A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon.

PubMed

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E; Moran, Emilio

2008-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.

A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon

PubMed Central

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E.; Moran, Emilio

2009-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin. PMID:19789716

A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements.

PubMed

Tannous, Halim; Istrate, Dan; Benlarbi-Delai, Aziz; Sarrazin, Julien; Gamet, Didier; Ho Ba Tho, Marie Christine; Dao, Tien Tuan

2016-11-15

Exergames have been proposed as a potential tool to improve the current practice of musculoskeletal rehabilitation. Inertial or optical motion capture sensors are commonly used to track the subject's movements. However, the use of these motion capture tools suffers from the lack of accuracy in estimating joint angles, which could lead to wrong data interpretation. In this study, we proposed a real time quaternion-based fusion scheme, based on the extended Kalman filter, between inertial and visual motion capture sensors, to improve the estimation accuracy of joint angles. The fusion outcome was compared to angles measured using a goniometer. The fusion output shows a better estimation, when compared to inertial measurement units and Kinect outputs. We noted a smaller error (3.96°) compared to the one obtained using inertial sensors (5.04°). The proposed multi-sensor fusion system is therefore accurate enough to be applied, in future works, to our serious game for musculoskeletal rehabilitation.

Improved accuracy and precision of tracer kinetic parameters by joint fitting to variable flip angle and dynamic contrast enhanced MRI data.

PubMed

Dickie, Ben R; Banerji, Anita; Kershaw, Lucy E; McPartlin, Andrew; Choudhury, Ananya; West, Catharine M; Rose, Chris J

2016-10-01

To improve the accuracy and precision of tracer kinetic model parameter estimates for use in dynamic contrast enhanced (DCE) MRI studies of solid tumors. Quantitative DCE-MRI requires an estimate of precontrast T1 , which is obtained prior to fitting a tracer kinetic model. As T1 mapping and tracer kinetic signal models are both a function of precontrast T1 it was hypothesized that its joint estimation would improve the accuracy and precision of both precontrast T1 and tracer kinetic model parameters. Accuracy and/or precision of two-compartment exchange model (2CXM) parameters were evaluated for standard and joint fitting methods in well-controlled synthetic data and for 36 bladder cancer patients. Methods were compared under a number of experimental conditions. In synthetic data, joint estimation led to statistically significant improvements in the accuracy of estimated parameters in 30 of 42 conditions (improvements between 1.8% and 49%). Reduced accuracy was observed in 7 of the remaining 12 conditions. Significant improvements in precision were observed in 35 of 42 conditions (between 4.7% and 50%). In clinical data, significant improvements in precision were observed in 18 of 21 conditions (between 4.6% and 38%). Accuracy and precision of DCE-MRI parameter estimates are improved when signal models are fit jointly rather than sequentially. Magn Reson Med 76:1270-1281, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

A neural network approach to cloud classification

NASA Technical Reports Server (NTRS)

Lee, Jonathan; Weger, Ronald C.; Sengupta, Sailes K.; Welch, Ronald M.

1990-01-01

It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery achieves an overall cloud identification accuracy of 93 percent. Cirrus can be distinguished from boundary layer cloudiness with an accuracy of 96 percent, without the use of an infrared channel. Stratocumulus is retrieved with an accuracy of 92 percent, cumulus at 90 percent. The use of the neural network does not improve cirrus classification accuracy. Rather, its main effect is in the improved separation between stratocumulus and cumulus cloudiness. While most cloud classification algorithms rely on linear parametric schemes, the present study is based on a nonlinear, nonparametric four-layer neural network approach. A three-layer neural network architecture, the nonparametric K-nearest neighbor approach, and the linear stepwise discriminant analysis procedure are compared. A significant finding is that significantly higher accuracies are attained with the nonparametric approaches using only 20 percent of the database as training data, compared to 67 percent of the database in the linear approach.

Introducing radiology report checklists among residents: adherence rates when suggesting versus requiring their use and early experience in improving accuracy.

PubMed

Powell, Daniel K; Lin, Eaton; Silberzweig, James E; Kagetsu, Nolan J

2014-03-01

To retrospectively compare resident adherence to checklist-style structured reporting for maxillofacial computed tomography (CT) from the emergency department (when required vs. suggested between two programs). To compare radiology resident reporting accuracy before and after introduction of the structured report and assess its ability to decrease the rate of undetected pathology. We introduced a reporting checklist for maxillofacial CT into our dictation software without specific training, requiring it at one program and suggesting it at another. We quantified usage among residents and compared reporting accuracy, before and after counting and categorizing faculty addenda. There was no significant change in resident accuracy in the first few months, with residents acting as their own controls (directly comparing performance with and without the checklist). Adherence to the checklist at program A (where it originated and was required) was 85% of reports compared to 9% of reports at program B (where it was suggested). When using program B as a secondary control, there was no significant difference in resident accuracy with or without using the checklist (comparing different residents using the checklist to those not using the checklist). Our results suggest that there is no automatic value of checklists for improving radiology resident reporting accuracy. They also suggest the importance of focused training, checklist flexibility, and a period of adjustment to a new reporting style. Mandatory checklists were readily adopted by residents but not when simply suggested. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Improved imputation accuracy in Hispanic/Latino populations with larger and more diverse reference panels: applications in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

PubMed Central

Nelson, Sarah C.; Stilp, Adrienne M.; Papanicolaou, George J.; Taylor, Kent D.; Rotter, Jerome I.; Thornton, Timothy A.; Laurie, Cathy C.

2016-01-01

Imputation is commonly used in genome-wide association studies to expand the set of genetic variants available for analysis. Larger and more diverse reference panels, such as the final Phase 3 of the 1000 Genomes Project, hold promise for improving imputation accuracy in genetically diverse populations such as Hispanics/Latinos in the USA. Here, we sought to empirically evaluate imputation accuracy when imputing to a 1000 Genomes Phase 3 versus a Phase 1 reference, using participants from the Hispanic Community Health Study/Study of Latinos. Our assessments included calculating the correlation between imputed and observed allelic dosage in a subset of samples genotyped on a supplemental array. We observed that the Phase 3 reference yielded higher accuracy at rare variants, but that the two reference panels were comparable at common variants. At a sample level, the Phase 3 reference improved imputation accuracy in Hispanic/Latino samples from the Caribbean more than for Mainland samples, which we attribute primarily to the additional reference panel samples available in Phase 3. We conclude that a 1000 Genomes Project Phase 3 reference panel can yield improved imputation accuracy compared with Phase 1, particularly for rare variants and for samples of certain genetic ancestry compositions. Our findings can inform imputation design for other genome-wide association studies of participants with diverse ancestries, especially as larger and more diverse reference panels continue to become available. PMID:27346520

Indexing of Diagnostic Accuracy Studies in MEDLINE and EMBASE

PubMed Central

Wilczynski, Nancy L.; Haynes, R. Brian

2007-01-01

Background: STAndards for Reporting of Diagnostic Accuracy (STARD) were published in 2003 and endorsed by some journals but not others. Objective: To determine whether the quality of indexing of diagnostic accuracy studies in MEDLINE and EMBASE has improved since the STARD statement was published. Design: Evaluate the change in the mean number of “accurate index terms” assigned to diagnostic accuracy studies, comparing STARD (endorsing) and non-STARD (non-endorsing) journals, for 2 years before and after STARD publication. Results: In MEDLINE, no differences in indexing quality were found for STARD and non-STARD journals before or after the STARD statement was published in 2003. In EMBASE, indexing in STARD journals improved compared with non-STARD journals (p = 0.02). However, articles in STARD journals had half the number of accurate indexing terms as articles in non-STARD journals, both before and after STARD statement publication (p < 0.001). PMID:18693947

Indexing of diagnosis accuracy studies in MEDLINE and EMBASE.

PubMed

Wilczynski, Nancy L; Haynes, R Brian

2007-10-11

STAndards for Reporting of Diagnostic Accuracy (STARD) were published in 2003 and endorsed by some journals but not others. To determine whether the quality of indexing of diagnostic accuracy studies in MEDLINE and EMBASE has improved since the STARD statement was published. Evaluate the change in the mean number of "accurate index terms" assigned to diagnostic accuracy studies, comparing STARD (endorsing) and non-STARD (non-endorsing) journals, for 2 years before and after STARD publication. In MEDLINE, no differences in indexing quality were found for STARD and non-STARD journals before or after the STARD statement was published in 2003. In EMBASE, indexing in STARD journals improved compared with non-STARD journals (p = 0.02). However, articles in STARD journals had half the number of accurate indexing terms as articles in non-STARD journals, both before and after STARD statement publication (p < 0.001).

Optimisation of shape kernel and threshold in image-processing motion analysers.

PubMed

Pedrocchi, A; Baroni, G; Sada, S; Marcon, E; Pedotti, A; Ferrigno, G

2001-09-01

The aim of the work is to optimise the image processing of a motion analyser. This is to improve accuracy, which is crucial for neurophysiological and rehabilitation applications. A new motion analyser, ELITE-S2, for installation on the International Space Station is described, with the focus on image processing. Important improvements are expected in the hardware of ELITE-S2 compared with ELITE and previous versions (ELITE-S and Kinelite). The core algorithm for marker recognition was based on the current ELITE version, using the cross-correlation technique. This technique was based on the matching of the expected marker shape, the so-called kernel, with image features. Optimisation of the kernel parameters was achieved using a genetic algorithm, taking into account noise rejection and accuracy. Optimisation was achieved by performing tests on six highly precise grids (with marker diameters ranging from 1.5 to 4 mm), representing all allowed marker image sizes, and on a noise image. The results of comparing the optimised kernels and the current ELITE version showed a great improvement in marker recognition accuracy, while noise rejection characteristics were preserved. An average increase in marker co-ordinate accuracy of +22% was achieved, corresponding to a mean accuracy of 0.11 pixel in comparison with 0.14 pixel, measured over all grids. An improvement of +37%, corresponding to an improvement from 0.22 pixel to 0.14 pixel, was observed over the grid with the biggest markers.

Effect of recent popularity on heat-conduction based recommendation models

NASA Astrophysics Data System (ADS)

Li, Wen-Jun; Dong, Qiang; Shi, Yang-Bo; Fu, Yan; He, Jia-Lin

2017-05-01

Accuracy and diversity are two important measures in evaluating the performance of recommender systems. It has been demonstrated that the recommendation model inspired by the heat conduction process has high diversity yet low accuracy. Many variants have been introduced to improve the accuracy while keeping high diversity, most of which regard the current node-degree of an item as its popularity. However in this way, a few outdated items of large degree may be recommended to an enormous number of users. In this paper, we take the recent popularity (recently increased item degrees) into account in the heat-conduction based methods, and propose accordingly the improved recommendation models. Experimental results on two benchmark data sets show that the accuracy can be largely improved while keeping the high diversity compared with the original models.

The Accuracy Benefit of Multiple Amperometric Glucose Sensors in People With Type 1 Diabetes

PubMed Central

Castle, Jessica R.; Pitts, Amy; Hanavan, Kathryn; Muhly, Rhonda; El Youssef, Joseph; Hughes-Karvetski, Colleen; Kovatchev, Boris; Ward, W. Kenneth

2012-01-01

OBJECTIVE To improve glucose sensor accuracy in subjects with type 1 diabetes by using multiple sensors and to assess whether the benefit of redundancy is affected by intersensor distance. RESEARCH DESIGN AND METHODS Nineteen adults with type 1 diabetes wore four Dexcom SEVEN PLUS subcutaneous glucose sensors during two 9-h studies. One pair of sensors was worn on each side of the abdomen, with each sensor pair placed at a predetermined distance apart and 20 cm away from the opposite pair. Arterialized venous blood glucose levels were measured every 15 min, and sensor glucose values were recorded every 5 min. Sensors were calibrated once at the beginning of the study. RESULTS The use of four sensors significantly reduced very large errors compared with one sensor (0.4 vs. 2.6% of errors ≥50% from reference glucose, P < 0.001) and also improved overall accuracy (mean absolute relative difference, 11.6 vs. 14.8%, P < 0.001). Using only two sensors also significantly improved very large errors and accuracy. Intersensor distance did not affect the function of sensor pairs. CONCLUSIONS Sensor accuracy is significantly improved with the use of multiple sensors compared with the use of a single sensor. The benefit of redundancy is present even when sensors are positioned very closely together (7 mm). These findings are relevant to the design of an artificial pancreas device. PMID:22357189

The accuracy benefit of multiple amperometric glucose sensors in people with type 1 diabetes.

PubMed

Castle, Jessica R; Pitts, Amy; Hanavan, Kathryn; Muhly, Rhonda; El Youssef, Joseph; Hughes-Karvetski, Colleen; Kovatchev, Boris; Ward, W Kenneth

2012-04-01

To improve glucose sensor accuracy in subjects with type 1 diabetes by using multiple sensors and to assess whether the benefit of redundancy is affected by intersensor distance. Nineteen adults with type 1 diabetes wore four Dexcom SEVEN PLUS subcutaneous glucose sensors during two 9-h studies. One pair of sensors was worn on each side of the abdomen, with each sensor pair placed at a predetermined distance apart and 20 cm away from the opposite pair. Arterialized venous blood glucose levels were measured every 15 min, and sensor glucose values were recorded every 5 min. Sensors were calibrated once at the beginning of the study. The use of four sensors significantly reduced very large errors compared with one sensor (0.4 vs. 2.6% of errors ≥50% from reference glucose, P < 0.001) and also improved overall accuracy (mean absolute relative difference, 11.6 vs. 14.8%, P < 0.001). Using only two sensors also significantly improved very large errors and accuracy. Intersensor distance did not affect the function of sensor pairs. Sensor accuracy is significantly improved with the use of multiple sensors compared with the use of a single sensor. The benefit of redundancy is present even when sensors are positioned very closely together (7 mm). These findings are relevant to the design of an artificial pancreas device.

The effect of a specialized dyslexia font, OpenDyslexic, on reading rate and accuracy.

PubMed

Wery, Jessica J; Diliberto, Jennifer A

2017-07-01

A single-subject alternating treatment design was used to investigate the extent to which a specialized dyslexia font, OpenDyslexic, impacted reading rate or accuracy compared to two commonly used fonts when used with elementary students identified as having dyslexia. OpenDyslexic was compared to Arial and Times New Roman in three reading tasks: (a) letter naming, (b) word reading, and (c) nonsense word reading. Data were analyzed through visual analysis and improvement rate difference, a nonparametric measure of nonoverlap for comparing treatments. Results from this alternating treatment experiment show no improvement in reading rate or accuracy for individual students with dyslexia, as well as the group as a whole. While some students commented that the font was "new" or "different", none of the participants reported preferring to read material presented in that font. These results indicate there may be no benefit for translating print materials to this font.

Intervention Planning Using a Laser Navigation System for CT-Guided Interventions: A Phantom and Patient Study

PubMed Central

Lee, Clara; Bolck, Jan; Naguib, Nagy N.N.; Schulz, Boris; Eichler, Katrin; Aschenbach, Rene; Wichmann, Julian L.; Vogl, Thomas. J.; Zangos, Stephan

2015-01-01

Objective To investigate the accuracy, efficiency and radiation dose of a novel laser navigation system (LNS) compared to those of free-handed punctures on computed tomography (CT). Materials and Methods Sixty punctures were performed using a phantom body to compare accuracy, timely effort, and radiation dose of the conventional free-handed procedure to those of the LNS-guided method. An additional 20 LNS-guided interventions were performed on another phantom to confirm accuracy. Ten patients subsequently underwent LNS-guided punctures. Results The phantom 1-LNS group showed a target point accuracy of 4.0 ± 2.7 mm (freehand, 6.3 ± 3.6 mm; p = 0.008), entrance point accuracy of 0.8 ± 0.6 mm (freehand, 6.1 ± 4.7 mm), needle angulation accuracy of 1.3 ± 0.9° (freehand, 3.4 ± 3.1°; p < 0.001), intervention time of 7.03 ± 5.18 minutes (freehand, 8.38 ± 4.09 minutes; p = 0.006), and 4.2 ± 3.6 CT images (freehand, 7.9 ± 5.1; p < 0.001). These results show significant improvement in 60 punctures compared to freehand. The phantom 2-LNS group showed a target point accuracy of 3.6 ± 2.5 mm, entrance point accuracy of 1.4 ± 2.0 mm, needle angulation accuracy of 1.0 ± 1.2°, intervention time of 1.44 ± 0.22 minutes, and 3.4 ± 1.7 CT images. The LNS group achieved target point accuracy of 5.0 ± 1.2 mm, entrance point accuracy of 2.0 ± 1.5 mm, needle angulation accuracy of 1.5 ± 0.3°, intervention time of 12.08 ± 3.07 minutes, and used 5.7 ± 1.6 CT-images for the first experience with patients. Conclusion Laser navigation system improved accuracy, duration of intervention, and radiation dose of CT-guided interventions. PMID:26175571

Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET

NASA Astrophysics Data System (ADS)

Ahn, Sangtae; Ross, Steven G.; Asma, Evren; Miao, Jun; Jin, Xiao; Cheng, Lishui; Wollenweber, Scott D.; Manjeshwar, Ravindra M.

2015-08-01

Ordered subset expectation maximization (OSEM) is the most widely used algorithm for clinical PET image reconstruction. OSEM is usually stopped early and post-filtered to control image noise and does not necessarily achieve optimal quantitation accuracy. As an alternative to OSEM, we have recently implemented a penalized likelihood (PL) image reconstruction algorithm for clinical PET using the relative difference penalty with the aim of improving quantitation accuracy without compromising visual image quality. Preliminary clinical studies have demonstrated visual image quality including lesion conspicuity in images reconstructed by the PL algorithm is better than or at least as good as that in OSEM images. In this paper we evaluate lesion quantitation accuracy of the PL algorithm with the relative difference penalty compared to OSEM by using various data sets including phantom data acquired with an anthropomorphic torso phantom, an extended oval phantom and the NEMA image quality phantom; clinical data; and hybrid clinical data generated by adding simulated lesion data to clinical data. We focus on mean standardized uptake values and compare them for PL and OSEM using both time-of-flight (TOF) and non-TOF data. The results demonstrate improvements of PL in lesion quantitation accuracy compared to OSEM with a particular improvement in cold background regions such as lungs.

Improved Discrete Ordinate Solutions in the Presence of an Anisotropically Reflecting Lower Boundary: Upgrades of the DISORT Computational Tool

NASA Technical Reports Server (NTRS)

Lin, Z.; Stamnes, S.; Jin, Z.; Laszlo, I.; Tsay, S. C.; Wiscombe, W. J.; Stamnes, K.

2015-01-01

A successor version 3 of DISORT (DISORT3) is presented with important upgrades that improve the accuracy, efficiency, and stability of the algorithm. Compared with version 2 (DISORT2 released in 2000) these upgrades include (a) a redesigned BRDF computation that improves both speed and accuracy, (b) a revised treatment of the single scattering correction, and (c) additional efficiency and stability upgrades for beam sources. In DISORT3 the BRDF computation is improved in the following three ways: (i) the Fourier decomposition is prepared "off-line", thus avoiding the repeated internal computations done in DISORT2; (ii) a large enough number of terms in the Fourier expansion of the BRDF is employed to guarantee accurate values of the expansion coefficients (default is 200 instead of 50 in DISORT2); (iii) in the post processing step the reflection of the direct attenuated beam from the lower boundary is included resulting in a more accurate single scattering correction. These improvements in the treatment of the BRDF have led to improved accuracy and a several-fold increase in speed. In addition, the stability of beam sources has been improved by removing a singularity occurring when the cosine of the incident beam angle is too close to the reciprocal of any of the eigenvalues. The efficiency for beam sources has been further improved from reducing by a factor of 2 (compared to DISORT2) the dimension of the linear system of equations that must be solved to obtain the particular solutions, and by replacing the LINPAK routines used in DISORT2 by LAPACK 3.5 in DISORT3. These beam source stability and efficiency upgrades bring enhanced stability and an additional 5-7% improvement in speed. Numerical results are provided to demonstrate and quantify the improvements in accuracy and efficiency of DISORT3 compared to DISORT2.

Statewide Quality Improvement Initiative to Reduce Early Elective Deliveries and Improve Birth Registry Accuracy.

PubMed

Kaplan, Heather C; King, Eileen; White, Beth E; Ford, Susan E; Fuller, Sandra; Krew, Michael A; Marcotte, Michael P; Iams, Jay D; Bailit, Jennifer L; Bouchard, Jo M; Friar, Kelly; Lannon, Carole M

2018-04-01

To evaluate the success of a quality improvement initiative to reduce early elective deliveries at less than 39 weeks of gestation and improve birth registry data accuracy rapidly and at scale in Ohio. Between February 2013 and March 2014, participating hospitals were involved in a quality improvement initiative to reduce early elective deliveries at less than 39 weeks of gestation and improve birth registry data. This initiative was designed as a learning collaborative model (group webinars and a single face-to-face meeting) and included individual quality improvement coaching. It was implemented using a stepped wedge design with hospitals divided into three balanced groups (waves) participating in the initiative sequentially. Birth registry data were used to assess hospital rates of nonmedically indicated inductions at less than 39 weeks of gestation. Comparisons were made between groups participating and those not participating in the initiative at two time points. To measure birth registry accuracy, hospitals conducted monthly audits comparing birth registry data with the medical record. Associations were assessed using generalized linear repeated measures models accounting for time effects. Seventy of 72 (97%) eligible hospitals participated. Based on birth registry data, nonmedically indicated inductions at less than 39 weeks of gestation declined in all groups with implementation (wave 1: 6.2-3.2%, P<.001; wave 2: 4.2-2.5%, P=.04; wave 3: 6.8-3.7%, P=.002). When waves 1 and 2 were participating in the initiative, they saw significant decreases in rates of early elective deliveries as compared with wave 3 (control; P=.018). All waves had significant improvement in birth registry accuracy (wave 1: 80-90%, P=.017; wave 2: 80-100%, P=.002; wave 3: 75-100%, P<.001). A quality improvement initiative enabled statewide spread of change strategies to decrease early elective deliveries and improve birth registry accuracy over 14 months and could be used for rapid dissemination of other evidence-based obstetric care practices across states or hospital systems.

Contrast-enhanced spectral mammography based on a photon-counting detector: quantitative accuracy and radiation dose

NASA Astrophysics Data System (ADS)

Lee, Seungwan; Kang, Sooncheol; Eom, Jisoo

2017-03-01

Contrast-enhanced mammography has been used to demonstrate functional information about a breast tumor by injecting contrast agents. However, a conventional technique with a single exposure degrades the efficiency of tumor detection due to structure overlapping. Dual-energy techniques with energy-integrating detectors (EIDs) also cause an increase of radiation dose and an inaccuracy of material decomposition due to the limitations of EIDs. On the other hands, spectral mammography with photon-counting detectors (PCDs) is able to resolve the issues induced by the conventional technique and EIDs using their energy-discrimination capabilities. In this study, the contrast-enhanced spectral mammography based on a PCD was implemented by using a polychromatic dual-energy model, and the proposed technique was compared with the dual-energy technique with an EID in terms of quantitative accuracy and radiation dose. The results showed that the proposed technique improved the quantitative accuracy as well as reduced radiation dose comparing to the dual-energy technique with an EID. The quantitative accuracy of the contrast-enhanced spectral mammography based on a PCD was slightly improved as a function of radiation dose. Therefore, the contrast-enhanced spectral mammography based on a PCD is able to provide useful information for detecting breast tumors and improving diagnostic accuracy.

Assessing disease severity: accuracy and reliability of rater estimates in relation to number of diagrams in a standard area diagram set

USDA-ARS?s Scientific Manuscript database

Error in rater estimates of plant disease severity occur, and standard area diagrams (SADs) help improve accuracy and reliability. The effects of diagram number in a SAD set on accuracy and reliability is unknown. The objective of this study was to compare estimates of pecan scab severity made witho...

Improving BeiDou real-time precise point positioning with numerical weather models

NASA Astrophysics Data System (ADS)

Lu, Cuixian; Li, Xingxing; Zus, Florian; Heinkelmann, Robert; Dick, Galina; Ge, Maorong; Wickert, Jens; Schuh, Harald

2017-09-01

Precise positioning with the current Chinese BeiDou Navigation Satellite System is proven to be of comparable accuracy to the Global Positioning System, which is at centimeter level for the horizontal components and sub-decimeter level for the vertical component. But the BeiDou precise point positioning (PPP) shows its limitation in requiring a relatively long convergence time. In this study, we develop a numerical weather model (NWM) augmented PPP processing algorithm to improve BeiDou precise positioning. Tropospheric delay parameters, i.e., zenith delays, mapping functions, and horizontal delay gradients, derived from short-range forecasts from the Global Forecast System of the National Centers for Environmental Prediction (NCEP) are applied into BeiDou real-time PPP. Observational data from stations that are capable of tracking the BeiDou constellation from the International GNSS Service (IGS) Multi-GNSS Experiments network are processed, with the introduced NWM-augmented PPP and the standard PPP processing. The accuracy of tropospheric delays derived from NCEP is assessed against with the IGS final tropospheric delay products. The positioning results show that an improvement in convergence time up to 60.0 and 66.7% for the east and vertical components, respectively, can be achieved with the NWM-augmented PPP solution compared to the standard PPP solutions, while only slight improvement in the solution convergence can be found for the north component. A positioning accuracy of 5.7 and 5.9 cm for the east component is achieved with the standard PPP that estimates gradients and the one that estimates no gradients, respectively, in comparison to 3.5 cm of the NWM-augmented PPP, showing an improvement of 38.6 and 40.1%. Compared to the accuracy of 3.7 and 4.1 cm for the north component derived from the two standard PPP solutions, the one of the NWM-augmented PPP solution is improved to 2.0 cm, by about 45.9 and 51.2%. The positioning accuracy for the up component improves from 11.4 and 13.2 cm with the two standard PPP solutions to 8.0 cm with the NWM-augmented PPP solution, an improvement of 29.8 and 39.4%, respectively.

Robotic Assistance Enables Inexperienced Surgeons to Perform Unicompartmental Knee Arthroplasties on Dry Bone Models with Accuracy Superior to Conventional Methods

PubMed Central

Masjedi, Milad; Andrews, Barry; Cobb, Justin

2013-01-01

Robotic systems have been shown to improve unicompartmental knee arthroplasty (UKA) component placement accuracy compared to conventional methods when used by experienced surgeons. We aimed to determine whether inexperienced UKA surgeons can position components accurately using robotic assistance when compared to conventional methods and to demonstrate the effect repetition has on accuracy. Sixteen surgeons were randomised to an active constraint robot or conventional group performing three UKAs over three weeks. Implanted component positions and orientations were compared to planned component positions in six degrees of freedom for both femoral and tibial components. Mean procedure time decreased for both robot (37.5 mins to 25.7 mins) (P = 0.002) and conventional (33.8 mins to 21.0 mins) (P = 0.002) groups by attempt three indicating the presence of a learning curve; however, neither group demonstrated changes in accuracy. Mean compound rotational and translational errors were lower in the robot group compared to the conventional group for both components at all attempts for which rotational error differences were significant at every attempt. The conventional group's positioning remained inaccurate even with repeated attempts although procedure time improved. In comparison, by limiting inaccuracies inherent in conventional equipment, robotic assistance enabled surgeons to achieve precision and accuracy when positioning UKA components irrespective of their experience. PMID:23862069

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning.

PubMed

Wang, Jinhua; Yang, Xi; Cai, Hongmin; Tan, Wanchang; Jin, Cangzheng; Li, Li

2016-06-07

Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer.

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

PubMed Central

Wang, Jinhua; Yang, Xi; Cai, Hongmin; Tan, Wanchang; Jin, Cangzheng; Li, Li

2016-01-01

Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer. PMID:27273294

Boosted classification trees result in minor to modest improvement in the accuracy in classifying cardiovascular outcomes compared to conventional classification trees

PubMed Central

Austin, Peter C; Lee, Douglas S

2011-01-01

Purpose: Classification trees are increasingly being used to classifying patients according to the presence or absence of a disease or health outcome. A limitation of classification trees is their limited predictive accuracy. In the data-mining and machine learning literature, boosting has been developed to improve classification. Boosting with classification trees iteratively grows classification trees in a sequence of reweighted datasets. In a given iteration, subjects that were misclassified in the previous iteration are weighted more highly than subjects that were correctly classified. Classifications from each of the classification trees in the sequence are combined through a weighted majority vote to produce a final classification. The authors' objective was to examine whether boosting improved the accuracy of classification trees for predicting outcomes in cardiovascular patients. Methods: We examined the utility of boosting classification trees for classifying 30-day mortality outcomes in patients hospitalized with either acute myocardial infarction or congestive heart failure. Results: Improvements in the misclassification rate using boosted classification trees were at best minor compared to when conventional classification trees were used. Minor to modest improvements to sensitivity were observed, with only a negligible reduction in specificity. For predicting cardiovascular mortality, boosted classification trees had high specificity, but low sensitivity. Conclusions: Gains in predictive accuracy for predicting cardiovascular outcomes were less impressive than gains in performance observed in the data mining literature. PMID:22254181

The value of vital sign trends for detecting clinical deterioration on the wards

PubMed Central

Churpek, Matthew M; Adhikari, Richa; Edelson, Dana P

2016-01-01

Aim Early detection of clinical deterioration on the wards may improve outcomes, and most early warning scores only utilize a patient’s current vital signs. The added value of vital sign trends over time is poorly characterized. We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends. Methods Patients admitted to five hospitals over a five-year period were included in this observational cohort study, with 60% of the data used for model derivation and 40% for validation. Vital signs were utilized to predict the combined outcome of cardiac arrest, intensive care unit transfer, and death. The accuracy of models utilizing both the current value and different trend methods were compared using the area under the receiver operating characteristic curve (AUC). Results A total of 269,999 patient admissions were included, which resulted in 16,452 outcomes. Overall, trends increased accuracy compared to a model containing only current vital signs (AUC 0.78 vs. 0.74; p<0.001). The methods that resulted in the greatest average increase in accuracy were the vital sign slope (AUC improvement 0.013) and minimum value (AUC improvement 0.012), while the change from the previous value resulted in an average worsening of the AUC (change in AUC −0.002). The AUC increased most for systolic blood pressure when trends were added (AUC improvement 0.05). Conclusion Vital sign trends increased the accuracy of models designed to detect critical illness on the wards. Our findings have important implications for clinicians at the bedside and for the development of early warning scores. PMID:26898412

The value of vital sign trends for detecting clinical deterioration on the wards.

PubMed

Churpek, Matthew M; Adhikari, Richa; Edelson, Dana P

2016-05-01

Early detection of clinical deterioration on the wards may improve outcomes, and most early warning scores only utilize a patient's current vital signs. The added value of vital sign trends over time is poorly characterized. We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends. Patients admitted to five hospitals over a five-year period were included in this observational cohort study, with 60% of the data used for model derivation and 40% for validation. Vital signs were utilized to predict the combined outcome of cardiac arrest, intensive care unit transfer, and death. The accuracy of models utilizing both the current value and different trend methods were compared using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patient admissions were included, which resulted in 16,452 outcomes. Overall, trends increased accuracy compared to a model containing only current vital signs (AUC 0.78 vs. 0.74; p<0.001). The methods that resulted in the greatest average increase in accuracy were the vital sign slope (AUC improvement 0.013) and minimum value (AUC improvement 0.012), while the change from the previous value resulted in an average worsening of the AUC (change in AUC -0.002). The AUC increased most for systolic blood pressure when trends were added (AUC improvement 0.05). Vital sign trends increased the accuracy of models designed to detect critical illness on the wards. Our findings have important implications for clinicians at the bedside and for the development of early warning scores. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Development and accuracy of a multipoint method for measuring visibility.

PubMed

Tai, Hongda; Zhuang, Zibo; Sun, Dongsong

2017-10-01

Accurate measurements of visibility are of great importance in many fields. This paper reports a multipoint visibility measurement (MVM) method to measure and calculate the atmospheric transmittance, extinction coefficient, and meteorological optical range (MOR). The relative errors of atmospheric transmittance and MOR measured by the MVM method and traditional transmissometer method are analyzed and compared. Experiments were conducted indoors, and the data were simultaneously processed. The results revealed that the MVM can effectively improve the accuracy under different visibility conditions. The greatest improvement of accuracy was 27%. The MVM can be used to calibrate and evaluate visibility meters.

A novel rotational matrix and translation vector algorithm: geometric accuracy for augmented reality in oral and maxillofacial surgeries.

PubMed

Murugesan, Yahini Prabha; Alsadoon, Abeer; Manoranjan, Paul; Prasad, P W C

2018-06-01

Augmented reality-based surgeries have not been successfully implemented in oral and maxillofacial areas due to limitations in geometric accuracy and image registration. This paper aims to improve the accuracy and depth perception of the augmented video. The proposed system consists of a rotational matrix and translation vector algorithm to reduce the geometric error and improve the depth perception by including 2 stereo cameras and a translucent mirror in the operating room. The results on the mandible/maxilla area show that the new algorithm improves the video accuracy by 0.30-0.40 mm (in terms of overlay error) and the processing rate to 10-13 frames/s compared to 7-10 frames/s in existing systems. The depth perception increased by 90-100 mm. The proposed system concentrates on reducing the geometric error. Thus, this study provides an acceptable range of accuracy with a shorter operating time, which provides surgeons with a smooth surgical flow. Copyright © 2018 John Wiley & Sons, Ltd.

Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.

PubMed

Trakoolwilaiwan, Thanawin; Behboodi, Bahareh; Lee, Jaeseok; Kim, Kyungsoo; Choi, Ji-Woong

2018-01-01

The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

Medium- and Long-term Prediction of LOD Change by the Leap-step Autoregressive Model

NASA Astrophysics Data System (ADS)

Wang, Qijie

2015-08-01

The accuracy of medium- and long-term prediction of length of day (LOD) change base on combined least-square and autoregressive (LS+AR) deteriorates gradually. Leap-step autoregressive (LSAR) model can significantly reduce the edge effect of the observation sequence. Especially, LSAR model greatly improves the resolution of signals’ low-frequency components. Therefore, it can improve the efficiency of prediction. In this work, LSAR is used to forecast the LOD change. The LOD series from EOP 08 C04 provided by IERS is modeled by both the LSAR and AR models. The results of the two models are analyzed and compared. When the prediction length is between 10-30 days, the accuracy improvement is less than 10%. When the prediction length amounts to above 30 day, the accuracy improved obviously, with the maximum being around 19%. The results show that the LSAR model has higher prediction accuracy and stability in medium- and long-term prediction.

Comparison of MSS and TM Data for Landcover Classification in the Chesapeake Bay Area: a Preliminary Report. [Taylor's Island, Maryland

NASA Technical Reports Server (NTRS)

Mulligan, P. J.; Gervin, J. C.; Lu, Y. C.

1985-01-01

An area bordering the Eastern Shore of the Chesapeake Bay was selected for study and classified using unsupervised techniques applied to LANDSAT-2 MSS data and several band combinations of LANDSAT-4 TM data. The accuracies of these Level I land cover classifications were verified using the Taylor's Island USGS 7.5 minute topographic map which was photointerpreted, digitized and rasterized. The the Taylor's Island map, comparing the MSS and TM three band (2 3 4) classifications, the increased resolution of TM produced a small improvement in overall accuracy of 1% correct due primarily to a small improvement, and 1% and 3%, in areas such as water and woodland. This was expected as the MSS data typically produce high accuracies for categories which cover large contiguous areas. However, in the categories covering smaller areas within the map there was generally an improvement of at least 10%. Classification of the important residential category improved 12%, and wetlands were mapped with 11% greater accuracy.

The Effects of Different Types of Environmental Noise on Academic Performance and Perceived Task Difficulty in Adolescents With ADHD.

PubMed

Batho, Lauren P; Martinussen, Rhonda; Wiener, Judith

2015-07-28

To examine the effects of environmental noises (speech and white noise) relative to a no noise control condition on the performance and difficulty ratings of youth with ADHD (N = 52) on academic tasks. Reading performance was measured by an oral retell (reading accuracy) and the time spent reading. Writing performance was measured through the proportion of correct writing sequences (writing accuracy) and the total words written on an essay. Participants in the white noise condition took less time to read the passage and wrote more words on the essay compared with participants in the other conditions, though white noise did not improve academic accuracy. The participants in the babble condition rated the tasks as most difficult. Although white noise appears to improve reading time and writing fluency, the findings suggest that white noise does not improve performance accuracy. Educational implications are discussed. © 2015 SAGE Publications.

Video-augmented feedback for procedural performance.

PubMed

Wittler, Mary; Hartman, Nicholas; Manthey, David; Hiestand, Brian; Askew, Kim

2016-06-01

Resident programs must assess residents' achievement of core competencies for clinical and procedural skills. Video-augmented feedback may facilitate procedural skill acquisition and promote more accurate self-assessment. A randomized controlled study to investigate whether video-augmented verbal feedback leads to increased procedural skill and improved accuracy of self-assessment compared to verbal only feedback. Participants were evaluated during procedural training for ultrasound guided internal jugular central venous catheter (US IJ CVC) placement. All participants received feedback based on a validated 30-point checklist for US IJ CVC placement and validated 6-point procedural global rating scale. Scores in both groups improved by a mean of 9.6 points (95% CI: 7.8-11.4) on the 30-point checklist, with no difference between groups in mean score improvement on the global rating scale. In regards to self-assessment, participant self-rating diverged from faculty scoring, increasingly so after receiving feedback. Residents rated highly by faculty underestimated their skill, while those rated more poorly demonstrated increasing overestimation. Accuracy of self-assessment was not improved by addition of video. While feedback advanced the skill of the resident, video-augmented feedback did not enhance skill acquisition or improve accuracy of resident self-assessment compared to standard feedback.

Systematic Calibration for Ultra-High Accuracy Inertial Measurement Units.

PubMed

Cai, Qingzhong; Yang, Gongliu; Song, Ningfang; Liu, Yiliang

2016-06-22

An inertial navigation system (INS) has been widely used in challenging GPS environments. With the rapid development of modern physics, an atomic gyroscope will come into use in the near future with a predicted accuracy of 5 × 10(-6)°/h or better. However, existing calibration methods and devices can not satisfy the accuracy requirements of future ultra-high accuracy inertial sensors. In this paper, an improved calibration model is established by introducing gyro g-sensitivity errors, accelerometer cross-coupling errors and lever arm errors. A systematic calibration method is proposed based on a 51-state Kalman filter and smoother. Simulation results show that the proposed calibration method can realize the estimation of all the parameters using a common dual-axis turntable. Laboratory and sailing tests prove that the position accuracy in a five-day inertial navigation can be improved about 8% by the proposed calibration method. The accuracy can be improved at least 20% when the position accuracy of the atomic gyro INS can reach a level of 0.1 nautical miles/5 d. Compared with the existing calibration methods, the proposed method, with more error sources and high order small error parameters calibrated for ultra-high accuracy inertial measurement units (IMUs) using common turntables, has a great application potential in future atomic gyro INSs.

A New Chemotherapeutic Investigation: Piracetam Effects on Dyslexia.

ERIC Educational Resources Information Center

Chase, Christopher H.; Schmitt, R. Larry

1984-01-01

Compared to placebo controls, 28 individuals treated with Piracetam (a new drug thought to enhance learning and memory consolidation) showed statistically significant improvements above baseline scores on measures of effective reading accuracy and comprehension, reading speed, and writing accuracy. The medication was well tolerated and showed no…

Reliability, Validity, and Classification Accuracy of the DSM-5 Diagnostic Criteria for Gambling Disorder and Comparison to DSM-IV.

PubMed

Stinchfield, Randy; McCready, John; Turner, Nigel E; Jimenez-Murcia, Susana; Petry, Nancy M; Grant, Jon; Welte, John; Chapman, Heather; Winters, Ken C

2016-09-01

The DSM-5 was published in 2013 and it included two substantive revisions for gambling disorder (GD). These changes are the reduction in the threshold from five to four criteria and elimination of the illegal activities criterion. The purpose of this study was to twofold. First, to assess the reliability, validity and classification accuracy of the DSM-5 diagnostic criteria for GD. Second, to compare the DSM-5-DSM-IV on reliability, validity, and classification accuracy, including an examination of the effect of the elimination of the illegal acts criterion on diagnostic accuracy. To compare DSM-5 and DSM-IV, eight datasets from three different countries (Canada, USA, and Spain; total N = 3247) were used. All datasets were based on similar research methods. Participants were recruited from outpatient gambling treatment services to represent the group with a GD and from the community to represent the group without a GD. All participants were administered a standardized measure of diagnostic criteria. The DSM-5 yielded satisfactory reliability, validity and classification accuracy. In comparing the DSM-5 to the DSM-IV, most comparisons of reliability, validity and classification accuracy showed more similarities than differences. There was evidence of modest improvements in classification accuracy for DSM-5 over DSM-IV, particularly in reduction of false negative errors. This reduction in false negative errors was largely a function of lowering the cut score from five to four and this revision is an improvement over DSM-IV. From a statistical standpoint, eliminating the illegal acts criterion did not make a significant impact on diagnostic accuracy. From a clinical standpoint, illegal acts can still be addressed in the context of the DSM-5 criterion of lying to others.

[Validation of an improved Demons deformable registration algorithm and its application in re-contouring in 4D-CT].

PubMed

Zhen, Xin; Zhou, Ling-hong; Lu, Wen-ting; Zhang, Shu-xu; Zhou, Lu

2010-12-01

To validate the efficiency and accuracy of an improved Demons deformable registration algorithm and evaluate its application in contour recontouring in 4D-CT. To increase the additional Demons force and reallocate the bilateral forces to accelerate convergent speed, we propose a novel energy function as the similarity measure, and utilize a BFGS method for optimization to avoid specifying the numbers of iteration. Mathematical transformed deformable CT images and home-made deformable phantom were used to validate the accuracy of the improved algorithm, and its effectiveness for contour recontouring was tested. The improved algorithm showed a relatively high registration accuracy and speed when compared with the classic Demons algorithm and optical flow based method. Visual inspection of the positions and shapes of the deformed contours agreed well with the physician-drawn contours. Deformable registration is a key technique in 4D-CT, and this improved Demons algorithm for contour recontouring can significantly reduce the workload of the physicians. The registration accuracy of this method proves to be sufficient for clinical needs.

Physiologically-based, predictive analytics using the heart-rate-to-Systolic-Ratio significantly improves the timeliness and accuracy of sepsis prediction compared to SIRS.

PubMed

Danner, Omar K; Hendren, Sandra; Santiago, Ethel; Nye, Brittany; Abraham, Prasad

2017-04-01

Enhancing the efficiency of diagnosis and treatment of severe sepsis by using physiologically-based, predictive analytical strategies has not been fully explored. We hypothesize assessment of heart-rate-to-systolic-ratio significantly increases the timeliness and accuracy of sepsis prediction after emergency department (ED) presentation. We evaluated the records of 53,313 ED patients from a large, urban teaching hospital between January and June 2015. The HR-to-systolic ratio was compared to SIRS criteria for sepsis prediction. There were 884 patients with discharge diagnoses of sepsis, severe sepsis, and/or septic shock. Variations in three presenting variables, heart rate, systolic BP and temperature were determined to be primary early predictors of sepsis with a 74% (654/884) accuracy compared to 34% (304/884) using SIRS criteria (p < 0.0001)in confirmed septic patients. Physiologically-based predictive analytics improved the accuracy and expediency of sepsis identification via detection of variations in HR-to-systolic ratio. This approach may lead to earlier sepsis workup and life-saving interventions. Copyright © 2017 Elsevier Inc. All rights reserved.

Concordance of adolescent human papillomavirus vaccination parental report with provider report in the National Immunization Survey-Teen (2008-2013).

PubMed

Hirth, Jacqueline; Kuo, Yong-Fang; Laz, Tabassum Haque; Starkey, Jonathan M; Rupp, Richard E; Rahman, Mahbubur; Berenson, Abbey B

2016-08-17

To examine the accuracy of parental report of HPV vaccination through examination of concordance, with healthcare provider vaccination report as the comparison. The 2008-2013 National Immunization Survey (NIS)-Teen was used to examine accuracy of parent reports of HPV vaccination for their female daughters aged 13-17years, as compared with provider report of initiation and number of doses. Multivariable logistic regression models were used to examine associations related to concordance of parent and provider report. Of 51,746 adolescents, 84% concordance for HPV vaccine initiation and 70% concordance for number of doses was observed. Accuracy varied by race/ethnicity, region, time, and income. The parent report of number of doses was more likely to be accurate among parents of 13 and 14year old females than 17year olds. Accuracy of initiation and number of doses were lower among Hispanic and black adolescents compared to white parents. The odds of over-report was higher among minorities compared to whites, but the odds of underreport was also markedly higher in these groups compared to parents of white teens. Accuracy of parental vaccine report decreased across time. These findings are important for healthcare providers who need to ascertain the vaccination status of young adults. Strengthening existing immunization registries to improve data sharing capabilities and record completeness could improve vaccination rates, while avoiding costs associated with over-vaccination. Copyright © 2016 Elsevier Ltd. All rights reserved.

Parameter estimation using weighted total least squares in the two-compartment exchange model.

PubMed

Garpebring, Anders; Löfstedt, Tommy

2018-01-01

The linear least squares (LLS) estimator provides a fast approach to parameter estimation in the linearized two-compartment exchange model. However, the LLS method may introduce a bias through correlated noise in the system matrix of the model. The purpose of this work is to present a new estimator for the linearized two-compartment exchange model that takes this noise into account. To account for the noise in the system matrix, we developed an estimator based on the weighted total least squares (WTLS) method. Using simulations, the proposed WTLS estimator was compared, in terms of accuracy and precision, to an LLS estimator and a nonlinear least squares (NLLS) estimator. The WTLS method improved the accuracy compared to the LLS method to levels comparable to the NLLS method. This improvement was at the expense of increased computational time; however, the WTLS was still faster than the NLLS method. At high signal-to-noise ratio all methods provided similar precisions while inconclusive results were observed at low signal-to-noise ratio. The proposed method provides improvements in accuracy compared to the LLS method, however, at an increased computational cost. Magn Reson Med 79:561-567, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Accuracies of univariate and multivariate genomic prediction models in African cassava.

PubMed

Okeke, Uche Godfrey; Akdemir, Deniz; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2017-12-04

Genomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for crop species such as cassava that have long breeding cycles. Practically, to implement GS in cassava breeding, it is necessary to evaluate different GS models and to develop suitable models for an optimized breeding pipeline. In this paper, we compared (1) prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for a single-environment genetic evaluation (Scenario 1), and (2) accuracies from a compound symmetric multi-environment model (uE) parameterized as a univariate multi-kernel model to a multivariate (ME) multi-environment mixed model that accounts for genotype-by-environment interaction for multi-environment genetic evaluation (Scenario 2). For these analyses, we used 16 years of public cassava breeding data for six target cassava traits and a fivefold cross-validation scheme with 10-repeat cycles to assess model prediction accuracies. In Scenario 1, the MT models had higher prediction accuracies than the uT models for all traits and locations analyzed, which amounted to on average a 40% improved prediction accuracy. For Scenario 2, we observed that the ME model had on average (across all locations and traits) a 12% improved prediction accuracy compared to the uE model. We recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.

Improving coding accuracy in an academic practice.

PubMed

Nguyen, Dana; O'Mara, Heather; Powell, Robert

2017-01-01

Practice management has become an increasingly important component of graduate medical education. This applies to every practice environment; private, academic, and military. One of the most critical aspects of practice management is documentation and coding for physician services, as they directly affect the financial success of any practice. Our quality improvement project aimed to implement a new and innovative method for teaching billing and coding in a longitudinal fashion in a family medicine residency. We hypothesized that implementation of a new teaching strategy would increase coding accuracy rates among residents and faculty. Design: single group, pretest-posttest. military family medicine residency clinic. Study populations: 7 faculty physicians and 18 resident physicians participated as learners in the project. Educational intervention: monthly structured coding learning sessions in the academic curriculum that involved learner-presented cases, small group case review, and large group discussion. overall coding accuracy (compliance) percentage and coding accuracy per year group for the subjects that were able to participate longitudinally. Statistical tests used: average coding accuracy for population; paired t test to assess improvement between 2 intervention periods, both aggregate and by year group. Overall coding accuracy rates remained stable over the course of time regardless of the modality of the educational intervention. A paired t test was conducted to compare coding accuracy rates at baseline (mean (M)=26.4%, SD=10%) to accuracy rates after all educational interventions were complete (M=26.8%, SD=12%); t24=-0.127, P=.90. Didactic teaching and small group discussion sessions did not improve overall coding accuracy in a residency practice. Future interventions could focus on educating providers at the individual level.

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks.

PubMed

Chai, Rifai; Ling, Sai Ho; San, Phyo Phyo; Naik, Ganesh R; Nguyen, Tuan N; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-01-01

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks

PubMed Central

Chai, Rifai; Ling, Sai Ho; San, Phyo Phyo; Naik, Ganesh R.; Nguyen, Tuan N.; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T.

2017-01-01

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively. PMID:28326009

Cow genotyping strategies for genomic selection in small dairy cattle population

USDA-ARS?s Scientific Manuscript database

This study compares how different cow genotyping strategies increase the accuracy of genomic estimated breeding values (EBV) in dairy cattle breeds with low numbers. In these breeds there are few sires with progeny records and genotyping cows can improve the accuracy of genomic EBV. The Guernsey bre...

Training General Education Pupils to Monitor Reading Using Curriculum-Based Measurement Procedures.

ERIC Educational Resources Information Center

Bentz, Johnell; And Others

1990-01-01

Although systematic monitoring of student progress has been associated with improved achievement, few teachers engage in progress monitoring because of testing-time requirements. Compared accuracy of 14 trained fourth- and fifth-grade general education students' curriculum-based reading assessments of second and third graders to accuracy of…

Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiers

PubMed Central

Racette, Lyne; Chiou, Christine Y.; Hao, Jiucang; Bowd, Christopher; Goldbaum, Michael H.; Zangwill, Linda M.; Lee, Te-Won; Weinreb, Robert N.; Sample, Pamela A.

2009-01-01

Purpose To investigate whether combining optic disc topography and short-wavelength automated perimetry (SWAP) data improves the diagnostic accuracy of relevance vector machine (RVM) classifiers for detecting glaucomatous eyes compared to using each test alone. Methods One eye of 144 glaucoma patients and 68 healthy controls from the Diagnostic Innovations in Glaucoma Study were included. RVM were trained and tested with cross-validation on optimized (backward elimination) SWAP features (thresholds plus age; pattern deviation (PD); total deviation (TD)) and on Heidelberg Retina Tomograph II (HRT) optic disc topography features, independently and in combination. RVM performance was also compared to two HRT linear discriminant functions (LDF) and to SWAP mean deviation (MD) and pattern standard deviation (PSD). Classifier performance was measured by the area under the receiver operating characteristic curves (AUROCs) generated for each feature set and by the sensitivities at set specificities of 75%, 90% and 96%. Results RVM trained on combined HRT and SWAP thresholds plus age had significantly higher AUROC (0.93) than RVM trained on HRT (0.88) and SWAP (0.76) alone. AUROCs for the SWAP global indices (MD: 0.68; PSD: 0.72) offered no advantage over SWAP thresholds plus age, while the LDF AUROCs were significantly lower than RVM trained on the combined SWAP and HRT feature set and on HRT alone feature set. Conclusions Training RVM on combined optimized HRT and SWAP data improved diagnostic accuracy compared to training on SWAP and HRT parameters alone. Future research may identify other combinations of tests and classifiers that can also improve diagnostic accuracy. PMID:19528827

A dynamic data source selection system for smartwatch platform.

PubMed

Nemati, Ebrahim; Sideris, Konstantinos; Kalantarian, Haik; Sarrafzadeh, Majid

2016-08-01

A novel data source selection algorithm is proposed for ambulatory activity tracking of elderly people. The algorithm introduces the concept of dynamic switching between the data collection modules (a smartwatch and a smartphone) to improve accuracy and battery life using contextual information. We show that by making offloading decisions as a function of activity, the proposed algorithm improves power consumption and accuracy of the previous work by 7 hours and 5% respectively compared to the baseline.

The use of computerized image guidance in lumbar disk arthroplasty.

PubMed

Smith, Harvey E; Vaccaro, Alexander R; Yuan, Philip S; Papadopoulos, Stephen; Sasso, Rick

2006-02-01

Surgical navigation systems have been increasingly studied and applied in the application of spinal instrumentation. Successful disk arthroplasty requires accurate midline and rotational positioning for optimal function and longevity. A surgical simulation study in human cadaver specimens was done to evaluate and compare the accuracy of standard fluoroscopy, computer-assisted fluoroscopic image guidance, and Iso-C3D image guidance in the placement of lumbar intervertebral disk replacements. Lumbar intervertebral disk prostheses were placed using three different image guidance techniques in three human cadaver spine specimens at multiple levels. Postinstrumentation accuracy was assessed with thin-cut computed tomography scans. Intervertebral disk replacements placed using the StealthStation with Iso-C3D were more accurately centered than those placed using the StealthStation with FluoroNav and standard fluoroscopy. Intervertebral disk replacements placed with Iso-C3D and FluoroNav had improved rotational divergence compared with standard fluoroscopy. Iso-C3D and FluoroNav had a smaller interprocedure variance than standard fluoroscopy. These results did not approach statistical significance. Relative to both virtual and standard fluoroscopy, use of the StealthStation with Iso-C3D resulted in improved accuracy in centering the lumbar disk prosthesis in the coronal midline. The StealthStation with FluoroNav appears to be at least equivalent to standard fluoroscopy and may offer improved accuracy with rotational alignment while minimizing radiation exposure to the surgeon. Surgical guidance systems may offer improved accuracy and less interprocedure variation in the placement of intervertebral disk replacements than standard fluoroscopy. Further study regarding surgical navigation systems for intervertebral disk replacement is warranted.

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm.

PubMed

Bahadure, Nilesh Bhaskarrao; Ray, Arun Kumar; Thethi, Har Pal

2018-01-17

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

Improved method for predicting protein fold patterns with ensemble classifiers.

PubMed

Chen, W; Liu, X; Huang, Y; Jiang, Y; Zou, Q; Lin, C

2012-01-27

Protein folding is recognized as a critical problem in the field of biophysics in the 21st century. Predicting protein-folding patterns is challenging due to the complex structure of proteins. In an attempt to solve this problem, we employed ensemble classifiers to improve prediction accuracy. In our experiments, 188-dimensional features were extracted based on the composition and physical-chemical property of proteins and 20-dimensional features were selected using a coupled position-specific scoring matrix. Compared with traditional prediction methods, these methods were superior in terms of prediction accuracy. The 188-dimensional feature-based method achieved 71.2% accuracy in five cross-validations. The accuracy rose to 77% when we used a 20-dimensional feature vector. These methods were used on recent data, with 54.2% accuracy. Source codes and dataset, together with web server and software tools for prediction, are available at: http://datamining.xmu.edu.cn/main/~cwc/ProteinPredict.html.

Improving Fermi Orbit Determination and Prediction in an Uncertain Atmospheric Drag Environment

NASA Technical Reports Server (NTRS)

Vavrina, Matthew A.; Newman, Clark P.; Slojkowski, Steven E.; Carpenter, J. Russell

2014-01-01

Orbit determination and prediction of the Fermi Gamma-ray Space Telescope trajectory is strongly impacted by the unpredictability and variability of atmospheric density and the spacecraft's ballistic coefficient. Operationally, Global Positioning System point solutions are processed with an extended Kalman filter for orbit determination, and predictions are generated for conjunction assessment with secondary objects. When these predictions are compared to Joint Space Operations Center radar-based solutions, the close approach distance between the two predictions can greatly differ ahead of the conjunction. This work explores strategies for improving prediction accuracy and helps to explain the prediction disparities. Namely, a tuning analysis is performed to determine atmospheric drag modeling and filter parameters that can improve orbit determination as well as prediction accuracy. A 45% improvement in three-day prediction accuracy is realized by tuning the ballistic coefficient and atmospheric density stochastic models, measurement frequency, and other modeling and filter parameters.

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition.

PubMed

Janousova, Eva; Schwarz, Daniel; Kasparek, Tomas

2015-06-30

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer-aided diagnostics of neuropsychiatric diseases. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Direct Position Determination of Multiple Non-Circular Sources with a Moving Coprime Array.

PubMed

Zhang, Yankui; Ba, Bin; Wang, Daming; Geng, Wei; Xu, Haiyun

2018-05-08

Direct position determination (DPD) is currently a hot topic in wireless localization research as it is more accurate than traditional two-step positioning. However, current DPD algorithms are all based on uniform arrays, which have an insufficient degree of freedom and limited estimation accuracy. To improve the DPD accuracy, this paper introduces a coprime array to the position model of multiple non-circular sources with a moving array. To maximize the advantages of this coprime array, we reconstruct the covariance matrix by vectorization, apply a spatial smoothing technique, and converge the subspace data from each measuring position to establish the cost function. Finally, we obtain the position coordinates of the multiple non-circular sources. The complexity of the proposed method is computed and compared with that of other methods, and the Cramer⁻Rao lower bound of DPD for multiple sources with a moving coprime array, is derived. Theoretical analysis and simulation results show that the proposed algorithm is not only applicable to circular sources, but can also improve the positioning accuracy of non-circular sources. Compared with existing two-step positioning algorithms and DPD algorithms based on uniform linear arrays, the proposed technique offers a significant improvement in positioning accuracy with a slight increase in complexity.

Different Coefficients and Exponents for Metabolic Body Weight in a Model to Estimate Individual Feed Intake for Growing-finishing Pigs

PubMed Central

Lee, S. A.; Kong, C.; Adeola, O.; Kim, B. G.

2016-01-01

Estimation of feed intake (FI) for individual animals within a pen is needed in situations where more than one animal share a feeder during feeding trials. A partitioning method (PM) was previously published as a model to estimate the individual FI (IFI). Briefly, the IFI of a pig within the pen was calculated by partitioning IFI into IFI for maintenance (IFIm) and IFI for growth. In the PM, IFIm is determined based on the metabolic body weight (BW), which is calculated using the coefficient of 106 and exponent of 0.75. Two simulation studies were conducted to test the hypothesis that the use of different coefficients and exponents for metabolic BW to calculate IFIm improves the accuracy of the estimates of IFI for pigs, and that PM is applied to pigs fed in group-housing systems. The accuracy of prediction represented by difference between actual and estimated IFI was compared using PM, ratio (RM), or averaging method (AM). In simulation studies 1 and 2, the PM estimated IFI better than the AM and RM during most of the periods (p<0.05). The use of 0.60 as the exponent and the coefficient of 197 to calculate metabolic BW did not improve the accuracy of the IFI estimates in both simulation studies 1 and 2. The results imply that the use of 197 kcal×kg BW0.60 as metabolizable energy for maintenance in PM does not improve the accuracy of IFI estimations compared with the use of 106 kcal×kg BW0.75 and that the PM estimates the IFI of pigs with greater accuracy compared with the averaging or ratio methods in group-housing systems. PMID:27608642

Improving the performances of autofocus based on adaptive retina-like sampling model

NASA Astrophysics Data System (ADS)

Hao, Qun; Xiao, Yuqing; Cao, Jie; Cheng, Yang; Sun, Ce

2018-03-01

An adaptive retina-like sampling model (ARSM) is proposed to balance autofocusing accuracy and efficiency. Based on the model, we carry out comparative experiments between the proposed method and the traditional method in terms of accuracy, the full width of the half maxima (FWHM) and time consumption. Results show that the performances of our method are better than that of the traditional method. Meanwhile, typical autofocus functions, including sum-modified-Laplacian (SML), Laplacian (LAP), Midfrequency-DCT (MDCT) and Absolute Tenengrad (ATEN) are compared through comparative experiments. The smallest FWHM is obtained by the use of LAP, which is more suitable for evaluating accuracy than other autofocus functions. The autofocus function of MDCT is most suitable to evaluate the real-time ability.

Ka-Band Radar Terminal Descent Sensor

NASA Technical Reports Server (NTRS)

Pollard, Brian; Berkun, Andrew; Tope, Michael; Andricos, Constantine; Okonek, Joseph; Lou, Yunling

2007-01-01

The terminal descent sensor (TDS) is a radar altimeter/velocimeter that improves the accuracy of velocity sensing by more than an order of magnitude when compared to existing sensors. The TDS is designed for the safe planetary landing of payloads, and may be used in helicopters and fixed-wing aircraft requiring high-accuracy velocity sensing

A Method for Improving the Pose Accuracy of a Robot Manipulator Based on Multi-Sensor Combined Measurement and Data Fusion

PubMed Central

Liu, Bailing; Zhang, Fumin; Qu, Xinghua

2015-01-01

An improvement method for the pose accuracy of a robot manipulator by using a multiple-sensor combination measuring system (MCMS) is presented. It is composed of a visual sensor, an angle sensor and a series robot. The visual sensor is utilized to measure the position of the manipulator in real time, and the angle sensor is rigidly attached to the manipulator to obtain its orientation. Due to the higher accuracy of the multi-sensor, two efficient data fusion approaches, the Kalman filter (KF) and multi-sensor optimal information fusion algorithm (MOIFA), are used to fuse the position and orientation of the manipulator. The simulation and experimental results show that the pose accuracy of the robot manipulator is improved dramatically by 38%∼78% with the multi-sensor data fusion. Comparing with reported pose accuracy improvement methods, the primary advantage of this method is that it does not require the complex solution of the kinematics parameter equations, increase of the motion constraints and the complicated procedures of the traditional vision-based methods. It makes the robot processing more autonomous and accurate. To improve the reliability and accuracy of the pose measurements of MCMS, the visual sensor repeatability is experimentally studied. An optimal range of 1 × 0.8 × 1 ∼ 2 × 0.8 × 1 m in the field of view (FOV) is indicated by the experimental results. PMID:25850067

Improved accuracy of intraocular lens power calculation with the Zeiss IOLMaster.

PubMed

Olsen, Thomas

2007-02-01

This study aimed to demonstrate how the level of accuracy in intraocular lens (IOL) power calculation can be improved with optical biometry using partial optical coherence interferometry (PCI) (Zeiss IOLMaster) and current anterior chamber depth (ACD) prediction algorithms. Intraocular lens power in 461 consecutive cataract operations was calculated using both PCI and ultrasound and the accuracy of the results of each technique were compared. To illustrate the importance of ACD prediction per se, predictions were calculated using both a recently published 5-variable method and the Haigis 2-variable method and the results compared. All calculations were optimized in retrospect to account for systematic errors, including IOL constants and other off-set errors. The average absolute IOL prediction error (observed minus expected refraction) was 0.65 dioptres with ultrasound and 0.43 D with PCI using the 5-variable ACD prediction method (p < 0.00001). The number of predictions within +/- 0.5 D, +/- 1.0 D and +/- 2.0 D of the expected outcome was 62.5%, 92.4% and 99.9% with PCI, compared with 45.5%, 77.3% and 98.4% with ultrasound, respectively (p < 0.00001). The 2-variable ACD method resulted in an average error in PCI predictions of 0.46 D, which was significantly higher than the error in the 5-variable method (p < 0.001). The accuracy of IOL power calculation can be significantly improved using calibrated axial length readings obtained with PCI and modern IOL power calculation formulas incorporating the latest generation ACD prediction algorithms.

Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Siyuan; Hwang, Youngdeok; Khabibrakhmanov, Ildar

With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual modelmore » has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.« less

Development of Neuromorphic Sift Operator with Application to High Speed Image Matching

NASA Astrophysics Data System (ADS)

Shankayi, M.; Saadatseresht, M.; Bitetto, M. A. V.

2015-12-01

There was always a speed/accuracy challenge in photogrammetric mapping process, including feature detection and matching. Most of the researches have improved algorithm's speed with simplifications or software modifications which increase the accuracy of the image matching process. This research tries to improve speed without enhancing the accuracy of the same algorithm using Neuromorphic techniques. In this research we have developed a general design of a Neuromorphic ASIC to handle algorithms such as SIFT. We also have investigated neural assignment in each step of the SIFT algorithm. With a rough estimation based on delay of the used elements including MAC and comparator, we have estimated the resulting chip's performance for 3 scenarios, Full HD movie (Videogrammetry), 24 MP (UAV photogrammetry), and 88 MP image sequence. Our estimations led to approximate 3000 fps for Full HD movie, 250 fps for 24 MP image sequence and 68 fps for 88MP Ultracam image sequence which can be a huge improvement for current photogrammetric processing systems. We also estimated the power consumption of less than10 watts which is not comparable to current workflows.

Improved Topographic Mapping Through Multi-Baseline SAR Interferometry with MAP Estimation

NASA Astrophysics Data System (ADS)

Dong, Yuting; Jiang, Houjun; Zhang, Lu; Liao, Mingsheng; Shi, Xuguo

2015-05-01

There is an inherent contradiction between the sensitivity of height measurement and the accuracy of phase unwrapping for SAR interferometry (InSAR) over rough terrain. This contradiction can be resolved by multi-baseline InSAR analysis, which exploits multiple phase observations with different normal baselines to improve phase unwrapping accuracy, or even avoid phase unwrapping. In this paper we propose a maximum a posteriori (MAP) estimation method assisted by SRTM DEM data for multi-baseline InSAR topographic mapping. Based on our method, a data processing flow is established and applied in processing multi-baseline ALOS/PALSAR dataset. The accuracy of resultant DEMs is evaluated by using a standard Chinese national DEM of scale 1:10,000 as reference. The results show that multi-baseline InSAR can improve DEM accuracy compared with single-baseline case. It is noteworthy that phase unwrapping is avoided and the quality of multi-baseline InSAR DEM can meet the DTED-2 standard.

Improving crop classification through attention to the timing of airborne radar acquisitions

NASA Technical Reports Server (NTRS)

Brisco, B.; Ulaby, F. T.; Protz, R.

1984-01-01

Radar remote sensors may provide valuable input to crop classification procedures because of (1) their independence of weather conditions and solar illumination, and (2) their ability to respond to differences in crop type. Manual classification of multidate synthetic aperture radar (SAR) imagery resulted in an overall accuracy of 83 percent for corn, forest, grain, and 'other' cover types. Forests and corn fields were identified with accuracies approaching or exceeding 90 percent. Grain fields and 'other' fields were often confused with each other, resulting in classification accuracies of 51 and 66 percent, respectively. The 83 percent correct classification represents a 10 percent improvement when compared to similar SAR data for the same area collected at alternate time periods in 1978. These results demonstrate that improvements in crop classification accuracy can be achieved with SAR data by synchronizing data collection times with crop growth stages in order to maximize differences in the geometric and dielectric properties of the cover types of interest.

Gravity compensation in a Strapdown Inertial Navigation System to improve the attitude accuracy

NASA Astrophysics Data System (ADS)

Zhu, Jing; Wang, Jun; Wang, Xingshu; Yang, Shuai

2017-10-01

Attitude errors in a strapdown inertial navigation system due to gravity disturbances and system noises can be relatively large, although they are bound within the Schuler and the Earth rotation period. The principal objective of the investigation is to determine to what extent accurate gravity data can improve the attitude accuracy. The way the gravity disturbances affect the attitude were analyzed and compared with system noises by the analytic solution and simulation. The gravity disturbances affect the attitude accuracy by introducing the initial attitude error and the equivalent accelerometer bias. With the development of the high precision inertial devices and the application of the rotation modulation technology, the gravity disturbance cannot be neglected anymore. The gravity compensation was performed using the EGM2008 and simulations with and without accurate gravity compensation under varying navigation conditions were carried out. The results show that the gravity compensation improves the horizontal components of attitude accuracy evidently while the yaw angle is badly affected by the uncompensated gyro bias in vertical channel.

Determination of Monensin in Bovine Tissues: A Bridging Study Comparing the Bioautographic Method (FSIS CLG-MON) with a Liquid Chromatography-Tandem Mass Spectrometry Method (OMA 2011.24).

PubMed

Mizinga, Kemmy M; Burnett, Thomas J; Brunelle, Sharon L; Wallace, Michael A; Coleman, Mark R

2018-05-01

The U.S. Department of Agriculture, Food Safety Inspection Service regulatory method for monensin, Chemistry Laboratory Guidebook CLG-MON, is a semiquantitative bioautographic method adopted in 1991. Official Method of AnalysisSM (OMA) 2011.24, a modern quantitative and confirmatory LC-tandem MS method, uses no chlorinated solvents and has several advantages, including ease of use, ready availability of reagents and materials, shorter run-time, and higher throughput than CLG-MON. Therefore, a bridging study was conducted to support the replacement of method CLG-MON with OMA 2011.24 for regulatory use. Using fortified bovine tissue samples, CLG-MON yielded accuracies of 80-120% in 44 of the 56 samples tested (one sample had no result, six samples had accuracies of >120%, and five samples had accuracies of 40-160%), but the semiquantitative nature of CLG-MON prevented assessment of precision, whereas OMA 2011.24 had accuracies of 88-110% and RSDr of 0.00-15.6%. Incurred residue results corroborated these results, demonstrating improved accuracy (83.3-114%) and good precision (RSDr of 2.6-20.5%) for OMA 2011.24 compared with CLG-MON (accuracy generally within 80-150%, with exceptions). Furthermore, χ2 analysis revealed no statistically significant difference between the two methods. Thus, the microbiological activity of monensin correlated with the determination of monensin A in bovine tissues, and OMA 2011.24 provided improved accuracy and precision over CLG-MON.

The power of timing: Adding a time-to-completion cutoff to the Word Choice Test and Recognition Memory Test improves classification accuracy.

PubMed

Erdodi, Laszlo A; Tyson, Bradley T; Shahein, Ayman G; Lichtenstein, Jonathan D; Abeare, Christopher A; Pelletier, Chantalle L; Zuccato, Brandon G; Kucharski, Brittany; Roth, Robert M

2017-05-01

The Recognition Memory Test (RMT) and Word Choice Test (WCT) are structurally similar, but psychometrically different. Previous research demonstrated that adding a time-to-completion cutoff improved the classification accuracy of the RMT. However, the contribution of WCT time-cutoffs to improve the detection of invalid responding has not been investigated. The present study was designed to evaluate the classification accuracy of time-to-completion on the WCT compared to the accuracy score and the RMT. Both tests were administered to 202 adults (M age  = 45.3 years, SD = 16.8; 54.5% female) clinically referred for neuropsychological assessment in counterbalanced order as part of a larger battery of cognitive tests. Participants obtained lower and more variable scores on the RMT (M = 44.1, SD = 7.6) than on the WCT (M = 46.9, SD = 5.7). Similarly, they took longer to complete the recognition trial on the RMT (M = 157.2 s,SD = 71.8) than the WCT (M = 137.2 s, SD = 75.7). The optimal cutoff on the RMT (≤43) produced .60 sensitivity at .87 specificity. The optimal cutoff on the WCT (≤47) produced .57 sensitivity at .87 specificity. Time-cutoffs produced comparable classification accuracies for both RMT (≥192 s; .48 sensitivity at .88 specificity) and WCT (≥171 s; .49 sensitivity at .91 specificity). They also identified an additional 6-10% of the invalid profiles missed by accuracy score cutoffs, while maintaining good specificity (.93-.95). Functional equivalence was reached at accuracy scores ≤43 (RMT) and ≤47 (WCT) or time-to-completion ≥192 s (RMT) and ≥171 s (WCT). Time-to-completion cutoffs are valuable additions to both tests. They can function as independent validity indicators or enhance the sensitivity of accuracy scores without requiring additional measures or extending standard administration time.

Correlation of ground tests and analyses of a dynamically scaled Space Station model configuration

NASA Technical Reports Server (NTRS)

Javeed, Mehzad; Edighoffer, Harold H.; Mcgowan, Paul E.

1993-01-01

Verification of analytical models through correlation with ground test results of a complex space truss structure is demonstrated. A multi-component, dynamically scaled space station model configuration is the focus structure for this work. Previously established test/analysis correlation procedures are used to develop improved component analytical models. Integrated system analytical models, consisting of updated component analytical models, are compared with modal test results to establish the accuracy of system-level dynamic predictions. Design sensitivity model updating methods are shown to be effective for providing improved component analytical models. Also, the effects of component model accuracy and interface modeling fidelity on the accuracy of integrated model predictions is examined.

The use of Landsat data to inventory cotton and soybean acreage in North Alabama

NASA Technical Reports Server (NTRS)

Downs, S. W., Jr.; Faust, N. L.

1980-01-01

This study was performed to determine if Landsat data could be used to improve the accuracy of the estimation of cotton acreage. A linear classification algorithm and a maximum likelihood algorithm were used for computer classification of the area, and the classification was compared with ground truth. The classification accuracy for some fields was greater than 90 percent; however, the overall accuracy was 71 percent for cotton and 56 percent for soybeans. The results of this research indicate that computer analysis of Landsat data has potential for improving upon the methods presently being used to determine cotton acreage; however, additional experiments and refinements are needed before the method can be used operationally.

Improved Short-Term Clock Prediction Method for Real-Time Positioning.

PubMed

Lv, Yifei; Dai, Zhiqiang; Zhao, Qile; Yang, Sheng; Zhou, Jinning; Liu, Jingnan

2017-06-06

The application of real-time precise point positioning (PPP) requires real-time precise orbit and clock products that should be predicted within a short time to compensate for the communication delay or data gap. Unlike orbit correction, clock correction is difficult to model and predict. The widely used linear model hardly fits long periodic trends with a small data set and exhibits significant accuracy degradation in real-time prediction when a large data set is used. This study proposes a new prediction model for maintaining short-term satellite clocks to meet the high-precision requirements of real-time clocks and provide clock extrapolation without interrupting the real-time data stream. Fast Fourier transform (FFT) is used to analyze the linear prediction residuals of real-time clocks. The periodic terms obtained through FFT are adopted in the sliding window prediction to achieve a significant improvement in short-term prediction accuracy. This study also analyzes and compares the accuracy of short-term forecasts (less than 3 h) by using different length observations. Experimental results obtained from International GNSS Service (IGS) final products and our own real-time clocks show that the 3-h prediction accuracy is better than 0.85 ns. The new model can replace IGS ultra-rapid products in the application of real-time PPP. It is also found that there is a positive correlation between the prediction accuracy and the short-term stability of on-board clocks. Compared with the accuracy of the traditional linear model, the accuracy of the static PPP using the new model of the 2-h prediction clock in N, E, and U directions is improved by about 50%. Furthermore, the static PPP accuracy of 2-h clock products is better than 0.1 m. When an interruption occurs in the real-time model, the accuracy of the kinematic PPP solution using 1-h clock prediction product is better than 0.2 m, without significant accuracy degradation. This model is of practical significance because it solves the problems of interruption and delay in data broadcast in real-time clock estimation and can meet the requirements of real-time PPP.

Data for free--can an electronic medical record provide outcome data for incontinence/prolapse repair procedures?

PubMed

Steidl, Matthew; Zimmern, Philippe

2013-01-01

We determined whether a custom computer program can improve the extraction and accuracy of key outcome measures from progress notes in an electronic medical record compared to a traditional data recording system for incontinence and prolapse repair procedures. Following institutional review board approval, progress notes were exported from the Epic electronic medical record system for outcome measure extraction by a custom computer program. The extracted data (D1) were compared against a manually maintained outcome measures database (D2). This work took place in 2 phases. During the first phase, volatile data such as questionnaires and standardized physical examination findings using the POP-Q (pelvic organ prolapse quantification) system were extracted from existing progress notes. The second phase used a progress note template incorporating key outcome measures to evaluate improvement in data accuracy and extraction rates. Phase 1 compared 6,625 individual outcome measures from 316 patients in D2 to 3,534 outcome measures extracted from progress notes in D1, resulting in an extraction rate of 53.3%. A subset of 3,763 outcome measures from D1 was created by excluding data that did not exist in the extraction, yielding an accuracy rate of 93.9%. With the use of the template in phase 2, the extraction rate improved to 91.9% (273 of 297) and the accuracy rate improved to 100% (273 of 273). In the field of incontinence and prolapse, the disciplined use of an electronic medical record template containing a preestablished set of key outcome measures can provide the ideal interface between required documentation and clinical research. Copyright © 2013 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

The use of video in standardized patient training to improve portrayal accuracy: A randomized post-test control group study.

PubMed

Schlegel, Claudia; Bonvin, Raphael; Rethans, Jan Joost; van der Vleuten, Cees

2014-10-14

Abstract Introduction: High-stake objective structured clinical examinations (OSCEs) with standardized patients (SPs) should offer the same conditions to all candidates throughout the exam. SP performance should therefore be as close to the original role script as possible during all encounters. In this study, we examined the impact of video in SP training on SPs' role accuracy, investigating how the use of different types of video during SP training improves the accuracy of SP portrayal. Methods: In a randomized post-test, control group design three groups of 12 SPs each with different types of video training and one control group of 12 SPs without video use in SP training were compared. The three intervention groups used role-modeling video, performance-feedback video, or a combination of both. Each SP from each group had four students encounter. Two blinded faculty members rated the 192 video-recorded encounters, using a case-specific rating instrument to assess SPs' role accuracy. Results: SPs trained by video showed significantly (p < 0.001) better role accuracy than SPs trained without video over the four sequential portrayals. There was no difference between the three types of video training. Discussion: Use of video during SP training enhances the accuracy of SP portrayal compared with no video, regardless of the type of video intervention used.

Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.

PubMed

Chowdhury, Alok Kumar; Tjondronegoro, Dian; Chandran, Vinod; Trost, Stewart G

2017-09-01

To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

Combining diffusion-weighted MRI with Gd-EOB-DTPA-enhanced MRI improves the detection of colorectal liver metastases.

PubMed

Koh, D-M; Collins, D J; Wallace, T; Chau, I; Riddell, A M

2012-07-01

To compare the diagnostic accuracy of gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA)-enhanced MRI, diffusion-weighted MRI (DW-MRI) and a combination of both techniques for the detection of colorectal hepatic metastases. 72 patients with suspected colorectal liver metastases underwent Gd-EOB-DTPA MRI and DW-MRI. Images were retrospectively reviewed with unenhanced T(1) and T(2) weighted images as Gd-EOB-DTPA image set, DW-MRI image set and combined image set by two independent radiologists. Each lesion detected was scored for size, location and likelihood of metastasis, and compared with surgery and follow-up imaging. Diagnostic accuracy was compared using receiver operating characteristics and interobserver agreement by kappa statistics. 417 lesions (310 metastases, 107 benign) were found in 72 patients. For both readers, diagnostic accuracy using the combined image set was higher [area under the curve (Az)=0.96, 0.97] than Gd-EOB-DTPA image set (Az=0.86, 0.89) or DW-MRI image set (Az=0.93, 0.92). Using combined image set improved identification of liver metastases compared with Gd-EOB-DTPA image set (p<0.001) or DW-MRI image set (p<0.001). There was very good interobserver agreement for lesion classification (κ=0.81-0.88). Combining DW-MRI with Gd-EOB-DTPA-enhanced T(1) weighted MRI significantly improved the detection of colorectal liver metastases.

Tightly Coupled Integration of GPS Ambiguity Fixed Precise Point Positioning and MEMS-INS through a Troposphere-Constrained Adaptive Kalman Filter

PubMed Central

Han, Houzeng; Xu, Tianhe; Wang, Jian

2016-01-01

Precise Point Positioning (PPP) makes use of the undifferenced pseudorange and carrier phase measurements with ionospheric-free (IF) combinations to achieve centimeter-level positioning accuracy. Conventionally, the IF ambiguities are estimated as float values. To improve the PPP positioning accuracy and shorten the convergence time, the integer phase clock model with between-satellites single-difference (BSSD) operation is used to recover the integer property. However, the continuity and availability of stand-alone PPP is largely restricted by the observation environment. The positioning performance will be significantly degraded when GPS operates under challenging environments, if less than five satellites are present. A commonly used approach is integrating a low cost inertial sensor to improve the positioning performance and robustness. In this study, a tightly coupled (TC) algorithm is implemented by integrating PPP with inertial navigation system (INS) using an Extended Kalman filter (EKF). The navigation states, inertial sensor errors and GPS error states are estimated together. The troposphere constrained approach, which utilizes external tropospheric delay as virtual observation, is applied to further improve the ambiguity-fixed height positioning accuracy, and an improved adaptive filtering strategy is implemented to improve the covariance modelling considering the realistic noise effect. A field vehicular test with a geodetic GPS receiver and a low cost inertial sensor was conducted to validate the improvement on positioning performance with the proposed approach. The results show that the positioning accuracy has been improved with inertial aiding. Centimeter-level positioning accuracy is achievable during the test, and the PPP/INS TC integration achieves a fast re-convergence after signal outages. For troposphere constrained solutions, a significant improvement for the height component has been obtained. The overall positioning accuracies of the height component are improved by 30.36%, 16.95% and 24.07% for three different convergence times, i.e., 60, 50 and 30 min, respectively. It shows that the ambiguity-fixed horizontal positioning accuracy has been significantly improved. When compared with the conventional PPP solution, it can be seen that position accuracies are improved by 19.51%, 61.11% and 23.53% for the north, east and height components, respectively, after one hour convergence through the troposphere constraint fixed PPP/INS with adaptive covariance model. PMID:27399721

Multi-grid finite element method used for enhancing the reconstruction accuracy in Cerenkov luminescence tomography

NASA Astrophysics Data System (ADS)

Guo, Hongbo; He, Xiaowei; Liu, Muhan; Zhang, Zeyu; Hu, Zhenhua; Tian, Jie

2017-03-01

Cerenkov luminescence tomography (CLT), as a promising optical molecular imaging modality, can be applied to cancer diagnostic and therapeutic. Most researches about CLT reconstruction are based on the finite element method (FEM) framework. However, the quality of FEM mesh grid is still a vital factor to restrict the accuracy of the CLT reconstruction result. In this paper, we proposed a multi-grid finite element method framework, which was able to improve the accuracy of reconstruction. Meanwhile, the multilevel scheme adaptive algebraic reconstruction technique (MLS-AART) based on a modified iterative algorithm was applied to improve the reconstruction accuracy. In numerical simulation experiments, the feasibility of our proposed method were evaluated. Results showed that the multi-grid strategy could obtain 3D spatial information of Cerenkov source more accurately compared with the traditional single-grid FEM.

Evaluating the potential for site-specific modification of LiDAR DEM derivatives to improve environmental planning-scale wetland identification using Random Forest classification

NASA Astrophysics Data System (ADS)

O'Neil, Gina L.; Goodall, Jonathan L.; Watson, Layne T.

2018-04-01

Wetlands are important ecosystems that provide many ecological benefits, and their quality and presence are protected by federal regulations. These regulations require wetland delineations, which can be costly and time-consuming to perform. Computer models can assist in this process, but lack the accuracy necessary for environmental planning-scale wetland identification. In this study, the potential for improvement of wetland identification models through modification of digital elevation model (DEM) derivatives, derived from high-resolution and increasingly available light detection and ranging (LiDAR) data, at a scale necessary for small-scale wetland delineations is evaluated. A novel approach of flow convergence modelling is presented where Topographic Wetness Index (TWI), curvature, and Cartographic Depth-to-Water index (DTW), are modified to better distinguish wetland from upland areas, combined with ancillary soil data, and used in a Random Forest classification. This approach is applied to four study sites in Virginia, implemented as an ArcGIS model. The model resulted in significant improvement in average wetland accuracy compared to the commonly used National Wetland Inventory (84.9% vs. 32.1%), at the expense of a moderately lower average non-wetland accuracy (85.6% vs. 98.0%) and average overall accuracy (85.6% vs. 92.0%). From this, we concluded that modifying TWI, curvature, and DTW provides more robust wetland and non-wetland signatures to the models by improving accuracy rates compared to classifications using the original indices. The resulting ArcGIS model is a general tool able to modify these local LiDAR DEM derivatives based on site characteristics to identify wetlands at a high resolution.

[Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].

PubMed

Zhou, Jinzhi; Tang, Xiaofang

2015-08-01

In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCD systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset IV a from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.

3D Higher Order Modeling in the BEM/FEM Hybrid Formulation

NASA Technical Reports Server (NTRS)

Fink, P. W.; Wilton, D. R.

2000-01-01

Higher order divergence- and curl-conforming bases have been shown to provide significant benefits, in both convergence rate and accuracy, in the 2D hybrid finite element/boundary element formulation (P. Fink and D. Wilton, National Radio Science Meeting, Boulder, CO, Jan. 2000). A critical issue in achieving the potential for accuracy of the approach is the accurate evaluation of all matrix elements. These involve products of high order polynomials and, in some instances, singular Green's functions. In the 2D formulation, the use of a generalized Gaussian quadrature method was found to greatly facilitate the computation and to improve the accuracy of the boundary integral equation self-terms. In this paper, a 3D, hybrid electric field formulation employing higher order bases and higher order elements is presented. The improvements in convergence rate and accuracy, compared to those resulting from lower order modeling, are established. Techniques developed to facilitate the computation of the boundary integral self-terms are also shown to improve the accuracy of these terms. Finally, simple preconditioning techniques are used in conjunction with iterative solution procedures to solve the resulting linear system efficiently. In order to handle the boundary integral singularities in the 3D formulation, the parent element- either a triangle or rectangle-is subdivided into a set of sub-triangles with a common vertex at the singularity. The contribution to the integral from each of the sub-triangles is computed using the Duffy transformation to remove the singularity. This method is shown to greatly facilitate t'pe self-term computation when the bases are of higher order. In addition, the sub-triangles can be further divided to achieve near arbitrary accuracy in the self-term computation. An efficient method for subdividing the parent element is presented. The accuracy obtained using higher order bases is compared to that obtained using lower order bases when the number of unknowns is approximately equal. Also, convergence rates obtained using higher order bases are compared to those obtained with lower order bases for selected sample

Audio visual speech source separation via improved context dependent association model

NASA Astrophysics Data System (ADS)

Kazemi, Alireza; Boostani, Reza; Sobhanmanesh, Fariborz

2014-12-01

In this paper, we exploit the non-linear relation between a speech source and its associated lip video as a source of extra information to propose an improved audio-visual speech source separation (AVSS) algorithm. The audio-visual association is modeled using a neural associator which estimates the visual lip parameters from a temporal context of acoustic observation frames. We define an objective function based on mean square error (MSE) measure between estimated and target visual parameters. This function is minimized for estimation of the de-mixing vector/filters to separate the relevant source from linear instantaneous or time-domain convolutive mixtures. We have also proposed a hybrid criterion which uses AV coherency together with kurtosis as a non-Gaussianity measure. Experimental results are presented and compared in terms of visually relevant speech detection accuracy and output signal-to-interference ratio (SIR) of source separation. The suggested audio-visual model significantly improves relevant speech classification accuracy compared to existing GMM-based model and the proposed AVSS algorithm improves the speech separation quality compared to reference ICA- and AVSS-based methods.

The Theory and Practice of Estimating the Accuracy of Dynamic Flight-Determined Coefficients

NASA Technical Reports Server (NTRS)

Maine, R. E.; Iliff, K. W.

1981-01-01

Means of assessing the accuracy of maximum likelihood parameter estimates obtained from dynamic flight data are discussed. The most commonly used analytical predictors of accuracy are derived and compared from both statistical and simplified geometrics standpoints. The accuracy predictions are evaluated with real and simulated data, with an emphasis on practical considerations, such as modeling error. Improved computations of the Cramer-Rao bound to correct large discrepancies due to colored noise and modeling error are presented. The corrected Cramer-Rao bound is shown to be the best available analytical predictor of accuracy, and several practical examples of the use of the Cramer-Rao bound are given. Engineering judgement, aided by such analytical tools, is the final arbiter of accuracy estimation.

Non-invasive cortical stimulation improves post-stroke attention decline.

PubMed

Kang, Eun Kyoung; Baek, Min Jae; Kim, Sangyun; Paik, Nam-Jong

2009-01-01

Attention decline after stroke is common and hampers the rehabilitation process, and non-invasive transcranial direct current stimulation (tDCS) has the potential to elicit behavioral changes by modulating cortical excitability. The authors tested the hypothesis that a single session of non-invasive cortical stimulation with excitatory anodal tDCS applied to the left dorsolateral prefrontal cortex (DLPFC) can improve attention in stroke patients. Ten patients with post-stroke cognitive decline (MMSE 25) and 10 age-matched healthy controls participated in this double blind, sham-controlled, crossover study involving the administration of real (2 mA for 20 min) or sham stimulation (2 mA for 1 min) to the left DLPFC. Attention was measured using a computerized Go/No-Go test before and after intervention. Improvements in accuracy and speed after stimulation relative to baseline were compared for real and sham stimulations. In healthy controls, no significant improvement in Go/No-Go test was observed after either real or sham stimulation. However, in stroke patients, tDCS led to a significant improvement in response accuracy at 1 hour post-stimulation relative to baseline, and this improvement was maintained until 3 hours post-stimulation (P< 0.05), whereas sham stimulation did not lead to a significant improvement in response accuracy (P> 0.05). Changes in reaction times were comparable for the two stimulations (P> 0.05). Non invasive anodal tDCS applied to the left DLPFC was found to improve attention versus sham stimulation in stroke patients, which suggests that non-invasive cortical intervention could potentially be used during rehabilitative training to improve attention.

Application of Numerical Integration and Data Fusion in Unit Vector Method

NASA Astrophysics Data System (ADS)

Zhang, J.

2012-01-01

The Unit Vector Method (UVM) is a series of orbit determination methods which are designed by Purple Mountain Observatory (PMO) and have been applied extensively. It gets the conditional equations for different kinds of data by projecting the basic equation to different unit vectors, and it suits for weighted process for different kinds of data. The high-precision data can play a major role in orbit determination, and accuracy of orbit determination is improved obviously. The improved UVM (PUVM2) promoted the UVM from initial orbit determination to orbit improvement, and unified the initial orbit determination and orbit improvement dynamically. The precision and efficiency are improved further. In this thesis, further research work has been done based on the UVM: Firstly, for the improvement of methods and techniques for observation, the types and decision of the observational data are improved substantially, it is also asked to improve the decision of orbit determination. The analytical perturbation can not meet the requirement. So, the numerical integration for calculating the perturbation has been introduced into the UVM. The accuracy of dynamical model suits for the accuracy of the real data, and the condition equations of UVM are modified accordingly. The accuracy of orbit determination is improved further. Secondly, data fusion method has been introduced into the UVM. The convergence mechanism and the defect of weighted strategy have been made clear in original UVM. The problem has been solved in this method, the calculation of approximate state transition matrix is simplified and the weighted strategy has been improved for the data with different dimension and different precision. Results of orbit determination of simulation and real data show that the work of this thesis is effective: (1) After the numerical integration has been introduced into the UVM, the accuracy of orbit determination is improved obviously, and it suits for the high-accuracy data of available observation apparatus. Compare with the classical differential improvement with the numerical integration, its calculation speed is also improved obviously. (2) After data fusion method has been introduced into the UVM, weighted distribution accords rationally with the accuracy of different kinds of data, all data are fully used and the new method is also good at numerical stability and rational weighted distribution.

Employment of sawtooth-shaped-function excitation signal and oversampling for improving resistance measurement accuracy

NASA Astrophysics Data System (ADS)

Lin, Ling; Li, Shujuan; Yan, Wenjuan; Li, Gang

2016-10-01

In order to achieve higher measurement accuracy of routine resistance without increasing the complexity and cost of the system circuit of existing methods, this paper presents a novel method that exploits a shaped-function excitation signal and oversampling technology. The excitation signal source for resistance measurement is modulated by the sawtooth-shaped-function signal, and oversampling technology is employed to increase the resolution and the accuracy of the measurement system. Compared with the traditional method of using constant amplitude excitation signal, this method can effectively enhance the measuring accuracy by almost one order of magnitude and reduce the root mean square error by 3.75 times under the same measurement conditions. The results of experiments show that the novel method can attain the aim of significantly improve the measurement accuracy of resistance on the premise of not increasing the system cost and complexity of the circuit, which is significantly valuable for applying in electronic instruments.

Orbit Determination Accuracy for Comets on Earth-Impacting Trajectories

NASA Technical Reports Server (NTRS)

Kay-Bunnell, Linda

2004-01-01

The results presented show the level of orbit determination accuracy obtainable for long-period comets discovered approximately one year before collision with Earth. Preliminary orbits are determined from simulated observations using Gauss' method. Additional measurements are incorporated to improve the solution through the use of a Kalman filter, and include non-gravitational perturbations due to outgassing. Comparisons between observatories in several different circular heliocentric orbits show that observatories in orbits with radii less than 1 AU result in increased orbit determination accuracy for short tracking durations due to increased parallax per unit time. However, an observatory at 1 AU will perform similarly if the tracking duration is increased, and accuracy is significantly improved if additional observatories are positioned at the Sun-Earth Lagrange points L3, L4, or L5. A single observatory at 1 AU capable of both optical and range measurements yields the highest orbit determination accuracy in the shortest amount of time when compared to other systems of observatories.

Vocal Accuracy and Neural Plasticity Following Micromelody-Discrimination Training

PubMed Central

Zarate, Jean Mary; Delhommeau, Karine; Wood, Sean; Zatorre, Robert J.

2010-01-01

Background Recent behavioral studies report correlational evidence to suggest that non-musicians with good pitch discrimination sing more accurately than those with poorer auditory skills. However, other studies have reported a dissociation between perceptual and vocal production skills. In order to elucidate the relationship between auditory discrimination skills and vocal accuracy, we administered an auditory-discrimination training paradigm to a group of non-musicians to determine whether training-enhanced auditory discrimination would specifically result in improved vocal accuracy. Methodology/Principal Findings We utilized micromelodies (i.e., melodies with seven different interval scales, each smaller than a semitone) as the main stimuli for auditory discrimination training and testing, and we used single-note and melodic singing tasks to assess vocal accuracy in two groups of non-musicians (experimental and control). To determine if any training-induced improvements in vocal accuracy would be accompanied by related modulations in cortical activity during singing, the experimental group of non-musicians also performed the singing tasks while undergoing functional magnetic resonance imaging (fMRI). Following training, the experimental group exhibited significant enhancements in micromelody discrimination compared to controls. However, we did not observe a correlated improvement in vocal accuracy during single-note or melodic singing, nor did we detect any training-induced changes in activity within brain regions associated with singing. Conclusions/Significance Given the observations from our auditory training regimen, we therefore conclude that perceptual discrimination training alone is not sufficient to improve vocal accuracy in non-musicians, supporting the suggested dissociation between auditory perception and vocal production. PMID:20567521

Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat

PubMed Central

Rutkoski, Jessica; Poland, Jesse; Mondal, Suchismita; Autrique, Enrique; Pérez, Lorena González; Crossa, José; Reynolds, Matthew; Singh, Ravi

2016-01-01

Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots. PMID:27402362

Navigation strategy and filter design for solar electric missions

NASA Technical Reports Server (NTRS)

Tapley, B. D.; Hagar, H., Jr.

1972-01-01

Methods which have been proposed to improve the navigation accuracy for the low-thrust space vehicle include modifications to the standard Sequential- and Batch-type orbit determination procedures and the use of inertial measuring units (IMU) which measures directly the acceleration applied to the vehicle. The navigation accuracy obtained using one of the more promising modifications to the orbit determination procedures is compared with a combined IMU-Standard. The unknown accelerations are approximated as both first-order and second-order Gauss-Markov processes. The comparison is based on numerical results obtained in a study of the navigation requirements of a numerically simulated 152-day low-thrust mission to the asteroid Eros. The results obtained in the simulation indicate that the DMC algorithm will yield a significant improvement over the navigation accuracies achieved with previous estimation algorithms. In addition, the DMC algorithms will yield better navigation accuracies than the IMU-Standard Orbit Determination algorithm, except for extremely precise IMU measurements, i.e., gyroplatform alignment .01 deg and accelerometer signal-to-noise ratio .07. Unless these accuracies are achieved, the IMU navigation accuracies are generally unacceptable.

A study on low-cost, high-accuracy, and real-time stereo vision algorithms for UAV power line inspection

NASA Astrophysics Data System (ADS)

Wang, Hongyu; Zhang, Baomin; Zhao, Xun; Li, Cong; Lu, Cunyue

2018-04-01

Conventional stereo vision algorithms suffer from high levels of hardware resource utilization due to algorithm complexity, or poor levels of accuracy caused by inadequacies in the matching algorithm. To address these issues, we have proposed a stereo range-finding technique that produces an excellent balance between cost, matching accuracy and real-time performance, for power line inspection using UAV. This was achieved through the introduction of a special image preprocessing algorithm and a weighted local stereo matching algorithm, as well as the design of a corresponding hardware architecture. Stereo vision systems based on this technique have a lower level of resource usage and also a higher level of matching accuracy following hardware acceleration. To validate the effectiveness of our technique, a stereo vision system based on our improved algorithms were implemented using the Spartan 6 FPGA. In comparative experiments, it was shown that the system using the improved algorithms outperformed the system based on the unimproved algorithms, in terms of resource utilization and matching accuracy. In particular, Block RAM usage was reduced by 19%, and the improved system was also able to output range-finding data in real time.

Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China.

PubMed

Yang, Xiaoyan; Chen, Longgao; Li, Yingkui; Xi, Wenjia; Chen, Longqian

2015-07-01

Land use/land cover (LULC) inventory provides an important dataset in regional planning and environmental assessment. To efficiently obtain the LULC inventory, we compared the LULC classifications based on single satellite imagery with a rule-based classification based on multi-seasonal imagery in Lianyungang City, a coastal city in China, using CBERS-02 (the 2nd China-Brazil Environmental Resource Satellites) images. The overall accuracies of the classification based on single imagery are 78.9, 82.8, and 82.0% in winter, early summer, and autumn, respectively. The rule-based classification improves the accuracy to 87.9% (kappa 0.85), suggesting that combining multi-seasonal images can considerably improve the classification accuracy over any single image-based classification. This method could also be used to analyze seasonal changes of LULC types, especially for those associated with tidal changes in coastal areas. The distribution and inventory of LULC types with an overall accuracy of 87.9% and a spatial resolution of 19.5 m can assist regional planning and environmental assessment efficiently in Lianyungang City. This rule-based classification provides a guidance to improve accuracy for coastal areas with distinct LULC temporal spectral features.

Parameter prediction based on Improved Process neural network and ARMA error compensation in Evaporation Process

NASA Astrophysics Data System (ADS)

Qian, Xiaoshan

2018-01-01

The traditional model of evaporation process parameters have continuity and cumulative characteristics of the prediction error larger issues, based on the basis of the process proposed an adaptive particle swarm neural network forecasting method parameters established on the autoregressive moving average (ARMA) error correction procedure compensated prediction model to predict the results of the neural network to improve prediction accuracy. Taking a alumina plant evaporation process to analyze production data validation, and compared with the traditional model, the new model prediction accuracy greatly improved, can be used to predict the dynamic process of evaporation of sodium aluminate solution components.

Effect of Carbohydrate and Caffeine Ingestion on Badminton Performance.

PubMed

Clarke, Neil D; Duncan, Michael J

2016-01-01

To investigate the effect of ingesting carbohydrate and caffeine solutions on measures that are central to success in badminton. Twelve male badminton players performed a badminton serve-accuracy test, coincidence-anticipation timing (CAT), and a choice reaction-time sprint test 60 min before exercise. Participants then consumed 7 mL/kg body mass of either water (PLA), 6.4% carbohydrate solution (CHO), a solution containing a caffeine dose of 4 mg/kg, or 6.4% carbohydrate and 4 mg/kg caffeine (C+C). All solutions were flavored with orange-flavored concentrate. During the 33-min fatigue protocol, participants were provided with an additional 3 mL/kg body mass of solution, which was ingested before the end of the protocol. As soon as the 33-min fatigue protocol was completed, all measures were recorded again. Short-serve accuracy was improved after the ingestion of CHO and C+C compared with PLA (P = .001, η(p)(2) = .50). Long-serve accuracy was improved after the ingestion of C+C compared with PLA (P < .001, η(p)(2) = .53). Absolute error in CAT demonstrated smaller deteriorations after the ingestion of C+C compared with PLA (P < .05; slow, η(p)(2) = .41; fast, η(p)(2) = .31). Choice reaction time improved in all trials with the exception of PLA, which demonstrated a reduction (P < .001, η(p)(2) = .85), although C+C was faster than all trials (P < .001, η(p)(2) = .76). These findings suggest that the ingestion of a caffeinated carbohydrate solution before and during a badminton match can maintain serve accuracy, anticipation timing, and sprinting actions around the court.

An Evaluation of Explicit Receptor Flexibility in Molecular Docking Using Molecular Dynamics and Torsion Angle Molecular Dynamics.

PubMed

Armen, Roger S; Chen, Jianhan; Brooks, Charles L

2009-10-13

Incorporating receptor flexibility into molecular docking should improve results for flexible proteins. However, the incorporation of explicit all-atom flexibility with molecular dynamics for the entire protein chain may also introduce significant error and "noise" that could decrease docking accuracy and deteriorate the ability of a scoring function to rank native-like poses. We address this apparent paradox by comparing the success of several flexible receptor models in cross-docking and multiple receptor ensemble docking for p38α mitogen-activated protein (MAP) kinase. Explicit all-atom receptor flexibility has been incorporated into a CHARMM-based molecular docking method (CDOCKER) using both molecular dynamics (MD) and torsion angle molecular dynamics (TAMD) for the refinement of predicted protein-ligand binding geometries. These flexible receptor models have been evaluated, and the accuracy and efficiency of TAMD sampling is directly compared to MD sampling. Several flexible receptor models are compared, encompassing flexible side chains, flexible loops, multiple flexible backbone segments, and treatment of the entire chain as flexible. We find that although including side chain and some backbone flexibility is required for improved docking accuracy as expected, docking accuracy also diminishes as additional and unnecessary receptor flexibility is included into the conformational search space. Ensemble docking results demonstrate that including protein flexibility leads to to improved agreement with binding data for 227 active compounds. This comparison also demonstrates that a flexible receptor model enriches high affinity compound identification without significantly increasing the number of false positives from low affinity compounds.

An Evaluation of Explicit Receptor Flexibility in Molecular Docking Using Molecular Dynamics and Torsion Angle Molecular Dynamics

PubMed Central

Armen, Roger S.; Chen, Jianhan; Brooks, Charles L.

2009-01-01

Incorporating receptor flexibility into molecular docking should improve results for flexible proteins. However, the incorporation of explicit all-atom flexibility with molecular dynamics for the entire protein chain may also introduce significant error and “noise” that could decrease docking accuracy and deteriorate the ability of a scoring function to rank native-like poses. We address this apparent paradox by comparing the success of several flexible receptor models in cross-docking and multiple receptor ensemble docking for p38α mitogen-activated protein (MAP) kinase. Explicit all-atom receptor flexibility has been incorporated into a CHARMM-based molecular docking method (CDOCKER) using both molecular dynamics (MD) and torsion angle molecular dynamics (TAMD) for the refinement of predicted protein-ligand binding geometries. These flexible receptor models have been evaluated, and the accuracy and efficiency of TAMD sampling is directly compared to MD sampling. Several flexible receptor models are compared, encompassing flexible side chains, flexible loops, multiple flexible backbone segments, and treatment of the entire chain as flexible. We find that although including side chain and some backbone flexibility is required for improved docking accuracy as expected, docking accuracy also diminishes as additional and unnecessary receptor flexibility is included into the conformational search space. Ensemble docking results demonstrate that including protein flexibility leads to to improved agreement with binding data for 227 active compounds. This comparison also demonstrates that a flexible receptor model enriches high affinity compound identification without significantly increasing the number of false positives from low affinity compounds. PMID:20160879

Classifying four-category visual objects using multiple ERP components in single-trial ERP.

PubMed

Qin, Yu; Zhan, Yu; Wang, Changming; Zhang, Jiacai; Yao, Li; Guo, Xiaojuan; Wu, Xia; Hu, Bin

2016-08-01

Object categorization using single-trial electroencephalography (EEG) data measured while participants view images has been studied intensively. In previous studies, multiple event-related potential (ERP) components (e.g., P1, N1, P2, and P3) were used to improve the performance of object categorization of visual stimuli. In this study, we introduce a novel method that uses multiple-kernel support vector machine to fuse multiple ERP component features. We investigate whether fusing the potential complementary information of different ERP components (e.g., P1, N1, P2a, and P2b) can improve the performance of four-category visual object classification in single-trial EEGs. We also compare the classification accuracy of different ERP component fusion methods. Our experimental results indicate that the classification accuracy increases through multiple ERP fusion. Additional comparative analyses indicate that the multiple-kernel fusion method can achieve a mean classification accuracy higher than 72 %, which is substantially better than that achieved with any single ERP component feature (55.07 % for the best single ERP component, N1). We compare the classification results with those of other fusion methods and determine that the accuracy of the multiple-kernel fusion method is 5.47, 4.06, and 16.90 % higher than those of feature concatenation, feature extraction, and decision fusion, respectively. Our study shows that our multiple-kernel fusion method outperforms other fusion methods and thus provides a means to improve the classification performance of single-trial ERPs in brain-computer interface research.

Study on Parameter Identification of Assembly Robot based on Screw Theory

NASA Astrophysics Data System (ADS)

Yun, Shi; Xiaodong, Zhang

2017-11-01

The kinematic model of assembly robot is one of the most important factors affecting repetitive precision. In order to improve the accuracy of model positioning, this paper first establishes the exponential product model of ER16-1600 assembly robot on the basis of screw theory, and then based on iterative least squares method, using ER16-1600 model robot parameter identification. By comparing the experiment before and after the calibration, it is proved that the method has obvious improvement on the positioning accuracy of the assembly robot.

Monotonically improving approximate answers to relational algebra queries

NASA Technical Reports Server (NTRS)

Smith, Kenneth P.; Liu, J. W. S.

1989-01-01

We present here a query processing method that produces approximate answers to queries posed in standard relational algebra. This method is monotone in the sense that the accuracy of the approximate result improves with the amount of time spent producing the result. This strategy enables us to trade the time to produce the result for the accuracy of the result. An approximate relational model that characterizes appromimate relations and a partial order for comparing them is developed. Relational operators which operate on and return approximate relations are defined.

Rolling bearing fault diagnosis based on information fusion using Dempster-Shafer evidence theory

NASA Astrophysics Data System (ADS)

Pei, Di; Yue, Jianhai; Jiao, Jing

2017-10-01

This paper presents a fault diagnosis method for rolling bearing based on information fusion. Acceleration sensors are arranged at different position to get bearing vibration data as diagnostic evidence. The Dempster-Shafer (D-S) evidence theory is used to fuse multi-sensor data to improve diagnostic accuracy. The efficiency of the proposed method is demonstrated by the high speed train transmission test bench. The results of experiment show that the proposed method in this paper improves the rolling bearing fault diagnosis accuracy compared with traditional signal analysis methods.

Diagnostic performance of an automated analysis software for the diagnosis of Alzheimer’s dementia with 18F FDG PET

PubMed Central

Partovi, Sasan; Yuh, Roger; Pirozzi, Sara; Lu, Ziang; Couturier, Spencer; Grosse, Ulrich; Schluchter, Mark D; Nelson, Aaron; Jones, Robert; O’Donnell, James K; Faulhaber, Peter

2017-01-01

The objective of this study was to assess the ability of a quantitative software-aided approach to improve the diagnostic accuracy of 18F FDG PET for Alzheimer’s dementia over visual analysis alone. Twenty normal subjects (M:F-12:8; mean age 80.6 years) and twenty mild AD subjects (M:F-12:8; mean age 70.6 years) with 18F FDG PET scans were obtained from the ADNI database. Three blinded readers interpreted these PET images first using a visual qualitative approach and then using a quantitative software-aided approach. Images were classified on two five-point scales based on normal/abnormal (1-definitely normal; 5-definitely abnormal) and presence of AD (1-definitely not AD; 5-definitely AD). Diagnostic sensitivity, specificity, and accuracy for both approaches were compared based on the aforementioned scales. The sensitivity, specificity, and accuracy for the normal vs. abnormal readings of all readers combined were higher when comparing the software-aided vs. visual approach (sensitivity 0.93 vs. 0.83 P = 0.0466; specificity 0.85 vs. 0.60 P = 0.0005; accuracy 0.89 vs. 0.72 P<0.0001). The specificity and accuracy for absence vs. presence of AD of all readers combined were higher when comparing the software-aided vs. visual approach (specificity 0.90 vs. 0.70 P = 0.0008; accuracy 0.81 vs. 0.72 P = 0.0356). Sensitivities of the software-aided and visual approaches did not differ significantly (0.72 vs. 0.73 P = 0.74). The quantitative software-aided approach appears to improve the performance of 18F FDG PET for the diagnosis of mild AD. It may be helpful for experienced 18F FDG PET readers analyzing challenging cases. PMID:28123864

Mapping seabed sediments: Comparison of manual, geostatistical, object-based image analysis and machine learning approaches

NASA Astrophysics Data System (ADS)

Diesing, Markus; Green, Sophie L.; Stephens, David; Lark, R. Murray; Stewart, Heather A.; Dove, Dayton

2014-08-01

Marine spatial planning and conservation need underpinning with sufficiently detailed and accurate seabed substrate and habitat maps. Although multibeam echosounders enable us to map the seabed with high resolution and spatial accuracy, there is still a lack of fit-for-purpose seabed maps. This is due to the high costs involved in carrying out systematic seabed mapping programmes and the fact that the development of validated, repeatable, quantitative and objective methods of swath acoustic data interpretation is still in its infancy. We compared a wide spectrum of approaches including manual interpretation, geostatistics, object-based image analysis and machine-learning to gain further insights into the accuracy and comparability of acoustic data interpretation approaches based on multibeam echosounder data (bathymetry, backscatter and derivatives) and seabed samples with the aim to derive seabed substrate maps. Sample data were split into a training and validation data set to allow us to carry out an accuracy assessment. Overall thematic classification accuracy ranged from 67% to 76% and Cohen's kappa varied between 0.34 and 0.52. However, these differences were not statistically significant at the 5% level. Misclassifications were mainly associated with uncommon classes, which were rarely sampled. Map outputs were between 68% and 87% identical. To improve classification accuracy in seabed mapping, we suggest that more studies on the effects of factors affecting the classification performance as well as comparative studies testing the performance of different approaches need to be carried out with a view to developing guidelines for selecting an appropriate method for a given dataset. In the meantime, classification accuracy might be improved by combining different techniques to hybrid approaches and multi-method ensembles.

The systematic component of phylogenetic error as a function of taxonomic sampling under parsimony.

PubMed

Debry, Ronald W

2005-06-01

The effect of taxonomic sampling on phylogenetic accuracy under parsimony is examined by simulating nucleotide sequence evolution. Random error is minimized by using very large numbers of simulated characters. This allows estimation of the consistency behavior of parsimony, even for trees with up to 100 taxa. Data were simulated on 8 distinct 100-taxon model trees and analyzed as stratified subsets containing either 25 or 50 taxa, in addition to the full 100-taxon data set. Overall accuracy decreased in a majority of cases when taxa were added. However, the magnitude of change in the cases in which accuracy increased was larger than the magnitude of change in the cases in which accuracy decreased, so, on average, overall accuracy increased as more taxa were included. A stratified sampling scheme was used to assess accuracy for an initial subsample of 25 taxa. The 25-taxon analyses were compared to 50- and 100-taxon analyses that were pruned to include only the original 25 taxa. On average, accuracy for the 25 taxa was improved by taxon addition, but there was considerable variation in the degree of improvement among the model trees and across different rates of substitution.

Improved performance of semiconductor laser tracking frequency gauge

NASA Astrophysics Data System (ADS)

Kaplan, D. M.; Roberts, T. J.; Phillips, J. D.; Reasenberg, R. D.

2018-03-01

We describe new results from the semiconductor-laser tracking frequency gauge, an instrument that can perform sub-picometer distance measurements and has applications in gravity research and in space-based astronomical instruments proposed for the study of light from extrasolar planets. Compared with previous results, we have improved incremental distance accuracy by a factor of two, to 0.9 pm in 80 s averaging time, and absolute distance accuracy by a factor of 20, to 0.17 μm in 1000 s. After an interruption of operation of a tracking frequency gauge used to control a distance, it is now possible, using a nonresonant measurement interferometer, to restore the distance to picometer accuracy by combining absolute and incremental distance measurements.

Significant improvement in one-dimensional cursor control using Laplacian electroencephalography over electroencephalography

NASA Astrophysics Data System (ADS)

Boudria, Yacine; Feltane, Amal; Besio, Walter

2014-06-01

Objective. Brain-computer interfaces (BCIs) based on electroencephalography (EEG) have been shown to accurately detect mental activities, but the acquisition of high levels of control require extensive user training. Furthermore, EEG has low signal-to-noise ratio and low spatial resolution. The objective of the present study was to compare the accuracy between two types of BCIs during the first recording session. EEG and tripolar concentric ring electrode (TCRE) EEG (tEEG) brain signals were recorded and used to control one-dimensional cursor movements. Approach. Eight human subjects were asked to imagine either ‘left’ or ‘right’ hand movement during one recording session to control the computer cursor using TCRE and disc electrodes. Main results. The obtained results show a significant improvement in accuracies using TCREs (44%-100%) compared to disc electrodes (30%-86%). Significance. This study developed the first tEEG-based BCI system for real-time one-dimensional cursor movements and showed high accuracies with little training.

Sampling strategies for improving tree accuracy and phylogenetic analyses: a case study in ciliate protists, with notes on the genus Paramecium.

PubMed

Yi, Zhenzhen; Strüder-Kypke, Michaela; Hu, Xiaozhong; Lin, Xiaofeng; Song, Weibo

2014-02-01

In order to assess how dataset-selection for multi-gene analyses affects the accuracy of inferred phylogenetic trees in ciliates, we chose five genes and the genus Paramecium, one of the most widely used model protist genera, and compared tree topologies of the single- and multi-gene analyses. Our empirical study shows that: (1) Using multiple genes improves phylogenetic accuracy, even when their one-gene topologies are in conflict with each other. (2) The impact of missing data on phylogenetic accuracy is ambiguous: resolution power and topological similarity, but not number of represented taxa, are the most important criteria of a dataset for inclusion in concatenated analyses. (3) As an example, we tested the three classification models of the genus Paramecium with a multi-gene based approach, and only the monophyly of the subgenus Paramecium is supported. Copyright © 2013 Elsevier Inc. All rights reserved.

Blind subjects implanted with the Argus II retinal prosthesis are able to improve performance in a spatial-motor task

PubMed Central

Ahuja, A K; Dorn, J D; Caspi, A; McMahon, M J; Dagnelie, G; daCruz, L; Stanga, P; Humayun, M S; Greenberg, R J

2012-01-01

Background/aims To determine to what extent subjects implanted with the Argus II retinal prosthesis can improve performance compared with residual native vision in a spatial-motor task. Methods High-contrast square stimuli (5.85 cm sides) were displayed in random locations on a 19″ (48.3 cm) touch screen monitor located 12″ (30.5 cm) in front of the subject. Subjects were instructed to locate and touch the square centre with the system on and then off (40 trials each). The coordinates of the square centre and location touched were recorded. Results Ninety-six percent (26/27) of subjects showed a significant improvement in accuracy and 93% (25/27) show a significant improvement in repeatability with the system on compared with off (p<0.05, Student t test). A group of five subjects that had both accuracy and repeatability values <250 pixels (7.4 cm) with the system off (ie, using only their residual vision) was significantly more accurate and repeatable than the remainder of the cohort (p<0.01). Of this group, four subjects showed a significant improvement in both accuracy and repeatability with the system on. Conclusion In a study on the largest cohort of visual prosthesis recipients to date, we found that artificial vision augments information from existing vision in a spatial-motor task. Clinical trials registry no NCT00407602. PMID:20881025

PPCM: Combing multiple classifiers to improve protein-protein interaction prediction

DOE PAGES

Yao, Jianzhuang; Guo, Hong; Yang, Xiaohan

2015-08-01

Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using anmore » assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed that PPCM significantly improved the PPI prediction accuracy over the corresponding individual classifiers. We found that additional classifiers incorporated into PPCM could lead to further improvement in the PPI prediction accuracy. Furthermore, cross species PPCM could achieve competitive and even better prediction accuracy compared to the single species PPCM. This study established a robust pipeline for PPI prediction by integrating multiple classifiers using Random Forests algorithm. Ultimately, this pipeline will be useful for predicting PPI in nonmodel species.« less

Electronic discharge summary and prescription: improving communication between hospital and primary care.

PubMed

Murphy, S F; Lenihan, L; Orefuwa, F; Colohan, G; Hynes, I; Collins, C G

2017-05-01

The discharge letter is a key component of the communication pathway between the hospital and primary care. Accuracy and timeliness of delivery are crucial to ensure continuity of patient care. Electronic discharge summaries (EDS) and prescriptions have been shown to improve quality of discharge information for general practitioners (GPs). The aim of this study was to evaluate the effect of a new EDS on GP satisfaction levels and accuracy of discharge diagnosis. A GP survey was carried out whereby semi-structured interviews were conducted with 13 GPs from three primary care centres who receive a high volume of discharge letters from the hospital. A chart review was carried out on 90 charts to compare accuracy of ICD-10 coding of Non-Consultant Hospital Doctors (NCHDs) with that of trained Hopital In-Patient Enquiry (HIPE) coders. GP satisfaction levels were over 90 % with most aspects of the EDS, including amount of information (97 %), accuracy (95 %), GP information and follow-up (97 %) and medications (91 %). 70 % of GPs received the EDS within 2 weeks. ICD-10 coding of discharge diagnosis by NCHDs had an accuracy of 33 %, compared with 95.6 % when done by trained coders (p < 0.00001). The introduction of the EDS and prescription has led to improved quality of timeliness of communication with primary care. It has led to a very high satisfaction rating with GPs. ICD-10 coding was found to be grossly inaccurate when carried out by NCHDs and it is more appropriate for this task to be carried out by trained coders.

High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections.

PubMed

Zhu, Xiangbin; Qiu, Huiling

2016-01-01

Human activity recognition(HAR) from the temporal streams of sensory data has been applied to many fields, such as healthcare services, intelligent environments and cyber security. However, the classification accuracy of most existed methods is not enough in some applications, especially for healthcare services. In order to improving accuracy, it is necessary to develop a novel method which will take full account of the intrinsic sequential characteristics for time-series sensory data. Moreover, each human activity may has correlated feature relationship at different levels. Therefore, in this paper, we propose a three-stage continuous hidden Markov model (TSCHMM) approach to recognize human activities. The proposed method contains coarse, fine and accurate classification. The feature reduction is an important step in classification processing. In this paper, sparse locality preserving projections (SpLPP) is exploited to determine the optimal feature subsets for accurate classification of the stationary-activity data. It can extract more discriminative activities features from the sensor data compared with locality preserving projections. Furthermore, all of the gyro-based features are used for accurate classification of the moving data. Compared with other methods, our method uses significantly less number of features, and the over-all accuracy has been obviously improved.

High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections

PubMed Central

2016-01-01

Human activity recognition(HAR) from the temporal streams of sensory data has been applied to many fields, such as healthcare services, intelligent environments and cyber security. However, the classification accuracy of most existed methods is not enough in some applications, especially for healthcare services. In order to improving accuracy, it is necessary to develop a novel method which will take full account of the intrinsic sequential characteristics for time-series sensory data. Moreover, each human activity may has correlated feature relationship at different levels. Therefore, in this paper, we propose a three-stage continuous hidden Markov model (TSCHMM) approach to recognize human activities. The proposed method contains coarse, fine and accurate classification. The feature reduction is an important step in classification processing. In this paper, sparse locality preserving projections (SpLPP) is exploited to determine the optimal feature subsets for accurate classification of the stationary-activity data. It can extract more discriminative activities features from the sensor data compared with locality preserving projections. Furthermore, all of the gyro-based features are used for accurate classification of the moving data. Compared with other methods, our method uses significantly less number of features, and the over-all accuracy has been obviously improved. PMID:27893761

An additional reference axis improves femoral rotation alignment in image-free computer navigation assisted total knee arthroplasty.

PubMed

Inui, Hiroshi; Taketomi, Shuji; Nakamura, Kensuke; Sanada, Takaki; Tanaka, Sakae; Nakagawa, Takumi

2013-05-01

Few studies have demonstrated improvement in accuracy of rotational alignment using image-free navigation systems mainly due to the inconsistent registration of anatomical landmarks. We have used an image-free navigation for total knee arthroplasty, which adopts the average algorithm between two reference axes (transepicondylar axis and axis perpendicular to the Whiteside axis) for femoral component rotation control. We hypothesized that addition of another axis (condylar twisting axis measured on a preoperative radiograph) would improve the accuracy. One group using the average algorithm (double-axis group) was compared with the other group using another axis to confirm the accuracy of the average algorithm (triple-axis group). Femoral components were more accurately implanted for rotational alignment in the triple-axis group (ideal: triple-axis group 100%, double-axis group 82%, P<0.05). Copyright © 2013 Elsevier Inc. All rights reserved.

An Improved Method of AGM for High Precision Geolocation of SAR Images

NASA Astrophysics Data System (ADS)

Zhou, G.; He, C.; Yue, T.; Huang, W.; Huang, Y.; Li, X.; Chen, Y.

2018-05-01

In order to take full advantage of SAR images, it is necessary to obtain the high precision location of the image. During the geometric correction process of images, to ensure the accuracy of image geometric correction and extract the effective mapping information from the images, precise image geolocation is important. This paper presents an improved analytical geolocation method (IAGM) that determine the high precision geolocation of each pixel in a digital SAR image. This method is based on analytical geolocation method (AGM) proposed by X. K. Yuan aiming at realizing the solution of RD model. Tests will be conducted using RADARSAT-2 SAR image. Comparing the predicted feature geolocation with the position as determined by high precision orthophoto, results indicate an accuracy of 50m is attainable with this method. Error sources will be analyzed and some recommendations about improving image location accuracy in future spaceborne SAR's will be given.

Clinical pathways for primary care: current use, interest and perceived usability.

PubMed

Waters, Richard C; Toy, Jennifer M; Drechsler, Adam

2018-02-26

Translating clinical evidence to daily practice remains a challenge and may improve with clinical pathways. We assessed interest in and usability of clinical pathways by primary care professionals. An online survey was created. Interest in pathways for patient care and learning was assessed at start and finish. Participants completed baseline questions then pathway-associated question sets related to management of 2 chronic diseases. Perceived pathway usability was assessed using the system usability scale. Accuracy and confidence of answers was compared for baseline and pathway-assisted questions. Of 115 participants, 17.4% had used clinical pathways, the lowest of decision support tool types surveyed. Accuracy and confidence in answers significantly improved for all pathways. Interest in using pathways daily or weekly was above 75% for the respondents. There is low utilization of, but high interest in, clinical pathways by primary care clinicians. Pathways improve accuracy and confidence in answering written clinical questions.

Physician involvement enhances coding accuracy to ensure national standards: an initiative to improve awareness among new junior trainees.

PubMed

Nallasivan, S; Gillott, T; Kamath, S; Blow, L; Goddard, V

2011-06-01

Record Keeping Standards is a development led by the Royal College of Physicians of London (RCP) Health Informatics Unit and funded by the National Health Service (NHS) Connecting for Health. A supplementary report produced by the RCP makes a number of recommendations based on a study held at an acute hospital trust. We audited the medical notes and coding to assess the accuracy, documentation by the junior doctors and also to correlate our findings with the RCP audit. Northern Lincolnshire & Goole Hospitals NHS Foundation Trust has 114,000 'finished consultant episodes' per year. A total of 100 consecutive medical (50) and rheumatology (50) discharges from Diana Princess of Wales Hospital from August-October 2009 were reviewed. The results showed an improvement in coding accuracy (10% errors), comparable to the RCP audit but with 5% documentation errors. Physician involvement needs enhancing to improve the effectiveness and to ensure clinical safety.

Eight-Week Battle Rope Training Improves Multiple Physical Fitness Dimensions and Shooting Accuracy in Collegiate Basketball Players.

PubMed

Chen, Wei-Han; Wu, Huey-June; Lo, Shin-Liang; Chen, Hui; Yang, Wen-Wen; Huang, Chen-Fu; Liu, Chiang

2018-05-28

Chen, WH, Wu, HJ, Lo, SL, Chen, H, Yang, WW, Huang, CF, and Liu, C. Eight-week battle rope training improves multiple physical fitness dimensions and shooting accuracy in collegiate basketball players. J Strength Cond Res XX(X): 000-000, 2018-Basketball players must possess optimally developed physical fitness in multiple dimensions and shooting accuracy. This study investigated whether (battle rope [BR]) training enhances multiple physical fitness dimensions, including aerobic capacity (AC), upper-body anaerobic power (AnP), upper-body and lower-body power, agility, and core muscle endurance, and shooting accuracy in basketball players and compared its effects with those of regular training (shuttle run [SR]). Thirty male collegiate basketball players were randomly assigned to the BR or SR groups (n = 15 per group). Both groups received 8-week interval training for 3 sessions per week; the protocol consisted of the same number of sets, exercise time, and rest interval time. The BR group exhibited significant improvements in AC (Progressive Aerobic Cardiovascular Endurance Run laps: 17.6%), upper-body AnP (mean power: 7.3%), upper-body power (basketball chest pass speed: 4.8%), lower-body power (jump height: 2.6%), core muscle endurance (flexion: 37.0%, extension: 22.8%, and right side bridge: 23.0%), and shooting accuracy (free throw: 14.0% and dynamic shooting: 36.2%). However, the SR group exhibited improvements in only AC (12.0%) and upper-body power (3.8%) (p < 0.05). The BR group demonstrated larger pre-post improvements in upper-body AnP (fatigue index) and dynamic shooting accuracy than the SR group did (p < 0.05). The BR group showed higher post-training performance in upper-body AnP (mean power and fatigue index) than the SR group did (p < 0.05). Thus, BR training effectively improves multiple physical fitness dimensions and shooting accuracy in collegiate basketball players.

Analysis on accuracy improvement of rotor-stator rubbing localization based on acoustic emission beamforming method.

PubMed

He, Tian; Xiao, Denghong; Pan, Qiang; Liu, Xiandong; Shan, Yingchun

2014-01-01

This paper attempts to introduce an improved acoustic emission (AE) beamforming method to localize rotor-stator rubbing fault in rotating machinery. To investigate the propagation characteristics of acoustic emission signals in casing shell plate of rotating machinery, the plate wave theory is used in a thin plate. A simulation is conducted and its result shows the localization accuracy of beamforming depends on multi-mode, dispersion, velocity and array dimension. In order to reduce the effect of propagation characteristics on the source localization, an AE signal pre-process method is introduced by combining plate wave theory and wavelet packet transform. And the revised localization velocity to reduce effect of array size is presented. The accuracy of rubbing localization based on beamforming and the improved method of present paper are compared by the rubbing test carried on a test table of rotating machinery. The results indicate that the improved method can localize rub fault effectively. Copyright © 2013 Elsevier B.V. All rights reserved.

Auditing an Online Self-reported Interventional Radiology Adverse Event Database for Compliance and Accuracy.

PubMed

Burch, Ezra A; Shyn, Paul B; Chick, Jeffrey F; Chauhan, Nikunj R

2017-04-01

The purpose of this study was to determine whether auditing an online self-reported interventional radiology quality assurance database improves compliance with record entry or improves the accuracy of adverse event (AE) reporting and grading. Physicians were trained in using the database before the study began. An audit of all database entries for the first 3 months, or the first quarter, was performed, at which point physicians were informed of the audit process; entries for the subsequent 3 months, or the second quarter, were again audited. Results between quarters were compared. Compliance with record entry improved from the first to second quarter, but reminders were necessary to ensure 100% compliance with record entry. Knowledge of the audit process did not significantly improve self-reporting of AE or accuracy of AE grading. However, auditing significantly changed the final AE reporting rates and grades. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

The combined effects of L-theanine and caffeine on cognitive performance and mood.

PubMed

Owen, Gail N; Parnell, Holly; De Bruin, Eveline A; Rycroft, Jane A

2008-08-01

The aim of this study was to compare 50 mg caffeine, with and without 100 mg L-theanine, on cognition and mood in healthy volunteers. The effects of these treatments on word recognition, rapid visual information processing, critical flicker fusion threshold, attention switching and mood were compared to placebo in 27 participants. Performance was measured at baseline and again 60 min and 90 min after each treatment (separated by a 7-day washout). Caffeine improved subjective alertness at 60 min and accuracy on the attention-switching task at 90 min. The L-theanine and caffeine combination improved both speed and accuracy of performance of the attention-switching task at 60 min, and reduced susceptibility to distracting information in the memory task at both 60 min and 90 min. These results replicate previous evidence which suggests that L-theanine and caffeine in combination are beneficial for improving performance on cognitively demanding tasks.

Single-Sided Deafness: Impact of Cochlear Implantation on Speech Perception in Complex Noise and on Auditory Localization Accuracy.

PubMed

Döge, Julia; Baumann, Uwe; Weissgerber, Tobias; Rader, Tobias

2017-12-01

To assess auditory localization accuracy and speech reception threshold (SRT) in complex noise conditions in adult patients with acquired single-sided deafness, after intervention with a cochlear implant (CI) in the deaf ear. Nonrandomized, open, prospective patient series. Tertiary referral university hospital. Eleven patients with late-onset single-sided deafness (SSD) and normal hearing in the unaffected ear, who received a CI. All patients were experienced CI users. Unilateral cochlear implantation. Speech perception was tested in a complex multitalker equivalent noise field consisting of multiple sound sources. Speech reception thresholds in noise were determined in aided (with CI) and unaided conditions. Localization accuracy was assessed in complete darkness. Acoustic stimuli were radiated by multiple loudspeakers distributed in the frontal horizontal plane between -60 and +60 degrees. In the aided condition, results show slightly improved speech reception scores compared with the unaided condition in most of the patients. For 8 of the 11 subjects, SRT was improved between 0.37 and 1.70 dB. Three of the 11 subjects showed deteriorations between 1.22 and 3.24 dB SRT. Median localization error decreased significantly by 12.9 degrees compared with the unaided condition. CI in single-sided deafness is an effective treatment to improve the auditory localization accuracy. Speech reception in complex noise conditions is improved to a lesser extent in 73% of the participating CI SSD patients. However, the absence of true binaural interaction effects (summation, squelch) impedes further improvements. The development of speech processing strategies that respect binaural interaction seems to be mandatory to advance speech perception in demanding listening situations in SSD patients.

Application of Sensor Fusion to Improve Uav Image Classification

NASA Astrophysics Data System (ADS)

Jabari, S.; Fathollahi, F.; Zhang, Y.

2017-08-01

Image classification is one of the most important tasks of remote sensing projects including the ones that are based on using UAV images. Improving the quality of UAV images directly affects the classification results and can save a huge amount of time and effort in this area. In this study, we show that sensor fusion can improve image quality which results in increasing the accuracy of image classification. Here, we tested two sensor fusion configurations by using a Panchromatic (Pan) camera along with either a colour camera or a four-band multi-spectral (MS) camera. We use the Pan camera to benefit from its higher sensitivity and the colour or MS camera to benefit from its spectral properties. The resulting images are then compared to the ones acquired by a high resolution single Bayer-pattern colour camera (here referred to as HRC). We assessed the quality of the output images by performing image classification tests. The outputs prove that the proposed sensor fusion configurations can achieve higher accuracies compared to the images of the single Bayer-pattern colour camera. Therefore, incorporating a Pan camera on-board in the UAV missions and performing image fusion can help achieving higher quality images and accordingly higher accuracy classification results.

Detection of dechallenge in spontaneous reporting systems: a comparison of Bayes methods.

PubMed

Banu, A Bazila; Alias Balamurugan, S Appavu; Thirumalaikolundusubramanian, Ponniah

2014-01-01

Dechallenge is a response observed for the reduction or disappearance of adverse drug reactions (ADR) on withdrawal of a drug from a patient. Currently available algorithms to detect dechallenge have limitations. Hence, there is a need to compare available new methods. To detect dechallenge in Spontaneous Reporting Systems, data-mining algorithms like Naive Bayes and Improved Naive Bayes were applied for comparing the performance of the algorithms in terms of accuracy and error. Analyzing the factors of dechallenge like outcome and disease category will help medical practitioners and pharmaceutical industries to determine the reasons for dechallenge in order to take essential steps toward drug safety. Adverse drug reactions of the year 2011 and 2012 were downloaded from the United States Food and Drug Administration's database. The outcome of classification algorithms showed that Improved Naive Bayes algorithm outperformed Naive Bayes with accuracy of 90.11% and error of 9.8% in detecting the dechallenge. Detecting dechallenge for unknown samples are essential for proper prescription. To overcome the issues exposed by Naive Bayes algorithm, Improved Naive Bayes algorithm can be used to detect dechallenge in terms of higher accuracy and minimal error.

Improvement of Quantitative Measurements in Multiplex Proteomics Using High-Field Asymmetric Waveform Spectrometry.

PubMed

Pfammatter, Sibylle; Bonneil, Eric; Thibault, Pierre

2016-12-02

Quantitative proteomics using isobaric reagent tandem mass tags (TMT) or isobaric tags for relative and absolute quantitation (iTRAQ) provides a convenient approach to compare changes in protein abundance across multiple samples. However, the analysis of complex protein digests by isobaric labeling can be undermined by the relative large proportion of co-selected peptide ions that lead to distorted reporter ion ratios and affect the accuracy and precision of quantitative measurements. Here, we investigated the use of high-field asymmetric waveform ion mobility spectrometry (FAIMS) in proteomic experiments to reduce sample complexity and improve protein quantification using TMT isobaric labeling. LC-FAIMS-MS/MS analyses of human and yeast protein digests led to significant reductions in interfering ions, which increased the number of quantifiable peptides by up to 68% while significantly improving the accuracy of abundance measurements compared to that with conventional LC-MS/MS. The improvement in quantitative measurements using FAIMS is further demonstrated for the temporal profiling of protein abundance of HEK293 cells following heat shock treatment.

A multidisciplinary approach to vascular surgery procedure coding improves coding accuracy, work relative value unit assignment, and reimbursement.

PubMed

Aiello, Francesco A; Judelson, Dejah R; Messina, Louis M; Indes, Jeffrey; FitzGerald, Gordon; Doucet, Danielle R; Simons, Jessica P; Schanzer, Andres

2016-08-01

Vascular surgery procedural reimbursement depends on accurate procedural coding and documentation. Despite the critical importance of correct coding, there has been a paucity of research focused on the effect of direct physician involvement. We hypothesize that direct physician involvement in procedural coding will lead to improved coding accuracy, increased work relative value unit (wRVU) assignment, and increased physician reimbursement. This prospective observational cohort study evaluated procedural coding accuracy of fistulograms at an academic medical institution (January-June 2014). All fistulograms were coded by institutional coders (traditional coding) and by a single vascular surgeon whose codes were verified by two institution coders (multidisciplinary coding). The coding methods were compared, and differences were translated into revenue and wRVUs using the Medicare Physician Fee Schedule. Comparison between traditional and multidisciplinary coding was performed for three discrete study periods: baseline (period 1), after a coding education session for physicians and coders (period 2), and after a coding education session with implementation of an operative dictation template (period 3). The accuracy of surgeon operative dictations during each study period was also assessed. An external validation at a second academic institution was performed during period 1 to assess and compare coding accuracy. During period 1, traditional coding resulted in a 4.4% (P = .004) loss in reimbursement and a 5.4% (P = .01) loss in wRVUs compared with multidisciplinary coding. During period 2, no significant difference was found between traditional and multidisciplinary coding in reimbursement (1.3% loss; P = .24) or wRVUs (1.8% loss; P = .20). During period 3, traditional coding yielded a higher overall reimbursement (1.3% gain; P = .26) than multidisciplinary coding. This increase, however, was due to errors by institution coders, with six inappropriately used codes resulting in a higher overall reimbursement that was subsequently corrected. Assessment of physician documentation showed improvement, with decreased documentation errors at each period (11% vs 3.1% vs 0.6%; P = .02). Overall, between period 1 and period 3, multidisciplinary coding resulted in a significant increase in additional reimbursement ($17.63 per procedure; P = .004) and wRVUs (0.50 per procedure; P = .01). External validation at a second academic institution was performed to assess coding accuracy during period 1. Similar to institution 1, traditional coding revealed an 11% loss in reimbursement ($13,178 vs $14,630; P = .007) and a 12% loss in wRVU (293 vs 329; P = .01) compared with multidisciplinary coding. Physician involvement in the coding of endovascular procedures leads to improved procedural coding accuracy, increased wRVU assignments, and increased physician reimbursement. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Enhancement of accuracy in shape sensing of surgical needles using optical frequency domain reflectometry in optical fibers.

PubMed

Parent, Francois; Loranger, Sebastien; Mandal, Koushik Kanti; Iezzi, Victor Lambin; Lapointe, Jerome; Boisvert, Jean-Sébastien; Baiad, Mohamed Diaa; Kadoury, Samuel; Kashyap, Raman

2017-04-01

We demonstrate a novel approach to enhance the precision of surgical needle shape tracking based on distributed strain sensing using optical frequency domain reflectometry (OFDR). The precision enhancement is provided by using optical fibers with high scattering properties. Shape tracking of surgical tools using strain sensing properties of optical fibers has seen increased attention in recent years. Most of the investigations made in this field use fiber Bragg gratings (FBG), which can be used as discrete or quasi-distributed strain sensors. By using a truly distributed sensing approach (OFDR), preliminary results show that the attainable accuracy is comparable to accuracies reported in the literature using FBG sensors for tracking applications (~1mm). We propose a technique that enhanced our accuracy by 47% using UV exposed fibers, which have higher light scattering compared to un-exposed standard single mode fibers. Improving the experimental setup will enhance the accuracy provided by shape tracking using OFDR and will contribute significantly to clinical applications.

Regression Analysis of Optical Coherence Tomography Disc Variables for Glaucoma Diagnosis.

PubMed

Richter, Grace M; Zhang, Xinbo; Tan, Ou; Francis, Brian A; Chopra, Vikas; Greenfield, David S; Varma, Rohit; Schuman, Joel S; Huang, David

2016-08-01

To report diagnostic accuracy of optical coherence tomography (OCT) disc variables using both time-domain (TD) and Fourier-domain (FD) OCT, and to improve the use of OCT disc variable measurements for glaucoma diagnosis through regression analyses that adjust for optic disc size and axial length-based magnification error. Observational, cross-sectional. In total, 180 normal eyes of 112 participants and 180 eyes of 138 participants with perimetric glaucoma from the Advanced Imaging for Glaucoma Study. Diagnostic variables evaluated from TD-OCT and FD-OCT were: disc area, rim area, rim volume, optic nerve head volume, vertical cup-to-disc ratio (CDR), and horizontal CDR. These were compared with overall retinal nerve fiber layer thickness and ganglion cell complex. Regression analyses were performed that corrected for optic disc size and axial length. Area-under-receiver-operating curves (AUROC) were used to assess diagnostic accuracy before and after the adjustments. An index based on multiple logistic regression that combined optic disc variables with axial length was also explored with the aim of improving diagnostic accuracy of disc variables. Comparison of diagnostic accuracy of disc variables, as measured by AUROC. The unadjusted disc variables with the highest diagnostic accuracies were: rim volume for TD-OCT (AUROC=0.864) and vertical CDR (AUROC=0.874) for FD-OCT. Magnification correction significantly worsened diagnostic accuracy for rim variables, and while optic disc size adjustments partially restored diagnostic accuracy, the adjusted AUROCs were still lower. Axial length adjustments to disc variables in the form of multiple logistic regression indices led to a slight but insignificant improvement in diagnostic accuracy. Our various regression approaches were not able to significantly improve disc-based OCT glaucoma diagnosis. However, disc rim area and vertical CDR had very high diagnostic accuracy, and these disc variables can serve to complement additional OCT measurements for diagnosis of glaucoma.

Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection

PubMed Central

Townsend, K A; Wollstein, G; Danks, D; Sung, K R; Ishikawa, H; Kagemann, L; Gabriele, M L; Schuman, J S

2010-01-01

Aims To assess performance of classifiers trained on Heidelberg Retina Tomograph 3 (HRT3) parameters for discriminating between healthy and glaucomatous eyes. Methods Classifiers were trained using HRT3 parameters from 60 healthy subjects and 140 glaucomatous subjects. The classifiers were trained on all 95 variables and smaller sets created with backward elimination. Seven types of classifiers, including Support Vector Machines with radial basis (SVM-radial), and Recursive Partitioning and Regression Trees (RPART), were trained on the parameters. The area under the ROC curve (AUC) was calculated for classifiers, individual parameters and HRT3 glaucoma probability scores (GPS). Classifier AUCs and leave-one-out accuracy were compared with the highest individual parameter and GPS AUCs and accuracies. Results The highest AUC and accuracy for an individual parameter were 0.848 and 0.79, for vertical cup/disc ratio (vC/D). For GPS, global GPS performed best with AUC 0.829 and accuracy 0.78. SVM-radial with all parameters showed significant improvement over global GPS and vC/ D with AUC 0.916 and accuracy 0.85. RPART with all parameters provided significant improvement over global GPS with AUC 0.899 and significant improvement over global GPS and vC/D with accuracy 0.875. Conclusions Machine learning classifiers of HRT3 data provide significant enhancement over current methods for detection of glaucoma. PMID:18523087

Research on High Accuracy Detection of Red Tide Hyperspecrral Based on Deep Learning Cnn

NASA Astrophysics Data System (ADS)

Hu, Y.; Ma, Y.; An, J.

2018-04-01

Increasing frequency in red tide outbreaks has been reported around the world. It is of great concern due to not only their adverse effects on human health and marine organisms, but also their impacts on the economy of the affected areas. this paper put forward a high accuracy detection method based on a fully-connected deep CNN detection model with 8-layers to monitor red tide in hyperspectral remote sensing images, then make a discussion of the glint suppression method for improving the accuracy of red tide detection. The results show that the proposed CNN hyperspectral detection model can detect red tide accurately and effectively. The red tide detection accuracy of the proposed CNN model based on original image and filter-image is 95.58 % and 97.45 %, respectively, and compared with the SVM method, the CNN detection accuracy is increased by 7.52 % and 2.25 %. Compared with SVM method base on original image, the red tide CNN detection accuracy based on filter-image increased by 8.62 % and 6.37 %. It also indicates that the image glint affects the accuracy of red tide detection seriously.

LASE measurements of water vapor, aerosol, and cloud distribution in hurricane environments and their role in hurricane development

NASA Technical Reports Server (NTRS)

Mahoney, M. J.; Ismail, S.; Browell, E. V.; Ferrare, R. A.; Kooi, S. A.; Brasseur, L.; Notari, A.; Petway, L.; Brackett, V.; Clayton, M.;

A community detection algorithm based on structural similarity

NASA Astrophysics Data System (ADS)

Guo, Xuchao; Hao, Xia; Liu, Yaqiong; Zhang, Li; Wang, Lu

2017-09-01

In order to further improve the efficiency and accuracy of community detection algorithm, a new algorithm named SSTCA (the community detection algorithm based on structural similarity with threshold) is proposed. In this algorithm, the structural similarities are taken as the weights of edges, and the threshold k is considered to remove multiple edges whose weights are less than the threshold, and improve the computational efficiency. Tests were done on the Zachary’s network, Dolphins’ social network and Football dataset by the proposed algorithm, and compared with GN and SSNCA algorithm. The results show that the new algorithm is superior to other algorithms in accuracy for the dense networks and the operating efficiency is improved obviously.

Genomic selection models double the accuracy of predicted breeding values for bacterial cold water disease resistance compared to a traditional pedigree-based model in rainbow trout aquaculture.

PubMed

Vallejo, Roger L; Leeds, Timothy D; Gao, Guangtu; Parsons, James E; Martin, Kyle E; Evenhuis, Jason P; Fragomeni, Breno O; Wiens, Gregory D; Palti, Yniv

2017-02-01

Previously, we have shown that bacterial cold water disease (BCWD) resistance in rainbow trout can be improved using traditional family-based selection, but progress has been limited to exploiting only between-family genetic variation. Genomic selection (GS) is a new alternative that enables exploitation of within-family genetic variation. We compared three GS models [single-step genomic best linear unbiased prediction (ssGBLUP), weighted ssGBLUP (wssGBLUP), and BayesB] to predict genomic-enabled breeding values (GEBV) for BCWD resistance in a commercial rainbow trout population, and compared the accuracy of GEBV to traditional estimates of breeding values (EBV) from a pedigree-based BLUP (P-BLUP) model. We also assessed the impact of sampling design on the accuracy of GEBV predictions. For these comparisons, we used BCWD survival phenotypes recorded on 7893 fish from 102 families, of which 1473 fish from 50 families had genotypes [57 K single nucleotide polymorphism (SNP) array]. Naïve siblings of the training fish (n = 930 testing fish) were genotyped to predict their GEBV and mated to produce 138 progeny testing families. In the following generation, 9968 progeny were phenotyped to empirically assess the accuracy of GEBV predictions made on their non-phenotyped parents. The accuracy of GEBV from all tested GS models were substantially higher than the P-BLUP model EBV. The highest increase in accuracy relative to the P-BLUP model was achieved with BayesB (97.2 to 108.8%), followed by wssGBLUP at iteration 2 (94.4 to 97.1%) and 3 (88.9 to 91.2%) and ssGBLUP (83.3 to 85.3%). Reducing the training sample size to n = ~1000 had no negative impact on the accuracy (0.67 to 0.72), but with n = ~500 the accuracy dropped to 0.53 to 0.61 if the training and testing fish were full-sibs, and even substantially lower, to 0.22 to 0.25, when they were not full-sibs. Using progeny performance data, we showed that the accuracy of genomic predictions is substantially higher than estimates obtained from the traditional pedigree-based BLUP model for BCWD resistance. Overall, we found that using a much smaller training sample size compared to similar studies in livestock, GS can substantially improve the selection accuracy and genetic gains for this trait in a commercial rainbow trout breeding population.

Information filtering via biased heat conduction.

PubMed

Liu, Jian-Guo; Zhou, Tao; Guo, Qiang

2011-09-01

The process of heat conduction has recently found application in personalized recommendation [Zhou et al., Proc. Natl. Acad. Sci. USA 107, 4511 (2010)], which is of high diversity but low accuracy. By decreasing the temperatures of small-degree objects, we present an improved algorithm, called biased heat conduction, which could simultaneously enhance the accuracy and diversity. Extensive experimental analyses demonstrate that the accuracy on MovieLens, Netflix, and Delicious datasets could be improved by 43.5%, 55.4% and 19.2%, respectively, compared with the standard heat conduction algorithm and also the diversity is increased or approximately unchanged. Further statistical analyses suggest that the present algorithm could simultaneously identify users' mainstream and special tastes, resulting in better performance than the standard heat conduction algorithm. This work provides a creditable way for highly efficient information filtering.

Satellite-Derived Sea Surface Temperature: Workshop 1

NASA Technical Reports Server (NTRS)

Njoku, E. G.

1983-01-01

Satellite measurements of sea surface temperature are now possible using a variety of sensors. The present accuracies of these methods are in the range of 0.5 to 2.0 C. This makes them potentially useful for synoptic studies of ocean currents and for global monitoring of climatological anomalies. To improve confidence in the satellite data, objective evaluations of sensor accuracies are necessary, and the conditions under which these accuracies degrade need to be understood. The Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite was studied. Sea surface temperatures, derived from November 1979 SMMR data, were compared globally against ship measurements and climatology, using facilities of the JPL Pilot Ocean Data System. Methods for improved data analysis and plans for additional workshops to incorporate data from other sensors were discussed.

How 3D patient-specific instruments improve accuracy of pelvic bone tumour resection in a cadaveric study.

PubMed

Sallent, A; Vicente, M; Reverté, M M; Lopez, A; Rodríguez-Baeza, A; Pérez-Domínguez, M; Velez, R

2017-10-01

To assess the accuracy of patient-specific instruments (PSIs) versus standard manual technique and the precision of computer-assisted planning and PSI-guided osteotomies in pelvic tumour resection. CT scans were obtained from five female cadaveric pelvises. Five osteotomies were designed using Mimics software: sacroiliac, biplanar supra-acetabular, two parallel iliopubic and ischial. For cases of the left hemipelvis, PSIs were designed to guide standard oscillating saw osteotomies and later manufactured using 3D printing. Osteotomies were performed using the standard manual technique in cases of the right hemipelvis. Post-resection CT scans were quantitatively analysed. Student's t -test and Mann-Whitney U test were used. Compared with the manual technique, PSI-guided osteotomies improved accuracy by a mean 9.6 mm (p < 0.008) in the sacroiliac osteotomies, 6.2 mm (p < 0.008) and 5.8 mm (p < 0.032) in the biplanar supra-acetabular, 3 mm (p < 0.016) in the ischial and 2.2 mm (p < 0.032) and 2.6 mm (p < 0.008) in the parallel iliopubic osteotomies, with a mean linear deviation of 4.9 mm (p < 0.001) for all osteotomies. Of the manual osteotomies, 53% (n = 16) had a linear deviation > 5 mm and 27% (n = 8) were > 10 mm. In the PSI cases, deviations were 10% (n = 3) and 0 % (n = 0), respectively. For angular deviation from pre-operative plans, we observed a mean improvement of 7.06° (p < 0.001) in pitch and 2.94° (p < 0.001) in roll, comparing PSI and the standard manual technique. In an experimental study, computer-assisted planning and PSIs improved accuracy in pelvic tumour resections, bringing osteotomy results closer to the parameters set in pre-operative planning, as compared with standard manual techniques. Cite this article : A. Sallent, M. Vicente, M. M. Reverté, A. Lopez, A. Rodríguez-Baeza, M. Pérez-Domínguez, R. Velez. How 3D patient-specific instruments improve accuracy of pelvic bone tumour resection in a cadaveric study. Bone Joint Res 2017;6:577-583. DOI: 10.1302/2046-3758.610.BJR-2017-0094.R1. © 2017 Sallent et al.

Evaluation of mathematical algorithms for automatic patient alignment in radiosurgery.

PubMed

Williams, Kenneth M; Schulte, Reinhard W; Schubert, Keith E; Wroe, Andrew J

2015-06-01

Image registration techniques based on anatomical features can serve to automate patient alignment for intracranial radiosurgery procedures in an effort to improve the accuracy and efficiency of the alignment process as well as potentially eliminate the need for implanted fiducial markers. To explore this option, four two-dimensional (2D) image registration algorithms were analyzed: the phase correlation technique, mutual information (MI) maximization, enhanced correlation coefficient (ECC) maximization, and the iterative closest point (ICP) algorithm. Digitally reconstructed radiographs from the treatment planning computed tomography scan of a human skull were used as the reference images, while orthogonal digital x-ray images taken in the treatment room were used as the captured images to be aligned. The accuracy of aligning the skull with each algorithm was compared to the alignment of the currently practiced procedure, which is based on a manual process of selecting common landmarks, including implanted fiducials and anatomical skull features. Of the four algorithms, three (phase correlation, MI maximization, and ECC maximization) demonstrated clinically adequate (ie, comparable to the standard alignment technique) translational accuracy and improvements in speed compared to the interactive, user-guided technique; however, the ICP algorithm failed to give clinically acceptable results. The results of this work suggest that a combination of different algorithms may provide the best registration results. This research serves as the initial groundwork for the translation of automated, anatomy-based 2D algorithms into a real-world system for 2D-to-2D image registration and alignment for intracranial radiosurgery. This may obviate the need for invasive implantation of fiducial markers into the skull and may improve treatment room efficiency and accuracy. © The Author(s) 2014.

Further development of the attitude difference method for estimating deflections of the vertical in real time

NASA Astrophysics Data System (ADS)

Zhu, Jing; Zhou, Zebo; Li, Yong; Rizos, Chris; Wang, Xingshu

2016-07-01

An improvement of the attitude difference method (ADM) to estimate deflections of the vertical (DOV) in real time is described in this paper. The ADM without offline processing estimates the DOV with a limited accuracy due to the response delay. The proposed model selection-based self-adaptive delay feedback (SDF) method takes the results of the ADM as the a priori information, then uses fitting and extrapolation to estimate the DOV at the current epoch. The active region selection factor F th is used to take full advantage of the Earth model EGM2008 and the SDF with different DOV exhibitions. The factors which affect the DOV estimation accuracy are analyzed and modeled. An external observation which is specified by the velocity difference between the global navigation satellite system (GNSS) and the inertial navigation system (INS) with DOV compensated is used to select the optimal model. The response delay induced by the weak observability of an integrated INS/GNSS to the violent DOV disturbances in the ADM is compensated. The DOV estimation accuracy of the SDF method is improved by approximately 40% and 50% respectively compared to that of the EGM2008 and the ADM. With an increase in GNSS accuracy, the DOV estimation accuracy could improve further.

A New Calibration Method Using Low Cost MEM IMUs to Verify the Performance of UAV-Borne MMS Payloads

PubMed Central

Chiang, Kai-Wei; Tsai, Meng-Lun; Naser, El-Sheimy; Habib, Ayman; Chu, Chien-Hsun

2015-01-01

Spatial information plays a critical role in remote sensing and mapping applications such as environment surveying and disaster monitoring. An Unmanned Aerial Vehicle (UAV)-borne mobile mapping system (MMS) can accomplish rapid spatial information acquisition under limited sky conditions with better mobility and flexibility than other means. This study proposes a long endurance Direct Geo-referencing (DG)-based fixed-wing UAV photogrammetric platform and two DG modules that each use different commercial Micro-Electro Mechanical Systems’ (MEMS) tactical grade Inertial Measurement Units (IMUs). Furthermore, this study develops a novel kinematic calibration method which includes lever arms, boresight angles and camera shutter delay to improve positioning accuracy. The new calibration method is then compared with the traditional calibration approach. The results show that the accuracy of the DG can be significantly improved by flying at a lower altitude using the new higher specification hardware. The new proposed method improves the accuracy of DG by about 20%. The preliminary results show that two-dimensional (2D) horizontal DG positioning accuracy is around 5.8 m at a flight height of 300 m using the newly designed tactical grade integrated Positioning and Orientation System (POS). The positioning accuracy in three-dimensions (3D) is less than 8 m. PMID:25808764

New calibration method using low cost MEM IMUs to verify the performance of UAV-borne MMS payloads.

PubMed

Chiang, Kai-Wei; Tsai, Meng-Lun; Naser, El-Sheimy; Habib, Ayman; Chu, Chien-Hsun

2015-03-19

Spatial information plays a critical role in remote sensing and mapping applications such as environment surveying and disaster monitoring. An Unmanned Aerial Vehicle (UAV)-borne mobile mapping system (MMS) can accomplish rapid spatial information acquisition under limited sky conditions with better mobility and flexibility than other means. This study proposes a long endurance Direct Geo-referencing (DG)-based fixed-wing UAV photogrammetric platform and two DG modules that each use different commercial Micro-Electro Mechanical Systems' (MEMS) tactical grade Inertial Measurement Units (IMUs). Furthermore, this study develops a novel kinematic calibration method which includes lever arms, boresight angles and camera shutter delay to improve positioning accuracy. The new calibration method is then compared with the traditional calibration approach. The results show that the accuracy of the DG can be significantly improved by flying at a lower altitude using the new higher specification hardware. The new proposed method improves the accuracy of DG by about 20%. The preliminary results show that two-dimensional (2D) horizontal DG positioning accuracy is around 5.8 m at a flight height of 300 m using the newly designed tactical grade integrated Positioning and Orientation System (POS). The positioning accuracy in three-dimensions (3D) is less than 8 m.

Improve accuracy for automatic acetabulum segmentation in CT images.

PubMed

Liu, Hao; Zhao, Jianning; Dai, Ning; Qian, Hongbo; Tang, Yuehong

2014-01-01

Separation of the femur head and acetabulum is one of main difficulties in the diseased hip joint due to deformed shapes and extreme narrowness of the joint space. To improve the segmentation accuracy is the key point of existing automatic or semi-automatic segmentation methods. In this paper, we propose a new method to improve the accuracy of the segmented acetabulum using surface fitting techniques, which essentially consists of three parts: (1) design a surface iterative process to obtain an optimization surface; (2) change the ellipsoid fitting to two-phase quadric surface fitting; (3) bring in a normal matching method and an optimization region method to capture edge points for the fitting quadric surface. Furthermore, this paper cited vivo CT data sets of 40 actual patients (with 79 hip joints). Test results for these clinical cases show that: (1) the average error of the quadric surface fitting method is 2.3 (mm); (2) the accuracy ratio of automatically recognized contours is larger than 89.4%; (3) the error ratio of section contours is less than 10% for acetabulums without severe malformation and less than 30% for acetabulums with severe malformation. Compared with similar methods, the accuracy of our method, which is applied in a software system, is significantly enhanced.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Cui, Mingjian; Hodge, Bri-Mathias

The large variability and uncertainty in wind power generation present a concern to power system operators, especially given the increasing amounts of wind power being integrated into the electric power system. Large ramps, one of the biggest concerns, can significantly influence system economics and reliability. The Wind Forecast Improvement Project (WFIP) was to improve the accuracy of forecasts and to evaluate the economic benefits of these improvements to grid operators. This paper evaluates the ramp forecasting accuracy gained by improving the performance of short-term wind power forecasting. This study focuses on the WFIP southern study region, which encompasses most ofmore » the Electric Reliability Council of Texas (ERCOT) territory, to compare the experimental WFIP forecasts to the existing short-term wind power forecasts (used at ERCOT) at multiple spatial and temporal scales. The study employs four significant wind power ramping definitions according to the power change magnitude, direction, and duration. The optimized swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental WFIP forecasts improve the accuracy of the wind power ramp forecasting. This improvement can result in substantial costs savings and power system reliability enhancements.« less

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PubMed Central

Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others. PMID:28376093

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PubMed

Weng, Stephen F; Reps, Jenna; Kai, Joe; Garibaldi, Jonathan M; Qureshi, Nadeem

2017-01-01

Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the 'receiver operating curve' (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723-0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739-0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755-0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755-0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759-0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

Assessment of an Euler-Interacting Boundary Layer Method Using High Reynolds Number Transonic Flight Data

NASA Technical Reports Server (NTRS)

Bonhaus, Daryl L.; Maddalon, Dal V.

1998-01-01

Flight-measured high Reynolds number turbulent-flow pressure distributions on a transport wing in transonic flow are compared to unstructured-grid calculations to assess the predictive ability of a three-dimensional Euler code (USM3D) coupled to an interacting boundary layer module. The two experimental pressure distributions selected for comparative analysis with the calculations are complex and turbulent but typical of an advanced technology laminar flow wing. An advancing front method (VGRID) was used to generate several tetrahedral grids for each test case. Initial calculations left considerable room for improvement in accuracy. Studies were then made of experimental errors, transition location, viscous effects, nacelle flow modeling, number and placement of spanwise boundary layer stations, and grid resolution. The most significant improvements in the accuracy of the calculations were gained by improvement of the nacelle flow model and by refinement of the computational grid. Final calculations yield results in close agreement with the experiment. Indications are that further grid refinement would produce additional improvement but would require more computer memory than is available. The appendix data compare the experimental attachment line location with calculations for different grid sizes. Good agreement is obtained between the experimental and calculated attachment line locations.

Accurate time delay technology in simulated test for high precision laser range finder

NASA Astrophysics Data System (ADS)

Chen, Zhibin; Xiao, Wenjian; Wang, Weiming; Xue, Mingxi

2015-10-01

With the continuous development of technology, the ranging accuracy of pulsed laser range finder (LRF) is higher and higher, so the maintenance demand of LRF is also rising. According to the dominant ideology of "time analog spatial distance" in simulated test for pulsed range finder, the key of distance simulation precision lies in the adjustable time delay. By analyzing and comparing the advantages and disadvantages of fiber and circuit delay, a method was proposed to improve the accuracy of the circuit delay without increasing the count frequency of the circuit. A high precision controllable delay circuit was designed by combining the internal delay circuit and external delay circuit which could compensate the delay error in real time. And then the circuit delay accuracy could be increased. The accuracy of the novel circuit delay methods proposed in this paper was actually measured by a high sampling rate oscilloscope actual measurement. The measurement result shows that the accuracy of the distance simulated by the circuit delay is increased from +/- 0.75m up to +/- 0.15m. The accuracy of the simulated distance is greatly improved in simulated test for high precision pulsed range finder.

We Can Have It All: Improved Surveillance Outcomes and Decreased Personnel Costs Associated With Electronic Reportable Disease Surveillance, North Carolina, 2010

PubMed Central

DiBiase, Lauren; Fangman, Mary T.; Fleischauer, Aaron T.; Waller, Anna E.; MacDonald, Pia D. M.

2013-01-01

Objectives. We assessed the timeliness, accuracy, and cost of a new electronic disease surveillance system at the local health department level. We describe practices associated with lower cost and better surveillance timeliness and accuracy. Methods. Interviews conducted May through August 2010 with local health department (LHD) staff at a simple random sample of 30 of 100 North Carolina counties provided information on surveillance practices and costs; we used surveillance system data to calculate timeliness and accuracy. We identified LHDs with best timeliness and accuracy and used these categories to compare surveillance practices and costs. Results. Local health departments in the top tertiles for surveillance timeliness and accuracy had a lower cost per case reported than LHDs with lower timeliness and accuracy ($71 and $124 per case reported, respectively; P = .03). Best surveillance practices fell into 2 domains: efficient use of the electronic surveillance system and use of surveillance data for local evaluation and program management. Conclusions. Timely and accurate surveillance can be achieved in the setting of restricted funding experienced by many LHDs. Adopting best surveillance practices may improve both efficiency and public health outcomes. PMID:24134385

Comparison of univariate and multivariate models for prediction of major and minor elements from laser-induced breakdown spectra with and without masking

NASA Astrophysics Data System (ADS)

Dyar, M. Darby; Fassett, Caleb I.; Giguere, Stephen; Lepore, Kate; Byrne, Sarah; Boucher, Thomas; Carey, CJ; Mahadevan, Sridhar

2016-09-01

This study uses 1356 spectra from 452 geologically-diverse samples, the largest suite of LIBS rock spectra ever assembled, to compare the accuracy of elemental predictions in models that use only spectral regions thought to contain peaks arising from the element of interest versus those that use information in the entire spectrum. Results show that for the elements Si, Al, Ti, Fe, Mg, Ca, Na, K, Ni, Mn, Cr, Co, and Zn, univariate predictions based on single emission lines are by far the least accurate, no matter how carefully the region of channels/wavelengths is chosen and despite the prominence of the selected emission lines. An automated iterative algorithm was developed to sweep through all 5485 channels of data and select the single region that produces the optimal prediction accuracy for each element using univariate analysis. For the eight major elements, use of this technique results in a 35% improvement in prediction accuracy; for minors, the improvement is 13%. The best wavelength region choice for any given univariate analysis is likely to be an inherent property of the specific training set that cannot be generalized. In comparison, multivariate analysis using partial least-squares (PLS) almost universally outperforms univariate analysis. PLS using all the same wavelength regions from the univariate analysis produces results that improve in accuracy by 63% for major elements and 3% for minor element. This difference is likely a reflection of signal to noise ratios, which are far better for major elements than for minor elements, and likely limit their prediction accuracy by any technique. We also compare predictions using specific wavelength ranges for each element against those employing all channels. Masking out channels to focus on emission lines from a specific element that occurs decreases prediction accuracy for major elements but is useful for minor elements with low signals and proportionally much higher noise; use of PLS rather than univariate analysis is still recommended. Finally, we tested the generalizability of our results by analyzing a second data set from a different instrument. Overall prediction accuracies for the mixed data sets are higher than for either set alone for all major and minor elements except Ni, Cr, and Co, where results are roughly comparable.

Effect of Piracetam on Dyslexic's Reading Ability.

ERIC Educational Resources Information Center

Wilsher, C.; And Others

1985-01-01

Forty-six dyslexic boys (aged eight to 13) were administered Piracetam or placebo in a double-blind, parallel experiment. Although, overall, there were no significant group effects, the within-subject design revealed improvements in reading speed and accuracy in Piracetam Ss. Dyslexics with higher reading ages improved significantly compared to…

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

PubMed

Zhou, Tao; Li, Zhaofu; Pan, Jianjun

2018-01-27

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

Genomic Prediction Accounting for Residual Heteroskedasticity

PubMed Central

Ou, Zhining; Tempelman, Robert J.; Steibel, Juan P.; Ernst, Catherine W.; Bates, Ronald O.; Bello, Nora M.

2015-01-01

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. PMID:26564950

Accuracy of Doppler echocardiographic estimates of pulmonary artery pressures in a canine model of pulmonary hypertension

PubMed Central

Soydan, Lydia C.; Kellihan, Heidi B.; Bates, Melissa L.; Stepien, Rebecca L.; Consigny, Daniel W.; Bellofiore, Alessandro; Francois, Christopher J.; Chesler, Naomi C.

2015-01-01

Objectives To compare noninvasive estimates of pulmonary artery pressure (PAP) obtained via echocardiography (ECHO) to invasive measurements of PAP obtained during right heart catheterization (RHC) across a wide range of PAP, to examine the accuracy of estimating right atrial pressure via ECHO (RAPECHO) compared to RAP measured by catheterization (RAPRHC), and to determine if adding RAPECHO improves the accuracy of noninvasive PAP estimations. Animals Fourteen healthy female beagle dogs. Methods ECHO and RHC performed at various data collection points, both at normal PAP and increased PAP (generated by microbead embolization). Results Noninvasive estimates of PAP were moderately but significantly correlated with invasive measurements of PAP. A high degree of variance was noted for all estimations, with increased variance at higher PAP. The addition of RAPECHO improved correlation and bias in all cases. RAPRHC was significantly correlated with RAPECHO and with subjectively assessed right atrial size (RA sizesubj). Conclusions Spectral Doppler assessments of tricuspid and pulmonic regurgitation are imperfect methods for predicting PAP as measured by catheterization despite an overall moderate correlation between invasive and noninvasive values. Noninvasive measurements may be better utilized as part of a comprehensive assessment of PAP in canine patients. RAPRHC appears best estimated based on subjective assessment of RA size. Including estimated RAPECHO in estimates of PAP improves the correlation and relatedness between noninvasive and invasive measures of PAP, but notable variability in accuracy of estimations persists. PMID:25601540

Are people excessive or judicious in their egocentrism? A modeling approach to understanding bias and accuracy in people's optimism.

PubMed

Windschitl, Paul D; Rose, Jason P; Stalkfleet, Michael T; Smith, Andrew R

2008-08-01

People are often egocentric when judging their likelihood of success in competitions, leading to overoptimism about winning when circumstances are generally easy and to overpessimism when the circumstances are difficult. Yet, egocentrism might be grounded in a rational tendency to favor highly reliable information (about the self) more so than less reliable information (about others). A general theory of probability called extended support theory was used to conceptualize and assess the role of egocentrism and its consequences for the accuracy of people's optimism in 3 competitions (Studies 1-3, respectively). Also, instructions were manipulated to test whether people who were urged to avoid egocentrism would show improved or worsened accuracy in their likelihood judgments. Egocentrism was found to have a potentially helpful effect on one form of accuracy, but people generally showed too much egocentrism. Debias instructions improved one form of accuracy but had no impact on another. The advantages of using the EST framework for studying optimism and other types of judgments (e.g., comparative ability judgments) are discussed. (c) 2008 APA, all rights reserved

An Improved DINEOF Algorithm for Filling Missing Values in Spatio-Temporal Sea Surface Temperature Data.

PubMed

Ping, Bo; Su, Fenzhen; Meng, Yunshan

2016-01-01

In this study, an improved Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm for determination of missing values in a spatio-temporal dataset is presented. Compared with the ordinary DINEOF algorithm, the iterative reconstruction procedure until convergence based on every fixed EOF to determine the optimal EOF mode is not necessary and the convergence criterion is only reached once in the improved DINEOF algorithm. Moreover, in the ordinary DINEOF algorithm, after optimal EOF mode determination, the initial matrix with missing data will be iteratively reconstructed based on the optimal EOF mode until the reconstruction is convergent. However, the optimal EOF mode may be not the best EOF for some reconstructed matrices generated in the intermediate steps. Hence, instead of using asingle EOF to fill in the missing data, in the improved algorithm, the optimal EOFs for reconstruction are variable (because the optimal EOFs are variable, the improved algorithm is called VE-DINEOF algorithm in this study). To validate the accuracy of the VE-DINEOF algorithm, a sea surface temperature (SST) data set is reconstructed by using the DINEOF, I-DINEOF (proposed in 2015) and VE-DINEOF algorithms. Four parameters (Pearson correlation coefficient, signal-to-noise ratio, root-mean-square error, and mean absolute difference) are used as a measure of reconstructed accuracy. Compared with the DINEOF and I-DINEOF algorithms, the VE-DINEOF algorithm can significantly enhance the accuracy of reconstruction and shorten the computational time.

A code-aided carrier synchronization algorithm based on improved nonbinary low-density parity-check codes

NASA Astrophysics Data System (ADS)

Bai, Cheng-lin; Cheng, Zhi-hui

2016-09-01

In order to further improve the carrier synchronization estimation range and accuracy at low signal-to-noise ratio ( SNR), this paper proposes a code-aided carrier synchronization algorithm based on improved nonbinary low-density parity-check (NB-LDPC) codes to study the polarization-division-multiplexing coherent optical orthogonal frequency division multiplexing (PDM-CO-OFDM) system performance in the cases of quadrature phase shift keying (QPSK) and 16 quadrature amplitude modulation (16-QAM) modes. The simulation results indicate that this algorithm can enlarge frequency and phase offset estimation ranges and enhance accuracy of the system greatly, and the bit error rate ( BER) performance of the system is improved effectively compared with that of the system employing traditional NB-LDPC code-aided carrier synchronization algorithm.

The accuracy of real-time procedure coding by theatre nurses: a comparison with the central national system.

PubMed

Maclean, Donald; Younes, Hakim Ben; Forrest, Margaret; Towers, Hazel K

2012-03-01

Accurate and timely clinical data are required for clinical and organisational purposes and is especially important for patient management, audit of surgical performance and the electronic health record. The recent introduction of computerised theatre management systems has enabled real-time (point-of-care) operative procedure coding by clinical staff. However the accuracy of these data is unknown. The aim of this Scottish study was to compare the accuracy of theatre nurses' real-time coding on the local theatre management system with the central Scottish Morbidity Record (SMR01). Paired procedural codes were recorded, qualitatively graded for precision and compared (n = 1038). In this study, real-time, point-of-care coding by theatre nurses resulted in significant coding errors compared with the central SMR01 database. Improved collaboration between full-time coders and clinical staff using computerised decision support systems is suggested.

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

A randomized, double-blind, controlled study of ultrasound-guided corticosteroid injection into the joint of patients with inflammatory arthritis.

PubMed

Cunnington, Joanna; Marshall, Nicola; Hide, Geoff; Bracewell, Claire; Isaacs, John; Platt, Philip; Kane, David

2010-07-01

Most corticosteroid injections into the joint are guided by the clinical examination (CE), but up to 70% are inaccurately placed, which may contribute to an inadequate response. The aim of this study was to investigate whether ultrasound (US) guidance improves the accuracy and clinical outcome of joint injections as compared with CE guidance in patients with inflammatory arthritis. A total of 184 patients with inflammatory arthritis and an inflamed joint (shoulder, elbow, wrist, knee, or ankle) were randomized to receive either US-guided or CE-guided corticosteroid injections. Visual analog scales (VAS) for assessment of function, pain, and stiffness of the target joint, a modified Health Assessment Questionnaire, and the EuroQol 5-domain questionnaire were obtained at baseline and at 2 weeks and 6 weeks postinjection. The erythrocyte sedimentation rate and C-reactive protein level were measured at baseline and 2 weeks. Contrast injected with the steroid was used to assess the accuracy of the joint injection. One-third of CE-guided injections were inaccurate. US-guided injections performed by a trainee rheumatologist were more accurate than the CE-guided injections performed by more senior rheumatologists (83% versus 66%; P = 0.010). There was no significant difference in clinical outcome between the group receiving US-guided injections and the group receiving CE-guided injections. Accurate injections led to greater improvement in joint function, as determined by VAS scores, at 6 weeks, as compared with inaccurate injections (30.6 mm versus 21.2 mm; P = 0.030). Clinicians who used US guidance reliably assessed the accuracy of joint injection (P < 0.001), whereas those who used CE guidance did not (P = 0.29). US guidance significantly improves the accuracy of joint injection, allowing a trainee to rapidly achieve higher accuracy than more experienced rheumatologists. US guidance did not improve the short-term outcome of joint injection.

Spreading a medication administration intervention organizationwide in six hospitals.

PubMed

Kliger, Julie; Singer, Sara; Hoffman, Frank; O'Neil, Edward

2012-02-01

Six hospitals from the San Francisco Bay Area participated in a 12-month quality improvement project conducted by the Integrated Nurse Leadership Program (INLP). A quality improvement intervention that focused on improving medication administration accuracy was spread from two pilot units to all inpatient units in the hospitals. INLP developed a 12-month curriculum, presented in a combination of off-site training sessions and hospital-based training and consultant-led meetings, to teach clinicians the key skills needed to drive organizationwide change. Each hospital established a nurse-led project team, as well as unit teams to address six safety processes designed to improve medication administration accuracy: compare medication to the medication administration record; keep medication labeled throughout; check two patient identifications; explain drug to patient (if applicable); chart immediately after administration; and protect process from distractions and interruptions. From baseline until one year after project completion, the six hospitals improved their medication accuracy rates, on average, from 83.4% to 98.0% in the spread units. The spread units also improved safety processes overall from 83.1% to 97.2%. During the same time, the initial pilot units also continued to improve accuracy from 94.0% to 96.8% and safety processes overall from 95.3% to 97.2%. With thoughtful planning, engaging those doing the work early and focusing on the "human side of change" along with technical knowledge of improvement methodologies, organizations can spread initiatives enterprisewide. This program required significant training of frontline workers in problem-solving skills, leading change, team management, data tracking, and communication.

Study design requirements for RNA sequencing-based breast cancer diagnostics.

PubMed

Mer, Arvind Singh; Klevebring, Daniel; Grönberg, Henrik; Rantalainen, Mattias

2016-02-01

Sequencing-based molecular characterization of tumors provides information required for individualized cancer treatment. There are well-defined molecular subtypes of breast cancer that provide improved prognostication compared to routine biomarkers. However, molecular subtyping is not yet implemented in routine breast cancer care. Clinical translation is dependent on subtype prediction models providing high sensitivity and specificity. In this study we evaluate sample size and RNA-sequencing read requirements for breast cancer subtyping to facilitate rational design of translational studies. We applied subsampling to ascertain the effect of training sample size and the number of RNA sequencing reads on classification accuracy of molecular subtype and routine biomarker prediction models (unsupervised and supervised). Subtype classification accuracy improved with increasing sample size up to N = 750 (accuracy = 0.93), although with a modest improvement beyond N = 350 (accuracy = 0.92). Prediction of routine biomarkers achieved accuracy of 0.94 (ER) and 0.92 (Her2) at N = 200. Subtype classification improved with RNA-sequencing library size up to 5 million reads. Development of molecular subtyping models for cancer diagnostics requires well-designed studies. Sample size and the number of RNA sequencing reads directly influence accuracy of molecular subtyping. Results in this study provide key information for rational design of translational studies aiming to bring sequencing-based diagnostics to the clinic.

CT image segmentation methods for bone used in medical additive manufacturing.

PubMed

van Eijnatten, Maureen; van Dijk, Roelof; Dobbe, Johannes; Streekstra, Geert; Koivisto, Juha; Wolff, Jan

2018-01-01

The accuracy of additive manufactured medical constructs is limited by errors introduced during image segmentation. The aim of this study was to review the existing literature on different image segmentation methods used in medical additive manufacturing. Thirty-two publications that reported on the accuracy of bone segmentation based on computed tomography images were identified using PubMed, ScienceDirect, Scopus, and Google Scholar. The advantages and disadvantages of the different segmentation methods used in these studies were evaluated and reported accuracies were compared. The spread between the reported accuracies was large (0.04 mm - 1.9 mm). Global thresholding was the most commonly used segmentation method with accuracies under 0.6 mm. The disadvantage of this method is the extensive manual post-processing required. Advanced thresholding methods could improve the accuracy to under 0.38 mm. However, such methods are currently not included in commercial software packages. Statistical shape model methods resulted in accuracies from 0.25 mm to 1.9 mm but are only suitable for anatomical structures with moderate anatomical variations. Thresholding remains the most widely used segmentation method in medical additive manufacturing. To improve the accuracy and reduce the costs of patient-specific additive manufactured constructs, more advanced segmentation methods are required. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Impact of time-resolved MRA on diagnostic accuracy in patients with symptomatic peripheral artery disease of the calf station.

PubMed

Hansmann, Jan; Michaely, Henrik J; Morelli, John N; Diehl, Steffen J; Meyer, Mathias; Schoenberg, Stefan O; Attenberger, Ulrike I

2013-12-01

The purpose of this article is to evaluate the added diagnostic accuracy of time-resolved MR angiography (MRA) of the calves compared with continuous-table-movement MRA in patients with symptomatic lower extremity peripheral artery disease (PAD) using digital subtraction angiography (DSA) correlation. Eighty-four consecutive patients with symptomatic PAD underwent a low-dose 3-T MRA protocol, consisting of continuous-table-movement MRA, acquired from the diaphragm to the calves, and an additional time-resolved MRA of the calves; 0.1 mmol/kg body weight (bw) of contrast material was used (0.07 mmol/kg bw for continuous-table-movement MRA and 0.03 mmol/kg bw for time-resolved MRA). Two radiologists rated image quality on a 4-point scale and stenosis degree on a 3-point scale. An additional assessment determined the degree of venous contamination and whether time-resolved MRA improved diagnostic confidence. The accuracy of stenosis gradation with continuous-table-movement and time-resolved MRA was compared with that of DSA as a correlation. Overall diagnostic accuracy was calculated for continuous-table-movement and time-resolved MRA. Median image quality was rated as good for 578 vessel segments with continuous-table-movement MRA and as excellent for 565 vessel segments with time-resolved MRA. Interreader agreement was excellent (κ = 0.80-0.84). Venous contamination interfered with diagnosis in more than 60% of continuous-table-movement MRA examinations. The degree of stenosis was assessed for 340 vessel segments. The diagnostic accuracies (continuous-table-movement MRA/time-resolved MRA) combined for the readers were obtained for the tibioperoneal trunk (84%/93%), anterior tibial (69%/87%), posterior tibial (85%/91%), and peroneal (67%/81%) arteries. The addition of time-resolved MRA improved diagnostic confidence in 69% of examinations. The addition of time-resolved MRA at the calf station improves diagnostic accuracy over continuous-table-movement MRA alone in symptomatic patients with PAD.

Evaluation of the geometric stability and the accuracy potential of digital cameras — Comparing mechanical stabilisation versus parameterisation

NASA Astrophysics Data System (ADS)

Rieke-Zapp, D.; Tecklenburg, W.; Peipe, J.; Hastedt, H.; Haig, Claudia

Recent tests on the geometric stability of several digital cameras that were not designed for photogrammetric applications have shown that the accomplished accuracies in object space are either limited or that the accuracy potential is not exploited to the fullest extent. A total of 72 calibrations were calculated with four different software products for eleven digital camera models with different hardware setups, some with mechanical fixation of one or more parts. The calibration procedure was chosen in accord to a German guideline for evaluation of optical 3D measuring systems [VDI/VDE, VDI/VDE 2634 Part 1, 2002. Optical 3D Measuring Systems-Imaging Systems with Point-by-point Probing. Beuth Verlag, Berlin]. All images were taken with ringflashes which was considered a standard method for close-range photogrammetry. In cases where the flash was mounted to the lens, the force exerted on the lens tube and the camera mount greatly reduced the accomplished accuracy. Mounting the ringflash to the camera instead resulted in a large improvement of accuracy in object space. For standard calibration best accuracies in object space were accomplished with a Canon EOS 5D and a 35 mm Canon lens where the focusing tube was fixed with epoxy (47 μm maximum absolute length measurement error in object space). The fixation of the Canon lens was fairly easy and inexpensive resulting in a sevenfold increase in accuracy compared with the same lens type without modification. A similar accuracy was accomplished with a Nikon D3 when mounting the ringflash to the camera instead of the lens (52 μm maximum absolute length measurement error in object space). Parameterisation of geometric instabilities by introduction of an image variant interior orientation in the calibration process improved results for most cameras. In this case, a modified Alpa 12 WA yielded the best results (29 μm maximum absolute length measurement error in object space). Extending the parameter model with FiBun software to model not only an image variant interior orientation, but also deformations in the sensor domain of the cameras, showed significant improvements only for a small group of cameras. The Nikon D3 camera yielded the best overall accuracy (25 μm maximum absolute length measurement error in object space) with this calibration procedure indicating at the same time the presence of image invariant error in the sensor domain. Overall, calibration results showed that digital cameras can be applied for an accurate photogrammetric survey and that only a little effort was sufficient to greatly improve the accuracy potential of digital cameras.

Comparison of 1.5- and 3-T MR imaging for evaluating the articular cartilage of the knee.

PubMed

Van Dyck, Pieter; Kenis, Christoph; Vanhoenacker, Filip M; Lambrecht, Valérie; Wouters, Kristien; Gielen, Jan L; Dossche, Lieven; Parizel, Paul M

2014-06-01

The aim of this prospective study was to compare routine MRI scans of the knee at 1.5 and 3 T obtained in the same individuals in terms of their performance in the diagnosis of cartilage lesions. One hundred patients underwent MRI of the knee at 1.5 and 3 T and subsequent knee arthroscopy. All MR examinations consisted of multiplanar 2D turbo spin-echo sequences. Three radiologists independently graded all articular surfaces of the knee joint seen at MRI. With arthroscopy as the reference standard, the sensitivity, specificity, and accuracy of 1.5- and 3-T MRI for detecting cartilage lesions and the proportion of correctly graded cartilage lesions within the knee joint were determined and compared using resampling statistics. For all readers and surfaces combined, the respective sensitivity, specificity, and accuracy for detecting all grades of cartilage lesions in the knee joint using MRI were 60, 96, and 87% at 1.5 T and 69, 96, and 90% at 3 T. There was a statistically significant improvement in sensitivity (p < 0.05), but not specificity or accuracy (n.s.) for the detection of cartilage lesions at 3 T. There was also a statistically significant (p < 0.05) improvement in the proportion of correctly graded cartilage lesions at 3 T as compared to 1.5 T. A 3-T MR protocol significantly improves diagnostic performance for the purpose of detecting cartilage lesions within the knee joint, when compared with a similar protocol performed at 1.5 T. III.

Q-adjusting technique applied to vertical deflections estimation in a single-axis rotation INS/GPS integrated system

NASA Astrophysics Data System (ADS)

Zhu, Jing; Wang, Xingshu; Wang, Jun; Dai, Dongkai; Xiong, Hao

2016-10-01

Former studies have proved that the attitude error in a single-axis rotation INS/GPS integrated system tracks the high frequency component of the deflections of the vertical (DOV) with a fixed delay and tracking error. This paper analyses the influence of the nominal process noise covariance matrix Q on the tracking error as well as the response delay, and proposed a Q-adjusting technique to obtain the attitude error which can track the DOV better. Simulation results show that different settings of Q lead to different response delay and tracking error; there exists optimal Q which leads to a minimum tracking error and a comparatively short response delay; for systems with different accuracy, different Q-adjusting strategy should be adopted. In this way, the DOV estimation accuracy of using the attitude error as the observation can be improved. According to the simulation results, the DOV estimation accuracy after using the Q-adjusting technique is improved by approximate 23% and 33% respectively compared to that of the Earth Model EGM2008 and the direct attitude difference method.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification.

PubMed

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification

PubMed Central

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database. PMID:26640813

Predictive accuracy of combined genetic and environmental risk scores.

PubMed

Dudbridge, Frank; Pashayan, Nora; Yang, Jian

2018-02-01

The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. © 2017 WILEY PERIODICALS, INC.

Predictive accuracy of combined genetic and environmental risk scores

PubMed Central

Pashayan, Nora; Yang, Jian

2017-01-01

ABSTRACT The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. PMID:29178508

Comparison of several von Willebrand factor (VWF) activity assays for monitoring patients undergoing treatment with VWF/FVIII concentrates: improved performance with a new modified automated method.

PubMed

Hillarp, A; Friedman, K D; Adcock-Funk, D; Tiefenbacher, S; Nichols, W L; Chen, D; Stadler, M; Schwartz, B A

2015-11-01

The ability of von Willebrand factor (VWF) to bind platelet GP Ib and promote platelet plug formation is measured in vitro using the ristocetin cofactor (VWF:RCo) assay. Automated assay systems make testing more accessible for diagnosis, but do not necessarily improve sensitivity and accuracy. We assessed the performance of a modified automated VWF:RCo assay protocol for the Behring Coagulation System (BCS(®) ) compared to other available assay methods. Results from different VWF:RCo assays in a number of specialized commercial and research testing laboratories were compared using plasma samples with varying VWF:RCo activities (0-1.2 IU mL(-1) ). Samples were prepared by mixing VWF concentrate or plasma standard into VWF-depleted plasma. Commercially available lyophilized standard human plasma was also studied. Emphasis was put on the low measuring range. VWF:RCo accuracy was calculated based on the expected values, whereas precision was obtained from repeated measurements. In the physiological concentration range, most of the automated tests resulted in acceptable accuracy, with varying reproducibility dependent on the method. However, several assays were inaccurate in the low measuring range. Only the modified BCS protocol showed acceptable accuracy over the entire measuring range with improved reproducibility. A modified BCS(®) VWF:RCo method can improve sensitivity and thus enhances the measuring range. Furthermore, the modified BCS(®) assay displayed good precision. This study indicates that the specific modifications - namely the combination of increased ristocetin concentration, reduced platelet content, VWF-depleted plasma as on-board diluent and a two-curve calculation mode - reduces the issues seen with current VWF:RCo activity assays. © 2015 John Wiley & Sons Ltd.

Improving the Accuracy of Mapping Urban Vegetation Carbon Density by Combining Shadow Remove, Spectral Unmixing Analysis and Spatial Modeling

NASA Astrophysics Data System (ADS)

Qie, G.; Wang, G.; Wang, M.

2016-12-01

Mixed pixels and shadows due to buildings in urban areas impede accurate estimation and mapping of city vegetation carbon density. In most of previous studies, these factors are often ignored, which thus result in underestimation of city vegetation carbon density. In this study we presented an integrated methodology to improve the accuracy of mapping city vegetation carbon density. Firstly, we applied a linear shadow remove analysis (LSRA) on remotely sensed Landsat 8 images to reduce the shadow effects on carbon estimation. Secondly, we integrated a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), and utilized and compared the integrated models on shadow-removed images to map vegetation carbon density. This methodology was examined in Shenzhen City of Southeast China. A data set from a total of 175 sample plots measured in 2013 and 2014 was used to train the models. The independent variables statistically significantly contributing to improving the fit of the models to the data and reducing the sum of squared errors were selected from a total of 608 variables derived from different image band combinations and transformations. The vegetation fraction from LSUA was then added into the models as an important independent variable. The estimates obtained were evaluated using a cross-validation method. Our results showed that higher accuracies were obtained from the integrated models compared with the ones using traditional methods which ignore the effects of mixed pixels and shadows. This study indicates that the integrated method has great potential on improving the accuracy of urban vegetation carbon density estimation. Key words: Urban vegetation carbon, shadow, spectral unmixing, spatial modeling, Landsat 8 images

Modeling of the Mode S tracking system in support of aircraft safety research

NASA Technical Reports Server (NTRS)

Sorensen, J. A.; Goka, T.

1982-01-01

This report collects, documents, and models data relating the expected accuracies of tracking variables to be obtained from the FAA's Mode S Secondary Surveillance Radar system. The data include measured range and azimuth to the tracked aircraft plus the encoded altitude transmitted via the Mode S data link. A brief summary is made of the Mode S system status and its potential applications for aircraft safety improvement including accident analysis. FAA flight test results are presented demonstrating Mode S range and azimuth accuracy and error characteristics and comparing Mode S to the current ATCRBS radar tracking system. Data are also presented that describe the expected accuracy and error characteristics of encoded altitude. These data are used to formulate mathematical error models of the Mode S variables and encoded altitude. A brief analytical assessment is made of the real-time tracking accuracy available from using Mode S and how it could be improved with down-linked velocity.

Adaptive time-variant models for fuzzy-time-series forecasting.

PubMed

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

Mean Expected Error in Prediction of Total Body Water: A True Accuracy Comparison between Bioimpedance Spectroscopy and Single Frequency Regression Equations

PubMed Central

Abtahi, Shirin; Abtahi, Farhad; Ellegård, Lars; Johannsson, Gudmundur; Bosaeus, Ingvar

2015-01-01

For several decades electrical bioimpedance (EBI) has been used to assess body fluid distribution and body composition. Despite the development of several different approaches for assessing total body water (TBW), it remains uncertain whether bioimpedance spectroscopic (BIS) approaches are more accurate than single frequency regression equations. The main objective of this study was to answer this question by calculating the expected accuracy of a single measurement for different EBI methods. The results of this study showed that all methods produced similarly high correlation and concordance coefficients, indicating good accuracy as a method. Even the limits of agreement produced from the Bland-Altman analysis indicated that the performance of single frequency, Sun's prediction equations, at population level was close to the performance of both BIS methods; however, when comparing the Mean Absolute Percentage Error value between the single frequency prediction equations and the BIS methods, a significant difference was obtained, indicating slightly better accuracy for the BIS methods. Despite the higher accuracy of BIS methods over 50 kHz prediction equations at both population and individual level, the magnitude of the improvement was small. Such slight improvement in accuracy of BIS methods is suggested insufficient to warrant their clinical use where the most accurate predictions of TBW are required, for example, when assessing over-fluidic status on dialysis. To reach expected errors below 4-5%, novel and individualized approaches must be developed to improve the accuracy of bioimpedance-based methods for the advent of innovative personalized health monitoring applications. PMID:26137489

Improving the spectral measurement accuracy based on temperature distribution and spectra-temperature relationship

NASA Astrophysics Data System (ADS)

Li, Zhe; Feng, Jinchao; Liu, Pengyu; Sun, Zhonghua; Li, Gang; Jia, Kebin

2018-05-01

Temperature is usually considered as a fluctuation in near-infrared spectral measurement. Chemometric methods were extensively studied to correct the effect of temperature variations. However, temperature can be considered as a constructive parameter that provides detailed chemical information when systematically changed during the measurement. Our group has researched the relationship between temperature-induced spectral variation (TSVC) and normalized squared temperature. In this study, we focused on the influence of temperature distribution in calibration set. Multi-temperature calibration set selection (MTCS) method was proposed to improve the prediction accuracy by considering the temperature distribution of calibration samples. Furthermore, double-temperature calibration set selection (DTCS) method was proposed based on MTCS method and the relationship between TSVC and normalized squared temperature. We compare the prediction performance of PLS models based on random sampling method and proposed methods. The results from experimental studies showed that the prediction performance was improved by using proposed methods. Therefore, MTCS method and DTCS method will be the alternative methods to improve prediction accuracy in near-infrared spectral measurement.

Prognostic scores in oesophageal or gastric variceal bleeding.

PubMed

Ohmann, C; Stöltzing, H; Wins, L; Busch, E; Thon, K

1990-05-01

Numerous scoring systems have been developed for the prediction of outcome of variceal bleeding; however, only a few have been evaluated adequately. The object of this study was to improve the classical Child-Pugh score (CPS) and to test other scores from the literature. Patients (n = 82) with endoscopically confirmed variceal bleeding and long-term sclerotherapy were included in the study. Linear logistic regression (LR) was applied to different sets of prognostic variables with regard to 30-day mortality. In addition, scores from the literature were evaluated on the data set. Performance was measured by the accuracy and receiver-operating characteristic curves. The application of LR to all five CPS variables (accuracy, 80%) was superior to the classical CPS (70%). LR with selection from the CPS variables or from other sets of variables resulted in no improvement. Compared with CPS only three scores from the literature, mainly based on subsets of the CPS variables, showed an improved accuracy. It is concluded that CPS is still a good scoring system; however, it can be improved by statistical analysis using the same variables.

Transmission versus reflectance spectroscopy for quantitation

NASA Astrophysics Data System (ADS)

Gardner, Craig M.

2018-01-01

The objective of this work was to compare the accuracy of analyte concentration estimation when using transmission versus diffuse reflectance spectroscopy of a scattering medium. Monte Carlo ray tracing of light through the medium was used in conjunction with pure component absorption spectra and Beer-Lambert absorption along each ray's pathlength to generate matched sets of pseudoabsorbance spectra, containing water and six analytes present in skin. PLS regression models revealed an improvement in accuracy when using transmission compared to reflectance for a range of medium thicknesses and instrument noise levels. An analytical expression revealed the source of the accuracy degradation with reflectance was due both to the reduced collection efficiency for a fixed instrument etendue and to the broad pathlength distribution that detected light travels in the medium before exiting from the incident side.

Transportation Modes Classification Using Sensors on Smartphones.

PubMed

Fang, Shih-Hau; Liao, Hao-Hsiang; Fei, Yu-Xiang; Chen, Kai-Hsiang; Huang, Jen-Wei; Lu, Yu-Ding; Tsao, Yu

2016-08-19

This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user's transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes.

Transportation Modes Classification Using Sensors on Smartphones

PubMed Central

Fang, Shih-Hau; Liao, Hao-Hsiang; Fei, Yu-Xiang; Chen, Kai-Hsiang; Huang, Jen-Wei; Lu, Yu-Ding; Tsao, Yu

2016-01-01

This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user’s transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes. PMID:27548182

Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database.

PubMed

Nakajima, Kenichi; Okuda, Koichi; Watanabe, Satoru; Matsuo, Shinro; Kinuya, Seigo; Toth, Karin; Edenbrandt, Lars

2018-03-07

An artificial neural network (ANN) has been applied to detect myocardial perfusion defects and ischemia. The present study compares the diagnostic accuracy of a more recent ANN version (1.1) with the initial version 1.0. We examined 106 patients (age, 77 ± 10 years) with coronary angiographic findings, comprising multi-vessel disease (≥ 50% stenosis) (52%) or old myocardial infarction (27%), or who had undergone coronary revascularization (30%). The ANN versions 1.0 and 1.1 were trained in Sweden (n = 1051) and Japan (n = 1001), respectively, using 99m Tc-methoxyisobutylisonitrile myocardial perfusion images. The ANN probabilities (from 0.0 to 1.0) of stress defects and ischemia were calculated in candidate regions of abnormalities. The diagnostic accuracy was compared using receiver-operating characteristics (ROC) analysis and the calculated area under the ROC curve (AUC) using expert interpretation as the gold standard. Although the AUC for stress defects was 0.95 and 0.93 (p = 0.27) for versions 1.1 and 1.0, respectively, that for detecting ischemia was significantly improved in version 1.1 (p = 0.0055): AUC 0.96 for version 1.1 (sensitivity 87%, specificity 96%) vs. 0.89 for version 1.0 (sensitivity 78%, specificity 97%). The improvement in the AUC shown by version 1.1 was also significant for patients with neither coronary revascularization nor old myocardial infarction (p = 0.0093): AUC = 0.98 for version 1.1 (sensitivity 88%, specificity 100%) and 0.88 for version 1.0 (sensitivity 76%, specificity 100%). Intermediate ANN probability between 0.1 and 0.7 was more often calculated by version 1.1 compared with version 1.0, which contributed to the improved diagnostic accuracy. The diagnostic accuracy of the new version was also improved in patients with either single-vessel disease or no stenosis (n = 47; AUC, 0.81 vs. 0.66 vs. p = 0.0060) when coronary stenosis was used as a gold standard. The diagnostic ability of the ANN version 1.1 was improved by retraining using the Japanese database, particularly for identifying ischemia.

Does probabilistic modelling of linkage disequilibrium evolution improve the accuracy of QTL location in animal pedigree?

PubMed

Cierco-Ayrolles, Christine; Dejean, Sébastien; Legarra, Andrés; Gilbert, Hélène; Druet, Tom; Ytournel, Florence; Estivals, Delphine; Oumouhou, Naïma; Mangin, Brigitte

2010-10-22

Since 2001, the use of more and more dense maps has made researchers aware that combining linkage and linkage disequilibrium enhances the feasibility of fine-mapping genes of interest. So, various method types have been derived to include concepts of population genetics in the analyses. One major drawback of many of these methods is their computational cost, which is very significant when many markers are considered. Recent advances in technology, such as SNP genotyping, have made it possible to deal with huge amount of data. Thus the challenge that remains is to find accurate and efficient methods that are not too time consuming. The study reported here specifically focuses on the half-sib family animal design. Our objective was to determine whether modelling of linkage disequilibrium evolution improved the mapping accuracy of a quantitative trait locus of agricultural interest in these populations. We compared two methods of fine-mapping. The first one was an association analysis. In this method, we did not model linkage disequilibrium evolution. Therefore, the modelling of the evolution of linkage disequilibrium was a deterministic process; it was complete at time 0 and remained complete during the following generations. In the second method, the modelling of the evolution of population allele frequencies was derived from a Wright-Fisher model. We simulated a wide range of scenarios adapted to animal populations and compared these two methods for each scenario. Our results indicated that the improvement produced by probabilistic modelling of linkage disequilibrium evolution was not significant. Both methods led to similar results concerning the location accuracy of quantitative trait loci which appeared to be mainly improved by using four flanking markers instead of two. Therefore, in animal half-sib designs, modelling linkage disequilibrium evolution using a Wright-Fisher model does not significantly improve the accuracy of the QTL location when compared to a simpler method assuming complete and constant linkage between the QTL and the marker alleles. Finally, given the high marker density available nowadays, the simpler method should be preferred as it gives accurate results in a reasonable computing time.

Improving the Accuracy of Laplacian Estimation with Novel Variable Inter-Ring Distances Concentric Ring Electrodes

PubMed Central

Makeyev, Oleksandr; Besio, Walter G.

2016-01-01

Noninvasive concentric ring electrodes are a promising alternative to conventional disc electrodes. Currently, the superiority of tripolar concentric ring electrodes over disc electrodes, in particular, in accuracy of Laplacian estimation, has been demonstrated in a range of applications. In our recent work, we have shown that accuracy of Laplacian estimation can be improved with multipolar concentric ring electrodes using a general approach to estimation of the Laplacian for an (n + 1)-polar electrode with n rings using the (4n + 1)-point method for n ≥ 2. This paper takes the next step toward further improving the Laplacian estimate by proposing novel variable inter-ring distances concentric ring electrodes. Derived using a modified (4n + 1)-point method, linearly increasing and decreasing inter-ring distances tripolar (n = 2) and quadripolar (n = 3) electrode configurations are compared to their constant inter-ring distances counterparts. Finite element method modeling and analytic results are consistent and suggest that increasing inter-ring distances electrode configurations may decrease the truncation error resulting in more accurate Laplacian estimates compared to respective constant inter-ring distances configurations. For currently used tripolar electrode configuration, the truncation error may be decreased more than two-fold, while for the quadripolar configuration more than a six-fold decrease is expected. PMID:27294933

Improving the Accuracy of Laplacian Estimation with Novel Variable Inter-Ring Distances Concentric Ring Electrodes.

PubMed

Makeyev, Oleksandr; Besio, Walter G

2016-06-10

Noninvasive concentric ring electrodes are a promising alternative to conventional disc electrodes. Currently, the superiority of tripolar concentric ring electrodes over disc electrodes, in particular, in accuracy of Laplacian estimation, has been demonstrated in a range of applications. In our recent work, we have shown that accuracy of Laplacian estimation can be improved with multipolar concentric ring electrodes using a general approach to estimation of the Laplacian for an (n + 1)-polar electrode with n rings using the (4n + 1)-point method for n ≥ 2. This paper takes the next step toward further improving the Laplacian estimate by proposing novel variable inter-ring distances concentric ring electrodes. Derived using a modified (4n + 1)-point method, linearly increasing and decreasing inter-ring distances tripolar (n = 2) and quadripolar (n = 3) electrode configurations are compared to their constant inter-ring distances counterparts. Finite element method modeling and analytic results are consistent and suggest that increasing inter-ring distances electrode configurations may decrease the truncation error resulting in more accurate Laplacian estimates compared to respective constant inter-ring distances configurations. For currently used tripolar electrode configuration, the truncation error may be decreased more than two-fold, while for the quadripolar configuration more than a six-fold decrease is expected.

Improved Accuracy Using Recursive Bayesian Estimation Based Language Model Fusion in ERP-Based BCI Typing Systems

PubMed Central

Orhan, U.; Erdogmus, D.; Roark, B.; Oken, B.; Purwar, S.; Hild, K. E.; Fowler, A.; Fried-Oken, M.

2013-01-01

RSVP Keyboard™ is an electroencephalography (EEG) based brain computer interface (BCI) typing system, designed as an assistive technology for the communication needs of people with locked-in syndrome (LIS). It relies on rapid serial visual presentation (RSVP) and does not require precise eye gaze control. Existing BCI typing systems which uses event related potentials (ERP) in EEG suffer from low accuracy due to low signal-to-noise ratio. Henceforth, RSVP Keyboard™ utilizes a context based decision making via incorporating a language model, to improve the accuracy of letter decisions. To further improve the contributions of the language model, we propose recursive Bayesian estimation, which relies on non-committing string decisions, and conduct an offline analysis, which compares it with the existing naïve Bayesian fusion approach. The results indicate the superiority of the recursive Bayesian fusion and in the next generation of RSVP Keyboard™ we plan to incorporate this new approach. PMID:23366432

Soil quality assessment using weighted fuzzy association rules

USGS Publications Warehouse

Xue, Yue-Ju; Liu, Shu-Guang; Hu, Yue-Ming; Yang, Jing-Feng

2010-01-01

Fuzzy association rules (FARs) can be powerful in assessing regional soil quality, a critical step prior to land planning and utilization; however, traditional FARs mined from soil quality database, ignoring the importance variability of the rules, can be redundant and far from optimal. In this study, we developed a method applying different weights to traditional FARs to improve accuracy of soil quality assessment. After the FARs for soil quality assessment were mined, redundant rules were eliminated according to whether the rules were significant or not in reducing the complexity of the soil quality assessment models and in improving the comprehensibility of FARs. The global weights, each representing the importance of a FAR in soil quality assessment, were then introduced and refined using a gradient descent optimization method. This method was applied to the assessment of soil resources conditions in Guangdong Province, China. The new approach had an accuracy of 87%, when 15 rules were mined, as compared with 76% from the traditional approach. The accuracy increased to 96% when 32 rules were mined, in contrast to 88% from the traditional approach. These results demonstrated an improved comprehensibility of FARs and a high accuracy of the proposed method.

Size at emergence improves accuracy of age estimates in forensically-useful beetle Creophilus maxillosus L. (Staphylinidae).

PubMed

Matuszewski, Szymon; Frątczak-Łagiewska, Katarzyna

2018-02-05

Insects colonizing human or animal cadavers may be used to estimate post-mortem interval (PMI) usually by aging larvae or pupae sampled on a crime scene. The accuracy of insect age estimates in a forensic context is reduced by large intraspecific variation in insect development time. Here we test the concept that insect size at emergence may be used to predict insect physiological age and accordingly to improve the accuracy of age estimates in forensic entomology. Using results of laboratory study on development of forensically-useful beetle Creophilus maxillosus (Linnaeus, 1758) (Staphylinidae) we demonstrate that its physiological age at emergence [i.e. thermal summation value (K) needed for emergence] fall with an increase of beetle size. In the validation study it was found that K estimated based on the adult insect size was significantly closer to the true K as compared to K from the general thermal summation model. Using beetle length at emergence as a predictor variable and male or female specific model regressing K against beetle length gave the most accurate predictions of age. These results demonstrate that size of C. maxillosus at emergence improves accuracy of age estimates in a forensic context.

Modeling of profilometry with laser focus sensors

NASA Astrophysics Data System (ADS)

Bischoff, Jörg; Manske, Eberhard; Baitinger, Henner

2011-05-01

Metrology is of paramount importance in submicron patterning. Particularly, line width and overlay have to be measured very accurately. Appropriated metrology techniques are scanning electron microscopy and optical scatterometry. The latter is non-invasive, highly accurate and enables optical cross sections of layer stacks but it requires periodic patterns. Scanning laser focus sensors are a viable alternative enabling the measurement of non-periodic features. Severe limitations are imposed by the diffraction limit determining the edge location accuracy. It will be shown that the accuracy can be greatly improved by means of rigorous modeling. To this end, a fully vectorial 2.5-dimensional model has been developed based on rigorous Maxwell solvers and combined with models for the scanning and various autofocus principles. The simulations are compared with experimental results. Moreover, the simulations are directly utilized to improve the edge location accuracy.

Information filtering via biased heat conduction

NASA Astrophysics Data System (ADS)

Liu, Jian-Guo; Zhou, Tao; Guo, Qiang

2011-09-01

The process of heat conduction has recently found application in personalized recommendation [Zhou , Proc. Natl. Acad. Sci. USA PNASA60027-842410.1073/pnas.1000488107107, 4511 (2010)], which is of high diversity but low accuracy. By decreasing the temperatures of small-degree objects, we present an improved algorithm, called biased heat conduction, which could simultaneously enhance the accuracy and diversity. Extensive experimental analyses demonstrate that the accuracy on MovieLens, Netflix, and Delicious datasets could be improved by 43.5%, 55.4% and 19.2%, respectively, compared with the standard heat conduction algorithm and also the diversity is increased or approximately unchanged. Further statistical analyses suggest that the present algorithm could simultaneously identify users' mainstream and special tastes, resulting in better performance than the standard heat conduction algorithm. This work provides a creditable way for highly efficient information filtering.

Determining successional stage of temperate coniferous forests with Landsat satellite data

NASA Technical Reports Server (NTRS)

Fiorella, Maria; Ripple, William J.

1993-01-01

Thematic Mapper (TM) digital imagery was used to map forest successional stages and to evaluate spectral differences between old-growth and mature forests in the central Cascade Range of Oregon. Relative sun incidence values were incorporated into the successional stage classification to compensate for topographic induced variation. Relative sun incidence improved the classification accuracy of young successional stages, but did not improve the classification accuracy of older, closed canopy forest classes or overall accuracy. TM bands 1, 2, and 4; the normalized difference vegetation index; and TM 4/3, 4/5, and 4/7 band ratio values for o|d-growth forests were found to be significantly lower than the values of mature forests. The Tasseled Cap features of brightness, greenness, and wetness also had significantly lower old-growth values as compared to mature forest values .

Financial impact of improved pressure ulcer staging in the acute hospital with use of a new tool, the NE1 Wound Assessment Tool.

PubMed

Young, Daniel L; Shen, Jay J; Estocado, Nancy; Landers, Merrill R

2012-04-01

The NE1 Wound Assessment Tool (NE1 WAT; Medline Industries, Inc, Mundelein, Illinois), previously called the N.E. One Can Stage, was shown to significantly improve accuracy of pressure ulcer (PrU) staging. Improved PrU staging has many potential benefits, including improved care for the patient and better reimbursement. Medicare has incentivized good care and accurate identification of PrUs in the acute care hospital through an additional payment, the Medicare Severity-Diagnosis Related Group (MS-DRG). This article examines the financial impact of NE1 WAT use on the acute care hospital relative to MS-DRG reimbursement. PrU staging accuracy with and without use of the NE1 WAT from previous data was compared with acute care hospital PrU rates obtained from the 2006 National Inpatient Sample. Hill-Rom International Pressure Ulcer Prevalence Survey data were used to estimate the number of MS-DRG-eligible PrUs. There are between 390,000 and 130,000 MS-DRG-eligible PrUs annually. Given current PrU staging accuracy, approximately $209 million in MS-DRG money is being collected. With the improved staging afforded by the NE1 WAT, this figure is approximately $763.9 million. Subtracting the 2 reveals $554.9 million in additional reimbursement that could be generated by using the NE1 WAT. There is a tremendous financial incentive to improve PrU staging. The NE1 WAT has been shown to improve PrU staging accuracy significantly. This improvement has the potential to improve the financial health of acute care hospitals caring for patients with PrUs.

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

Experimental analysis of robot-assisted needle insertion into porcine liver.

PubMed

Wang, Wendong; Shi, Yikai; Goldenberg, Andrew A; Yuan, Xiaoqing; Zhang, Peng; He, Lijing; Zou, Yingjie

2015-01-01

How to improve placement accuracy of needle insertion into liver tissue is of paramount interest to physicians. A robot-assisted system was developed to experimentally demonstrate its advantages in needle insertion surgeries. Experiments of needle insertion into porcine liver tissue were performed with conic tip needle (diameter 8 mm) and bevel tip needle (diameter 1.5 mm) in this study. Manual operation was designed to compare the performance of the presented robot-assisted system. The real-time force curves show outstanding advantages of robot-assisted operation in improving the controllability and stability of needle insertion process by comparing manual operation. The statistics of maximum force and average force further demonstrates robot-assisted operation causes less oscillation. The difference of liver deformation created by manual operation and robot-assisted operation is very low, 1 mm for average deformation and 2 mm for maximum deformation. To conclude, the presented robot-assisted system can improve placement accuracy of needle by stably control insertion process.

Variability in interhospital trauma data coding and scoring: A challenge to the accuracy of aggregated trauma registries.

PubMed

Arabian, Sandra S; Marcus, Michael; Captain, Kevin; Pomphrey, Michelle; Breeze, Janis; Wolfe, Jennefer; Bugaev, Nikolay; Rabinovici, Reuven

2015-09-01

Analyses of data aggregated in state and national trauma registries provide the platform for clinical, research, development, and quality improvement efforts in trauma systems. However, the interhospital variability and accuracy in data abstraction and coding have not yet been directly evaluated. This multi-institutional, Web-based, anonymous study examines interhospital variability and accuracy in data coding and scoring by registrars. Eighty-two American College of Surgeons (ACS)/state-verified Level I and II trauma centers were invited to determine different data elements including diagnostic, procedure, and Abbreviated Injury Scale (AIS) coding as well as selected National Trauma Data Bank definitions for the same fictitious case. Variability and accuracy in data entries were assessed by the maximal percent agreement among the registrars for the tested data elements, and 95% confidence intervals were computed to compare this level of agreement to the ideal value of 100%. Variability and accuracy in all elements were compared (χ testing) based on Trauma Quality Improvement Program (TQIP) membership, level of trauma center, ACS verification, and registrar's certifications. Fifty registrars (61%) completed the survey. The overall accuracy for all tested elements was 64%. Variability was noted in all examined parameters except for the place of occurrence code in all groups and the lower extremity AIS code in Level II trauma centers and in the Certified Specialist in Trauma Registry- and Certified Abbreviated Injury Scale Specialist-certified registrar groups. No differences in variability were noted when groups were compared based on TQIP membership, level of center, ACS verification, and registrar's certifications, except for prehospital Glasgow Coma Scale (GCS), where TQIP respondents agreed more than non-TQIP centers (p = 0.004). There is variability and inaccuracy in interhospital data coding and scoring of injury information. This finding casts doubt on the validity of registry data used in all aspects of trauma care and injury surveillance.

Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data

USGS Publications Warehouse

Myint, S.W.; Yuan, M.; Cerveny, R.S.; Giri, C.P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and objectoriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. ?? 2008 by MDPI.

Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data

PubMed Central

Myint, Soe W.; Yuan, May; Cerveny, Randall S.; Giri, Chandra P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. PMID:27879757

Accuracy optimization with wavelength tunability in overlay imaging technology

NASA Astrophysics Data System (ADS)

Lee, Honggoo; Kang, Yoonshik; Han, Sangjoon; Shim, Kyuchan; Hong, Minhyung; Kim, Seungyoung; Lee, Jieun; Lee, Dongyoung; Oh, Eungryong; Choi, Ahlin; Kim, Youngsik; Marciano, Tal; Klein, Dana; Hajaj, Eitan M.; Aharon, Sharon; Ben-Dov, Guy; Lilach, Saltoun; Serero, Dan; Golotsvan, Anna

2018-03-01

As semiconductor manufacturing technology progresses and the dimensions of integrated circuit elements shrink, overlay budget is accordingly being reduced. Overlay budget closely approaches the scale of measurement inaccuracies due to both optical imperfections of the measurement system and the interaction of light with geometrical asymmetries of the measured targets. Measurement inaccuracies can no longer be ignored due to their significant effect on the resulting device yield. In this paper we investigate a new approach for imaging based overlay (IBO) measurements by optimizing accuracy rather than contrast precision, including its effect over the total target performance, using wavelength tunable overlay imaging metrology. We present new accuracy metrics based on theoretical development and present their quality in identifying the measurement accuracy when compared to CD-SEM overlay measurements. The paper presents the theoretical considerations and simulation work, as well as measurement data, for which tunability combined with the new accuracy metrics is shown to improve accuracy performance.

Prediction of Industrial Electric Energy Consumption in Anhui Province Based on GA-BP Neural Network

NASA Astrophysics Data System (ADS)

Zhang, Jiajing; Yin, Guodong; Ni, Youcong; Chen, Jinlan

2018-01-01

In order to improve the prediction accuracy of industrial electrical energy consumption, a prediction model of industrial electrical energy consumption was proposed based on genetic algorithm and neural network. The model use genetic algorithm to optimize the weights and thresholds of BP neural network, and the model is used to predict the energy consumption of industrial power in Anhui Province, to improve the prediction accuracy of industrial electric energy consumption in Anhui province. By comparing experiment of GA-BP prediction model and BP neural network model, the GA-BP model is more accurate with smaller number of neurons in the hidden layer.

Activity Recognition for Personal Time Management

NASA Astrophysics Data System (ADS)

Prekopcsák, Zoltán; Soha, Sugárka; Henk, Tamás; Gáspár-Papanek, Csaba

We describe an accelerometer based activity recognition system for mobile phones with a special focus on personal time management. We compare several data mining algorithms for the automatic recognition task in the case of single user and multiuser scenario, and improve accuracy with heuristics and advanced data mining methods. The results show that daily activities can be recognized with high accuracy and the integration with the RescueTime software can give good insights for personal time management.

The Role of Teacher's Corrective Feedback in Improving Iranian EFL Learners' Writing Accuracy over Time: Is Learner's Mother Tongue Relevant?

ERIC Educational Resources Information Center

Rahimi, Mohammad

2009-01-01

The purpose of the present study was to investigate the impact of feedback on writing accuracy over time and examine the relevance of the students' mother tongue to the feedback effect. To this end, the study compared two groups of Iranian English majors (N = 56) over a period of four months: one with indirect grammar feedback and the other with…

Measurement of slow-moving along-track displacement from an efficient multiple-aperture SAR interferometry (MAI) stacking

USGS Publications Warehouse

Jo, Min-Jeong; Jung, Hyung-Sup; Won, Joong-Sun; Poland, Michael; Miklius, Asta; Lu, Zhong

2015-01-01

Multiple-aperture SAR interferometry (MAI) has demonstrated outstanding measurement accuracy of along-track displacement when compared to pixel-offset-tracking methods; however, measuring slow-moving (cm/year) surface displacement remains a challenge. Stacking of multi-temporal observations is a potential approach to reducing noise and increasing measurement accuracy, but it is difficult to achieve a significant improvement by applying traditional stacking methods to multi-temporal MAI interferograms. This paper proposes an efficient MAI stacking method, where multi-temporal forward- and backward-looking residual interferograms are individually stacked before the MAI interferogram is generated. We tested the performance of this method using ENVISAT data from Kīlauea Volcano, Hawai‘i, where displacement on the order of several centimeters per year is common. By comparing results from the proposed stacking methods with displacements from GPS data, we documented measurement accuracies of about 1.03 and 1.07 cm/year for the descending and ascending tracks, respectively—an improvement of about a factor of two when compared with that from the conventional stacking approach. Three-dimensional surface-displacement maps can be constructed by combining stacked InSAR and MAI observations, which will contribute to a better understanding of a variety of geological phenomena.

Researches on High Accuracy Prediction Methods of Earth Orientation Parameters

NASA Astrophysics Data System (ADS)

Xu, X. Q.

2015-09-01

The Earth rotation reflects the coupling process among the solid Earth, atmosphere, oceans, mantle, and core of the Earth on multiple spatial and temporal scales. The Earth rotation can be described by the Earth's orientation parameters, which are abbreviated as EOP (mainly including two polar motion components PM_X and PM_Y, and variation in the length of day ΔLOD). The EOP is crucial in the transformation between the terrestrial and celestial reference systems, and has important applications in many areas such as the deep space exploration, satellite precise orbit determination, and astrogeodynamics. However, the EOP products obtained by the space geodetic technologies generally delay by several days to two weeks. The growing demands for modern space navigation make high-accuracy EOP prediction be a worthy topic. This thesis is composed of the following three aspects, for the purpose of improving the EOP forecast accuracy. (1) We analyze the relation between the length of the basic data series and the EOP forecast accuracy, and compare the EOP prediction accuracy for the linear autoregressive (AR) model and the nonlinear artificial neural network (ANN) method by performing the least squares (LS) extrapolations. The results show that the high precision forecast of EOP can be realized by appropriate selection of the basic data series length according to the required time span of EOP prediction: for short-term prediction, the basic data series should be shorter, while for the long-term prediction, the series should be longer. The analysis also showed that the LS+AR model is more suitable for the short-term forecasts, while the LS+ANN model shows the advantages in the medium- and long-term forecasts. (2) We develop for the first time a new method which combines the autoregressive model and Kalman filter (AR+Kalman) in short-term EOP prediction. The equations of observation and state are established using the EOP series and the autoregressive coefficients respectively, which are used to improve/re-evaluate the AR model. Comparing to the single AR model, the AR+Kalman method performs better in the prediction of UT1-UTC and ΔLOD, and the improvement in the prediction of the polar motion is significant. (3) Following the successful Earth Orientation Parameter Prediction Comparison Campaign (EOP PCC), the Earth Orientation Parameter Combination of Prediction Pilot Project (EOPC PPP) was sponsored in 2010. As one of the participants from China, we update and submit the short- and medium-term (1 to 90 days) EOP predictions every day. From the current comparative statistics, our prediction accuracy is on the medium international level. We will carry out more innovative researches to improve the EOP forecast accuracy and enhance our level in EOP forecast.

Remote kinematic training for patients with chronic neck pain: a randomised controlled trial.

PubMed

Sarig Bahat, Hilla; Croft, Kate; Carter, Courtney; Hoddinott, Anna; Sprecher, Elliot; Treleaven, Julia

2018-06-01

To evaluate short- and intermediate-term effects of kinematic training (KT) using virtual reality (VR) or laser in patients with chronic neck pain. A randomised controlled trial with three arms (laser, VR, control) to post-intervention (N = 90), and two arms (laser or VR) continuing to 3 months follow-up. Home training intervention was provided during 4 weeks to VR and laser groups while control group waited. Primary outcome measures included neck disability index (NDI), global perceived effect (GPE), and cervical motion velocity (mean and peak). Secondary outcome measures included pain intensity (VAS), health status (EQ5D), kinesiophobia (TSK), range, smoothness, and accuracy of neck motion as measured by the neck VR system. Measures were taken at baseline, immediately post-training, and 3 months later. Ninety patients with neck pain were randomised to the trial, of which 76 completed 1 month follow-up, and 56 the 3 months follow-up. Significant improvements were demonstrated in NDI and velocity with good effect sizes in intervention groups compared to control. No within-group changes were presented in the control group, compared to global improvements in intervention groups. Velocity significantly improved at both time points in both groups. NDI, VAS, EQ5D, TSK and accuracy significantly improved at both time points in VR and in laser at 3 months evaluation in all but TSK. GPE scores showed 74-84% of participants perceived improvement and/or were satisfied. Significant advantages to the VR group compared to laser were found in velocity, pain intensity, health status and accuracy at both time points. The results support home kinematic training using VR or laser for improving disability, neck pain and kinematics in the short and intermediate term with an advantage to the VR group. The results provide directions for future research, use and development. ACTRN12615000231549.

Accuracy Assessment of Coastal Topography Derived from Uav Images

NASA Astrophysics Data System (ADS)

Long, N.; Millescamps, B.; Pouget, F.; Dumon, A.; Lachaussée, N.; Bertin, X.

2016-06-01

To monitor coastal environments, Unmanned Aerial Vehicle (UAV) is a low-cost and easy to use solution to enable data acquisition with high temporal frequency and spatial resolution. Compared to Light Detection And Ranging (LiDAR) or Terrestrial Laser Scanning (TLS), this solution produces Digital Surface Model (DSM) with a similar accuracy. To evaluate the DSM accuracy on a coastal environment, a campaign was carried out with a flying wing (eBee) combined with a digital camera. Using the Photoscan software and the photogrammetry process (Structure From Motion algorithm), a DSM and an orthomosaic were produced. Compared to GNSS surveys, the DSM accuracy is estimated. Two parameters are tested: the influence of the methodology (number and distribution of Ground Control Points, GCPs) and the influence of spatial image resolution (4.6 cm vs 2 cm). The results show that this solution is able to reproduce the topography of a coastal area with a high vertical accuracy (< 10 cm). The georeferencing of the DSM require a homogeneous distribution and a large number of GCPs. The accuracy is correlated with the number of GCPs (use 19 GCPs instead of 10 allows to reduce the difference of 4 cm); the required accuracy should be dependant of the research problematic. Last, in this particular environment, the presence of very small water surfaces on the sand bank does not allow to improve the accuracy when the spatial resolution of images is decreased.

Genomic Prediction Accounting for Residual Heteroskedasticity.

PubMed

Ou, Zhining; Tempelman, Robert J; Steibel, Juan P; Ernst, Catherine W; Bates, Ronald O; Bello, Nora M

2015-11-12

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. Copyright © 2016 Ou et al.

Comparing the Efficiency of Repeated Reading and Listening-While-Reading to Improve Fluency and Comprehension

ERIC Educational Resources Information Center

Hawkins, Renee O.; Marsicano, Richard; Schmitt, Ara J.; McCallum, Elizabeth; Musti-Rao, Shobana

2015-01-01

An alternating treatments design was used to compare the effects of two reading fluency interventions on the oral reading fluency and maze accuracy of four fourth-grade students. Also, by taking into account time spent in intervention, the efficiency of the two interventions was compared. In the adult-mediated repeated reading (RR) condition,…

Utility of Water Siphon Maneuver for Eliciting Gastroesophageal Reflux During Barium Esophagography: Correlation With Histologic Findings.

PubMed

Dane, B; Doshi, A; Khan, A; Megibow, A

2018-06-12

The objective of this study is to evaluate whether the water siphon maneuver improves detection of gastroesophageal (GE) reflux during barium esophagography compared with observation for spontaneous reflux only. Histopathologic analysis is the reference standard. This retrospective study assessed 87 outpatients who underwent both barium esophagography and upper endoscopy-guided biopsy within a 30-day interval. The water siphon maneuver was routinely performed when spontaneous GE reflux was not observed during the fluoroscopic study. Radiology reports were reviewed for mentions of the presence of reflux and the circumstances in which it was observed (as a spontaneous occurrence or as a result of the water siphon maneuver). Pathology reports from subsequent endoscopic biopsies were reviewed to identify histologic changes of reflux disease. The sensitivity, specificity, and accuracy of esophagography, observation for spontaneous reflux, and the water siphon maneuver were calculated and then compared using a McNemar test. Of the 87 patients, 57 (65.5%) had GE reflux diagnosed on the basis of histologic changes noted on endoscopy, and 30 (34.5%) did not. A total of 57 patients (65.5%) showed reflux during esophagography, 41 (71.9%) of whom had reflux diagnosed by the water siphon maneuver, and 16 (28.1%) had reflux diagnosed on the basis of observation of spontaneous reflux. Forty-four patients had reflux diagnosed on the basis of both a barium study and histologic findings; 13 patients had reflux noted on esophagography but had negative histologic findings. The overall sensitivity, specificity, and accuracy of esophagography for reflux were 77.2%, 56.7%, and 70.1%, respectively. Spontaneous reflux alone had a sensitivity, specificity, and accuracy of 21.1%, 86.7%, and 43.7%, respectively. The water siphon maneuver showed a sensitivity of 71.1%, a specificity of 65.4%, and accuracy of 69.0%. The differences in the sensitivity, specificity, and accuracy of the water siphon maneuver versus observation of spontaneous reflux were statistically significant (p ≤ 0.004). A properly performed and interpreted water siphon maneuver significantly increases the sensitivity and accuracy for GE reflux during esophagography, compared with observation for spontaneous reflux alone. The water siphon maneuver is a simple addition to barium esophagography that improves sensitivity and accuracy for the diagnosis of GE reflux compared with observation alone.

Analytical Models of Exoplanetary Atmospheres. IV. Improved Two-stream Radiative Transfer for the Treatment of Aerosols

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heng, Kevin; Kitzmann, Daniel, E-mail: kevin.heng@csh.unibe.ch, E-mail: daniel.kitzmann@csh.unibe.ch

We present a novel generalization of the two-stream method of radiative transfer, which allows for the accurate treatment of radiative transfer in the presence of strong infrared scattering by aerosols. We prove that this generalization involves only a simple modification of the coupling coefficients and transmission functions in the hemispheric two-stream method. This modification originates from allowing the ratio of the first Eddington coefficients to depart from unity. At the heart of the method is the fact that this ratio may be computed once and for all over the entire range of values of the single-scattering albedo and scattering asymmetrymore » factor. We benchmark our improved two-stream method by calculating the fraction of flux reflected by a single atmospheric layer (the reflectivity) and comparing these calculations to those performed using a 32-stream discrete-ordinates method. We further compare our improved two-stream method to the two-stream source function (16 streams) and delta-Eddington methods, demonstrating that it is often more accurate at the order-of-magnitude level. Finally, we illustrate its accuracy using a toy model of the early Martian atmosphere hosting a cloud layer composed of carbon dioxide ice particles. The simplicity of implementation and accuracy of our improved two-stream method renders it suitable for implementation in three-dimensional general circulation models. In other words, our improved two-stream method has the ease of implementation of a standard two-stream method, but the accuracy of a 32-stream method.« less

Population variation in isotopic composition of shorebird feathers: Implications for determining molting grounds

USGS Publications Warehouse

Torres-Dowdall, J.; Farmer, A.H.; Bucher, E.H.; Rye, R.O.; Landis, G.

2009-01-01

Stable isotope analyses have revolutionized the study of migratory connectivity. However, as with all tools, their limitations must be understood in order to derive the maximum benefit of a particular application. The goal of this study was to evaluate the efficacy of stable isotopes of C, N, H, O and S for assigning known-origin feathers to the molting sites of migrant shorebird species wintering and breeding in Argentina. Specific objectives were to: 1) compare the efficacy of the technique for studying shorebird species with different migration patterns, life histories and habitat-use patterns; 2) evaluate the grouping of species with similar migration and habitat use patterns in a single analysis to potentially improve prediction accuracy; and 3) evaluate the potential gains in prediction accuracy that might be achieved from using multiple stable isotopes. The efficacy of stable isotope ratios to determine origin was found to vary with species. While one species (White-rumped Sandpiper, Calidris fuscicollis) had high levels of accuracy assigning samples to known origin (91% of samples correctly assigned), another (Collared Plover, Charadrius collaris) showed low levels of accuracy (52% of samples correctly assigned). Intra-individual variability may account for this difference in efficacy. The prediction model for three species with similar migration and habitat-use patterns performed poorly compared with the model for just one of the species (71% versus 91% of samples correctly assigned). Thus, combining multiple sympatric species may not improve model prediction accuracy. Increasing the number of stable isotopes in the analyses increased the accuracy of assigning shorebirds to their molting origin, but the best combination - involving a subset of all the isotopes analyzed - varied among species.

Highly Sensitive Troponin I Followed by Advanced Coronary Artery Disease Assessment Using Computed Tomography Angiography Improves Acute Coronary Syndrome Risk Stratification Accuracy and Work-up in Acute Chest Pain Patients: Results from ROMICAT II Trial

PubMed Central

Mayrhofer, Thomas; Puchner, Stefan B.; Lu, Michael T.; Maurovich-Horvat, Pal; Pope, J. Hector; Truong, Quynh A.; Udelson, James E.; Peacock, W. Frank; White, Charles S.; Woodard, Pamela K.; Fleg, Jerome L.; Nagurney, John T.; Januzzi, James L.; Hoffmann, Udo

2015-01-01

Objectives We compared diagnostic accuracy of conventional troponin/traditional coronary artery disease (CAD) assessment and highly sensitive troponin (hsTn) I/advanced CAD assessment for acute coronary syndrome (ACS) during the index hospitalization. Background HsTn I and advanced assessment of CAD using coronary computed tomography angiography (CTA) are promising candidates to improve the accuracy of emergency department (ED) evaluation of patients with suspected ACS. Methods We performed an observational cohort study in patients with suspected ACS enrolled in the ROMICAT II trial and randomized to coronary CTA who also had hsTn I measurement at the time of the ED presentation. We assessed coronary CTA for traditional (no CAD, non-obstructive CAD, ≥50% stenosis) and advanced features of CAD (≥50% stenosis, high-risk plaque features: positive remodeling, low <30 Hounsfield Units plaque, napkin ring sign, spotty calcium). Results Of 160 patients (mean age: 53±8 years, 40% women) 10.6% were diagnosed with ACS. The ACS rate in patients with HsTn I below the limit of detection (n=9, 5.6%), intermediate (n=139, 86.9%), and above the 99th percentile (n=12, 7.5%) was 0%, 8.6%, and 58.3%, respectively. Absence of ≥50% stenosis and high-risk plaque ruled out ACS in patients with intermediate hsTn I (n=87, 54.4%; ACS rate 0%), while patients with both ≥50% stenosis and high-risk plaque were at high risk (n=13, 8.1%; ACS rate 69.2%) and patients with either ≥50% stenosis or high-risk plaque were at intermediate risk for ACS (n=39, 24.4%; ACS rate 7.7%). HsTn I/advanced coronary CTA assessment significantly improved the diagnostic accuracy for ACS as compared to conventional troponin/traditional coronary CTA (AUC 0.84, 95%CI 0.80-0.88 vs. 0.74, 95%CI 0.70-0.78; p<0.001). Conclusions HsTn I at the time of presentation followed by early advanced coronary CTA assessment improves the risk stratification and diagnostic accuracy for ACS as compared to conventional troponin and traditional coronary CTA assessment. (Multicenter Study to Rule Out Myocardial Infarction/Ischemia by Cardiac Computed Tomography [ROMICAT-II]; NCT01084239) PMID:26476506

60 seconds to survival: A pilot study of a disaster triage video game for prehospital providers.

PubMed

Cicero, Mark X; Whitfill, Travis; Munjal, Kevin; Madhok, Manu; Diaz, Maria Carmen G; Scherzer, Daniel J; Walsh, Barbara M; Bowen, Angela; Redlener, Michael; Goldberg, Scott A; Symons, Nadine; Burkett, James; Santos, Joseph C; Kessler, David; Barnicle, Ryan N; Paesano, Geno; Auerbach, Marc A

2017-01-01

Disaster triage training for emergency medical service (EMS) providers is not standardized. Simulation training is costly and time-consuming. In contrast, educational video games enable low-cost and more time-efficient standardized training. We hypothesized that players of the video game "60 Seconds to Survival" (60S) would have greater improvements in disaster triage accuracy compared to control subjects who did not play 60S. Participants recorded their demographics and highest EMS training level and were randomized to play 60S (intervention) or serve as controls. At baseline, all participants completed a live school-shooting simulation in which manikins and standardized patients depicted 10 adult and pediatric victims. The intervention group then played 60S at least three times over the course of 13 weeks (time 2). Players triaged 12 patients in three scenarios (school shooting, house fire, tornado), and received in-game performance feedback. At time 2, the same live simulation was conducted for all participants. Controls had no disaster training during the study. The main outcome was improvement in triage accuracy in live simulations from baseline to time 2. Physicians and EMS providers predetermined expected triage level (RED/YELLOW/GREEN/BLACK) via modified Delphi method. There were 26 participants in the intervention group and 21 in the control group. There was no difference in gender, level of training, or years of EMS experience (median 5.5 years intervention, 3.5 years control, p = 0.49) between the groups. At baseline, both groups demonstrated median triage accuracy of 80 percent (IQR 70-90 percent, p = 0.457). At time 2, the intervention group had a significant improvement from baseline (median accuracy = 90 percent [IQR: 80-90 percent], p = 0.005), while the control group did not (median accuracy = 80 percent [IQR:80-95], p = 0.174). However, the mean improvement from baseline was not significant between the two groups (difference = 6.5, p = 0.335). The intervention demonstrated a significant improvement in accuracy from baseline to time 2 while the control did not. However, there was no significant difference in the improvement between the intervention and control groups. These results may be due to small sample size. Future directions include assessment of the game's effect on triage accuracy with a larger, multisite site cohort and iterative development to improve 60S.

Precise Tracking of the Magellan and Pioneer Venus Orbiters by Same-Beam Interferometry. Part 2: Orbit Determination Analysis

NASA Technical Reports Server (NTRS)

Folkner, W. M.; Border, J. S.; Nandi, S.; Zukor, K. S.

1993-01-01

A new radio metric positioning technique has demonstrated improved orbit determination accuracy for the Magellan and Pioneer Venus Orbiter orbiters. The new technique, known as Same-Beam Interferometry (SBI), is applicable to the positioning of multiple planetary rovers, landers, and orbiters which may simultaneously be observed in the same beamwidth of Earth-based radio antennas. Measurements of carrier phase are differenced between spacecraft and between receiving stations to determine the plane-of-sky components of the separation vector(s) between the spacecraft. The SBI measurements complement the information contained in line-of-sight Doppler measurements, leading to improved orbit determination accuracy. Orbit determination solutions have been obtained for a number of 48-hour data arcs using combinations of Doppler, differenced-Doppler, and SBI data acquired in the spring of 1991. Orbit determination accuracy is assessed by comparing orbit solutions from adjacent data arcs. The orbit solution differences are shown to agree with expected orbit determination uncertainties. The results from this demonstration show that the orbit determination accuracy for Magellan obtained by using Doppler plus SBI data is better than the accuracy achieved using Doppler plus differenced-Doppler by a factor of four and better than the accuracy achieved using only Doppler by a factor of eighteen. The orbit determination accuracy for Pioneer Venus Orbiter using Doppler plus SBI data is better than the accuracy using only Doppler data by 30 percent.

Spatially distributed modeling of soil organic carbon across China with improved accuracy

NASA Astrophysics Data System (ADS)

Li, Qi-quan; Zhang, Hao; Jiang, Xin-ye; Luo, Youlin; Wang, Chang-quan; Yue, Tian-xiang; Li, Bing; Gao, Xue-song

2017-06-01

There is a need for more detailed spatial information on soil organic carbon (SOC) for the accurate estimation of SOC stock and earth system models. As it is effective to use environmental factors as auxiliary variables to improve the prediction accuracy of spatially distributed modeling, a combined method (HASM_EF) was developed to predict the spatial pattern of SOC across China using high accuracy surface modeling (HASM), artificial neural network (ANN), and principal component analysis (PCA) to introduce land uses, soil types, climatic factors, topographic attributes, and vegetation cover as predictors. The performance of HASM_EF was compared with ordinary kriging (OK), OK, and HASM combined, respectively, with land uses and soil types (OK_LS and HASM_LS), and regression kriging combined with land uses and soil types (RK_LS). Results showed that HASM_EF obtained the lowest prediction errors and the ratio of performance to deviation (RPD) presented the relative improvements of 89.91%, 63.77%, 55.86%, and 42.14%, respectively, compared to the other four methods. Furthermore, HASM_EF generated more details and more realistic spatial information on SOC. The improved performance of HASM_EF can be attributed to the introduction of more environmental factors, to explicit consideration of the multicollinearity of selected factors and the spatial nonstationarity and nonlinearity of relationships between SOC and selected factors, and to the performance of HASM and ANN. This method may play a useful tool in providing more precise spatial information on soil parameters for global modeling across large areas.

a New Approach for Accuracy Improvement of Pulsed LIDAR Remote Sensing Data

NASA Astrophysics Data System (ADS)

Zhou, G.; Huang, W.; Zhou, X.; He, C.; Li, X.; Huang, Y.; Zhang, L.

2018-05-01

In remote sensing applications, the accuracy of time interval measurement is one of the most important parameters that affect the quality of pulsed lidar data. The traditional time interval measurement technique has the disadvantages of low measurement accuracy, complicated circuit structure and large error. A high-precision time interval data cannot be obtained in these traditional methods. In order to obtain higher quality of remote sensing cloud images based on the time interval measurement, a higher accuracy time interval measurement method is proposed. The method is based on charging the capacitance and sampling the change of capacitor voltage at the same time. Firstly, the approximate model of the capacitance voltage curve in the time of flight of pulse is fitted based on the sampled data. Then, the whole charging time is obtained with the fitting function. In this method, only a high-speed A/D sampler and capacitor are required in a single receiving channel, and the collected data is processed directly in the main control unit. The experimental results show that the proposed method can get error less than 3 ps. Compared with other methods, the proposed method improves the time interval accuracy by at least 20 %.

On Accuracy of Adaptive Grid Methods for Captured Shocks

NASA Technical Reports Server (NTRS)

Yamaleev, Nail K.; Carpenter, Mark H.

2002-01-01

The accuracy of two grid adaptation strategies, grid redistribution and local grid refinement, is examined by solving the 2-D Euler equations for the supersonic steady flow around a cylinder. Second- and fourth-order linear finite difference shock-capturing schemes, based on the Lax-Friedrichs flux splitting, are used to discretize the governing equations. The grid refinement study shows that for the second-order scheme, neither grid adaptation strategy improves the numerical solution accuracy compared to that calculated on a uniform grid with the same number of grid points. For the fourth-order scheme, the dominant first-order error component is reduced by the grid adaptation, while the design-order error component drastically increases because of the grid nonuniformity. As a result, both grid adaptation techniques improve the numerical solution accuracy only on the coarsest mesh or on very fine grids that are seldom found in practical applications because of the computational cost involved. Similar error behavior has been obtained for the pressure integral across the shock. A simple analysis shows that both grid adaptation strategies are not without penalties in the numerical solution accuracy. Based on these results, a new grid adaptation criterion for captured shocks is proposed.

Real-Time Tropospheric Product Establishment and Accuracy Assessment in China

NASA Astrophysics Data System (ADS)

Chen, M.; Guo, J.; Wu, J.; Song, W.; Zhang, D.

2018-04-01

Tropospheric delay has always been an important issue in Global Navigation Satellite System (GNSS) processing. Empirical tropospheric delay models are difficult to simulate complex and volatile atmospheric environments, resulting in poor accuracy of the empirical model and difficulty in meeting precise positioning demand. In recent years, some scholars proposed to establish real-time tropospheric product by using real-time or near-real-time GNSS observations in a small region, and achieved some good results. This paper uses real-time observing data of 210 Chinese national GNSS reference stations to estimate the tropospheric delay, and establishes ZWD grid model in the country wide. In order to analyze the influence of tropospheric grid product on wide-area real-time PPP, this paper compares the method of taking ZWD grid product as a constraint with the model correction method. The results show that the ZWD grid product estimated based on the national reference stations can improve PPP accuracy and convergence speed. The accuracy in the north (N), east (E) and up (U) direction increase by 31.8 %,15.6 % and 38.3 %, respectively. As with the convergence speed, the accuracy of U direction experiences the most improvement.

Real-time, resource-constrained object classification on a micro-air vehicle

NASA Astrophysics Data System (ADS)

Buck, Louis; Ray, Laura

2013-12-01

A real-time embedded object classification algorithm is developed through the novel combination of binary feature descriptors, a bag-of-visual-words object model and the cortico-striatal loop (CSL) learning algorithm. The BRIEF, ORB and FREAK binary descriptors are tested and compared to SIFT descriptors with regard to their respective classification accuracies, execution times, and memory requirements when used with CSL on a 12.6 g ARM Cortex embedded processor running at 800 MHz. Additionally, the effect of x2 feature mapping and opponent-color representations used with these descriptors is examined. These tests are performed on four data sets of varying sizes and difficulty, and the BRIEF descriptor is found to yield the best combination of speed and classification accuracy. Its use with CSL achieves accuracies between 67% and 95% of those achieved with SIFT descriptors and allows for the embedded classification of a 128x192 pixel image in 0.15 seconds, 60 times faster than classification with SIFT. X2 mapping is found to provide substantial improvements in classification accuracy for all of the descriptors at little cost, while opponent-color descriptors are offer accuracy improvements only on colorful datasets.

A drift line bias estimator: ARMA-based filter or calibration method, and its application in BDS/GPS-based attitude determination

NASA Astrophysics Data System (ADS)

Liang, Zhang; Yanqing, Hou; Jie, Wu

2016-12-01

The multi-antenna synchronized receiver (using a common clock) is widely applied in GNSS-based attitude determination (AD) or terrain deformations monitoring, and many other applications, since the high-accuracy single-differenced carrier phase can be used to improve the positioning or AD accuracy. Thus, the line bias (LB) parameter (fractional bias isolating) should be calibrated in the single-differenced phase equations. In the past decades, all researchers estimated the LB as a constant parameter in advance and compensated it in real time. However, the constant LB assumption is inappropriate in practical applications because of the physical length and permittivity changes of the cables, caused by the environmental temperature variation and the instability of receiver-self inner circuit transmitting delay. Considering the LB drift (or colored LB) in practical circumstances, this paper initiates a real-time estimator using auto regressive moving average-based (ARMA) prediction/whitening filter model or Moving average-based (MA) constant calibration model. In the ARMA-based filter model, four cases namely AR(1), ARMA(1, 1), AR(2) and ARMA(2, 1) are applied for the LB prediction. The real-time relative positioning model using the ARMA-based predicting LB is derived and it is theoretically proved that the positioning accuracy is better than the traditional double difference carrier phase (DDCP) model. The drifting LB is defined with a phase temperature changing rate integral function, which is a random walk process if the phase temperature changing rate is white noise, and is validated by the analysis of the AR model coefficient. The auto covariance function shows that the LB is indeed varying in time and estimating it as a constant is not safe, which is also demonstrated by the analysis on LB variation of each visible satellite during a zero and short baseline BDS/GPS experiment. Compared to the DDCP approach, in the zero-baseline experiment, the LB constant calibration (LBCC) and MA approaches improved the positioning accuracy of the vertical component, while slightly degrading the accuracy of the horizontal components. The ARMA(1, 0) model, however, improved the positioning accuracy of all three components, with 40 and 50 % improvement of the vertical component for BDS and GPS, respectively. In the short baseline experiment, compared to the DDCP approach, the LBCC approach yielded bad positioning solutions and degraded the AD accuracy; both MA and ARMA-based filter approaches improved the AD accuracy. Moreover, the ARMA(1, 0) and ARMA(1, 1) models have relatively better performance, improving to 55 % and 48 % the elevation angle in ARMA(1, 1) and MA model for GPS, respectively. Furthermore, the drifting LB variation is found to be continuous and slowly cumulative; the variation magnitudes in the unit of length are almost identical on different frequency carrier phases, so the LB variation does not show obvious correlation between different frequencies. Consequently, the wide-lane LB in the unit of cycle is very stable, while the narrow-lane LB varies largely in time. This reasoning probably also explains the phenomenon that the wide-lane LB originating in the satellites is stable, while the narrow-lane LB varies. The results of ARMA-based filters are better than the MA model, which probably implies that the modeling for drifting LB can further improve the precise point positioning accuracy.

An Improved Method of Heterogeneity Compensation for the Convolution / Superposition Algorithm

NASA Astrophysics Data System (ADS)

Jacques, Robert; McNutt, Todd

2014-03-01

Purpose: To improve the accuracy of convolution/superposition (C/S) in heterogeneous material by developing a new algorithm: heterogeneity compensated superposition (HCS). Methods: C/S has proven to be a good estimator of the dose deposited in a homogeneous volume. However, near heterogeneities electron disequilibrium occurs, leading to the faster fall-off and re-buildup of dose. We propose to filter the actual patient density in a position and direction sensitive manner, allowing the dose deposited near interfaces to be increased or decreased relative to C/S. We implemented the effective density function as a multivariate first-order recursive filter and incorporated it into GPU-accelerated, multi-energetic C/S implementation. We compared HCS against C/S using the ICCR 2000 Monte-Carlo accuracy benchmark, 23 similar accuracy benchmarks and 5 patient cases. Results: Multi-energetic HCS increased the dosimetric accuracy for the vast majority of voxels; in many cases near Monte-Carlo results were achieved. We defined the per-voxel error, %|mm, as the minimum of the distance to agreement in mm and the dosimetric percentage error relative to the maximum MC dose. HCS improved the average mean error by 0.79 %|mm for the patient volumes; reducing the average mean error from 1.93 %|mm to 1.14 %|mm. Very low densities (i.e. < 0.1 g / cm3) remained problematic, but may be solvable with a better filter function. Conclusions: HCS improved upon C/S's density scaled heterogeneity correction with a position and direction sensitive density filter. This method significantly improved the accuracy of the GPU based algorithm reaching the accuracy levels of Monte Carlo based methods with performance in a few tenths of seconds per beam. Acknowledgement: Funding for this research was provided by the NSF Cooperative Agreement EEC9731748, Elekta / IMPAC Medical Systems, Inc. and the Johns Hopkins University. James Satterthwaite provided the Monte Carlo benchmark simulations.

Speech and Nonspeech Sequence Skill Learning in Adults Who Stutter

ERIC Educational Resources Information Center

Smits-Bandstra, Sarah; De Nil, Luc; Saint-Cyr, Jean A.

2006-01-01

Two studies compared the speech and nonspeech sequence skill learning of nine persons who stutter (PWS) and nine matched fluent speakers (PNS). Sequence skill learning was defined as a continuing process of stable improvement in speed and/or accuracy of sequencing performance over practice and was measured by comparing PWS's and PNS's performance…

Increased capture of pediatric surgical complications utilizing a novel case-log web application to enhance quality improvement.

PubMed

Fisher, Jason C; Kuenzler, Keith A; Tomita, Sandra S; Sinha, Prashant; Shah, Paresh; Ginsburg, Howard B

2017-01-01

Documenting surgical complications is limited by multiple barriers and is not fostered in the electronic health record. Tracking complications is essential for quality improvement (QI) and required for board certification. Current registry platforms do not facilitate meaningful complication reporting. We developed a novel web application that improves accuracy and reduces barriers to documenting complications. We deployed a custom web application that allows pediatric surgeons to maintain case logs. The program includes a module for entering complication data in real time. Reminders to enter outcome data occur at key postoperative intervals to optimize recall of events. Between October 1, 2014, and March 31, 2015, frequencies of surgical complications captured by the existing hospital reporting system were compared with data aggregated by our application. 780 cases were captured by the web application, compared with 276 cases registered by the hospital system. We observed an increase in the capture of major complications when compared to the hospital dataset (14 events vs. 4 events). This web application improved real-time reporting of surgical complications, exceeding the accuracy of administrative datasets. Custom informatics solutions may help reduce barriers to self-reporting of adverse events and improve the data that presently inform pediatric surgical QI. Diagnostic study/Retrospective study. Level III - case control study. Copyright © 2017 Elsevier Inc. All rights reserved.

Transmission versus reflectance spectroscopy for quantitation.

PubMed

Gardner, Craig M

2018-01-01

The objective of this work was to compare the accuracy of analyte concentration estimation when using transmission versus diffuse reflectance spectroscopy of a scattering medium. Monte Carlo ray tracing of light through the medium was used in conjunction with pure component absorption spectra and Beer-Lambert absorption along each ray's pathlength to generate matched sets of pseudoabsorbance spectra, containing water and six analytes present in skin. PLS regression models revealed an improvement in accuracy when using transmission compared to reflectance for a range of medium thicknesses and instrument noise levels. An analytical expression revealed the source of the accuracy degradation with reflectance was due both to the reduced collection efficiency for a fixed instrument etendue and to the broad pathlength distribution that detected light travels in the medium before exiting from the incident side. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Thematic accuracy of the NLCD 2001 land cover for the conterminous United States

USGS Publications Warehouse

Wickham, J.D.; Stehman, S.V.; Fry, J.A.; Smith, J.H.; Homer, Collin G.

2010-01-01

The land-cover thematic accuracy of NLCD 2001 was assessed from a probability-sample of 15,000 pixels. Nationwide, NLCD 2001 overall Anderson Level II and Level I accuracies were 78.7% and 85.3%, respectively. By comparison, overall accuracies at Level II and Level I for the NLCD 1992 were 58% and 80%. Forest and cropland were two classes showing substantial improvements in accuracy in NLCD 2001 relative to NLCD 1992. NLCD 2001 forest and cropland user's accuracies were 87% and 82%, respectively, compared to 80% and 43% for NLCD 1992. Accuracy results are reported for 10 geographic regions of the United States, with regional overall accuracies ranging from 68% to 86% for Level II and from 79% to 91% at Level I. Geographic variation in class-specific accuracy was strongly associated with the phenomenon that regionally more abundant land-cover classes had higher accuracy. Accuracy estimates based on several definitions of agreement are reported to provide an indication of the potential impact of reference data error on accuracy. Drawing on our experience from two NLCD national accuracy assessments, we discuss the use of designs incorporating auxiliary data to more seamlessly quantify reference data quality as a means to further advance thematic map accuracy assessment.

Improved spatial accuracy of functional maps in the rat olfactory bulb using supervised machine learning approach.

PubMed

Murphy, Matthew C; Poplawsky, Alexander J; Vazquez, Alberto L; Chan, Kevin C; Kim, Seong-Gi; Fukuda, Mitsuhiro

2016-08-15

Functional MRI (fMRI) is a popular and important tool for noninvasive mapping of neural activity. As fMRI measures the hemodynamic response, the resulting activation maps do not perfectly reflect the underlying neural activity. The purpose of this work was to design a data-driven model to improve the spatial accuracy of fMRI maps in the rat olfactory bulb. This system is an ideal choice for this investigation since the bulb circuit is well characterized, allowing for an accurate definition of activity patterns in order to train the model. We generated models for both cerebral blood volume weighted (CBVw) and blood oxygen level dependent (BOLD) fMRI data. The results indicate that the spatial accuracy of the activation maps is either significantly improved or at worst not significantly different when using the learned models compared to a conventional general linear model approach, particularly for BOLD images and activity patterns involving deep layers of the bulb. Furthermore, the activation maps computed by CBVw and BOLD data show increased agreement when using the learned models, lending more confidence to their accuracy. The models presented here could have an immediate impact on studies of the olfactory bulb, but perhaps more importantly, demonstrate the potential for similar flexible, data-driven models to improve the quality of activation maps calculated using fMRI data. Copyright © 2016 Elsevier Inc. All rights reserved.

Intraoperative perception and estimates on extent of resection during awake glioma surgery: overcoming the learning curve.

PubMed

Lau, Darryl; Hervey-Jumper, Shawn L; Han, Seunggu J; Berger, Mitchel S

2018-05-01

OBJECTIVE There is ample evidence that extent of resection (EOR) is associated with improved outcomes for glioma surgery. However, it is often difficult to accurately estimate EOR intraoperatively, and surgeon accuracy has yet to be reviewed. In this study, the authors quantitatively assessed the accuracy of intraoperative perception of EOR during awake craniotomy for tumor resection. METHODS A single-surgeon experience of performing awake craniotomies for tumor resection over a 17-year period was examined. Retrospective review of operative reports for quantitative estimation of EOR was recorded. Definitive EOR was based on postoperative MRI. Analysis of accuracy of EOR estimation was examined both as a general outcome (gross-total resection [GTR] or subtotal resection [STR]), and quantitatively (5% within EOR on postoperative MRI). Patient demographics, tumor characteristics, and surgeon experience were examined. The effects of accuracy on motor and language outcomes were assessed. RESULTS A total of 451 patients were included in the study. Overall accuracy of intraoperative perception of whether GTR or STR was achieved was 79.6%, and overall accuracy of quantitative perception of resection (within 5% of postoperative MRI) was 81.4%. There was a significant difference (p = 0.049) in accuracy for gross perception over the 17-year period, with improvement over the later years: 1997-2000 (72.6%), 2001-2004 (78.5%), 2005-2008 (80.7%), and 2009-2013 (84.4%). Similarly, there was a significant improvement (p = 0.015) in accuracy of quantitative perception of EOR over the 17-year period: 1997-2000 (72.2%), 2001-2004 (69.8%), 2005-2008 (84.8%), and 2009-2013 (93.4%). This improvement in accuracy is demonstrated by the significantly higher odds of correctly estimating quantitative EOR in the later years of the series on multivariate logistic regression. Insular tumors were associated with the highest accuracy of gross perception (89.3%; p = 0.034), but lowest accuracy of quantitative perception (61.1% correct; p < 0.001) compared with tumors in other locations. Even after adjusting for surgeon experience, this particular trend for insular tumors remained true. The absence of 1p19q co-deletion was associated with higher quantitative perception accuracy (96.9% vs 81.5%; p = 0.051). Tumor grade, recurrence, diagnosis, and isocitrate dehydrogenase-1 (IDH-1) status were not associated with accurate perception of EOR. Overall, new neurological deficits occurred in 8.4% of cases, and 42.1% of those new neurological deficits persisted after the 3-month follow-up. Correct quantitative perception was associated with lower postoperative motor deficits (2.4%) compared with incorrect perceptions (8.0%; p = 0.029). There were no detectable differences in language outcomes based on perception of EOR. CONCLUSIONS The findings from this study suggest that there is a learning curve associated with the ability to accurately assess intraoperative EOR during glioma surgery, and it may take more than a decade to be truly proficient. Understanding the factors associated with this ability to accurately assess EOR will provide safer surgeries while maximizing tumor resection.

Adapting and Modifying the Apparatus for Students to Accurately Determine the Freezing Point of a Solvent and Solution

ERIC Educational Resources Information Center

Li, Shirong; Guo, Jianzhong; Wang, Kewang; Chen, Lin; Hu, Daodao; Bai, Yunshan

2017-01-01

An improved apparatus for measuring freezing points has been developed. Compared to the traditional Beckmann freezing point instrument, the improved one overcame prior difficulties with solidification of liquid and made the solid-liquid equilibrium reversible with heat compensation from a heating tube. The reliability and accuracy were carefully…

On-board error correction improves IR earth sensor accuracy

NASA Astrophysics Data System (ADS)

Alex, T. K.; Kasturirangan, K.; Shrivastava, S. K.

1989-10-01

Infra-red earth sensors are used in satellites for attitude sensing. Their accuracy is limited by systematic and random errors. The sources of errors in a scanning infra-red earth sensor are analyzed in this paper. The systematic errors arising from seasonal variation of infra-red radiation, oblate shape of the earth, ambient temperature of sensor, changes in scan/spin rates have been analyzed. Simple relations are derived using least square curve fitting for on-board correction of these errors. Random errors arising out of noise from detector and amplifiers, instability of alignment and localized radiance anomalies are analyzed and possible correction methods are suggested. Sun and Moon interference on earth sensor performance has seriously affected a number of missions. The on-board processor detects Sun/Moon interference and corrects the errors on-board. It is possible to obtain eight times improvement in sensing accuracy, which will be comparable with ground based post facto attitude refinement.

Myocardial perfusion imaging with PET

PubMed Central

Nakazato, Ryo; Berman, Daniel S; Alexanderson, Erick; Slomka, Piotr

2013-01-01

PET-myocardial perfusion imaging (MPI) allows accurate measurement of myocardial perfusion, absolute myocardial blood flow and function at stress and rest in a single study session performed in approximately 30 min. Various PET tracers are available for MPI, and rubidium-82 or nitrogen-13-ammonia is most commonly used. In addition, a new fluorine-18-based PET-MPI tracer is currently being evaluated. Relative quantification of PET perfusion images shows very high diagnostic accuracy for detection of obstructive coronary artery disease. Dynamic myocardial blood flow analysis has demonstrated additional prognostic value beyond relative perfusion imaging. Patient radiation dose can be reduced and image quality can be improved with latest advances in PET/CT equipment. Simultaneous assessment of both anatomy and perfusion by hybrid PET/CT can result in improved diagnostic accuracy. Compared with SPECT-MPI, PET-MPI provides higher diagnostic accuracy, using lower radiation doses during a shorter examination time period for the detection of coronary artery disease. PMID:23671459

An Interactive Image Segmentation Method in Hand Gesture Recognition

PubMed Central

Chen, Disi; Li, Gongfa; Sun, Ying; Kong, Jianyi; Jiang, Guozhang; Tang, Heng; Ju, Zhaojie; Yu, Hui; Liu, Honghai

2017-01-01

In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy. PMID:28134818

An automatic step adjustment method for average power analysis technique used in fiber amplifiers

NASA Astrophysics Data System (ADS)

Liu, Xue-Ming

2006-04-01

An automatic step adjustment (ASA) method for average power analysis (APA) technique used in fiber amplifiers is proposed in this paper for the first time. In comparison with the traditional APA technique, the proposed method has suggested two unique merits such as a higher order accuracy and an ASA mechanism, so that it can significantly shorten the computing time and improve the solution accuracy. A test example demonstrates that, by comparing to the APA technique, the proposed method increases the computing speed by more than a hundredfold under the same errors. By computing the model equations of erbium-doped fiber amplifiers, the numerical results show that our method can improve the solution accuracy by over two orders of magnitude at the same amplifying section number. The proposed method has the capacity to rapidly and effectively compute the model equations of fiber Raman amplifiers and semiconductor lasers.

Accurate Grid-based Clustering Algorithm with Diagonal Grid Searching and Merging

NASA Astrophysics Data System (ADS)

Liu, Feng; Ye, Chengcheng; Zhu, Erzhou

2017-09-01

Due to the advent of big data, data mining technology has attracted more and more attentions. As an important data analysis method, grid clustering algorithm is fast but with relatively lower accuracy. This paper presents an improved clustering algorithm combined with grid and density parameters. The algorithm first divides the data space into the valid meshes and invalid meshes through grid parameters. Secondly, from the starting point located at the first point of the diagonal of the grids, the algorithm takes the direction of “horizontal right, vertical down” to merge the valid meshes. Furthermore, by the boundary grid processing, the invalid grids are searched and merged when the adjacent left, above, and diagonal-direction grids are all the valid ones. By doing this, the accuracy of clustering is improved. The experimental results have shown that the proposed algorithm is accuracy and relatively faster when compared with some popularly used algorithms.

A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches

NASA Astrophysics Data System (ADS)

Ye, Su; Pontius, Robert Gilmore; Rakshit, Rahul

2018-07-01

Object-based image analysis (OBIA) has gained widespread popularity for creating maps from remotely sensed data. Researchers routinely claim that OBIA procedures outperform pixel-based procedures; however, it is not immediately obvious how to evaluate the degree to which an OBIA map compares to reference information in a manner that accounts for the fact that the OBIA map consists of objects that vary in size and shape. Our study reviews 209 journal articles concerning OBIA published between 2003 and 2017. We focus on the three stages of accuracy assessment: (1) sampling design, (2) response design and (3) accuracy analysis. First, we report the literature's overall characteristics concerning OBIA accuracy assessment. Simple random sampling was the most used method among probability sampling strategies, slightly more than stratified sampling. Office interpreted remotely sensed data was the dominant reference source. The literature reported accuracies ranging from 42% to 96%, with an average of 85%. A third of the articles failed to give sufficient information concerning accuracy methodology such as sampling scheme and sample size. We found few studies that focused specifically on the accuracy of the segmentation. Second, we identify a recent increase of OBIA articles in using per-polygon approaches compared to per-pixel approaches for accuracy assessment. We clarify the impacts of the per-pixel versus the per-polygon approaches respectively on sampling, response design and accuracy analysis. Our review defines the technical and methodological needs in the current per-polygon approaches, such as polygon-based sampling, analysis of mixed polygons, matching of mapped with reference polygons and assessment of segmentation accuracy. Our review summarizes and discusses the current issues in object-based accuracy assessment to provide guidance for improved accuracy assessments for OBIA.

Improved measurement performance of the Physikalisch-Technische Bundesanstalt nanometer comparator by integration of a new Zerodur sample carriage

NASA Astrophysics Data System (ADS)

Flügge, Jens; Köning, Rainer; Schötka, Eugen; Weichert, Christoph; Köchert, Paul; Bosse, Harald; Kunzmann, Horst

2014-12-01

The paper describes recent improvements of Physikalisch-Technische Bundesanstalt's (PTB) reference measuring instrument for length graduations, the so-called nanometer comparator, intended to achieve a measurement uncertainty in the domain of 1 nm for a length up to 300 mm. The improvements are based on the design and realization of a new sample carriage, integrated into the existing structure and the optimization of coupling this new device to the vacuum interferometer, by which the length measuring range of approximately 540 mm with sub-nm resolution is given. First, measuring results of the enhanced nanometer comparator are presented and discussed, which show the improvements of the measuring capabilities and verify the step toward the sub-nm accuracy level.

Apollo oxygen tank stratification analysis, volume 2

NASA Technical Reports Server (NTRS)

Barton, J. E.; Patterson, H. W.

1972-01-01

An analysis of flight performance of the Apollo 15 cryogenic oxygen tanks was conducted with the variable grid stratification math model developed earlier in the program. Flight conditions investigated were the CMP-EVA and one passive thermal control period which exhibited heater temperature characteristics not previously observed. Heater temperatures for these periods were simulated with the math model using flight acceleration data. Simulation results (heater temperature and tank pressure) compared favorably with the Apollo 15 flight data, and it was concluded that tank performance was nominal. Math model modifications were also made to improve the simulation accuracy. The modifications included the addition of the effects of the tank wall thermal mass and an improved system flow distribution model. The modifications improved the accuracy of simulated pressure response based on comparisons with flight data.

A deformable particle-in-cell method for advective transport in geodynamic modeling

NASA Astrophysics Data System (ADS)

Samuel, Henri

2018-06-01

This paper presents an improvement of the particle-in-cell method commonly used in geodynamic modeling for solving pure advection of sharply varying fields. Standard particle-in-cell approaches use particle kernels to transfer the information carried by the Lagrangian particles to/from the Eulerian grid. These kernels are generally one-dimensional and non-evolutive, which leads to the development of under- and over-sampling of the spatial domain by the particles. This reduces the accuracy of the solution, and may require the use of a prohibitive amount of particles in order to maintain the solution accuracy to an acceptable level. The new proposed approach relies on the use of deformable kernels that account for the strain history in the vicinity of particles. It results in a significant improvement of the spatial sampling by the particles, leading to a much higher accuracy of the numerical solution, for a reasonable computational extra cost. Various 2D tests were conducted to compare the performances of the deformable particle-in-cell method with the particle-in-cell approach. These consistently show that at comparable accuracy, the deformable particle-in-cell method was found to be four to six times more efficient than standard particle-in-cell approaches. The method could be adapted to 3D space and generalized to cases including motionless transport.

Enhancing lineup identification accuracy: two codes are better than one.

PubMed

Melara, R D; DeWitt-Rickards, T S; O'Brien, T P

1989-10-01

Ways of improving identification accuracy were explored by comparing the conventional visual lineup with an auditory/visual lineup, one that paired color photographs with voice recordings. This bimodal lineup necessitated sequential presentation of lineup members; Experiment 1 showed that performance in sequential lineups was better than performance in traditional simultaneous lineups. In Experiments 2A and 2B unimodal and bimodal lineups were compared by using a multiple-lineup paradigm: Ss viewed 3 videotaped episodes depicting standard police procedures and were tested in 4 sequential lineups. Bimodal lineups were more diagnostic than either visual or auditory lineups alone. The bimodal lineup led to a 126% improvement in number of correct identifications over the conventional visual lineup, with no concomitant increase in number of false identifications. These results imply strongly that bimodal procedures should be adopted in real-world lineups. The nature of memorial processes underlying this bimodal advantage is discussed.

Comprehensive Numerical Analysis of Finite Difference Time Domain Methods for Improving Optical Waveguide Sensor Accuracy

PubMed Central

Samak, M. Mosleh E. Abu; Bakar, A. Ashrif A.; Kashif, Muhammad; Zan, Mohd Saiful Dzulkifly

2016-01-01

This paper discusses numerical analysis methods for different geometrical features that have limited interval values for typically used sensor wavelengths. Compared with existing Finite Difference Time Domain (FDTD) methods, the alternating direction implicit (ADI)-FDTD method reduces the number of sub-steps by a factor of two to three, which represents a 33% time savings in each single run. The local one-dimensional (LOD)-FDTD method has similar numerical equation properties, which should be calculated as in the previous method. Generally, a small number of arithmetic processes, which result in a shorter simulation time, are desired. The alternating direction implicit technique can be considered a significant step forward for improving the efficiency of unconditionally stable FDTD schemes. This comparative study shows that the local one-dimensional method had minimum relative error ranges of less than 40% for analytical frequencies above 42.85 GHz, and the same accuracy was generated by both methods.

Prediction of welding shrinkage deformation of bridge steel box girder based on wavelet neural network

NASA Astrophysics Data System (ADS)

Tao, Yulong; Miao, Yunshui; Han, Jiaqi; Yan, Feiyun

2018-05-01

Aiming at the low accuracy of traditional forecasting methods such as linear regression method, this paper presents a prediction method for predicting the relationship between bridge steel box girder and its displacement with wavelet neural network. Compared with traditional forecasting methods, this scheme has better local characteristics and learning ability, which greatly improves the prediction ability of deformation. Through analysis of the instance and found that after compared with the traditional prediction method based on wavelet neural network, the rigid beam deformation prediction accuracy is higher, and is superior to the BP neural network prediction results, conform to the actual demand of engineering design.

An Improved WiFi Indoor Positioning Algorithm by Weighted Fusion

PubMed Central

Ma, Rui; Guo, Qiang; Hu, Changzhen; Xue, Jingfeng

2015-01-01

The rapid development of mobile Internet has offered the opportunity for WiFi indoor positioning to come under the spotlight due to its low cost. However, nowadays the accuracy of WiFi indoor positioning cannot meet the demands of practical applications. To solve this problem, this paper proposes an improved WiFi indoor positioning algorithm by weighted fusion. The proposed algorithm is based on traditional location fingerprinting algorithms and consists of two stages: the offline acquisition and the online positioning. The offline acquisition process selects optimal parameters to complete the signal acquisition, and it forms a database of fingerprints by error classification and handling. To further improve the accuracy of positioning, the online positioning process first uses a pre-match method to select the candidate fingerprints to shorten the positioning time. After that, it uses the improved Euclidean distance and the improved joint probability to calculate two intermediate results, and further calculates the final result from these two intermediate results by weighted fusion. The improved Euclidean distance introduces the standard deviation of WiFi signal strength to smooth the WiFi signal fluctuation and the improved joint probability introduces the logarithmic calculation to reduce the difference between probability values. Comparing the proposed algorithm, the Euclidean distance based WKNN algorithm and the joint probability algorithm, the experimental results indicate that the proposed algorithm has higher positioning accuracy. PMID:26334278

An Improved WiFi Indoor Positioning Algorithm by Weighted Fusion.

PubMed

Ma, Rui; Guo, Qiang; Hu, Changzhen; Xue, Jingfeng

2015-08-31

The rapid development of mobile Internet has offered the opportunity for WiFi indoor positioning to come under the spotlight due to its low cost. However, nowadays the accuracy of WiFi indoor positioning cannot meet the demands of practical applications. To solve this problem, this paper proposes an improved WiFi indoor positioning algorithm by weighted fusion. The proposed algorithm is based on traditional location fingerprinting algorithms and consists of two stages: the offline acquisition and the online positioning. The offline acquisition process selects optimal parameters to complete the signal acquisition, and it forms a database of fingerprints by error classification and handling. To further improve the accuracy of positioning, the online positioning process first uses a pre-match method to select the candidate fingerprints to shorten the positioning time. After that, it uses the improved Euclidean distance and the improved joint probability to calculate two intermediate results, and further calculates the final result from these two intermediate results by weighted fusion. The improved Euclidean distance introduces the standard deviation of WiFi signal strength to smooth the WiFi signal fluctuation and the improved joint probability introduces the logarithmic calculation to reduce the difference between probability values. Comparing the proposed algorithm, the Euclidean distance based WKNN algorithm and the joint probability algorithm, the experimental results indicate that the proposed algorithm has higher positioning accuracy.

Identification of Aeromonas isolates by matrix-assisted laser desorption ionization time-of-flight mass spectrometry.

PubMed

Lamy, Brigitte; Kodjo, Angeli; Laurent, Frédéric

2011-09-01

We evaluated the accuracy of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for identifying aeromonads with an extraction procedure. Genus-level accuracy was 100%. Compared to rpoB gene sequencing, species-level accuracy was 90.6% (29/32) for type and reference strains and 91.4% for a collection of 139 clinical and environmental isolates, making this system one of the most accurate and rapid methods for phenotypic identification. The reliability of this technique was very promising, although some improvements in database composition, taxonomy, and discriminatory power are needed. Copyright © 2011 Elsevier Inc. All rights reserved.

Diagnostic accuracy of optical coherence tomography in actinic keratosis and basal cell carcinoma.

PubMed

Olsen, J; Themstrup, L; De Carvalho, N; Mogensen, M; Pellacani, G; Jemec, G B E

2016-12-01

Early diagnosis of non-melanoma skin cancer (NMSC) is potentially possible using optical coherence tomography (OCT) which provides non-invasive, real-time images of skin with micrometre resolution and an imaging depth of up to 2mm. OCT technology for skin imaging has undergone significant developments, improving image quality substantially. The diagnostic accuracy of any method is influenced by continuous technological development making it necessary to regularly re-evaluate methods. The objective of this study is to estimate the diagnostic accuracy of OCT in basal cell carcinomas (BCC) and actinic keratosis (AK) as well as differentiating these lesions from normal skin. A study set consisting of 142 OCT images meeting selection criterea for image quality and diagnosis of AK, BCC and normal skin was presented uniformly to two groups of blinded observers: 5 dermatologists experienced in OCT-image interpretation and 5 dermatologists with no experience in OCT. During the presentation of the study set the observers filled out a standardized questionnaire regarding the OCT diagnosis. Images were captured using a commercially available OCT machine (Vivosight ® , Michelson Diagnostics, UK). Skilled OCT observers were able to diagnose BCC lesions with a sensitivity of 86% to 95% and a specificity of 81% to 98%. Skilled observers with at least one year of OCT-experience showed an overall higher diagnostic accuracy compared to inexperienced observers. The study shows an improved diagnostic accuracy of OCT in differentiating AK and BCC from healthy skin using state-of-the-art technology compared to earlier OCT technology, especially concerning BCC diagnosis. Copyright © 2016 Elsevier B.V. All rights reserved.

Comparative Diagnostic Accuracy of the ACE-III, MIS, MMSE, MoCA, and RUDAS for Screening of Alzheimer Disease.

PubMed

Matías-Guiu, Jordi A; Valles-Salgado, María; Rognoni, Teresa; Hamre-Gil, Frank; Moreno-Ramos, Teresa; Matías-Guiu, Jorge

2017-01-01

Our aim was to evaluate and compare the diagnostic properties of 5 screening tests for the diagnosis of mild Alzheimer disease (AD). We conducted a prospective and cross-sectional study of 92 patients with mild AD and of 68 healthy controls from our Department of Neurology. The diagnostic properties of the following tests were compared: Mini-Mental State Examination (MMSE), Addenbrooke's Cognitive Examination III (ACE-III), Memory Impairment Screen (MIS), Montreal Cognitive Assessment (MoCA), and Rowland Universal Dementia Assessment Scale (RUDAS). All tests yielded high diagnostic accuracy, with the ACE-III achieving the best diagnostic properties. The area under the curve was 0.897 for the ACE-III, 0.889 for the RUDAS, 0.874 for the MMSE, 0.866 for the MIS, and 0.856 for the MoCA. The Mini-ACE score from the ACE-III showed the highest diagnostic capacity (area under the curve 0.939). Memory scores of the ACE-III and of the RUDAS showed a better diagnostic accuracy than those of the MMSE and of the MoCA. All tests, especially the ACE-III, conveyed a higher diagnostic accuracy in patients with full primary education than in the less educated group. Implementing normative data improved the diagnostic accuracy of the ACE-III but not that of the other tests. The ACE-III achieved the highest diagnostic accuracy. This better discrimination was more evident in the more educated group. © 2017 S. Karger AG, Basel.

GPS vertical axis performance enhancement for helicopter precision landing approach

NASA Technical Reports Server (NTRS)

Denaro, Robert P.; Beser, Jacques

1986-01-01

Several areas were investigated for improving vertical accuracy for a rotorcraft using the differential Global Positioning System (GPS) during a landing approach. Continuous deltaranging was studied and the potential improvement achieved by estimating acceleration was studied by comparing the performance on a constant acceleration turn and a rough landing profile of several filters: a position-velocity (PV) filter, a position-velocity-constant acceleration (PVAC) filter, and a position-velocity-turning acceleration (PVAT) filter. In overall statistics, the PVAC filter was found to be most efficient with the more complex PVAT performing equally well. Vertical performance was not significantly different among the filters. Satellite selection algorithms based on vertical errors only (vertical dilution of precision or VDOP) and even-weighted cross-track and vertical errors (XVDOP) were tested. The inclusion of an altimeter was studied by modifying the PVAC filter to include a baro bias estimate. Improved vertical accuracy during degraded DOP conditions resulted. Flight test results for raw differential results excluding filter effects indicated that the differential performance significantly improved overall navigation accuracy. A landing glidepath steering algorithm was devised which exploits the flexibility of GPS in determining precise relative position. A method for propagating the steering command over the GPS update interval was implemented.

Improving the Numerical Stability of Fast Matrix Multiplication

DOE PAGES

Ballard, Grey; Benson, Austin R.; Druinsky, Alex; ...

2016-10-04

Fast algorithms for matrix multiplication, namely those that perform asymptotically fewer scalar operations than the classical algorithm, have been considered primarily of theoretical interest. Apart from Strassen's original algorithm, few fast algorithms have been efficiently implemented or used in practical applications. However, there exist many practical alternatives to Strassen's algorithm with varying performance and numerical properties. Fast algorithms are known to be numerically stable, but because their error bounds are slightly weaker than the classical algorithm, they are not used even in cases where they provide a performance benefit. We argue in this study that the numerical sacrifice of fastmore » algorithms, particularly for the typical use cases of practical algorithms, is not prohibitive, and we explore ways to improve the accuracy both theoretically and empirically. The numerical accuracy of fast matrix multiplication depends on properties of the algorithm and of the input matrices, and we consider both contributions independently. We generalize and tighten previous error analyses of fast algorithms and compare their properties. We discuss algorithmic techniques for improving the error guarantees from two perspectives: manipulating the algorithms, and reducing input anomalies by various forms of diagonal scaling. In conclusion, we benchmark performance and demonstrate our improved numerical accuracy.« less

Effect of intraarticular inoculation of mesenchymal stem cells in dogs with hip osteoarthritis by means of objective force platform gait analysis: concordance with numeric subjective scoring scales.

PubMed

Vilar, Jose M; Cuervo, Belen; Rubio, Monica; Sopena, Joaquín; Domínguez, Juan M; Santana, Angelo; Carrillo, Jose M

2016-10-07

Subjective pain assessment scales have been widely used for assessing lameness in response to pain, but the accuracy of these scales has been questioned. To assess scale accuracy, 10 lame, presa Canario dogs with osteoarthritis (OA) associated with bilateral hip dysplasia were first treated with mesenchymal stem cells. Then, potential lameness improvement was analyzed using two pain scales (Bioarth and visual analog scale). These data were compared with similar data collected using a force platform with the same animals during a period of 6 months after treatment. The F test for intraclass correlation showed that concordance in pain/lameness scores between the 2 measuring methodologies was not significant (P value ≥ 0.9213; 95 % confidence interval, -0.56, 0.11). Although subjective pain assessment showed improvement after 6 months, force platform data demonstrated those same animals had returned to the initial lameness state. Use of pain assessment scales to measure lameness associated with OA did not have great accuracy and concordance when compared with quantitative force platform gait analysis.

Accuracy improvement of the H-drive air-levitating wafer inspection stage based on error analysis and compensation

NASA Astrophysics Data System (ADS)

Zhang, Fan; Liu, Pinkuan

2018-04-01

In order to improve the inspection precision of the H-drive air-bearing stage for wafer inspection, in this paper the geometric error of the stage is analyzed and compensated. The relationship between the positioning errors and error sources are initially modeled, and seven error components are identified that are closely related to the inspection accuracy. The most effective factor that affects the geometric error is identified by error sensitivity analysis. Then, the Spearman rank correlation method is applied to find the correlation between different error components, aiming at guiding the accuracy design and error compensation of the stage. Finally, different compensation methods, including the three-error curve interpolation method, the polynomial interpolation method, the Chebyshev polynomial interpolation method, and the B-spline interpolation method, are employed within the full range of the stage, and their results are compared. Simulation and experiment show that the B-spline interpolation method based on the error model has better compensation results. In addition, the research result is valuable for promoting wafer inspection accuracy and will greatly benefit the semiconductor industry.

Satellite SAR geocoding with refined RPC model

NASA Astrophysics Data System (ADS)

Zhang, Lu; Balz, Timo; Liao, Mingsheng

2012-04-01

Recent studies have proved that the Rational Polynomial Camera (RPC) model is able to act as a reliable replacement of the rigorous Range-Doppler (RD) model for the geometric processing of satellite SAR datasets. But its capability in absolute geolocation of SAR images has not been evaluated quantitatively. Therefore, in this article the problems of error analysis and refinement of SAR RPC model are primarily investigated to improve the absolute accuracy of SAR geolocation. Range propagation delay and azimuth timing error are identified as two major error sources for SAR geolocation. An approach based on SAR image simulation and real-to-simulated image matching is developed to estimate and correct these two errors. Afterwards a refined RPC model can be built from the error-corrected RD model and then used in satellite SAR geocoding. Three experiments with different settings are designed and conducted to comprehensively evaluate the accuracies of SAR geolocation with both ordinary and refined RPC models. All the experimental results demonstrate that with RPC model refinement the absolute location accuracies of geocoded SAR images can be improved significantly, particularly in Easting direction. In another experiment the computation efficiencies of SAR geocoding with both RD and RPC models are compared quantitatively. The results show that by using the RPC model such efficiency can be remarkably improved by at least 16 times. In addition the problem of DEM data selection for SAR image simulation in RPC model refinement is studied by a comparative experiment. The results reveal that the best choice should be using the proper DEM datasets of spatial resolution comparable to that of the SAR images.

Inclusion of Population-specific Reference Panel from India to the 1000 Genomes Phase 3 Panel Improves Imputation Accuracy.

PubMed

Ahmad, Meraj; Sinha, Anubhav; Ghosh, Sreya; Kumar, Vikrant; Davila, Sonia; Yajnik, Chittaranjan S; Chandak, Giriraj R

2017-07-27

Imputation is a computational method based on the principle of haplotype sharing allowing enrichment of genome-wide association study datasets. It depends on the haplotype structure of the population and density of the genotype data. The 1000 Genomes Project led to the generation of imputation reference panels which have been used globally. However, recent studies have shown that population-specific panels provide better enrichment of genome-wide variants. We compared the imputation accuracy using 1000 Genomes phase 3 reference panel and a panel generated from genome-wide data on 407 individuals from Western India (WIP). The concordance of imputed variants was cross-checked with next-generation re-sequencing data on a subset of genomic regions. Further, using the genome-wide data from 1880 individuals, we demonstrate that WIP works better than the 1000 Genomes phase 3 panel and when merged with it, significantly improves the imputation accuracy throughout the minor allele frequency range. We also show that imputation using only South Asian component of the 1000 Genomes phase 3 panel works as good as the merged panel, making it computationally less intensive job. Thus, our study stresses that imputation accuracy using 1000 Genomes phase 3 panel can be further improved by including population-specific reference panels from South Asia.

Efficient use of unlabeled data for protein sequence classification: a comparative study.

PubMed

Kuksa, Pavel; Huang, Pai-Hsi; Pavlovic, Vladimir

2009-04-29

Recent studies in computational primary protein sequence analysis have leveraged the power of unlabeled data. For example, predictive models based on string kernels trained on sequences known to belong to particular folds or superfamilies, the so-called labeled data set, can attain significantly improved accuracy if this data is supplemented with protein sequences that lack any class tags-the unlabeled data. In this study, we present a principled and biologically motivated computational framework that more effectively exploits the unlabeled data by only using the sequence regions that are more likely to be biologically relevant for better prediction accuracy. As overly-represented sequences in large uncurated databases may bias the estimation of computational models that rely on unlabeled data, we also propose a method to remove this bias and improve performance of the resulting classifiers. Combined with state-of-the-art string kernels, our proposed computational framework achieves very accurate semi-supervised protein remote fold and homology detection on three large unlabeled databases. It outperforms current state-of-the-art methods and exhibits significant reduction in running time. The unlabeled sequences used under the semi-supervised setting resemble the unpolished gemstones; when used as-is, they may carry unnecessary features and hence compromise the classification accuracy but once cut and polished, they improve the accuracy of the classifiers considerably.

Analysis of variance to assess statistical significance of Laplacian estimation accuracy improvement due to novel variable inter-ring distances concentric ring electrodes.

PubMed

Makeyev, Oleksandr; Joe, Cody; Lee, Colin; Besio, Walter G

2017-07-01

Concentric ring electrodes have shown promise in non-invasive electrophysiological measurement demonstrating their superiority to conventional disc electrodes, in particular, in accuracy of Laplacian estimation. Recently, we have proposed novel variable inter-ring distances concentric ring electrodes. Analytic and finite element method modeling results for linearly increasing distances electrode configurations suggested they may decrease the truncation error resulting in more accurate Laplacian estimates compared to currently used constant inter-ring distances configurations. This study assesses statistical significance of Laplacian estimation accuracy improvement due to novel variable inter-ring distances concentric ring electrodes. Full factorial design of analysis of variance was used with one categorical and two numerical factors: the inter-ring distances, the electrode diameter, and the number of concentric rings in the electrode. The response variables were the Relative Error and the Maximum Error of Laplacian estimation computed using a finite element method model for each of the combinations of levels of three factors. Effects of the main factors and their interactions on Relative Error and Maximum Error were assessed and the obtained results suggest that all three factors have statistically significant effects in the model confirming the potential of using inter-ring distances as a means of improving accuracy of Laplacian estimation.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

PubMed Central

Pan, Jianjun

2018-01-01

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively. PMID:29382073

PACE: Probabilistic Assessment for Contributor Estimation- A machine learning-based assessment of the number of contributors in DNA mixtures.

PubMed

Marciano, Michael A; Adelman, Jonathan D

2017-03-01

The deconvolution of DNA mixtures remains one of the most critical challenges in the field of forensic DNA analysis. In addition, of all the data features required to perform such deconvolution, the number of contributors in the sample is widely considered the most important, and, if incorrectly chosen, the most likely to negatively influence the mixture interpretation of a DNA profile. Unfortunately, most current approaches to mixture deconvolution require the assumption that the number of contributors is known by the analyst, an assumption that can prove to be especially faulty when faced with increasingly complex mixtures of 3 or more contributors. In this study, we propose a probabilistic approach for estimating the number of contributors in a DNA mixture that leverages the strengths of machine learning. To assess this approach, we compare classification performances of six machine learning algorithms and evaluate the model from the top-performing algorithm against the current state of the art in the field of contributor number classification. Overall results show over 98% accuracy in identifying the number of contributors in a DNA mixture of up to 4 contributors. Comparative results showed 3-person mixtures had a classification accuracy improvement of over 6% compared to the current best-in-field methodology, and that 4-person mixtures had a classification accuracy improvement of over 20%. The Probabilistic Assessment for Contributor Estimation (PACE) also accomplishes classification of mixtures of up to 4 contributors in less than 1s using a standard laptop or desktop computer. Considering the high classification accuracy rates, as well as the significant time commitment required by the current state of the art model versus seconds required by a machine learning-derived model, the approach described herein provides a promising means of estimating the number of contributors and, subsequently, will lead to improved DNA mixture interpretation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Establishment of a Site-Specific Tropospheric Model Based on Ground Meteorological Parameters over the China Region.

PubMed

Zhou, Chongchong; Peng, Bibo; Li, Wei; Zhong, Shiming; Ou, Jikun; Chen, Runjing; Zhao, Xinglong

2017-07-27

China is a country of vast territory with complicated geographical environment and climate conditions. With the rapid progress of the Chinese BeiDou satellite navigation system (BDS); more accurate tropospheric models must be applied to improve the accuracy of navigation and positioning. Based on the formula of the Saastamoinen and Callahan models; this study develops two single-site tropospheric models (named SAAS_S and CH_S models) for the Chinese region using radiosonde data from 2005 to 2012. We assess the two single-site tropospheric models with radiosonde data for 2013 and zenith tropospheric delay (ZTD) data from four International GNSS Service (IGS) stations and compare them to the results of the Saastamoinen and Callahan models. The experimental results show that: the mean accuracy of the SAAS_S model (bias: 0.19 cm; RMS: 3.19 cm) at all radiosonde stations is superior to those of the Saastamoinen (bias: 0.62 cm; RMS: 3.62 cm) and CH_S (bias: -0.05 cm; RMS: 3.38 cm) models. In most Chinese regions; the RMS values of the SAAS_S and CH_S models are about 0.51~2.12 cm smaller than those of their corresponding source models. The SAAS_S model exhibits a clear improvement in the accuracy over the Saastamoinen model in low latitude regions. When the SAAS_S model is replaced by the SAAS model in the positioning of GNSS; the mean accuracy of vertical direction in the China region can be improved by 1.12~1.55 cm and the accuracy of vertical direction in low latitude areas can be improved by 1.33~7.63 cm. The residuals of the SAAS_S model are closer to a normal distribution compared to those of the Saastamoinen model. Single-site tropospheric models based on the short period of the most recent data (for example 2 years) can also achieve a satisfactory accuracy. The average performance of the SAAS_S model (bias: 0.83 cm; RMS: 3.24 cm) at four IGS stations is superior to that of the Saastamoinen (bias: -0.86 cm; RMS: 3.59 cm) and CH_S (bias: 0.45 cm; RMS: 3.38 cm) models.

Establishment of a Site-Specific Tropospheric Model Based on Ground Meteorological Parameters over the China Region

PubMed Central

Zhou, Chongchong; Peng, Bibo; Li, Wei; Zhong, Shiming; Ou, Jikun; Chen, Runjing; Zhao, Xinglong

2017-01-01

China is a country of vast territory with complicated geographical environment and climate conditions. With the rapid progress of the Chinese BeiDou satellite navigation system (BDS); more accurate tropospheric models must be applied to improve the accuracy of navigation and positioning. Based on the formula of the Saastamoinen and Callahan models; this study develops two single-site tropospheric models (named SAAS_S and CH_S models) for the Chinese region using radiosonde data from 2005 to 2012. We assess the two single-site tropospheric models with radiosonde data for 2013 and zenith tropospheric delay (ZTD) data from four International GNSS Service (IGS) stations and compare them to the results of the Saastamoinen and Callahan models. The experimental results show that: the mean accuracy of the SAAS_S model (bias: 0.19 cm; RMS: 3.19 cm) at all radiosonde stations is superior to those of the Saastamoinen (bias: 0.62 cm; RMS: 3.62 cm) and CH_S (bias: −0.05 cm; RMS: 3.38 cm) models. In most Chinese regions; the RMS values of the SAAS_S and CH_S models are about 0.51~2.12 cm smaller than those of their corresponding source models. The SAAS_S model exhibits a clear improvement in the accuracy over the Saastamoinen model in low latitude regions. When the SAAS_S model is replaced by the SAAS model in the positioning of GNSS; the mean accuracy of vertical direction in the China region can be improved by 1.12~1.55 cm and the accuracy of vertical direction in low latitude areas can be improved by 1.33~7.63 cm. The residuals of the SAAS_S model are closer to a normal distribution compared to those of the Saastamoinen model. Single-site tropospheric models based on the short period of the most recent data (for example 2 years) can also achieve a satisfactory accuracy. The average performance of the SAAS_S model (bias: 0.83 cm; RMS: 3.24 cm) at four IGS stations is superior to that of the Saastamoinen (bias: −0.86 cm; RMS: 3.59 cm) and CH_S (bias: 0.45 cm; RMS: 3.38 cm) models. PMID:28749429

Not all ultrasounds are created equal: general sonography versus musculoskeletal sonography in the detection of rotator cuff tears

PubMed Central

Cole, Brandi; Twibill, Kristen; Lam, Patrick; Hackett, Lisa

2016-01-01

Background This cross-sectional analytic diagnostic accuracy study was designed to compare the accuracy of ultrasound performed by general sonographers in local radiology practices with ultrasound performed by an experienced musculoskeletal sonographer for the detection of rotator cuff tears. Methods In total, 238 patients undergoing arthroscopy who had previously had an ultrasound performed by both a general sonographer and a specialist musculoskeletal sonographer made up the study cohort. Accuracy of diagnosis was compared with the findings at arthroscopy. Results When analyzed as all tears versus no tears, musculoskeletal sonography had an accuracy of 97%, a sensitivity of 97% and a specificity of 95%, whereas general sonography had an accuracy of 91%, a sensitivity of 91% and a specificity of 86%. When the partial tears were split with those ≥ 50% thickness in the tear group and those < 50% thickness in the no-tear group, musculoskeletal sonography had an accuracy of 97%, a sensitivity of 97% and a specificity of 100% and general sonography had an accuracy of 85%, a sensitivity of 84% and a specificity of 87%. Conclusions Ultrasound in the hands of an experienced musculoskeletal sonographer is highly accurate for the diagnosis of rotator cuff tears. General sonography has improved subsequent to earlier studies but remains inferior to an ultrasound performed by a musculoskeletal sonographer. PMID:27660657

Can verbal working memory training improve reading?

PubMed

Banales, Erin; Kohnen, Saskia; McArthur, Genevieve

2015-01-01

The aim of the current study was to determine whether poor verbal working memory is associated with poor word reading accuracy because the former causes the latter, or the latter causes the former. To this end, we tested whether (a) verbal working memory training improves poor verbal working memory or poor word reading accuracy, and whether (b) reading training improves poor reading accuracy or verbal working memory in a case series of four children with poor word reading accuracy and verbal working memory. Each child completed 8 weeks of verbal working memory training and 8 weeks of reading training. Verbal working memory training improved verbal working memory in two of the four children, but did not improve their reading accuracy. Similarly, reading training improved word reading accuracy in all children, but did not improve their verbal working memory. These results suggest that the causal links between verbal working memory and reading accuracy may not be as direct as has been assumed.

SU-E-T-13: A Feasibility Study of the Use of Hybrid Computational Phantoms for Improved Historical Dose Reconstruction in the Study of Late Radiation Effects for Hodgkin's Lymphoma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Petroccia, H; O'Reilly, S; Bolch, W

Purpose: Radiation-induced cancer effects are well-documented following radiotherapy. Further investigation is needed to more accurately determine a dose-response relationship for late radiation effects. Recent dosimetry studies tend to use representative patients (Taylor 2009) or anthropomorphic phantoms (Wirth 2008) for estimating organ mean doses. In this study, we compare hybrid computational phantoms to patient-specific voxel phantoms to test the accuracy of University of Florida Hybrid Phantom Library (UFHP Library) for historical dose reconstructions. Methods: A cohort of 10 patients with CT images was used to reproduce the data that was collected historically for Hodgkin's lymphoma patients (i.e. caliper measurements and photographs).more » Four types of phantoms were generated to show a range of refinement from reference hybrid-computational phantom to patient-specific phantoms. Each patient is matched to a reference phantom from the UFHP Library based on height and weight. The reference phantom is refined in the anterior/posterior direction to create a ‘caliper-scaled phantom’. A photograph is simulated using a surface rendering from segmented CT images. Further refinement in the lateral direction is performed using ratios from a simulated-photograph to create a ‘photograph and caliper-scaled phantom’; breast size and position is visually adjusted. Patient-specific hybrid phantoms, with matched organ volumes, are generated and show the capabilities of the UF Hybrid Phantom Library. Reference, caliper-scaled, photograph and caliper-scaled, and patient-specific hybrid phantoms are compared with patient-specific voxel phantoms to determine the accuracy of the study. Results: Progression from reference phantom to patient specific hybrid shows good agreement with the patient specific voxel phantoms. Each stage of refinement shows an overall trend of improvement in dose accuracy within the study, which suggests that computational phantoms can show improved accuracy in historical dose estimates. Conclusion: Computational hybrid phantoms show promise for improved accuracy within retrospective studies when CTs and other x-ray images are not available.« less

Power Search.

ERIC Educational Resources Information Center

Haskin, David

1997-01-01

Compares six leading Web search engines (AltaVista, Excite, HotBot, Infoseek, Lycos, and Northern Light), looking at the breadth of their coverage, accuracy, and ease of use, and finds a clear favorite of the six. Includes tips that can improve search results. (AEF)

Assessment of the Accuracy and Cut-Failure Rates of Eye Bank-Cut Corneas for Use in Endothelial Keratoplasty: A Comparison of Outcomes Between 2010 and 2013.

PubMed

Katzman, Lee R; Hoover, Caroline K; Khalifa, Yousuf M; Jeng, Bennie H

2015-11-01

To evaluate the accuracy of eye bank-prepared precut donor corneas over time by comparing cut-failure rates and corneal thickness measurements in 2010 and 2013. A total of 2511 human corneas cut by a technician-operated mechanical microkeratome intended for endothelial keratoplasty were evaluated prospectively at one large eye bank facility in 2010 and in 2013. The endothelium was evaluated by slit lamp, and specular microscopy both before and after cutting was performed. Graft thickness as measured by pachymetry and/or optical coherence tomography was collected to assess the accuracy of the cut tissue. Cut-failure rates were compared between normal donor tissue and tissue with significant preexisting scarring. The combined cut-failure rate in 2010 and 2013 was 2.3% (23/1000) and 1.6% (24/1511), respectively (P = 0.23). The cut-failure rate among normal tissue in 2010 and 2013 was 2.0% (19/927) and 1.4% (19/1400), respectively (P = 0.24). The cut-failure rate among previously scarred tissue in 2010 and 2013 was 5.5% (4/73) and 4.5% (5/111), respectively (P = 0.74). The mean surgeon-requested graft thickness was 144.7 μm (range 100-150, SD 13.6) and 127.2 μm (range 75-150, SD 25.2) in 2010 and 2013, respectively (P < 0.0001). The mean deviation from target graft thickness was 21.3 μm (SD 16.3) and 13.6 μm (SD 12.5) in 2010 and 2013, respectively (P < 0.0001). From 2010 to 2013, the combined cut-failure rates trended toward improvement, while the accuracy of graft thickness improved. This study suggests that the accuracy and success rates of tissue preparation for endothelial keratoplasty improve with experience and volume.

An Improved Compressive Sensing and Received Signal Strength-Based Target Localization Algorithm with Unknown Target Population for Wireless Local Area Networks.

PubMed

Yan, Jun; Yu, Kegen; Chen, Ruizhi; Chen, Liang

2017-05-30

In this paper a two-phase compressive sensing (CS) and received signal strength (RSS)-based target localization approach is proposed to improve position accuracy by dealing with the unknown target population and the effect of grid dimensions on position error. In the coarse localization phase, by formulating target localization as a sparse signal recovery problem, grids with recovery vector components greater than a threshold are chosen as the candidate target grids. In the fine localization phase, by partitioning each candidate grid, the target position in a grid is iteratively refined by using the minimum residual error rule and the least-squares technique. When all the candidate target grids are iteratively partitioned and the measurement matrix is updated, the recovery vector is re-estimated. Threshold-based detection is employed again to determine the target grids and hence the target population. As a consequence, both the target population and the position estimation accuracy can be significantly improved. Simulation results demonstrate that the proposed approach achieves the best accuracy among all the algorithms compared.

Orion Pad Abort 1 Flight Test: Simulation Predictions Versus Flight Data

NASA Technical Reports Server (NTRS)

Stillwater, Ryan Allanque; Merritt, Deborah S.

2011-01-01

The presentation covers the pre-flight simulation predictions of the Orion Pad Abort 1. The pre-flight simulation predictions are compared to the Orion Pad Abort 1 flight test data. Finally the flight test data is compared to the updated simulation predictions, which show a ove rall improvement in the accuracy of the simulation predictions.

Accuracy investigation of phthalate metabolite standards.

PubMed

Langlois, Éric; Leblanc, Alain; Simard, Yves; Thellen, Claude

2012-05-01

Phthalates are ubiquitous compounds whose metabolites are usually determined in urine for biomonitoring studies. Following suspect and unexplained results from our laboratory in an external quality-assessment scheme, we investigated the accuracy of all phthalate metabolite standards in our possession by comparing them with those of several suppliers. Our findings suggest that commercial phthalate metabolite certified solutions are not always accurate and that lot-to-lot discrepancies significantly affect the accuracy of the results obtained with several of these standards. These observations indicate that the reliability of the results obtained from different lots of standards is not equal, which reduces the possibility of intra-laboratory and inter-laboratory comparisons of results. However, agreements of accuracy have been observed for a majority of neat standards obtained from different suppliers, which indicates that a solution to this issue is available. Data accuracy of phthalate metabolites should be of concern for laboratories performing phthalate metabolite analysis because of the standards used. The results of our investigation are presented from the perspective that laboratories performing phthalate metabolite analysis can obtain accurate and comparable results in the future. Our findings will contribute to improving the quality of future phthalate metabolite analyses and will affect the interpretation of past results.

Relative Navigation of Formation-Flying Satellites

NASA Technical Reports Server (NTRS)

Long, Anne; Kelbel, David; Lee, Taesul; Leung, Dominic; Carpenter, J. Russell; Grambling, Cheryl

2002-01-01

This paper compares autonomous relative navigation performance for formations in eccentric, medium and high-altitude Earth orbits using Global Positioning System (GPS) Standard Positioning Service (SPS), crosslink, and celestial object measurements. For close formations, the relative navigation accuracy is highly dependent on the magnitude of the uncorrelated measurement errors. A relative navigation position accuracy of better than 10 centimeters root-mean-square (RMS) can be achieved for medium-altitude formations that can continuously track at least one GPS signal. A relative navigation position accuracy of better than 15 meters RMS can be achieved for high-altitude formations that have sparse tracking of the GPS signals. The addition of crosslink measurements can significantly improve relative navigation accuracy for formations that use sparse GPS tracking or celestial object measurements for absolute navigation.

Silver Coating for High-Mass-Accuracy Imaging Mass Spectrometry of Fingerprints on Nanostructured Silicon.

PubMed

Guinan, Taryn M; Gustafsson, Ove J R; McPhee, Gordon; Kobus, Hilton; Voelcker, Nicolas H

2015-11-17

Nanostructure imaging mass spectrometry (NIMS) using porous silicon (pSi) is a key technique for molecular imaging of exogenous and endogenous low molecular weight compounds from fingerprints. However, high-mass-accuracy NIMS can be difficult to achieve as time-of-flight (ToF) mass analyzers, which dominate the field, cannot sufficiently compensate for shifts in measured m/z values. Here, we show internal recalibration using a thin layer of silver (Ag) sputter-coated onto functionalized pSi substrates. NIMS peaks for several previously reported fingerprint components were selected and mass accuracy was compared to theoretical values. Mass accuracy was improved by more than an order of magnitude in several cases. This straightforward method should form part of the standard guidelines for NIMS studies for spatial characterization of small molecules.

Clinical Study of Orthogonal-View Phase-Matched Digital Tomosynthesis for Lung Tumor Localization.

PubMed

Zhang, You; Ren, Lei; Vergalasova, Irina; Yin, Fang-Fang

2017-01-01

Compared to cone-beam computed tomography, digital tomosynthesis imaging has the benefits of shorter scanning time, less imaging dose, and better mechanical clearance for tumor localization in radiation therapy. However, for lung tumors, the localization accuracy of the conventional digital tomosynthesis technique is affected by the lack of depth information and the existence of lung tumor motion. This study investigates the clinical feasibility of using an orthogonal-view phase-matched digital tomosynthesis technique to improve the accuracy of lung tumor localization. The proposed orthogonal-view phase-matched digital tomosynthesis technique benefits from 2 major features: (1) it acquires orthogonal-view projections to improve the depth information in reconstructed digital tomosynthesis images and (2) it applies respiratory phase-matching to incorporate patient motion information into the synthesized reference digital tomosynthesis sets, which helps to improve the localization accuracy of moving lung tumors. A retrospective study enrolling 14 patients was performed to evaluate the accuracy of the orthogonal-view phase-matched digital tomosynthesis technique. Phantom studies were also performed using an anthropomorphic phantom to investigate the feasibility of using intratreatment aggregated kV and beams' eye view cine MV projections for orthogonal-view phase-matched digital tomosynthesis imaging. The localization accuracy of the orthogonal-view phase-matched digital tomosynthesis technique was compared to that of the single-view digital tomosynthesis techniques and the digital tomosynthesis techniques without phase-matching. The orthogonal-view phase-matched digital tomosynthesis technique outperforms the other digital tomosynthesis techniques in tumor localization accuracy for both the patient study and the phantom study. For the patient study, the orthogonal-view phase-matched digital tomosynthesis technique localizes the tumor to an average (± standard deviation) error of 1.8 (0.7) mm for a 30° total scan angle. For the phantom study using aggregated kV-MV projections, the orthogonal-view phase-matched digital tomosynthesis localizes the tumor to an average error within 1 mm for varying magnitudes of scan angles. The pilot clinical study shows that the orthogonal-view phase-matched digital tomosynthesis technique enables fast and accurate localization of moving lung tumors.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, R.; Harrison, D. E. Jr.

A variable time step integration algorithm for carrying out molecular dynamics simulations of atomic collision cascades is proposed which evaluates the interaction forces only once per time step. The algorithm is tested on some model problems which have exact solutions and is compared against other common methods. These comparisons show that the method has good stability and accuracy. Applications to Ar/sup +/ bombardment of Cu and Si show good accuracy and improved speed to the original method (D. E. Harrison, W. L. Gay, and H. M. Effron, J. Math. Phys. /bold 10/, 1179 (1969)).

Diagnostic performance of dual-energy contrast-enhanced subtracted mammography in dense breasts compared to mammography alone: interobserver blind-reading analysis.

PubMed

Cheung, Yun-Chung; Lin, Yu-Ching; Wan, Yung-Liang; Yeow, Kee-Min; Huang, Pei-Chin; Lo, Yung-Feng; Tsai, Hsiu-Pei; Ueng, Shir-Hwa; Chang, Chee-Jen

2014-10-01

To analyse the accuracy of dual-energy contrast-enhanced spectral mammography in dense breasts in comparison with contrast-enhanced subtracted mammography (CESM) and conventional mammography (Mx). CESM cases of dense breasts with histological proof were evaluated in the present study. Four radiologists with varying experience in mammography interpretation blindly read Mx first, followed by CESM. The diagnostic profiles, consistency and learning curve were analysed statistically. One hundred lesions (28 benign and 72 breast malignancies) in 89 females were analysed. Use of CESM improved the cancer diagnosis by 21.2 % in sensitivity (71.5 % to 92.7 %), by 16.1 % in specificity (51.8 % to 67.9 %) and by 19.8 % in accuracy (65.9 % to 85.8 %) compared with Mx. The interobserver diagnostic consistency was markedly higher using CESM than using Mx alone (0.6235 vs. 0.3869 using the kappa ratio). The probability of a correct prediction was elevated from 80 % to 90 % after 75 consecutive case readings. CESM provided additional information with consistent improvement of the cancer diagnosis in dense breasts compared to Mx alone. The prediction of the diagnosis could be improved by the interpretation of a significant number of cases in the presence of 6 % benign contrast enhancement in this study. • DE-CESM improves the cancer diagnosis in dense breasts compared with mammography. • DE-CESM shows greater consistency than mammography alone by interobserver blind reading. • Diagnostic improvement of DE-CESM is independent of the mammographic reading experience.

Improved solution accuracy for Landsat-4 (TDRSS-user) orbit determination

NASA Technical Reports Server (NTRS)

Oza, D. H.; Niklewski, D. J.; Doll, C. E.; Mistretta, G. D.; Hart, R. C.

1994-01-01

This paper presents the results of a study to compare the orbit determination accuracy for a Tracking and Data Relay Satellite System (TDRSS) user spacecraft, Landsat-4, obtained using a Prototype Filter Smoother (PFS), with the accuracy of an established batch-least-squares system, the Goddard Trajectory Determination System (GTDS). The results of Landsat-4 orbit determination will provide useful experience for the Earth Observing System (EOS) series of satellites. The Landsat-4 ephemerides were estimated for the January 17-23, 1991, timeframe, during which intensive TDRSS tracking data for Landsat-4 were available. Independent assessments were made of the consistencies (overlap comparisons for the batch case and convariances for the sequential case) of solutions produced by the batch and sequential methods. The filtered and smoothed PFS orbit solutions were compared with the definitive GTDS orbit solutions for Landsat-4; the solution differences were generally less than 15 meters.

Mind the gap: Increased inter-letter spacing as a means of improving reading performance.

PubMed

Dotan, Shahar; Katzir, Tami

2018-06-05

Theeffects of text display, specificallywithin-word spacing, on children's reading at different developmental levels has barely been investigated.This study explored the influence of manipulating inter-letter spacing on reading performance (accuracy and rate) of beginner Hebrew readers compared with older readers and of low-achieving readers compared with age-matched high-achieving readers.A computer-based isolated word reading task was performed by 132 first and third graders. Words were displayed under two spacing conditions: standard spacing (100%) and increased spacing (150%). Words were balanced for length and frequency across conditions. Results indicated that increased spacing contributed to reading accuracy without affecting reading rate. Interestingly, all first graders benefitted fromthe spaced condition. Thiseffect was found only in long words but not in short words. Among third graders, only low-achieving readers gained in accuracy fromthespaced condition. Thetheoretical and clinical effects ofthefindings are discussed. Copyright © 2018 Elsevier Inc. All rights reserved.

Individuals Achieve More Accurate Results with Meters That Are Codeless and Employ Dynamic Electrochemistry

PubMed Central

Rao, Anoop; Wiley, Meg; Iyengar, Sridhar; Nadeau, Dan; Carnevale, Julie

2010-01-01

Background Studies have shown that controlling blood glucose can reduce the onset and progression of the long-term microvascular and neuropathic complications associated with the chronic course of diabetes mellitus. Improved glycemic control can be achieved by frequent testing combined with changes in medication, exercise, and diet. Technological advancements have enabled improvements in analytical accuracy of meters, and this paper explores two such parameters to which that accuracy can be attributed. Methods Four blood glucose monitoring systems (with or without dynamic electrochemistry algorithms, codeless or requiring coding prior to testing) were evaluated and compared with respect to their accuracy. Results Altogether, 108 blood glucose values were obtained for each system from 54 study participants and compared with the reference values. The analysis depicted in the International Organization for Standardization table format indicates that the devices with dynamic electrochemistry and the codeless feature had the highest proportion of acceptable results overall (System A, 101/103). Results were significant when compared at the 10% bias level with meters that were codeless and utilized static electrochemistry (p = .017) or systems that had static electrochemistry but needed coding (p = .008). Conclusions Analytical performance of these blood glucose meters differed significantly depending on their technologic features. Meters that utilized dynamic electrochemistry and did not require coding were more accurate than meters that used static electrochemistry or required coding. PMID:20167178

Individuals achieve more accurate results with meters that are codeless and employ dynamic electrochemistry.

PubMed

Rao, Anoop; Wiley, Meg; Iyengar, Sridhar; Nadeau, Dan; Carnevale, Julie

2010-01-01

Studies have shown that controlling blood glucose can reduce the onset and progression of the long-term microvascular and neuropathic complications associated with the chronic course of diabetes mellitus. Improved glycemic control can be achieved by frequent testing combined with changes in medication, exercise, and diet. Technological advancements have enabled improvements in analytical accuracy of meters, and this paper explores two such parameters to which that accuracy can be attributed. Four blood glucose monitoring systems (with or without dynamic electrochemistry algorithms, codeless or requiring coding prior to testing) were evaluated and compared with respect to their accuracy. Altogether, 108 blood glucose values were obtained for each system from 54 study participants and compared with the reference values. The analysis depicted in the International Organization for Standardization table format indicates that the devices with dynamic electrochemistry and the codeless feature had the highest proportion of acceptable results overall (System A, 101/103). Results were significant when compared at the 10% bias level with meters that were codeless and utilized static electrochemistry (p = .017) or systems that had static electrochemistry but needed coding (p = .008). Analytical performance of these blood glucose meters differed significantly depending on their technologic features. Meters that utilized dynamic electrochemistry and did not require coding were more accurate than meters that used static electrochemistry or required coding. 2010 Diabetes Technology Society.

An Improved P-Phase Arrival Picking Method S/L-K-A with an Application to the Yongshaba Mine in China

NASA Astrophysics Data System (ADS)

Shang, Xueyi; Li, Xibing; Morales-Esteban, A.; Dong, Longjun

2018-02-01

Automatic microseismic P-phase arrival picking is paramount for microseismic event identification, event location and source mechanism analysis. The commonly used STA/LTA picker, PAI-K picker, AIC picker and three proposed pickers have been applied to determine the P-phase arrivals of 580 microseismic signals (the sampling frequency is 6000 Hz). These have been obtained from the Institute of Mine Seismology (IMS) acquisition system of the Yongshaba mine in China. Then, the six above-mentioned pickers have been compared in their picking accuracy, typical waveforms, signal-to-noise ratio (SNR) adaptabilities and quantitative evaluation. The results have shown that: (1) the triggered STA/LTA picker has a good picking stability but a low picking accuracy. While the PAI-K and the AIC pickers have a higher picking accuracy but a poorer picking stability compared with the triggered STA/LTA picker. Moreover, the AIC picker usually has a better picking result than the PAI-K picker; (2) the S/L-K-A picker significantly improves the STA/LTA, the PAI-K and the S/L + PAI-K pickers. Moreover, it obviously improves the AIC and the S/L + AIC pickers' large picking error (> 30 ms) signals; (3) the picking error ratios of the S/L-K-A picker within 10, 20 and 30 ms achieve 92.76, 95.86 and 97.41%, respectively. The S/L-K-A picker enhances the picking adaptability to different waveforms and SNRs. In conclusion, the S/L-K-A picker provides a new method for automatic microseismic P-phase arrival picking with a high accuracy and a good stability.

Meta-analytical prognostic accuracy of the Comprehensive Assessment of at Risk Mental States (CAARMS): The need for refined prediction.

PubMed

Oliver, D; Kotlicka-Antczak, M; Minichino, A; Spada, G; McGuire, P; Fusar-Poli, P

2018-03-01

Primary indicated prevention is reliant on accurate tools to predict the onset of psychosis. The gold standard assessment for detecting individuals at clinical high risk (CHR-P) for psychosis in the UK and many other countries is the Comprehensive Assessment for At Risk Mental States (CAARMS). While the prognostic accuracy of CHR-P instruments has been assessed in general, this is the first study to specifically analyse that of the CAARMS. As such, the CAARMS was used as the index test, with the reference index being psychosis onset within 2 years. Six independent studies were analysed using MIDAS (STATA 14), with a total of 1876 help-seeking subjects referred to high risk services (CHR-P+: n=892; CHR-P-: n=984). Area under the curve (AUC), summary receiver operating characteristic curves (SROC), quality assessment, likelihood ratios, and probability modified plots were computed, along with sensitivity analyses and meta-regressions. The current meta-analysis confirmed that the 2-year prognostic accuracy of the CAARMS is only acceptable (AUC=0.79 95% CI: 0.75-0.83) and not outstanding as previously reported. In particular, specificity was poor. Sensitivity of the CAARMS is inferior compared to the SIPS, while specificity is comparably low. However, due to the difficulties in performing these types of studies, power in this meta-analysis was low. These results indicate that refining and improving the prognostic accuracy of the CAARMS should be the mainstream area of research for the next era. Avenues of prediction improvement are critically discussed and presented to better benefit patients and improve outcomes of first episode psychosis. Copyright © 2017 The Authors. Published by Elsevier Masson SAS.. All rights reserved.

Two-dimensional antiscatter grid: A novel scatter rejection device for Cone-beam computed tomography.

PubMed

Alexeev, Timur; Kavanagh, Brian; Miften, Moyed; Altunbas, Cem

2018-02-01

Scattered radiation remains to be a major cause of image quality degradation in Flat Panel Detector (FPD)-based Cone-beam computed tomography (CBCT). We have been investigating a novel two-dimensional antiscatter grid (2D-ASG) concept to reduce scatter intensity, and hence improve CBCT image quality. We present the first CBCT imaging experiments performed with the 2D-ASG prototype, and demonstrate its efficacy in improving CBCT image quality. A 2D-ASG prototype with septa focused to x-ray source was additively manufactured from tungsten and mounted on a Varian TrueBeam CBCT system. CBCT projections of phantoms were acquired with an offset detector geometry using TrueBeam's "developer" mode. To minimize the effect of gantry flex, projections were gain corrected on angle-specific bases. CBCT images were reconstructed using a filtered backprojection algorithm and image quality improvement was quantified by measuring contrast-to-noise ratio (CNR) and CT number accuracy in images acquired with no antiscatter grid (NO-ASG), conventional one dimensional antiscatter grid (1D-ASG), and the 2D-ASG prototype. A significant improvement in contrast resolution was achieved using our 2D-ASG prototype compared to results of 1D-ASG and NO-ASG acquisitions. Compared to NO-ASG and 1D-ASG experiments, the CNR of material inserts improved by as much as 86% and 54% respectively. Using 2D-ASG, CT number underestimation in water equivalent material section of the phantom was reduced by up to 325 HU when compared to NO-ASG and up to 179 HU when compared to 1D-ASG. We successfully performed the first CBCT imaging experiments with a 2D-ASG prototype. 2D-ASG provided significantly higher CT number accuracy, higher CNR, and diminished scatter-induced image artifacts in qualitative evaluations. We strongly believe that utilization of a 2D-ASG may potentially lead to better soft tissue visualization in CBCT and may enable novel clinical applications that require high CT number accuracy. © 2017 American Association of Physicists in Medicine.

Imperfect practice makes perfect: error management training improves transfer of learning.

PubMed

Dyre, Liv; Tabor, Ann; Ringsted, Charlotte; Tolsgaard, Martin G

2017-02-01

Traditionally, trainees are instructed to practise with as few errors as possible during simulation-based training. However, transfer of learning may improve if trainees are encouraged to commit errors. The aim of this study was to assess the effects of error management instructions compared with error avoidance instructions during simulation-based ultrasound training. Medical students (n = 60) with no prior ultrasound experience were randomised to error management training (EMT) (n = 32) or error avoidance training (EAT) (n = 28). The EMT group was instructed to deliberately make errors during training. The EAT group was instructed to follow the simulator instructions and to commit as few errors as possible. Training consisted of 3 hours of simulation-based ultrasound training focusing on fetal weight estimation. Simulation-based tests were administered before and after training. Transfer tests were performed on real patients 7-10 days after the completion of training. Primary outcomes were transfer test performance scores and diagnostic accuracy. Secondary outcomes included performance scores and diagnostic accuracy during the simulation-based pre- and post-tests. A total of 56 participants completed the study. On the transfer test, EMT group participants attained higher performance scores (mean score: 67.7%, 95% confidence interval [CI]: 62.4-72.9%) than EAT group members (mean score: 51.7%, 95% CI: 45.8-57.6%) (p < 0.001; Cohen's d = 1.1, 95% CI: 0.5-1.7). There was a moderate improvement in diagnostic accuracy in the EMT group compared with the EAT group (16.7%, 95% CI: 10.2-23.3% weight deviation versus 26.6%, 95% CI: 16.5-36.7% weight deviation [p = 0.082; Cohen's d = 0.46, 95% CI: -0.06 to 1.0]). No significant interaction effects between group and performance improvements between the pre- and post-tests were found in either performance scores (p = 0.25) or diagnostic accuracy (p = 0.09). The provision of error management instructions during simulation-based training improves the transfer of learning to the clinical setting compared with error avoidance instructions. Rather than teaching to avoid errors, the use of errors for learning should be explored further in medical education theory and practice. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Validity of Predictive Equations for Resting Energy Expenditure Developed for Obese Patients: Impact of Body Composition Method

PubMed Central

Achamrah, Najate; Jésus, Pierre; Grigioni, Sébastien; Rimbert, Agnès; Petit, André; Déchelotte, Pierre; Folope, Vanessa; Coëffier, Moïse

2018-01-01

Predictive equations have been specifically developed for obese patients to estimate resting energy expenditure (REE). Body composition (BC) assessment is needed for some of these equations. We assessed the impact of BC methods on the accuracy of specific predictive equations developed in obese patients. REE was measured (mREE) by indirect calorimetry and BC assessed by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). mREE, percentages of prediction accuracy (±10% of mREE) were compared. Predictive equations were studied in 2588 obese patients. Mean mREE was 1788 ± 6.3 kcal/24 h. Only the Müller (BIA) and Harris & Benedict (HB) equations provided REE with no difference from mREE. The Huang, Müller, Horie-Waitzberg, and HB formulas provided a higher accurate prediction (>60% of cases). The use of BIA provided better predictions of REE than DXA for the Huang and Müller equations. Inversely, the Horie-Waitzberg and Lazzer formulas provided a higher accuracy using DXA. Accuracy decreased when applied to patients with BMI ≥ 40, except for the Horie-Waitzberg and Lazzer (DXA) formulas. Müller equations based on BIA provided a marked improvement of REE prediction accuracy than equations not based on BC. The interest of BC to improve REE predictive equations accuracy in obese patients should be confirmed. PMID:29320432

Phonemic accuracy development in children with cochlear implants up to five years of age by using Levenshtein distance.

PubMed

Faes, Jolien; Gillis, Joris; Gillis, Steven

2016-01-01

Phonemic accuracy of children with cochlear implants (CI) is often reported to be lower in comparison with normally hearing (NH) age-matched children. In this study, we compare phonemic accuracy development in the spontaneous speech of Dutch-speaking children with CI and NH age-matched peers. A dynamic cost model of Levenshtein distance is used to compute the accuracy of each word token. We set up a longitudinal design with monthly data for comparisons up to age two and a cross-sectional design with yearly data between three and five years of age. The main finding is that phonemic accuracy steadily increases throughout the period studied. Children with CI's accuracy is lower than that of their NH age mates, but this difference is not statistically significant in the earliest stages of lexical development. But accuracy of children with CI initially improves significantly less steeply than that of NH peers. Furthermore, the number of syllables in the target word and target word's complexity influence children's accuracy, as longer and more complex target words are less accurately produced. Up to age four, children with CI are significantly less accurate than NH children with increasing word length and word complexity. This difference has disappeared at age five. Finally, hearing age is shown to influence accuracy development of children with CI, while age of implant activation is not. This article informs the reader about phonemic accuracy development in children. The reader will be able to (a) discuss different metrics to measure phonemic accuracy development, (b) discuss phonemic accuracy of children with CI up to five years of age and compare them with NH children, (c) discuss the influence of target word's complexity and target word's syllable length on phonemic accuracy, (d) discuss the influence of hearing experience and age of implantation on phonemic accuracy of children with CI. Copyright © 2015 Elsevier Inc. All rights reserved.

Suitability of the echo-time-shift method as laboratory standard for thermal ultrasound dosimetry

NASA Astrophysics Data System (ADS)

Fuhrmann, Tina; Georg, Olga; Haller, Julian; Jenderka, Klaus-Vitold

2017-03-01

Ultrasound therapy is a promising, non-invasive application with potential to significantly improve cancer therapies like surgery, viro- or immunotherapy. This therapy needs faster, cheaper and more easy-to-handle quality assurance tools for therapy devices as well as possibilities to verify treatment plans and for dosimetry. This limits comparability and safety of treatments. Accurate spatial and temporal temperature maps could be used to overcome these shortcomings. In this contribution first results of suitability and accuracy investigations of the echo-time-shift method for two-dimensional temperature mapping during and after sonication are presented. The analysis methods used to calculate time-shifts were a discrete frame-to-frame and a discrete frame-to-base-frame algorithm as well as a sigmoid fit for temperature calculation. In the future accuracy could be significantly enhanced by using continuous methods for time-shift calculation. Further improvements can be achieved by improving filtering algorithms and interpolation of sampled diagnostic ultrasound data. It might be a comparatively accurate, fast and affordable method for laboratory and clinical quality control.

Contrast and harmonic imaging improves accuracy and efficiency of novice readers for dobutamine stress echocardiography

NASA Technical Reports Server (NTRS)

Vlassak, Irmien; Rubin, David N.; Odabashian, Jill A.; Garcia, Mario J.; King, Lisa M.; Lin, Steve S.; Drinko, Jeanne K.; Morehead, Annitta J.; Prior, David L.; Asher, Craig R.;

Vascular input function correction of inflow enhancement for improved pharmacokinetic modeling of liver DCE-MRI.

PubMed

Ning, Jia; Schubert, Tilman; Johnson, Kevin M; Roldán-Alzate, Alejandro; Chen, Huijun; Yuan, Chun; Reeder, Scott B

2018-06-01

To propose a simple method to correct vascular input function (VIF) due to inflow effects and to test whether the proposed method can provide more accurate VIFs for improved pharmacokinetic modeling. A spoiled gradient echo sequence-based inflow quantification and contrast agent concentration correction method was proposed. Simulations were conducted to illustrate improvement in the accuracy of VIF estimation and pharmacokinetic fitting. Animal studies with dynamic contrast-enhanced MR scans were conducted before, 1 week after, and 2 weeks after portal vein embolization (PVE) was performed in the left portal circulation of pigs. The proposed method was applied to correct the VIFs for model fitting. Pharmacokinetic parameters fitted using corrected and uncorrected VIFs were compared between different lobes and visits. Simulation results demonstrated that the proposed method can improve accuracy of VIF estimation and pharmacokinetic fitting. In animal study results, pharmacokinetic fitting using corrected VIFs demonstrated changes in perfusion consistent with changes expected after PVE, whereas the perfusion estimates derived by uncorrected VIFs showed no significant changes. The proposed correction method improves accuracy of VIFs and therefore provides more precise pharmacokinetic fitting. This method may be promising in improving the reliability of perfusion quantification. Magn Reson Med 79:3093-3102, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Improved dense trajectories for action recognition based on random projection and Fisher vectors

NASA Astrophysics Data System (ADS)

Ai, Shihui; Lu, Tongwei; Xiong, Yudian

2018-03-01

As an important application of intelligent monitoring system, the action recognition in video has become a very important research area of computer vision. In order to improve the accuracy rate of the action recognition in video with improved dense trajectories, one advanced vector method is introduced. Improved dense trajectories combine Fisher Vector with Random Projection. The method realizes the reduction of the characteristic trajectory though projecting the high-dimensional trajectory descriptor into the low-dimensional subspace based on defining and analyzing Gaussian mixture model by Random Projection. And a GMM-FV hybrid model is introduced to encode the trajectory feature vector and reduce dimension. The computational complexity is reduced by Random Projection which can drop Fisher coding vector. Finally, a Linear SVM is used to classifier to predict labels. We tested the algorithm in UCF101 dataset and KTH dataset. Compared with existed some others algorithm, the result showed that the method not only reduce the computational complexity but also improved the accuracy of action recognition.

Aerothermal modeling program, phase 2. Element B: Flow interaction experiment

NASA Technical Reports Server (NTRS)

Nikjooy, M.; Mongia, H. C.; Murthy, S. N. B.; Sullivan, J. P.

1986-01-01

The design process was improved and the efficiency, life, and maintenance costs of the turbine engine hot section was enhanced. Recently, there has been much emphasis on the need for improved numerical codes for the design of efficient combustors. For the development of improved computational codes, there is a need for an experimentally obtained data base to be used at test cases for the accuracy of the computations. The purpose of Element-B is to establish a benchmark quality velocity and scalar measurements of the flow interaction of circular jets with swirling flow typical of that in the dome region of annular combustor. In addition to the detailed experimental effort, extensive computations of the swirling flows are to be compared with the measurements for the purpose of assessing the accuracy of current and advanced turbulence and scalar transport models.

Incorporating conditional random fields and active learning to improve sentiment identification.

PubMed

Zhang, Kunpeng; Xie, Yusheng; Yang, Yi; Sun, Aaron; Liu, Hengchang; Choudhary, Alok

2014-10-01

Many machine learning, statistical, and computational linguistic methods have been developed to identify sentiment of sentences in documents, yielding promising results. However, most of state-of-the-art methods focus on individual sentences and ignore the impact of context on the meaning of a sentence. In this paper, we propose a method based on conditional random fields to incorporate sentence structure and context information in addition to syntactic information for improving sentiment identification. We also investigate how human interaction affects the accuracy of sentiment labeling using limited training data. We propose and evaluate two different active learning strategies for labeling sentiment data. Our experiments with the proposed approach demonstrate a 5%-15% improvement in accuracy on Amazon customer reviews compared to existing supervised learning and rule-based methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Preserved Discrimination Performance and Neural Processing during Crossmodal Attention in Aging

PubMed Central

Mishra, Jyoti; Gazzaley, Adam

2013-01-01

In a recent study in younger adults (19-29 year olds) we showed evidence that distributed audiovisual attention resulted in improved discrimination performance for audiovisual stimuli compared to focused visual attention. Here, we extend our findings to healthy older adults (60-90 year olds), showing that performance benefits of distributed audiovisual attention in this population match those of younger adults. Specifically, improved performance was revealed in faster response times for semantically congruent audiovisual stimuli during distributed relative to focused visual attention, without any differences in accuracy. For semantically incongruent stimuli, discrimination accuracy was significantly improved during distributed relative to focused attention. Furthermore, event-related neural processing showed intact crossmodal integration in higher performing older adults similar to younger adults. Thus, there was insufficient evidence to support an age-related deficit in crossmodal attention. PMID:24278464

Use of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) for improving the accuracy of the risk classification of type 1 diabetes.

PubMed

Sosenko, Jay M; Skyler, Jay S; Mahon, Jeffrey; Krischer, Jeffrey P; Greenbaum, Carla J; Rafkin, Lisa E; Beam, Craig A; Boulware, David C; Matheson, Della; Cuthbertson, David; Herold, Kevan C; Eisenbarth, George; Palmer, Jerry P

2014-04-01

OBJECTIVE We studied the utility of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) for improving the accuracy of type 1 diabetes (T1D) risk classification in TrialNet Natural History Study (TNNHS) participants. RESEARCH DESIGN AND METHODS The cumulative incidence of T1D was compared between normoglycemic individuals with DPTRS values >7.00 and dysglycemic individuals in the TNNHS (n = 991). It was also compared between individuals with DPTRS values <7.00 or >7.00 among those with dysglycemia and those with multiple autoantibodies in the TNNHS. DPTRS values >7.00 were compared with dysglycemia for characterizing risk in Diabetes Prevention Trial-Type 1 (DPT-1) (n = 670) and TNNHS participants. The reliability of DPTRS values >7.00 was compared with dysglycemia in the TNNHS. RESULTS The cumulative incidence of T1D for normoglycemic TNNHS participants with DPTRS values >7.00 was comparable to those with dysglycemia. Among those with dysglycemia, the cumulative incidence was much higher (P < 0.001) for those with DPTRS values >7.00 than for those with values <7.00 (3-year risks: 0.16 for <7.00 and 0.46 for >7.00). Dysglycemic individuals in DPT-1 were at much higher risk for T1D than those with dysglycemia in the TNNHS (P < 0.001); there was no significant difference in risk between the studies among those with DPTRS values >7.00. The proportion in the TNNHS reverting from dysglycemia to normoglycemia at the next visit was higher than the proportion reverting from DPTRS values >7.00 to values <7.00 (36 vs. 23%). CONCLUSIONS DPTRS thresholds can improve T1D risk classification accuracy by identifying high-risk normoglycemic and low-risk dysglycemic individuals. The 7.00 DPTRS threshold characterizes risk more consistently between populations and has greater reliability than dysglycemia.

Accurate ECG diagnosis of atrial tachyarrhythmias using quantitative analysis: a prospective diagnostic and cost-effectiveness study.

PubMed

Krummen, David E; Patel, Mitul; Nguyen, Hong; Ho, Gordon; Kazi, Dhruv S; Clopton, Paul; Holland, Marian C; Greenberg, Scott L; Feld, Gregory K; Faddis, Mitchell N; Narayan, Sanjiv M

2010-11-01

Quantitative ECG Analysis. Optimal atrial tachyarrhythmia management is facilitated by accurate electrocardiogram interpretation, yet typical atrial flutter (AFl) may present without sawtooth F-waves or RR regularity, and atrial fibrillation (AF) may be difficult to separate from atypical AFl or rapid focal atrial tachycardia (AT). We analyzed whether improved diagnostic accuracy using a validated analysis tool significantly impacts costs and patient care. We performed a prospective, blinded, multicenter study using a novel quantitative computerized algorithm to identify atrial tachyarrhythmia mechanism from the surface ECG in patients referred for electrophysiology study (EPS). In 122 consecutive patients (age 60 ± 12 years) referred for EPS, 91 sustained atrial tachyarrhythmias were studied. ECGs were also interpreted by 9 physicians from 3 specialties for comparison and to allow healthcare system modeling. Diagnostic accuracy was compared to the diagnosis at EPS. A Markov model was used to estimate the impact of improved arrhythmia diagnosis. We found 13% of typical AFl ECGs had neither sawtooth flutter waves nor RR regularity, and were misdiagnosed by the majority of clinicians (0/6 correctly diagnosed by consensus visual interpretation) but correctly by quantitative analysis in 83% (5/6, P = 0.03). AF diagnosis was also improved through use of the algorithm (92%) versus visual interpretation (primary care: 76%, P < 0.01). Economically, we found that these improvements in diagnostic accuracy resulted in an average cost-savings of $1,303 and 0.007 quality-adjusted-life-years per patient. Typical AFl and AF are frequently misdiagnosed using visual criteria. Quantitative analysis improves diagnostic accuracy and results in improved healthcare costs and patient outcomes. © 2010 Wiley Periodicals, Inc.

SU-E-J-109: Accurate Contour Transfer Between Different Image Modalities Using a Hybrid Deformable Image Registration and Fuzzy Connected Image Segmentation Method.

PubMed

Yang, C; Paulson, E; Li, X

2012-06-01

To develop and evaluate a tool that can improve the accuracy of contour transfer between different image modalities under challenging conditions of low image contrast and large image deformation, comparing to a few commonly used methods, for radiation treatment planning. The software tool includes the following steps and functionalities: (1) accepting input of images of different modalities, (2) converting existing contours on reference images (e.g., MRI) into delineated volumes and adjusting the intensity within the volumes to match target images (e.g., CT) intensity distribution for enhanced similarity metric, (3) registering reference and target images using appropriate deformable registration algorithms (e.g., B-spline, demons) and generate deformed contours, (4) mapping the deformed volumes on target images, calculating mean, variance, and center of mass as the initialization parameters for consecutive fuzzy connectedness (FC) image segmentation on target images, (5) generate affinity map from FC segmentation, (6) achieving final contours by modifying the deformed contours using the affinity map with a gradient distance weighting algorithm. The tool was tested with the CT and MR images of four pancreatic cancer patients acquired at the same respiration phase to minimize motion distortion. Dice's Coefficient was calculated against direct delineation on target image. Contours generated by various methods, including rigid transfer, auto-segmentation, deformable only transfer and proposed method, were compared. Fuzzy connected image segmentation needs careful parameter initialization and user involvement. Automatic contour transfer by multi-modality deformable registration leads up to 10% of accuracy improvement over the rigid transfer. Two extra proposed steps of adjusting intensity distribution and modifying the deformed contour with affinity map improve the transfer accuracy further to 14% averagely. Deformable image registration aided by contrast adjustment and fuzzy connectedness segmentation improves the contour transfer accuracy between multi-modality images, particularly with large deformation and low image contrast. © 2012 American Association of Physicists in Medicine.

Animated graphics for comparing two risks: a cautionary tale.

PubMed

Zikmund-Fisher, Brian J; Witteman, Holly O; Fuhrel-Forbis, Andrea; Exe, Nicole L; Kahn, Valerie C; Dickson, Mark

2012-07-25

The increasing use of computer-administered risk communications affords the potential to replace static risk graphics with animations that use motion cues to reinforce key risk messages. Research on the use of animated graphics, however, has yielded mixed findings, and little research exists to identify the specific animations that might improve risk knowledge and patients' decision making. To test whether viewing animated forms of standard pictograph (icon array) risk graphics displaying risks of side effects would improve people's ability to select the treatment with the lowest risk profile, as compared with viewing static images of the same risks. A total of 4198 members of a demographically diverse Internet panel read a scenario about two hypothetical treatments for thyroid cancer. Each treatment was described as equally effective but varied in side effects (with one option slightly better than the other). Participants were randomly assigned to receive all risk information in 1 of 10 pictograph formats in a quasi-factorial design. We compared a control condition of static grouped icons with a static scattered icon display and with 8 Flash-based animated versions that incorporated different combinations of (1) building the risk 1 icon at a time, (2) having scattered risk icons settle into a group, or (3) having scattered risk icons shuffle themselves (either automatically or by user control). We assessed participants' ability to choose the better treatment (choice accuracy), their gist knowledge of side effects (knowledge accuracy), and their graph evaluation ratings, controlling for subjective numeracy and need for cognition. When compared against static grouped-icon arrays, no animations significantly improved any outcomes, and most showed significant performance degradations. However, participants who received animations of grouped icons in which at-risk icons appeared 1 at a time performed as well on all outcomes as the static grouped-icon control group. Displays with scattered icons (static or animated) performed particularly poorly unless they included the settle animation that allowed users to view event icons grouped. Many combinations of animation, especially those with scattered icons that shuffle randomly, appear to inhibit knowledge accuracy in this context. Static pictographs that group risk icons, however, perform very well on measures of knowledge and choice accuracy. These findings parallel recent evidence in other data communication contexts that less can be more-that is, that simpler, more focused information presentation can result in improved understanding. Decision aid designers and health educators should proceed with caution when considering the use of animated risk graphics to compare two risks, given that evidence-based, static risk graphics appear optimal.

Investigation of the interpolation method to improve the distributed strain measurement accuracy in optical frequency domain reflectometry systems.

PubMed

Cui, Jiwen; Zhao, Shiyuan; Yang, Di; Ding, Zhenyang

2018-02-20

We use a spectrum interpolation technique to improve the distributed strain measurement accuracy in a Rayleigh-scatter-based optical frequency domain reflectometry sensing system. We demonstrate that strain accuracy is not limited by the "uncertainty principle" that exists in the time-frequency analysis. Different interpolation methods are investigated and used to improve the accuracy of peak position of the cross-correlation and, therefore, improve the accuracy of the strain. Interpolation implemented by padding zeros on one side of the windowed data in the spatial domain, before the inverse fast Fourier transform, is found to have the best accuracy. Using this method, the strain accuracy and resolution are both improved without decreasing the spatial resolution. The strain of 3 μϵ within the spatial resolution of 1 cm at the position of 21.4 m is distinguished, and the measurement uncertainty is 3.3 μϵ.

SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease.

PubMed

Ozcift, Akin

2012-08-01

Parkinson disease (PD) is an age-related deterioration of certain nerve systems, which affects movement, balance, and muscle control of clients. PD is one of the common diseases which affect 1% of people older than 60 years. A new classification scheme based on support vector machine (SVM) selected features to train rotation forest (RF) ensemble classifiers is presented for improving diagnosis of PD. The dataset contains records of voice measurements from 31 people, 23 with PD and each record in the dataset is defined with 22 features. The diagnosis model first makes use of a linear SVM to select ten most relevant features from 22. As a second step of the classification model, six different classifiers are trained with the subset of features. Subsequently, at the third step, the accuracies of classifiers are improved by the utilization of RF ensemble classification strategy. The results of the experiments are evaluated using three metrics; classification accuracy (ACC), Kappa Error (KE) and Area under the Receiver Operating Characteristic (ROC) Curve (AUC). Performance measures of two base classifiers, i.e. KStar and IBk, demonstrated an apparent increase in PD diagnosis accuracy compared to similar studies in literature. After all, application of RF ensemble classification scheme improved PD diagnosis in 5 of 6 classifiers significantly. We, numerically, obtained about 97% accuracy in RF ensemble of IBk (a K-Nearest Neighbor variant) algorithm, which is a quite high performance for Parkinson disease diagnosis.

Contrast-enhanced spectral mammography improves diagnostic accuracy in the symptomatic setting.

PubMed

Tennant, S L; James, J J; Cornford, E J; Chen, Y; Burrell, H C; Hamilton, L J; Girio-Fragkoulakis, C

2016-11-01

To assess the diagnostic accuracy of contrast-enhanced spectral mammography (CESM), and gauge its "added value" in the symptomatic setting. A retrospective multi-reader review of 100 consecutive CESM examinations was performed. Anonymised low-energy (LE) images were reviewed and given a score for malignancy. At least 3 weeks later, the entire examination (LE and recombined images) was reviewed. Histopathology data were obtained for all cases. Differences in performance were assessed using receiver operator characteristic (ROC) analysis. Sensitivity, specificity, and lesion size (versus MRI or histopathology) differences were calculated. Seventy-three percent of cases were malignant at final histology, 27% were benign following standard triple assessment. ROC analysis showed improved overall performance of CESM over LE alone, with area under the curve of 0.93 versus 0.83 (p<0.025). CESM showed increased sensitivity (95% versus 84%, p<0.025) and specificity (81% versus 63%, p<0.025) compared to LE alone, with all five readers showing improved accuracy. Tumour size estimation at CESM was significantly more accurate than LE alone, the latter tending to undersize lesions. In 75% of cases, CESM was deemed a useful or significant aid to diagnosis. CESM provides immediately available, clinically useful information in the symptomatic clinic in patients with suspicious palpable abnormalities. Radiologist sensitivity, specificity, and size accuracy for breast cancer detection and staging are all improved using CESM as the primary mammographic investigation. Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Improved localization accuracy in stochastic super-resolution fluorescence microscopy by K-factor image deshadowing

PubMed Central

Ilovitsh, Tali; Meiri, Amihai; Ebeling, Carl G.; Menon, Rajesh; Gerton, Jordan M.; Jorgensen, Erik M.; Zalevsky, Zeev

2013-01-01

Localization of a single fluorescent particle with sub-diffraction-limit accuracy is a key merit in localization microscopy. Existing methods such as photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) achieve localization accuracies of single emitters that can reach an order of magnitude lower than the conventional resolving capabilities of optical microscopy. However, these techniques require a sparse distribution of simultaneously activated fluorophores in the field of view, resulting in larger time needed for the construction of the full image. In this paper we present the use of a nonlinear image decomposition algorithm termed K-factor, which reduces an image into a nonlinear set of contrast-ordered decompositions whose joint product reassembles the original image. The K-factor technique, when implemented on raw data prior to localization, can improve the localization accuracy of standard existing methods, and also enable the localization of overlapping particles, allowing the use of increased fluorophore activation density, and thereby increased data collection speed. Numerical simulations of fluorescence data with random probe positions, and especially at high densities of activated fluorophores, demonstrate an improvement of up to 85% in the localization precision compared to single fitting techniques. Implementing the proposed concept on experimental data of cellular structures yielded a 37% improvement in resolution for the same super-resolution image acquisition time, and a decrease of 42% in the collection time of super-resolution data with the same resolution. PMID:24466491

Modeling hemodynamics in intracranial aneurysms: Comparing accuracy of CFD solvers based on finite element and finite volume schemes.

PubMed

Botti, Lorenzo; Paliwal, Nikhil; Conti, Pierangelo; Antiga, Luca; Meng, Hui

2018-06-01

Image-based computational fluid dynamics (CFD) has shown potential to aid in the clinical management of intracranial aneurysms (IAs) but its adoption in the clinical practice has been missing, partially due to lack of accuracy assessment and sensitivity analysis. To numerically solve the flow-governing equations CFD solvers generally rely on two spatial discretization schemes: Finite Volume (FV) and Finite Element (FE). Since increasingly accurate numerical solutions are obtained by different means, accuracies and computational costs of FV and FE formulations cannot be compared directly. To this end, in this study we benchmark two representative CFD solvers in simulating flow in a patient-specific IA model: (1) ANSYS Fluent, a commercial FV-based solver and (2) VMTKLab multidGetto, a discontinuous Galerkin (dG) FE-based solver. The FV solver's accuracy is improved by increasing the spatial mesh resolution (134k, 1.1m, 8.6m and 68.5m tetrahedral element meshes). The dGFE solver accuracy is increased by increasing the degree of polynomials (first, second, third and fourth degree) on the base 134k tetrahedral element mesh. Solutions from best FV and dGFE approximations are used as baseline for error quantification. On average, velocity errors for second-best approximations are approximately 1cm/s for a [0,125]cm/s velocity magnitude field. Results show that high-order dGFE provide better accuracy per degree of freedom but worse accuracy per Jacobian non-zero entry as compared to FV. Cross-comparison of velocity errors demonstrates asymptotic convergence of both solvers to the same numerical solution. Nevertheless, the discrepancy between under-resolved velocity fields suggests that mesh independence is reached following different paths. This article is protected by copyright. All rights reserved.

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

PubMed Central

Shi, Tiezhu; Liu, Huizeng; Chen, Yiyun; Fei, Teng; Wang, Junjie; Wu, Guofeng

2017-01-01

This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF), artificial neural network (ANN), radial basis function- and linear function- based support vector machine (RBF- and LF-SVM) were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs). The statistical significance of the difference between models was evaluated by using McNemar’s test (Z value). The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%), ANN (OA = 89%), RBF- (OA = 89%) and LF-SVM (OA = 87%) had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05). These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies. PMID:28471412

On-line analysis of algae in water by discrete three-dimensional fluorescence spectroscopy.

PubMed

Zhao, Nanjing; Zhang, Xiaoling; Yin, Gaofang; Yang, Ruifang; Hu, Li; Chen, Shuang; Liu, Jianguo; Liu, Wenqing

2018-03-19

In view of the problem of the on-line measurement of algae classification, a method of algae classification and concentration determination based on the discrete three-dimensional fluorescence spectra was studied in this work. The discrete three-dimensional fluorescence spectra of twelve common species of algae belonging to five categories were analyzed, the discrete three-dimensional standard spectra of five categories were built, and the recognition, classification and concentration prediction of algae categories were realized by the discrete three-dimensional fluorescence spectra coupled with non-negative weighted least squares linear regression analysis. The results show that similarities between discrete three-dimensional standard spectra of different categories were reduced and the accuracies of recognition, classification and concentration prediction of the algae categories were significantly improved. By comparing with that of the chlorophyll a fluorescence excitation spectra method, the recognition accuracy rate in pure samples by discrete three-dimensional fluorescence spectra is improved 1.38%, and the recovery rate and classification accuracy in pure diatom samples 34.1% and 46.8%, respectively; the recognition accuracy rate of mixed samples by discrete-three dimensional fluorescence spectra is enhanced by 26.1%, the recovery rate of mixed samples with Chlorophyta 37.8%, and the classification accuracy of mixed samples with diatoms 54.6%.

Solving the stability-accuracy-diversity dilemma of recommender systems

NASA Astrophysics Data System (ADS)

Hou, Lei; Liu, Kecheng; Liu, Jianguo; Zhang, Runtong

2017-02-01

Recommender systems are of great significance in predicting the potential interesting items based on the target user's historical selections. However, the recommendation list for a specific user has been found changing vastly when the system changes, due to the unstable quantification of item similarities, which is defined as the recommendation stability problem. To improve the similarity stability and recommendation stability is crucial for the user experience enhancement and the better understanding of user interests. While the stability as well as accuracy of recommendation could be guaranteed by recommending only popular items, studies have been addressing the necessity of diversity which requires the system to recommend unpopular items. By ranking the similarities in terms of stability and considering only the most stable ones, we present a top- n-stability method based on the Heat Conduction algorithm (denoted as TNS-HC henceforth) for solving the stability-accuracy-diversity dilemma. Experiments on four benchmark data sets indicate that the TNS-HC algorithm could significantly improve the recommendation stability and accuracy simultaneously and still retain the high-diversity nature of the Heat Conduction algorithm. Furthermore, we compare the performance of the TNS-HC algorithm with a number of benchmark recommendation algorithms. The result suggests that the TNS-HC algorithm is more efficient in solving the stability-accuracy-diversity triple dilemma of recommender systems.

Exploiting the chaotic behaviour of atmospheric models with reconfigurable architectures

NASA Astrophysics Data System (ADS)

Russell, Francis P.; Düben, Peter D.; Niu, Xinyu; Luk, Wayne; Palmer, T. N.

2017-12-01

Reconfigurable architectures are becoming mainstream: Amazon, Microsoft and IBM are supporting such architectures in their data centres. The computationally intensive nature of atmospheric modelling is an attractive target for hardware acceleration using reconfigurable computing. Performance of hardware designs can be improved through the use of reduced-precision arithmetic, but maintaining appropriate accuracy is essential. We explore reduced-precision optimisation for simulating chaotic systems, targeting atmospheric modelling, in which even minor changes in arithmetic behaviour will cause simulations to diverge quickly. The possibility of equally valid simulations having differing outcomes means that standard techniques for comparing numerical accuracy are inappropriate. We use the Hellinger distance to compare statistical behaviour between reduced-precision CPU implementations to guide reconfigurable designs of a chaotic system, then analyse accuracy, performance and power efficiency of the resulting implementations. Our results show that with only a limited loss in accuracy corresponding to less than 10% uncertainty in input parameters, the throughput and energy efficiency of a single-precision chaotic system implemented on a Xilinx Virtex-6 SX475T Field Programmable Gate Array (FPGA) can be more than doubled.

Cytology-based treatment decision in primary lung cancer: is it accurate enough?

PubMed

Sakr, Lama; Roll, Patrice; Payan, Marie-José; Liprandi, Agnès; Dutau, Hervé; Astoul, Philippe; Robaglia-Schlupp, Andrée; Loundou, Anderson; Barlesi, Fabrice

2012-03-01

Accurate distinction of lung cancer types has become increasingly important as recent trials have shown differential response to chemotherapy among non-small cell lung carcinoma (NSCLC) subtypes. Cytological procedures are frequently used but their diagnostic accuracy has been previously questioned. However, new endoscopic and cytological techniques might have improved cytological accuracy in comparison with prior findings. The aim of this study was to reassess cytological accuracy for diagnosis of lung cancer subtypes. A retrospective chart review of subjects who underwent fiberoptic bronchoscopy (FOB) for suspicion of lung cancer in 2007-2008, was undertaken. Reports of bronchoscopically derived cytological specimens were compared to those of histological material. Endoscopic findings and specific investigational techniques were taken into account. A total of 467 FOB with both cytological and histological diagnostic techniques were performed in 449 subjects. Patients consisted of 345 men and 104 women (median age, 65 yrs). Cytology proved malignancy in 157 patients. Cytologically diagnosed carcinomas were classified into squamous cell carcinoma (SqCC) in 56, adenocarcinoma (ADC) in 6, small cell lung carcinoma (SCLC) in 12, non-small cell lung carcinoma not otherwise specified (NSCLC-NOS) in 71, and unclassified carcinoma in 12. Cytology correlated fairly with biopsy specimens, as agreement was observed in 83% of SCLC, 100% of ADC, 74% of SqCC and 8% of NSCLC-NOS. Interestingly, 61% of cytologically identified NSCLC-NOS were classified as ADC by histology. Cytological accuracy improved in case of an endobronchial lesion, mainly for SqCC. These results indicate that cytological accuracy remains fair with regard to diagnosis of squamous and non-squamous lung cancer subtypes. Improvement of cytological accuracy is expected however with novel diagnostic strategies. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

An index with improved diagnostic accuracy for the diagnosis of Crohn's disease derived from the Lennard-Jones criteria.

PubMed

Reinisch, S; Schweiger, K; Pablik, E; Collet-Fenetrier, B; Peyrin-Biroulet, L; Alfaro, I; Panés, J; Moayyedi, P; Reinisch, W

2016-09-01

The Lennard-Jones criteria are considered the gold standard for diagnosing Crohn's disease (CD) and include the items granuloma, macroscopic discontinuity, transmural inflammation, fibrosis, lymphoid aggregates and discontinuous inflammation on histology. The criteria have never been subjected to a formal validation process. To develop a validated and improved diagnostic index based on the items of Lennard-Jones criteria. Included were 328 adult patients with long-standing CD (median disease duration 10 years) from three centres and classified as 'established', 'probable' or 'non-CD' by Lennard-Jones criteria at time of diagnosis. Controls were patients with ulcerative colitis (n = 170). The performance of each of the six diagnostic items of Lennard-Jones criteria was modelled by logistic regression and a new index based on stepwise backward selection and cut-offs was developed. The diagnostic value of the new index was analysed by comparing sensitivity, specificity and accuracy vs. Lennard-Jones criteria. By Lennard-Jones criteria 49% (n = 162) of CD patients would have been diagnosed as 'non-CD' at time of diagnosis (sensitivity/specificity/accuracy, 'established' CD: 0.34/0.99/0.67; 'probable' CD: 0.51/0.95/0.73). A new index was derived from granuloma, fibrosis, transmural inflammation and macroscopic discontinuity, but excluded lymphoid aggregates and discontinuous inflammation on histology. Our index provided improved diagnostic accuracy for 'established' and 'probable' CD (sensitivity/specificity/accuracy, 'established' CD: 0.45/1/0.72; 'probable' CD: 0.8/0.85/0.82), including the subgroup isolated colonic CD ('probable' CD, new index: 0.73/0.85/0.79; Lennard-Jones criteria: 0.43/0.95/0.69). We developed an index based on items of Lennard-Jones criteria providing improved diagnostic accuracy for the differential diagnosis between CD and UC. © 2016 John Wiley & Sons Ltd.

Deep multi-scale convolutional neural network for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Zhang, Feng-zhe; Yang, Xia

2018-04-01

In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

Characterization and classification of lupus patients based on plasma thermograms

PubMed Central

Chaires, Jonathan B.; Mekmaysy, Chongkham S.; DeLeeuw, Lynn; Sivils, Kathy L.; Harley, John B.; Rovin, Brad H.; Kulasekera, K. B.; Jarjour, Wael N.

2017-01-01

Objective Plasma thermograms (thermal stability profiles of blood plasma) are being utilized as a new diagnostic approach for clinical assessment. In this study, we investigated the ability of plasma thermograms to classify systemic lupus erythematosus (SLE) patients versus non SLE controls using a sample of 300 SLE and 300 control subjects from the Lupus Family Registry and Repository. Additionally, we evaluated the heterogeneity of thermograms along age, sex, ethnicity, concurrent health conditions and SLE diagnostic criteria. Methods Thermograms were visualized graphically for important differences between covariates and summarized using various measures. A modified linear discriminant analysis was used to segregate SLE versus control subjects on the basis of the thermograms. Classification accuracy was measured based on multiple training/test splits of the data and compared to classification based on SLE serological markers. Results Median sensitivity, specificity, and overall accuracy based on classification using plasma thermograms was 86%, 83%, and 84% compared to 78%, 95%, and 86% based on a combination of five antibody tests. Combining thermogram and serology information together improved sensitivity from 78% to 86% and overall accuracy from 86% to 89% relative to serology alone. Predictive accuracy of thermograms for distinguishing SLE and osteoarthritis / rheumatoid arthritis patients was comparable. Both gender and anemia significantly interacted with disease status for plasma thermograms (p<0.001), with greater separation between SLE and control thermograms for females relative to males and for patients with anemia relative to patients without anemia. Conclusion Plasma thermograms constitute an additional biomarker which may help improve diagnosis of SLE patients, particularly when coupled with standard diagnostic testing. Differences in thermograms according to patient sex, ethnicity, clinical and environmental factors are important considerations for application of thermograms in a clinical setting. PMID:29149219

Improving the histopathologic diagnosis of pediatric malignancies in a low-resource setting by combining focused training and telepathology strategies.

PubMed

Santiago, Teresa C; Jenkins, Jesse J; Pedrosa, Francisco; Billups, Catherine; Quintana, Yuri; Ribeiro, Raul C; Qaddoumi, Ibrahim

2012-08-01

Accurate diagnosis is critical for optimal management of pediatric cancer. Pathologists with experience in pediatric oncology are in short supply in the developing world. Telepathology is increasingly used for consultations but its overall contribution to diagnostic accuracy is unknown. We developed a strategy to provide a focused training in pediatric cancer and telepathology support to pathologists in the developing world. After the training period, we compared trainee's diagnoses with those of an experienced pathologist. We next compared the effectiveness of static versus dynamic telepathology review in 127 cases. Results were compared by Fisher's exact test. The diagnoses of the trainee and the expert pathologist differed in only 6.5% of cases (95% CI, 1.2-20.0%). The overall concordance between the telepathology and original diagnoses was 90.6% (115/127; 95% CI, 84.1-94.6%). Brief, focused training in pediatric cancer histopathology can improve diagnostic accuracy. Dynamic and static telepathology analyses are equally effective for diagnostic review. Copyright © 2012 Wiley Periodicals, Inc.

High-accuracy user identification using EEG biometrics.

PubMed

Koike-Akino, Toshiaki; Mahajan, Ruhi; Marks, Tim K; Ye Wang; Watanabe, Shinji; Tuzel, Oncel; Orlik, Philip

2016-08-01

We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various different combinations of a dimensionality reduction technique followed by a classification algorithm. Experimental results show that an identification accuracy of 72% can be achieved using only a single 800 ms ERP epoch. In addition, we demonstrate that the user identification accuracy can be significantly improved to more than 96.7% by joint classification of multiple epochs.

Adaptive Kalman filter for indoor localization using Bluetooth Low Energy and inertial measurement unit.

PubMed

Yoon, Paul K; Zihajehzadeh, Shaghayegh; Bong-Soo Kang; Park, Edward J

2015-08-01

This paper proposes a novel indoor localization method using the Bluetooth Low Energy (BLE) and an inertial measurement unit (IMU). The multipath and non-line-of-sight errors from low-power wireless localization systems commonly result in outliers, affecting the positioning accuracy. We address this problem by adaptively weighting the estimates from the IMU and BLE in our proposed cascaded Kalman filter (KF). The positioning accuracy is further improved with the Rauch-Tung-Striebel smoother. The performance of the proposed algorithm is compared against that of the standard KF experimentally. The results show that the proposed algorithm can maintain high accuracy for position tracking the sensor in the presence of the outliers.

A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: using point and gridded FLUXNET and water balance ET

USGS Publications Warehouse

Velpuri, Naga M.; Senay, Gabriel B.; Singh, Ramesh K.; Bohms, Stefanie; Verdin, James P.

2013-01-01

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. Extensive evaluation of such large scale estimates is necessary before they can be used in various applications. In this study, two monthly MODIS 1 km ET products, MODIS global ET (MOD16) and Operational Simplified Surface Energy Balance (SSEBop) ET, are validated over the conterminous United States at both point and basin scales. Point scale validation was performed using eddy covariance FLUXNET ET (FLET) data (2001–2007) aggregated by year, land cover, elevation and climate zone. Basin scale validation was performed using annual gridded FLUXNET ET (GFET) and annual basin water balance ET (WBET) data aggregated by various hydrologic unit code (HUC) levels. Point scale validation using monthly data aggregated by years revealed that the MOD16 ET and SSEBop ET products showed overall comparable annual accuracies. For most land cover types, both ET products showed comparable results. However, SSEBop showed higher performance for Grassland and Forest classes; MOD16 showed improved performance in the Woody Savanna class. Accuracy of both the ET products was also found to be comparable over different climate zones. However, SSEBop data showed higher skill score across the climate zones covering the western United States. Validation results at different HUC levels over 2000–2011 using GFET as a reference indicate higher accuracies for MOD16 ET data. MOD16, SSEBop and GFET data were validated against WBET (2000–2009), and results indicate that both MOD16 and SSEBop ET matched the accuracies of the global GFET dataset at different HUC levels. Our results indicate that both MODIS ET products effectively reproduced basin scale ET response (up to 25% uncertainty) compared to CONUS-wide point-based ET response (up to 50–60% uncertainty) illustrating the reliability of MODIS ET products for basin-scale ET estimation. Results from this research would guide the additional parameter refinement required for the MOD16 and SSEBop algorithms in order to further improve their accuracy and performance for agro-hydrologic applications.

An improved parallel fuzzy connected image segmentation method based on CUDA.

PubMed

Wang, Liansheng; Li, Dong; Huang, Shaohui

2016-05-12

Fuzzy connectedness method (FC) is an effective method for extracting fuzzy objects from medical images. However, when FC is applied to large medical image datasets, its running time will be greatly expensive. Therefore, a parallel CUDA version of FC (CUDA-kFOE) was proposed by Ying et al. to accelerate the original FC. Unfortunately, CUDA-kFOE does not consider the edges between GPU blocks, which causes miscalculation of edge points. In this paper, an improved algorithm is proposed by adding a correction step on the edge points. The improved algorithm can greatly enhance the calculation accuracy. In the improved method, an iterative manner is applied. In the first iteration, the affinity computation strategy is changed and a look up table is employed for memory reduction. In the second iteration, the error voxels because of asynchronism are updated again. Three different CT sequences of hepatic vascular with different sizes were used in the experiments with three different seeds. NVIDIA Tesla C2075 is used to evaluate our improved method over these three data sets. Experimental results show that the improved algorithm can achieve a faster segmentation compared to the CPU version and higher accuracy than CUDA-kFOE. The calculation results were consistent with the CPU version, which demonstrates that it corrects the edge point calculation error of the original CUDA-kFOE. The proposed method has a comparable time cost and has less errors compared to the original CUDA-kFOE as demonstrated in the experimental results. In the future, we will focus on automatic acquisition method and automatic processing.

Sliding-mode control combined with improved adaptive feedforward for wafer scanner

NASA Astrophysics Data System (ADS)

Li, Xiaojie; Wang, Yiguang

2018-03-01

In this paper, a sliding-mode control method combined with improved adaptive feedforward is proposed for wafer scanner to improve the tracking performance of the closed-loop system. Particularly, In addition to the inverse model, the nonlinear force ripple effect which may degrade the tracking accuracy of permanent magnet linear motor (PMLM) is considered in the proposed method. The dominant position periodicity of force ripple is determined by using the Fast Fourier Transform (FFT) analysis for experimental data and the improved feedforward control is achieved by the online recursive least-squares (RLS) estimation of the inverse model and the force ripple. The improved adaptive feedforward is given in a general form of nth-order model with force ripple effect. This proposed method is motivated by the motion controller design of the long-stroke PMLM and short-stroke voice coil motor for wafer scanner. The stability of the closed-loop control system and the convergence of the motion tracking are guaranteed by the proposed sliding-mode feedback and adaptive feedforward methods theoretically. Comparative experiments on a precision linear motion platform can verify the correctness and effectiveness of the proposed method. The experimental results show that comparing to traditional method the proposed one has better performance of rapidity and robustness, especially for high speed motion trajectory. And, the improvements on both tracking accuracy and settling time can be achieved.

Very high resolution aerial films

NASA Astrophysics Data System (ADS)

Becker, Rolf

1986-11-01

The use of very high resolution aerial films in aerial photography is evaluated. Commonly used panchromatic, color, and CIR films and their high resolution equivalents are compared. Based on practical experience and systematic investigations, the very high image quality and improved height accuracy that can be achieved using these films are demonstrated. Advantages to be gained from this improvement and operational restrictions encountered when using high resolution film are discussed.

Assessment of a high-SNR chemical-shift-encoded MRI with complex reconstruction for proton density fat fraction (PDFF) estimation overall and in the low-fat range.

PubMed

Park, Charlie C; Hooker, Catherine; Hooker, Jonathan C; Bass, Emily; Haufe, William; Schlein, Alexandra; Covarrubias, Yesenia; Heba, Elhamy; Bydder, Mark; Wolfson, Tanya; Gamst, Anthony; Loomba, Rohit; Schwimmer, Jeffrey; Hernando, Diego; Reeder, Scott B; Middleton, Michael; Sirlin, Claude B; Hamilton, Gavin

2018-04-29

Improving the signal-to-noise ratio (SNR) of chemical-shift-encoded MRI acquisition with complex reconstruction (MRI-C) may improve the accuracy and precision of noninvasive proton density fat fraction (PDFF) quantification in patients with hepatic steatosis. To assess the accuracy of high SNR (Hi-SNR) MRI-C versus standard MRI-C acquisition to estimate hepatic PDFF in adult and pediatric nonalcoholic fatty liver disease (NAFLD) using an MR spectroscopy (MRS) sequence as the reference standard. Prospective. In all, 231 adult and pediatric patients with known or suspected NAFLD. PDFF estimated at 3T by three MR techniques: standard MRI-C; a Hi-SNR MRI-C variant with increased slice thickness, decreased matrix size, and no parallel imaging; and MRS (reference standard). MRI-PDFF was measured by image analysts using a region of interest coregistered with the MRS-PDFF voxel. Linear regression analyses were used to assess accuracy and precision of MRI-estimated PDFF for MRS-PDFF as a function of MRI-PDFF using the standard and Hi-SNR MRI-C for all patients and for patients with MRS-PDFF <10%. In all, 271 exams from 231 patients were included (mean MRS-PDFF: 12.6% [SD: 10.4]; range: 0.9-41.9). High agreement between MRI-PDFF and MRS-PDFF was demonstrated across the overall range of PDFF, with a regression slope of 1.035 for the standard MRI-C and 1.008 for Hi-SNR MRI-C. Hi-SNR MRI-C, compared to standard MRI-C, provided small but statistically significant improvements in the slope (respectively, 1.008 vs. 1.035, P = 0.004) and mean bias (0.412 vs. 0.673, P < 0.0001) overall. In the low-fat patients only, Hi-SNR MRI-C provided improvements in the slope (1.058 vs. 1.190, P = 0.002), mean bias (0.168 vs. 0.368, P = 0.007), intercept (-0.153 vs. -0.796, P < 0.0001), and borderline improvement in the R 2 (0.888 vs. 0.813, P = 0.01). Compared to standard MRI-C, Hi-SNR MRI-C provides slightly higher MRI-PDFF estimation accuracy across the overall range of PDFF and improves both accuracy and precision in the low PDFF range. 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

solGS: a web-based tool for genomic selection

USDA-ARS?s Scientific Manuscript database

Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, ana...

Photovoltaic pumping system - Comparative study analysis between direct and indirect coupling mode

NASA Astrophysics Data System (ADS)

Harrag, Abdelghani; Titraoui, Abdessalem; Bahri, Hamza; Messalti, Sabir

2017-02-01

In this paper, P&O algorithm is used in order to improve the performance of photovoltaic water pumping system in both dynamic and static response. The efficiency of the proposed algorithm has been studied successfully using a DC motor-pump powered using controller by thirty six PV modules via DC-DC boost converter derived by a P&O MPPT algorithm. Comparative study results between the direct and indirect modes coupling confirm that the proposed algorithm can effectively improve simultaneously: accuracy, rapidity, ripple and overshoot.

United States Military Cancer Institute Clinical Trials Group (USMCI GI-01) randomized controlled trial comparing targeted nodal assessment and ultrastaging with standard pathological evaluation for colon cancer.

PubMed

Nissan, Aviram; Protic, Mladjan; Bilchik, Anton J; Howard, Robin S; Peoples, George E; Stojadinovic, Alexander

2012-09-01

Our randomized controlled trial previously demonstrated improved staging accuracy with targeted nodal assessment and ultrastaging (TNA-us) in colon cancer (CC). Our objective was to test the hypothesis that TNA-us improves disease-free survival (DFS) in CC. In this randomized trial, targeted nodal assessment and ultrastaging resulted in enhanced lymph node diagnostic yield associated with improved staging accuracy, which was further associated with improved disease-free survival in early colon cancer. Clinical parameters of the control (n = 94) and TNA-us (n = 98) groups were comparable. Median (interquartile range) lymph node yield was higher in the TNA-us arm: 16 (12-22) versus 13 (10-18); P = 0.002. Median follow-up was 46 (29-70) months. Overall 5-year DFS was 61% in the control arm and 71% in the TNA-us arm (P = 0.11). Clinical parameters of node-negative patients in the control (n = 51) and TNA-us (n = 55) groups were comparable. Lymph node yield was higher in the TNA-us arm: 15 (12-21) versus 13 (8-18); P = 0.03. Five-year DFS differed significantly between groups with node-negative CC (control 71% vs TNA-us 86%; P = 0.04). Survival among stage II CC alone was higher in the TNA-us group, 83% versus 65%; P = 0.03. Adjuvant chemotherapy use was nearly identical between groups. TNA-us stratified CC prognosis; DFS differed significantly between ultrastaged and conventionally staged node-negative patients [control pN0 72% vs TNA-us pN0(i-) 87%; P = 0.03]. Survival varied according to lymph node yield in patients with node-negative CC [5-year DFS: <12 lymph nodes = 57% vs 12+ lymph nodes = 85%; P = 0.011] but not in stage III CC. TNA-us is associated with improved nodal diagnostic yield and enhanced staging accuracy (stage migration), which is further associated with improved DFS in early CC. This study is registered at clinicaltrials.gov under the registration number: NCT01623258.

A novel method for accurate needle-tip identification in trans-rectal ultrasound-based high-dose-rate prostate brachytherapy.

PubMed

Zheng, Dandan; Todor, Dorin A

2011-01-01

In real-time trans-rectal ultrasound (TRUS)-based high-dose-rate prostate brachytherapy, the accurate identification of needle-tip position is critical for treatment planning and delivery. Currently, needle-tip identification on ultrasound images can be subject to large uncertainty and errors because of ultrasound image quality and imaging artifacts. To address this problem, we developed a method based on physical measurements with simple and practical implementation to improve the accuracy and robustness of needle-tip identification. Our method uses measurements of the residual needle length and an off-line pre-established coordinate transformation factor, to calculate the needle-tip position on the TRUS images. The transformation factor was established through a one-time systematic set of measurements of the probe and template holder positions, applicable to all patients. To compare the accuracy and robustness of the proposed method and the conventional method (ultrasound detection), based on the gold-standard X-ray fluoroscopy, extensive measurements were conducted in water and gel phantoms. In water phantom, our method showed an average tip-detection accuracy of 0.7 mm compared with 1.6 mm of the conventional method. In gel phantom (more realistic and tissue-like), our method maintained its level of accuracy while the uncertainty of the conventional method was 3.4mm on average with maximum values of over 10mm because of imaging artifacts. A novel method based on simple physical measurements was developed to accurately detect the needle-tip position for TRUS-based high-dose-rate prostate brachytherapy. The method demonstrated much improved accuracy and robustness over the conventional method. Copyright © 2011 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

A new fault diagnosis algorithm for AUV cooperative localization system

NASA Astrophysics Data System (ADS)

Shi, Hongyang; Miao, Zhiyong; Zhang, Yi

2017-10-01

Multiple AUVs cooperative localization as a new kind of underwater positioning technology, not only can improve the positioning accuracy, but also has many advantages the single AUV does not have. It is necessary to detect and isolate the fault to increase the reliability and availability of the AUVs cooperative localization system. In this paper, the Extended Multiple Model Adaptive Cubature Kalmam Filter (EMMACKF) method is presented to detect the fault. The sensor failures are simulated based on the off-line experimental data. Experimental results have shown that the faulty apparatus can be diagnosed effectively using the proposed method. Compared with Multiple Model Adaptive Extended Kalman Filter and Multi-Model Adaptive Unscented Kalman Filter, both accuracy and timelines have been improved to some extent.

The performance evaluation model of mining project founded on the weight optimization entropy value method

NASA Astrophysics Data System (ADS)

Mao, Chao; Chen, Shou

2017-01-01

According to the traditional entropy value method still have low evaluation accuracy when evaluating the performance of mining projects, a performance evaluation model of mineral project founded on improved entropy is proposed. First establish a new weight assignment model founded on compatible matrix analysis of analytic hierarchy process (AHP) and entropy value method, when the compatibility matrix analysis to achieve consistency requirements, if it has differences between subjective weights and objective weights, moderately adjust both proportions, then on this basis, the fuzzy evaluation matrix for performance evaluation. The simulation experiments show that, compared with traditional entropy and compatible matrix analysis method, the proposed performance evaluation model of mining project based on improved entropy value method has higher accuracy assessment.

An improved graph cut segmentation method for cervical lymph nodes on sonograms and its relationship with node's shape assessment.

PubMed

Zhang, Junhua; Wang, Yuanyuan; Shi, Xinling

2009-12-01

A modified graph cut was proposed under the elliptical shape constraint to segment cervical lymph nodes on sonograms, and its effect on the measurement of short axis to long axis ratio (S/L) was investigated by using the relative ultimate measurement accuracy (RUMA). Under the same user inputs, the proposed algorithm successfully segmented all 60 sonograms tested, while the traditional graph cut failed. The mean RUMA resulted from the developed method was comparable to that resulted from the manual segmentation. Results indicated that utilizing the elliptical shape prior could appreciably improve the graph cut for nodes segmentation, and the proposed method satisfied the accuracy requirement of S/L measurement.

Metal Artifact Reduction in X-ray Computed Tomography Using Computer-Aided Design Data of Implants as Prior Information.

PubMed

Ruth, Veikko; Kolditz, Daniel; Steiding, Christian; Kalender, Willi A

2017-06-01

The performance of metal artifact reduction (MAR) methods in x-ray computed tomography (CT) suffers from incorrect identification of metallic implants in the artifact-affected volumetric images. The aim of this study was to investigate potential improvements of state-of-the-art MAR methods by using prior information on geometry and material of the implant. The influence of a novel prior knowledge-based segmentation (PS) compared with threshold-based segmentation (TS) on 2 MAR methods (linear interpolation [LI] and normalized-MAR [NORMAR]) was investigated. The segmentation is the initial step of both MAR methods. Prior knowledge-based segmentation uses 3-dimensional registered computer-aided design (CAD) data as prior knowledge to estimate the correct position and orientation of the metallic objects. Threshold-based segmentation uses an adaptive threshold to identify metal. Subsequently, for LI and NORMAR, the selected voxels are projected into the raw data domain to mark metal areas. Attenuation values in these areas are replaced by different interpolation schemes followed by a second reconstruction. Finally, the previously selected metal voxels are replaced by the metal voxels determined by PS or TS in the initial reconstruction. First, we investigated in an elaborate phantom study if the knowledge of the exact implant shape extracted from the CAD data provided by the manufacturer of the implant can improve the MAR result. Second, the leg of a human cadaver was scanned using a clinical CT system before and after the implantation of an artificial knee joint. The results were compared regarding segmentation accuracy, CT number accuracy, and the restoration of distorted structures. The use of PS improved the efficacy of LI and NORMAR compared with TS. Artifacts caused by insufficient segmentation were reduced, and additional information was made available within the projection data. The estimation of the implant shape was more exact and not dependent on a threshold value. Consequently, the visibility of structures was improved when comparing the new approach to the standard method. This was further confirmed by improved CT value accuracy and reduced image noise. The PS approach based on prior implant information provides image quality which is superior to TS-based MAR, especially when the shape of the metallic implant is complex. The new approach can be useful for improving MAR methods and dose calculations within radiation therapy based on the MAR corrected CT images.

Perceptual experience and posttest improvements in perceptual accuracy and consistency.

PubMed

Wagman, Jeffrey B; McBride, Dawn M; Trefzger, Amanda J

2008-08-01

Two experiments investigated the relationship between perceptual experience (during practice) and posttest improvements in perceptual accuracy and consistency. Experiment 1 investigated the potential relationship between how often knowledge of results (KR) is provided during a practice session and posttest improvements in perceptual accuracy. Experiment 2 investigated the potential relationship between how often practice (PR) is provided during a practice session and posttest improvements in perceptual consistency. The results of both experiments are consistent with previous findings that perceptual accuracy improves only when practice includes KR and that perceptual consistency improves regardless of whether practice includes KR. In addition, the results showed that although there is a relationship between how often KR is provided during a practice session and posttest improvements in perceptual accuracy, there is no relationship between how often PR is provided during a practice session and posttest improvements in consistency.

Improved water-level forecasting for the Northwest European Shelf and North Sea through direct modelling of tide, surge and non-linear interaction

NASA Astrophysics Data System (ADS)

Zijl, Firmijn; Verlaan, Martin; Gerritsen, Herman

2013-07-01

In real-time operational coastal forecasting systems for the northwest European shelf, the representation accuracy of tide-surge models commonly suffers from insufficiently accurate tidal representation, especially in shallow near-shore areas with complex bathymetry and geometry. Therefore, in conventional operational systems, the surge component from numerical model simulations is used, while the harmonically predicted tide, accurately known from harmonic analysis of tide gauge measurements, is added to forecast the full water-level signal at tide gauge locations. Although there are errors associated with this so-called astronomical correction (e.g. because of the assumption of linearity of tide and surge), for current operational models, astronomical correction has nevertheless been shown to increase the representation accuracy of the full water-level signal. The simulated modulation of the surge through non-linear tide-surge interaction is affected by the poor representation of the tide signal in the tide-surge model, which astronomical correction does not improve. Furthermore, astronomical correction can only be applied to locations where the astronomic tide is known through a harmonic analysis of in situ measurements at tide gauge stations. This provides a strong motivation to improve both tide and surge representation of numerical models used in forecasting. In the present paper, we propose a new generation tide-surge model for the northwest European Shelf (DCSMv6). This is the first application on this scale in which the tidal representation is such that astronomical correction no longer improves the accuracy of the total water-level representation and where, consequently, the straightforward direct model forecasting of total water levels is better. The methodology applied to improve both tide and surge representation of the model is discussed, with emphasis on the use of satellite altimeter data and data assimilation techniques for reducing parameter uncertainty. Historic DCSMv6 model simulations are compared against shelf wide observations for a full calendar year. For a selection of stations, these results are compared to those with astronomical correction, which confirms that the tide representation in coastal regions has sufficient accuracy, and that forecasting total water levels directly yields superior results.

Modeling tropospheric wet delays with national GNSS reference network in China for BeiDou precise point positioning

NASA Astrophysics Data System (ADS)

Zheng, Fu; Lou, Yidong; Gu, Shengfeng; Gong, Xiaopeng; Shi, Chuang

2017-10-01

During past decades, precise point positioning (PPP) has been proven to be a well-known positioning technique for centimeter or decimeter level accuracy. However, it needs long convergence time to get high-accuracy positioning, which limits the prospects of PPP, especially in real-time applications. It is expected that the PPP convergence time can be reduced by introducing high-quality external information, such as ionospheric or tropospheric corrections. In this study, several methods for tropospheric wet delays modeling over wide areas are investigated. A new, improved model is developed, applicable in real-time applications in China. Based on the GPT2w model, a modified parameter of zenith wet delay exponential decay wrt. height is introduced in the modeling of the real-time tropospheric delay. The accuracy of this tropospheric model and GPT2w model in different seasons is evaluated with cross-validation, the root mean square of the zenith troposphere delay (ZTD) is 1.2 and 3.6 cm on average, respectively. On the other hand, this new model proves to be better than the tropospheric modeling based on water-vapor scale height; it can accurately express tropospheric delays up to 10 km altitude, which potentially has benefits in many real-time applications. With the high-accuracy ZTD model, the augmented PPP convergence performance for BeiDou navigation satellite system (BDS) and GPS is evaluated. It shows that the contribution of the high-quality ZTD model on PPP convergence performance has relation with the constellation geometry. As BDS constellation geometry is poorer than GPS, the improvement for BDS PPP is more significant than that for GPS PPP. Compared with standard real-time PPP, the convergence time is reduced by 2-7 and 20-50% for the augmented BDS PPP, while GPS PPP only improves about 6 and 18% (on average), in horizontal and vertical directions, respectively. When GPS and BDS are combined, the geometry is greatly improved, which is good enough to get a reliable PPP solution, the augmentation PPP improves insignificantly comparing with standard PPP.

Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils.

PubMed

Devos, Olivier; Downey, Gerard; Duponchel, Ludovic

2014-04-01

Classification is an important task in chemometrics. For several years now, support vector machines (SVMs) have proven to be powerful for infrared spectral data classification. However such methods require optimisation of parameters in order to control the risk of overfitting and the complexity of the boundary. Furthermore, it is established that the prediction ability of classification models can be improved using pre-processing in order to remove unwanted variance in the spectra. In this paper we propose a new methodology based on genetic algorithm (GA) for the simultaneous optimisation of SVM parameters and pre-processing (GENOPT-SVM). The method has been tested for the discrimination of the geographical origin of Italian olive oil (Ligurian and non-Ligurian) on the basis of near infrared (NIR) or mid infrared (FTIR) spectra. Different classification models (PLS-DA, SVM with mean centre data, GENOPT-SVM) have been tested and statistically compared using McNemar's statistical test. For the two datasets, SVM with optimised pre-processing give models with higher accuracy than the one obtained with PLS-DA on pre-processed data. In the case of the NIR dataset, most of this accuracy improvement (86.3% compared with 82.8% for PLS-DA) occurred using only a single pre-processing step. For the FTIR dataset, three optimised pre-processing steps are required to obtain SVM model with significant accuracy improvement (82.2%) compared to the one obtained with PLS-DA (78.6%). Furthermore, this study demonstrates that even SVM models have to be developed on the basis of well-corrected spectral data in order to obtain higher classification rates. Copyright © 2013 Elsevier Ltd. All rights reserved.

Social desirability, not dietary restraint, is related to accuracy of reported dietary intake of a laboratory meal in females during a 24-hour recall.

PubMed

Schoch, Ashlee H; Raynor, Hollie A

2012-01-01

Underreporting in self-reported dietary intake has been linked to dietary restraint (DR) and social desirability (SD), however few investigations have examined the influence of both DR and SD on reporting accuracy and used objective, rather than estimated, measures to determine dietary reporting accuracy. This study investigated accuracy of reporting consumption of a laboratory meal during a 24-hour dietary recall (24HR) in 38 healthy, college-aged, normal-weight women, categorized as high or low in DR and SD. Participants consumed a lunch of four foods (sandwich wrap, chips, fruit, and ice cream) in a laboratory and completed a telephone 24HR the following day. Accuracy of reported energy intake of the meal=((reported energy intake-measured energy intake)/measured energy intake)×100 [positive numbers=overreporting]. Overreporting of energy intake occurred in all groups (overall accuracy rate=43.1±49.9%). SD-high as compared to SD-low more accurately reported energy intake of chips (19.8±56.2% vs. 117.1±141.3%, p<0.05) and ice cream (17.2±78.2% vs. 71.6±82.7%, p<0.05). SD-high as compared to SD-low more accurately reported overall energy intake (29.8±48.2% vs. 58.0±48.8%, p<0.05). To improve accuracy of dietary assessment, future research should investigate factors contributing to inaccuracies in dietary reporting and the best methodology to use to determine dietary reporting accuracy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Comparison of Diagnostic Accuracy of Clinical Measures of Breast Cancer–Related Lymphedema: Area Under the Curve

PubMed Central

Smoot, Betty J.; Wong, Josephine F.; Dodd, Marylin J.

2013-01-01

Objective To compare diagnostic accuracy of measures of breast cancer–related lymphedema (BCRL). Design Cross-sectional design comparing clinical measures with the criterion standard of previous diagnosis of BCRL. Setting University of California San Francisco Translational Science Clinical Research Center. Participants Women older than 18 years and more than 6 months posttreatment for breast cancer (n=141; 70 with BCRL, 71 without BCRL). Interventions Not applicable. Main Outcome Measures Sensitivity, specificity, receiver operator characteristic curve, and area under the curve (AUC) were used to evaluate accuracy. Results A total of 141 women were categorized as having (n=70) or not having (n=71) BCRL based on past diagnosis by a health care provider, which was used as the reference standard. Analyses of ROC curves for the continuous outcomes yielded AUC of .68 to .88 (P<.001); of the physical measures bioimpedance spectroscopy yielded the highest accuracy with an AUC of .88 (95% confidence interval, .80–.96) for women whose dominant arm was the affected arm. The lowest accuracy was found using the 2-cm diagnostic cutoff score to identify previously diagnosed BCRL (AUC, .54–.65). Conclusions Our findings support the use of bioimpedance spectroscopy in the assessment of existing BCRL. Refining diagnostic cutoff values may improve accuracy of diagnosis and warrant further investigation. PMID:21440706

The effect of using genealogy-based haplotypes for genomic prediction

PubMed Central

2013-01-01

Background Genomic prediction uses two sources of information: linkage disequilibrium between markers and quantitative trait loci, and additive genetic relationships between individuals. One way to increase the accuracy of genomic prediction is to capture more linkage disequilibrium by regression on haplotypes instead of regression on individual markers. The aim of this study was to investigate the accuracy of genomic prediction using haplotypes based on local genealogy information. Methods A total of 4429 Danish Holstein bulls were genotyped with the 50K SNP chip. Haplotypes were constructed using local genealogical trees. Effects of haplotype covariates were estimated with two types of prediction models: (1) assuming that effects had the same distribution for all haplotype covariates, i.e. the GBLUP method and (2) assuming that a large proportion (π) of the haplotype covariates had zero effect, i.e. a Bayesian mixture method. Results About 7.5 times more covariate effects were estimated when fitting haplotypes based on local genealogical trees compared to fitting individuals markers. Genealogy-based haplotype clustering slightly increased the accuracy of genomic prediction and, in some cases, decreased the bias of prediction. With the Bayesian method, accuracy of prediction was less sensitive to parameter π when fitting haplotypes compared to fitting markers. Conclusions Use of haplotypes based on genealogy can slightly increase the accuracy of genomic prediction. Improved methods to cluster the haplotypes constructed from local genealogy could lead to additional gains in accuracy. PMID:23496971

The effect of using genealogy-based haplotypes for genomic prediction.

PubMed

Edriss, Vahid; Fernando, Rohan L; Su, Guosheng; Lund, Mogens S; Guldbrandtsen, Bernt

2013-03-06

Genomic prediction uses two sources of information: linkage disequilibrium between markers and quantitative trait loci, and additive genetic relationships between individuals. One way to increase the accuracy of genomic prediction is to capture more linkage disequilibrium by regression on haplotypes instead of regression on individual markers. The aim of this study was to investigate the accuracy of genomic prediction using haplotypes based on local genealogy information. A total of 4429 Danish Holstein bulls were genotyped with the 50K SNP chip. Haplotypes were constructed using local genealogical trees. Effects of haplotype covariates were estimated with two types of prediction models: (1) assuming that effects had the same distribution for all haplotype covariates, i.e. the GBLUP method and (2) assuming that a large proportion (π) of the haplotype covariates had zero effect, i.e. a Bayesian mixture method. About 7.5 times more covariate effects were estimated when fitting haplotypes based on local genealogical trees compared to fitting individuals markers. Genealogy-based haplotype clustering slightly increased the accuracy of genomic prediction and, in some cases, decreased the bias of prediction. With the Bayesian method, accuracy of prediction was less sensitive to parameter π when fitting haplotypes compared to fitting markers. Use of haplotypes based on genealogy can slightly increase the accuracy of genomic prediction. Improved methods to cluster the haplotypes constructed from local genealogy could lead to additional gains in accuracy.

Accuracy of PSA Self-Reports among Low-Income Men with Prostate Cancer after a Public Health Nursing Intervention.

PubMed

Zavala, Mary Wassel; Yule, Arthur; Kwan, Lorna; Lambrechts, Sylvia; Maliski, Sally L; Litwin, Mark S

2016-11-01

To examine accuracy of patient-reported prostate-specific antigen (PSA) levels among indigent, uninsured men in a state-funded prostate cancer treatment program that provides case management, care coordination, and health education. Program evaluation. About 114 men with matched self- and lab-reported PSA levels at program enrollment and another time point within 18 months. Abstraction of self- and lab-reported PSA levels to determine self-report as "accurate" or "inaccurate," and evaluate accuracy change over time, before and after nursing interventions. Chi-square tests compared patients with accurate versus inaccurate PSA values. Nonlinear multivariate analyses explored trends in self-reported accuracy over time. Program enrollees receive prostate cancer education from a Nurse Case Manager (NCM), including significance of PSA levels. Men self-report PSA results to their NCM following lab draws and appointments. The NCM provides ongoing education about PSA levels. Of the sample, 46% (n = 53) accurately reported PSA levels. Accuracy of PSA self-reports improved with increasing time since program enrollment. Compared with men at public facilities, those treated at private facilities showed increasing accuracy in self-reported PSA (p = .038). A targeted nursing intervention may increase specific knowledge of PSA levels. Additionally, the provider/treatment setting significantly impacts a patient's disease education and knowledge. © 2016 Wiley Periodicals, Inc.

Broad-band frequency references in the near-infrared: Accurate dual comb spectroscopy of methane and acetylene

NASA Astrophysics Data System (ADS)

Zolot, A. M.; Giorgetta, F. R.; Baumann, E.; Swann, W. C.; Coddington, I.; Newbury, N. R.

2013-03-01

The Doppler-limited spectra of methane between 176 THz and 184 THz (5870-6130 cm-1) and acetylene between 193 THz and 199 THz (6430-6630 cm-1) are acquired via comb-tooth resolved dual comb spectroscopy with frequency accuracy traceable to atomic standards. A least squares analysis of the measured absorbance and phase line shapes provides line center frequencies with absolute accuracy of 0.2 MHz, or less than one thousandth of the room temperature Doppler width. This accuracy is verified through comparison with previous saturated absorption spectroscopy of 37 strong isolated lines of acetylene. For the methane spectrum, the center frequencies of 46 well-isolated strong lines are determined with similar high accuracy, along with the center frequencies for 1107 non-isolated lines at lower accuracy. The measured methane line-center frequencies have an uncertainty comparable to the few available laser heterodyne measurements in this region but span a much larger optical bandwidth, marking the first broad-band measurements of the methane 2ν3 region directly referenced to atomic frequency standards. This study demonstrates the promise of dual comb spectroscopy to obtain high resolution broadband spectra that are comparable to state-of-the-art Fourier-transform spectrometer measurements but with much improved frequency accuracy.Work of the US government, not subject to US copyright.

Compact Intraoperative MRI: Stereotactic Accuracy and Future Directions.

PubMed

Markowitz, Daniel; Lin, Dishen; Salas, Sussan; Kohn, Nina; Schulder, Michael

2017-01-01

Intraoperative imaging must supply data that can be used for accurate stereotactic navigation. This information should be at least as accurate as that acquired from diagnostic imagers. The aim of this study was to compare the stereotactic accuracy of an updated compact intraoperative MRI (iMRI) device based on a 0.15-T magnet to standard surgical navigation on a 1.5-T diagnostic scan MRI and to navigation with an earlier model of the same system. The accuracy of each system was assessed using a water-filled phantom model of the brain. Data collected with the new system were compared to those obtained in a previous study assessing the older system. The accuracy of the new iMRI was measured against standard surgical navigation on a 1.5-T MRI using T1-weighted (W) images. The mean error with the iMRI using T1W images was lower than that based on images from the 1.5-T scan (1.24 vs. 2.43 mm). T2W images from the newer iMRI yielded a lower navigation error than those acquired with the prior model (1.28 vs. 3.15 mm). Improvements in magnet design can yield progressive increases in accuracy, validating the concept of compact, low-field iMRI. Avoiding the need for registration between image and surgical space increases navigation accuracy. © 2017 S. Karger AG, Basel.

Autonomous Navigation of Small Uavs Based on Vehicle Dynamic Model

NASA Astrophysics Data System (ADS)

Khaghani, M.; Skaloud, J.

2016-03-01

This paper presents a novel approach to autonomous navigation for small UAVs, in which the vehicle dynamic model (VDM) serves as the main process model within the navigation filter. The proposed method significantly increases the accuracy and reliability of autonomous navigation, especially for small UAVs with low-cost IMUs on-board. This is achieved with no extra sensor added to the conventional INS/GNSS setup. This improvement is of special interest in case of GNSS outages, where inertial coasting drifts very quickly. In the proposed architecture, the solution to VDM equations provides the estimate of position, velocity, and attitude, which is updated within the navigation filter based on available observations, such as IMU data or GNSS measurements. The VDM is also fed with the control input to the UAV, which is available within the control/autopilot system. The filter is capable of estimating wind velocity and dynamic model parameters, in addition to navigation states and IMU sensor errors. Monte Carlo simulations reveal major improvements in navigation accuracy compared to conventional INS/GNSS navigation system during the autonomous phase, when satellite signals are not available due to physical obstruction or electromagnetic interference for example. In case of GNSS outages of a few minutes, position and attitude accuracy experiences improvements of orders of magnitude compared to inertial coasting. It means that during such scenario, the position-velocity-attitude (PVA) determination is sufficiently accurate to navigate the UAV to a home position without any signal that depends on vehicle environment.

Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals

PubMed Central

Hosseini, Seyyed Abed; Khalilzadeh, Mohammad Ali; Naghibi-Sistani, Mohammad Bagher; Homam, Seyyed Mehran

2015-01-01

Background: This paper proposes a new emotional stress assessment system using multi-modal bio-signals. Electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. Methods: We design an efficient acquisition protocol to acquire the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) and peripheral signals such as blood volume pulse, skin conductance (SC) and respiration, under images induction (calm-neutral and negatively excited) for the participants. The visual stimuli images are selected from the subset International Affective Picture System database. The qualitative and quantitative evaluation of peripheral signals are used to select suitable segments of EEG signals for improving the accuracy of signal labeling according to emotional stress states. After pre-processing, wavelet coefficients, fractal dimension, and Lempel-Ziv complexity are used to extract the features of the EEG signals. The vast number of features leads to the problem of dimensionality, which is solved using the genetic algorithm as a feature selection method. Results: The results show that the average classification accuracy is 89.6% for two categories of emotional stress states using the support vector machine (SVM). Conclusion: This is a great improvement in results compared to other similar researches. We achieve a noticeable improvement of 11.3% in accuracy using SVM classifier, in compared to previous studies. Therefore, a new fusion between EEG and peripheral signals are more robust in comparison to the separate signals. PMID:26622979

Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals.

PubMed

Hosseini, Seyyed Abed; Khalilzadeh, Mohammad Ali; Naghibi-Sistani, Mohammad Bagher; Homam, Seyyed Mehran

2015-07-06

This paper proposes a new emotional stress assessment system using multi-modal bio-signals. Electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. We design an efficient acquisition protocol to acquire the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) and peripheral signals such as blood volume pulse, skin conductance (SC) and respiration, under images induction (calm-neutral and negatively excited) for the participants. The visual stimuli images are selected from the subset International Affective Picture System database. The qualitative and quantitative evaluation of peripheral signals are used to select suitable segments of EEG signals for improving the accuracy of signal labeling according to emotional stress states. After pre-processing, wavelet coefficients, fractal dimension, and Lempel-Ziv complexity are used to extract the features of the EEG signals. The vast number of features leads to the problem of dimensionality, which is solved using the genetic algorithm as a feature selection method. The results show that the average classification accuracy is 89.6% for two categories of emotional stress states using the support vector machine (SVM). This is a great improvement in results compared to other similar researches. We achieve a noticeable improvement of 11.3% in accuracy using SVM classifier, in compared to previous studies. Therefore, a new fusion between EEG and peripheral signals are more robust in comparison to the separate signals.

Are general surgeons able to accurately self-assess their level of technical skills?

PubMed

Rizan, C; Ansell, J; Tilston, T W; Warren, N; Torkington, J

2015-11-01

Self-assessment is a way of improving technical capabilities without the need for trainer feedback. It can identify areas for improvement and promote professional medical development. The aim of this review was to identify whether self-assessment is an accurate form of technical skills appraisal in general surgery. The PubMed, MEDLINE(®), Embase(™) and Cochrane databases were searched for studies assessing the reliability of self-assessment of technical skills in general surgery. For each study, we recorded the skills assessed and the evaluation methods used. Common endpoints between studies were compared to provide recommendations based on the levels of evidence. Twelve studies met the inclusion criteria from 22,292 initial papers. There was no level 1 evidence published. All papers compared the correlation between self-appraisal versus an expert score but differed in the technical skills assessment and the evaluation tools used. The accuracy of self-assessment improved with increasing experience (level 2 recommendation), age (level 3 recommendation) and the use of video playback (level 3 recommendation). Accuracy was reduced by stressful learning environments (level 2 recommendation), lack of familiarity with assessment tools (level 3 recommendation) and in advanced surgical procedures (level 3 recommendation). Evidence exists to support the reliability of self-assessment of technical skills in general surgery. Several variables have been shown to affect the accuracy of self-assessment of technical skills. Future work should focus on evaluating the reliability of self-assessment during live operating procedures.

Combining geostatistics with Moran's I analysis for mapping soil heavy metals in Beijing, China.

PubMed

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-03-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran's I analysis was used to supplement the traditional geostatistics. According to Moran's I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran's I and the standardized Moran's I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran's I analysis was better than traditional geostatistics. Thus, Moran's I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals.

Combining Geostatistics with Moran’s I Analysis for Mapping Soil Heavy Metals in Beijing, China

PubMed Central

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-01-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran’s I analysis was used to supplement the traditional geostatistics. According to Moran’s I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran’s I and the standardized Moran’s I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran’s I analysis was better than traditional geostatistics. Thus, Moran’s I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals. PMID:22690179

Motion direction estimation based on active RFID with changing environment

NASA Astrophysics Data System (ADS)

Jie, Wu; Minghua, Zhu; Wei, He

2018-05-01

The gate system is used to estimate the direction of RFID tags carriers when they are going through the gate. Normally, it is difficult to achieve and keep a high accuracy in estimating motion direction of RFID tags because the received signal strength of tag changes sharply according to the changing electromagnetic environment. In this paper, a method of motion direction estimation for RFID tags is presented. To improve estimation accuracy, the machine leaning algorithm is used to get the fitting function of the received data by readers which are deployed inside and outside gate respectively. Then the fitted data are sampled to get the standard vector. We compare the stand vector with template vectors to get the motion direction estimation result. Then the corresponding template vector is updated according to the surrounding environment. We conducted the simulation and implement of the proposed method and the result shows that the proposed method in this work can improve and keep a high accuracy under the condition of the constantly changing environment.

Positioning stability improvement with inter-system biases on multi-GNSS PPP

NASA Astrophysics Data System (ADS)

Choi, Byung-Kyu; Yoon, Hasu

2018-07-01

The availability of multiple signals from different Global Navigation Satellite System (GNSS) constellations provides opportunities for improving positioning accuracy and initial convergence time. With dual-frequency observations from the four constellations (GPS, GLONASS, Galileo, and BeiDou), it is possible to investigate combined GNSS precise point positioning (PPP) accuracy and stability. The differences between GNSS systems result in inter-system biases (ISBs). We consider several ISB values such as GPS-GLONASS, GPS-Galileo, and GPS-BeiDou. These biases are compliant with key parameters defined in the multi-GNSS PPP processing. In this study, we present a unified PPP method that sets ISB values as fixed or constant. A comprehensive analysis that includes satellite visibility, position dilution of precision, position accuracy is performed to evaluate a unified PPP method with constrained cut-off elevation angles. Compared to the conventional PPP solutions, our approach shows more stable positioning at a constrained cut-off elevation angle of 50 degrees.

Enhanced differential evolution to combine optical mouse sensor with image structural patches for robust endoscopic navigation.

PubMed

Luo, Xiongbiao; Jayarathne, Uditha L; McLeod, A Jonathan; Mori, Kensaku

2014-01-01

Endoscopic navigation generally integrates different modalities of sensory information in order to continuously locate an endoscope relative to suspicious tissues in the body during interventions. Current electromagnetic tracking techniques for endoscopic navigation have limited accuracy due to tissue deformation and magnetic field distortion. To avoid these limitations and improve the endoscopic localization accuracy, this paper proposes a new endoscopic navigation framework that uses an optical mouse sensor to measure the endoscope movements along its viewing direction. We then enhance the differential evolution algorithm by modifying its mutation operation. Based on the enhanced differential evolution method, these movement measurements and image structural patches in endoscopic videos are fused to accurately determine the endoscope position. An evaluation on a dynamic phantom demonstrated that our method provides a more accurate navigation framework. Compared to state-of-the-art methods, it improved the navigation accuracy from 2.4 to 1.6 mm and reduced the processing time from 2.8 to 0.9 seconds.

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach.

PubMed

Yang, Hao-Fan; Dillon, Tharam S; Chen, Yi-Ping Phoebe

2017-10-01

Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy. The proposed model is designed using the Taguchi method to develop an optimized structure and to learn traffic flow features through layer-by-layer feature granulation with a greedy layerwise unsupervised learning algorithm. It is applied to real-world data collected from the M6 freeway in the U.K. and is compared with three existing traffic predictors. To the best of our knowledge, this is the first time that an optimized structure of the traffic flow forecasting model with a deep learning approach is presented. The evaluation results demonstrate that the proposed model with an optimized structure has superior performance in traffic flow forecasting.

Intra-patient semi-automated segmentation of the cervix-uterus in CT-images for adaptive radiotherapy of cervical cancer

NASA Astrophysics Data System (ADS)

Luiza Bondar, M.; Hoogeman, Mischa; Schillemans, Wilco; Heijmen, Ben

2013-08-01

For online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and (2) to improve the segmentation accuracy by including prior knowledge on the daily bladder volume or on the daily coordinates of implanted fiducial markers. The tested methods were: shape deformation (SD) and atlas-based segmentation (ABAS) using two non-rigid registration methods: demons and a hierarchical algorithm. Tests on 102 CT-scans of 13 patients demonstrated that the segmentation accuracy significantly increased by including the bladder volume predicted with a simple 1D model based on a manually defined bladder top. Moreover, manually identified implanted fiducial markers significantly improved the accuracy of the SD method. For patients with large cervix-uterus volume regression, the use of CT-data acquired toward the end of the treatment was required to improve segmentation accuracy. Including prior knowledge, the segmentation results of SD (Dice similarity coefficient 85 ± 6%, error margin 2.2 ± 2.3 mm, average time around 1 min) and of ABAS using hierarchical non-rigid registration (Dice 82 ± 10%, error margin 3.1 ± 2.3 mm, average time around 30 s) support their use for image guided online adaptive radiotherapy of cervical cancer.

Intra-patient semi-automated segmentation of the cervix-uterus in CT-images for adaptive radiotherapy of cervical cancer.

PubMed

Bondar, M Luiza; Hoogeman, Mischa; Schillemans, Wilco; Heijmen, Ben

2013-08-07

For online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and (2) to improve the segmentation accuracy by including prior knowledge on the daily bladder volume or on the daily coordinates of implanted fiducial markers. The tested methods were: shape deformation (SD) and atlas-based segmentation (ABAS) using two non-rigid registration methods: demons and a hierarchical algorithm. Tests on 102 CT-scans of 13 patients demonstrated that the segmentation accuracy significantly increased by including the bladder volume predicted with a simple 1D model based on a manually defined bladder top. Moreover, manually identified implanted fiducial markers significantly improved the accuracy of the SD method. For patients with large cervix-uterus volume regression, the use of CT-data acquired toward the end of the treatment was required to improve segmentation accuracy. Including prior knowledge, the segmentation results of SD (Dice similarity coefficient 85 ± 6%, error margin 2.2 ± 2.3 mm, average time around 1 min) and of ABAS using hierarchical non-rigid registration (Dice 82 ± 10%, error margin 3.1 ± 2.3 mm, average time around 30 s) support their use for image guided online adaptive radiotherapy of cervical cancer.

Efficient use of unlabeled data for protein sequence classification: a comparative study

PubMed Central

Kuksa, Pavel; Huang, Pai-Hsi; Pavlovic, Vladimir

2009-01-01

Background Recent studies in computational primary protein sequence analysis have leveraged the power of unlabeled data. For example, predictive models based on string kernels trained on sequences known to belong to particular folds or superfamilies, the so-called labeled data set, can attain significantly improved accuracy if this data is supplemented with protein sequences that lack any class tags–the unlabeled data. In this study, we present a principled and biologically motivated computational framework that more effectively exploits the unlabeled data by only using the sequence regions that are more likely to be biologically relevant for better prediction accuracy. As overly-represented sequences in large uncurated databases may bias the estimation of computational models that rely on unlabeled data, we also propose a method to remove this bias and improve performance of the resulting classifiers. Results Combined with state-of-the-art string kernels, our proposed computational framework achieves very accurate semi-supervised protein remote fold and homology detection on three large unlabeled databases. It outperforms current state-of-the-art methods and exhibits significant reduction in running time. Conclusion The unlabeled sequences used under the semi-supervised setting resemble the unpolished gemstones; when used as-is, they may carry unnecessary features and hence compromise the classification accuracy but once cut and polished, they improve the accuracy of the classifiers considerably. PMID:19426450

Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks

NASA Astrophysics Data System (ADS)

Zhu, Shijia; Wang, Yadong

2015-12-01

Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is ‘stationarity’, and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational efficiency. Additionally, we derived a novel generalized Bayesian Information Criterion under the non-stationary assumption (called BWBIC), which can help significantly improve the reconstruction accuracy and largely reduce over-fitting. Moreover, the re-estimation formulas for all parameters of our model are derived, enabling us to avoid reliance on parameter settings. Compared to the state-of-the-art methods, the experimental evaluation of our proposed method on both synthetic and real biological data demonstrates more stably high prediction accuracy and significantly improved computation efficiency, even with no prior knowledge and parameter settings.

Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins

PubMed Central

Yang, Jing; He, Bao-Ji; Jang, Richard; Zhang, Yang; Shen, Hong-Bin

2015-01-01

Abstract Motivation: Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g. >3 bonds, is too low to effectively assist structure assembly simulations. Results: We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins. Availability and implementation: http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/ Contact: zhng@umich.edu or hbshen@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26254435

Improved Motor-Timing: Effects of Synchronized Metro-Nome Training on Golf Shot Accuracy

PubMed Central

Sommer, Marius; Rönnqvist, Louise

2009-01-01

This study investigates the effect of synchronized metronome training (SMT) on motor timing and how this training might affect golf shot accuracy. Twenty-six experienced male golfers participated (mean age 27 years; mean golf handicap 12.6) in this study. Pre- and post-test investigations of golf shots made by three different clubs were conducted by use of a golf simulator. The golfers were randomized into two groups: a SMT group and a Control group. After the pre-test, the golfers in the SMT group completed a 4-week SMT program designed to improve their motor timing, the golfers in the Control group were merely training their golf-swings during the same time period. No differences between the two groups were found from the pre-test outcomes, either for motor timing scores or for golf shot accuracy. However, the post-test results after the 4-weeks SMT showed evident motor timing improvements. Additionally, significant improvements for golf shot accuracy were found for the SMT group and with less variability in their performance. No such improvements were found for the golfers in the Control group. As with previous studies that used a SMT program, this study’s results provide further evidence that motor timing can be improved by SMT and that such timing improvement also improves golf accuracy. Key points This study investigates the effect of synchronized metronome training (SMT) on motor timing and how this training might affect golf shot accuracy. A randomized control group design was used. The 4 week SMT intervention showed significant improvements in motor timing, golf shot accuracy, and lead to less variability. We conclude that this study’s results provide further evidence that motor timing can be improved by SMT training and that such timing improvement also improves golf accuracy. PMID:24149608

Low-dose CT imaging of a total hip arthroplasty phantom using model-based iterative reconstruction and orthopedic metal artifact reduction.

PubMed

Wellenberg, R H H; Boomsma, M F; van Osch, J A C; Vlassenbroek, A; Milles, J; Edens, M A; Streekstra, G J; Slump, C H; Maas, M

2017-05-01

To compare quantitative measures of image quality, in terms of CT number accuracy, noise, signal-to-noise-ratios (SNRs), and contrast-to-noise ratios (CNRs), at different dose levels with filtered-back-projection (FBP), iterative reconstruction (IR), and model-based iterative reconstruction (MBIR) alone and in combination with orthopedic metal artifact reduction (O-MAR) in a total hip arthroplasty (THA) phantom. Scans were acquired from high- to low-dose (CTDI vol : 40.0, 32.0, 24.0, 16.0, 8.0, and 4.0 mGy) at 120- and 140- kVp. Images were reconstructed using FBP, IR (iDose 4 level 2, 4, and 6) and MBIR (IMR, level 1, 2, and 3) with and without O-MAR. CT number accuracy in Hounsfield Units (HU), noise or standard deviation, SNRs, and CNRs were analyzed. The IMR technique showed lower noise levels (p < 0.01), higher SNRs (p < 0.001) and CNRs (p < 0.001) compared with FBP and iDose 4 in all acquisitions from high- to low-dose with constant CT numbers. O-MAR reduced noise (p < 0.01) and improved SNRs (p < 0.01) and CNRs (p < 0.001) while improving CT number accuracy only at a low dose. At the low dose of 4.0 mGy, IMR level 1, 2, and 3 showed 83%, 89%, and 95% lower noise values, a factor 6.0, 9.2, and 17.9 higher SNRs, and 5.7, 8.8, and 18.2 higher CNRs compared with FBP respectively. Based on quantitative analysis of CT number accuracy, noise values, SNRs, and CNRs, we conclude that the combined use of IMR and O-MAR enables a reduction in radiation dose of 83% compared with FBP and iDose 4 in the CT imaging of a THA phantom.

The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly

PubMed Central

2013-01-01

Background This study aims to improve accuracy of Bioelectrical Impedance Analysis (BIA) prediction equations for estimating fat free mass (FFM) of the elderly by using non-linear Back Propagation Artificial Neural Network (BP-ANN) model and to compare the predictive accuracy with the linear regression model by using energy dual X-ray absorptiometry (DXA) as reference method. Methods A total of 88 Taiwanese elderly adults were recruited in this study as subjects. Linear regression equations and BP-ANN prediction equation were developed using impedances and other anthropometrics for predicting the reference FFM measured by DXA (FFMDXA) in 36 male and 26 female Taiwanese elderly adults. The FFM estimated by BIA prediction equations using traditional linear regression model (FFMLR) and BP-ANN model (FFMANN) were compared to the FFMDXA. The measuring results of an additional 26 elderly adults were used to validate than accuracy of the predictive models. Results The results showed the significant predictors were impedance, gender, age, height and weight in developed FFMLR linear model (LR) for predicting FFM (coefficient of determination, r2 = 0.940; standard error of estimate (SEE) = 2.729 kg; root mean square error (RMSE) = 2.571kg, P < 0.001). The above predictors were set as the variables of the input layer by using five neurons in the BP-ANN model (r2 = 0.987 with a SD = 1.192 kg and relatively lower RMSE = 1.183 kg), which had greater (improved) accuracy for estimating FFM when compared with linear model. The results showed a better agreement existed between FFMANN and FFMDXA than that between FFMLR and FFMDXA. Conclusion When compared the performance of developed prediction equations for estimating reference FFMDXA, the linear model has lower r2 with a larger SD in predictive results than that of BP-ANN model, which indicated ANN model is more suitable for estimating FFM. PMID:23388042

Comparing a paper based monitoring and evaluation system to a mHealth system to support the national community health worker programme, South Africa: an evaluation

PubMed Central

2014-01-01

Background In an attempt to address a complex disease burden, including improving progress towards MDGs 4 and 5, South Africa recently introduced a re-engineered Primary Health Care (PHC) strategy, which has led to the development of a national community health worker (CHW) programme. The present study explored the development of a cell phone-based and paper-based monitoring and evaluation (M&E) system to support the work of the CHWs. Methods One sub-district in the North West province was identified for the evaluation. One outreach team comprising ten CHWs maintained both the paper forms and mHealth system to record household data on community-based services. A comparative analysis was done to calculate the correspondence between the paper and phone records. A focus group discussion was conducted with the CHWs. Clinical referrals, data accuracy and supervised visits were compared and analysed for the paper and phone systems. Results Compared to the mHealth system where data accuracy was assured, 40% of the CHWs showed a consistently high level (>90% correspondence) of data transfer accuracy on paper. Overall, there was an improvement over time, and by the fifth month, all CHWs achieved a correspondence of 90% or above between phone and paper data. The most common error that occurred was summing the total number of visits and/or activities across the five household activity indicators. Few supervised home visits were recorded in either system and there was no evidence of the team leader following up on the automatic notifications received on their cell phones. Conclusions The evaluation emphasizes the need for regular supervision for both systems and rigorous and ongoing assessments of data quality for the paper system. Formalization of a mHealth M&E system for PHC outreach teams delivering community based services could offer greater accuracy of M&E and enhance supervision systems for CHWs. PMID:25106499

Hierarchical image segmentation via recursive superpixel with adaptive regularity

NASA Astrophysics Data System (ADS)

Nakamura, Kensuke; Hong, Byung-Woo

2017-11-01

A fast and accurate segmentation algorithm in a hierarchical way based on a recursive superpixel technique is presented. We propose a superpixel energy formulation in which the trade-off between data fidelity and regularization is dynamically determined based on the local residual in the energy optimization procedure. We also present an energy optimization algorithm that allows a pixel to be shared by multiple regions to improve the accuracy and appropriate the number of segments. The qualitative and quantitative evaluations demonstrate that our algorithm, combining the proposed energy and optimization, outperforms the conventional k-means algorithm by up to 29.10% in F-measure. We also perform comparative analysis with state-of-the-art algorithms in the hierarchical segmentation. Our algorithm yields smooth regions throughout the hierarchy as opposed to the others that include insignificant details. Our algorithm overtakes the other algorithms in terms of balance between accuracy and computational time. Specifically, our method runs 36.48% faster than the region-merging approach, which is the fastest of the comparing algorithms, while achieving a comparable accuracy.

Accuracy of patient-specific guided glenoid baseplate positioning for reverse shoulder arthroplasty.

PubMed

Levy, Jonathan C; Everding, Nathan G; Frankle, Mark A; Keppler, Louis J

2014-10-01

The accuracy of reproducing a surgical plan during shoulder arthroplasty is improved by computer assistance. Intraoperative navigation, however, is challenged by increased surgical time and additional technically difficult steps. Patient-matched instrumentation has the potential to reproduce a similar degree of accuracy without the need for additional surgical steps. The purpose of this study was to examine the accuracy of patient-specific planning and a patient-specific drill guide for glenoid baseplate placement in reverse shoulder arthroplasty. A patient-specific glenoid baseplate drill guide for reverse shoulder arthroplasty was produced for 14 cadaveric shoulders based on a plan developed by a virtual preoperative 3-dimensional planning system using thin-cut computed tomography images. Using this patient-specific guide, high-volume shoulder surgeons exposed the glenoid through a deltopectoral approach and drilled the bicortical pathway defined by the guide. The trajectory of the drill path was compared with the virtual preoperative planned position using similar thin-cut computed tomography images to define accuracy. The drill pathway defined by the patient-matched guide was found to be highly accurate when compared with the preoperative surgical plan. The translational accuracy was 1.2 ± 0.7 mm. The accuracy of inferior tilt was 1.2° ± 1.2°. The accuracy of glenoid version was 2.6° ± 1.7°. The use of patient-specific glenoid baseplate guides is highly accurate in reproducing a virtual 3-dimensional preoperative plan. This technique delivers the accuracy observed using computerized navigation without any additional surgical steps or technical challenges. Copyright © 2014 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.

PubMed

Marafino, Ben J; Davies, Jason M; Bardach, Naomi S; Dean, Mitzi L; Dudley, R Adams

2014-01-01

Existing risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times. To develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment. We selected notes from 2001-2008 for 4191 neonatal ICU (NICU) and 2198 adult ICU patients from the MIMIC-II database from the Beth Israel Deaconess Medical Center. Using these notes, we developed an implementation of the SVM classifier to identify procedures (mechanical ventilation and phototherapy in NICU notes) and diagnoses (jaundice in NICU and intracranial hemorrhage (ICH) in adult ICU). On the jaundice classification task, we also compared classifier performance using n-gram features to unigrams with application of a negation algorithm (NegEx). Our classifier accurately identified mechanical ventilation (accuracy=0.982, F1=0.954) and phototherapy use (accuracy=0.940, F1=0.912), as well as jaundice (accuracy=0.898, F1=0.884) and ICH diagnoses (accuracy=0.938, F1=0.943). Including bigram features improved performance on the jaundice (accuracy=0.898 vs 0.865) and ICH (0.938 vs 0.927) tasks, and outperformed NegEx-derived unigram features (accuracy=0.898 vs 0.863) on the jaundice task. Overall, a classifier using n-gram support vectors displayed excellent performance characteristics. The classifier generalizes to diverse patient populations, diagnoses, and procedures. SVM-based classifiers can accurately identify procedure status and diagnoses among ICU patients, and including n-gram features improves performance, compared to existing methods. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

SU-F-T-441: Dose Calculation Accuracy in CT Images Reconstructed with Artifact Reduction Algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ng, C; Chan, S; Lee, F

Purpose: Accuracy of radiotherapy dose calculation in patients with surgical implants is complicated by two factors. First is the accuracy of CT number, second is the dose calculation accuracy. We compared measured dose with dose calculated on CT images reconstructed with FBP and an artifact reduction algorithm (OMAR, Philips) for a phantom with high density inserts. Dose calculation were done with Varian AAA and AcurosXB. Methods: A phantom was constructed with solid water in which 2 titanium or stainless steel rods could be inserted. The phantom was scanned with the Philips Brillance Big Bore CT. Image reconstruction was done withmore » FBP and OMAR. Two 6 MV single field photon plans were constructed for each phantom. Radiochromic films were placed at different locations to measure the dose deposited. One plan has normal incidence on the titanium/steel rods. In the second plan, the beam is at almost glancing incidence on the metal rods. Measurements were then compared with dose calculated with AAA and AcurosXB. Results: The use of OMAR images slightly improved the dose calculation accuracy. The agreement between measured and calculated dose was best with AXB and image reconstructed with OMAR. Dose calculated on titanium phantom has better agreement with measurement. Large discrepancies were seen at points directly above and below the high density inserts. Both AAA and AXB underestimated the dose directly above the metal surface, while overestimated the dose below the metal surface. Doses measured downstream of metal were all within 3% of calculated values. Conclusion: When doing treatment planning for patients with metal implants, care must be taken to acquire correct CT images to improve dose calculation accuracy. Moreover, great discrepancies in measured and calculated dose were observed at metal/tissue interface. Care must be taken in estimating the dose in critical structures that come into contact with metals.« less

A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures

PubMed Central

2014-01-01

Background Improving accuracy and efficiency of computational methods that predict pseudoknotted RNA secondary structures is an ongoing challenge. Existing methods based on free energy minimization tend to be very slow and are limited in the types of pseudoknots that they can predict. Incorporating known structural information can improve prediction accuracy; however, there are not many methods for prediction of pseudoknotted structures that can incorporate structural information as input. There is even less understanding of the relative robustness of these methods with respect to partial information. Results We present a new method, Iterative HFold, for pseudoknotted RNA secondary structure prediction. Iterative HFold takes as input a pseudoknot-free structure, and produces a possibly pseudoknotted structure whose energy is at least as low as that of any (density-2) pseudoknotted structure containing the input structure. Iterative HFold leverages strengths of earlier methods, namely the fast running time of HFold, a method that is based on the hierarchical folding hypothesis, and the energy parameters of HotKnots V2.0. Our experimental evaluation on a large data set shows that Iterative HFold is robust with respect to partial information, with average accuracy on pseudoknotted structures steadily increasing from roughly 54% to 79% as the user provides up to 40% of the input structure. Iterative HFold is much faster than HotKnots V2.0, while having comparable accuracy. Iterative HFold also has significantly better accuracy than IPknot on our HK-PK and IP-pk168 data sets. Conclusions Iterative HFold is a robust method for prediction of pseudoknotted RNA secondary structures, whose accuracy with more than 5% information about true pseudoknot-free structures is better than that of IPknot, and with about 35% information about true pseudoknot-free structures compares well with that of HotKnots V2.0 while being significantly faster. Iterative HFold and all data used in this work are freely available at http://www.cs.ubc.ca/~hjabbari/software.php. PMID:24884954

GStream: Improving SNP and CNV Coverage on Genome-Wide Association Studies

PubMed Central

Alonso, Arnald; Marsal, Sara; Tortosa, Raül; Canela-Xandri, Oriol; Julià, Antonio

2013-01-01

We present GStream, a method that combines genome-wide SNP and CNV genotyping in the Illumina microarray platform with unprecedented accuracy. This new method outperforms previous well-established SNP genotyping software. More importantly, the CNV calling algorithm of GStream dramatically improves the results obtained by previous state-of-the-art methods and yields an accuracy that is close to that obtained by purely CNV-oriented technologies like Comparative Genomic Hybridization (CGH). We demonstrate the superior performance of GStream using microarray data generated from HapMap samples. Using the reference CNV calls generated by the 1000 Genomes Project (1KGP) and well-known studies on whole genome CNV characterization based either on CGH or genotyping microarray technologies, we show that GStream can increase the number of reliably detected variants up to 25% compared to previously developed methods. Furthermore, the increased genome coverage provided by GStream allows the discovery of CNVs in close linkage disequilibrium with SNPs, previously associated with disease risk in published Genome-Wide Association Studies (GWAS). These results could provide important insights into the biological mechanism underlying the detected disease risk association. With GStream, large-scale GWAS will not only benefit from the combined genotyping of SNPs and CNVs at an unprecedented accuracy, but will also take advantage of the computational efficiency of the method. PMID:23844243

Detecting the borders between coding and non-coding DNA regions in prokaryotes based on recursive segmentation and nucleotide doublets statistics

PubMed Central

2012-01-01

Background Detecting the borders between coding and non-coding regions is an essential step in the genome annotation. And information entropy measures are useful for describing the signals in genome sequence. However, the accuracies of previous methods of finding borders based on entropy segmentation method still need to be improved. Methods In this study, we first applied a new recursive entropic segmentation method on DNA sequences to get preliminary significant cuts. A 22-symbol alphabet is used to capture the differential composition of nucleotide doublets and stop codon patterns along three phases in both DNA strands. This process requires no prior training datasets. Results Comparing with the previous segmentation methods, the experimental results on three bacteria genomes, Rickettsia prowazekii, Borrelia burgdorferi and E.coli, show that our approach improves the accuracy for finding the borders between coding and non-coding regions in DNA sequences. Conclusions This paper presents a new segmentation method in prokaryotes based on Jensen-Rényi divergence with a 22-symbol alphabet. For three bacteria genomes, comparing to A12_JR method, our method raised the accuracy of finding the borders between protein coding and non-coding regions in DNA sequences. PMID:23282225

3D prostate MR-TRUS non-rigid registration using dual optimization with volume-preserving constraint

NASA Astrophysics Data System (ADS)

Qiu, Wu; Yuan, Jing; Fenster, Aaron

2016-03-01

We introduce an efficient and novel convex optimization-based approach to the challenging non-rigid registration of 3D prostate magnetic resonance (MR) and transrectal ultrasound (TRUS) images, which incorporates a new volume preserving constraint to essentially improve the accuracy of targeting suspicious regions during the 3D TRUS guided prostate biopsy. Especially, we propose a fast sequential convex optimization scheme to efficiently minimize the employed highly nonlinear image fidelity function using the robust multi-channel modality independent neighborhood descriptor (MIND) across the two modalities of MR and TRUS. The registration accuracy was evaluated using 10 patient images by calculating the target registration error (TRE) using manually identified corresponding intrinsic fiducials in the whole prostate gland. We also compared the MR and TRUS manually segmented prostate surfaces in the registered images in terms of the Dice similarity coefficient (DSC), mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD). Experimental results showed that the proposed method with the introduced volume-preserving prior significantly improves the registration accuracy comparing to the method without the volume-preserving constraint, by yielding an overall mean TRE of 2:0+/-0:7 mm, and an average DSC of 86:5+/-3:5%, MAD of 1:4+/-0:6 mm and MAXD of 6:5+/-3:5 mm.

Improving the accuracy of Laplacian estimation with novel multipolar concentric ring electrodes

PubMed Central

Ding, Quan; Besio, Walter G.

2015-01-01

Conventional electroencephalography with disc electrodes has major drawbacks including poor spatial resolution, selectivity and low signal-to-noise ratio that are critically limiting its use. Concentric ring electrodes, consisting of several elements including the central disc and a number of concentric rings, are a promising alternative with potential to improve all of the aforementioned aspects significantly. In our previous work, the tripolar concentric ring electrode was successfully used in a wide range of applications demonstrating its superiority to conventional disc electrode, in particular, in accuracy of Laplacian estimation. This paper takes the next step toward further improving the Laplacian estimation with novel multipolar concentric ring electrodes by completing and validating a general approach to estimation of the Laplacian for an (n + 1)-polar electrode with n rings using the (4n + 1)-point method for n ≥ 2 that allows cancellation of all the truncation terms up to the order of 2n. An explicit formula based on inversion of a square Vandermonde matrix is derived to make computation of multipolar Laplacian more efficient. To confirm the analytic result of the accuracy of Laplacian estimate increasing with the increase of n and to assess the significance of this gain in accuracy for practical applications finite element method model analysis has been performed. Multipolar concentric ring electrode configurations with n ranging from 1 ring (bipolar electrode configuration) to 6 rings (septapolar electrode configuration) were directly compared and obtained results suggest the significance of the increase in Laplacian accuracy caused by increase of n. PMID:26693200

Improving the accuracy of Laplacian estimation with novel multipolar concentric ring electrodes.

PubMed

Makeyev, Oleksandr; Ding, Quan; Besio, Walter G

2016-02-01

Conventional electroencephalography with disc electrodes has major drawbacks including poor spatial resolution, selectivity and low signal-to-noise ratio that are critically limiting its use. Concentric ring electrodes, consisting of several elements including the central disc and a number of concentric rings, are a promising alternative with potential to improve all of the aforementioned aspects significantly. In our previous work, the tripolar concentric ring electrode was successfully used in a wide range of applications demonstrating its superiority to conventional disc electrode, in particular, in accuracy of Laplacian estimation. This paper takes the next step toward further improving the Laplacian estimation with novel multipolar concentric ring electrodes by completing and validating a general approach to estimation of the Laplacian for an ( n + 1)-polar electrode with n rings using the (4 n + 1)-point method for n ≥ 2 that allows cancellation of all the truncation terms up to the order of 2 n . An explicit formula based on inversion of a square Vandermonde matrix is derived to make computation of multipolar Laplacian more efficient. To confirm the analytic result of the accuracy of Laplacian estimate increasing with the increase of n and to assess the significance of this gain in accuracy for practical applications finite element method model analysis has been performed. Multipolar concentric ring electrode configurations with n ranging from 1 ring (bipolar electrode configuration) to 6 rings (septapolar electrode configuration) were directly compared and obtained results suggest the significance of the increase in Laplacian accuracy caused by increase of n .

Higher-order triangular spectral element method with optimized cubature points for seismic wavefield modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Youshan, E-mail: ysliu@mail.iggcas.ac.cn; Teng, Jiwen, E-mail: jwteng@mail.iggcas.ac.cn; Xu, Tao, E-mail: xutao@mail.iggcas.ac.cn

2017-05-01

The mass-lumped method avoids the cost of inverting the mass matrix and simultaneously maintains spatial accuracy by adopting additional interior integration points, known as cubature points. To date, such points are only known analytically in tensor domains, such as quadrilateral or hexahedral elements. Thus, the diagonal-mass-matrix spectral element method (SEM) in non-tensor domains always relies on numerically computed interpolation points or quadrature points. However, only the cubature points for degrees 1 to 6 are known, which is the reason that we have developed a p-norm-based optimization algorithm to obtain higher-order cubature points. In this way, we obtain and tabulate newmore » cubature points with all positive integration weights for degrees 7 to 9. The dispersion analysis illustrates that the dispersion relation determined from the new optimized cubature points is comparable to that of the mass and stiffness matrices obtained by exact integration. Simultaneously, the Lebesgue constant for the new optimized cubature points indicates its surprisingly good interpolation properties. As a result, such points provide both good interpolation properties and integration accuracy. The Courant–Friedrichs–Lewy (CFL) numbers are tabulated for the conventional Fekete-based triangular spectral element (TSEM), the TSEM with exact integration, and the optimized cubature-based TSEM (OTSEM). A complementary study demonstrates the spectral convergence of the OTSEM. A numerical example conducted on a half-space model demonstrates that the OTSEM improves the accuracy by approximately one order of magnitude compared to the conventional Fekete-based TSEM. In particular, the accuracy of the 7th-order OTSEM is even higher than that of the 14th-order Fekete-based TSEM. Furthermore, the OTSEM produces a result that can compete in accuracy with the quadrilateral SEM (QSEM). The high accuracy of the OTSEM is also tested with a non-flat topography model. In terms of computational efficiency, the OTSEM is more efficient than the Fekete-based TSEM, although it is slightly costlier than the QSEM when a comparable numerical accuracy is required. - Highlights: • Higher-order cubature points for degrees 7 to 9 are developed. • The effects of quadrature rule on the mass and stiffness matrices has been conducted. • The cubature points have always positive integration weights. • Freeing from the inversion of a wide bandwidth mass matrix. • The accuracy of the TSEM has been improved in about one order of magnitude.« less

Geoid undulation accuracy

NASA Technical Reports Server (NTRS)

Rapp, Richard H.

1993-01-01

The determination of the geoid and equipotential surface of the Earth's gravity field, has long been of interest to geodesists and oceanographers. The geoid provides a surface to which the actual ocean surface can be compared with the differences implying information on the circulation patterns of the oceans. For use in oceanographic applications the geoid is ideally needed to a high accuracy and to a high resolution. There are applications that require geoid undulation information to an accuracy of +/- 10 cm with a resolution of 50 km. We are far from this goal today but substantial improvement in geoid determination has been made. In 1979 the cumulative geoid undulation error to spherical harmonic degree 20 was +/- 1.4 m for the GEM10 potential coefficient model. Today the corresponding value has been reduced to +/- 25 cm for GEM-T3 or +/- 11 cm for the OSU91A model. Similar improvements are noted by harmonic degree (wave-length) and in resolution. Potential coefficient models now exist to degree 360 based on a combination of data types. This paper discusses the accuracy changes that have taken place in the past 12 years in the determination of geoid undulations.

Diagnostic relevance of high field MRI in clinical neuroradiology: the advantages and challenges of driving a sports car.

PubMed

Wattjes, Mike P; Barkhof, Frederik

2012-11-01

High field MRI operating at 3 T is increasingly being used in the field of neuroradiology on the grounds that higher magnetic field strength should theoretically lead to a higher diagnostic accuracy in the diagnosis of several disease entities. This Editorial discusses the exhaustive review by Wardlaw and colleagues of research comparing 3 T MRI with 1.5 T MRI in the field of neuroradiology. Interestingly, the authors found no convincing evidence of improved image quality, diagnostic accuracy, or reduced total examination times using 3 T MRI instead of 1.5 T MRI. These findings are highly relevant since a new generation of high field MRI systems operating at 7 T has recently been introduced. • Higher magnetic field strengths do not necessarily lead to a better diagnostic accuracy. • Disadvantages of high field MR systems have to be considered in clinical practice. • Higher field strengths are needed for functional imaging, spectroscopy, etc. • Disappointingly there are few direct comparisons of 1.5 and 3 T MRI. • Whether the next high field MR generation (7 T) will improve diagnostic accuracy has to be investigated.

A Rapid Coordinate Transformation Method Applied in Industrial Robot Calibration Based on Characteristic Line Coincidence.

PubMed

Liu, Bailing; Zhang, Fumin; Qu, Xinghua; Shi, Xiaojia

2016-02-18

Coordinate transformation plays an indispensable role in industrial measurements, including photogrammetry, geodesy, laser 3-D measurement and robotics. The widely applied methods of coordinate transformation are generally based on solving the equations of point clouds. Despite the high accuracy, this might result in no solution due to the use of ill conditioned matrices. In this paper, a novel coordinate transformation method is proposed, not based on the equation solution but based on the geometric transformation. We construct characteristic lines to represent the coordinate systems. According to the space geometry relation, the characteristic line scan is made to coincide by a series of rotations and translations. The transformation matrix can be obtained using matrix transformation theory. Experiments are designed to compare the proposed method with other methods. The results show that the proposed method has the same high accuracy, but the operation is more convenient and flexible. A multi-sensor combined measurement system is also presented to improve the position accuracy of a robot with the calibration of the robot kinematic parameters. Experimental verification shows that the position accuracy of robot manipulator is improved by 45.8% with the proposed method and robot calibration.

Pseudorange Measurement Method Based on AIS Signals.

PubMed

Zhang, Jingbo; Zhang, Shufang; Wang, Jinpeng

2017-05-22

In order to use the existing automatic identification system (AIS) to provide additional navigation and positioning services, a complete pseudorange measurements solution is presented in this paper. Through the mathematical analysis of the AIS signal, the bit-0-phases in the digital sequences were determined as the timestamps. Monte Carlo simulation was carried out to compare the accuracy of the zero-crossing and differential peak, which are two timestamp detection methods in the additive white Gaussian noise (AWGN) channel. Considering the low-speed and low-dynamic motion characteristics of ships, an optimal estimation method based on the minimum mean square error is proposed to improve detection accuracy. Furthermore, the α difference filter algorithm was used to achieve the fusion of the optimal estimation results of the two detection methods. The results show that the algorithm can greatly improve the accuracy of pseudorange estimation under low signal-to-noise ratio (SNR) conditions. In order to verify the effectiveness of the scheme, prototypes containing the measurement scheme were developed and field tests in Xinghai Bay of Dalian (China) were performed. The test results show that the pseudorange measurement accuracy was better than 28 m (σ) without any modification of the existing AIS system.

Pseudorange Measurement Method Based on AIS Signals

PubMed Central

Zhang, Jingbo; Zhang, Shufang; Wang, Jinpeng

2017-01-01

In order to use the existing automatic identification system (AIS) to provide additional navigation and positioning services, a complete pseudorange measurements solution is presented in this paper. Through the mathematical analysis of the AIS signal, the bit-0-phases in the digital sequences were determined as the timestamps. Monte Carlo simulation was carried out to compare the accuracy of the zero-crossing and differential peak, which are two timestamp detection methods in the additive white Gaussian noise (AWGN) channel. Considering the low-speed and low-dynamic motion characteristics of ships, an optimal estimation method based on the minimum mean square error is proposed to improve detection accuracy. Furthermore, the α difference filter algorithm was used to achieve the fusion of the optimal estimation results of the two detection methods. The results show that the algorithm can greatly improve the accuracy of pseudorange estimation under low signal-to-noise ratio (SNR) conditions. In order to verify the effectiveness of the scheme, prototypes containing the measurement scheme were developed and field tests in Xinghai Bay of Dalian (China) were performed. The test results show that the pseudorange measurement accuracy was better than 28 m (σ) without any modification of the existing AIS system. PMID:28531153

Improving the Accuracy of Diagnosing Placenta Previa on Transvaginal Ultrasound by Distinguishing between the Uterine Isthmus and Cervix: A Prospective Multicenter Observational Study.

PubMed

Hasegawa, Junichi; Kawabata, Ikuno; Takeda, Yoshiharu; Aoki, Hiroaki; Fukami, Takehiko; Tajima, Atsushi; Miyakoshi, Kei; Otsuki, Katsufumi; Shinozuka, Norio; Matsuda, Yoshio; Iwashita, Mitsutoshi; Okai, Takashi; Nakai, Akihito

2017-01-01

To clarify whether distinguishing between the uterine isthmus and cervix can improve the accuracy of diagnosing placenta previa at term. A multicenter prospective observational study was conducted among pregnant women with suspected placenta previa at 20-24 weeks' gestation. Subjects were divided into the open isthmus group and closed isthmus group. The accuracy of diagnosing placenta previa at term was compared between the 2 groups. We screened 9,341 patients, and 53 (0.6%) met the inclusion criteria. Nineteen cases with an open isthmus and 34 with a closed isthmus were followed. The accuracy for diagnosing placenta previa or a low-lying placenta at term was 94.7% in the open isthmus group and 26.5% in the closed isthmus group (p < 0.001). Elective or emergency Cesarean section was required in 100% of cases in the open isthmus group and 20.6% in the closed isthmus group (p < 0.001). A high prediction rate of placenta previa was obtained by using transvaginal ultrasound at 20-24 weeks' gestation after the isthmus opened by carefully distinguishing between the cervix and isthmus. © 2016 S. Karger AG, Basel.

Exploring the Relationship Between Eye Movements and Electrocardiogram Interpretation Accuracy

NASA Astrophysics Data System (ADS)

Davies, Alan; Brown, Gavin; Vigo, Markel; Harper, Simon; Horseman, Laura; Splendiani, Bruno; Hill, Elspeth; Jay, Caroline

2016-12-01

Interpretation of electrocardiograms (ECGs) is a complex task involving visual inspection. This paper aims to improve understanding of how practitioners perceive ECGs, and determine whether visual behaviour can indicate differences in interpretation accuracy. A group of healthcare practitioners (n = 31) who interpret ECGs as part of their clinical role were shown 11 commonly encountered ECGs on a computer screen. The participants’ eye movement data were recorded as they viewed the ECGs and attempted interpretation. The Jensen-Shannon distance was computed for the distance between two Markov chains, constructed from the transition matrices (visual shifts from and to ECG leads) of the correct and incorrect interpretation groups for each ECG. A permutation test was then used to compare this distance against 10,000 randomly shuffled groups made up of the same participants. The results demonstrated a statistically significant (α  0.05) result in 5 of the 11 stimuli demonstrating that the gaze shift between the ECG leads is different between the groups making correct and incorrect interpretations and therefore a factor in interpretation accuracy. The results shed further light on the relationship between visual behaviour and ECG interpretation accuracy, providing information that can be used to improve both human and automated interpretation approaches.

A Rapid Coordinate Transformation Method Applied in Industrial Robot Calibration Based on Characteristic Line Coincidence

PubMed Central

Liu, Bailing; Zhang, Fumin; Qu, Xinghua; Shi, Xiaojia

2016-01-01

Coordinate transformation plays an indispensable role in industrial measurements, including photogrammetry, geodesy, laser 3-D measurement and robotics. The widely applied methods of coordinate transformation are generally based on solving the equations of point clouds. Despite the high accuracy, this might result in no solution due to the use of ill conditioned matrices. In this paper, a novel coordinate transformation method is proposed, not based on the equation solution but based on the geometric transformation. We construct characteristic lines to represent the coordinate systems. According to the space geometry relation, the characteristic line scan is made to coincide by a series of rotations and translations. The transformation matrix can be obtained using matrix transformation theory. Experiments are designed to compare the proposed method with other methods. The results show that the proposed method has the same high accuracy, but the operation is more convenient and flexible. A multi-sensor combined measurement system is also presented to improve the position accuracy of a robot with the calibration of the robot kinematic parameters. Experimental verification shows that the position accuracy of robot manipulator is improved by 45.8% with the proposed method and robot calibration. PMID:26901203

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

Metaheuristic Algorithms for Convolution Neural Network

PubMed Central

Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

2016-01-01

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent). PMID:27375738

Metaheuristic Algorithms for Convolution Neural Network.

PubMed

Rere, L M Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

2016-01-01

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).

Implant alignment in total elbow arthroplasty: conventional vs. navigated techniques

NASA Astrophysics Data System (ADS)

McDonald, Colin P.; Johnson, James A.; King, Graham J. W.; Peters, Terry M.

2009-02-01

Incorrect selection of the native flexion-extension axis during implant alignment in elbow replacement surgery is likely a significant contributor to failure of the prosthesis. Computer and image-assisted surgery is emerging as a useful surgical tool in terms of improving the accuracy of orthopaedic procedures. This study evaluated the accuracy of implant alignment using an image-based navigation technique compared against a conventional non-navigated approach. Implant alignment error was 0.8 +/- 0.3 mm in translation and 1.1 +/- 0.4° in rotation for the navigated alignment, compared with 3.1 +/- 1.3 mm and 5.0 +/- 3.8° for the non-navigated alignment. Five (5) of the 11 non-navigated alignments were malaligned greater than 5° while none of the navigated alignments were placed with an error of greater than 2.0°. It is likely that improved implant positioning will lead to reduced implant loading and wear, resulting in fewer implantrelated complications and revision surgeries.

Surface wind accuracy for modeling mineral dust emissions: Comparing two regional models in a Bodélé case study

NASA Astrophysics Data System (ADS)

Laurent, B.; Heinold, B.; Tegen, I.; Bouet, C.; Cautenet, G.

2008-05-01

After a decade of research on improving the description of surface and soil features in desert regions to accurately model mineral dust emissions, we now emphasize the need for deeper evaluating the accuracy of modeled 10-m surface wind speeds U 10 . Two mesoscale models, the Lokal-Modell (LM) and the Regional Atmospheric Modeling System (RAMS), coupled with an explicit dust emission model have previously been used to simulate mineral dust events in the Bodélé region. We compare LM and RAMS U 10 , together with measurements at the Chicha site (BoDEx campaign) and Faya-Largeau meteorological station. Surface features and soil schemes are investigated to correctly simulate U 10 intensity and diurnal variability. The uncertainties in dust emissions computed with LM and RAMS U 10 and different soil databases are estimated. This sensitivity study shows the importance of accurate computation of surface winds to improve the quantification of regional dust emissions from the Bodélé

Wire-positioning algorithm for coreless Hall array sensors in current measurement

NASA Astrophysics Data System (ADS)

Chen, Wenli; Zhang, Huaiqing; Chen, Lin; Gu, Shanyun

2018-05-01

This paper presents a scheme of circular-arrayed, coreless Hall-effect current transformers. It can satisfy the demands of wide dynamic range and bandwidth current in the distribution system, as well as the demand of AC and DC simultaneous measurements. In order to improve the signal to noise ratio (SNR) of the sensor, a wire-positioning algorithm is proposed, which can improve the measurement accuracy based on the post-processing of measurement data. The simulation results demonstrate that the maximum errors are 70%, 6.1% and 0.95% corresponding to Ampère’s circuital method, approximate positioning algorithm and precise positioning algorithm, respectively. It is obvious that the accuracy of the positioning algorithm is significantly improved when compared with that of the Ampère’s circuital method. The maximum error of the positioning algorithm is smaller in the experiment.

Template-Directed Instrumentation Reduces Cost and Improves Efficiency for Total Knee Arthroplasty: An Economic Decision Analysis and Pilot Study.

PubMed

McLawhorn, Alexander S; Carroll, Kaitlin M; Blevins, Jason L; DeNegre, Scott T; Mayman, David J; Jerabek, Seth A

2015-10-01

Template-directed instrumentation (TDI) for total knee arthroplasty (TKA) may streamline operating room (OR) workflow and reduce costs by preselecting implants and minimizing instrument tray burden. A decision model simulated the economics of TDI. Sensitivity analyses determined thresholds for model variables to ensure TDI success. A clinical pilot was reviewed. The accuracy of preoperative templates was validated, and 20 consecutive primary TKAs were performed using TDI. The model determined that preoperative component size estimation should be accurate to ±1 implant size for 50% of TKAs to implement TDI. The pilot showed that preoperative template accuracy exceeded 97%. There were statistically significant improvements in OR turnover time and in-room time for TDI compared to an historical cohort of TKAs. TDI reduces costs and improves OR efficiency. Copyright © 2015 Elsevier Inc. All rights reserved.

Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization

PubMed Central

Wang, Jie-sheng; Li, Shu-xia; Song, Jiang-di

2015-01-01

In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy. PMID:26366164

The impact of conventional surface data upon VAS regression retrievals in the lower troposphere

NASA Technical Reports Server (NTRS)

Lee, T. H.; Chesters, D.; Mostek, A.

1983-01-01

Surface temperature and dewpoint reports are added to the infrared radiances from the VISSR Atmospheric Sounder (VAS) in order to improve the retrieval of temperature and moisture profiles in the lower troposphere. The conventional (airways) surface data are combined with the twelve VAS channels as additional predictors in a ridge regression retrieval scheme, with the aim of using all available data to make high resolution space-time interpolations of the radiosonde network. For one day of VAS observations, retrievals using only VAS radiances are compared with retrievals using VAS radiances plus surface data. Temperature retrieval accuracy evaluated at coincident radiosonde sites shows a significant impact within the boundary layer. Dewpoint retrieval accuracy shows a broader improvement within the lowest tropospheric layers. The most dramatic impact of surface data is observed in the improved relative spatial and temporal continuity of low-level fields retrieved over the Midwestern United States.

Improved P300 speller performance using electrocorticography, spectral features, and natural language processing.

PubMed

Speier, William; Fried, Itzhak; Pouratian, Nader

2013-07-01

The P300 speller is a system designed to restore communication to patients with advanced neuromuscular disorders. This study was designed to explore the potential improvement from using electrocorticography (ECoG) compared to the more traditional usage of electroencephalography (EEG). We tested the P300 speller on two epilepsy patients with temporary subdural electrode arrays over the occipital and temporal lobes respectively. We then performed offline analysis to determine the accuracy and bit rate of the system and integrated spectral features into the classifier and used a natural language processing (NLP) algorithm to further improve the results. The subject with the occipital grid achieved an accuracy of 82.77% and a bit rate of 41.02, which improved to 96.31% and 49.47 respectively using a language model and spectral features. The temporal grid patient achieved an accuracy of 59.03% and a bit rate of 18.26 with an improvement to 75.81% and 27.05 respectively using a language model and spectral features. Spatial analysis of the individual electrodes showed best performance using signals generated and recorded near the occipital pole. Using ECoG and integrating language information and spectral features can improve the bit rate of a P300 speller system. This improvement is sensitive to the electrode placement and likely depends on visually evoked potentials. This study shows that there can be an improvement in BCI performance when using ECoG, but that it is sensitive to the electrode location. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Automatic disease diagnosis using optimised weightless neural networks for low-power wearable devices

PubMed Central

Edla, Damodar Reddy; Kuppili, Venkatanareshbabu; Dharavath, Ramesh; Beechu, Nareshkumar Reddy

2017-01-01

Low-power wearable devices for disease diagnosis are used at anytime and anywhere. These are non-invasive and pain-free for the better quality of life. However, these devices are resource constrained in terms of memory and processing capability. Memory constraint allows these devices to store a limited number of patterns and processing constraint provides delayed response. It is a challenging task to design a robust classification system under above constraints with high accuracy. In this Letter, to resolve this problem, a novel architecture for weightless neural networks (WNNs) has been proposed. It uses variable sized random access memories to optimise the memory usage and a modified binary TRIE data structure for reducing the test time. In addition, a bio-inspired-based genetic algorithm has been employed to improve the accuracy. The proposed architecture is experimented on various disease datasets using its software and hardware realisations. The experimental results prove that the proposed architecture achieves better performance in terms of accuracy, memory saving and test time as compared to standard WNNs. It also outperforms in terms of accuracy as compared to conventional neural network-based classifiers. The proposed architecture is a powerful part of most of the low-power wearable devices for the solution of memory, accuracy and time issues. PMID:28868148

Prospective evaluation of the VITEK MS for the routine identification of bacteria and yeast in the clinical microbiology laboratory: assessment of accuracy of identification and turnaround time.

PubMed

Charnot-Katsikas, Angella; Tesic, Vera; Boonlayangoor, Sue; Bethel, Cindy; Frank, Karen M

2014-02-01

This study assessed the accuracy of bacterial and yeast identification using the VITEK MS, and the time to reporting of isolates before and after its implementation in routine clinical practice. Three hundred and sixty-two isolates of bacteria and yeast, consisting of a variety of clinical isolates and American Type Culture Collection strains, were tested. Results were compared with reference identifications from the VITEK 2 system and with 16S rRNA sequence analysis. The VITEK MS provided an acceptable identification to species level for 283 (78 %) isolates. Considering organisms for which genus-level identification is acceptable for routine clinical care, 315 isolates (87 %) had an acceptable identification. Six isolates (2 %) were identified incorrectly, five of which were Shigella species. Finally, the time for reporting the identifications was decreased significantly after implementation of the VITEK MS for a total mean reduction in time of 10.52 h (P<0.0001). Overall, accuracy of the VITEK MS was comparable or superior to that from the VITEK 2. The findings were also comparable to other studies examining the accuracy of the VITEK MS, although differences exist, depending on the diversity of species represented as well as on the versions of the databases used. The VITEK MS can be incorporated effectively into routine use in a clinical microbiology laboratory and future expansion of the database should provide improved accuracy for the identification of micro-organisms.

a Gsa-Svm Hybrid System for Classification of Binary Problems

NASA Astrophysics Data System (ADS)

Sarafrazi, Soroor; Nezamabadi-pour, Hossein; Barahman, Mojgan

2011-06-01

This paperhybridizesgravitational search algorithm (GSA) with support vector machine (SVM) and made a novel GSA-SVM hybrid system to improve the classification accuracy in binary problems. GSA is an optimization heuristic toolused to optimize the value of SVM kernel parameter (in this paper, radial basis function (RBF) is chosen as the kernel function). The experimental results show that this newapproach can achieve high classification accuracy and is comparable to or better than the particle swarm optimization (PSO)-SVM and genetic algorithm (GA)-SVM, which are two hybrid systems for classification.

Fuzzy PID control algorithm based on PSO and application in BLDC motor

NASA Astrophysics Data System (ADS)

Lin, Sen; Wang, Guanglong

2017-06-01

A fuzzy PID control algorithm is studied based on improved particle swarm optimization (PSO) to perform Brushless DC (BLDC) motor control which has high accuracy, good anti-jamming capability and steady state accuracy compared with traditional PID control. The mathematical and simulation model is established for BLDC motor by simulink software, and the speed loop of the fuzzy PID controller is designed. The simulation results show that the fuzzy PID control algorithm based on PSO has higher stability, high control precision and faster dynamic response speed.

[Contrast agent improves diagnostic value of dobutamine stress echocardiography].

PubMed

Uehara, H; Yamamoto, T; Hirano, Y; Ozasa, Y; Yamada, S; Ikawa, H; Ishikawa, K

2001-03-01

Suboptimal endocardial definition reduces the diagnostic value of stress echocardiography for coronary artery disease, but intravenous infusion of a left ventricular contrast agent (Albunex) may enhance endocardial border delineation and improve the diagnostic value of dobutamine stress echocardiography. Fifty-six patients, 38 with myocardial infarction, 16 with angina pectoris and two normal subjects, were enrolled in this study. Dobutamine was infused in scalar doses of 5 to 40 micrograms/kg/min. Intravenous infusion of Albunex (0.15 ml/kg) was administered at rest and during peak dobutamine stress during monitoring of the apical four-chamber view. The left ventricle in the apical four-chamber view was divided into six segments and an endocardial delineation score of 0 to 3 (none to excellent visualization) was given to each segment. Endocardial delineation score was increased after Albunex infusion from 2.0 to 2.3 in the basal-septal, 2.0 to 2.4 in the mid-septal, 1.1 to 1.8 in the apical-septal, 0.7 to 1.2 in the apical-lateral, 0.9 to 1.6 in the mid-lateral, and 1.2 to 1.9 in the basal-lateral segments during peak dobutamine administration. Endocardial border resolution in the lateral wall showed greater improvement than in the septal wall after Albunex infusion. Diagnostic values in the left anterior descending artery territory failed to improve with Albunex infusion (sensitivity 82% to 89%, specificity 94% to 89%, and accuracy 86% to 89%), whereas a higher diagnostic accuracy was noted in the left circumflex artery territory with Albunex compared to without Albunex (sensitivity 63% to 81%, specificity 88% to 98%, and accuracy 80% to 93%, p < 0.05). Contrast agent improves the diagnostic accuracy of dobutamine stress echocardiography in the left circumflex artery territory.

Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm.

PubMed

Banchhor, Sumit K; Londhe, Narendra D; Araki, Tadashi; Saba, Luca; Radeva, Petia; Laird, John R; Suri, Jasjit S

2017-12-01

Planning of percutaneous interventional procedures involves a pre-screening and risk stratification of the coronary artery disease. Current screening tools use stand-alone plaque texture-based features and therefore lack the ability to stratify the risk. This IRB approved study presents a novel strategy for coronary artery disease risk stratification using an amalgamation of IVUS plaque texture-based and wall-based measurement features. Due to common genetic plaque makeup, carotid plaque burden was chosen as a gold standard for risk labels during training-phase of machine learning (ML) paradigm. Cross-validation protocol was adopted to compute the accuracy of the ML framework. A set of 59 plaque texture-based features was padded with six wall-based measurement features to show the improvement in stratification accuracy. The ML system was executed using principle component analysis-based framework for dimensionality reduction and uses support vector machine classifier for training and testing-phases. The ML system produced a stratification accuracy of 91.28%, demonstrating an improvement of 5.69% when wall-based measurement features were combined with plaque texture-based features. The fused system showed an improvement in mean sensitivity, specificity, positive predictive value, and area under the curve by: 6.39%, 4.59%, 3.31% and 5.48%, respectively when compared to the stand-alone system. While meeting the stability criteria of 5%, the ML system also showed a high average feature retaining power and mean reliability of 89.32% and 98.24%, respectively. The ML system showed an improvement in risk stratification accuracy when the wall-based measurement features were fused with the plaque texture-based features. Copyright © 2017 Elsevier Ltd. All rights reserved.

Pediatric Surgeon-Directed Wound Classification Improves Accuracy

PubMed Central

Zens, Tiffany J.; Rusy, Deborah A.; Gosain, Ankush

2015-01-01

Background Surgical wound classification (SWC) communicates the degree of contamination in the surgical field and is used to stratify risk of surgical site infection and compare outcomes amongst centers. We hypothesized that changing from nurse-directed to surgeon-directed SWC during a structured operative debrief we will improve accuracy of documentation. Methods An IRB-approved retrospective chart review was performed. Two time periods were defined: initially, SWC was determined and recorded by the circulating nurse (Pre-Debrief 6/2012-5/2013) and allowing six months for adoption and education, we implemented a structured operative debriefing including surgeon-directed SWC (Post-Debrief 1/2014-8/2014). Accuracy of SWC was determined for four commonly performed Pediatric General Surgery operations: inguinal hernia repair (clean), gastrostomy +/− Nissen fundoplication (clean-contaminated), appendectomy without perforation (contaminated), and appendectomy with perforation (dirty). Results 183 cases Pre-Debrief and 142 cases Post-Debrief met inclusion criteria. No differences between time periods were noted in regards to patient demographics, ASA class, or case mix. Accuracy of wound classification improved Post-Debrief (42% vs. 58.5%, p=0.003). Pre-Debrief, 26.8% of cases were overestimated or underestimated by more than one wound class, vs. 3.5% of cases Post-Debrief (p<0.001). Interestingly, the majority of Post-Debrief contaminated cases were incorrectly classified as clean-contaminated. Conclusions Implementation of a structured operative debrief including surgeon-directed SWC improves the percentage of correctly classified wounds and decreases the degree of inaccuracy in incorrectly classified cases. However, following implementation of the debriefing, we still observed a 41.5% rate of incorrect documentation, most notably in contaminated cases, indicating further education and process improvement is needed. PMID:27020829

Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions

PubMed Central

Sükösd, Zsuzsanna; Swenson, M. Shel; Kjems, Jørgen; Heitsch, Christine E.

2013-01-01

Recent advances in RNA structure determination include using data from high-throughput probing experiments to improve thermodynamic prediction accuracy. We evaluate the extent and nature of improvements in data-directed predictions for a diverse set of 16S/18S ribosomal sequences using a stochastic model of experimental SHAPE data. The average accuracy for 1000 data-directed predictions always improves over the original minimum free energy (MFE) structure. However, the amount of improvement varies with the sequence, exhibiting a correlation with MFE accuracy. Further analysis of this correlation shows that accurate MFE base pairs are typically preserved in a data-directed prediction, whereas inaccurate ones are not. Thus, the positive predictive value of common base pairs is consistently higher than the directed prediction accuracy. Finally, we confirm sequence dependencies in the directability of thermodynamic predictions and investigate the potential for greater accuracy improvements in the worst performing test sequence. PMID:23325843

Iterative refinement of structure-based sequence alignments by Seed Extension

PubMed Central

Kim, Changhoon; Tai, Chin-Hsien; Lee, Byungkook

2009-01-01

Background Accurate sequence alignment is required in many bioinformatics applications but, when sequence similarity is low, it is difficult to obtain accurate alignments based on sequence similarity alone. The accuracy improves when the structures are available, but current structure-based sequence alignment procedures still mis-align substantial numbers of residues. In order to correct such errors, we previously explored the possibility of replacing the residue-based dynamic programming algorithm in structure alignment procedures with the Seed Extension algorithm, which does not use a gap penalty. Here, we describe a new procedure called RSE (Refinement with Seed Extension) that iteratively refines a structure-based sequence alignment. Results RSE uses SE (Seed Extension) in its core, which is an algorithm that we reported recently for obtaining a sequence alignment from two superimposed structures. The RSE procedure was evaluated by comparing the correctly aligned fractions of residues before and after the refinement of the structure-based sequence alignments produced by popular programs. CE, DaliLite, FAST, LOCK2, MATRAS, MATT, TM-align, SHEBA and VAST were included in this analysis and the NCBI's CDD root node set was used as the reference alignments. RSE improved the average accuracy of sequence alignments for all programs tested when no shift error was allowed. The amount of improvement varied depending on the program. The average improvements were small for DaliLite and MATRAS but about 5% for CE and VAST. More substantial improvements have been seen in many individual cases. The additional computation times required for the refinements were negligible compared to the times taken by the structure alignment programs. Conclusion RSE is a computationally inexpensive way of improving the accuracy of a structure-based sequence alignment. It can be used as a standalone procedure following a regular structure-based sequence alignment or to replace the traditional iterative refinement procedures based on residue-level dynamic programming algorithm in many structure alignment programs. PMID:19589133

An improved KCF tracking algorithm based on multi-feature and multi-scale

NASA Astrophysics Data System (ADS)

Wu, Wei; Wang, Ding; Luo, Xin; Su, Yang; Tian, Weiye

2018-02-01

The purpose of visual tracking is to associate the target object in a continuous video frame. In recent years, the method based on the kernel correlation filter has become the research hotspot. However, the algorithm still has some problems such as video capture equipment fast jitter, tracking scale transformation. In order to improve the ability of scale transformation and feature description, this paper has carried an innovative algorithm based on the multi feature fusion and multi-scale transform. The experimental results show that our method solves the problem that the target model update when is blocked or its scale transforms. The accuracy of the evaluation (OPE) is 77.0%, 75.4% and the success rate is 69.7%, 66.4% on the VOT and OTB datasets. Compared with the optimal one of the existing target-based tracking algorithms, the accuracy of the algorithm is improved by 6.7% and 6.3% respectively. The success rates are improved by 13.7% and 14.2% respectively.

Real-time Kinematic Positioning of INS Tightly Aided Multi-GNSS Ionospheric Constrained PPP

PubMed Central

Gao, Zhouzheng; Shen, Wenbin; Zhang, Hongping; Niu, Xiaoji; Ge, Maorong

2016-01-01

Real-time Precise Point Positioning (PPP) technique is being widely applied for providing precise positioning services with the significant improvement on satellite precise products accuracy. With the rapid development of the multi-constellation Global Navigation Satellite Systems (multi-GNSS), currently, about 80 navigation satellites are operational in orbit. Obviously, PPP performance is dramatically improved with all satellites compared to that of GPS-only PPP. However, the performance of PPP could be evidently affected by unexpected and unavoidable severe observing environments, especially in the dynamic applications. Consequently, we apply Inertial Navigation System (INS) to the Ionospheric-Constrained (IC) PPP to overcome such drawbacks. The INS tightly aided multi-GNSS IC-PPP model can make full use of GNSS and INS observations to improve the PPP performance in terms of accuracy, availability, continuity, and convergence speed. Then, a set of airborne data is analyzed to evaluate and validate the improvement of multi-GNSS and INS on the performance of IC-PPP. PMID:27470270

Real-time Kinematic Positioning of INS Tightly Aided Multi-GNSS Ionospheric Constrained PPP.

PubMed

Gao, Zhouzheng; Shen, Wenbin; Zhang, Hongping; Niu, Xiaoji; Ge, Maorong

2016-07-29

Real-time Precise Point Positioning (PPP) technique is being widely applied for providing precise positioning services with the significant improvement on satellite precise products accuracy. With the rapid development of the multi-constellation Global Navigation Satellite Systems (multi-GNSS), currently, about 80 navigation satellites are operational in orbit. Obviously, PPP performance is dramatically improved with all satellites compared to that of GPS-only PPP. However, the performance of PPP could be evidently affected by unexpected and unavoidable severe observing environments, especially in the dynamic applications. Consequently, we apply Inertial Navigation System (INS) to the Ionospheric-Constrained (IC) PPP to overcome such drawbacks. The INS tightly aided multi-GNSS IC-PPP model can make full use of GNSS and INS observations to improve the PPP performance in terms of accuracy, availability, continuity, and convergence speed. Then, a set of airborne data is analyzed to evaluate and validate the improvement of multi-GNSS and INS on the performance of IC-PPP.

Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

PubMed

Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

2015-01-01

A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

Utilisation of three-dimensional printed heart models for operative planning of complex congenital heart defects.

PubMed

Olejník, Peter; Nosal, Matej; Havran, Tomas; Furdova, Adriana; Cizmar, Maros; Slabej, Michal; Thurzo, Andrej; Vitovic, Pavol; Klvac, Martin; Acel, Tibor; Masura, Jozef

2017-01-01

To evaluate the accuracy of the three-dimensional (3D) printing of cardiovascular structures. To explore whether utilisation of 3D printed heart replicas can improve surgical and catheter interventional planning in patients with complex congenital heart defects. Between December 2014 and November 2015 we fabricated eight cardiovascular models based on computed tomography data in patients with complex spatial anatomical relationships of cardiovascular structures. A Bland-Altman analysis was used to assess the accuracy of 3D printing by comparing dimension measurements at analogous anatomical locations between the printed models and digital imagery data, as well as between printed models and in vivo surgical findings. The contribution of 3D printed heart models for perioperative planning improvement was evaluated in the four most representative patients. Bland-Altman analysis confirmed the high accuracy of 3D cardiovascular printing. Each printed model offered an improved spatial anatomical orientation of cardiovascular structures. Current 3D printers can produce authentic copies of patients` cardiovascular systems from computed tomography data. The use of 3D printed models can facilitate surgical or catheter interventional procedures in patients with complex congenital heart defects due to better preoperative planning and intraoperative orientation.

An Examination of a Small-Group Decoding Intervention for Struggling Readers: Comparing Accuracy and Automaticity Criteria

ERIC Educational Resources Information Center

Hudson, Roxanne F.; Isakson, Carole; Richman, Taylor; Lane, Holly B.; Arriaza-Allen, Stephanie

2011-01-01

In this study, we compared methods to improve the decoding and reading fluency of struggling readers. Second-grade poor readers were randomly assigned to one of the two practice conditions within a repeated reading intervention. Both interventions were in small groups, were 20-28 min long, took place 2-4 days per week, and consisted of phonemic…

Global and Regional 3D Tomography for Improved Seismic Event Location and Uncertainty in Explosion Monitoring

NASA Astrophysics Data System (ADS)

Downey, N.; Begnaud, M. L.; Hipp, J. R.; Ballard, S.; Young, C. S.; Encarnacao, A. V.

2017-12-01

The SALSA3D global 3D velocity model of the Earth was developed to improve the accuracy and precision of seismic travel time predictions for a wide suite of regional and teleseismic phases. Recently, the global SALSA3D model was updated to include additional body wave phases including mantle phases, core phases, reflections off the core-mantle boundary and underside reflections off the surface of the Earth. We show that this update improves travel time predictions and leads directly to significant improvements in the accuracy and precision of seismic event locations as compared to locations computed using standard 1D velocity models like ak135, or 2½D models like RSTT. A key feature of our inversions is that path-specific model uncertainty of travel time predictions are calculated using the full 3D model covariance matrix computed during tomography, which results in more realistic uncertainty ellipses that directly reflect tomographic data coverage. Application of this method can also be done at a regional scale: we present a velocity model with uncertainty obtained using data obtained from the University of Utah Seismograph Stations. These results show a reduction in travel-time residuals for re-located events compared with those obtained using previously published models.

Towards online iris and periocular recognition under relaxed imaging constraints.

PubMed

Tan, Chun-Wei; Kumar, Ajay

2013-10-01

Online iris recognition using distantly acquired images in a less imaging constrained environment requires the development of a efficient iris segmentation approach and recognition strategy that can exploit multiple features available for the potential identification. This paper presents an effective solution toward addressing such a problem. The developed iris segmentation approach exploits a random walker algorithm to efficiently estimate coarsely segmented iris images. These coarsely segmented iris images are postprocessed using a sequence of operations that can effectively improve the segmentation accuracy. The robustness of the proposed iris segmentation approach is ascertained by providing comparison with other state-of-the-art algorithms using publicly available UBIRIS.v2, FRGC, and CASIA.v4-distance databases. Our experimental results achieve improvement of 9.5%, 4.3%, and 25.7% in the average segmentation accuracy, respectively, for the UBIRIS.v2, FRGC, and CASIA.v4-distance databases, as compared with most competing approaches. We also exploit the simultaneously extracted periocular features to achieve significant performance improvement. The joint segmentation and combination strategy suggest promising results and achieve average improvement of 132.3%, 7.45%, and 17.5% in the recognition performance, respectively, from the UBIRIS.v2, FRGC, and CASIA.v4-distance databases, as compared with the related competing approaches.

Confidence and Information Access in Clinical Decision-Making: An Examination of the Cognitive Processes that affect the Information-seeking Behavior of Physicians.

PubMed

Uy, Raymonde Charles; Sarmiento, Raymond Francis; Gavino, Alex; Fontelo, Paul

2014-01-01

Clinical decision-making involves the interplay between cognitive processes and physicians' perceptions of confidence in the context of their information-seeking behavior. The objectives of the study are: to examine how these concepts interact, to determine whether physician confidence, defined in relation to information need, affects clinical decision-making, and if information access improves decision accuracy. We analyzed previously collected data about resident physicians' perceptions of information need from a study comparing abstracts and full-text articles in clinical decision accuracy. We found that there is a significant relation between confidence and accuracy (φ=0.164, p<0.01). We also found various differences in the alignment of confidence and accuracy, demonstrating the concepts of underconfidence and overconfidence across years of clinical experience. Access to online literature also has a significant effect on accuracy (p<0.001). These results highlight possible CDSS strategies to reduce medical errors.

Geolocation and Pointing Accuracy Analysis for the WindSat Sensor

NASA Technical Reports Server (NTRS)

Meissner, Thomas; Wentz, Frank J.; Purdy, William E.; Gaiser, Peter W.; Poe, Gene; Uliana, Enzo A.

2006-01-01

Geolocation and pointing accuracy analyses of the WindSat flight data are presented. The two topics were intertwined in the flight data analysis and will be addressed together. WindSat has no unusual geolocation requirements relative to other sensors, but its beam pointing knowledge accuracy is especially critical to support accurate polarimetric radiometry. Pointing accuracy was improved and verified using geolocation analysis in conjunction with scan bias analysis. nvo methods were needed to properly identify and differentiate between data time tagging and pointing knowledge errors. Matchups comparing coastlines indicated in imagery data with their known geographic locations were used to identify geolocation errors. These coastline matchups showed possible pointing errors with ambiguities as to the true source of the errors. Scan bias analysis of U, the third Stokes parameter, and of vertical and horizontal polarizations provided measurement of pointing offsets resolving ambiguities in the coastline matchup analysis. Several geolocation and pointing bias sources were incfementally eliminated resulting in pointing knowledge and geolocation accuracy that met all design requirements.

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

PubMed

Zhang, He-Hua; Yang, Liuyang; Liu, Yuchuan; Wang, Pin; Yin, Jun; Li, Yongming; Qiu, Mingguo; Zhu, Xueru; Yan, Fang

2016-11-16

The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.

Improving Precision, Maintaining Accuracy, and Reducing Acquisition Time for Trace Elements in EPMA

NASA Astrophysics Data System (ADS)

Donovan, J.; Singer, J.; Armstrong, J. T.

2016-12-01

Trace element precision in electron probe micro analysis (EPMA) is limited by intrinsic random variation in the x-ray continuum. Traditionally we characterize background intensity by measuring on either side of the emission line and interpolating the intensity underneath the peak to obtain the net intensity. Alternatively, we can measure the background intensity at the on-peak spectrometer position using a number of standard materials that do not contain the element of interest. This so-called mean atomic number (MAN) background calibration (Donovan, et al., 2016) uses a set of standard measurements, covering an appropriate range of average atomic number, to iteratively estimate the continuum intensity for the unknown composition (and hence average atomic number). We will demonstrate that, at least for materials with a relatively simple matrix such as SiO2, TiO2, ZrSiO4, etc. where one may obtain a matrix matched standard for use in the so called "blank correction", we can obtain trace element accuracy comparable to traditional off-peak methods, and with improved precision, in about half the time. Donovan, Singer and Armstrong, A New EPMA Method for Fast Trace Element Analysis in Simple Matrices ", American Mineralogist, v101, p1839-1853, 2016 Figure 1. Uranium concentration line profiles from quantitative x-ray maps (20 keV, 100 nA, 5 um beam size and 4000 msec per pixel), for both off-peak and MAN background methods without (a), and with (b), the blank correction applied. We see precision significantly improved compared with traditional off-peak measurements while, in this case, the blank correction provides a small but discernable improvement in accuracy.

Dimensionality-varied convolutional neural network for spectral-spatial classification of hyperspectral data

NASA Astrophysics Data System (ADS)

Liu, Wanjun; Liang, Xuejian; Qu, Haicheng

2017-11-01

Hyperspectral image (HSI) classification is one of the most popular topics in remote sensing community. Traditional and deep learning-based classification methods were proposed constantly in recent years. In order to improve the classification accuracy and robustness, a dimensionality-varied convolutional neural network (DVCNN) was proposed in this paper. DVCNN was a novel deep architecture based on convolutional neural network (CNN). The input of DVCNN was a set of 3D patches selected from HSI which contained spectral-spatial joint information. In the following feature extraction process, each patch was transformed into some different 1D vectors by 3D convolution kernels, which were able to extract features from spectral-spatial data. The rest of DVCNN was about the same as general CNN and processed 2D matrix which was constituted by by all 1D data. So that the DVCNN could not only extract more accurate and rich features than CNN, but also fused spectral-spatial information to improve classification accuracy. Moreover, the robustness of network on water-absorption bands was enhanced in the process of spectral-spatial fusion by 3D convolution, and the calculation was simplified by dimensionality varied convolution. Experiments were performed on both Indian Pines and Pavia University scene datasets, and the results showed that the classification accuracy of DVCNN improved by 32.87% on Indian Pines and 19.63% on Pavia University scene than spectral-only CNN. The maximum accuracy improvement of DVCNN achievement was 13.72% compared with other state-of-the-art HSI classification methods, and the robustness of DVCNN on water-absorption bands noise was demonstrated.

An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms.

PubMed

Amaral, Jorge L M; Lopes, Agnaldo J; Jansen, José M; Faria, Alvaro C D; Melo, Pedro L

2013-12-01

The purpose of this study was to develop an automatic classifier to increase the accuracy of the forced oscillation technique (FOT) for diagnosing early respiratory abnormalities in smoking patients. The data consisted of FOT parameters obtained from 56 volunteers, 28 healthy and 28 smokers with low tobacco consumption. Many supervised learning techniques were investigated, including logistic linear classifiers, k nearest neighbor (KNN), neural networks and support vector machines (SVM). To evaluate performance, the ROC curve of the most accurate parameter was established as baseline. To determine the best input features and classifier parameters, we used genetic algorithms and a 10-fold cross-validation using the average area under the ROC curve (AUC). In the first experiment, the original FOT parameters were used as input. We observed a significant improvement in accuracy (KNN=0.89 and SVM=0.87) compared with the baseline (0.77). The second experiment performed a feature selection on the original FOT parameters. This selection did not cause any significant improvement in accuracy, but it was useful in identifying more adequate FOT parameters. In the third experiment, we performed a feature selection on the cross products of the FOT parameters. This selection resulted in a further increase in AUC (KNN=SVM=0.91), which allows for high diagnostic accuracy. In conclusion, machine learning classifiers can help identify early smoking-induced respiratory alterations. The use of FOT cross products and the search for the best features and classifier parameters can markedly improve the performance of machine learning classifiers. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Protein docking prediction using predicted protein-protein interface.

PubMed

Li, Bin; Kihara, Daisuke

2012-01-10

Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

Multiple confidence estimates as indices of eyewitness memory.

PubMed

Sauer, James D; Brewer, Neil; Weber, Nathan

2008-08-01

Eyewitness identification decisions are vulnerable to various influences on witnesses' decision criteria that contribute to false identifications of innocent suspects and failures to choose perpetrators. An alternative procedure using confidence estimates to assess the degree of match between novel and previously viewed faces was investigated. Classification algorithms were applied to participants' confidence data to determine when a confidence value or pattern of confidence values indicated a positive response. Experiment 1 compared confidence group classification accuracy with a binary decision control group's accuracy on a standard old-new face recognition task and found superior accuracy for the confidence group for target-absent trials but not for target-present trials. Experiment 2 used a face mini-lineup task and found reduced target-present accuracy offset by large gains in target-absent accuracy. Using a standard lineup paradigm, Experiments 3 and 4 also found improved classification accuracy for target-absent lineups and, with a more sophisticated algorithm, for target-present lineups. This demonstrates the accessibility of evidence for recognition memory decisions and points to a more sensitive index of memory quality than is afforded by binary decisions.

Radio interferometric measurements for accurate planetary orbiter navigation

NASA Technical Reports Server (NTRS)

Poole, S. R.; Ananda, M.; Hildebrand, C. E.

1979-01-01

The use of narrowband delta-VLBI to achieve accurate orbit determination is presented by viewing a spacecraft from widely separated stations followed by viewing a nearby quasar from the same stations. Current analysis is examined that establishes the orbit determination accuracy achieved with data arcs spanning up to 3.5 d. Strategies for improving prediction accuracy are given, and the performance of delta-VLBI is compared with conventional radiometric tracking data. It is found that accuracy 'within the fit' is on the order of 0.5 km for data arcs having delta-VLBI on the ends of the arcs and for arc lengths varying from one baseline to 3.5 d. The technique is discussed with reference to the proposed Venus Orbiting Imaging Radar mission.

Selective classification for improved robustness of myoelectric control under nonideal conditions.

PubMed

Scheme, Erik J; Englehart, Kevin B; Hudgins, Bernard S

2011-06-01

Recent literature in pattern recognition-based myoelectric control has highlighted a disparity between classification accuracy and the usability of upper limb prostheses. This paper suggests that the conventionally defined classification accuracy may be idealistic and may not reflect true clinical performance. Herein, a novel myoelectric control system based on a selective multiclass one-versus-one classification scheme, capable of rejecting unknown data patterns, is introduced. This scheme is shown to outperform nine other popular classifiers when compared using conventional classification accuracy as well as a form of leave-one-out analysis that may be more representative of real prosthetic use. Additionally, the classification scheme allows for real-time, independent adjustment of individual class-pair boundaries making it flexible and intuitive for clinical use.

Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering

PubMed Central

Zheng, Ming; Sun, Ying; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang

2012-01-01

Background Gravitation field algorithm (GFA) is a new optimization algorithm which is based on an imitation of natural phenomena. GFA can do well both for searching global minimum and multi-minima in computational biology. But GFA needs to be improved for increasing efficiency, and modified for applying to some discrete data problems in system biology. Method An improved GFA called IGFA was proposed in this paper. Two parts were improved in IGFA. The first one is the rule of random division, which is a reasonable strategy and makes running time shorter. The other one is rotation factor, which can improve the accuracy of IGFA. And to apply IGFA to the hierarchical clustering, the initial part and the movement operator were modified. Results Two kinds of experiments were used to test IGFA. And IGFA was applied to hierarchical clustering. The global minimum experiment was used with IGFA, GFA, GA (genetic algorithm) and SA (simulated annealing). Multi-minima experiment was used with IGFA and GFA. The two experiments results were compared with each other and proved the efficiency of IGFA. IGFA is better than GFA both in accuracy and running time. For the hierarchical clustering, IGFA is used to optimize the smallest distance of genes pairs, and the results were compared with GA and SA, singular-linkage clustering, UPGMA. The efficiency of IGFA is proved. PMID:23173043

Efficient measurement of amylose content in cereal grains.

USDA-ARS?s Scientific Manuscript database

Rapid and economical measurement of amylose content in barley is important for genetic study and breeding improvement of this trait. Seventeen genotypes with a wide range of amylose contents were used to compare the amylose measurement accuracy of the cost-effective iodine-potassium iodide (I:KI) me...

A systematic review of the PTSD Checklist's diagnostic accuracy studies using QUADAS.

PubMed

McDonald, Scott D; Brown, Whitney L; Benesek, John P; Calhoun, Patrick S

2015-09-01

Despite the popularity of the PTSD Checklist (PCL) as a clinical screening test, there has been no comprehensive quality review of studies evaluating its diagnostic accuracy. A systematic quality assessment of 22 diagnostic accuracy studies of the English-language PCL using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) assessment tool was conducted to examine (a) the quality of diagnostic accuracy studies of the PCL, and (b) whether quality has improved since the 2003 STAndards for the Reporting of Diagnostic accuracy studies (STARD) initiative regarding reporting guidelines for diagnostic accuracy studies. Three raters independently applied the QUADAS tool to each study, and a consensus among the 4 authors is reported. Findings indicated that although studies generally met standards in several quality areas, there is still room for improvement. Areas for improvement include establishing representativeness, adequately describing clinical and demographic characteristics of the sample, and presenting better descriptions of important aspects of test and reference standard execution. Only 2 studies met each of the 14 quality criteria. In addition, study quality has not appreciably improved since the publication of the STARD Statement in 2003. Recommendations for the improvement of diagnostic accuracy studies of the PCL are discussed. (c) 2015 APA, all rights reserved).

Accurate and diverse recommendations via eliminating redundant correlations

NASA Astrophysics Data System (ADS)

Zhou, Tao; Su, Ri-Qi; Liu, Run-Ran; Jiang, Luo-Luo; Wang, Bing-Hong; Zhang, Yi-Cheng

2009-12-01

In this paper, based on a weighted projection of a bipartite user-object network, we introduce a personalized recommendation algorithm, called network-based inference (NBI), which has higher accuracy than the classical algorithm, namely collaborative filtering. In NBI, the correlation resulting from a specific attribute may be repeatedly counted in the cumulative recommendations from different objects. By considering the higher order correlations, we design an improved algorithm that can, to some extent, eliminate the redundant correlations. We test our algorithm on two benchmark data sets, MovieLens and Netflix. Compared with NBI, the algorithmic accuracy, measured by the ranking score, can be further improved by 23 per cent for MovieLens and 22 per cent for Netflix. The present algorithm can even outperform the Latent Dirichlet Allocation algorithm, which requires much longer computational time. Furthermore, most previous studies considered the algorithmic accuracy only; in this paper, we argue that the diversity and popularity, as two significant criteria of algorithmic performance, should also be taken into account. With more or less the same accuracy, an algorithm giving higher diversity and lower popularity is more favorable. Numerical results show that the present algorithm can outperform the standard one simultaneously in all five adopted metrics: lower ranking score and higher precision for accuracy, larger Hamming distance and lower intra-similarity for diversity, as well as smaller average degree for popularity.

Impact of Linearity and Write Noise of Analog Resistive Memory Devices in a Neural Algorithm Accelerator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jacobs-Gedrim, Robin B.; Agarwal, Sapan; Knisely, Kathrine E.

Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm level. In order to assess this degradation, analog properties of ReRAM devices were measured and hand-written digit recognition accuracy was modeled for the training using backpropagation. Bipolar filamentary devices utilizing three material systems were measured and compared: one oxygen vacancy system, Ta-TaO x, andmore » two conducting metallization systems, Cu-SiO 2, and Ag/chalcogenide. Analog properties and conductance ranges of the devices are optimized by measuring the response to varying voltage pulse characteristics. Key analog device properties which degrade the accuracy are update linearity and write noise. Write noise may improve as a function of device manufacturing maturity, but write nonlinearity appears relatively consistent among the different device material systems and is found to be the most significant factor affecting accuracy. As a result, this suggests that new materials and/or fundamentally different resistive switching mechanisms may be required to improve device linearity and achieve higher algorithm training accuracy.« less

Impact of Linearity and Write Noise of Analog Resistive Memory Devices in a Neural Algorithm Accelerator

DOE PAGES

Jacobs-Gedrim, Robin B.; Agarwal, Sapan; Knisely, Kathrine E.; ...

2017-12-01

Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm level. In order to assess this degradation, analog properties of ReRAM devices were measured and hand-written digit recognition accuracy was modeled for the training using backpropagation. Bipolar filamentary devices utilizing three material systems were measured and compared: one oxygen vacancy system, Ta-TaO x, andmore » two conducting metallization systems, Cu-SiO 2, and Ag/chalcogenide. Analog properties and conductance ranges of the devices are optimized by measuring the response to varying voltage pulse characteristics. Key analog device properties which degrade the accuracy are update linearity and write noise. Write noise may improve as a function of device manufacturing maturity, but write nonlinearity appears relatively consistent among the different device material systems and is found to be the most significant factor affecting accuracy. As a result, this suggests that new materials and/or fundamentally different resistive switching mechanisms may be required to improve device linearity and achieve higher algorithm training accuracy.« less

Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited.

PubMed

Thomas, Cibu; Ye, Frank Q; Irfanoglu, M Okan; Modi, Pooja; Saleem, Kadharbatcha S; Leopold, David A; Pierpaoli, Carlo

2014-11-18

Tractography based on diffusion-weighted MRI (DWI) is widely used for mapping the structural connections of the human brain. Its accuracy is known to be limited by technical factors affecting in vivo data acquisition, such as noise, artifacts, and data undersampling resulting from scan time constraints. It generally is assumed that improvements in data quality and implementation of sophisticated tractography methods will lead to increasingly accurate maps of human anatomical connections. However, assessing the anatomical accuracy of DWI tractography is difficult because of the lack of independent knowledge of the true anatomical connections in humans. Here we investigate the future prospects of DWI-based connectional imaging by applying advanced tractography methods to an ex vivo DWI dataset of the macaque brain. The results of different tractography methods were compared with maps of known axonal projections from previous tracer studies in the macaque. Despite the exceptional quality of the DWI data, none of the methods demonstrated high anatomical accuracy. The methods that showed the highest sensitivity showed the lowest specificity, and vice versa. Additionally, anatomical accuracy was highly dependent upon parameters of the tractography algorithm, with different optimal values for mapping different pathways. These results suggest that there is an inherent limitation in determining long-range anatomical projections based on voxel-averaged estimates of local fiber orientation obtained from DWI data that is unlikely to be overcome by improvements in data acquisition and analysis alone.

High-accuracy reference standards for two-photon absorption in the 680–1050 nm wavelength range

PubMed Central

de Reguardati, Sophie; Pahapill, Juri; Mikhailov, Alexander; Stepanenko, Yuriy; Rebane, Aleksander

2016-01-01

Degenerate two-photon absorption (2PA) of a series of organic fluorophores is measured using femtosecond fluorescence excitation method in the wavelength range, λ2PA = 680–1050 nm, and ~100 MHz pulse repetition rate. The function of relative 2PA spectral shape is obtained with estimated accuracy 5%, and the absolute 2PA cross section is measured at selected wavelengths with the accuracy 8%. Significant improvement of the accuracy is achieved by means of rigorous evaluation of the quadratic dependence of the fluorescence signal on the incident photon flux in the whole wavelength range, by comparing results obtained from two independent experiments, as well as due to meticulous evaluation of critical experimental parameters, including the excitation spatial- and temporal pulse shape, laser power and sample geometry. Application of the reference standards in nonlinear transmittance measurements is discussed. PMID:27137334

Effect of Lamina Thickness of Prepreg on the Surface Accuracy of Carbon Fiber Composite Space Mirrors

NASA Astrophysics Data System (ADS)

Yang, Zhiyong; Tang, Zhanwen; Xie, Yongjie; Shi, Hanqiao; Zhang, Boming; Guo, Hongjun

2018-02-01

Composite space mirror can completely replicate the high-precision surface of mould by replication process, but the actual surface accuracy of the replication composite mirror always decreases. Lamina thickness of prepreg affects the layers and layup sequence of composite space mirror, and which would affect surface accuracy of space mirror. In our research, two groups of contrasting cases through finite element analyses (FEA) and comparative experiments were studied; the effect of different lamina thicknesses of prepreg and corresponding lay-up sequences was focused as well. We describe a special analysis model, validated process and result analysis. The simulated and measured surface figures both get the same conclusion. Reducing lamina thickness of prepreg used in replicating composite space mirror is propitious to optimal design of layup sequence for fabricating composite mirror, and could improve its surface accuracy.

Navigating highly elliptical earth orbiters with simultaneous VLBI from orthogonal baseline pairs

NASA Technical Reports Server (NTRS)

Frauenholz, Raymond B.

1986-01-01

Navigation strategies for determining highly elliptical orbits with VLBI are described. The predicted performance of wideband VLBI and Delta VLBI measurements obtained by orthogonal baseline pairs are compared for a 16-hr equatorial orbit. It is observed that the one-sigma apogee position accuracy improves two orders of magnitude to the meter level when Delta VLBI measurements are added to coherent Doppler and range, and the simpler VLBI strategy provides nearly the same orbit accuracy. The effects of differential measurement noise and acquisition geometry on orbit accuracy are investigated. The data reveal that quasar position uncertainty limits the accuracy of wideband Delta VLBI measurements, and that polar motion and baseline uncertainties and offsets between station clocks affect the wideband VLBI data. It is noted that differential one-way range (DOR) has performance nearly equal to that of the more complex Delta DOR and is recommended for use on spacecraft in high elliptical orbits.

Accuracy improvement techniques in Precise Point Positioning method using multiple GNSS constellations

NASA Astrophysics Data System (ADS)

Vasileios Psychas, Dimitrios; Delikaraoglou, Demitris

2016-04-01

The future Global Navigation Satellite Systems (GNSS), including modernized GPS, GLONASS, Galileo and BeiDou, offer three or more signal carriers for civilian use and much more redundant observables. The additional frequencies can significantly improve the capabilities of the traditional geodetic techniques based on GPS signals at two frequencies, especially with regard to the availability, accuracy, interoperability and integrity of high-precision GNSS applications. Furthermore, highly redundant measurements can allow for robust simultaneous estimation of static or mobile user states including more parameters such as real-time tropospheric biases and more reliable ambiguity resolution estimates. This paper presents an investigation and analysis of accuracy improvement techniques in the Precise Point Positioning (PPP) method using signals from the fully operational (GPS and GLONASS), as well as the emerging (Galileo and BeiDou) GNSS systems. The main aim was to determine the improvement in both the positioning accuracy achieved and the time convergence it takes to achieve geodetic-level (10 cm or less) accuracy. To this end, freely available observation data from the recent Multi-GNSS Experiment (MGEX) of the International GNSS Service, as well as the open source program RTKLIB were used. Following a brief background of the PPP technique and the scope of MGEX, the paper outlines the various observational scenarios that were used in order to test various data processing aspects of PPP solutions with multi-frequency, multi-constellation GNSS systems. Results from the processing of multi-GNSS observation data from selected permanent MGEX stations are presented and useful conclusions and recommendations for further research are drawn. As shown, data fusion from GPS, GLONASS, Galileo and BeiDou systems is becoming increasingly significant nowadays resulting in a position accuracy increase (mostly in the less favorable East direction) and a large reduction of convergence time in PPP static and kinematic solutions compared to GPS-only PPP solutions for various observational session durations. However, this is mostly observed when the visibility of Galileo and BeiDou satellites is substantially long within an observational session. In GPS-only cases dealing with data from high elevation cut-off angles, the number of GPS satellites decreases dramatically, leading to a position accuracy and convergence time deviating from satisfactory geodetic thresholds. By contrast, respective multi-GNSS PPP solutions not only show improvement, but also lead to geodetic level accuracies even in 30° elevation cut-off. Finally, the GPS ambiguity resolution in PPP processing is investigated using the GPS satellite wide-lane fractional cycle biases, which are included in the clock products by CNES. It is shown that their addition shortens the convergence time and increases the position accuracy of PPP solutions, especially in kinematic mode. Analogous improvement is obtained in respective multi-GNSS solutions, even though the GLONASS, Galileo and BeiDou ambiguities remain float, since information about them is not provided in the clock products available to date.

Improving the inhibitory control task to detect minimal hepatic encephalopathy.

PubMed

Amodio, Piero; Ridola, Lorenzo; Schiff, Sami; Montagnese, Sara; Pasquale, Chiara; Nardelli, Silvia; Pentassuglio, Ilaria; Trezza, Maria; Marzano, Chiara; Flaiban, Cristiana; Angeli, Paolo; Cona, Giorgia; Bisiacchi, Patrizia; Gatta, Angelo; Riggio, Oliviero

2010-08-01

Quantification of the number of noninhibited responses (lures) in the inhibitory control task (ICT) has been proposed for the diagnosis of minimal hepatic encephalopathy (MHE). We assessed the efficacy of ICT compared with recommended diagnostic standards. We studied patients with cirrhosis and healthy individuals (controls) who underwent the ICT at 2 centers (center A: n=51 patients and 41 controls, center B: n=24 patients and 14 controls). Subjects were evaluated for MHE by psychometric hepatic encephalopathy score (PHES). Patients from center B also were assessed for MHE by critical flicker frequency and spectral electroencephalogram analyses. Patients with cirrhosis had higher ICT lures (23.2+/-12.8 vs 12.9+/-5.8, respectively, P<.01) and lower ICT target accuracy (0.88+/-0.17 vs 0.96+/-0.03, respectively, P<.01) compared with controls. However, lures were comparable (25.2+/-12.5 vs 21.4+/-13.9, respectively, P=.32) among patients with/without altered PHES (center A). There was a reverse, U-shaped relationship between ICT lure and target accuracy; a variable adjusting lures was devised based on target accuracy (weighted lures at center B). This variable differed between patients with and without MHE. The variable weighted lures was then validated from data collected at center A by receiver operator characteristic curve analysis; it discriminated between patients with and without PHES alterations (area under the curve=0.71+/-0.07). However, target accuracy alone was as effective as a stand-alone variable (area under the curve=0.81+/-0.06). The ICT is not useful for the diagnosis of MHE, unless adjusted by target accuracy. Testing inhibition (lures) does not seem to be superior to testing attention (target accuracy) for the detection of MHE. Copyright (c) 2010 AGA Institute. Published by Elsevier Inc. All rights reserved.

Highly accurate detection of ovarian cancer using CA125 but limited improvement with serum matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling.

PubMed

Tiss, Ali; Timms, John F; Smith, Celia; Devetyarov, Dmitry; Gentry-Maharaj, Aleksandra; Camuzeaux, Stephane; Burford, Brian; Nouretdinov, Ilia; Ford, Jeremy; Luo, Zhiyuan; Jacobs, Ian; Menon, Usha; Gammerman, Alex; Cramer, Rainer

2010-12-01

Our objective was to test the performance of CA125 in classifying serum samples from a cohort of malignant and benign ovarian cancers and age-matched healthy controls and to assess whether combining information from matrix-assisted laser desorption/ionization (MALDI) time-of-flight profiling could improve diagnostic performance. Serum samples from women with ovarian neoplasms and healthy volunteers were subjected to CA125 assay and MALDI time-of-flight mass spectrometry (MS) profiling. Models were built from training data sets using discriminatory MALDI MS peaks in combination with CA125 values and tested their ability to classify blinded test samples. These were compared with models using CA125 threshold levels from 193 patients with ovarian cancer, 290 with benign neoplasm, and 2236 postmenopausal healthy controls. Using a CA125 cutoff of 30 U/mL, an overall sensitivity of 94.8% (96.6% specificity) was obtained when comparing malignancies versus healthy postmenopausal controls, whereas a cutoff of 65 U/mL provided a sensitivity of 83.9% (99.6% specificity). High classification accuracies were obtained for early-stage cancers (93.5% sensitivity). Reasons for high accuracies include recruitment bias, restriction to postmenopausal women, and inclusion of only primary invasive epithelial ovarian cancer cases. The combination of MS profiling information with CA125 did not significantly improve the specificity/accuracy compared with classifications on the basis of CA125 alone. We report unexpectedly good performance of serum CA125 using threshold classification in discriminating healthy controls and women with benign masses from those with invasive ovarian cancer. This highlights the dependence of diagnostic tests on the characteristics of the study population and the crucial need for authors to provide sufficient relevant details to allow comparison. Our study also shows that MS profiling information adds little to diagnostic accuracy. This finding is in contrast with other reports and shows the limitations of serum MS profiling for biomarker discovery and as a diagnostic tool.

Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter.

PubMed

Chu, Hairong; Sun, Tingting; Zhang, Baiqiang; Zhang, Hongwei; Chen, Yang

2017-01-14

In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the "Velocity and Attitude" matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment.

Research on segmentation based on multi-atlas in brain MR image

NASA Astrophysics Data System (ADS)

Qian, Yuejing

2018-03-01

Accurate segmentation of specific tissues in brain MR image can be effectively achieved with the multi-atlas-based segmentation method, and the accuracy mainly depends on the image registration accuracy and fusion scheme. This paper proposes an automatic segmentation method based on the multi-atlas for brain MR image. Firstly, to improve the registration accuracy in the area to be segmented, we employ a target-oriented image registration method for the refinement. Then In the label fusion, we proposed a new algorithm to detect the abnormal sparse patch and simultaneously abandon the corresponding abnormal sparse coefficients, this method is made based on the remaining sparse coefficients combined with the multipoint label estimator strategy. The performance of the proposed method was compared with those of the nonlocal patch-based label fusion method (Nonlocal-PBM), the sparse patch-based label fusion method (Sparse-PBM) and majority voting method (MV). Based on our experimental results, the proposed method is efficient in the brain MR images segmentation compared with MV, Nonlocal-PBM, and Sparse-PBM methods.

Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter

PubMed Central

Chu, Hairong; Sun, Tingting; Zhang, Baiqiang; Zhang, Hongwei; Chen, Yang

2017-01-01

In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the “Velocity and Attitude” matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment. PMID:28098829

Influence of Layup and Curing on the Surface Accuracy in the Manufacturing of Carbon Fiber Reinforced Polymer (CFRP) Composite Space Mirrors

NASA Astrophysics Data System (ADS)

Yang, Zhiyong; Zhang, Jianbao; Xie, Yongjie; Zhang, Boming; Sun, Baogang; Guo, Hongjun

2017-12-01

Carbon fiber reinforced polymer, CFRP, composite materials have been used to fabricate space mirror. Usually the composite space mirror can completely replicate the high-precision surface of mould by replication process, but the actual surface accuracy of replicated space mirror is always reduced, still needed further study. We emphatically studied the error caused by layup and curing on the surface accuracy of space mirror through comparative experiments and analyses, the layup and curing influence factors include curing temperature, cooling rate of curing, method of prepreg lay-up, and area weight of fiber. Focusing on the four factors, we analyzed the error influence rule and put forward corresponding control measures to improve the surface figure of space mirror. For comparative analysis, six CFRP composite mirrors were fabricated and surface profile of mirrors were measured. Four guiding control measures were described here. Curing process of composite space mirror is our next focus.

EVALUATING RISK-PREDICTION MODELS USING DATA FROM ELECTRONIC HEALTH RECORDS.

PubMed

Wang, L E; Shaw, Pamela A; Mathelier, Hansie M; Kimmel, Stephen E; French, Benjamin

2016-03-01

The availability of data from electronic health records facilitates the development and evaluation of risk-prediction models, but estimation of prediction accuracy could be limited by outcome misclassification, which can arise if events are not captured. We evaluate the robustness of prediction accuracy summaries, obtained from receiver operating characteristic curves and risk-reclassification methods, if events are not captured (i.e., "false negatives"). We derive estimators for sensitivity and specificity if misclassification is independent of marker values. In simulation studies, we quantify the potential for bias in prediction accuracy summaries if misclassification depends on marker values. We compare the accuracy of alternative prognostic models for 30-day all-cause hospital readmission among 4548 patients discharged from the University of Pennsylvania Health System with a primary diagnosis of heart failure. Simulation studies indicate that if misclassification depends on marker values, then the estimated accuracy improvement is also biased, but the direction of the bias depends on the direction of the association between markers and the probability of misclassification. In our application, 29% of the 1143 readmitted patients were readmitted to a hospital elsewhere in Pennsylvania, which reduced prediction accuracy. Outcome misclassification can result in erroneous conclusions regarding the accuracy of risk-prediction models.

Improved Accuracy of Automated Estimation of Cardiac Output Using Circulation Time in Patients with Heart Failure.

PubMed

Dajani, Hilmi R; Hosokawa, Kazuya; Ando, Shin-Ichi

2016-11-01

Lung-to-finger circulation time of oxygenated blood during nocturnal periodic breathing in heart failure patients measured using polysomnography correlates negatively with cardiac function but possesses limited accuracy for cardiac output (CO) estimation. CO was recalculated from lung-to-finger circulation time using a multivariable linear model with information on age and average overnight heart rate in 25 patients who underwent evaluation of heart failure. The multivariable model decreased the percentage error to 22.3% relative to invasive CO measured during cardiac catheterization. This improved automated noninvasive CO estimation using multiple variables meets a recently proposed performance criterion for clinical acceptability of noninvasive CO estimation, and compares very favorably with other available methods. Copyright © 2016 Elsevier Inc. All rights reserved.

Accuracy of shade matching performed by colour blind and normal dental students using 3D Master and Vita Lumin shade guides.

PubMed

Vafaee, F; Rakhshan, V; Vafaei, M; Khoshhal, M

2012-03-01

The purpose of this study was to investigate whether 3D Master or VitaLumin shade guides could improve colour selection in individuals with normal and defective colour vision. First, colour perception of 260 dental students was evaluated. Afterwards, 9 colour blind and 9 matched normal subjects tried to detect colours of 10 randomly selected tabs from each kit and the correct/false answers were counted. Of the colour-defective subjects, 47.8% and 33.3% correctly detected the shade using 3D Master and VitaLumin, respectively. These statistics were 62.2% and 42.2% in normal subjects. In normal participants, but not in colour blind ones, 3D Master significantly improved shade matching accuracy compared to VitaLumin.

Four Reasons to Question the Accuracy of a Biotic Index; the Risk of Metric Bias and the Scope to Improve Accuracy

PubMed Central

Monaghan, Kieran A.

2016-01-01

Natural ecological variability and analytical design can bias the derived value of a biotic index through the variable influence of indicator body-size, abundance, richness, and ascribed tolerance scores. Descriptive statistics highlight this risk for 26 aquatic indicator systems; detailed analysis is provided for contrasting weighted-average indices applying the example of the BMWP, which has the best supporting data. Differences in body size between taxa from respective tolerance classes is a common feature of indicator systems; in some it represents a trend ranging from comparatively small pollution tolerant to larger intolerant organisms. Under this scenario, the propensity to collect a greater proportion of smaller organisms is associated with negative bias however, positive bias may occur when equipment (e.g. mesh-size) selectively samples larger organisms. Biotic indices are often derived from systems where indicator taxa are unevenly distributed along the gradient of tolerance classes. Such skews in indicator richness can distort index values in the direction of taxonomically rich indicator classes with the subsequent degree of bias related to the treatment of abundance data. The misclassification of indicator taxa causes bias that varies with the magnitude of the misclassification, the relative abundance of misclassified taxa and the treatment of abundance data. These artifacts of assessment design can compromise the ability to monitor biological quality. The statistical treatment of abundance data and the manipulation of indicator assignment and class richness can be used to improve index accuracy. While advances in methods of data collection (i.e. DNA barcoding) may facilitate improvement, the scope to reduce systematic bias is ultimately limited to a strategy of optimal compromise. The shortfall in accuracy must be addressed by statistical pragmatism. At any particular site, the net bias is a probabilistic function of the sample data, resulting in an error variance around an average deviation. Following standardized protocols and assigning precise reference conditions, the error variance of their comparative ratio (test-site:reference) can be measured and used to estimate the accuracy of the resultant assessment. PMID:27392036

Effect of tropospheric models on derived precipitable water vapor over Southeast Asia

NASA Astrophysics Data System (ADS)

Rahimi, Zhoobin; Mohd Shafri, Helmi Zulhaidi; Othman, Faridah; Norman, Masayu

2017-05-01

An interesting subject in the field of GPS technology is estimating variation of precipitable water vapor (PWV). This estimation can be used as a data source to assess and monitor rapid changes in meteorological conditions. So far, numerous GPS stations are distributed across the world and the number of GPS networks is increasing. Despite these developments, a challenging aspect of estimating PWV through GPS networks is the need of tropospheric parameters such as temperature, pressure, and relative humidity (Liu et al., 2015). To estimate the tropospheric parameters, global pressure temperature (GPT) model developed by Boehm et al. (2007) is widely used in geodetic analysis for GPS observations. To improve the accuracy, Lagler et al. (2013) introduced GPT2 model by adding annual and semi-annual variation effects to GPT model. Furthermore, Boehm et al. (2015) proposed the GPT2 wet (GPT2w) model which uses water vapor pressure to improve the calculations. The global accuracy of GPT2 and GPT2w models has been evaluated by previous researches (Fund et al., 2011; Munekane and Boehm, 2010); however, investigations to assess the accuracy of global tropospheric models in tropical regions such as Southeast Asia is not sufficient. This study tests and examines the accuracy of GPT2w as one of the most recent versions of tropospheric models (Boehm et al., 2015). We developed a new regional model called Malaysian Pressure Temperature (MPT) model, and compared this model with GPT2w model. The compared results at one international GNSS service (IGS) station located in the south of Peninsula Malaysia shows that MPT model has a better performance than GPT2w model to produce PWV during monsoon season. According to the results, MPT has improved the accuracy of estimated pressure and temperature by 30% and 10%, respectively, in comparison with GPT2w model. These results indicate that MPT model can be a good alternative tool in the absence of meteorological sensors at GPS stations in Peninsula Malaysia. Therefore, for GPS-based studies, we recommend MPT model to be used as a complementary tool for the Malaysia Real-Time Kinematic Network to develop a real-time PWV monitoring system.

Improving communication of medication changes using a pharmacist-prepared discharge medication management summary.

PubMed

Ooi, Choon Ean; Rofe, Olivia; Vienet, Michelle; Elliott, Rohan A

2017-04-01

Background Discontinuity of care between hospital and primary care is often due to poor information transfer. Medication information in medical discharge summaries (DS) is often incomplete or incorrect. The effectiveness and feasibility of hospital pharmacists communicating medication information, including changes made in the hospital, is not clearly defined. Objective To explore the impact of a pharmacist-prepared Discharge Medication Management Summary (DMMS) on the accuracy of information about medication changes provided to patients' general practitioners (GPs). Setting Two medical wards at a major metropolitan hospital in Australia. Method An intervention was developed in which ward pharmacists communicated medication change information to GPs using the DMMS. Retrospective audits were conducted at baseline and after implementation of the DMMS to compare the accuracy of information provided by doctors and pharmacists. GPs' satisfaction with the DMMS was assessed through a faxed survey. Main outcome measure Accuracy of medication change information communicated to GPs; GP satisfaction and feasibility of a pharmacist-prepared DMMS. Results At baseline, 263/573 (45.9%) medication changes were documented by doctors in the DS. In the post-intervention audit, more medication changes were documented in the pharmacist-prepared DMMS compared to the doctor-prepared DS (72.8% vs. 31.5%; p < 0.001). Most GPs (73.3%) were satisfied with the information provided and wanted to receive the DMMS in the future. Completing the DMMS took pharmacists an average of 11.7 minutes. Conclusion The accuracy of medication information transferred upon discharge can be improved by expanding the role of hospital pharmacists to include documenting medication changes.

Diagnostic Accuracy of Cerebrospinal Fluid Amyloid-β Isoforms for Early and Differential Dementia Diagnosis.

PubMed

Struyfs, Hanne; Van Broeck, Bianca; Timmers, Maarten; Fransen, Erik; Sleegers, Kristel; Van Broeckhoven, Christine; De Deyn, Peter P; Streffer, Johannes R; Mercken, Marc; Engelborghs, Sebastiaan

2015-01-01

Overlapping cerebrospinal fluid biomarkers (CSF) levels between Alzheimer's disease (AD) and non-AD patients decrease differential diagnostic accuracy of the AD core CSF biomarkers. Amyloid-β (Aβ) isoforms might improve the AD versus non-AD differential diagnosis. To determine the added diagnostic value of Aβ isoforms, Aβ(1-37), Aβ(1-38), and Aβ(1-40), as compared to the AD CSF biomarkers Aβ(1-42), T-tau, and P-tau(181P). CSF from patients with dementia due to AD (n = 50), non-AD dementias (n = 50), mild cognitive impairment due to AD (n = 50) and non-demented controls (n = 50) was analyzed with a prototype multiplex assay using MSD detection technology. The non-AD group consisted of frontotemporal dementia (FTD; n = 17), dementia with Lewy bodies (DLB; n = 17), and vascular dementia (n = 16). Aβ(1-37) and Aβ(1-38) increased accuracy to differentiate AD from FTD or DLB. Aβ(1-37), Aβ(1-38), and Aβ(1-40) levels correlated with Mini-Mental State Examination scores and disease duration in dementia due to AD. The Aβ(1-42)/Aβ(1-40) ratio improved diagnostic performance of Aβ(1-42) in most differential diagnostic situations. Aβ(1-42) levels were lower in APOE ε4 carriers compared to non-carriers. Aβ isoforms help to differentiate AD from FTD and DLB. Aβ isoforms increase diagnostic performance of Aβ(1-42). In contrast to Aβ1-42, Aβ isoforms seem to be correlated with disease severity in AD. Adding the Aβ isoforms to the current biomarker panel could enhance diagnostic accuracy.

A novel computerized surgeon-machine interface for robot-assisted laser phonomicrosurgery.

PubMed

Mattos, Leonardo S; Deshpande, Nikhil; Barresi, Giacinto; Guastini, Luca; Peretti, Giorgio

2014-08-01

To introduce a novel computerized surgical system for improved usability, intuitiveness, accuracy, and controllability in robot-assisted laser phonomicrosurgery. Pilot technology assessment. The novel system was developed involving a newly designed motorized laser micromanipulator, a touch-screen display, and a graphics stylus. The system allows the control of a CO2 laser through interaction between the stylus and the live video of the surgical area. This empowers the stylus with the ability to have actual effect on the surgical site. Surgical enhancements afforded by this system were established through a pilot technology assessment using randomized trials comparing its performance with a state-of-the-art laser microsurgery system. Resident surgeons and medical students were chosen as subjects in performing sets of trajectory-following exercises. Image processing-based techniques were used for an objective performance assessment. A System Usability Scale-based questionnaire was used for the qualitative assessment. The computerized interface demonstrated superiority in usability, accuracy, and controllability over the state-of-the-art system. Significant ease of use and learning experienced by the subjects were demonstrated by the usability score assigned to the two compared interfaces: computerized interface = 83.96% versus state-of-the-art = 68.02%. The objective analysis showed a significant enhancement in accuracy and controllability: computerized interface = 90.02% versus state-of-the-art = 75.59%. The novel system significantly enhances the accuracy, usability, and controllability in laser phonomicrosurgery. The design provides an opportunity to improve the ergonomics and safety of current surgical setups. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

Experimental study of sector and linear array ultrasound accuracy and the influence of navigated 3D-reconstruction as compared to MRI in a brain tumor model.

PubMed

Siekmann, Max; Lothes, Thomas; König, Ralph; Wirtz, Christian Rainer; Coburger, Jan

2018-03-01

Currently, intraoperative ultrasound in brain tumor surgery is a rapidly propagating option in imaging technology. We examined the accuracy and resolution limits of different ultrasound probes and the influence of 3D-reconstruction in a phantom and compared these results to MRI in an intraoperative setting (iMRI). An agarose gel phantom with predefined gel targets was examined with iMRI, a sector (SUS) and a linear (LUS) array probe with two-dimensional images. Additionally, 3D-reconstructed sweeps in perpendicular directions were made of every target with both probes, resulting in 392 measurements. Statistical calculations were performed, and comparative boxplots were generated. Every measurement of iMRI and LUS was more precise than SUS, while there was no apparent difference in height of iMRI and 3D-reconstructed LUS. Measurements with 3D-reconstructed LUS were always more accurate than in 2D-LUS, while 3D-reconstruction of SUS showed nearly no differences to 2D-SUS in some measurements. We found correlations of 3D-reconstructed SUS and LUS length and width measurements with 2D results in the same image orientation. LUS provides an accuracy and resolution comparable to iMRI, while SUS is less exact than LUS and iMRI. 3D-reconstruction showed the potential to distinctly improve accuracy and resolution of ultrasound images, although there is a strong correlation with the sweep direction during data acquisition.

Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what?

PubMed Central

2017-01-01

Assessing the accuracy of predictive models is critical because predictive models have been increasingly used across various disciplines and predictive accuracy determines the quality of resultant predictions. Pearson product-moment correlation coefficient (r) and the coefficient of determination (r2) are among the most widely used measures for assessing predictive models for numerical data, although they are argued to be biased, insufficient and misleading. In this study, geometrical graphs were used to illustrate what were used in the calculation of r and r2 and simulations were used to demonstrate the behaviour of r and r2 and to compare three accuracy measures under various scenarios. Relevant confusions about r and r2, has been clarified. The calculation of r and r2 is not based on the differences between the predicted and observed values. The existing error measures suffer various limitations and are unable to tell the accuracy. Variance explained by predictive models based on cross-validation (VEcv) is free of these limitations and is a reliable accuracy measure. Legates and McCabe’s efficiency (E1) is also an alternative accuracy measure. The r and r2 do not measure the accuracy and are incorrect accuracy measures. The existing error measures suffer limitations. VEcv and E1 are recommended for assessing the accuracy. The applications of these accuracy measures would encourage accuracy-improved predictive models to be developed to generate predictions for evidence-informed decision-making. PMID:28837692

Interlaboratory Analytical Comparison of Fatty Acid Concentrations in Serum or Plasma

PubMed Central

Schantz, Michele M.; Powers, Carissa D.; Schleicher, Rosemary L.; Betz, Joseph M.; Wise, Stephen A.

2016-01-01

The National Institute of Standards and Technology, in collaboration with the National Institutes of Health Office of Dietary Supplements and the Centers for Disease Control and Prevention, is conducting an accuracy-based program for improving the comparability of individual fatty acid measurements in serum and plasma. To date, two exercises of the Fatty Acid Quality Assurance Program (FAQAP) were conducted with 11 and 14 participants, respectively. The results from these two exercises indicate the need to improve the within-lab repeatability and between-lab reproducibility thus providing more confidence in the comparability of fatty acid measurements. PMID:27662814

Contrast-enhanced small-animal PET/CT in cancer research: strong improvement of diagnostic accuracy without significant alteration of quantitative accuracy and NEMA NU 4-2008 image quality parameters.

PubMed

Lasnon, Charline; Quak, Elske; Briand, Mélanie; Gu, Zheng; Louis, Marie-Hélène; Aide, Nicolas

2013-01-17

The use of iodinated contrast media in small-animal positron emission tomography (PET)/computed tomography (CT) could improve anatomic referencing and tumor delineation but may introduce inaccuracies in the attenuation correction of the PET images. This study evaluated the diagnostic performance and accuracy of quantitative values in contrast-enhanced small-animal PET/CT (CEPET/CT) as compared to unenhanced small animal PET/CT (UEPET/CT). Firstly, a NEMA NU 4-2008 phantom (filled with 18F-FDG or 18F-FDG plus contrast media) and a homemade phantom, mimicking an abdominal tumor surrounded by water or contrast media, were used to evaluate the impact of iodinated contrast media on the image quality parameters and accuracy of quantitative values for a pertinent-sized target. Secondly, two studies in 22 abdominal tumor-bearing mice and rats were performed. The first animal experiment studied the impact of a dual-contrast media protocol, comprising the intravenous injection of a long-lasting contrast agent mixed with 18F-FDG and the intraperitoneal injection of contrast media, on tumor delineation and the accuracy of quantitative values. The second animal experiment compared the diagnostic performance and quantitative values of CEPET/CT versus UEPET/CT by sacrificing the animals after the tracer uptake period and imaging them before and after intraperitoneal injection of contrast media. There was minimal impact on IQ parameters (%SDunif and spillover ratios in air and water) when the NEMA NU 4-2008 phantom was filled with 18F-FDG plus contrast media. In the homemade phantom, measured activity was similar to true activity (-0.02%) and overestimated by 10.30% when vials were surrounded by water or by an iodine solution, respectively. The first animal experiment showed excellent tumor delineation and a good correlation between small-animal (SA)-PET and ex vivo quantification (r2 = 0.87, P < 0.0001). The second animal experiment showed a good correlation between CEPET/CT and UEPET/CT quantitative values (r2 = 0.99, P < 0.0001). Receiver operating characteristic analysis demonstrated better diagnostic accuracy of CEPET/CT versus UEPET/CT (senior researcher, area under the curve (AUC) 0.96 versus 0.77, P = 0.004; junior researcher, AUC 0.78 versus 0.58, P = 0.004). The use of iodinated contrast media for small-animal PET imaging significantly improves tumor delineation and diagnostic performance, without significant alteration of SA-PET quantitative accuracy and NEMA NU 4-2008 IQ parameters.

A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery.

PubMed

Huang, Huasheng; Deng, Jizhong; Lan, Yubin; Yang, Aqing; Deng, Xiaoling; Zhang, Lei

2018-01-01

Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery.

Optimization of camera exposure durations for multi-exposure speckle imaging of the microcirculation

PubMed Central

Kazmi, S. M. Shams; Balial, Satyajit; Dunn, Andrew K.

2014-01-01

Improved Laser Speckle Contrast Imaging (LSCI) blood flow analyses that incorporate inverse models of the underlying laser-tissue interaction have been used to develop more quantitative implementations of speckle flowmetry such as Multi-Exposure Speckle Imaging (MESI). In this paper, we determine the optimal camera exposure durations required for obtaining flow information with comparable accuracy with the prevailing MESI implementation utilized in recent in vivo rodent studies. A looping leave-one-out (LOO) algorithm was used to identify exposure subsets which were analyzed for accuracy against flows obtained from analysis with the original full exposure set over 9 animals comprising n = 314 regional flow measurements. From the 15 original exposures, 6 exposures were found using the LOO process to provide comparable accuracy, defined as being no more than 10% deviant, with the original flow measurements. The optimal subset of exposures provides a basis set of camera durations for speckle flowmetry studies of the microcirculation and confers a two-fold faster acquisition rate and a 28% reduction in processing time without sacrificing accuracy. Additionally, the optimization process can be used to identify further reductions in the exposure subsets for tailoring imaging over less expansive flow distributions to enable even faster imaging. PMID:25071956

Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors.

PubMed

Lee, David; Park, Sang-Hoon; Lee, Sang-Goog

2017-10-07

In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain-computer interfaces. The proposed method is configured as follows: first, wavelet transforms are applied to extract the feature vectors for identification of motor imagery electroencephalography (EEG) and principal component analyses are used to reduce the dimensionality of the feature vectors and linearly combine them. Subsequently, the GMM universal background model is trained by the expectation-maximization (EM) algorithm to purify the training data and reduce its size. Finally, a purified and reduced GMM-supervector is used to train the support vector machine classifier. The performance of the proposed method was evaluated for three different motor imagery datasets in terms of accuracy, kappa, mutual information, and computation time, and compared with the state-of-the-art algorithms. The results from the study indicate that the proposed method achieves high accuracy with a small amount of training data compared with the state-of-the-art algorithms in motor imagery EEG classification.

Influence of different topographic correction strategies on mountain vegetation classification accuracy in the Lancang Watershed, China

NASA Astrophysics Data System (ADS)

Zhang, Zhiming; de Wulf, Robert R.; van Coillie, Frieke M. B.; Verbeke, Lieven P. C.; de Clercq, Eva M.; Ou, Xiaokun

2011-01-01

Mapping of vegetation using remote sensing in mountainous areas is considerably hampered by topographic effects on the spectral response pattern. A variety of topographic normalization techniques have been proposed to correct these illumination effects due to topography. The purpose of this study was to compare six different topographic normalization methods (Cosine correction, Minnaert correction, C-correction, Sun-canopy-sensor correction, two-stage topographic normalization, and slope matching technique) for their effectiveness in enhancing vegetation classification in mountainous environments. Since most of the vegetation classes in the rugged terrain of the Lancang Watershed (China) did not feature a normal distribution, artificial neural networks (ANNs) were employed as a classifier. Comparing the ANN classifications, none of the topographic correction methods could significantly improve ETM+ image classification overall accuracy. Nevertheless, at the class level, the accuracy of pine forest could be increased by using topographically corrected images. On the contrary, oak forest and mixed forest accuracies were significantly decreased by using corrected images. The results also showed that none of the topographic normalization strategies was satisfactorily able to correct for the topographic effects in severely shadowed areas.

Validation of Australian data in the Australasian Vascular Audit.

PubMed

Beiles, Charles Barry; Bourke, Bernie M

2014-09-01

Accuracy of data is important in any clinical audit. It is necessary to determine how complete the dataset is as well as the accuracy of the data that have been entered. The Australasian Vascular Audit has been operational for 4 years and a data validation process has been undertaken. An independent data source is available, which is collected by the Australian Institute of Health and Welfare. This collects all public and private hospital data and is available for interrogation. Similarly, private-only data are available from the Medicare website. This has been compared with the Australasian Vascular Audit dataset to establish completeness of data collection. Quality of data collected has been verified by comparing accuracy of data fields with that present in patient records in a 5% random sample. For the 2 years studied, there was a 63% capture rate in Australia for all patients. In the private sector, only 50% of patients were captured with a significant decrease noted in 2013. The quality of data entered had a 2.6% error rate. There is a need to increase compliance with vascular audit in Australia and data accuracy is acceptable but could be improved. © 2014 Royal Australasian College of Surgeons.

The availability of prior ECGs improves paramedic accuracy in recognizing ST-segment elevation myocardial infarction.

PubMed

O'Donnell, Daniel; Mancera, Mike; Savory, Eric; Christopher, Shawn; Schaffer, Jason; Roumpf, Steve

2015-01-01

Early and accurate identification of ST-elevation myocardial infarction (STEMI) by prehospital providers has been shown to significantly improve door to balloon times and improve patient outcomes. Previous studies have shown that paramedic accuracy in reading 12 lead ECGs can range from 86% to 94%. However, recent studies have demonstrated that accuracy diminishes for the more uncommon STEMI presentations (e.g. lateral). Unlike hospital physicians, paramedics rarely have the ability to review previous ECGs for comparison. Whether or not a prior ECG can improve paramedic accuracy is not known. The availability of prior ECGs improves paramedic accuracy in ECG interpretation. 130 paramedics were given a single clinical scenario. Then they were randomly assigned 12 computerized prehospital ECGs, 6 with and 6 without an accompanying prior ECG. All ECGs were obtained from a local STEMI registry. For each ECG paramedics were asked to determine whether or not there was a STEMI and to rate their confidence in their interpretation. To determine if the old ECGs improved accuracy we used a mixed effects logistic regression model to calculate p-values between the control and intervention. The addition of a previous ECG improved the accuracy of identifying STEMIs from 75.5% to 80.5% (p=0.015). A previous ECG also increased paramedic confidence in their interpretation (p=0.011). The availability of previous ECGs improves paramedic accuracy and enhances their confidence in interpreting STEMIs. Further studies are needed to evaluate this impact in a clinical setting. Copyright © 2015 Elsevier Inc. All rights reserved.

A Comparison between the Effect of Free Time and Daily Report Cards on the Academic Behavior of Junior High School Students.

ERIC Educational Resources Information Center

Martin, R. C.; McLaughlin, T. F.

1981-01-01

When the effectiveness of free time and daily report card systems on assignment completion and accuracy of four junior high school special education students were compared, results indicated that both procedures improved students' performance. (Author)

Multi-site evaluation of IKONOS data for classification of tropical coral reef environments

USGS Publications Warehouse

Andrefouet, S.; Kramer, Philip; Torres-Pulliza, D.; Joyce, K.E.; Hochberg, E.J.; Garza-Perez, R.; Mumby, P.J.; Riegl, Bernhard; Yamano, H.; White, W.H.; Zubia, M.; Brock, J.C.; Phinn, S.R.; Naseer, A.; Hatcher, B.G.; Muller-Karger, F. E.

2003-01-01

Ten IKONOS images of different coral reef sites distributed around the world were processed to assess the potential of 4-m resolution multispectral data for coral reef habitat mapping. Complexity of reef environments, established by field observation, ranged from 3 to 15 classes of benthic habitats containing various combinations of sediments, carbonate pavement, seagrass, algae, and corals in different geomorphologic zones (forereef, lagoon, patch reef, reef flats). Processing included corrections for sea surface roughness and bathymetry, unsupervised or supervised classification, and accuracy assessment based on ground-truth data. IKONOS classification results were compared with classified Landsat 7 imagery for simple to moderate complexity of reef habitats (5-11 classes). For both sensors, overall accuracies of the classifications show a general linear trend of decreasing accuracy with increasing habitat complexity. The IKONOS sensor performed better, with a 15-20% improvement in accuracy compared to Landsat. For IKONOS, overall accuracy was 77% for 4-5 classes, 71% for 7-8 classes, 65% in 9-11 classes, and 53% for more than 13 classes. The Landsat classification accuracy was systematically lower, with an average of 56% for 5-10 classes. Within this general trend, inter-site comparisons and specificities demonstrate the benefits of different approaches. Pre-segmentation of the different geomorphologic zones and depth correction provided different advantages in different environments. Our results help guide scientists and managers in applying IKONOS-class data for coral reef mapping applications. ?? 2003 Elsevier Inc. All rights reserved.

Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images.

PubMed

Knauer, Uwe; Matros, Andrea; Petrovic, Tijana; Zanker, Timothy; Scott, Eileen S; Seiffert, Udo

2017-01-01

Hyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of the full spectral information, appropriate selection of individual bands, or calculation of spectral indices. Limitations of such approaches are reduced classification accuracy, reduced robustness due to spatial variation of the spectral information across the surface of the objects measured as well as a loss of information intrinsic to band selection and use of spectral indices. In this paper we present an improved spatial-spectral segmentation approach for the analysis of hyperspectral imaging data and its application for the prediction of powdery mildew infection levels (disease severity) of intact Chardonnay grape bunches shortly before veraison. Instead of calculating texture features (spatial features) for the huge number of spectral bands independently, dimensionality reduction by means of Linear Discriminant Analysis (LDA) was applied first to derive a few descriptive image bands. Subsequent classification was based on modified Random Forest classifiers and selective extraction of texture parameters from the integral image representation of the image bands generated. Dimensionality reduction, integral images, and the selective feature extraction led to improved classification accuracies of up to [Formula: see text] for detached berries used as a reference sample (training dataset). Our approach was validated by predicting infection levels for a sample of 30 intact bunches. Classification accuracy improved with the number of decision trees of the Random Forest classifier. These results corresponded with qPCR results. An accuracy of 0.87 was achieved in classification of healthy, infected, and severely diseased bunches. However, discrimination between visually healthy and infected bunches proved to be challenging for a few samples, perhaps due to colonized berries or sparse mycelia hidden within the bunch or airborne conidia on the berries that were detected by qPCR. An advanced approach to hyperspectral image classification based on combined spatial and spectral image features, potentially applicable to many available hyperspectral sensor technologies, has been developed and validated to improve the detection of powdery mildew infection levels of Chardonnay grape bunches. The spatial-spectral approach improved especially the detection of light infection levels compared with pixel-wise spectral data analysis. This approach is expected to improve the speed and accuracy of disease detection once the thresholds for fungal biomass detected by hyperspectral imaging are established; it can also facilitate monitoring in plant phenotyping of grapevine and additional crops.

Accuracy of pedicle screw placement comparing robot-assisted technology and the free-hand with fluoroscopy-guided method in spine surgery: An updated meta-analysis.

PubMed

Fan, Yong; Du, Jin Peng; Liu, Ji Jun; Zhang, Jia Nan; Qiao, Huan Huan; Liu, Shi Chang; Hao, Ding Jun

2018-06-01

A miniature spine-mounted robot has recently been introduced to further improve the accuracy of pedicle screw placement in spine surgery. However, the differences in accuracy between the robotic-assisted (RA) technique and the free-hand with fluoroscopy-guided (FH) method for pedicle screw placement are controversial. A meta-analysis was conducted to focus on this problem. Several randomized controlled trials (RCTs) and cohort studies involving RA and FH and published before January 2017 were searched for using the Cochrane Library, Ovid, Web of Science, PubMed, and EMBASE databases. A total of 55 papers were selected. After the full-text assessment, 45 clinical trials were excluded. The final meta-analysis included 10 articles. The accuracy of pedicle screw placement within the RA group was significantly greater than the accuracy within the FH group (odds ratio 95%, "perfect accuracy" confidence interval: 1.38-2.07, P < .01; odds ratio 95% "clinically acceptable" Confidence Interval: 1.17-2.08, P < .01). There are significant differences in accuracy between RA surgery and FH surgery. It was demonstrated that the RA technique is superior to the conventional method in terms of the accuracy of pedicle screw placement.

Investigation of a Hydrostatic Weighing Method for a 1 kg Mass Comparator

NASA Astrophysics Data System (ADS)

Probst, R.; Kochsiek, M.

1984-01-01

A mass comparator for the comparison of 1 kg weights was built according to a hydrostatic weighing principle, where the buoyancy in a liquid serves to compensate the force due to gravity. In accordance with the method known for hydrometers or areometers, the immersion depth of a float is measured as a function of the force due to gravity, using a laser interferometer. The substitution principle can thus be quite simply realized at constant load. An essential advantage of this weighing method compared with the mechanical beam balance results from the frictionless and vibration-resistant bearing of the float in the liquid. For achieving a high accuracy with this technique, two prerequisites were important: the reduction of the influence of temperature by adapting the coefficients of expansion of buoyant body and liquid to each other, and the improvement of the wetting property of the liquid by adding a surfactant. The accuracy was further improved by the use of an electromagnetic feedback control to keep the immersion depth constant. By this method, a relative standard deviation of the weighings of better than 5 × 10-9 could be achieved.

A multi-time-step noise reduction method for measuring velocity statistics from particle tracking velocimetry

NASA Astrophysics Data System (ADS)

Machicoane, Nathanaël; López-Caballero, Miguel; Bourgoin, Mickael; Aliseda, Alberto; Volk, Romain

2017-10-01

We present a method to improve the accuracy of velocity measurements for fluid flow or particles immersed in it, based on a multi-time-step approach that allows for cancellation of noise in the velocity measurements. Improved velocity statistics, a critical element in turbulent flow measurements, can be computed from the combination of the velocity moments computed using standard particle tracking velocimetry (PTV) or particle image velocimetry (PIV) techniques for data sets that have been collected over different values of time intervals between images. This method produces Eulerian velocity fields and Lagrangian velocity statistics with much lower noise levels compared to standard PIV or PTV measurements, without the need of filtering and/or windowing. Particle displacement between two frames is computed for multiple different time-step values between frames in a canonical experiment of homogeneous isotropic turbulence. The second order velocity structure function of the flow is computed with the new method and compared to results from traditional measurement techniques in the literature. Increased accuracy is also demonstrated by comparing the dissipation rate of turbulent kinetic energy measured from this function against previously validated measurements.

An index of refraction algorithm for seawater over temperature, pressure, salinity, density, and wavelength

NASA Astrophysics Data System (ADS)

Millard, R. C.; Seaver, G.

1990-12-01

A 27-term index of refraction algorithm for pure and sea waters has been developed using four experimental data sets of differing accuracies. They cover the range 500-700 nm in wavelength, 0-30°C in temperature, 0-40 psu in salinity, and 0-11,000 db in pressure. The index of refraction algorithm has an accuracy that varies from 0.4 ppm for pure water at atmospheric pressure to 80 ppm at high pressures, but preserves the accuracy of each original data set. This algorithm is a significant improvement over existing descriptions as it is in analytical form with a better and more carefully defined accuracy. A salinometer algorithm with the same uncertainty has been created by numerically inverting the index algorithm using the Newton-Raphson method. The 27-term index algorithm was used to generate a pseudo-data set at the sodium D wavelength (589.26 nm) from which a 6-term densitometer algorithm was constructed. The densitometer algorithm also produces salinity as an intermediate step in the salinity inversion. The densitometer residuals have a standard deviation of 0.049 kg m -3 which is not accurate enough for most oceanographic applications. However, the densitometer algorithm was used to explore the sensitivity of density from this technique to temperature and pressure uncertainties. To achieve a deep ocean densitometer of 0.001 kg m -3 accuracy would require the index of refraction to have an accuracy of 0.3 ppm, the temperature an accuracy of 0.01°C and the pressure 1 db. Our assessment of the currently available index of refraction measurements finds that only the data for fresh water at atmospheric pressure produce an algorithm satisfactory for oceanographic use (density to 0.4 ppm). The data base for the algorithm at higher pressures and various salinities requires an order of magnitude or better improvement in index measurement accuracy before the resultant density accuracy will be comparable to the currently available oceanographic algorithm.

Dispersal and extrapolation on the accuracy of temporal predictions from distribution models for the Darwin's frog.

PubMed

Uribe-Rivera, David E; Soto-Azat, Claudio; Valenzuela-Sánchez, Andrés; Bizama, Gustavo; Simonetti, Javier A; Pliscoff, Patricio

2017-07-01

Climate change is a major threat to biodiversity; the development of models that reliably predict its effects on species distributions is a priority for conservation biogeography. Two of the main issues for accurate temporal predictions from Species Distribution Models (SDM) are model extrapolation and unrealistic dispersal scenarios. We assessed the consequences of these issues on the accuracy of climate-driven SDM predictions for the dispersal-limited Darwin's frog Rhinoderma darwinii in South America. We calibrated models using historical data (1950-1975) and projected them across 40 yr to predict distribution under current climatic conditions, assessing predictive accuracy through the area under the ROC curve (AUC) and True Skill Statistics (TSS), contrasting binary model predictions against temporal-independent validation data set (i.e., current presences/absences). To assess the effects of incorporating dispersal processes we compared the predictive accuracy of dispersal constrained models with no dispersal limited SDMs; and to assess the effects of model extrapolation on the predictive accuracy of SDMs, we compared this between extrapolated and no extrapolated areas. The incorporation of dispersal processes enhanced predictive accuracy, mainly due to a decrease in the false presence rate of model predictions, which is consistent with discrimination of suitable but inaccessible habitat. This also had consequences on range size changes over time, which is the most used proxy for extinction risk from climate change. The area of current climatic conditions that was absent in the baseline conditions (i.e., extrapolated areas) represents 39% of the study area, leading to a significant decrease in predictive accuracy of model predictions for those areas. Our results highlight (1) incorporating dispersal processes can improve predictive accuracy of temporal transference of SDMs and reduce uncertainties of extinction risk assessments from global change; (2) as geographical areas subjected to novel climates are expected to arise, they must be reported as they show less accurate predictions under future climate scenarios. Consequently, environmental extrapolation and dispersal processes should be explicitly incorporated to report and reduce uncertainties in temporal predictions of SDMs, respectively. Doing so, we expect to improve the reliability of the information we provide for conservation decision makers under future climate change scenarios. © 2017 by the Ecological Society of America.

CT colonography: influence of 3D viewing and polyp candidate features on interpretation with computer-aided detection.

PubMed

Shi, Rong; Schraedley-Desmond, Pamela; Napel, Sandy; Olcott, Eric W; Jeffrey, R Brooke; Yee, Judy; Zalis, Michael E; Margolis, Daniel; Paik, David S; Sherbondy, Anthony J; Sundaram, Padmavathi; Beaulieu, Christopher F

2006-06-01

To retrospectively determine if three-dimensional (3D) viewing improves radiologists' accuracy in classifying true-positive (TP) and false-positive (FP) polyp candidates identified with computer-aided detection (CAD) and to determine candidate polyp features that are associated with classification accuracy, with known polyps serving as the reference standard. Institutional review board approval and informed consent were obtained; this study was HIPAA compliant. Forty-seven computed tomographic (CT) colonography data sets were obtained in 26 men and 10 women (age range, 42-76 years). Four radiologists classified 705 polyp candidates (53 TP candidates, 652 FP candidates) identified with CAD; initially, only two-dimensional images were used, but these were later supplemented with 3D rendering. Another radiologist unblinded to colonoscopy findings characterized the features of each candidate, assessed colon distention and preparation, and defined the true nature of FP candidates. Receiver operating characteristic curves were used to compare readers' performance, and repeated-measures analysis of variance was used to test features that affect interpretation. Use of 3D viewing improved classification accuracy for three readers and increased the area under the receiver operating characteristic curve to 0.96-0.97 (P<.001). For TP candidates, maximum polyp width (P=.038), polyp height (P=.019), and preparation (P=.004) significantly affected accuracy. For FP candidates, colonic segment (P=.007), attenuation (P<.001), surface smoothness (P<.001), distention (P=.034), preparation (P<.001), and true nature of candidate lesions (P<.001) significantly affected accuracy. Use of 3D viewing increases reader accuracy in the classification of polyp candidates identified with CAD. Polyp size and examination quality are significantly associated with accuracy. Copyright (c) RSNA, 2006.

The effect of search term on the quality and accuracy of online information regarding distal radius fractures.

PubMed

Dy, Christopher J; Taylor, Samuel A; Patel, Ronak M; Kitay, Alison; Roberts, Timothy R; Daluiski, Aaron

2012-09-01

Recent emphasis on shared decision making and patient-centered research has increased the importance of patient education and health literacy. The internet is rapidly growing as a source of self-education for patients. However, concern exists over the quality, accuracy, and readability of the information. Our objective was to determine whether the quality, accuracy, and readability of information online about distal radius fractures vary with the search term. This was a prospective evaluation of 3 search engines using 3 different search terms of varying sophistication ("distal radius fracture," "wrist fracture," and "broken wrist"). We evaluated 70 unique Web sites for quality, accuracy, and readability. We used comparative statistics to determine whether the search term affected the quality, accuracy, and readability of the Web sites found. Three orthopedic surgeons independently gauged quality and accuracy of information using a set of predetermined scoring criteria. We evaluated the readability of the Web site using the Fleisch-Kincaid score for reading grade level. There were significant differences in the quality, accuracy, and readability of information found, depending on the search term. We found higher quality and accuracy resulted from the search term "distal radius fracture," particularly compared with Web sites resulting from the term "broken wrist." The reading level was higher than recommended in 65 of the 70 Web sites and was significantly higher when searching with "distal radius fracture" than "wrist fracture" or "broken wrist." There was no correlation between Web site reading level and quality or accuracy. The readability of information about distal radius fractures in most Web sites was higher than the recommended reading level for the general public. The quality and accuracy of the information found significantly varied with the sophistication of the search term used. Physicians, professional societies, and search engines should consider efforts to improve internet access to high-quality information at an understandable level. Copyright © 2012 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.

Control over structure-specific flexibility improves anatomical accuracy for point-based deformable registration in bladder cancer radiotherapy.

PubMed

Wognum, S; Bondar, L; Zolnay, A G; Chai, X; Hulshof, M C C M; Hoogeman, M S; Bel, A

2013-02-01

Future developments in image guided adaptive radiotherapy (IGART) for bladder cancer require accurate deformable image registration techniques for the precise assessment of tumor and bladder motion and deformation that occur as a result of large bladder volume changes during the course of radiotherapy treatment. The aim was to employ an extended version of a point-based deformable registration algorithm that allows control over tissue-specific flexibility in combination with the authors' unique patient dataset, in order to overcome two major challenges of bladder cancer registration, i.e., the difficulty in accounting for the difference in flexibility between the bladder wall and tumor and the lack of visible anatomical landmarks for validation. The registration algorithm used in the current study is an extension of the symmetric-thin plate splines-robust point matching (S-TPS-RPM) algorithm, a symmetric feature-based registration method. The S-TPS-RPM algorithm has been previously extended to allow control over the degree of flexibility of different structures via a weight parameter. The extended weighted S-TPS-RPM algorithm was tested and validated on CT data (planning- and four to five repeat-CTs) of five urinary bladder cancer patients who received lipiodol injections before radiotherapy. The performance of the weighted S-TPS-RPM method, applied to bladder and tumor structures simultaneously, was compared with a previous version of the S-TPS-RPM algorithm applied to bladder wall structure alone and with a simultaneous nonweighted S-TPS-RPM registration of the bladder and tumor structures. Performance was assessed in terms of anatomical and geometric accuracy. The anatomical accuracy was calculated as the residual distance error (RDE) of the lipiodol markers and the geometric accuracy was determined by the surface distance, surface coverage, and inverse consistency errors. Optimal parameter values for the flexibility and bladder weight parameters were determined for the weighted S-TPS-RPM. The weighted S-TPS-RPM registration algorithm with optimal parameters significantly improved the anatomical accuracy as compared to S-TPS-RPM registration of the bladder alone and reduced the range of the anatomical errors by half as compared with the simultaneous nonweighted S-TPS-RPM registration of the bladder and tumor structures. The weighted algorithm reduced the RDE range of lipiodol markers from 0.9-14 mm after rigid bone match to 0.9-4.0 mm, compared to a range of 1.1-9.1 mm with S-TPS-RPM of bladder alone and 0.9-9.4 mm for simultaneous nonweighted registration. All registration methods resulted in good geometric accuracy on the bladder; average error values were all below 1.2 mm. The weighted S-TPS-RPM registration algorithm with additional weight parameter allowed indirect control over structure-specific flexibility in multistructure registrations of bladder and bladder tumor, enabling anatomically coherent registrations. The availability of an anatomically validated deformable registration method opens up the horizon for improvements in IGART for bladder cancer.

Radial k-t SPIRiT: autocalibrated parallel imaging for generalized phase-contrast MRI.

PubMed

Santelli, Claudio; Schaeffter, Tobias; Kozerke, Sebastian

2014-11-01

To extend SPIRiT to additionally exploit temporal correlations for highly accelerated generalized phase-contrast MRI and to compare the performance of the proposed radial k-t SPIRiT method relative to frame-by-frame SPIRiT and radial k-t GRAPPA reconstruction for velocity and turbulence mapping in the aortic arch. Free-breathing navigator-gated two-dimensional radial cine imaging with three-directional multi-point velocity encoding was implemented and fully sampled data were obtained in the aortic arch of healthy volunteers. Velocities were encoded with three different first gradient moments per axis to permit quantification of mean velocity and turbulent kinetic energy. Velocity and turbulent kinetic energy maps from up to 14-fold undersampled data were compared for k-t SPIRiT, frame-by-frame SPIRiT, and k-t GRAPPA relative to the fully sampled reference. Using k-t SPIRiT, improvements in magnitude and velocity reconstruction accuracy were found. Temporally resolved magnitude profiles revealed a reduction in spatial blurring with k-t SPIRiT compared with frame-by-frame SPIRiT and k-t GRAPPA for all velocity encodings, leading to improved estimates of turbulent kinetic energy. k-t SPIRiT offers improved reconstruction accuracy at high radial undersampling factors and hence facilitates the use of generalized phase-contrast MRI for routine use. Copyright © 2013 Wiley Periodicals, Inc.

Impact of point-of-care implementation of Xpert® MTB/RIF: product vs. process innovation.

PubMed

Schumacher, S G; Thangakunam, B; Denkinger, C M; Oliver, A A; Shakti, K B; Qin, Z Z; Michael, J S; Luo, R; Pai, M; Christopher, D J

2015-09-01

Both product innovation (e.g., more sensitive tests) and process innovation (e.g., a point-of-care [POC] testing programme) could improve patient outcomes. To study the respective contributions of product and process innovation in improving patient outcomes. We implemented a POC programme using Xpert(®) MTB/RIF in an out-patient clinic of a tertiary care hospital in India. We measured the impact of process innovation by comparing time to diagnosis with routine testing vs. POC testing. We measured the impact of product innovation by comparing accuracy and time to diagnosis using smear microscopy vs. POC Xpert. We enrolled 1012 patients over a 15-month period. Xpert had high accuracy, but the incremental value of one Xpert over two smears was only 6% (95%CI 3-12). Implementing Xpert as a routine laboratory test did not reduce the time to diagnosis compared to smear-based diagnosis. In contrast, the POC programme reduced the time to diagnosis by 5.5 days (95%CI 4.3-6.7), but required dedicated staff and substantial adaptation of clinic workflow. Process innovation by way of a POC Xpert programme had a greater impact on time to diagnosis than the product per se, and can yield important improvements in patient care that are complementary to those achieved by introducing innovative technologies.

Comparative study of surrogate models for groundwater contamination source identification at DNAPL-contaminated sites

NASA Astrophysics Data System (ADS)

Hou, Zeyu; Lu, Wenxi

2018-05-01

Knowledge of groundwater contamination sources is critical for effectively protecting groundwater resources, estimating risks, mitigating disaster, and designing remediation strategies. Many methods for groundwater contamination source identification (GCSI) have been developed in recent years, including the simulation-optimization technique. This study proposes utilizing a support vector regression (SVR) model and a kernel extreme learning machine (KELM) model to enrich the content of the surrogate model. The surrogate model was itself key in replacing the simulation model, reducing the huge computational burden of iterations in the simulation-optimization technique to solve GCSI problems, especially in GCSI problems of aquifers contaminated by dense nonaqueous phase liquids (DNAPLs). A comparative study between the Kriging, SVR, and KELM models is reported. Additionally, there is analysis of the influence of parameter optimization and the structure of the training sample dataset on the approximation accuracy of the surrogate model. It was found that the KELM model was the most accurate surrogate model, and its performance was significantly improved after parameter optimization. The approximation accuracy of the surrogate model to the simulation model did not always improve with increasing numbers of training samples. Using the appropriate number of training samples was critical for improving the performance of the surrogate model and avoiding unnecessary computational workload. It was concluded that the KELM model developed in this work could reasonably predict system responses in given operation conditions. Replacing the simulation model with a KELM model considerably reduced the computational burden of the simulation-optimization process and also maintained high computation accuracy.

Proposed hybrid-classifier ensemble algorithm to map snow cover area

NASA Astrophysics Data System (ADS)

Nijhawan, Rahul; Raman, Balasubramanian; Das, Josodhir

2018-01-01

Metaclassification ensemble approach is known to improve the prediction performance of snow-covered area. The methodology adopted in this case is based on neural network along with four state-of-art machine learning algorithms: support vector machine, artificial neural networks, spectral angle mapper, K-mean clustering, and a snow index: normalized difference snow index. An AdaBoost ensemble algorithm related to decision tree for snow-cover mapping is also proposed. According to available literature, these methods have been rarely used for snow-cover mapping. Employing the above techniques, a study was conducted for Raktavarn and Chaturangi Bamak glaciers, Uttarakhand, Himalaya using multispectral Landsat 7 ETM+ (enhanced thematic mapper) image. The study also compares the results with those obtained from statistical combination methods (majority rule and belief functions) and accuracies of individual classifiers. Accuracy assessment is performed by computing the quantity and allocation disagreement, analyzing statistic measures (accuracy, precision, specificity, AUC, and sensitivity) and receiver operating characteristic curves. A total of 225 combinations of parameters for individual classifiers were trained and tested on the dataset and results were compared with the proposed approach. It was observed that the proposed methodology produced the highest classification accuracy (95.21%), close to (94.01%) that was produced by the proposed AdaBoost ensemble algorithm. From the sets of observations, it was concluded that the ensemble of classifiers produced better results compared to individual classifiers.

Accuracy and Injection Force of the Gla-300 Injection Device Compared With Other Commercialized Disposable Insulin Pens.

PubMed

Klonoff, David; Nayberg, Irina; Thonius, Marissa; See, Florian; Abdel-Tawab, Mona; Erbstein, Frank; Haak, Thomas

2015-08-26

To deliver insulin glargine 300 U/mL (Gla-300), the widely used SoloSTAR(®) pen has been modified to allow for accurate and precise delivery of required insulin units in one-third of the volume compared with insulin glargine 100 U/mL, while improving usability. Here we compare the accuracy and injection force of 3 disposable insulin pens: Gla-300 SoloSTAR(®), FlexPen(®), and KwikPen™. For the accuracy assessment, 60 of each of the 3 tested devices were used for the delivery of 3 different doses (1 U, half-maximal dose, and maximal dose), which were measured gravimetrically. For the injection force assessment, 20 pens of each of the 3 types were tested twice at half-maximal and once at maximal dose, at an injection speed of 6 U/s. All tested pens met the International Organization for Standardization (ISO) requirements for dosing accuracy, with Gla-300 SoloSTAR showing the lowest between-dose variation (greatest reproducibility) at all dose levels. Mean injection force was significantly lower for Gla-300 SoloSTAR than for the other 2 pens at both half maximal and maximal doses (P < .0271). All tested pens were accurate according to ISO criteria, and the Gla-300 SoloSTAR pen displayed the greatest reproducibility and lowest injection force of any of the 3 tested devices. © 2015 Diabetes Technology Society.

Changes in skill and physical fitness following training in talent-identified volleyball players.

PubMed

Gabbett, Tim; Georgieff, Boris; Anderson, Steve; Cotton, Brad; Savovic, Darko; Nicholson, Lee

2006-02-01

This study investigated the effect of a skill-based training program on measurements of skill and physical fitness in talent-identified volleyball players. Twenty-six talented junior volleyball players (mean +/- SE age, 15.5 +/- 0.2 years) participated in an 8-week skill-based training program that included 3 skill-based court sessions per week. Skills sessions were designed to develop passing, setting, serving, spiking, and blocking technique and accuracy as well as game tactics and positioning skills. Coaches used a combination of technical and instructional coaching, coupled with skill-based games to facilitate learning. Subjects performed measurements of skill (passing, setting, serving, and spiking technique and accuracy), standard anthropometry (height, standing-reach height, body mass, and sum of 7 skinfolds), lower-body muscular power (vertical jump, spike jump), upper-body muscular power (overhead medicine-ball throw), speed (5- and 10-m sprint), agility (T-test), and maximal aerobic power (multistage fitness test) before and after training. Training induced significant (p < 0.05) improvements in spiking, setting, and passing accuracy and spiking and passing technique. Compared with pretraining, there were significant (p < 0.05) improvements in 5- and 10-m speed and agility. There were no significant differences between pretraining and posttraining for body mass, skinfold thickness, lower-body muscular power, upper-body muscular power, and maximal aerobic power. These findings demonstrate that skill-based volleyball training improves spiking, setting, and passing accuracy and spiking and passing technique, but has little effect on the physiological and anthropometric characteristics of players.

Facilitating text reading in posterior cortical atrophy.

PubMed

Yong, Keir X X; Rajdev, Kishan; Shakespeare, Timothy J; Leff, Alexander P; Crutch, Sebastian J

2015-07-28

We report (1) the quantitative investigation of text reading in posterior cortical atrophy (PCA), and (2) the effects of 2 novel software-based reading aids that result in dramatic improvements in the reading ability of patients with PCA. Reading performance, eye movements, and fixations were assessed in patients with PCA and typical Alzheimer disease and in healthy controls (experiment 1). Two reading aids (single- and double-word) were evaluated based on the notion that reducing the spatial and oculomotor demands of text reading might support reading in PCA (experiment 2). Mean reading accuracy in patients with PCA was significantly worse (57%) compared with both patients with typical Alzheimer disease (98%) and healthy controls (99%); spatial aspects of passages were the primary determinants of text reading ability in PCA. Both aids led to considerable gains in reading accuracy (PCA mean reading accuracy: single-word reading aid = 96%; individual patient improvement range: 6%-270%) and self-rated measures of reading. Data suggest a greater efficiency of fixations and eye movements under the single-word reading aid in patients with PCA. These findings demonstrate how neurologic characterization of a neurodegenerative syndrome (PCA) and detailed cognitive analysis of an important everyday skill (reading) can combine to yield aids capable of supporting important everyday functional abilities. This study provides Class III evidence that for patients with PCA, 2 software-based reading aids (single-word and double-word) improve reading accuracy. © 2015 American Academy of Neurology.

Facilitating text reading in posterior cortical atrophy

PubMed Central

Rajdev, Kishan; Shakespeare, Timothy J.; Leff, Alexander P.; Crutch, Sebastian J.

2015-01-01

Objective: We report (1) the quantitative investigation of text reading in posterior cortical atrophy (PCA), and (2) the effects of 2 novel software-based reading aids that result in dramatic improvements in the reading ability of patients with PCA. Methods: Reading performance, eye movements, and fixations were assessed in patients with PCA and typical Alzheimer disease and in healthy controls (experiment 1). Two reading aids (single- and double-word) were evaluated based on the notion that reducing the spatial and oculomotor demands of text reading might support reading in PCA (experiment 2). Results: Mean reading accuracy in patients with PCA was significantly worse (57%) compared with both patients with typical Alzheimer disease (98%) and healthy controls (99%); spatial aspects of passages were the primary determinants of text reading ability in PCA. Both aids led to considerable gains in reading accuracy (PCA mean reading accuracy: single-word reading aid = 96%; individual patient improvement range: 6%–270%) and self-rated measures of reading. Data suggest a greater efficiency of fixations and eye movements under the single-word reading aid in patients with PCA. Conclusions: These findings demonstrate how neurologic characterization of a neurodegenerative syndrome (PCA) and detailed cognitive analysis of an important everyday skill (reading) can combine to yield aids capable of supporting important everyday functional abilities. Classification of evidence: This study provides Class III evidence that for patients with PCA, 2 software-based reading aids (single-word and double-word) improve reading accuracy. PMID:26138948

[A practical procedure to improve the accuracy of radiochromic film dosimetry: a integration with a correction method of uniformity correction and a red/blue correction method].

PubMed

Uehara, Ryuzo; Tachibana, Hidenobu; Ito, Yasushi; Yoshino, Shinichi; Matsubayashi, Fumiyasu; Sato, Tomoharu

2013-06-01

It has been reported that the light scattering could worsen the accuracy of dose distribution measurement using a radiochromic film. The purpose of this study was to investigate the accuracy of two different films, EDR2 and EBT2, as film dosimetry tools. The effectiveness of a correction method for the non-uniformity caused from EBT2 film and the light scattering was also evaluated. In addition the efficacy of this correction method integrated with the red/blue correction method was assessed. EDR2 and EBT2 films were read using a flatbed charge-coupled device scanner (EPSON 10000G). Dose differences on the axis perpendicular to the scanner lamp movement axis were within 1% with EDR2, but exceeded 3% (Maximum: +8%) with EBT2. The non-uniformity correction method, after a single film exposure, was applied to the readout of the films. A corrected dose distribution data was subsequently created. The correction method showed more than 10%-better pass ratios in dose difference evaluation than when the correction method was not applied. The red/blue correction method resulted in 5%-improvement compared with the standard procedure that employed red color only. The correction method with EBT2 proved to be able to rapidly correct non-uniformity, and has potential for routine clinical IMRT dose verification if the accuracy of EBT2 is required to be similar to that of EDR2. The use of red/blue correction method may improve the accuracy, but we recommend we should use the red/blue correction method carefully and understand the characteristics of EBT2 for red color only and the red/blue correction method.

Improving zero-training brain-computer interfaces by mixing model estimators

NASA Astrophysics Data System (ADS)

Verhoeven, T.; Hübner, D.; Tangermann, M.; Müller, K. R.; Dambre, J.; Kindermans, P. J.

2017-06-01

Objective. Brain-computer interfaces (BCI) based on event-related potentials (ERP) incorporate a decoder to classify recorded brain signals and subsequently select a control signal that drives a computer application. Standard supervised BCI decoders require a tedious calibration procedure prior to every session. Several unsupervised classification methods have been proposed that tune the decoder during actual use and as such omit this calibration. Each of these methods has its own strengths and weaknesses. Our aim is to improve overall accuracy of ERP-based BCIs without calibration. Approach. We consider two approaches for unsupervised classification of ERP signals. Learning from label proportions (LLP) was recently shown to be guaranteed to converge to a supervised decoder when enough data is available. In contrast, the formerly proposed expectation maximization (EM) based decoding for ERP-BCI does not have this guarantee. However, while this decoder has high variance due to random initialization of its parameters, it obtains a higher accuracy faster than LLP when the initialization is good. We introduce a method to optimally combine these two unsupervised decoding methods, letting one method’s strengths compensate for the weaknesses of the other and vice versa. The new method is compared to the aforementioned methods in a resimulation of an experiment with a visual speller. Main results. Analysis of the experimental results shows that the new method exceeds the performance of the previous unsupervised classification approaches in terms of ERP classification accuracy and symbol selection accuracy during the spelling experiment. Furthermore, the method shows less dependency on random initialization of model parameters and is consequently more reliable. Significance. Improving the accuracy and subsequent reliability of calibrationless BCIs makes these systems more appealing for frequent use.

Evaluation of factors affecting CGMS calibration.

PubMed

Buckingham, Bruce A; Kollman, Craig; Beck, Roy; Kalajian, Andrea; Fiallo-Scharer, Rosanna; Tansey, Michael J; Fox, Larry A; Wilson, Darrell M; Weinzimer, Stuart A; Ruedy, Katrina J; Tamborlane, William V

2006-06-01

The optimal number/timing of calibrations entered into the CGMS (Medtronic MiniMed, Northridge, CA) continuous glucose monitoring system have not been previously described. Fifty subjects with Type 1 diabetes mellitus (10-18 years old) were hospitalized in a clinical research center for approximately 24 h on two separate days. CGMS and OneTouch Ultra meter (LifeScan, Milpitas, CA) data were obtained. The CGMS was retrospectively recalibrated using the Ultra data varying the number and timing of calibrations. Resulting CGMS values were compared against laboratory reference values. There was a modest improvement in accuracy with increasing number of calibrations. The median relative absolute deviation (RAD) was 14%, 15%, 13%, and 13% when using three, four, five, and seven calibration values, respectively (P < 0.001). Corresponding percentages of CGMS-reference pairs meeting the International Organisation for Standardisation criteria were 66%, 67%, 71%, and 72% (P < 0.001). Nighttime accuracy improved when daytime calibrations (pre-lunch and pre-dinner) were removed leaving only two calibrations at 9 p.m. and 6 a.m. (median difference, -2 vs. -9 mg/dL, P < 0.001; median RAD, 12% vs. 15%, P = 0.001). Accuracy was better on visits where the average absolute rate of glucose change at the times of calibration was lower. On visits with average absolute rates <0.5, 0.5 to <1.0, 1.0 to <1.5, and >or=1.5 mg/dL/min, median RAD values were 13% versus 14% versus 17% versus 19%, respectively (P = 0.05). Although accuracy is slightly improved with more calibrations, the timing of the calibrations appears more important. Modifying the algorithm to put less weight on daytime calibrations for nighttime values and calibrating during times of relative glucose stability may have greater impact on accuracy.

Evaluation of Factors Affecting CGMS Calibration

PubMed Central

2006-01-01

Background The optimal number/timing of calibrations entered into the Continuous Glucose Monitoring System (“CGMS”; Medtronic MiniMed, Northridge, CA) have not been previously described. Methods Fifty subjects with T1DM (10–18y) were hospitalized in a clinical research center for ~24h on two separate days. CGMS and OneTouch® Ultra® Meter (“Ultra”; LifeScan, Milpitas, CA) data were obtained. The CGMS was retrospectively recalibrated using the Ultra data varying the number and timing of calibrations. Resulting CGMS values were compared against laboratory reference values. Results There was a modest improvement in accuracy with increasing number of calibrations. The median relative absolute deviation (RAD) was 14%, 15%, 13% and 13% when using 3, 4, 5 and 7 calibration values, respectively (p<0.001). Corresponding percentages of CGMS-reference pairs meeting the ISO criteria were 66%, 67%, 71% and 72% (p<0.001). Nighttime accuracy improved when daytime calibrations (pre-lunch and pre-dinner) were removed leaving only two calibrations at 9p.m. and 6a.m. (median difference: −2 vs. −9mg/dL, p<0.001; median RAD: 12% vs. 15%, p=0.001). Accuracy was better on visits where the average absolute rate of glucose change at the times of calibration was lower. On visits with average absolute rates <0.5, 0.5-<1.0, 1.0-<1.5 and ≥1.5mg/dL/min, median RAD values were 13% vs. 14% vs. 17% vs. 19%, respectively (p=0.05). Conclusions Although accuracy is slightly improved with more calibrations, the timing of the calibrations appears more important. Modifying the algorithm to put less weight on daytime calibrations for nighttime values and calibrating during times of relative glucose stability may have greater impact on accuracy. PMID:16800753

Comparison of Accuracy Between a Conventional and Two Digital Intraoral Impression Techniques.

PubMed

Malik, Junaid; Rodriguez, Jose; Weisbloom, Michael; Petridis, Haralampos

To compare the accuracy (ie, precision and trueness) of full-arch impressions fabricated using either a conventional polyvinyl siloxane (PVS) material or one of two intraoral optical scanners. Full-arch impressions of a reference model were obtained using addition silicone impression material (Aquasil Ultra; Dentsply Caulk) and two optical scanners (Trios, 3Shape, and CEREC Omnicam, Sirona). Surface matching software (Geomagic Control, 3D Systems) was used to superimpose the scans within groups to determine the mean deviations in precision and trueness (μm) between the scans, which were calculated for each group and compared statistically using one-way analysis of variance with post hoc Bonferroni (trueness) and Games-Howell (precision) tests (IBM SPSS ver 24, IBM UK). Qualitative analysis was also carried out from three-dimensional maps of differences between scans. Means and standard deviations (SD) of deviations in precision for conventional, Trios, and Omnicam groups were 21.7 (± 5.4), 49.9 (± 18.3), and 36.5 (± 11.12) μm, respectively. Means and SDs for deviations in trueness were 24.3 (± 5.7), 87.1 (± 7.9), and 80.3 (± 12.1) μm, respectively. The conventional impression showed statistically significantly improved mean precision (P < .006) and mean trueness (P < .001) compared to both digital impression procedures. There were no statistically significant differences in precision (P = .153) or trueness (P = .757) between the digital impressions. The qualitative analysis revealed local deviations along the palatal surfaces of the molars and incisal edges of the anterior teeth of < 100 μm. Conventional full-arch PVS impressions exhibited improved mean accuracy compared to two direct optical scanners. No significant differences were found between the two digital impression methods.

Comparison of vessel enhancement algorithms applied to time-of-flight MRA images for cerebrovascular segmentation.

PubMed

Phellan, Renzo; Forkert, Nils D

2017-11-01

Vessel enhancement algorithms are often used as a preprocessing step for vessel segmentation in medical images to improve the overall segmentation accuracy. Each algorithm uses different characteristics to enhance vessels, such that the most suitable algorithm may vary for different applications. This paper presents a comparative analysis of the accuracy gains in vessel segmentation generated by the use of nine vessel enhancement algorithms: Multiscale vesselness using the formulas described by Erdt (MSE), Frangi (MSF), and Sato (MSS), optimally oriented flux (OOF), ranking orientations responses path operator (RORPO), the regularized Perona-Malik approach (RPM), vessel enhanced diffusion (VED), hybrid diffusion with continuous switch (HDCS), and the white top hat algorithm (WTH). The filters were evaluated and compared based on time-of-flight MRA datasets and corresponding manual segmentations from 5 healthy subjects and 10 patients with an arteriovenous malformation. Additionally, five synthetic angiographic datasets with corresponding ground truth segmentation were generated with three different noise levels (low, medium, and high) and also used for comparison. The parameters for each algorithm and subsequent segmentation were optimized using leave-one-out cross evaluation. The Dice coefficient, Matthews correlation coefficient, area under the ROC curve, number of connected components, and true positives were used for comparison. The results of this study suggest that vessel enhancement algorithms do not always lead to more accurate segmentation results compared to segmenting nonenhanced images directly. Multiscale vesselness algorithms, such as MSE, MSF, and MSS proved to be robust to noise, while diffusion-based filters, such as RPM, VED, and HDCS ranked in the top of the list in scenarios with medium or no noise. Filters that assume tubular-shapes, such as MSE, MSF, MSS, OOF, RORPO, and VED show a decrease in accuracy when considering patients with an AVM, because vessels may vary from its tubular-shape in this case. Vessel enhancement algorithms can help to improve the accuracy of the segmentation of the vascular system. However, their contribution to accuracy has to be evaluated as it depends on the specific applications, and in some cases it can lead to a reduction of the overall accuracy. No specific filter was suitable for all tested scenarios. © 2017 American Association of Physicists in Medicine.

The Value of Diffusion-Weighted Imaging in Combination With Conventional Magnetic Resonance Imaging for Improving Tumor Detection for Early Cervical Carcinoma Treated With Fertility-Sparing Surgery.

PubMed

Li, Xiulei; Wang, Ling; Li, Yong; Song, Peiji

2017-10-01

This study aimed to investigate the value of diffusion-weighted imaging (DWI) in combination with conventional magnetic resonance imaging (MRI) for improving tumor detection in young patients treated with fertility-sparing surgery because of early cervical carcinoma. Fifty-four patients with stage Ia or Ib1 cervical carcinoma were enrolled into this study. Magnetic resonance examinations were performed for these patients using conventional MRI (including T1-weighted imaging, T2-weighted imaging, and dynamic contrast-enhanced MRI) and DWI. The apparent diffusion coefficient (ADC) values of cervical carcinoma were analyzed quantitatively and compared with that of adjacent epithelium. Sensitivity, positive predictive value, and accuracy of 2 sets of MRI sequences were calculated on the basis of histologic results, and the diagnostic ability of conventional MRI/DWI combinations was compared with that of conventional MRI. The mean ADC value from cervical carcinoma (mean, 786 × 10 mm/s ± 100) was significantly lower than that from adjacent epithelium (mean, 1352 × 10 mm/s ± 147) (P = 0.01). When the threshold ADC value set as 1010 × 10 mm/s, the sensitivity and specificity for differentiating cervical carcinoma from nontumor epithelium were 78.2% and 67.2%, respectively. The sensitivity and accuracy of conventional MRI for tumor detection were 76.0% and 70.4%, whereas the sensitivity and accuracy of conventional MRI/DWI combinations were 91.7% and 90.7%, respectively. Conventional MRI/DWI combinations revealed a positive predictive value of 97.8% and only 4 false-negative findings. The addition of DWI to conventional MRI considerably improves the sensitivity and accuracy of tumor detection in young patients treated with fertility-sparing surgery, which supports the inclusion quantitative analysis of ADC value in routine MRI protocol before fertility-sparing surgery.

Temporal regularization of ultrasound-based liver motion estimation for image-guided radiation therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

O’Shea, Tuathan P., E-mail: tuathan.oshea@icr.ac.uk; Bamber, Jeffrey C.; Harris, Emma J.

Purpose: Ultrasound-based motion estimation is an expanding subfield of image-guided radiation therapy. Although ultrasound can detect tissue motion that is a fraction of a millimeter, its accuracy is variable. For controlling linear accelerator tracking and gating, ultrasound motion estimates must remain highly accurate throughout the imaging sequence. This study presents a temporal regularization method for correlation-based template matching which aims to improve the accuracy of motion estimates. Methods: Liver ultrasound sequences (15–23 Hz imaging rate, 2.5–5.5 min length) from ten healthy volunteers under free breathing were used. Anatomical features (blood vessels) in each sequence were manually annotated for comparison withmore » normalized cross-correlation based template matching. Five sequences from a Siemens Acuson™ scanner were used for algorithm development (training set). Results from incremental tracking (IT) were compared with a temporal regularization method, which included a highly specific similarity metric and state observer, known as the α–β filter/similarity threshold (ABST). A further five sequences from an Elekta Clarity™ system were used for validation, without alteration of the tracking algorithm (validation set). Results: Overall, the ABST method produced marked improvements in vessel tracking accuracy. For the training set, the mean and 95th percentile (95%) errors (defined as the difference from manual annotations) were 1.6 and 1.4 mm, respectively (compared to 6.2 and 9.1 mm, respectively, for IT). For each sequence, the use of the state observer leads to improvement in the 95% error. For the validation set, the mean and 95% errors for the ABST method were 0.8 and 1.5 mm, respectively. Conclusions: Ultrasound-based motion estimation has potential to monitor liver translation over long time periods with high accuracy. Nonrigid motion (strain) and the quality of the ultrasound data are likely to have an impact on tracking performance. A future study will investigate spatial uniformity of motion and its effect on the motion estimation errors.« less

Improved automation of dissolved organic carbon sampling for organic-rich surface waters.

PubMed

Grayson, Richard P; Holden, Joseph

2016-02-01

In-situ UV-Vis spectrophotometers offer the potential for improved estimates of dissolved organic carbon (DOC) fluxes for organic-rich systems such as peatlands because they are able to sample and log DOC proxies automatically through time at low cost. In turn, this could enable improved total carbon budget estimates for peatlands. The ability of such instruments to accurately measure DOC depends on a number of factors, not least of which is how absorbance measurements relate to DOC and the environmental conditions. Here we test the ability of a S::can Spectro::lyser™ for measuring DOC in peatland streams with routinely high DOC concentrations. Through analysis of the spectral response data collected by the instrument we have been able to accurately measure DOC up to 66 mg L(-1), which is more than double the original upper calibration limit for this particular instrument. A linear regression modelling approach resulted in an accuracy >95%. The greatest accuracy was achieved when absorbance values for several different wavelengths were used at the same time in the model. However, an accuracy >90% was achieved using absorbance values for a single wavelength to predict DOC concentration. Our calculations indicated that, for organic-rich systems, in-situ measurement with a scanning spectrophotometer can improve fluvial DOC flux estimates by 6 to 8% compared with traditional sampling methods. Thus, our techniques pave the way for improved long-term carbon budget calculations from organic-rich systems such as peatlands. Copyright © 2015 Elsevier B.V. All rights reserved.

Implementation of a hospital-based quality assessment program for rectal cancer.

PubMed

Hendren, Samantha; McKeown, Ellen; Morris, Arden M; Wong, Sandra L; Oerline, Mary; Poe, Lyndia; Campbell, Darrell A; Birkmeyer, Nancy J

2014-05-01

Quality improvement programs in Europe have had a markedly beneficial effect on the processes and outcomes of rectal cancer care. The quality of rectal cancer care in the United States is not as well understood, and scalable quality improvement programs have not been developed. The purpose of this article is to describe the implementation of a hospital-based quality assessment program for rectal cancer, targeting both community and academic hospitals. We recruited 10 hospitals from a surgical quality improvement organization. Nurse reviewers were trained to abstract rectal cancer data from hospital medical records, and abstracts were assessed for accuracy. We conducted two surveys to assess the training program and limitations of the data abstraction. We validated data completeness and accuracy by comparing hospital medical record and tumor registry data. Nine of 10 hospitals successfully performed abstractions with ≥ 90% accuracy. Experienced nurse reviewers were challenged by the technical details in operative and pathology reports. Although most variables had less than 10% missing data, outpatient testing information was lacking from some hospitals' inpatient records. This implementation project yielded a final quality assessment program consisting of 20 medical records variables and 11 tumor registry variables. An innovative program linking tumor registry data to quality-improvement data for rectal cancer quality assessment was successfully implemented in 10 hospitals. This data platform and training program can serve as a template for other organizations that are interested in assessing and improving the quality of rectal cancer care. Copyright © 2014 by American Society of Clinical Oncology.

Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter.

PubMed

Sung, Kwangjae; Lee, Dong Kyu 'Roy'; Kim, Hwangnam

2018-05-26

The reliable and accurate indoor pedestrian positioning is one of the biggest challenges for location-based systems and applications. Most pedestrian positioning systems have drift error and large bias due to low-cost inertial sensors and random motions of human being, as well as unpredictable and time-varying radio-frequency (RF) signals used for position determination. To solve this problem, many indoor positioning approaches that integrate the user's motion estimated by dead reckoning (DR) method and the location data obtained by RSS fingerprinting through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed to achieve higher positioning accuracy in indoor environments. Among Bayesian filtering methods, PF is the most popular integrating approach and can provide the best localization performance. However, since PF uses a large number of particles for the high performance, it can lead to considerable computational cost. This paper presents an indoor positioning system implemented on a smartphone, which uses simple dead reckoning (DR), RSS fingerprinting using iBeacon and machine learning scheme, and improved KF. The core of the system is the enhanced KF called a sigma-point Kalman particle filter (SKPF), which localize the user leveraging both the unscented transform of UKF and the weighting method of PF. The SKPF algorithm proposed in this study is used to provide the enhanced positioning accuracy by fusing positional data obtained from both DR and fingerprinting with uncertainty. The SKPF algorithm can achieve better positioning accuracy than KF and UKF and comparable performance compared to PF, and it can provide higher computational efficiency compared with PF. iBeacon in our positioning system is used for energy-efficient localization and RSS fingerprinting. We aim to design the localization scheme that can realize the high positioning accuracy, computational efficiency, and energy efficiency through the SKPF and iBeacon indoors. Empirical experiments in real environments show that the use of the SKPF algorithm and iBeacon in our indoor localization scheme can achieve very satisfactory performance in terms of localization accuracy, computational cost, and energy efficiency.

Improved Estimation of Orbits and Physical Properties of Objects in GEO

NASA Astrophysics Data System (ADS)

Bradley, B.; Axelrad, P.

2013-09-01

Orbital debris is a major concern for satellite operators, both commercial and military. Debris in the geosynchronous (GEO) belt is of particular concern because this unique region is such a valuable, limited resource, and, from the ground we cannot reliably track and characterize GEO objects smaller than 1 meter in diameter. Space-based space surveillance (SBSS) is required to observe GEO objects without weather restriction and with improved viewing geometry. SBSS satellites have thus far been placed in Sun-synchronous orbits. This paper investigates the benefits to GEO orbit determination (including the estimation of mass, area, and shape) that arises from placing observing satellites in geosynchronous transfer orbit (GTO) and a sub-GEO orbit. Recently, several papers have reported on simulation studies to estimate orbits and physical properties; however, these studies use simulated objects and ground-based measurements, often with dense and long data arcs. While this type of simulation provides valuable insight into what is possible, as far as state estimation goes, it is not a very realistic observing scenario and thus may not yield meaningful accuracies. Our research improves upon simulations published to date by utilizing publicly available ephemerides for the WAAS satellites (Anik F1R and Galaxy 15), accurate at the meter level. By simulating and deliberately degrading right ascension and declination observations, consistent with these ephemerides, a realistic assessment of the achievable orbit determination accuracy using GTO and sub-GEO SBSS platforms is performed. Our results show that orbit accuracy is significantly improved as compared to a Sun-synchronous platform. Physical property estimation is also performed using simulated astrometric and photometric data taken from GTO and sub-GEO sensors. Simulations of SBSS-only as well as combined SBSS and ground-based observation tracks are used to study the improvement in area, mass, and shape estimation gained by the proposed systems. Again our work improves upon previous research by investigating realistic observation scheduling scenarios to gain insight into achievable accuracies.

New progress of ranging technology at Wuhan Satellite Laser Ranging Station

NASA Technical Reports Server (NTRS)

Xia, Zhiz-Hong; Ye, Wen-Wei; Cai, Qing-Fu

1993-01-01

A satellite laser ranging system with an accuracy of the level of centimeter has been successfully developed at the Institute of Seismology, State Seismological Bureau with the cooperation of the Institute of Geodesy and Geophysics, Chinese Academy of Science. With significant improvements on the base of the second generation SLR system developed in 1985, ranging accuracy of the new system has been upgraded from 15 cm to 3-4 cm. Measuring range has also been expanded, so that the ETALON satellite with an orbit height of 20,000 km launched by the former U.S.S.R. can now be tracked. Compared with the 2nd generation SLR system, the newly developed system has the following improvements. A Q modulated laser is replaced by a mode-locked YAG laser. The new device has a pulse width of 150 ps and a repetition rate of 1-4 pps. A quick response photomultiplier has been adopted as the receiver for echo; for example, the adoption of the MCP tube has obviously reduced the jitter error of the transit time and has improved the ranging accuracy. The whole system is controlled by an IBM PC/XT Computer to guide automatic tracking and measurement. It can carry out these functions for satellite orbit calculation, real-time tracking and adjusting, data acquisition and the preprocessed of observing data, etc. The automatization level and reliability of the observation have obviously improved.

Staggered Mesh Ewald: An extension of the Smooth Particle-Mesh Ewald method adding great versatility

PubMed Central

Cerutti, David S.; Duke, Robert E.; Darden, Thomas A.; Lybrand, Terry P.

2009-01-01

We draw on an old technique for improving the accuracy of mesh-based field calculations to extend the popular Smooth Particle Mesh Ewald (SPME) algorithm as the Staggered Mesh Ewald (StME) algorithm. StME improves the accuracy of computed forces by up to 1.2 orders of magnitude and also reduces the drift in system momentum inherent in the SPME method by averaging the results of two separate reciprocal space calculations. StME can use charge mesh spacings roughly 1.5× larger than SPME to obtain comparable levels of accuracy; the one mesh in an SPME calculation can therefore be replaced with two separate meshes, each less than one third of the original size. Coarsening the charge mesh can be balanced with reductions in the direct space cutoff to optimize performance: the efficiency of StME rivals or exceeds that of SPME calculations with similarly optimized parameters. StME may also offer advantages for parallel molecular dynamics simulations because it permits the use of coarser meshes without requiring higher orders of charge interpolation and also because the two reciprocal space calculations can be run independently if that is most suitable for the machine architecture. We are planning other improvements to the standard SPME algorithm, and anticipate that StME will work synergistically will all of them to dramatically improve the efficiency and parallel scaling of molecular simulations. PMID:20174456

Improving Oil Palm Classification in the Peruvian Amazon by Combining Active and Passive Remote Sensing Data

NASA Astrophysics Data System (ADS)

Gutierrez-Velez, V. H.; DeFries, R. S.

2011-12-01

Oil palm expansion has led to clearing of extensive forest areas in the tropics. However quantitative assessments of the magnitude of oil palm expansion to deforestation have been challenging due in large part to the limitations presented by conventional optical data sets for discriminating plantations from forests and other tree cover vegetations. Recently available information from active remote sensors has opened the possibility of using these data sources to overcome these limitations. The purpose of this analysis is to evaluate the accuracy of oil palm classification when using ALOS/PALSAR active satellite data in conjunction with Landsat information, compared to the use of Landsat data only. The analysis takes place in a focused region around the city of Pucallpa in the Ucayali province of the Peruvian Amazon for the year 2010. Oil palm plantations were separated in five categories consisting of four age classes (0-3, 3-5, 5-10 and > 10 yrs) and an additional class accounting for degraded plantations older than 15 yr. Other land covers were water bodies, unvegetated land, short and tall grass, fallow, secondary vegetation, and forest. Classifications were performed using random forests. Training points for calibration and validation consisted of 411 polygons measured in areas representative of the land covers of interest and totaled 6,367 ha. Overall classification accuracy increased from 89.9% using only Landsat data sets to 94.3% using both Landast and ALOS/PALSAR. Both user's and producer's accuracy increased in all classes when using both data sets except for producer's accuracy in short grass which decreased by 1%. The largest increase in user's accuracy was obtained in oil palm plantations older than 10 years from 62 to 80% while producer's accuracy improved the most in plantations in age class 3-5 from 63 to 80%. Results demonstrate the suitability of data from ALOS/PALSAR and other active remote sensors to improve classification of oil palm plantations in age classes and discriminate them from other land covers. Results suggest a potential for improving discrimination of other tree cover types using a combination of active and conventional optical remote sensors.

Feature instructions improve face-matching accuracy

PubMed Central

Bindemann, Markus

2018-01-01

Identity comparisons of photographs of unfamiliar faces are prone to error but important for applied settings, such as person identification at passport control. Finding techniques to improve face-matching accuracy is therefore an important contemporary research topic. This study investigated whether matching accuracy can be improved by instruction to attend to specific facial features. Experiment 1 showed that instruction to attend to the eyebrows enhanced matching accuracy for optimized same-day same-race face pairs but not for other-race faces. By contrast, accuracy was unaffected by instruction to attend to the eyes, and declined with instruction to attend to ears. Experiment 2 replicated the eyebrow-instruction improvement with a different set of same-race faces, comprising both optimized same-day and more challenging different-day face pairs. These findings suggest that instruction to attend to specific features can enhance face-matching accuracy, but feature selection is crucial and generalization across face sets may be limited. PMID:29543822

Higher-order triangular spectral element method with optimized cubature points for seismic wavefield modeling

NASA Astrophysics Data System (ADS)

Liu, Youshan; Teng, Jiwen; Xu, Tao; Badal, José

2017-05-01

The mass-lumped method avoids the cost of inverting the mass matrix and simultaneously maintains spatial accuracy by adopting additional interior integration points, known as cubature points. To date, such points are only known analytically in tensor domains, such as quadrilateral or hexahedral elements. Thus, the diagonal-mass-matrix spectral element method (SEM) in non-tensor domains always relies on numerically computed interpolation points or quadrature points. However, only the cubature points for degrees 1 to 6 are known, which is the reason that we have developed a p-norm-based optimization algorithm to obtain higher-order cubature points. In this way, we obtain and tabulate new cubature points with all positive integration weights for degrees 7 to 9. The dispersion analysis illustrates that the dispersion relation determined from the new optimized cubature points is comparable to that of the mass and stiffness matrices obtained by exact integration. Simultaneously, the Lebesgue constant for the new optimized cubature points indicates its surprisingly good interpolation properties. As a result, such points provide both good interpolation properties and integration accuracy. The Courant-Friedrichs-Lewy (CFL) numbers are tabulated for the conventional Fekete-based triangular spectral element (TSEM), the TSEM with exact integration, and the optimized cubature-based TSEM (OTSEM). A complementary study demonstrates the spectral convergence of the OTSEM. A numerical example conducted on a half-space model demonstrates that the OTSEM improves the accuracy by approximately one order of magnitude compared to the conventional Fekete-based TSEM. In particular, the accuracy of the 7th-order OTSEM is even higher than that of the 14th-order Fekete-based TSEM. Furthermore, the OTSEM produces a result that can compete in accuracy with the quadrilateral SEM (QSEM). The high accuracy of the OTSEM is also tested with a non-flat topography model. In terms of computational efficiency, the OTSEM is more efficient than the Fekete-based TSEM, although it is slightly costlier than the QSEM when a comparable numerical accuracy is required.

Patient-specific instrument can achieve same accuracy with less resection time than navigation assistance in periacetabular pelvic tumor surgery: a cadaveric study.

PubMed

Wong, Kwok-Chuen; Sze, Kwan-Yik; Wong, Irene Oi-Ling; Wong, Chung-Ming; Kumta, Shekhar-Madhukar

2016-02-01

Inaccurate resection in pelvic tumors can result in compromised margins with increase local recurrence. Navigation-assisted and patient-specific instrument (PSI) techniques have recently been reported in assisting pelvic tumor surgery with the tendency of improving surgical accuracy. We examined and compared the accuracy of transferring a virtual pelvic resection plan to actual surgery using navigation-assisted or PSI technique in a cadaver study. We performed CT scan in twelve cadaveric bodies including whole pelvic bones. Either supraacetabular or partial acetabular resection was virtually planned in a hemipelvis using engineering software. The virtual resection plan was transferred to a CT-based navigation system or was used for design and fabrication of PSI. Pelvic resections were performed using navigation assistance in six cadavers and PSI in another six. Post-resection images were co-registered with preoperative planning for comparative analysis of resection accuracy in the two techniques. The mean average deviation error from the planned resection was no different ([Formula: see text]) for the navigation and the PSI groups: 1.9 versus 1.4 mm, respectively. The mean time required for the bone resection was greater ([Formula: see text]) for the navigation group than for the PSI group: 16.2 versus 1.1 min, respectively. In simulated periacetabular pelvic tumor resections, PSI technique enabled surgeons to reproduce the virtual surgical plan with similar accuracy but with less bone resection time when compared with navigation assistance. Further studies are required to investigate the clinical benefits of PSI technique in pelvic tumor surgery.

Quality improvement of International Classification of Diseases, 9th revision, diagnosis coding in radiation oncology: single-institution prospective study at University of California, San Francisco.

PubMed

Chen, Chien P; Braunstein, Steve; Mourad, Michelle; Hsu, I-Chow J; Haas-Kogan, Daphne; Roach, Mack; Fogh, Shannon E

2015-01-01

Accurate International Classification of Diseases (ICD) diagnosis coding is critical for patient care, billing purposes, and research endeavors. In this single-institution study, we evaluated our baseline ICD-9 (9th revision) diagnosis coding accuracy, identified the most common errors contributing to inaccurate coding, and implemented a multimodality strategy to improve radiation oncology coding. We prospectively studied ICD-9 coding accuracy in our radiation therapy--specific electronic medical record system. Baseline ICD-9 coding accuracy was obtained from chart review targeting ICD-9 coding accuracy of all patients treated at our institution between March and June of 2010. To improve performance an educational session highlighted common coding errors, and a user-friendly software tool, RadOnc ICD Search, version 1.0, for coding radiation oncology specific diagnoses was implemented. We then prospectively analyzed ICD-9 coding accuracy for all patients treated from July 2010 to June 2011, with the goal of maintaining 80% or higher coding accuracy. Data on coding accuracy were analyzed and fed back monthly to individual providers. Baseline coding accuracy for physicians was 463 of 661 (70%) cases. Only 46% of physicians had coding accuracy above 80%. The most common errors involved metastatic cases, whereby primary or secondary site ICD-9 codes were either incorrect or missing, and special procedures such as stereotactic radiosurgery cases. After implementing our project, overall coding accuracy rose to 92% (range, 86%-96%). The median accuracy for all physicians was 93% (range, 77%-100%) with only 1 attending having accuracy below 80%. Incorrect primary and secondary ICD-9 codes in metastatic cases showed the most significant improvement (10% vs 2% after intervention). Identifying common coding errors and implementing both education and systems changes led to significantly improved coding accuracy. This quality assurance project highlights the potential problem of ICD-9 coding accuracy by physicians and offers an approach to effectively address this shortcoming. Copyright © 2015. Published by Elsevier Inc.

Improved Extreme Learning Machine based on the Sensitivity Analysis

NASA Astrophysics Data System (ADS)

Cui, Licheng; Zhai, Huawei; Wang, Benchao; Qu, Zengtang

2018-03-01

Extreme learning machine and its improved ones is weak in some points, such as computing complex, learning error and so on. After deeply analyzing, referencing the importance of hidden nodes in SVM, an novel analyzing method of the sensitivity is proposed which meets people’s cognitive habits. Based on these, an improved ELM is proposed, it could remove hidden nodes before meeting the learning error, and it can efficiently manage the number of hidden nodes, so as to improve the its performance. After comparing tests, it is better in learning time, accuracy and so on.

An algorithm to improve diagnostic accuracy in diabetes in computerised problem orientated medical records (POMR) compared with an established algorithm developed in episode orientated records (EOMR).

PubMed

de Lusignan, Simon; Liaw, Siaw-Teng; Dedman, Daniel; Khunti, Kamlesh; Sadek, Khaled; Jones, Simon

2015-06-05

An algorithm that detects errors in diagnosis, classification or coding of diabetes in primary care computerised medial record (CMR) systems is currently available. However, this was developed on CMR systems that are episode orientated medical records (EOMR); and do not force the user to always code a problem or link data to an existing one. More strictly problem orientated medical record (POMR) systems mandate recording a problem and linking consultation data to them. To compare the rates of detection of diagnostic accuracy using an algorithm developed in EOMR with a new POMR specific algorithm. We used data from The Health Improvement Network (THIN) database (N = 2,466,364) to identify a population of 100,513 (4.08%) patients considered likely to have diabetes. We recalibrated algorithms designed to classify cases of diabetes to take account of that POMR enforced coding consistency in the computerised medical record systems [In Practice Systems (InPS) Vision] that contribute data to THIN. We explored the different proportions of people classified as having type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) and with diabetes unclassifiable as either T1DM or T2DM. We compared proportions using chi-square tests and used Tukey's test to compare the characteristics of the people in each group. The prevalence of T1DM using the original EOMR algorithm was 0.38% (9,264/2,466,364), and for T2DM 3.22% (79,417/2,466,364). The prevalence using the new POMR algorithm was 0.31% (7,750/2,466,364) T1DM and 3.65% (89,990/2,466,364) T2DM. The EOMR algorithms also left more people unclassified 11,439 (12%), as to their type of diabetes compared with 2,380 (2.4%), for the new algorithm. Those people who were only classified by the EOMR system differed in terms of older age, and apparently better glycaemic control, despite not being prescribed medication for their diabetes (p < 0.005). Increasing the degree of problem orientation of the medical record system can improve the accuracy of recording of diagnoses and, therefore, the accuracy of using routinely collected data from CMRs to determine the prevalence of diabetes mellitus; data processing strategies should reflect the degree of problem orientation.

Dynamic Filtering Improves Attentional State Prediction with fNIRS

NASA Technical Reports Server (NTRS)

Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

2016-01-01

Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).

Accuracy evaluation of an ASTER-Derived Global Digital Elevation Model (GDEM) Version 1 and Version 2 for two sites in western Africa

USGS Publications Warehouse

Chirico, Peter G.; Malpeli, Katherine C.; Trimble, Sarah M.

2012-01-01

This study compares the ASTER Global DEM version 1 (GDEMv1) and version 2 (GDEMv2) for two study sites with distinct terrain and land cover characteristics in western Africa. The effects of land cover, slope, relief, and stack number are evaluated through both absolute and relative DEM statistical comparisons. While GDEMv2 at times performed better than GDEMv1, this improvement was not consistent, revealing the complex nature and interaction of terrain and land cover characteristics, which influences the accuracy of GDEM tiles on local and regional scales.

An experimental study of interstitial lung tissue classification in HRCT images using ANN and role of cost functions

NASA Astrophysics Data System (ADS)

Dash, Jatindra K.; Kale, Mandar; Mukhopadhyay, Sudipta; Khandelwal, Niranjan; Prabhakar, Nidhi; Garg, Mandeep; Kalra, Naveen

2017-03-01

In this paper, we investigate the effect of the error criteria used during a training phase of the artificial neural network (ANN) on the accuracy of the classifier for classification of lung tissues affected with Interstitial Lung Diseases (ILD). Mean square error (MSE) and the cross-entropy (CE) criteria are chosen being most popular choice in state-of-the-art implementations. The classification experiment performed on the six interstitial lung disease (ILD) patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Micronodules, Fibrosis and Healthy from MedGIFT database. The texture features from an arbitrary region of interest (AROI) are extracted using Gabor filter. Two different neural networks are trained with the scaled conjugate gradient back propagation algorithm with MSE and CE error criteria function respectively for weight updation. Performance is evaluated in terms of average accuracy of these classifiers using 4 fold cross-validation. Each network is trained for five times for each fold with randomly initialized weight vectors and accuracies are computed. Significant improvement in classification accuracy is observed when ANN is trained by using CE (67.27%) as error function compared to MSE (63.60%). Moreover, standard deviation of the classification accuracy for the network trained with CE (6.69) error criteria is found less as compared to network trained with MSE (10.32) criteria.

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra.

PubMed

Peng, Yi; Xiong, Xiong; Adhikari, Kabindra; Knadel, Maria; Grunwald, Sabine; Greve, Mogens Humlekrog

2015-01-01

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l'Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the 'upland model' was able to more accurately predict SOC compared with the 'upland & wetland model'. However, the separately calibrated 'upland and wetland model' did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra

PubMed Central

Peng, Yi; Xiong, Xiong; Adhikari, Kabindra; Knadel, Maria; Grunwald, Sabine; Greve, Mogens Humlekrog

2015-01-01

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l’Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the ‘upland model’ was able to more accurately predict SOC compared with the ‘upland & wetland model’. However, the separately calibrated ‘upland and wetland model’ did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM). PMID:26555071