A multistage approach to improve performance of computer-aided detection of pulmonary embolisms depicted on CT images: preliminary investigation.

PubMed

Park, Sang Cheol; Chapman, Brian E; Zheng, Bin

2011-06-01

This study developed a computer-aided detection (CAD) scheme for pulmonary embolism (PE) detection and investigated several approaches to improve CAD performance. In the study, 20 computed tomography examinations with various lung diseases were selected, which include 44 verified PE lesions. The proposed CAD scheme consists of five basic steps: 1) lung segmentation; 2) PE candidate extraction using an intensity mask and tobogganing region growing; 3) PE candidate feature extraction; 4) false-positive (FP) reduction using an artificial neural network (ANN); and 5) a multifeature-based k-nearest neighbor for positive/negative classification. In this study, we also investigated the following additional methods to improve CAD performance: 1) grouping 2-D detected features into a single 3-D object; 2) selecting features with a genetic algorithm (GA); and 3) limiting the number of allowed suspicious lesions to be cued in one examination. The results showed that 1) CAD scheme using tobogganing, an ANN, and grouping method achieved the maximum detection sensitivity of 79.2%; 2) the maximum scoring method achieved the superior performance over other scoring fusion methods; 3) GA was able to delete "redundant" features and further improve CAD performance; and 4) limiting the maximum number of cued lesions in an examination reduced FP rate by 5.3 times. Combining these approaches, CAD scheme achieved 63.2% detection sensitivity with 18.4 FP lesions per examination. The study suggested that performance of CAD schemes for PE detection depends on many factors that include 1) optimizing the 2-D region grouping and scoring methods; 2) selecting the optimal feature set; and 3) limiting the number of allowed cueing lesions per examination.

Fusion of local and global detection systems to detect tuberculosis in chest radiographs.

PubMed

Hogeweg, Laurens; Mol, Christian; de Jong, Pim A; Dawson, Rodney; Ayles, Helen; van Ginneken, Bramin

2010-01-01

Automatic detection of tuberculosis (TB) on chest radiographs is a difficult problem because of the diverse presentation of the disease. A combination of detection systems for abnormalities and normal anatomy is used to improve detection performance. A textural abnormality detection system operating at the pixel level is combined with a clavicle detection system to suppress false positive responses. The output of a shape abnormality detection system operating at the image level is combined in a next step to further improve performance by reducing false negatives. Strategies for combining systems based on serial and parallel configurations were evaluated using the minimum, maximum, product, and mean probability combination rules. The performance of TB detection increased, as measured using the area under the ROC curve, from 0.67 for the textural abnormality detection system alone to 0.86 when the three systems were combined. The best result was achieved using the sum and product rule in a parallel combination of outputs.

Performance analysis of a fault inferring nonlinear detection system algorithm with integrated avionics flight data

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.

1985-01-01

This paper presents the performance analysis results of a fault inferring nonlinear detection system (FINDS) using integrated avionics sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment. First, an overview of the FINDS algorithm structure is given. Then, aircraft state estimate time histories and statistics for the flight data sensors are discussed. This is followed by an explanation of modifications made to the detection and decision functions in FINDS to improve false alarm and failure detection performance. Next, the failure detection and false alarm performance of the FINDS algorithm are analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minutes of flight data. Results indicate that the detection speed, failure level estimation, and false alarm performance show a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed is faster for filter measurement sensors such as MLS than for filter input sensors such as flight control accelerometers. Finally, the progress in modifications of the FINDS algorithm design to accommodate flight computer constraints is discussed.

A matter of time: improvement of visual temporal processing during training-induced restoration of light detection performance

PubMed Central

Poggel, Dorothe A.; Treutwein, Bernhard; Sabel, Bernhard A.; Strasburger, Hans

2015-01-01

The issue of how basic sensory and temporal processing are related is still unresolved. We studied temporal processing, as assessed by simple visual reaction times (RT) and double-pulse resolution (DPR), in patients with partial vision loss after visual pathway lesions and investigated whether vision restoration training (VRT), a training program designed to improve light detection performance, would also affect temporal processing. Perimetric and campimetric visual field tests as well as maps of DPR thresholds and RT were acquired before and after a 3 months training period with VRT. Patient performance was compared to that of age-matched healthy subjects. Intact visual field size increased during training. Averaged across the entire visual field, DPR remained constant while RT improved slightly. However, in transition zones between the blind and intact areas (areas of residual vision) where patients had shown between 20 and 80% of stimulus detection probability in pre-training visual field tests, both DPR and RT improved markedly. The magnitude of improvement depended on the defect depth (or degree of intactness) of the respective region at baseline. Inter-individual training outcome variability was very high, with some patients showing little change and others showing performance approaching that of healthy controls. Training-induced improvement of light detection in patients with visual field loss thus generalized to dynamic visual functions. The findings suggest that similar neural mechanisms may underlie the impairment and subsequent training-induced functional recovery of both light detection and temporal processing. PMID:25717307

TORNADO-WARNING PERFORMANCE IN THE PAST AND FUTURE: A Perspective from Signal Detection Theory.

NASA Astrophysics Data System (ADS)

Brooks, Harold E.

2004-06-01

Changes over the years in tornado-warning performance in the United States can be modeled from the perspective of signal detection theory. From this view, it can be seen that there have been distinct periods of change in performance, most likely associated with deployment of radars, and changes in scientific understanding and training. The model also makes it clear that improvements in the false alarm ratio can only occur at the cost of large decreases in the probability of detection, or with large improvements in the overall quality of the warning system.

Optimizing Robinson Operator with Ant Colony Optimization As a Digital Image Edge Detection Method

NASA Astrophysics Data System (ADS)

Yanti Nasution, Tarida; Zarlis, Muhammad; K. M Nasution, Mahyuddin

2017-12-01

Edge detection serves to identify the boundaries of an object against a background of mutual overlap. One of the classic method for edge detection is operator Robinson. Operator Robinson produces a thin, not assertive and grey line edge. To overcome these deficiencies, the proposed improvements to edge detection method with the approach graph with Ant Colony Optimization algorithm. The repairs may be performed are thicken the edge and connect the edges cut off. Edge detection research aims to do optimization of operator Robinson with Ant Colony Optimization then compare the output and generated the inferred extent of Ant Colony Optimization can improve result of edge detection that has not been optimized and improve the accuracy of the results of Robinson edge detection. The parameters used in performance measurement of edge detection are morphology of the resulting edge line, MSE and PSNR. The result showed that Robinson and Ant Colony Optimization method produces images with a more assertive and thick edge. Ant Colony Optimization method is able to be used as a method for optimizing operator Robinson by improving the image result of Robinson detection average 16.77 % than classic Robinson result.

Unsupervised iterative detection of land mines in highly cluttered environments.

PubMed

Batman, Sinan; Goutsias, John

2003-01-01

An unsupervised iterative scheme is proposed for land mine detection in heavily cluttered scenes. This scheme is based on iterating hybrid multispectral filters that consist of a decorrelating linear transform coupled with a nonlinear morphological detector. Detections extracted from the first pass are used to improve results in subsequent iterations. The procedure stops after a predetermined number of iterations. The proposed scheme addresses several weaknesses associated with previous adaptations of morphological approaches to land mine detection. Improvement in detection performance, robustness with respect to clutter inhomogeneities, a completely unsupervised operation, and computational efficiency are the main highlights of the method. Experimental results reveal excellent performance.

Improved assessment of accuracy and performance using a rotational paper-based device for multiplexed detection of heavy metals.

PubMed

Sun, Xiange; Li, Bowei; Qi, Anjin; Tian, Chongguo; Han, Jinglong; Shi, Yajun; Lin, Bingcheng; Chen, Lingxin

2018-02-01

In this work, a novel rotational microfluidic paper-based device was developed to improve the accuracy and performance of the multiplexed colorimetric detection by effectively avoiding the diffusion of colorimetric reagent on the detection zone. The integrated paper-based rotational valves were used to control the connection or disconnection between detection zones and fluid channels. Based on the manipulation of the rotational valves, this rotational paper-based device could prevent the random diffusion of colorimetric reagent and reduce the error of quantitative analysis considerably. The multiplexed colorimetric detection of heavy metals Ni(II), Cu(II) and Cr(VI) were implemented on the rotational device and the detection limits could be found to be 4.8, 1.6, and 0.18mg/L, respectively. The developed rotational device showed the great advantage in improving the detection accuracy and was expected to be a low-cost, portable analytical platform for the on-site detection. Copyright © 2017 Elsevier B.V. All rights reserved.

Improved Detection of Winter Lightning in the Tohoku Region of Japan using Vaisala’s LS700x Technology

NASA Astrophysics Data System (ADS)

Cummins, Kenneth L.; Honma, Noriyasu; Pifer, Alburt E.; Rogers, Tim; Tatsumi, Masataka

The demand for both data quality and the range of Cloud-to-Ground (CG) lightning parameters is highest for forensic applications within the electric utility industry. For years, the research and operational communities within this industry in Japan have pointed out a limitation of these LLS networks in the detection and location of damaging (high-current and/or large charge transfer) lightning flashes during the winter months (so-called “Winter Lightning”). Most of these flashes appear to be upward-connecting discharges, frequently referred to as “Ground-to-Cloud” (GC) flashes. The basic architecture and design of Vaisala’s new LS700x lightning sensor was developed in-part to improve detection of these unusual and complex flashes. This paper presents our progress-to-date on this effort. We include a review of the winter lightning detection problem, an overview of the LS700x architecture, a discussion of how this architecture was exploited to evaluate and improve performance for winter lightning, and a presentation of results-to-date on performance improvement. A comparison of GC detection performance between Tohoku’s operational 9-sensor IMPACT (ALDF 141-T) LLS and its 6-sensor LS700x research network indicates roughly a factor-of-two improvement for this class of discharges, with an overall detection of 23/24 (96%) of GC flashes.

Continuous detection of weak sensory signals in afferent spike trains: the role of anti-correlated interspike intervals in detection performance.

PubMed

Goense, J B M; Ratnam, R

2003-10-01

An important problem in sensory processing is deciding whether fluctuating neural activity encodes a stimulus or is due to variability in baseline activity. Neurons that subserve detection must examine incoming spike trains continuously, and quickly and reliably differentiate signals from baseline activity. Here we demonstrate that a neural integrator can perform continuous signal detection, with performance exceeding that of trial-based procedures, where spike counts in signal- and baseline windows are compared. The procedure was applied to data from electrosensory afferents of weakly electric fish (Apteronotus leptorhynchus), where weak perturbations generated by small prey add approximately 1 spike to a baseline of approximately 300 spikes s(-1). The hypothetical postsynaptic neuron, modeling an electrosensory lateral line lobe cell, could detect an added spike within 10-15 ms, achieving near ideal detection performance (80-95%) at false alarm rates of 1-2 Hz, while trial-based testing resulted in only 30-35% correct detections at that false alarm rate. The performance improvement was due to anti-correlations in the afferent spike train, which reduced both the amplitude and duration of fluctuations in postsynaptic membrane activity, and so decreased the number of false alarms. Anti-correlations can be exploited to improve detection performance only if there is memory of prior decisions.

Lesion detection and quantification performance of the Tachyon-I time-of-flight PET scanner: phantom and human studies.

PubMed

Zhang, Xuezhu; Peng, Qiyu; Zhou, Jian; Huber, Jennifer S; Moses, William W; Qi, Jinyi

2018-03-16

The first generation Tachyon PET (Tachyon-I) is a demonstration single-ring PET scanner that reaches a coincidence timing resolution of 314 ps using LSO scintillator crystals coupled to conventional photomultiplier tubes. The objective of this study was to quantify the improvement in both lesion detection and quantification performance resulting from the improved time-of-flight (TOF) capability of the Tachyon-I scanner. We developed a quantitative TOF image reconstruction method for the Tachyon-I and evaluated its TOF gain for lesion detection and quantification. Scans of either a standard NEMA torso phantom or healthy volunteers were used as the normal background data. Separately scanned point source and sphere data were superimposed onto the phantom or human data after accounting for the object attenuation. We used the bootstrap method to generate multiple independent noisy datasets with and without a lesion present. The signal-to-noise ratio (SNR) of a channelized hotelling observer (CHO) was calculated for each lesion size and location combination to evaluate the lesion detection performance. The bias versus standard deviation trade-off of each lesion uptake was also calculated to evaluate the quantification performance. The resulting CHO-SNR measurements showed improved performance in lesion detection with better timing resolution. The detection performance was also dependent on the lesion size and location, in addition to the background object size and shape. The results of bias versus noise trade-off showed that the noise (standard deviation) reduction ratio was about 1.1-1.3 over the TOF 500 ps and 1.5-1.9 over the non-TOF modes, similar to the SNR gains for lesion detection. In conclusion, this Tachyon-I PET study demonstrated the benefit of improved time-of-flight capability on lesion detection and ROI quantification for both phantom and human subjects.

Lesion detection and quantification performance of the Tachyon-I time-of-flight PET scanner: phantom and human studies

NASA Astrophysics Data System (ADS)

Zhang, Xuezhu; Peng, Qiyu; Zhou, Jian; Huber, Jennifer S.; Moses, William W.; Qi, Jinyi

2018-03-01

The first generation Tachyon PET (Tachyon-I) is a demonstration single-ring PET scanner that reaches a coincidence timing resolution of 314 ps using LSO scintillator crystals coupled to conventional photomultiplier tubes. The objective of this study was to quantify the improvement in both lesion detection and quantification performance resulting from the improved time-of-flight (TOF) capability of the Tachyon-I scanner. We developed a quantitative TOF image reconstruction method for the Tachyon-I and evaluated its TOF gain for lesion detection and quantification. Scans of either a standard NEMA torso phantom or healthy volunteers were used as the normal background data. Separately scanned point source and sphere data were superimposed onto the phantom or human data after accounting for the object attenuation. We used the bootstrap method to generate multiple independent noisy datasets with and without a lesion present. The signal-to-noise ratio (SNR) of a channelized hotelling observer (CHO) was calculated for each lesion size and location combination to evaluate the lesion detection performance. The bias versus standard deviation trade-off of each lesion uptake was also calculated to evaluate the quantification performance. The resulting CHO-SNR measurements showed improved performance in lesion detection with better timing resolution. The detection performance was also dependent on the lesion size and location, in addition to the background object size and shape. The results of bias versus noise trade-off showed that the noise (standard deviation) reduction ratio was about 1.1–1.3 over the TOF 500 ps and 1.5–1.9 over the non-TOF modes, similar to the SNR gains for lesion detection. In conclusion, this Tachyon-I PET study demonstrated the benefit of improved time-of-flight capability on lesion detection and ROI quantification for both phantom and human subjects.

Evaluation of a fault tolerant system for an integrated avionics sensor configuration with TSRV flight data

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Godiwala, P. M.

1985-01-01

The performance analysis results of a fault inferring nonlinear detection system (FINDS) using sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment is presented. First, a statistical analysis of the flight recorded sensor data was made in order to determine the characteristics of sensor inaccuracies. Next, modifications were made to the detection and decision functions in the FINDS algorithm in order to improve false alarm and failure detection performance under real modelling errors present in the flight data. Finally, the failure detection and false alarm performance of the FINDS algorithm were analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minute flight data. In general, the detection speed, failure level estimation, and false alarm performance showed a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed was faster for filter measurement sensors soon as MLS than for filter input sensors such as flight control accelerometers.

Radioactive threat detection using scintillant-based detectors

NASA Astrophysics Data System (ADS)

Chalmers, Alex

2004-09-01

An update to the performance of AS&E's Radioactive Threat Detection sensor technology. A model is presented detailing the components of the scintillant-based RTD system employed in AS&E products aimed at detecting radiological WMD. An overview of recent improvements in the sensors, electrical subsystems and software algorithms are presented. The resulting improvements in performance are described and sample results shown from existing systems. Advanced and future capabilities are described with an assessment of their feasibility and their application to Homeland Defense.

Performance of an improved thermal neutron activation detector for buried bulk explosives

NASA Astrophysics Data System (ADS)

McFee, J. E.; Faust, A. A.; Andrews, H. R.; Clifford, E. T. H.; Mosquera, C. M.

2013-06-01

First generation thermal neutron activation (TNA) sensors, employing an isotopic source and NaI(Tl) gamma ray detectors, were deployed by Canadian Forces in 2002 as confirmation sensors on multi-sensor landmine detection systems. The second generation TNA detector is being developed with a number of improvements aimed at increasing sensitivity and facilitating ease of operation. Among these are an electronic neutron generator to increase sensitivity for deeper and horizontally displaced explosives; LaBr3(Ce) scintillators, to improve time response and energy resolution; improved thermal and electronic stability; improved sensor head geometry to minimize spatial response nonuniformity; and more robust data processing. The sensor is described, with emphasis on the improvements. Experiments to characterize the performance of the second generation TNA in detecting buried landmines and improvised explosive devices (IEDs) hidden in culverts are described. Performance results, including comparisons between the performance of the first and second generation systems are presented.

Improved explosive collection and detection with rationally assembled surface sampling materials

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

Chouyyok, Wilaiwan; Bays, J. Timothy; Gerasimenko, Aleksandr A.

Sampling and detection of trace explosives is a key analytical process in modern transportation safety. In this work we have explored some of the fundamental analytical processes for collection and detection of trace level explosive on surfaces with the most widely utilized system, thermal desorption IMS. The performance of the standard muslin swipe material was compared with chemically modified fiberglass cloth. The fiberglass surface was modified to include phenyl functional groups. When compared to standard muslin, the phenyl functionalized fiberglass sampling material showed better analyte release from the sampling material as well as improved response and repeatability from multiple usesmore » of the same swipe. The improved sample release of the functionalized fiberglass swipes resulted in a significant increase in sensitivity. Various physical and chemical properties were systematically explored to determine optimal performance. The results herein have relevance to improving the detection of other explosive compounds and potentially to a wide range of other chemical sampling and field detection challenges.« less

Prolonged maturation of auditory perception and learning in gerbils

PubMed Central

Sarro, Emma C.; Sanes, Dan H.

2011-01-01

In humans, auditory perception reaches maturity over a broad age range, extending through adolescence. Despite this slow maturation, children are considered to be outstanding learners, suggesting that immature perceptual skills might actually be advantageous to improvement on an acoustic task as a result of training (perceptual learning). Previous non-human studies have not employed an identical task when comparing perceptual performance of young and mature subjects, making it difficult to assess learning. Here, we used an identical procedure on juvenile and adult gerbils to examine the perception of amplitude modulation (AM), a stimulus feature that is an important component of most natural sounds. On average, Adult animals could detect smaller fluctuations in amplitude (i.e. smaller modulation depths) than Juveniles, indicating immature perceptual skills in Juveniles. However, the population variance was much greater for Juveniles, a few animals displaying adult-like AM detection. To determine whether immature perceptual skills facilitated learning, we compared naïve performance on the AM detection task with the amount of improvement following additional training. The amount of improvement in Adults correlated with naïve performance: those with the poorest naïve performance improved the most. In contrast, the naïve performance of Juveniles did not predict the amount of learning. Those Juveniles with immature AM detection thresholds did not display greater learning than Adults. Furthermore, for several of the Juveniles with adult-like thresholds, AM detection deteriorated with repeated testing. Thus, immature perceptual skills in young animals were not associated with greater learning. PMID:20506133

Utilization of optical emission endpoint in photomask dry etch processing

NASA Astrophysics Data System (ADS)

Faure, Thomas B.; Huynh, Cuc; Lercel, Michael J.; Smith, Adam; Wagner, Thomas

2002-03-01

Use of accurate and repeatable endpoint detection during dry etch processing of photomask is very important for obtaining good mask mean-to-target and CD uniformity performance. It was found that the typical laser reflectivity endpoint detecting system used on photomask dry etch systems had several key limitations that caused unnecessary scrap and non-optimum image size performance. Consequently, work to develop and implement use of a more robust optical emission endpoint detection system for chrome dry etch processing of photomask was performed. Initial feasibility studies showed that the emission technique was sensitive enough to monitor pattern loadings on contact and via level masks down to 3 percent pattern coverage. Additional work was performed to further improve this to 1 percent pattern coverage by optimizing the endpoint detection parameters. Comparison studies of mask mean-to-target performance and CD uniformity were performed with the use of optical emission endpoint versus laser endpoint for masks built using TOK IP3600 and ZEP 7000 resist systems. It was found that an improvement in mean-to-target performance and CD uniformity was realized on several types of production masks. In addition, part-to-part endpoint time repeatability was found to be significantly improved with the use of optical emission endpoint.

An improved pi/4-QPSK with nonredundant error correction for satellite mobile broadcasting

NASA Technical Reports Server (NTRS)

Feher, Kamilo; Yang, Jiashi

1991-01-01

An improved pi/4-quadrature phase-shift keying (QPSK) receiver that incorporates a simple nonredundant error correction (NEC) structure is proposed for satellite and land-mobile digital broadcasting. The bit-error-rate (BER) performance of the pi/4-QPSK with NEC is analyzed and evaluated in a fast Rician fading and additive white Gaussian noise (AWGN) environment using computer simulation. It is demonstrated that with simple electronics the performance of a noncoherently detected pi/4-QPSK signal in both AWGN and fast Rician fading can be improved. When the K-factor (a ratio of average power of multipath signal to direct path power) of the Rician channel decreases, the improvement increases. An improvement of 1.2 dB could be obtained at a BER of 0.0001 in the AWGN channel. This performance gain is achieved without requiring any signal redundancy and additional bandwidth. Three types of noncoherent detection schemes of pi/4-QPSK with NEC structure, such as IF band differential detection, baseband differential detection, and FM discriminator, are discussed. It is concluded that the pi/4-QPSK with NEC is an attractive scheme for power-limited satellite land-mobile broadcasting systems.

Nano-immunoassay with improved performance for detection of cancer biomarkers

DOE PAGES

Krasnoslobodtsev, Alexey V.; Torres, Maria P.; Kaur, Sukhwinder; ...

2015-01-01

Nano-immunoassay utilizing surface-enhanced Raman scattering (SERS) effect is a promising analytical technique for the early detection of cancer. In its current standing the assay is capable of discriminating samples of healthy individuals from samples of pancreatic cancer patients. Further improvements in sensitivity and reproducibility will extend practical applications of the SERS-based detection platforms to wider range of problems. In this report, we discuss several strategies designed to improve performance of the SERS-based detection system. We demonstrate that reproducibility of the platform is enhanced by using atomically smooth mica surface as a template for preparation of capture surface in SERS sandwichmore » immunoassay. Furthermore, the assay's stability and sensitivity can be further improved by using either polymer or graphene monolayer as a thin protective layer applied on top of the assay addresses. The protective layer renders the signal to be more stable against photo-induced damage and carbonaceous contamination.« less

Integrating Oil Debris and Vibration Gear Damage Detection Technologies Using Fuzzy Logic

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.; Afjeh, Abdollah A.

2002-01-01

A diagnostic tool for detecting damage to spur gears was developed. Two different measurement technologies, wear debris analysis and vibration, were integrated into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual measurement technologies. This diagnostic tool was developed and evaluated experimentally by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Test Rig. Experimental data were collected during experiments performed in this test rig with and without pitting. Results show combining the two measurement technologies improves the detection of pitting damage on spur gears.

Spiral Bevel Gear Damage Detection Using Decision Fusion Analysis

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.; Handschuh, Robert F.; Afjeh, Abdollah A.

2002-01-01

A diagnostic tool for detecting damage to spiral bevel gears was developed. Two different monitoring technologies, oil debris analysis and vibration, were integrated using data fusion into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual monitoring technologies. This diagnostic tool was evaluated by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spiral Bevel Gear Fatigue Rigs. Data was collected during experiments performed in this test rig when pitting damage occurred. Results show that combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spiral bevel gears.

Efficient cooperative compressive spectrum sensing by identifying multi-candidate and exploiting deterministic matrix

NASA Astrophysics Data System (ADS)

Li, Jia; Wang, Qiang; Yan, Wenjie; Shen, Yi

2015-12-01

Cooperative spectrum sensing exploits the spatial diversity to improve the detection of occupied channels in cognitive radio networks (CRNs). Cooperative compressive spectrum sensing (CCSS) utilizing the sparsity of channel occupancy further improves the efficiency by reducing the number of reports without degrading detection performance. In this paper, we firstly and mainly propose the referred multi-candidate orthogonal matrix matching pursuit (MOMMP) algorithms to efficiently and effectively detect occupied channels at fusion center (FC), where multi-candidate identification and orthogonal projection are utilized to respectively reduce the number of required iterations and improve the probability of exact identification. Secondly, two common but different approaches based on threshold and Gaussian distribution are introduced to realize the multi-candidate identification. Moreover, to improve the detection accuracy and energy efficiency, we propose the matrix construction based on shrinkage and gradient descent (MCSGD) algorithm to provide a deterministic filter coefficient matrix of low t-average coherence. Finally, several numerical simulations validate that our proposals provide satisfactory performance with higher probability of detection, lower probability of false alarm and less detection time.

Analysis of On-board Hazard Detection and Avoidance for Safe Lunar Landing

NASA Technical Reports Server (NTRS)

Johnson, Andrew E.; Huertas, Andres; Werner, Robert A.; Montgomery, James F.

2008-01-01

Landing hazard detection and avoidance technology is being pursued within NASA to improve landing safety and increase access to sites of interest on the lunar surface. The performance of a hazard detection and avoidance system depends on properties of the terrain, sensor performance, algorithm design, vehicle characteristics and the overall all guidance navigation and control architecture. This paper analyzes the size of the region that must be imaged, sensor performance parameters and the impact of trajectory angle on hazard detection performance. The analysis shows that vehicle hazard tolerance is the driving parameter for hazard detection system design.

Cue combination in a combined feature contrast detection and figure identification task.

PubMed

Meinhardt, Günter; Persike, Malte; Mesenholl, Björn; Hagemann, Cordula

2006-11-01

Target figures defined by feature contrast in spatial frequency, orientation or both cues had to be detected in Gabor random fields and their shape had to be identified in a dual task paradigm. Performance improved with increasing feature contrast and was strongly correlated among both tasks. Subjects performed significantly better with combined cues than with single cues. The improvement due to cue summation was stronger than predicted by the assumption of independent feature specific mechanisms, and increased with the performance level achieved with single cues until it was limited by ceiling effects. Further, cue summation was also strongly correlated among tasks: when there was benefit due to the additional cue in feature contrast detection, there was also benefit in figure identification. For the same performance level achieved with single cues, cue summation was generally larger in figure identification than in feature contrast detection, indicating more benefit when processes of shape and surface formation are involved. Our results suggest that cue combination improves spatial form completion and figure-ground segregation in noisy environments, and therefore leads to more stable object vision.

An integrative 'omics' solution to the detection of recombinant human erythropoietin and blood doping.

PubMed

Pitsiladis, Yannis P; Durussel, Jérôme; Rabin, Olivier

2014-05-01

Administration of recombinant human erythropoietin (rHumanEPO) improves sporting performance and hence is frequently subject to abuse by athletes, although rHumanEPO is prohibited by the WADA. Approaches to detect rHumanEPO doping have improved significantly in recent years but remain imperfect. A new transcriptomic-based longitudinal screening approach is being developed that has the potential to improve the analytical performance of current detection methods. In particular, studies are being funded by WADA to identify a 'molecular signature' of rHumanEPO doping and preliminary results are promising. In the first systematic study to be conducted, the expression of hundreds of genes were found to be altered by rHumanEPO with numerous gene transcripts being differentially expressed after the first injection and further transcripts profoundly upregulated during and subsequently downregulated up to 4 weeks postadministration of the drug; with the same transcriptomic pattern observed in all participants. The identification of a blood 'molecular signature' of rHumanEPO administration is the strongest evidence to date that gene biomarkers have the potential to substantially improve the analytical performance of current antidoping methods such as the Athlete Biological Passport for rHumanEPO detection. Given the early promise of transcriptomics, research using an 'omics'-based approach involving genomics, transcriptomics, proteomics and metabolomics should be intensified in order to achieve improved detection of rHumanEPO and other doping substances and methods difficult to detect such a recombinant human growth hormone and blood transfusions.

Detection of fatigue cracks by nondestructive testing methods

NASA Technical Reports Server (NTRS)

Anderson, R. T.; Delacy, T. J.; Stewart, R. C.

1973-01-01

The effectiveness was assessed of various NDT methods to detect small tight cracks by randomly introducing fatigue cracks into aluminum sheets. The study included optimizing NDT methods calibrating NDT equipment with fatigue cracked standards, and evaluating a number of cracked specimens by the optimized NDT methods. The evaluations were conducted by highly trained personnel, provided with detailed procedures, in order to minimize the effects of human variability. These personnel performed the NDT on the test specimens without knowledge of the flaw locations and reported on the flaws detected. The performance of these tests was measured by comparing the flaws detected against the flaws present. The principal NDT methods utilized were radiographic, ultrasonic, penetrant, and eddy current. Holographic interferometry, acoustic emission monitoring, and replication methods were also applied on a reduced number of specimens. Generally, the best performance was shown by eddy current, ultrasonic, penetrant and holographic tests. Etching provided no measurable improvement, while proof loading improved flaw detectability. Data are shown that quantify the performances of the NDT methods applied.

Early detection monitoring of aquatic invasive species: Measuring performance success in a Lake Superior pilot network

EPA Science Inventory

The Great Lakes Water Quality Agreement, Annex 6 calls for a U.S.-Canada, basin-wide aquatic invasive species early detection network by 2015. The objective of our research is to explore survey design strategies that can improve detection efficiency, and to develop performance me...

High resolution colonoscopy in a bowel cancer screening program improves polyp detection

PubMed Central

Banks, Matthew R; Haidry, Rehan; Butt, M Adil; Whitley, Lisa; Stein, Judith; Langmead, Louise; Bloom, Stuart L; O’Bichere, Austin; McCartney, Sara; Basherdas, Kalpesh; Rodriguez-Justo, Manuel; Lovat, Laurence B

2011-01-01

AIM: To compare high resolution colonoscopy (Olympus Lucera) with a megapixel high resolution system (Pentax HiLine) as an in-service evaluation. METHODS: Polyp detection rates and measures of performance were collected for 269 colonoscopy procedures. Five colonoscopists conducted the study over a three month period, as part of the United Kingdom bowel cancer screening program. RESULTS:There were no differences in procedure duration (χ2 P = 0.98), caecal intubation rates (χ2 P = 0.67), or depth of sedation (χ2 P = 0.64). Mild discomfort was more common in the Pentax group (χ2 P = 0.036). Adenoma detection rate was significantly higher in the Pentax group (χ2 test for trend P = 0.01). Most of the extra polyps detected were flat or sessile adenomas. CONCLUSION: Megapixel definition colonoscopes improve adenoma detection without compromising other measures of endoscope performance. Increased polyp detection rates may improve future outcomes in bowel cancer screening programs. PMID:22090787

A methodology for evaluating detection performance of ultrasonic array imaging algorithms for coarse-grained materials.

PubMed

Van Pamel, Anton; Brett, Colin R; Lowe, Michael J S

2014-12-01

Improving the ultrasound inspection capability for coarse-grained metals remains of longstanding interest and is expected to become increasingly important for next-generation electricity power plants. Conventional ultrasonic A-, B-, and C-scans have been found to suffer from strong background noise caused by grain scattering, which can severely limit the detection of defects. However, in recent years, array probes and full matrix capture (FMC) imaging algorithms have unlocked exciting possibilities for improvements. To improve and compare these algorithms, we must rely on robust methodologies to quantify their performance. This article proposes such a methodology to evaluate the detection performance of imaging algorithms. For illustration, the methodology is applied to some example data using three FMC imaging algorithms; total focusing method (TFM), phase-coherent imaging (PCI), and decomposition of the time-reversal operator with multiple scattering filter (DORT MSF). However, it is important to note that this is solely to illustrate the methodology; this article does not attempt the broader investigation of different cases that would be needed to compare the performance of these algorithms in general. The methodology considers the statistics of detection, presenting the detection performance as probability of detection (POD) and probability of false alarm (PFA). A test sample of coarse-grained nickel super alloy, manufactured to represent materials used for future power plant components and containing some simple artificial defects, is used to illustrate the method on the candidate algorithms. The data are captured in pulse-echo mode using 64-element array probes at center frequencies of 1 and 5 MHz. In this particular case, it turns out that all three algorithms are shown to perform very similarly when comparing their flaw detection capabilities.

Auditory enhancement of visual perception at threshold depends on visual abilities.

PubMed

Caclin, Anne; Bouchet, Patrick; Djoulah, Farida; Pirat, Elodie; Pernier, Jacques; Giard, Marie-Hélène

2011-06-17

Whether or not multisensory interactions can improve detection thresholds, and thus widen the range of perceptible events is a long-standing debate. Here we revisit this question, by testing the influence of auditory stimuli on visual detection threshold, in subjects exhibiting a wide range of visual-only performance. Above the perceptual threshold, crossmodal interactions have indeed been reported to depend on the subject's performance when the modalities are presented in isolation. We thus tested normal-seeing subjects and short-sighted subjects wearing their usual glasses. We used a paradigm limiting potential shortcomings of previous studies: we chose a criterion-free threshold measurement procedure and precluded exogenous cueing effects by systematically presenting a visual cue whenever a visual target (a faint Gabor patch) might occur. Using this carefully controlled procedure, we found that concurrent sounds only improved visual detection thresholds in the sub-group of subjects exhibiting the poorest performance in the visual-only conditions. In these subjects, for oblique orientations of the visual stimuli (but not for vertical or horizontal targets), the auditory improvement was still present when visual detection was already helped with flanking visual stimuli generating a collinear facilitation effect. These findings highlight that crossmodal interactions are most efficient to improve perceptual performance when an isolated modality is deficient. Copyright © 2011 Elsevier B.V. All rights reserved.

Sequential feature selection for detecting buried objects using forward looking ground penetrating radar

NASA Astrophysics Data System (ADS)

Shaw, Darren; Stone, Kevin; Ho, K. C.; Keller, James M.; Luke, Robert H.; Burns, Brian P.

2016-05-01

Forward looking ground penetrating radar (FLGPR) has the benefit of detecting objects at a significant standoff distance. The FLGPR signal is radiated over a large surface area and the radar signal return is often weak. Improving detection, especially for buried in road targets, while maintaining an acceptable false alarm rate remains to be a challenging task. Various kinds of features have been developed over the years to increase the FLGPR detection performance. This paper focuses on investigating the use of as many features as possible for detecting buried targets and uses the sequential feature selection technique to automatically choose the features that contribute most for improving performance. Experimental results using data collected at a government test site are presented.

Improving resolution of dynamic communities in human brain networks through targeted node removal

PubMed Central

Turner, Benjamin O.; Miller, Michael B.; Carlson, Jean M.

2017-01-01

Current approaches to dynamic community detection in complex networks can fail to identify multi-scale community structure, or to resolve key features of community dynamics. We propose a targeted node removal technique to improve the resolution of community detection. Using synthetic oscillator networks with well-defined “ground truth” communities, we quantify the community detection performance of a common modularity maximization algorithm. We show that the performance of the algorithm on communities of a given size deteriorates when these communities are embedded in multi-scale networks with communities of different sizes, compared to the performance in a single-scale network. We demonstrate that targeted node removal during community detection improves performance on multi-scale networks, particularly when removing the most functionally cohesive nodes. Applying this approach to network neuroscience, we compare dynamic functional brain networks derived from fMRI data taken during both repetitive single-task and varied multi-task experiments. After the removal of regions in visual cortex, the most coherent functional brain area during the tasks, community detection is better able to resolve known functional brain systems into communities. In addition, node removal enables the algorithm to distinguish clear differences in brain network dynamics between these experiments, revealing task-switching behavior that was not identified with the visual regions present in the network. These results indicate that targeted node removal can improve spatial and temporal resolution in community detection, and they demonstrate a promising approach for comparison of network dynamics between neuroscientific data sets with different resolution parameters. PMID:29261662

An improved method to detect correct protein folds using partial clustering.

PubMed

Zhou, Jianjun; Wishart, David S

2013-01-16

Structure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient "partial" clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods. We propose a new scheme that performs rapid but incomplete clustering on protein decoys. Our method detects structurally similar decoys (measured using either C(α) RMSD or GDT-TS score) and extracts representatives from them without assigning every decoy to a cluster. We integrated our new clustering strategy with several different scoring functions to assess both the performance and speed in identifying correct or near-correct folds. Experimental results on 35 Rosetta decoy sets and 40 I-TASSER decoy sets show that our method can improve the correct fold detection rate as assessed by two different quality criteria. This improvement is significantly better than two recently published clustering methods, Durandal and Calibur-lite. Speed and efficiency testing shows that our method can handle much larger decoy sets and is up to 22 times faster than Durandal and Calibur-lite. The new method, named HS-Forest, avoids the computationally expensive task of clustering every decoy, yet still allows superior correct-fold selection. Its improved speed, efficiency and decoy-selection performance should enable structure prediction researchers to work with larger decoy sets and significantly improve their ab initio structure prediction performance.

An improved method to detect correct protein folds using partial clustering

PubMed Central

2013-01-01

Background Structure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient “partial“ clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods. Results We propose a new scheme that performs rapid but incomplete clustering on protein decoys. Our method detects structurally similar decoys (measured using either Cα RMSD or GDT-TS score) and extracts representatives from them without assigning every decoy to a cluster. We integrated our new clustering strategy with several different scoring functions to assess both the performance and speed in identifying correct or near-correct folds. Experimental results on 35 Rosetta decoy sets and 40 I-TASSER decoy sets show that our method can improve the correct fold detection rate as assessed by two different quality criteria. This improvement is significantly better than two recently published clustering methods, Durandal and Calibur-lite. Speed and efficiency testing shows that our method can handle much larger decoy sets and is up to 22 times faster than Durandal and Calibur-lite. Conclusions The new method, named HS-Forest, avoids the computationally expensive task of clustering every decoy, yet still allows superior correct-fold selection. Its improved speed, efficiency and decoy-selection performance should enable structure prediction researchers to work with larger decoy sets and significantly improve their ab initio structure prediction performance. PMID:23323835

Behavioral training to improve collision detection

PubMed Central

DeLoss, Denton J.; Bian, Zheng; Watanabe, Takeo; Andersen, George J.

2015-01-01

Young drivers are a high-risk group for vehicle crashes due to inexperience in detecting an impending collision and are one group that may benefit from perceptual learning (PL) training. The present study assessed whether PL could be used to improve performance in collision detection. Ten college-aged subjects participated in the first experiment, which consisted of seven 1-hr sessions conducted on separate days. Thresholds at three observer/object speeds were measured prior to training using a two-alternative forced choice procedure during which they indicated whether an approaching object would result in a collision or noncollision event. Participants were then trained near threshold at one of these speeds for 5 days. After training, participants showed a significant reduction in the time needed to detect a collision at the trained speed. This improvement was also found to transfer to the higher observer speed condition. A second experiment was conducted to determine whether this improvement was due to training near threshold or whether this improvement was merely due to practice with the task. Training with stimuli well above threshold showed no significant improvement in performance, indicating that the improvement seen in the first experiment was not solely due to task practice. PMID:26230917

Hepatocellular Carcinoma Screening Associated with Early Tumor Detection and Improved Survival Among Patients with Cirrhosis in the US.

PubMed

Singal, Amit G; Mittal, Sahil; Yerokun, Olutola A; Ahn, Chul; Marrero, Jorge A; Yopp, Adam C; Parikh, Neehar D; Scaglione, Steve J

2017-09-01

Professional societies recommend hepatocellular carcinoma screening in patients with cirrhosis, but high-quality data evaluating its effectiveness to improve early tumor detection and survival in "real world" clinical practice are needed. We aim to characterize the association between hepatocellular carcinoma screening and early tumor detection, curative treatment, and overall survival among patients with cirrhosis. We performed a retrospective cohort study of patients diagnosed with hepatocellular carcinoma between June 2012 and May 2013 at 4 health systems in the US. Patients were categorized in the screening group if hepatocellular carcinoma was detected by imaging performed for screening purposes. Generalized linear models and multivariate Cox regression with frailty adjustment were used to compare early detection, curative treatment, and survival between screen-detected and non-screen-detected patients. Among 374 hepatocellular carcinoma patients, 42% (n = 157) were detected by screening. Screen-detected patients had a significantly higher proportion of early tumors (Barcelona Clinic Liver Cancer stage A 63.1% vs 36.4%, P <.001) and were more likely to undergo curative treatment (31% vs 13%, P = .02). Hepatocellular carcinoma screening was significantly associated with improved survival in multivariate analysis (hazards ratio 0.41; 95% confidence interval, 0.26-0.65) after adjusting for patient demographics, Child-Pugh class, and performance status. Median survival of screen-detected patients was 14.6 months, compared with 6.0 months for non-screen-detected patients, with the difference remaining significant after adjusting for lead-time bias (hazards ratio 0.59, 95% confidence interval, 0.37-0.93). Hepatocellular carcinoma screening is associated with increased early tumor detection and improved survival; however, a minority of hepatocellular carcinoma patients are detected by screening. Interventions to increase screening use in patients with cirrhosis may help curb hepatocellular carcinoma mortality rates. Copyright © 2017 Elsevier Inc. All rights reserved.

Change Detection: Training and Transfer

PubMed Central

Gaspar, John G.; Neider, Mark B.; Simons, Daniel J.; McCarley, Jason S.; Kramer, Arthur F.

2013-01-01

Observers often fail to notice even dramatic changes to their environment, a phenomenon known as change blindness. If training could enhance change detection performance in general, then it might help to remedy some real-world consequences of change blindness (e.g. failing to detect hazards while driving). We examined whether adaptive training on a simple change detection task could improve the ability to detect changes in untrained tasks for young and older adults. Consistent with an effective training procedure, both young and older adults were better able to detect changes to trained objects following training. However, neither group showed differential improvement on untrained change detection tasks when compared to active control groups. Change detection training led to improvements on the trained task but did not generalize to other change detection tasks. PMID:23840775

Repeated Induction of Inattentional Blindness in a Simulated Aviation Environment

NASA Technical Reports Server (NTRS)

Kennedy, Kellie D.; Stephens, Chad L.; Williams, Ralph A.; Schutte, Paul C.

2017-01-01

The study reported herein is a subset of a larger investigation on the role of automation in the context of the flight deck and used a fixed-based, human-in-the-loop simulator. This paper explored the relationship between automation and inattentional blindness (IB) occurrences in a repeated induction paradigm using two types of runway incursions. The critical stimuli for both runway incursions were directly relevant to primary task performance. Sixty non-pilot participants performed the final five minutes of a landing scenario twice in one of three automation conditions: full automation (FA), partial automation (PA), and no automation (NA). The first induction resulted in a 70 percent (42 of 60) detection failure rate with those in the PA condition significantly more likely to detect the incursion compared to the FA condition or the NA condition. The second induction yielded a 50 percent detection failure rate. Although detection improved (detection failure rates declined) in all conditions, those in the FA condition demonstrated the greatest improvement with doubled detection rates. The detection behavior in the first trial did not preclude a failed detection in the second induction. Group membership (IB vs. Detection) in the FA condition showed a greater improvement than those in the NA condition and rated the Mental Demand and Effort subscales of the NASA-TLX (NASA Task Load Index) significantly higher for Time 2 compared Time 1. Participants in the FA condition used the experience of IB exposure to improve task performance whereas those in the NA condition did not, indicating the availability and reallocation of attentional resources in the FA condition. These findings support the role of engagement in operational attention detriment and the consideration of attentional failure causation to determine appropriate mitigation strategies.

Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool.

PubMed

Di Segni, Mattia; de Soccio, Valeria; Cantisani, Vito; Bonito, Giacomo; Rubini, Antonello; Di Segni, Gabriele; Lamorte, Sveva; Magri, Valentina; De Vito, Corrado; Migliara, Giuseppe; Bartolotta, Tommaso Vincenzo; Metere, Alessio; Giacomelli, Laura; de Felice, Carlo; D'Ambrosio, Ferdinando

2018-06-01

To assess the diagnostic performance and the potential as a teaching tool of S-detect in the assessment of focal breast lesions. 61 patients (age 21-84 years) with benign breast lesions in follow-up or candidate to pathological sampling or with suspicious lesions candidate to biopsy were enrolled. The study was based on a prospective and on a retrospective phase. In the prospective phase, after completion of baseline US by an experienced breast radiologist and S-detect assessment, 5 operators with different experience and dedication to breast radiology performed elastographic exams. In the retrospective phase, the 5 operators performed a retrospective assessment and categorized lesions with BI-RADS 2013 lexicon. Integration of S-detect to in-training operators evaluations was performed by giving priority to S-detect analysis in case of disagreement. 2 × 2 contingency tables and ROC analysis were used to assess the diagnostic performances; inter-rater agreement was measured with Cohen's k; Bonferroni's test was used to compare performances. A significance threshold of p = 0.05 was adopted. All operators showed sensitivity > 90% and varying specificity (50-75%); S-detect showed sensitivity > 90 and 70.8% specificity, with inter-rater agreement ranging from moderate to good. Lower specificities were improved by the addition of S-detect. The addition of elastography did not lead to any improvement of the diagnostic performance. S-detect is a feasible tool for the characterization of breast lesions; it has a potential as a teaching tool for the less experienced operators.

LEA Detection and Tracking Method for Color-Independent Visual-MIMO

PubMed Central

Kim, Jai-Eun; Kim, Ji-Won; Kim, Ki-Doo

2016-01-01

Communication performance in the color-independent visual-multiple input multiple output (visual-MIMO) technique is deteriorated by light emitting array (LEA) detection and tracking errors in the received image because the image sensor included in the camera must be used as the receiver in the visual-MIMO system. In this paper, in order to improve detection reliability, we first set up the color-space-based region of interest (ROI) in which an LEA is likely to be placed, and then use the Harris corner detection method. Next, we use Kalman filtering for robust tracking by predicting the most probable location of the LEA when the relative position between the camera and the LEA varies. In the last step of our proposed method, the perspective projection is used to correct the distorted image, which can improve the symbol decision accuracy. Finally, through numerical simulation, we show the possibility of robust detection and tracking of the LEA, which results in a symbol error rate (SER) performance improvement. PMID:27384563

LEA Detection and Tracking Method for Color-Independent Visual-MIMO.

PubMed

Kim, Jai-Eun; Kim, Ji-Won; Kim, Ki-Doo

2016-07-02

Communication performance in the color-independent visual-multiple input multiple output (visual-MIMO) technique is deteriorated by light emitting array (LEA) detection and tracking errors in the received image because the image sensor included in the camera must be used as the receiver in the visual-MIMO system. In this paper, in order to improve detection reliability, we first set up the color-space-based region of interest (ROI) in which an LEA is likely to be placed, and then use the Harris corner detection method. Next, we use Kalman filtering for robust tracking by predicting the most probable location of the LEA when the relative position between the camera and the LEA varies. In the last step of our proposed method, the perspective projection is used to correct the distorted image, which can improve the symbol decision accuracy. Finally, through numerical simulation, we show the possibility of robust detection and tracking of the LEA, which results in a symbol error rate (SER) performance improvement.

A conceptual model to empower software requirements conflict detection and resolution with rule-based reasoning

NASA Astrophysics Data System (ADS)

Ahmad, Sabrina; Jalil, Intan Ermahani A.; Ahmad, Sharifah Sakinah Syed

2016-08-01

It is seldom technical issues which impede the process of eliciting software requirements. The involvement of multiple stakeholders usually leads to conflicts and therefore the need of conflict detection and resolution effort is crucial. This paper presents a conceptual model to further improve current efforts. Hence, this paper forwards an improved conceptual model to assist the conflict detection and resolution effort which extends the model ability and improves overall performance. The significant of the new model is to empower the automation of conflicts detection and its severity level with rule-based reasoning.

Validation of an automated seizure detection algorithm for term neonates

PubMed Central

Mathieson, Sean R.; Stevenson, Nathan J.; Low, Evonne; Marnane, William P.; Rennie, Janet M.; Temko, Andrey; Lightbody, Gordon; Boylan, Geraldine B.

2016-01-01

Objective The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres. Methods EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed. Results Between sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6–75.0%, with false detection (FD) rates of 0.04–0.36 FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen’s Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures. Conclusion The SDA achieved promising performance and warrants further testing in a live clinical evaluation. Significance The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens. PMID:26055336

The price of performance: a cost and performance analysis of the implementation of cell-free fetal DNA testing for Down syndrome in Ontario, Canada.

PubMed

Okun, N; Teitelbaum, M; Huang, T; Dewa, C S; Hoch, J S

2014-04-01

To examine the cost and performance implications of introducing cell-free fetal DNA (cffDNA) testing within modeled scenarios in a publicly funded Canadian provincial Down syndrome (DS) prenatal screening program. Two clinical algorithms were created: the first to represent the current screening program and the second to represent one that incorporates cffDNA testing. From these algorithms, eight distinct scenarios were modeled to examine: (1) the current program (no cffDNA), (2) the current program with first trimester screening (FTS) as the nuchal translucency-based primary screen (no cffDNA), (3) a program substituting current screening with primary cffDNA, (4) contingent cffDNA with current FTS performance, (5) contingent cffDNA at a fixed price to result in overall cost neutrality,(6) contingent cffDNA with an improved detection rate (DR) of FTS, (7) contingent cffDNA with higher uptake of FTS, and (8) contingent cffDNA with optimized FTS (higher uptake and improved DR). This modeling study demonstrates that introducing contingent cffDNA testing improves performance by increasing the number of cases of DS detected prenatally, and reducing the number of amniocenteses performed and concomitant iatrogenic pregnancy loss of pregnancies not affected by DS. Costs are modestly increased, although the cost per case of DS detected is decreased with contingent cffDNA testing. Contingent models of cffDNA testing can improve overall screening performance while maintaining the provision of an 11- to 13-week scan. Costs are modestly increased, but cost per prenatally detected case of DS is decreased. © 2013 John Wiley & Sons, Ltd.

Double Threshold Energy Detection Based Cooperative Spectrum Sensing for Cognitive Radio Networks with QoS Guarantee

NASA Astrophysics Data System (ADS)

Hu, Hang; Yu, Hong; Zhang, Yongzhi

2013-03-01

Cooperative spectrum sensing, which can greatly improve the ability of discovering the spectrum opportunities, is regarded as an enabling mechanism for cognitive radio (CR) networks. In this paper, we employ a double threshold detection method in energy detector to perform spectrum sensing, only the CR users with reliable sensing information are allowed to transmit one bit local decision to the fusion center. Simulation results will show that our proposed double threshold detection method could not only improve the sensing performance but also save the bandwidth of the reporting channel compared with the conventional detection method with one threshold. By weighting the sensing performance and the consumption of system resources in a utility function that is maximized with respect to the number of CR users, it has been shown that the optimal number of CR users is related to the price of these Quality-of-Service (QoS) requirements.

Integrating conflict detection and attentional control mechanisms.

PubMed

Walsh, Bong J; Buonocore, Michael H; Carter, Cameron S; Mangun, George R

2011-09-01

Human behavior involves monitoring and adjusting performance to meet established goals. Performance-monitoring systems that act by detecting conflict in stimulus and response processing have been hypothesized to influence cortical control systems to adjust and improve performance. Here we used fMRI to investigate the neural mechanisms of conflict monitoring and resolution during voluntary spatial attention. We tested the hypothesis that the ACC would be sensitive to conflict during attentional orienting and influence activity in the frontoparietal attentional control network that selectively modulates visual information processing. We found that activity in ACC increased monotonically with increasing attentional conflict. This increased conflict detection activity was correlated with both increased activity in the attentional control network and improved speed and accuracy from one trial to the next. These results establish a long hypothesized interaction between conflict detection systems and neural systems supporting voluntary control of visual attention.

Boosting instance prototypes to detect local dermoscopic features.

PubMed

Situ, Ning; Yuan, Xiaojing; Zouridakis, George

2010-01-01

Local dermoscopic features are useful in many dermoscopic criteria for skin cancer detection. We address the problem of detecting local dermoscopic features from epiluminescence (ELM) microscopy skin lesion images. We formulate the recognition of local dermoscopic features as a multi-instance learning (MIL) problem. We employ the method of diverse density (DD) and evidence confidence (EC) function to convert MIL to a single-instance learning (SIL) problem. We apply Adaboost to improve the classification performance with support vector machines (SVMs) as the base classifier. We also propose to boost the selection of instance prototypes through changing the data weights in the DD function. We validate the methods on detecting ten local dermoscopic features from a dataset with 360 images. We compare the performance of the MIL approach, its boosting version, and a baseline method without using MIL. Our results show that boosting can provide performance improvement compared to the other two methods.

Improved thermal neutron activation sensor for detection of bulk explosives

NASA Astrophysics Data System (ADS)

McFee, John E.; Faust, Anthony A.; Andrews, H. Robert; Clifford, Edward T. H.; Mosquera, Cristian M.

2012-06-01

Defence R&D Canada - Suffield and Bubble Technology Industries have been developing thermal neutron activation (TNA) sensors for detection of buried bulk explosives since 1994. First generation sensors, employing an isotopic source and NaI(Tl) gamma ray detectors, were deployed by Canadian Forces in 2002 as confirmation sensors on the ILDS teleoperated, vehicle-mounted, multi-sensor anti-tank landmine detection systems. The first generation TNA could detect anti-tank mines buried 10 cm or less in no more than a minute, but deeper mines and those significantly displaced horizontally required considerably longer times. Mines as deep as 30 cm could be detected with long counting times (1000 s). The second generation TNA detector is being developed with a number of improvements aimed at increasing sensitivity and facilitating ease of operation. Among these are an electronic neutron generator to increase sensitivity for deeper and horizontally displaced explosives; LaBr3(Ce) scintillators, to improve time response and energy resolution; improved thermal and electronic stability; improved sensor head geometry to minimize spatial response nonuniformity; and more robust data processing. This improved sensitivity can translate to either decreased counting times, decreased minimum detectable explosive quantities, increased maximum sensor-to-target displacement, or a trade off among all three. Experiments to characterize the performance of the latest generation TNA in detecting buried landmines and IEDs hidden in culverts were conducted during 2011. This paper describes the second generation system. The experimental setup and methodology are detailed and preliminary comparisons between the performance of first and second generation systems are presented.

Brief group training of medical students in focused cardiac ultrasound may improve diagnostic accuracy of physical examination.

PubMed

Stokke, Thomas M; Ruddox, Vidar; Sarvari, Sebastian I; Otterstad, Jan E; Aune, Erlend; Edvardsen, Thor

2014-11-01

Physical examination and auscultation can be challenging for medical students. The aim of this study was to investigate whether a brief session of group training in focused cardiac ultrasound (FCU) with a pocket-sized device would allow medical students to improve their ability to detect clinically relevant cardiac lesions at the bedside. Twenty-one medical students in their clinical curriculum completed 4 hours of FCU training in groups. The students examined patients referred for echocardiography with emphasis on auscultation, followed by FCU. Findings from physical examination and FCU were compared with those from standard echocardiography performed and analyzed by cardiologists. In total, 72 patients were included in the study, and 110 examinations were performed. With a stethoscope, sensitivity to detect clinically relevant (moderate or greater) valvular disease was 29% for mitral regurgitation, 33% for aortic regurgitation, and 67% for aortic stenosis. FCU improved sensitivity to detect mitral regurgitation (69%, P < .001). However, sensitivity to detect aortic regurgitation (43%) and aortic stenosis (70%) did not improve significantly. Specificity was ≥89% for all valvular diagnoses by both methods. For nonvalvular diagnoses, FCU's sensitivity to detect moderate or greater left ventricular dysfunction (90%) was excellent, detection of right ventricular dysfunction (79%) was good, while detection of dilated left atrium (53%), dilated right atrium (49%), pericardial effusion (40%), and dilated aortic root (25%) was less accurate. Specificity varied from 57% to 94%. After brief group training in FCU, medical students could detect mitral regurgitation significantly better compared with physical examination, whereas detection of aortic regurgitation and aortic stenosis did not improve. Left ventricular dysfunction was detected with high sensitivity. More extensive training is advised. Copyright © 2014 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.

Detecting Corresponding Vertex Pairs between Planar Tessellation Datasets with Agglomerative Hierarchical Cell-Set Matching.

PubMed

Huh, Yong; Yu, Kiyun; Park, Woojin

2016-01-01

This paper proposes a method to detect corresponding vertex pairs between planar tessellation datasets. Applying an agglomerative hierarchical co-clustering, the method finds geometrically corresponding cell-set pairs from which corresponding vertex pairs are detected. Then, the map transformation is performed with the vertex pairs. Since these pairs are independently detected for each corresponding cell-set pairs, the method presents improved matching performance regardless of locally uneven positional discrepancies between dataset. The proposed method was applied to complicated synthetic cell datasets assumed as a cadastral map and a topographical map, and showed an improved result with the F-measures of 0.84 comparing to a previous matching method with the F-measure of 0.48.

Improvement of automatic hemorrhage detection methods using brightness correction on fundus images

NASA Astrophysics Data System (ADS)

Hatanaka, Yuji; Nakagawa, Toshiaki; Hayashi, Yoshinori; Kakogawa, Masakatsu; Sawada, Akira; Kawase, Kazuhide; Hara, Takeshi; Fujita, Hiroshi

2008-03-01

We have been developing several automated methods for detecting abnormalities in fundus images. The purpose of this study is to improve our automated hemorrhage detection method to help diagnose diabetic retinopathy. We propose a new method for preprocessing and false positive elimination in the present study. The brightness of the fundus image was changed by the nonlinear curve with brightness values of the hue saturation value (HSV) space. In order to emphasize brown regions, gamma correction was performed on each red, green, and blue-bit image. Subsequently, the histograms of each red, blue, and blue-bit image were extended. After that, the hemorrhage candidates were detected. The brown regions indicated hemorrhages and blood vessels and their candidates were detected using density analysis. We removed the large candidates such as blood vessels. Finally, false positives were removed by using a 45-feature analysis. To evaluate the new method for the detection of hemorrhages, we examined 125 fundus images, including 35 images with hemorrhages and 90 normal images. The sensitivity and specificity for the detection of abnormal cases was were 80% and 88%, respectively. These results indicate that the new method may effectively improve the performance of our computer-aided diagnosis system for hemorrhages.

Research on Aircraft Target Detection Algorithm Based on Improved Radial Gradient Transformation

NASA Astrophysics Data System (ADS)

Zhao, Z. M.; Gao, X. M.; Jiang, D. N.; Zhang, Y. Q.

2018-04-01

Aiming at the problem that the target may have different orientation in the unmanned aerial vehicle (UAV) image, the target detection algorithm based on the rotation invariant feature is studied, and this paper proposes a method of RIFF (Rotation-Invariant Fast Features) based on look up table and polar coordinate acceleration to be used for aircraft target detection. The experiment shows that the detection performance of this method is basically equal to the RIFF, and the operation efficiency is greatly improved.

Enhanced change detection performance reveals improved strategy use in avid action video game players.

PubMed

Clark, Kait; Fleck, Mathias S; Mitroff, Stephen R

2011-01-01

Recent research has shown that avid action video game players (VGPs) outperform non-video game players (NVGPs) on a variety of attentional and perceptual tasks. However, it remains unknown exactly why and how such differences arise; while some prior research has demonstrated that VGPs' improvements stem from enhanced basic perceptual processes, other work indicates that they can stem from enhanced attentional control. The current experiment used a change-detection task to explore whether top-down strategies can contribute to VGPs' improved abilities. Participants viewed alternating presentations of an image and a modified version of the image and were tasked with detecting and localizing the changed element. Consistent with prior claims of enhanced perceptual abilities, VGPs were able to detect the changes while requiring less exposure to the change than NVGPs. Further analyses revealed this improved change detection performance may result from altered strategy use; VGPs employed broader search patterns when scanning scenes for potential changes. These results complement prior demonstrations of VGPs' enhanced bottom-up perceptual benefits by providing new evidence of VGPs' potentially enhanced top-down strategic benefits. Copyright © 2010 Elsevier B.V. All rights reserved.

Transmission Bearing Damage Detection Using Decision Fusion Analysis

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.; Lewicki, David G.; Decker, Harry J.

2004-01-01

A diagnostic tool was developed for detecting fatigue damage to rolling element bearings in an OH-58 main rotor transmission. Two different monitoring technologies, oil debris analysis and vibration, were integrated using data fusion into a health monitoring system for detecting bearing surface fatigue pitting damage. This integrated system showed improved detection and decision-making capabilities as compared to using individual monitoring technologies. This diagnostic tool was evaluated by collecting vibration and oil debris data from tests performed in the NASA Glenn 500 hp Helicopter Transmission Test Stand. Data was collected during experiments performed in this test rig when two unanticipated bearing failures occurred. Results show that combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spiral bevel gears duplex ball bearings and spiral bevel pinion triplex ball bearings in a main rotor transmission.

Enhancing detection of steady-state visual evoked potentials using individual training data.

PubMed

Wang, Yijun; Nakanishi, Masaki; Wang, Yu-Te; Jung, Tzyy-Ping

2014-01-01

Although the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has improved gradually in the past decades, it still does not meet the requirement of a high communication speed in many applications. A major challenge is the interference of spontaneous background EEG activities in discriminating SSVEPs. An SSVEP BCI using frequency coding typically does not have a calibration procedure since the frequency of SSVEPs can be recognized by power spectrum density analysis (PSDA). However, the detection rate can be deteriorated by the spontaneous EEG activities within the same frequency range because phase information of SSVEPs is ignored in frequency detection. To address this problem, this study proposed to incorporate individual SSVEP training data into canonical correlation analysis (CCA) to improve the frequency detection of SSVEPs. An eight-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment was used for performance evaluation. Compared to the standard CCA method, the proposed method obtained significantly improved detection accuracy (95.2% vs. 88.4%, p<0.05) and information transfer rates (ITR) (104.6 bits/min vs. 89.1 bits/min, p<0.05). The results suggest that the employment of individual SSVEP training data can significantly improve the detection rate and thereby facilitate the implementation of a high-speed BCI.

Throughput of Coded Optical CDMA Systems with AND Detectors

NASA Astrophysics Data System (ADS)

Memon, Kehkashan A.; Umrani, Fahim A.; Umrani, A. W.; Umrani, Naveed A.

2012-09-01

Conventional detection techniques used in optical code-division multiple access (OCDMA) systems are not optimal and result in poor bit error rate performance. This paper analyzes the coded performance of optical CDMA systems with AND detectors for enhanced throughput efficiencies and improved error rate performance. The results show that the use of AND detectors significantly improve the performance of an optical channel.

Vision restoration through extrastriate stimulation in patients with visual field defects: a double-blind and randomized experimental study.

PubMed

Jobke, Sandra; Kasten, Erich; Sabel, Bernhard A

2009-01-01

. Vision restoration therapy (VRT) to treat visual field defects used single-point visual stimulation in areas of residual vision up to now. The question arises if the efficiency of restoration can be increased when the entire region of blindness is trained by a visual stimulus aimed at activating extrastriate pathways (extrastriate VRT). . In this crossover study, 18 patients with visual field defects with prior VRT experience were treated with 2 training paradigms. Group 1 (n = 8) first used extrastriate VRT followed by conventional standard VRT. Group 2 (n = 10) trained in reverse order. Visual field size was assessed with computer-based perimetry and subjective vision with the National Eye Institute Visual Function Questionnaire (NEI-VFQ). . In group 1, stimulus detection in high-resolution perimetry (HRP) improved by 5.9% (P < .01) after extrastriate VRT. After the second training period (standard VRT), detection further improved by 1.8% (P = .093). In group 2, detection performance improved after standard VRT by 2.9% (P < .05) and after extrastriate VRT by 2.9% (P < .05). Detection performance increased twice as much after extrastriate VRT (4.2%) than after standard VRT (2.4%; P < .05). All changes in fixation performance were unrelated to detection improvements. NEI-VFQ did not show any significant changes. . Greater improvement after extrastriate VRT is interpreted as an activation of extrastriate pathways by massive "spiral-like" stimulation. These pathways bypass the damaged visual cortex, stimulating extrastriate cortical regions, and are thought to be involved in blindsight.

Feature Transformation Detection Method with Best Spectral Band Selection Process for Hyper-spectral Imaging

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike; Brickhouse, Mark

2015-11-01

We present a newly developed feature transformation (FT) detection method for hyper-spectral imagery (HSI) sensors. In essence, the FT method, by transforming the original features (spectral bands) to a different feature domain, may considerably increase the statistical separation between the target and background probability density functions, and thus may significantly improve the target detection and identification performance, as evidenced by the test results in this paper. We show that by differentiating the original spectral, one can completely separate targets from the background using a single spectral band, leading to perfect detection results. In addition, we have proposed an automated best spectral band selection process with a double-threshold scheme that can rank the available spectral bands from the best to the worst for target detection. Finally, we have also proposed an automated cross-spectrum fusion process to further improve the detection performance in lower spectral range (<1000 nm) by selecting the best spectral band pair with multivariate analysis. Promising detection performance has been achieved using a small background material signature library for concept-proving, and has then been further evaluated and verified using a real background HSI scene collected by a HYDICE sensor.

Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description

PubMed Central

Liu, Yi-Hung; Chen, Yan-Jen

2011-01-01

Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms. PMID:22016625

Automatic defect detection for TFT-LCD array process using quasiconformal kernel support vector data description.

PubMed

Liu, Yi-Hung; Chen, Yan-Jen

2011-01-01

Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms.

Target recognition of ladar range images using slice image: comparison of four improved algorithms

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Cao, Jingya; Wang, Liang; Zhai, Yu; Cheng, Yang

2017-07-01

Compared with traditional 3-D shape data, ladar range images possess properties of strong noise, shape degeneracy, and sparsity, which make feature extraction and representation difficult. The slice image is an effective feature descriptor to resolve this problem. We propose four improved algorithms on target recognition of ladar range images using slice image. In order to improve resolution invariance of the slice image, mean value detection instead of maximum value detection is applied in these four improved algorithms. In order to improve rotation invariance of the slice image, three new improved feature descriptors-which are feature slice image, slice-Zernike moments, and slice-Fourier moments-are applied to the last three improved algorithms, respectively. Backpropagation neural networks are used as feature classifiers in the last two improved algorithms. The performance of these four improved recognition systems is analyzed comprehensively in the aspects of the three invariances, recognition rate, and execution time. The final experiment results show that the improvements for these four algorithms reach the desired effect, the three invariances of feature descriptors are not directly related to the final recognition performance of recognition systems, and these four improved recognition systems have different performances under different conditions.

High operation temperature of HgCdTe photodiodes by bulk defect passivation

NASA Astrophysics Data System (ADS)

Boieriu, Paul; Velicu, S.; Bommena, R.; Buurma, C.; Blisset, C.; Grein, C.; Sivananthan, S.; Hagler, P.

2013-01-01

Spatial noise and the loss of photogenerated current due material non-uniformities limit the performance of long wavelength infrared (LWIR) HgCdTe detector arrays. Reducing the electrical activity of defects is equivalent to lowering their density, thereby allowing detection and discrimination over longer ranges. Infrared focal plane arrays (IRFPAs) in other spectral bands will also benefit from detectivity and uniformity improvements. Larger signal-to-noise ratios permit either improved accuracy of detection/discrimination when an IRFPA is employed under current operating conditions, or provide similar performance with the IRFPA operating under less stringent conditions such as higher system temperature, increased system jitter or damaged read out integrated circuit (ROIC) wells. The bulk passivation of semiconductors with hydrogen continues to be investigated for its potential to become a tool for the fabrication of high performance devices. Inductively coupled plasmas have been shown to improve the quality and uniformity of semiconductor materials and devices. The retention of the benefits following various aging conditions is discussed here.

High-performance computer aided detection system for polyp detection in CT colonography with fluid and fecal tagging

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Wang, Shijun; Kabadi, Suraj; Summers, Ronald M.

2009-02-01

CT colonography (CTC) is a feasible and minimally invasive method for the detection of colorectal polyps and cancer screening. Computer-aided detection (CAD) of polyps has improved consistency and sensitivity of virtual colonoscopy interpretation and reduced interpretation burden. A CAD system typically consists of four stages: (1) image preprocessing including colon segmentation; (2) initial detection generation; (3) feature selection; and (4) detection classification. In our experience, three existing problems limit the performance of our current CAD system. First, highdensity orally administered contrast agents in fecal-tagging CTC have scatter effects on neighboring tissues. The scattering manifests itself as an artificial elevation in the observed CT attenuation values of the neighboring tissues. This pseudo-enhancement phenomenon presents a problem for the application of computer-aided polyp detection, especially when polyps are submerged in the contrast agents. Second, general kernel approach for surface curvature computation in the second stage of our CAD system could yield erroneous results for thin structures such as small (6-9 mm) polyps and for touching structures such as polyps that lie on haustral folds. Those erroneous curvatures will reduce the sensitivity of polyp detection. The third problem is that more than 150 features are selected from each polyp candidate in the third stage of our CAD system. These high dimensional features make it difficult to learn a good decision boundary for detection classification and reduce the accuracy of predictions. Therefore, an improved CAD system for polyp detection in CTC data is proposed by introducing three new techniques. First, a scale-based scatter correction algorithm is applied to reduce pseudo-enhancement effects in the image pre-processing stage. Second, a cubic spline interpolation method is utilized to accurately estimate curvatures for initial detection generation. Third, a new dimensionality reduction classifier, diffusion map and local linear embedding (DMLLE), is developed for classification and false positives (FP) reduction. Performance of the improved CAD system is evaluated and compared with our existing CAD system (without applying those techniques) using CT scans of 1186 patients. These scans are divided into a training set and a test set. The sensitivity of the improved CAD system increased 18% on training data at a rate of 5 FPs per patient and 15% on test data at a rate of 5 FPs per patient. Our results indicated that the improved CAD system achieved significantly better performance on medium-sized colonic adenomas with higher sensitivity and lower FP rate in CTC.

Older Adults’ Functional Performance and Health Knowledge After a Combination Exercise, Health Education, and Bingo Game

PubMed Central

Crandall, K. Jason; Steenbergen, Katryn I.

2015-01-01

Combining exercise, health education, and the game of bingo may help older adults remain independent. The objective was to determine whether a 10-week health promotion program (Bingocize®) improves functional performance and health knowledge in older adults. Participants were assigned to experimental (n = 13) or control (n = 14) groups. The intervention was administered twice per week at two independent living facilities. Pre and postfunctional performance and health knowledge were measured. Mixed between–within subject ANOVA was used to detect differences between groups (p < .05). Improvements were found in all dependent variables except lower body flexibility, systolic blood pressure, and health knowledge. Adherence was 97.31% ± 2.59%. Bingocize® has the potential to help older adults remain independent by improving functional performance. Statistical improvements in health knowledge were not found, but future researchers may explore modifying the health education component or using a different measure of health knowledge to detect changes. PMID:28138476

No psychological effect of color context in a low level vision task

PubMed Central

Pedley, Adam; Wade, Alex R

2013-01-01

Background: A remarkable series of recent papers have shown that colour can influence performance in cognitive tasks. In particular, they suggest that viewing a participant number printed in red ink or other red ancillary stimulus elements improves performance in tasks requiring local processing and impedes performance in tasks requiring global processing whilst the reverse is true for the colour blue. The tasks in these experiments require high level cognitive processing such as analogy solving or remote association tests and the chromatic effect on local vs. global processing is presumed to involve widespread activation of the autonomic nervous system. If this is the case, we might expect to see similar effects on all local vs. global task comparisons. To test this hypothesis, we asked whether chromatic cues also influence performance in tasks involving low level visual feature integration. Methods: Subjects performed either local (contrast detection) or global (form detection) tasks on achromatic dynamic Glass pattern stimuli. Coloured instructions, target frames and fixation points were used to attempt to bias performance to different task types. Based on previous literature, we hypothesised that red cues would improve performance in the (local) contrast detection task but would impede performance in the (global) form detection task.  Results: A two-way, repeated measures, analysis of covariance (2×2 ANCOVA) with gender as a covariate, revealed no influence of colour on either task, F(1,29) = 0.289, p = 0.595, partial η 2 = 0.002. Additional analysis revealed no significant differences in only the first attempts of the tasks or in the improvement in performance between trials. Discussion: We conclude that motivational processes elicited by colour perception do not influence neuronal signal processing in the early visual system, in stark contrast to their putative effects on processing in higher areas. PMID:25075280

No psychological effect of color context in a low level vision task.

PubMed

Pedley, Adam; Wade, Alex R

2013-01-01

A remarkable series of recent papers have shown that colour can influence performance in cognitive tasks. In particular, they suggest that viewing a participant number printed in red ink or other red ancillary stimulus elements improves performance in tasks requiring local processing and impedes performance in tasks requiring global processing whilst the reverse is true for the colour blue. The tasks in these experiments require high level cognitive processing such as analogy solving or remote association tests and the chromatic effect on local vs. global processing is presumed to involve widespread activation of the autonomic nervous system. If this is the case, we might expect to see similar effects on all local vs. global task comparisons. To test this hypothesis, we asked whether chromatic cues also influence performance in tasks involving low level visual feature integration. Subjects performed either local (contrast detection) or global (form detection) tasks on achromatic dynamic Glass pattern stimuli. Coloured instructions, target frames and fixation points were used to attempt to bias performance to different task types. Based on previous literature, we hypothesised that red cues would improve performance in the (local) contrast detection task but would impede performance in the (global) form detection task.  A two-way, repeated measures, analysis of covariance (2×2 ANCOVA) with gender as a covariate, revealed no influence of colour on either task, F(1,29) = 0.289, p = 0.595, partial η (2) = 0.002. Additional analysis revealed no significant differences in only the first attempts of the tasks or in the improvement in performance between trials. We conclude that motivational processes elicited by colour perception do not influence neuronal signal processing in the early visual system, in stark contrast to their putative effects on processing in higher areas.

Improved detection and false alarm rejection for chemical vapors using passive hyperspectral imaging

NASA Astrophysics Data System (ADS)

Marinelli, William J.; Miyashiro, Rex; Gittins, Christopher M.; Konno, Daisei; Chang, Shing; Farr, Matt; Perkins, Brad

2013-05-01

Two AIRIS sensors were tested at Dugway Proving Grounds against chemical agent vapor simulants. The primary objectives of the test were to: 1) assess performance of algorithm improvements designed to reduce false alarm rates with a special emphasis on solar effects, and 3) evaluate performance in target detection at 5 km. The tests included 66 total releases comprising alternating 120 kg glacial acetic acid (GAA) and 60 kg triethyl phosphate (TEP) events. The AIRIS sensors had common algorithms, detection thresholds, and sensor parameters. The sensors used the target set defined for the Joint Service Lightweight Chemical Agent Detector (JSLSCAD) with TEP substituted for GA and GAA substituted for VX. They were exercised at two sites located at either 3 km or 5 km from the release point. Data from the tests will be presented showing that: 1) excellent detection capability was obtained at both ranges with significantly shorter alarm times at 5 km, 2) inter-sensor comparison revealed very comparable performance, 3) false alarm rates < 1 incident per 10 hours running time over 143 hours of sensor operations were achieved, 4) algorithm improvements eliminated both solar and cloud false alarms. The algorithms enabling the improved false alarm rejection will be discussed. The sensor technology has recently been extended to address the problem of detection of liquid and solid chemical agents and toxic industrial chemical on surfaces. The phenomenology and applicability of passive infrared hyperspectral imaging to this problem will be discussed and demonstrated.

Constant-Envelope Waveform Design for Optimal Target-Detection and Autocorrelation Performances

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

Sen, Satyabrata

2013-01-01

We propose an algorithm to directly synthesize in time-domain a constant-envelope transmit waveform that achieves the optimal performance in detecting an extended target in the presence of signal-dependent interference. This approach is in contrast to the traditional indirect methods that synthesize the transmit signal following the computation of the optimal energy spectral density. Additionally, we aim to maintain a good autocorrelation property of the designed signal. Therefore, our waveform design technique solves a bi-objective optimization problem in order to simultaneously improve the detection and autocorrelation performances, which are in general conflicting in nature. We demonstrate this compromising characteristics of themore » detection and autocorrelation performances with numerical examples. Furthermore, in the absence of the autocorrelation criterion, our designed signal is shown to achieve a near-optimum detection performance.« less

Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement

PubMed Central

Negri, Lucas; Nied, Ademir; Kalinowski, Hypolito; Paterno, Aleksander

2011-01-01

This paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroid and gaussian fitting algorithms are shown to have the highest precision but produce systematic errors that depend on the FBG refractive index modulation profile. The proposed neural network displays relatively good precision with reduced systematic errors and improved computational performance when compared to other networks. Additionally, suitable algorithms may be chosen with the general guidelines presented. PMID:22163806

Impurity-induced deep centers in Tl 6SI 4

DOE PAGES

Shi, Hongliang; Lin, Wenwen; Kanatzidis, Mercouri G.; ...

2017-04-13

Tl 6SI 4 is a promising material for room-temperature semiconductor radiation detection applications. The history of the development of semiconductor radiation detection materials has demonstrated that impurities strongly affect the carrier transport and that material purification is a critically important step in improving the carrier transport and thereby the detector performance. Here, we report combined experimental and theoretical studies of impurities in Tl 6SI 4. Impurity concentrations in Tl 6SI 4 were analyzed by glow discharge mass spectrometry. Purification of the raw material by multi-pass vertical narrow zone refining was found to be effective in reducing the concentrations of mostmore » impurities. Density functional theory calculations were also performed to study the trapping levels introduced by the main impurities detected in experiments. We show that, among dozens of detected impurities, most are either electrically inactive or shallow. In the purified Tl 6SI 4 sample, only Bi has a significant concentration (0.2 ppm wt) and introduces deep electron trapping levels in the band gap. Lastly, improvement of the purification processes is expected to further reduce the impurity concentrations and their impact on carrier transport in Tl 6SI 4, leading to improved detector performance.« less

Bayesian image reconstruction for improving detection performance of muon tomography.

PubMed

Wang, Guobao; Schultz, Larry J; Qi, Jinyi

2009-05-01

Muon tomography is a novel technology that is being developed for detecting high-Z materials in vehicles or cargo containers. Maximum likelihood methods have been developed for reconstructing the scattering density image from muon measurements. However, the instability of maximum likelihood estimation often results in noisy images and low detectability of high-Z targets. In this paper, we propose using regularization to improve the image quality of muon tomography. We formulate the muon reconstruction problem in a Bayesian framework by introducing a prior distribution on scattering density images. An iterative shrinkage algorithm is derived to maximize the log posterior distribution. At each iteration, the algorithm obtains the maximum a posteriori update by shrinking an unregularized maximum likelihood update. Inverse quadratic shrinkage functions are derived for generalized Laplacian priors and inverse cubic shrinkage functions are derived for generalized Gaussian priors. Receiver operating characteristic studies using simulated data demonstrate that the Bayesian reconstruction can greatly improve the detection performance of muon tomography.

Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study.

PubMed

Sunwoo, Leonard; Kim, Young Jae; Choi, Seung Hong; Kim, Kwang-Gi; Kang, Ji Hee; Kang, Yeonah; Bae, Yun Jung; Yoo, Roh-Eul; Kim, Jihang; Lee, Kyong Joon; Lee, Seung Hyun; Choi, Byung Se; Jung, Cheolkyu; Sohn, Chul-Ho; Kim, Jae Hyoung

2017-01-01

To assess the effect of computer-aided detection (CAD) of brain metastasis (BM) on radiologists' diagnostic performance in interpreting three-dimensional brain magnetic resonance (MR) imaging using follow-up imaging and consensus as the reference standard. The institutional review board approved this retrospective study. The study cohort consisted of 110 consecutive patients with BM and 30 patients without BM. The training data set included MR images of 80 patients with 450 BM nodules. The test set included MR images of 30 patients with 134 BM nodules and 30 patients without BM. We developed a CAD system for BM detection using template-matching and K-means clustering algorithms for candidate detection and an artificial neural network for false-positive reduction. Four reviewers (two neuroradiologists and two radiology residents) interpreted the test set images before and after the use of CAD in a sequential manner. The sensitivity, false positive (FP) per case, and reading time were analyzed. A jackknife free-response receiver operating characteristic (JAFROC) method was used to determine the improvement in the diagnostic accuracy. The sensitivity of CAD was 87.3% with an FP per case of 302.4. CAD significantly improved the diagnostic performance of the four reviewers with a figure-of-merit (FOM) of 0.874 (without CAD) vs. 0.898 (with CAD) according to JAFROC analysis (p < 0.01). Statistically significant improvement was noted only for less-experienced reviewers (FOM without vs. with CAD, 0.834 vs. 0.877, p < 0.01). The additional time required to review the CAD results was approximately 72 sec (40% of the total review time). CAD as a second reader helps radiologists improve their diagnostic performance in the detection of BM on MR imaging, particularly for less-experienced reviewers.

The development of real-time stability supports visual working memory performance: Young children's feature binding can be improved through perceptual structure.

PubMed

Simmering, Vanessa R; Wood, Chelsey M

2017-08-01

Working memory is a basic cognitive process that predicts higher-level skills. A central question in theories of working memory development is the generality of the mechanisms proposed to explain improvements in performance. Prior theories have been closely tied to particular tasks and/or age groups, limiting their generalizability. The cognitive dynamics theory of visual working memory development has been proposed to overcome this limitation. From this perspective, developmental improvements arise through the coordination of cognitive processes to meet demands of different behavioral tasks. This notion is described as real-time stability, and can be probed through experiments that assess how changing task demands impact children's performance. The current studies test this account by probing visual working memory for colors and shapes in a change detection task that compares detection of changes to new features versus swaps in color-shape binding. In Experiment 1, 3- to 4-year-old children showed impairments specific to binding swaps, as predicted by decreased real-time stability early in development; 5- to 6-year-old children showed a slight advantage on binding swaps, but 7- to 8-year-old children and adults showed no difference across trial types. Experiment 2 tested the proposed explanation of young children's binding impairment through added perceptual structure, which supported the stability and precision of feature localization in memory-a process key to detecting binding swaps. This additional structure improved young children's binding swap detection, but not new-feature detection or adults' performance. These results provide further evidence for the cognitive dynamics and real-time stability explanation of visual working memory development. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

On-line early fault detection and diagnosis of municipal solid waste incinerators

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

Zhao Jinsong; Huang Jianchao; Sun Wei

A fault detection and diagnosis framework is proposed in this paper for early fault detection and diagnosis (FDD) of municipal solid waste incinerators (MSWIs) in order to improve the safety and continuity of production. In this framework, principal component analysis (PCA), one of the multivariate statistical technologies, is used for detecting abnormal events, while rule-based reasoning performs the fault diagnosis and consequence prediction, and also generates recommendations for fault mitigation once an abnormal event is detected. A software package, SWIFT, is developed based on the proposed framework, and has been applied in an actual industrial MSWI. The application shows thatmore » automated real-time abnormal situation management (ASM) of the MSWI can be achieved by using SWIFT, resulting in an industrially acceptable low rate of wrong diagnosis, which has resulted in improved process continuity and environmental performance of the MSWI.« less

Multiparametric ultrasound in the detection of prostate cancer: a systematic review.

PubMed

Postema, Arnoud; Mischi, Massimo; de la Rosette, Jean; Wijkstra, Hessel

2015-11-01

To investigate the advances and clinical results of the different ultrasound modalities and the progress in combining them into multiparametric UltraSound (mpUS). A systematic literature search on mpUS and the different ultrasound modalities included: greyscale ultrasound, computerized transrectal ultrasound, Doppler and power Doppler techniques, dynamic contrast-enhanced ultrasound and (shear wave) elastography. Limited research available on combining ultrasound modalities has presented improvement in diagnostic performance. The data of two studies suggest that even adding a lower performing ultrasound modality to a better performing modality using crude methods can already improve the sensitivity by 13-51 %. The different modalities detect different tumours. No study has tried to combine ultrasound modalities employing a system similar to the PIRADS system used for mpMRI or more advanced classifying algorithms. Available evidence confirms that combining different ultrasound modalities significantly improves diagnostic performance.

Fusion solution for soldier wearable gunfire detection systems

NASA Astrophysics Data System (ADS)

Cakiades, George; Desai, Sachi; Deligeorges, Socrates; Buckland, Bruce E.; George, Jemin

2012-06-01

Currently existing acoustic based Gunfire Detection Systems (GDS) such as soldier wearable, vehicle mounted, and fixed site devices provide enemy detection and localization capabilities to the user. However, the solution to the problem of portability versus performance tradeoff remains elusive. The Data Fusion Module (DFM), described herein, is a sensor/platform agnostic software supplemental tool that addresses this tradeoff problem by leveraging existing soldier networks to enhance GDS performance across a Tactical Combat Unit (TCU). The DFM software enhances performance by leveraging all available acoustic GDS information across the TCU synergistically to calculate highly accurate solutions more consistently than any individual GDS in the TCU. The networked sensor architecture provides additional capabilities addressing the multiple shooter/fire-fight problems in addition to sniper detection/localization. The addition of the fusion solution to the overall Size, Weight and Power & Cost (SWaP&C) is zero to negligible. At the end of the first-year effort, the DFM integrated sensor network's performance was impressive showing improvements upwards of 50% in comparison to a single sensor solution. Further improvements are expected when the networked sensor architecture created in this effort is fully exploited.

Rate change detection of frequency modulated signals: developmental trends.

PubMed

Cohen-Mimran, Ravit; Sapir, Shimon

2011-08-26

The aim of this study was to examine developmental trends in rate change detection of auditory rhythmic signals (repetitive sinusoidally frequency modulated tones). Two groups of children (9-10 years old and 11-12 years old) and one group of young adults performed a rate change detection (RCD) task using three types of stimuli. The rate of stimulus modulation was either constant (CR), raised by 1 Hz in the middle of the stimulus (RR1) or raised by 2 Hz in the middle of the stimulus (RR2). Performance on the RCD task significantly improved with age. Also, the different stimuli showed different developmental trajectories. When the RR2 stimulus was used, results showed adult-like performance by the age of 10 years but when the RR1 stimulus was used performance continued to improve beyond 12 years of age. Rate change detection of repetitive sinusoidally frequency modulated tones show protracted development beyond the age of 12 years. Given evidence for abnormal processing of auditory rhythmic signals in neurodevelopmental conditions, such as dyslexia, the present methodology might help delineate the nature of these conditions.

Modifications developed to improve x-ray detection devices

NASA Technical Reports Server (NTRS)

1994-01-01

Improvements in the development of x-ray detection devices are described. Emphasis is placed on lowering the temperature in order to achieve better x-ray response. A simplified charge integrator schematic is presented along with supporting tables. By using cryogenic operating temperatures, these x-ray detectors may eventually surpass the performance of the best semiconductor detectors.

Improved computer-aided detection of small polyps in CT colonography using interpolation for curvature estimationa

PubMed Central

Liu, Jiamin; Kabadi, Suraj; Van Uitert, Robert; Petrick, Nicholas; Deriche, Rachid; Summers, Ronald M.

2011-01-01

Purpose: Surface curvatures are important geometric features for the computer-aided analysis and detection of polyps in CT colonography (CTC). However, the general kernel approach for curvature computation can yield erroneous results for small polyps and for polyps that lie on haustral folds. Those erroneous curvatures will reduce the performance of polyp detection. This paper presents an analysis of interpolation’s effect on curvature estimation for thin structures and its application on computer-aided detection of small polyps in CTC. Methods: The authors demonstrated that a simple technique, image interpolation, can improve the accuracy of curvature estimation for thin structures and thus significantly improve the sensitivity of small polyp detection in CTC. Results: Our experiments showed that the merits of interpolating included more accurate curvature values for simulated data, and isolation of polyps near folds for clinical data. After testing on a large clinical data set, it was observed that sensitivities with linear, quadratic B-spline and cubic B-spline interpolations significantly improved the sensitivity for small polyp detection. Conclusions: The image interpolation can improve the accuracy of curvature estimation for thin structures and thus improve the computer-aided detection of small polyps in CTC. PMID:21859029

ICCD: interactive continuous collision detection between deformable models using connectivity-based culling.

PubMed

Tang, Min; Curtis, Sean; Yoon, Sung-Eui; Manocha, Dinesh

2009-01-01

We present an interactive algorithm for continuous collision detection between deformable models. We introduce multiple techniques to improve the culling efficiency and the overall performance of continuous collision detection. First, we present a novel formulation for continuous normal cones and use these normal cones to efficiently cull large regions of the mesh as part of self-collision tests. Second, we introduce the concept of "procedural representative triangles" to remove all redundant elementary tests between nonadjacent triangles. Finally, we exploit the mesh connectivity and introduce the concept of "orphan sets" to eliminate redundant elementary tests between adjacent triangle primitives. In practice, we can reduce the number of elementary tests by two orders of magnitude. These culling techniques have been combined with bounding volume hierarchies and can result in one order of magnitude performance improvement as compared to prior collision detection algorithms for deformable models. We highlight the performance of our algorithm on several benchmarks, including cloth simulations, N-body simulations, and breaking objects.

Effect of time-of-flight and point spread function modeling on detectability of myocardial defects in PET

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

Schaefferkoetter, Joshua, E-mail: dnrjds@nus.edu.sg; Ouyang, Jinsong; Rakvongthai, Yothin

2014-06-15

Purpose: A study was designed to investigate the impact of time-of-flight (TOF) and point spread function (PSF) modeling on the detectability of myocardial defects. Methods: Clinical FDG-PET data were used to generate populations of defect-present and defect-absent images. Defects were incorporated at three contrast levels, and images were reconstructed by ordered subset expectation maximization (OSEM) iterative methods including ordinary Poisson, alone and with PSF, TOF, and PSF+TOF. Channelized Hotelling observer signal-to-noise ratio (SNR) was the surrogate for human observer performance. Results: For three iterations, 12 subsets, and no postreconstruction smoothing, TOF improved overall defect detection SNR by 8.6% as comparedmore » to its non-TOF counterpart for all the defect contrasts. Due to the slow convergence of PSF reconstruction, PSF yielded 4.4% less SNR than non-PSF. For reconstruction parameters (iteration number and postreconstruction smoothing kernel size) optimizing observer SNR, PSF showed larger improvement for faint defects. The combination of TOF and PSF improved mean detection SNR as compared to non-TOF and non-PSF counterparts by 3.0% and 3.2%, respectively. Conclusions: For typical reconstruction protocol used in clinical practice, i.e., less than five iterations, TOF improved defect detectability. In contrast, PSF generally yielded less detectability. For large number of iterations, TOF+PSF yields the best observer performance.« less

Comparison of Colonoscopy Quality Measures Across Various Practice Settings and the Impact of Performance Scorecards.

PubMed

Inra, Jennifer A; Nayor, Jennifer; Rosenblatt, Margery; Mutinga, Muthoka; Reddy, Sarathchandra I; Syngal, Sapna; Kastrinos, Fay

2017-04-01

Quality performance measures for screening colonoscopy vary among endoscopists. The impact of practice setting is unknown. We aimed to (1) compare screening colonoscopy performance measures among three different US practice settings; (2) evaluate factors associated with adenoma detection; and (3) assess a scorecard intervention on performance metrics. This multi-center prospective study compared patient, endoscopist, and colonoscopy characteristics performed at a tertiary care hospital (TCH), community-based hospital (CBH), and private practice group (PPG). Withdrawal times (WT), cecal intubation, and adenoma detection rates (ADR) were compared by site at baseline and 12 weeks following scorecard distribution. Generalized linear mixed models identified factors associated with adenoma detection. Twenty-eight endoscopists performed colonoscopies on 1987 asymptomatic, average-risk individuals ≥50 years. Endoscopist and patient characteristics were similar across sites. The PPG screened more men (TCH: 42.8%, CBH: 45.0%, PPG: 54.2%; p < 0.0001). Preparation quality varied with good/excellent results in 70.6, 88.3, and 92% of TCH, CBH, and PPG cases, respectively (p < 0.0001). Male ADRs, cecal intubation, and WT exceeded recommended benchmarks despite variable results at each site; female ADRs were <15% at the PPG which screened the fewest females. Performance remained unchanged following scorecard distribution. Adenoma detection was associated with increasing patient age, male gender, WT, adequate preparation, but not practice setting. Each practice performed high-quality screening colonoscopy. Scorecards did not improve performance metrics. Preparation quality varies among practice settings and can be modified to improve adenoma detection.

Using dual tasks to test immediate transfer of training between naturalistic movements: a proof-of-principle study.

PubMed

Schaefer, Sydney Y; Lang, Catherine E

2012-01-01

Theories of motor learning predict that training a movement reduces the amount of attention needed for its performance (i.e., more automatic). If training one movement transfers, then the amount of attention needed for performing a second movement should also be reduced, as measured under dual task conditions. The authors' purpose was to test whether dual task paradigms are feasible for detecting transfer of training between two naturalistic movements. Immediately following motor training, subjects improved performance of a second untrained movement under single and dual task conditions. Subjects with no training did not. Improved performance in the untrained movement was likely due to transfer, and suggests that dual tasks may be feasible for detecting transfer between naturalistic actions.

Trace gas detection in hyperspectral imagery using the wavelet packet subspace

NASA Astrophysics Data System (ADS)

Salvador, Mark A. Z.

This dissertation describes research into a new remote sensing method to detect trace gases in hyperspectral and ultra-spectral data. This new method is based on the wavelet packet transform. It attempts to improve both the computational tractability and the detection of trace gases in airborne and spaceborne spectral imagery. Atmospheric trace gas research supports various Earth science disciplines to include climatology, vulcanology, pollution monitoring, natural disasters, and intelligence and military applications. Hyperspectral and ultra-spectral data significantly increases the data glut of existing Earth science data sets. Spaceborne spectral data in particular significantly increases spectral resolution while performing daily global collections of the earth. Application of the wavelet packet transform to the spectral space of hyperspectral and ultra-spectral imagery data potentially improves remote sensing detection algorithms. It also facilities the parallelization of these methods for high performance computing. This research seeks two science goals, (1) developing a new spectral imagery detection algorithm, and (2) facilitating the parallelization of trace gas detection in spectral imagery data.

Using trend templates in a neonatal seizure algorithm improves detection of short seizures in a foetal ovine model.

PubMed

Zwanenburg, Alex; Andriessen, Peter; Jellema, Reint K; Niemarkt, Hendrik J; Wolfs, Tim G A M; Kramer, Boris W; Delhaas, Tammo

2015-03-01

Seizures below one minute in duration are difficult to assess correctly using seizure detection algorithms. We aimed to improve neonatal detection algorithm performance for short seizures through the use of trend templates for seizure onset and end. Bipolar EEG were recorded within a transiently asphyxiated ovine model at 0.7 gestational age, a common experimental model for studying brain development in humans of 30-34 weeks of gestation. Transient asphyxia led to electrographic seizures within 6-8 h. A total of 3159 seizures, 2386 shorter than one minute, were annotated in 1976 h-long EEG recordings from 17 foetal lambs. To capture EEG characteristics, five features, sensitive to seizures, were calculated and used to derive trend information. Feature values and trend information were used as input for support vector machine classification and subsequently post-processed. Performance metrics, calculated after post-processing, were compared between analyses with and without employing trend information. Detector performance was assessed after five-fold cross-validation conducted ten times with random splits. The use of trend templates for seizure onset and end in a neonatal seizure detection algorithm significantly improves the correct detection of short seizures using two-channel EEG recordings from 54.3% (52.6-56.1) to 59.5% (58.5-59.9) at FDR 2.0 (median (range); p < 0.001, Wilcoxon signed rank test). Using trend templates might therefore aid in detection of short seizures by EEG monitoring at the NICU.

Improved wheal detection from skin prick test images

NASA Astrophysics Data System (ADS)

Bulan, Orhan

2014-03-01

Skin prick test is a commonly used method for diagnosis of allergic diseases (e.g., pollen allergy, food allergy, etc.) in allergy clinics. The results of this test are erythema and wheal provoked on the skin where the test is applied. The sensitivity of the patient against a specific allergen is determined by the physical size of the wheal, which can be estimated from images captured by digital cameras. Accurate wheal detection from these images is an important step for precise estimation of wheal size. In this paper, we propose a method for improved wheal detection on prick test images captured by digital cameras. Our method operates by first localizing the test region by detecting calibration marks drawn on the skin. The luminance variation across the localized region is eliminated by applying a color transformation from RGB to YCbCr and discarding the luminance channel. We enhance the contrast of the captured images for the purpose of wheal detection by performing principal component analysis on the blue-difference (Cb) and red-difference (Cr) color channels. We finally, perform morphological operations on the contrast enhanced image to detect the wheal on the image plane. Our experiments performed on images acquired from 36 different patients show the efficiency of the proposed method for wheal detection from skin prick test images captured in an uncontrolled environment.

Detection of Multiple Stationary Humans Using UWB MIMO Radar.

PubMed

Liang, Fulai; Qi, Fugui; An, Qiang; Lv, Hao; Chen, Fuming; Li, Zhao; Wang, Jianqi

2016-11-16

Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls.

Detection of Multiple Stationary Humans Using UWB MIMO Radar

PubMed Central

Liang, Fulai; Qi, Fugui; An, Qiang; Lv, Hao; Chen, Fuming; Li, Zhao; Wang, Jianqi

2016-01-01

Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls. PMID:27854356

Laser Spot Center Detection and Comparison Test

NASA Astrophysics Data System (ADS)

Zhu, Jun; Xu, Zhengjie; Fu, Deli; Hu, Cong

2018-04-01

High efficiency and precision of the pot center detection are the foundations of avionics instrument navigation and optics measurement basis for many applications. It has noticeable impact on overall system performance. Among them, laser spot detection is very important in the optical measurement technology. In order to improve the low accuracy of the spot center position, the algorithm is improved on the basis of the circle fitting. The pretreatment is used by circle fitting, and the improved adaptive denoising filter for TV repair technology can effectively improves the accuracy of the spot center position. At the same time, the pretreatment and de-noising can effectively reduce the influence of Gaussian white noise, which enhances the anti-jamming capability.

Effects of threshold on single-target detection by using modified amplitude-modulated joint transform correlator

NASA Astrophysics Data System (ADS)

Kaewkasi, Pitchaya; Widjaja, Joewono; Uozumi, Jun

2007-03-01

Effects of threshold value on detection performance of the modified amplitude-modulated joint transform correlator are quantitatively studied using computer simulation. Fingerprint and human face images are used as test scenes in the presence of noise and a contrast difference. Simulation results demonstrate that this correlator improves detection performance for both types of image used, but moreso for human face images. Optimal detection of low-contrast human face images obscured by strong noise can be obtained by selecting an appropriate threshold value.

A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability.

PubMed

Awais, Muhammad; Badruddin, Nasreen; Drieberg, Micheal

2017-08-31

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t -tests to select only statistically significant features ( p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system's performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.

A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

PubMed Central

Badruddin, Nasreen

2017-01-01

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear. PMID:28858220

Rhythm-based heartbeat duration normalization for atrial fibrillation detection.

PubMed

Islam, Md Saiful; Ammour, Nassim; Alajlan, Naif; Aboalsamh, Hatim

2016-05-01

Screening of atrial fibrillation (AF) for high-risk patients including all patients aged 65 years and older is important for prevention of risk of stroke. Different technologies such as modified blood pressure monitor, single lead ECG-based finger-probe, and smart phone using plethysmogram signal have been emerging for this purpose. All these technologies use irregularity of heartbeat duration as a feature for AF detection. We have investigated a normalization method of heartbeat duration for improved AF detection. AF is an arrhythmia in which heartbeat duration generally becomes irregularly irregular. From a window of heartbeat duration, we estimate the possible rhythm of the majority of heartbeats and normalize duration of all heartbeats in the window based on the rhythm so that we can measure the irregularity of heartbeats for both AF and non-AF rhythms in the same scale. Irregularity is measured by the entropy of distribution of the normalized duration. Then we classify a window of heartbeats as AF or non-AF by thresholding the measured irregularity. The effect of this normalization is evaluated by comparing AF detection performances using duration with the normalization, without normalization, and with other existing normalizations. Sensitivity and specificity of AF detection using normalized heartbeat duration were tested on two landmark databases available online and compared with results of other methods (with/without normalization) by receiver operating characteristic (ROC) curves. ROC analysis showed that the normalization was able to improve the performance of AF detection and it was consistent for a wide range of sensitivity and specificity for use of different thresholds. Detection accuracy was also computed for equal rates of sensitivity and specificity for different methods. Using normalized heartbeat duration, we obtained 96.38% accuracy which is more than 4% improvement compared to AF detection without normalization. The proposed normalization method was found useful for improving performance and robustness of AF detection. Incorporation of this method in a screening device could be crucial to reduce the risk of AF-related stroke. In general, the incorporation of the rhythm-based normalization in an AF detection method seems important for developing a robust AF screening device. Copyright © 2016 Elsevier Ltd. All rights reserved.

Two-stage Keypoint Detection Scheme for Region Duplication Forgery Detection in Digital Images.

PubMed

Emam, Mahmoud; Han, Qi; Zhang, Hongli

2018-01-01

In digital image forensics, copy-move or region duplication forgery detection became a vital research topic recently. Most of the existing keypoint-based forgery detection methods fail to detect the forgery in the smooth regions, rather than its sensitivity to geometric changes. To solve these problems and detect points which cover all the regions, we proposed two steps for keypoint detection. First, we employed the scale-invariant feature operator to detect the spatially distributed keypoints from the textured regions. Second, the keypoints from the missing regions are detected using Harris corner detector with nonmaximal suppression to evenly distribute the detected keypoints. To improve the matching performance, local feature points are described using Multi-support Region Order-based Gradient Histogram descriptor. Based on precision-recall rates and commonly tested dataset, comprehensive performance evaluation is performed. The results demonstrated that the proposed scheme has better detection and robustness against some geometric transformation attacks compared with state-of-the-art methods. © 2017 American Academy of Forensic Sciences.

DeepSig: deep learning improves signal peptide detection in proteins.

PubMed

Savojardo, Castrense; Martelli, Pier Luigi; Fariselli, Piero; Casadio, Rita

2018-05-15

The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website. pierluigi.martelli@unibo.it. Supplementary data are available at Bioinformatics online.

Detection of no-model input-output pairs in closed-loop systems.

PubMed

Potts, Alain Segundo; Alvarado, Christiam Segundo Morales; Garcia, Claudio

2017-11-01

The detection of no-model input-output (IO) pairs is important because it can speed up the multivariable system identification process, since all the pairs with null transfer functions are previously discarded and it can also improve the identified model quality, thus improving the performance of model based controllers. In the available literature, the methods focus just on the open-loop case, since in this case there is not the effect of the controller forcing the main diagonal in the transfer matrix to one and all the other terms to zero. In this paper, a modification of a previous method able to detect no-model IO pairs in open-loop systems is presented, but adapted to perform this duty in closed-loop systems. Tests are performed by using the traditional methods and the proposed one to show its effectiveness. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Error detection and response adjustment in youth with mild spastic cerebral palsy: an event-related brain potential study.

PubMed

Hakkarainen, Elina; Pirilä, Silja; Kaartinen, Jukka; van der Meere, Jaap J

2013-06-01

This study evaluated the brain activation state during error making in youth with mild spastic cerebral palsy and a peer control group while carrying out a stimulus recognition task. The key question was whether patients were detecting their own errors and subsequently improving their performance in a future trial. Findings indicated that error responses of the group with cerebral palsy were associated with weak motor preparation, as indexed by the amplitude of the late contingent negative variation. However, patients were detecting their errors as indexed by the amplitude of the response-locked negativity and thus improved their performance in a future trial. Findings suggest that the consequence of error making on future performance is intact in a sample of youth with mild spastic cerebral palsy. Because the study group is small, the present findings need replication using a larger sample.

Systematic evaluation of deep learning based detection frameworks for aerial imagery

NASA Astrophysics Data System (ADS)

Sommer, Lars; Steinmann, Lucas; Schumann, Arne; Beyerer, Jürgen

2018-04-01

Object detection in aerial imagery is crucial for many applications in the civil and military domain. In recent years, deep learning based object detection frameworks significantly outperformed conventional approaches based on hand-crafted features on several datasets. However, these detection frameworks are generally designed and optimized for common benchmark datasets, which considerably differ from aerial imagery especially in object sizes. As already demonstrated for Faster R-CNN, several adaptations are necessary to account for these differences. In this work, we adapt several state-of-the-art detection frameworks including Faster R-CNN, R-FCN, and Single Shot MultiBox Detector (SSD) to aerial imagery. We discuss adaptations that mainly improve the detection accuracy of all frameworks in detail. As the output of deeper convolutional layers comprise more semantic information, these layers are generally used in detection frameworks as feature map to locate and classify objects. However, the resolution of these feature maps is insufficient for handling small object instances, which results in an inaccurate localization or incorrect classification of small objects. Furthermore, state-of-the-art detection frameworks perform bounding box regression to predict the exact object location. Therefore, so called anchor or default boxes are used as reference. We demonstrate how an appropriate choice of anchor box sizes can considerably improve detection performance. Furthermore, we evaluate the impact of the performed adaptations on two publicly available datasets to account for various ground sampling distances or differing backgrounds. The presented adaptations can be used as guideline for further datasets or detection frameworks.

Multiratio fusion change detection with adaptive thresholding

NASA Astrophysics Data System (ADS)

Hytla, Patrick C.; Balster, Eric J.; Vasquez, Juan R.; Neuroth, Robert M.

2017-04-01

A ratio-based change detection method known as multiratio fusion (MRF) is proposed and tested. The MRF framework builds on other change detection components proposed in this work: dual ratio (DR) and multiratio (MR). The DR method involves two ratios coupled with adaptive thresholds to maximize detected changes and minimize false alarms. The use of two ratios is shown to outperform the single ratio case when the means of the image pairs are not equal. MR change detection builds on the DR method by including negative imagery to produce four total ratios with adaptive thresholds. Inclusion of negative imagery is shown to improve detection sensitivity and to boost detection performance in certain target and background cases. MRF further expands this concept by fusing together the ratio outputs using a routine in which detections must be verified by two or more ratios to be classified as a true changed pixel. The proposed method is tested with synthetically generated test imagery and real datasets with results compared to other methods found in the literature. DR is shown to significantly outperform the standard single ratio method. MRF produces excellent change detection results that exhibit up to a 22% performance improvement over other methods from the literature at low false-alarm rates.

Multiplex Amplification Coupled with COLD-PCR and High Resolution Melting Enables Identification of Low-Abundance Mutations in Cancer Samples with Low DNA Content

PubMed Central

Milbury, Coren A.; Chen, Clark C.; Mamon, Harvey; Liu, Pingfang; Santagata, Sandro; Makrigiorgos, G. Mike

2011-01-01

Thorough screening of cancer-specific biomarkers, such as DNA mutations, can require large amounts of genomic material; however, the amount of genomic material obtained from some specimens (such as biopsies, fine-needle aspirations, circulating-DNA or tumor cells, and histological slides) may limit the analyses that can be performed. Furthermore, mutant alleles may be at low-abundance relative to wild-type DNA, reducing detection ability. We present a multiplex-PCR approach tailored to amplify targets of interest from small amounts of precious specimens, for extensive downstream detection of low-abundance alleles. Using 3 ng of DNA (1000 genome-equivalents), we amplified the 1 coding exons (2-11) of TP53 via multiplex-PCR. Following multiplex-PCR, we performed COLD-PCR (co-amplification of major and minor alleles at lower denaturation temperature) to enrich low-abundance variants and high resolution melting (HRM) to screen for aberrant melting profiles. Mutation-positive samples were sequenced. Evaluation of mutation-containing dilutions revealed improved sensitivities after COLD-PCR over conventional-PCR. COLD-PCR improved HRM sensitivity by approximately threefold to sixfold. Similarly, COLD-PCR improved mutation identification in sequence-chromatograms over conventional PCR. In clinical specimens, eight mutations were detected via conventional-PCR-HRM, whereas 12 were detected by COLD-PCR-HRM, yielding a 33% improvement in mutation detection. In summary, we demonstrate an efficient approach to increase screening capabilities from limited DNA material via multiplex-PCR and improve mutation detection sensitivity via COLD-PCR amplification. PMID:21354058

Computer-aided detection (CAD) of breast cancer on full field digital and screening film mammograms

NASA Astrophysics Data System (ADS)

Sun, Xuejun; Qian, Wei; Song, Xiaoshan; Qian, Yuyan; Song, Dansheng; Clark, Robert A.

2003-05-01

Full-field digital mammography (FFDM) as a new breast imaging modality has potential to detect more breast cancers or to detect them at smaller sizes and earlier stages compared with screening film mammography (SFM). However, its performance needs verification, and it would pose new problems for the development of CAD methods for breast cancer detection and diagnosis. Performance evaluation of CAD systems on FFDM and SFM has been conducted in this study, respectively. First, an adaptive CAD system employing a series of advanced modules has been developed on FFDM. Second, a standardization approach has been developed to make the CAD system independent of characteristics of digitizer or imaging modalities for mammography. CAD systems developed previously for SFM and developed in this study for FFDM have been evaluated on FFDM and SFM images without and with standardization, respectively, to examine the performance improvement of the CAD system developed in this study. Computerized free-response receiver operating characteristic (FROC) analysis has been adopted as performance evaluation method. Compared with previous one, the CAD system developed in this study demonstrated significantly performance improvements. However, the comparison results have shown that the performances of final CAD system in this study are not significantly different on FFDM and on SFM after standardization. It needs further study on the assessment of CAD system performance on FFDM and SFM modalities.

Capacity building and predictors of success for HIV-1 drug resistance testing in the Asia-Pacific region and Africa

PubMed Central

Land, Sally; Zhou, Julian; Cunningham, Philip; Sohn, Annette H; Singtoroj, Thida; Katzenstein, David; Mann, Marita; Sayer, David; Kantor, Rami

2013-01-01

Background The TREAT Asia Quality Assessment Scheme (TAQAS) was developed as a quality assessment programme through expert education and training, for laboratories in the Asia-Pacific and Africa that perform HIV drug-resistance (HIVDR) genotyping. We evaluated the programme performance and factors associated with high-quality HIVDR genotyping. Methods Laboratories used their standard protocols to test panels of human immunodeficiency virus (HIV)-positive plasma samples or electropherograms. Protocols were documented and performance was evaluated according to a newly developed scoring system, agreement with panel-specific consensus sequence, and detection of drug-resistance mutations (DRMs) and mixtures of wild-type and resistant virus (mixtures). High-quality performance was defined as detection of ≥95% DRMs. Results Over 4.5 years, 23 participating laboratories in 13 countries tested 45 samples (30 HIV-1 subtype B; 15 non-B subtypes) in nine panels. Median detection of DRMs was 88–98% in plasma panels and 90–97% in electropherogram panels. Laboratories were supported to amend and improve their test outcomes as appropriate. Three laboratories that detected <80% DRMs in early panels demonstrated subsequent improvement. Sample complexity factors – number of DRMs (p<0.001) and number of DRMs as mixtures (p<0.001); and laboratory performance factors – detection of mixtures (p<0.001) and agreement with consensus sequence (p<0.001), were associated with high performance; sample format (plasma or electropherogram), subtype and genotyping protocol were not. Conclusion High-quality HIVDR genotyping was achieved in the TAQAS collaborative laboratory network. Sample complexity and detection of mixtures were associated with performance quality. Laboratories conducting HIVDR genotyping are encouraged to participate in quality assessment programmes. PMID:23845227

Improving adaptive/responsive signal control performance : implications of non-invasive detection and legacy timing practices : final report.

DOT National Transportation Integrated Search

2017-02-01

This project collected and analyzed event based vehicle detection data from multiple technologies at four different sites across Oregon to provide guidance for deployment of non-invasive detection for use in adaptive control, as well as develop a tru...

The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy.

PubMed

Fleming, Alan D; Goatman, Keith A; Philip, Sam; Williams, Graeme J; Prescott, Gordon J; Scotland, Graham S; McNamee, Paul; Leese, Graham P; Wykes, William N; Sharp, Peter F; Olson, John A

2010-06-01

Automated grading has the potential to improve the efficiency of diabetic retinopathy screening services. While disease/no disease grading can be performed using only microaneurysm detection and image-quality assessment, automated recognition of other types of lesions may be advantageous. This study investigated whether inclusion of automated recognition of exudates and haemorrhages improves the detection of observable/referable diabetic retinopathy. Images from 1253 patients with observable/referable retinopathy and 6333 patients with non-referable retinopathy were obtained from three grading centres. All images were reference-graded, and automated disease/no disease assessments were made based on microaneurysm detection and combined microaneurysm, exudate and haemorrhage detection. Introduction of algorithms for exudates and haemorrhages resulted in a statistically significant increase in the sensitivity for detection of observable/referable retinopathy from 94.9% (95% CI 93.5 to 96.0) to 96.6% (95.4 to 97.4) without affecting manual grading workload. Automated detection of exudates and haemorrhages improved the detection of observable/referable retinopathy.

Computer-aided detection of pulmonary embolism at CT pulmonary angiography: can it improve performance of inexperienced readers?

PubMed

Blackmon, Kevin N; Florin, Charles; Bogoni, Luca; McCain, Joshua W; Koonce, James D; Lee, Heon; Bastarrika, Gorka; Thilo, Christian; Costello, Philip; Salganicoff, Marcos; Joseph Schoepf, U

2011-06-01

To evaluate the effect of a computer-aided detection (CAD) algorithm on the performance of novice readers for detection of pulmonary embolism (PE) at CT pulmonary angiography (CTPA). We included CTPA examinations of 79 patients (50 female, 52 ± 18 years). Studies were evaluated by two independent inexperienced readers who marked all vessels containing PE. After 3 months all studies were reevaluated by the same two readers, this time aided by CAD prototype. A consensus read by three expert radiologists served as the reference standard. Statistical analysis used χ(2) and McNemar testing. Expert consensus revealed 119 PEs in 32 studies. For PE detection, the sensitivity of CAD alone was 78%. Inexperienced readers' initial interpretations had an average per-PE sensitivity of 50%, which improved to 71% (p < 0.001) with CAD as a second reader. False positives increased from 0.18 to 0.25 per study (p = 0.03). Per-study, the readers initially detected 27/32 positive studies (84%); with CAD this number increased to 29.5 studies (92%; p = 0.125). Our results suggest that CAD significantly improves the sensitivity of PE detection for inexperienced readers with a small but appreciable increase in the rate of false positives.

Evaluation of Long-Range Lightning Detection Networks Using TRMM/LIS Observations

NASA Technical Reports Server (NTRS)

Rudlosky, Scott D.; Holzworth, Robert H.; Carey, Lawrence D.; Schultz, Chris J.; Bateman, Monte; Cecil, Daniel J.; Cummins, Kenneth L.; Petersen, Walter A.; Blakeslee, Richard J.; Goodman, Steven J.

2011-01-01

Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. Toward this end, the present study evaluates data from the World Wide Lightning Location Network (WWLLN) using observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study documents the WWLLN detection efficiency and location accuracy relative to LIS observations, describes the spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by WWLLN. Improved knowledge of the WWLLN detection capabilities will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM).

Noise-robust speech triage.

PubMed

Bartos, Anthony L; Cipr, Tomas; Nelson, Douglas J; Schwarz, Petr; Banowetz, John; Jerabek, Ladislav

2018-04-01

A method is presented in which conventional speech algorithms are applied, with no modifications, to improve their performance in extremely noisy environments. It has been demonstrated that, for eigen-channel algorithms, pre-training multiple speaker identification (SID) models at a lattice of signal-to-noise-ratio (SNR) levels and then performing SID using the appropriate SNR dependent model was successful in mitigating noise at all SNR levels. In those tests, it was found that SID performance was optimized when the SNR of the testing and training data were close or identical. In this current effort multiple i-vector algorithms were used, greatly improving both processing throughput and equal error rate classification accuracy. Using identical approaches in the same noisy environment, performance of SID, language identification, gender identification, and diarization were significantly improved. A critical factor in this improvement is speech activity detection (SAD) that performs reliably in extremely noisy environments, where the speech itself is barely audible. To optimize SAD operation at all SNR levels, two algorithms were employed. The first maximized detection probability at low levels (-10 dB ≤ SNR < +10 dB) using just the voiced speech envelope, and the second exploited features extracted from the original speech to improve overall accuracy at higher quality levels (SNR ≥ +10 dB).

A Frequency-Domain Adaptive Matched Filter for Active Sonar Detection.

PubMed

Zhao, Zhishan; Zhao, Anbang; Hui, Juan; Hou, Baochun; Sotudeh, Reza; Niu, Fang

2017-07-04

The most classical detector of active sonar and radar is the matched filter (MF), which is the optimal processor under ideal conditions. Aiming at the problem of active sonar detection, we propose a frequency-domain adaptive matched filter (FDAMF) with the use of a frequency-domain adaptive line enhancer (ALE). The FDAMF is an improved MF. In the simulations in this paper, the signal to noise ratio (SNR) gain of the FDAMF is about 18.6 dB higher than that of the classical MF when the input SNR is -10 dB. In order to improve the performance of the FDAMF with a low input SNR, we propose a pre-processing method, which is called frequency-domain time reversal convolution and interference suppression (TRC-IS). Compared with the classical MF, the FDAMF combined with the TRC-IS method obtains higher SNR gain, a lower detection threshold, and a better receiver operating characteristic (ROC) in the simulations in this paper. The simulation results show that the FDAMF has higher processing gain and better detection performance than the classical MF under ideal conditions. The experimental results indicate that the FDAMF does improve the performance of the MF, and can adapt to actual interference in a way. In addition, the TRC-IS preprocessing method works well in an actual noisy ocean environment.

Significance of MPEG-7 textural features for improved mass detection in mammography.

PubMed

Eltonsy, Nevine H; Tourassi, Georgia D; Fadeev, Aleksey; Elmaghraby, Adel S

2006-01-01

The purpose of the study is to investigate the significance of MPEG-7 textural features for improving the detection of masses in screening mammograms. The detection scheme was originally based on morphological directional neighborhood features extracted from mammographic regions of interest (ROIs). Receiver Operating Characteristics (ROC) was performed to evaluate the performance of each set of features independently and merged into a back-propagation artificial neural network (BPANN) using the leave-one-out sampling scheme (LOOSS). The study was based on a database of 668 mammographic ROIs (340 depicting cancer regions and 328 depicting normal parenchyma). Overall, the ROC area index of the BPANN using the directional morphological features was Az=0.85+/-0.01. The MPEG-7 edge histogram descriptor-based BPNN showed an ROC area index of Az=0.71+/-0.01 while homogeneous textural descriptors using 30 and 120 channels helped the BPNN achieve similar ROC area indexes of Az=0.882+/-0.02 and Az=0.877+/-0.01 respectively. After merging the MPEG-7 homogeneous textural features with the directional neighborhood features the performance of the BPANN increased providing an ROC area index of Az=0.91+/-0.01. MPEG-7 homogeneous textural descriptor significantly improved the morphology-based detection scheme.

Using dual tasks to test immediate transfer of training between naturalistic movements: A proof-of-principle study

PubMed Central

Schaefer, Sydney Y.; Lang, Catherine E.

2012-01-01

Theories of motor learning predict that training a movement reduces the amount of attention needed for its performance (i.e. more automatic). If training one movement transfers, then the amount of attention needed for performing a second movement should also be reduced, as measured under dual task conditions. The purpose of this study was to test whether dual task paradigms are feasible for detecting transfer of training between two naturalistic movements. Immediately following motor training, subjects improved performance of a second untrained movement under both single and dual task conditions. Subjects with no training did not. Improved performance in the untrained movement was likely due to transfer, and suggests that dual tasks may be feasible for detecting transfer between naturalistic actions. PMID:22934682

Redundancy management of inertial systems.

NASA Technical Reports Server (NTRS)

Mckern, R. A.; Musoff, H.

1973-01-01

The paper reviews developments in failure detection and isolation techniques applicable to gimballed and strapdown systems. It examines basic redundancy management goals of improved reliability, performance and logistic costs, and explores mechanizations available for both input and output data handling. The meaning of redundant system reliability in terms of available coverage, system MTBF, and mission time is presented and the practical hardware performance limitations of failure detection and isolation techniques are explored. Simulation results are presented illustrating implementation coverages attainable considering IMU performance models and mission detection threshold requirements. The implications of a complete GN&C redundancy management method on inertial techniques are also explored.

Interlaboratory validation of an improved U.S. Food and Drug Administration method for detection of Cyclospora cayetanensis in produce using TaqMan real-time PCR

USDA-ARS?s Scientific Manuscript database

A collaborative validation study was performed to evaluate the performance of a new U.S. Food and Drug Administration method developed for detection of the protozoan parasite, Cyclospora cayetanensis, on cilantro and raspberries. The method includes a sample preparation step in which oocysts are re...

Impact of three task demand factors on simulated unmanned system intelligence, surveillance, and reconnaissance operations.

PubMed

Abich, Julian; Reinerman-Jones, Lauren; Matthews, Gerald

2017-06-01

The present study investigated how three task demand factors influenced performance, subjective workload and stress of novice intelligence, surveillance, and reconnaissance operators within a simulation of an unmanned ground vehicle. Manipulations were task type, dual-tasking and event rate. Participants were required to discriminate human targets within a street scene from a direct video feed (threat detection [TD] task) and detect changes in symbols presented in a map display (change detection [CD] task). Dual-tasking elevated workload and distress, and impaired performance for both tasks. However, with increasing event rate, CD task deteriorated, but TD improved. Thus, standard workload models provide a better guide to evaluating the demands of abstract symbols than to processing realistic human characters. Assessment of stress and workload may be especially important in the design and evaluation of systems in which human character critical signals must be detected in video images. Practitioner Summary: This experiment assessed subjective workload and stress during threat and CD tasks performed alone and in combination. Results indicated an increase in event rate led to significant improvements in performance during TD, but decrements during CD, yet both had associated increases in workload and engagement.

Optimization of a chemical identification algorithm

NASA Astrophysics Data System (ADS)

Chyba, Thomas H.; Fisk, Brian; Gunning, Christin; Farley, Kevin; Polizzi, Amber; Baughman, David; Simpson, Steven; Slamani, Mohamed-Adel; Almassy, Robert; Da Re, Ryan; Li, Eunice; MacDonald, Steve; Slamani, Ahmed; Mitchell, Scott A.; Pendell-Jones, Jay; Reed, Timothy L.; Emge, Darren

2010-04-01

A procedure to evaluate and optimize the performance of a chemical identification algorithm is presented. The Joint Contaminated Surface Detector (JCSD) employs Raman spectroscopy to detect and identify surface chemical contamination. JCSD measurements of chemical warfare agents, simulants, toxic industrial chemicals, interferents and bare surface backgrounds were made in the laboratory and under realistic field conditions. A test data suite, developed from these measurements, is used to benchmark algorithm performance throughout the improvement process. In any one measurement, one of many possible targets can be present along with interferents and surfaces. The detection results are expressed as a 2-category classification problem so that Receiver Operating Characteristic (ROC) techniques can be applied. The limitations of applying this framework to chemical detection problems are discussed along with means to mitigate them. Algorithmic performance is optimized globally using robust Design of Experiments and Taguchi techniques. These methods require figures of merit to trade off between false alarms and detection probability. Several figures of merit, including the Matthews Correlation Coefficient and the Taguchi Signal-to-Noise Ratio are compared. Following the optimization of global parameters which govern the algorithm behavior across all target chemicals, ROC techniques are employed to optimize chemical-specific parameters to further improve performance.

Review of Literature for Model Assisted Probability of Detection

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

Meyer, Ryan M.; Crawford, Susan L.; Lareau, John P.

This is a draft technical letter report for NRC client documenting a literature review of model assisted probability of detection (MAPOD) for potential application to nuclear power plant components for improvement of field NDE performance estimations.

Airport surface detection equipment ASDE-3 radar set : appendix I

DOT National Transportation Integrated Search

1973-02-01

This specification establishes the performance, design, development, and test requirements for the Airport Surface Detection Equipment, the ASDE-3 Radar Set, intended as a replacement for the currently FAA-commissioned ASDE-2. It provides improvement...

Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection

NASA Astrophysics Data System (ADS)

Li, Jiangtong; Luo, Yongdao; Dai, Honglin

2018-01-01

Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.

Real-time method for establishing a detection map for a network of sensors

DOEpatents

Nguyen, Hung D; Koch, Mark W; Giron, Casey; Rondeau, Daniel M; Russell, John L

2012-09-11

A method for establishing a detection map of a dynamically configurable sensor network. This method determines an appropriate set of locations for a plurality of sensor units of a sensor network and establishes a detection map for the network of sensors while the network is being set up; the detection map includes the effects of the local terrain and individual sensor performance. Sensor performance is characterized during the placement of the sensor units, which enables dynamic adjustment or reconfiguration of the placement of individual elements of the sensor network during network set-up to accommodate variations in local terrain and individual sensor performance. The reconfiguration of the network during initial set-up to accommodate deviations from idealized individual sensor detection zones improves the effectiveness of the sensor network in detecting activities at a detection perimeter and can provide the desired sensor coverage of an area while minimizing unintentional gaps in coverage.

Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.

PubMed

Tang, Tianyu; Zhou, Shilin; Deng, Zhipeng; Zou, Huanxin; Lei, Lin

2017-02-10

Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.

Evaluation of ultrasonic array imaging algorithms for inspection of a coarse grained material

NASA Astrophysics Data System (ADS)

Van Pamel, A.; Lowe, M. J. S.; Brett, C. R.

2014-02-01

Improving the ultrasound inspection capability for coarse grain metals remains of longstanding interest to industry and the NDE research community and is expected to become increasingly important for next generation power plants. A test sample of coarse grained Inconel 625 which is representative of future power plant components has been manufactured to test the detectability of different inspection techniques. Conventional ultrasonic A, B, and C-scans showed the sample to be extraordinarily difficult to inspect due to its scattering behaviour. However, in recent years, array probes and Full Matrix Capture (FMC) imaging algorithms, which extract the maximum amount of information possible, have unlocked exciting possibilities for improvements. This article proposes a robust methodology to evaluate the detection performance of imaging algorithms, applying this to three FMC imaging algorithms; Total Focusing Method (TFM), Phase Coherent Imaging (PCI), and Decomposition of the Time Reversal Operator with Multiple Scattering (DORT MSF). The methodology considers the statistics of detection, presenting the detection performance as Probability of Detection (POD) and probability of False Alarm (PFA). The data is captured in pulse-echo mode using 64 element array probes at centre frequencies of 1MHz and 5MHz. All three algorithms are shown to perform very similarly when comparing their flaw detection capabilities on this particular case.

Sensor data fusion for spectroscopy-based detection of explosives

NASA Astrophysics Data System (ADS)

Shah, Pratik V.; Singh, Abhijeet; Agarwal, Sanjeev; Sedigh, Sahra; Ford, Alan; Waterbury, Robert

2009-05-01

In-situ trace detection of explosive compounds such as RDX, TNT, and ammonium nitrate, is an important problem for the detection of IEDs and IED precursors. Spectroscopic techniques such as LIBS and Raman have shown promise for the detection of residues of explosive compounds on surfaces from standoff distances. Individually, both LIBS and Raman techniques suffer from various limitations, e.g., their robustness and reliability suffers due to variations in peak strengths and locations. However, the orthogonal nature of the spectral and compositional information provided by these techniques makes them suitable candidates for the use of sensor fusion to improve the overall detection performance. In this paper, we utilize peak energies in a region by fitting Lorentzian or Gaussian peaks around the location of interest. The ratios of peak energies are used for discrimination, in order to normalize the effect of changes in overall signal strength. Two data fusion techniques are discussed in this paper. Multi-spot fusion is performed on a set of independent samples from the same region based on the maximum likelihood formulation. Furthermore, the results from LIBS and Raman sensors are fused using linear discriminators. Improved detection performance with significantly reduced false alarm rates is reported using fusion techniques on data collected for sponsor demonstration at Fort Leonard Wood.

On analyzing colour constancy approach for improving SURF detector performance

NASA Astrophysics Data System (ADS)

Zulkiey, Mohd Asyraf; Zaki, Wan Mimi Diyana Wan; Hussain, Aini; Mustafa, Mohd. Marzuki

2012-04-01

Robust key point detector plays a crucial role in obtaining a good tracking feature. The main challenge in outdoor tracking is the illumination change due to various reasons such as weather fluctuation and occlusion. This paper approaches the illumination change problem by transforming the input image through colour constancy algorithm before applying the SURF detector. Masked grey world approach is chosen because of its ability to perform well under local as well as global illumination change. Every image is transformed to imitate the canonical illuminant and Gaussian distribution is used to model the global change. The simulation results show that the average number of detected key points have increased by 69.92%. Moreover, the average of improved performance cases far out weight the degradation case where the former is improved by 215.23%. The approach is suitable for tracking implementation where sudden illumination occurs frequently and robust key point detection is needed.

Acoustic Analysis and Electroglottography in Elite Vocal Performers.

PubMed

Villafuerte-Gonzalez, Rocio; Valadez-Jimenez, Victor M; Sierra-Ramirez, Jose A; Ysunza, Pablo Antonio; Chavarria-Villafuerte, Karen; Hernandez-Lopez, Xochiquetzal

2017-05-01

Acoustic analysis of voice (AAV) and electroglottography (EGG) have been used for assessing vocal quality in patients with voice disorders. The effectiveness of these procedures for detecting mild disturbances in vocal quality in elite vocal performers has been controversial. To compare acoustic parameters obtained by AAV and EGG before and after vocal training to determine the effectiveness of these procedures for detecting vocal improvements in elite vocal performers. Thirty-three elite vocal performers were studied. The study group included 14 males and 19 females, ages 18-40 years, without a history of voice disorders. Acoustic parameters were obtained through AAV and EGG before and after vocal training using the Linklater method. Nonsignificant differences (P > 0.05) were found between values of fundamental frequency (F 0 ), shimmer, and jitter obtained by both procedures before vocal training. Mean F 0 was similar after vocal training. Jitter percentage as measured by AAV showed nonsignificant differences (P > 0.05) before and after vocal training. Shimmer percentage as measured by AAV demonstrated a significant reduction (P < 0.05) after vocal training. As measured by EGG after vocal training, shimmer and jitter were significantly reduced (P < 0.05); open quotient was significantly increased (P < 0.05); and irregularity was significantly reduced (P < 0.05). AAV and EGG were effective for detecting improvements in vocal function after vocal training in male and female elite vocal performers undergoing vocal training. EGG demonstrated better efficacy for detecting improvements and provided additional parameters as compared to AAV. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor.

PubMed

Ansari, A H; Cherian, P J; Dereymaeker, A; Matic, V; Jansen, K; De Wispelaere, L; Dielman, C; Vervisch, J; Swarte, R M; Govaert, P; Naulaers, G; De Vos, M; Van Huffel, S

2016-09-01

After identifying the most seizure-relevant characteristics by a previously developed heuristic classifier, a data-driven post-processor using a novel set of features is applied to improve the performance. The main characteristics of the outputs of the heuristic algorithm are extracted by five sets of features including synchronization, evolution, retention, segment, and signal features. Then, a support vector machine and a decision making layer remove the falsely detected segments. Four datasets including 71 neonates (1023h, 3493 seizures) recorded in two different university hospitals, are used to train and test the algorithm without removing the dubious seizures. The heuristic method resulted in a false alarm rate of 3.81 per hour and good detection rate of 88% on the entire test databases. The post-processor, effectively reduces the false alarm rate by 34% while the good detection rate decreases by 2%. This post-processing technique improves the performance of the heuristic algorithm. The structure of this post-processor is generic, improves our understanding of the core visually determined EEG features of neonatal seizures and is applicable for other neonatal seizure detectors. The post-processor significantly decreases the false alarm rate at the expense of a small reduction of the good detection rate. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Strategies for Improved Interpretation of Computer-Aided Detections for CT Colonography Utilizing Distributed Human Intelligence

PubMed Central

McKenna, Matthew T.; Wang, Shijun; Nguyen, Tan B.; Burns, Joseph E.; Petrick, Nicholas; Summers, Ronald M.

2012-01-01

Computer-aided detection (CAD) systems have been shown to improve the diagnostic performance of CT colonography (CTC) in the detection of premalignant colorectal polyps. Despite the improvement, the overall system is not optimal. CAD annotations on true lesions are incorrectly dismissed, and false positives are misinterpreted as true polyps. Here, we conduct an observer performance study utilizing distributed human intelligence in the form of anonymous knowledge workers (KWs) to investigate human performance in classifying polyp candidates under different presentation strategies. We evaluated 600 polyp candidates from 50 patients, each case having at least one polyp • 6 mm, from a large database of CTC studies. Each polyp candidate was labeled independently as a true or false polyp by 20 KWs and an expert radiologist. We asked each labeler to determine whether the candidate was a true polyp after looking at a single 3D-rendered image of the candidate and after watching a video fly-around of the candidate. We found that distributed human intelligence improved significantly when presented with the additional information in the video fly-around. We noted that performance degraded with increasing interpretation time and increasing difficulty, but distributed human intelligence performed better than our CAD classifier for “easy” and “moderate” polyp candidates. Further, we observed numerous parallels between the expert radiologist and the KWs. Both showed similar improvement in classification moving from single-image to video interpretation. Additionally, difficulty estimates obtained from the KWs using an expectation maximization algorithm correlated well with the difficulty rating assigned by the expert radiologist. Our results suggest that distributed human intelligence is a powerful tool that will aid in the development of CAD for CTC. PMID:22705287

Strategies for improved interpretation of computer-aided detections for CT colonography utilizing distributed human intelligence.

PubMed

McKenna, Matthew T; Wang, Shijun; Nguyen, Tan B; Burns, Joseph E; Petrick, Nicholas; Summers, Ronald M

2012-08-01

Computer-aided detection (CAD) systems have been shown to improve the diagnostic performance of CT colonography (CTC) in the detection of premalignant colorectal polyps. Despite the improvement, the overall system is not optimal. CAD annotations on true lesions are incorrectly dismissed, and false positives are misinterpreted as true polyps. Here, we conduct an observer performance study utilizing distributed human intelligence in the form of anonymous knowledge workers (KWs) to investigate human performance in classifying polyp candidates under different presentation strategies. We evaluated 600 polyp candidates from 50 patients, each case having at least one polyp ≥6 mm, from a large database of CTC studies. Each polyp candidate was labeled independently as a true or false polyp by 20 KWs and an expert radiologist. We asked each labeler to determine whether the candidate was a true polyp after looking at a single 3D-rendered image of the candidate and after watching a video fly-around of the candidate. We found that distributed human intelligence improved significantly when presented with the additional information in the video fly-around. We noted that performance degraded with increasing interpretation time and increasing difficulty, but distributed human intelligence performed better than our CAD classifier for "easy" and "moderate" polyp candidates. Further, we observed numerous parallels between the expert radiologist and the KWs. Both showed similar improvement in classification moving from single-image to video interpretation. Additionally, difficulty estimates obtained from the KWs using an expectation maximization algorithm correlated well with the difficulty rating assigned by the expert radiologist. Our results suggest that distributed human intelligence is a powerful tool that will aid in the development of CAD for CTC. Copyright © 2012. Published by Elsevier B.V.

Brightness-preserving fuzzy contrast enhancement scheme for the detection and classification of diabetic retinopathy disease.

PubMed

Datta, Niladri Sekhar; Dutta, Himadri Sekhar; Majumder, Koushik

2016-01-01

The contrast enhancement of retinal image plays a vital role for the detection of microaneurysms (MAs), which are an early sign of diabetic retinopathy disease. A retinal image contrast enhancement method has been presented to improve the MA detection technique. The success rate on low-contrast noisy retinal image analysis shows the importance of the proposed method. Overall, 587 retinal input images are tested for performance analysis. The average sensitivity and specificity are obtained as 95.94% and 99.21%, respectively. The area under curve is found as 0.932 for the receiver operating characteristics analysis. The classifications of diabetic retinopathy disease are also performed here. The experimental results show that the overall MA detection method performs better than the current state-of-the-art MA detection algorithms.

Detection of person borne IEDs using multiple cooperative sensors

NASA Astrophysics Data System (ADS)

MacIntosh, Scott; Deming, Ross; Hansen, Thorkild; Kishan, Neel; Tang, Ling; Shea, Jing; Lang, Stephen

2011-06-01

The use of multiple cooperative sensors for the detection of person borne IEDs is investigated. The purpose of the effort is to evaluate the performance benefits of adding multiple sensor data streams into an aided threat detection algorithm, and a quantitative analysis of which sensor data combinations improve overall detection performance. Testing includes both mannequins and human subjects with simulated suicide bomb devices of various configurations, materials, sizes and metal content. Aided threat recognition algorithms are being developed to test detection performance of individual sensors against combined fused sensors inputs. Sensors investigated include active and passive millimeter wave imaging systems, passive infrared, 3-D profiling sensors and acoustic imaging. The paper describes the experimental set-up and outlines the methodology behind a decision fusion algorithm-based on the concept of a "body model".

Comparison of human observer and algorithmic target detection in nonurban forward-looking infrared imagery

NASA Astrophysics Data System (ADS)

Weber, Bruce A.

2005-07-01

We have performed an experiment that compares the performance of human observers with that of a robust algorithm for the detection of targets in difficult, nonurban forward-looking infrared imagery. Our purpose was to benchmark the comparison and document performance differences for future algorithm improvement. The scale-insensitive detection algorithm, used as a benchmark by the Night Vision Electronic Sensors Directorate for algorithm evaluation, employed a combination of contrastlike features to locate targets. Detection receiver operating characteristic curves and observer-confidence analyses were used to compare human and algorithmic responses and to gain insight into differences. The test database contained ground targets, in natural clutter, whose detectability, as judged by human observers, ranged from easy to very difficult. In general, as compared with human observers, the algorithm detected most of the same targets, but correlated confidence with correct detections poorly and produced many more false alarms at any useful level of performance. Though characterizing human performance was not the intent of this study, results suggest that previous observational experience was not a strong predictor of human performance, and that combining individual human observations by majority vote significantly reduced false-alarm rates.

Topological anomaly detection performance with multispectral polarimetric imagery

NASA Astrophysics Data System (ADS)

Gartley, M. G.; Basener, W.,

2009-05-01

Polarimetric imaging has demonstrated utility for increasing contrast of manmade targets above natural background clutter. Manual detection of manmade targets in multispectral polarimetric imagery can be challenging and a subjective process for large datasets. Analyst exploitation may be improved utilizing conventional anomaly detection algorithms such as RX. In this paper we examine the performance of a relatively new approach to anomaly detection, which leverages topology theory, applied to spectral polarimetric imagery. Detection results for manmade targets embedded in a complex natural background will be presented for both the RX and Topological Anomaly Detection (TAD) approaches. We will also present detailed results examining detection sensitivities relative to: (1) the number of spectral bands, (2) utilization of Stoke's images versus intensity images, and (3) airborne versus spaceborne measurements.

Effective DQE (eDQE) for monoscopic and stereoscopic chest radiography imaging systems with the incorporation of anatomical noise.

PubMed

Boyce, Sarah J; Choudhury, Kingshuk Roy; Samei, Ehsan

2013-09-01

Stereoscopic chest biplane correlation imaging (stereo∕BCI) has been proposed as an alternative modality to single view chest x-ray (CXR). The metrics effective modulation transfer function (eMTF), effective normalized noise power spectrum (eNNPS), and effective detective quantum efficiency (eDQE) have been proposed as clinically relevant metrics for assessing clinical system performance taking into consideration the magnification and scatter effects. This study compared the metrics eMTF, eNNPS, eDQE, and detectability index for stereo∕BCI and single view CXR under isodose conditions at two magnifications for two anthropomorphic phantoms of differing sizes. Measurements for the eMTF were taken for two phantom sizes with an opaque edge test device using established techniques. The eNNPS was measured at two isodose conditions for two phantoms using established techniques. The scatter was measured for two phantoms using an established beam stop method. All measurements were also taken at two different magnifications with two phantoms. A geometrical phantom was used for comparison with prior results for CXR although the results for an anatomy free phantom are not expected to vary for BCI. Stereo∕BCI resulted in improved metrics compared to single view CXR. Results indicated that magnification can potentially improve the detection performance primarily due to the air gap which reduced scatter by ∼20%. For both phantoms, at isodose, eDQE(0) for stereo∕BCI was ∼100 times higher than that for CXR. Magnification at isodose improved eDQE(0) by ∼10 times for stereo∕BCI. Increasing the dose did not improve eDQE. The detectability index for stereo∕BCI was ∼100 times better than single view CXR for all conditions. The detectability index was also not improved with increased dose. The findings indicate that stereo∕BCI with magnification may improve detectability of subtle lung nodules compared to single view CXR. Results were improved with magnification for the smaller phantom but not for the larger phantom. The effective DQE and the detectability index did not improve with increasing dose.

Effective DQE (eDQE) for monoscopic and stereoscopic chest radiography imaging systems with the incorporation of anatomical noise

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

Boyce, Sarah J.; Choudhury, Kingshuk Roy; Samei, Ehsan

2013-09-15

Purpose: Stereoscopic chest biplane correlation imaging (stereo/BCI) has been proposed as an alternative modality to single view chest x-ray (CXR). The metrics effective modulation transfer function (eMTF), effective normalized noise power spectrum (eNNPS), and effective detective quantum efficiency (eDQE) have been proposed as clinically relevant metrics for assessing clinical system performance taking into consideration the magnification and scatter effects. This study compared the metrics eMTF, eNNPS, eDQE, and detectability index for stereo/BCI and single view CXR under isodose conditions at two magnifications for two anthropomorphic phantoms of differing sizes.Methods: Measurements for the eMTF were taken for two phantom sizes withmore » an opaque edge test device using established techniques. The eNNPS was measured at two isodose conditions for two phantoms using established techniques. The scatter was measured for two phantoms using an established beam stop method. All measurements were also taken at two different magnifications with two phantoms. A geometrical phantom was used for comparison with prior results for CXR although the results for an anatomy free phantom are not expected to vary for BCI.Results: Stereo/BCI resulted in improved metrics compared to single view CXR. Results indicated that magnification can potentially improve the detection performance primarily due to the air gap which reduced scatter by ∼20%. For both phantoms, at isodose, eDQE(0) for stereo/BCI was ∼100 times higher than that for CXR. Magnification at isodose improved eDQE(0) by ∼10 times for stereo/BCI. Increasing the dose did not improve eDQE. The detectability index for stereo/BCI was ∼100 times better than single view CXR for all conditions. The detectability index was also not improved with increased dose.Conclusions: The findings indicate that stereo/BCI with magnification may improve detectability of subtle lung nodules compared to single view CXR. Results were improved with magnification for the smaller phantom but not for the larger phantom. The effective DQE and the detectability index did not improve with increasing dose.« less

in silico Surveillance: evaluating outbreak detection with simulation models

PubMed Central

2013-01-01

Background Detecting outbreaks is a crucial task for public health officials, yet gaps remain in the systematic evaluation of outbreak detection protocols. The authors’ objectives were to design, implement, and test a flexible methodology for generating detailed synthetic surveillance data that provides realistic geographical and temporal clustering of cases and use to evaluate outbreak detection protocols. Methods A detailed representation of the Boston area was constructed, based on data about individuals, locations, and activity patterns. Influenza-like illness (ILI) transmission was simulated, producing 100 years of in silico ILI data. Six different surveillance systems were designed and developed using gathered cases from the simulated disease data. Performance was measured by inserting test outbreaks into the surveillance streams and analyzing the likelihood and timeliness of detection. Results Detection of outbreaks varied from 21% to 95%. Increased coverage did not linearly improve detection probability for all surveillance systems. Relaxing the decision threshold for signaling outbreaks greatly increased false-positives, improved outbreak detection slightly, and led to earlier outbreak detection. Conclusions Geographical distribution can be more important than coverage level. Detailed simulations of infectious disease transmission can be configured to represent nearly any conceivable scenario. They are a powerful tool for evaluating the performance of surveillance systems and methods used for outbreak detection. PMID:23343523

Ultrahigh photo-responsivity and detectivity in multilayer InSe nanosheets phototransistors with broadband response

DOE PAGES

Feng, Wei; Wu, Jing-Bin; Li, Xiaoli; ...

2015-05-20

In this paper, we demonstrate the strategies and principles for the performance improvement of layered semiconductor based photodetectors using multilayer indium selenide (InSe) as the model material. It is discovered that multiple reflection interference at the interfaces in the phototransistor device leads to a thickness-dependent photo-response, which provides a guideline to improve the performance of layered semiconductor based phototransistors. The responsivity and detectivity of InSe nanosheet phototransistor can be adjustable using applied gate voltage. Our InSe nanosheet phototransistor exhibits ultrahigh responsivity and detectivity. An ultrahigh external photo-responsivity of ~10 4 A W -1 can be achieved from broad spectra rangingmore » from UV to near infrared wavelength using our InSe nanosheet photodetectors. The detectivity of multilayer InSe devices is ~10 12 to 10 13 Jones, which surpasses that of the currently exploited InGaAs photodetectors (10 11 to 10 12 Jones). Finally, this research shows that multilayer InSe nanosheets are promising materials for high performance photodetectors.« less

A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan W.

2014-01-01

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.

A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan Walker

2015-01-01

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.

Detecting changes in dynamic and complex acoustic environments

PubMed Central

Boubenec, Yves; Lawlor, Jennifer; Górska, Urszula; Shamma, Shihab; Englitz, Bernhard

2017-01-01

Natural sounds such as wind or rain, are characterized by the statistical occurrence of their constituents. Despite their complexity, listeners readily detect changes in these contexts. We here address the neural basis of statistical decision-making using a combination of psychophysics, EEG and modelling. In a texture-based, change-detection paradigm, human performance and reaction times improved with longer pre-change exposure, consistent with improved estimation of baseline statistics. Change-locked and decision-related EEG responses were found in a centro-parietal scalp location, whose slope depended on change size, consistent with sensory evidence accumulation. The potential's amplitude scaled with the duration of pre-change exposure, suggesting a time-dependent decision threshold. Auditory cortex-related potentials showed no response to the change. A dual timescale, statistical estimation model accounted for subjects' performance. Furthermore, a decision-augmented auditory cortex model accounted for performance and reaction times, suggesting that the primary cortical representation requires little post-processing to enable change-detection in complex acoustic environments. DOI: http://dx.doi.org/10.7554/eLife.24910.001 PMID:28262095

Evaluation of two 4th generation point-of-care assays for the detection of Human Immunodeficiency Virus infection.

PubMed

Stafylis, Chrysovalantis; Klausner, Jeffrey D

2017-01-01

Fourth generation assays detect simultaneously antibodies for HIV and the p24 antigen, identifying HIV infection earlier than previous generation tests. Previous studies have shown that the Alere Determine HIV-1/2 Combo has lower than anticipated performance in detecting antibodies for HIV and the p24 antigen. Furthermore, there are currently very few studies evaluating the performance of Standard Diagnostics BIOLINE HIV Ag/Ab Combo. To evaluate the performance of the Alere Determine HIV-1/2 Combo and the Standard Diagnostics BIOLINE HIV Ag/Ab Combo in a panel of frozen serum samples. The testing panel included 133 previously frozen serum specimens from the UCLA Clinical Microbiology & Immunoserology laboratory. Reference testing included testing for HIV antibodies by a 3rd generation enzyme immunoassay followed by HIV RNA detection. Antibody negative and RNA positive sera were also tested by a laboratory 4th generation HIV Ab/Ag enzyme immunoassay. Reference testing yielded 97 positives for HIV infection and 36 negative samples. Sensitivity of the Alere test was 95% (88-98%), while the SD Bioline sensitivity was 91% (83-96%). Both assays showed 100% (90-100%) specificity. No indeterminate or invalid results were recorded. Among 13 samples with acute infection (HIV RNA positive, HIV antibody negative), 12 were found positive by the first assay and 8 by the second. The antigen component of the Alere assay detected 10 acute samples, while the SD Bioline assay detected only one. Both rapid assays showed very good overall performance in detecting HIV infection in frozen serum samples, but further improvements are required to improve the performance in acute infection.

Testing & Evaluation of Close-Range SAR for Monitoring & Automatically Detecting Pavement Conditions

DOT National Transportation Integrated Search

2012-01-01

This report summarizes activities in support of the DOT contract on Testing & Evaluating Close-Range SAR for Monitoring & Automatically Detecting Pavement Conditions & Improve Visual Inspection Procedures. The work of this project was performed by Dr...

Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome.

PubMed

Koivu, Aki; Korpimäki, Teemu; Kivelä, Petri; Pahikkala, Tapio; Sairanen, Mikko

2018-05-04

Prenatal screening generates a great amount of data that is used for predicting risk of various disorders. Prenatal risk assessment is based on multiple clinical variables and overall performance is defined by how well the risk algorithm is optimized for the population in question. This article evaluates machine learning algorithms to improve performance of first trimester screening of Down syndrome. Machine learning algorithms pose an adaptive alternative to develop better risk assessment models using the existing clinical variables. Two real-world data sets were used to experiment with multiple classification algorithms. Implemented models were tested with a third, real-world, data set and performance was compared to a predicate method, a commercial risk assessment software. Best performing deep neural network model gave an area under the curve of 0.96 and detection rate of 78% with 1% false positive rate with the test data. Support vector machine model gave area under the curve of 0.95 and detection rate of 61% with 1% false positive rate with the same test data. When compared with the predicate method, the best support vector machine model was slightly inferior, but an optimized deep neural network model was able to give higher detection rates with same false positive rate or similar detection rate but with markedly lower false positive rate. This finding could further improve the first trimester screening for Down syndrome, by using existing clinical variables and a large training data derived from a specific population. Copyright © 2018 Elsevier Ltd. All rights reserved.

An incremental knowledge assimilation system (IKAS) for mine detection

NASA Astrophysics Data System (ADS)

Porway, Jake; Raju, Chaitanya; Varadarajan, Karthik Mahesh; Nguyen, Hieu; Yadegar, Joseph

2010-04-01

In this paper we present an adaptive incremental learning system for underwater mine detection and classification that utilizes statistical models of seabed texture and an adaptive nearest-neighbor classifier to identify varied underwater targets in many different environments. The first stage of processing uses our Background Adaptive ANomaly detector (BAAN), which identifies statistically likely target regions using Gabor filter responses over the image. Using this information, BAAN classifies the background type and updates its detection using background-specific parameters. To perform classification, a Fully Adaptive Nearest Neighbor (FAAN) determines the best label for each detection. FAAN uses an extremely fast version of Nearest Neighbor to find the most likely label for the target. The classifier perpetually assimilates new and relevant information into its existing knowledge database in an incremental fashion, allowing improved classification accuracy and capturing concept drift in the target classes. Experiments show that the system achieves >90% classification accuracy on underwater mine detection tasks performed on synthesized datasets provided by the Office of Naval Research. We have also demonstrated that the system can incrementally improve its detection accuracy by constantly learning from new samples.

An integrated logit model for contamination event detection in water distribution systems.

PubMed

Housh, Mashor; Ostfeld, Avi

2015-05-15

The problem of contamination event detection in water distribution systems has become one of the most challenging research topics in water distribution systems analysis. Current attempts for event detection utilize a variety of approaches including statistical, heuristics, machine learning, and optimization methods. Several existing event detection systems share a common feature in which alarms are obtained separately for each of the water quality indicators. Unifying those single alarms from different indicators is usually performed by means of simple heuristics. A salient feature of the current developed approach is using a statistically oriented model for discrete choice prediction which is estimated using the maximum likelihood method for integrating the single alarms. The discrete choice model is jointly calibrated with other components of the event detection system framework in a training data set using genetic algorithms. The fusing process of each indicator probabilities, which is left out of focus in many existing event detection system models, is confirmed to be a crucial part of the system which could be modelled by exploiting a discrete choice model for improving its performance. The developed methodology is tested on real water quality data, showing improved performances in decreasing the number of false positive alarms and in its ability to detect events with higher probabilities, compared to previous studies. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evaluation of field performance of poplar clones using selected competition indices.

Treesearch

Chandler Brodie; D.S. DeBell

2004-01-01

Use of competition indices in the analysis of forestry experiments may improve detection and understanding of treatment effects, and thereby improve the application of results. In this paper, we compared the performance of eight indices in an analysis of a spacing trial of four Populus clones planted in pure and mixed clonal plots. Indices were...

Analytical sensitivity of current best-in-class malaria rapid diagnostic tests.

PubMed

Jimenez, Alfons; Rees-Channer, Roxanne R; Perera, Rushini; Gamboa, Dionicia; Chiodini, Peter L; González, Iveth J; Mayor, Alfredo; Ding, Xavier C

2017-03-24

Rapid diagnostic tests (RDTs) are today the most widely used method for malaria diagnosis and are recommended, alongside microscopy, for the confirmation of suspected cases before the administration of anti-malarial treatment. The diagnostic performance of RDTs, as compared to microscopy or PCR is well described but the actual analytical sensitivity of current best-in-class tests is poorly documented. This value is however a key performance indicator and a benchmark value needed to developed new RDTs of improved sensitivity. Thirteen RDTs detecting either the Plasmodium falciparum histidine rich protein 2 (HRP2) or the plasmodial lactate dehydrogenase (pLDH) antigens were selected from the best performing RDTs according to the WHO-FIND product testing programme. The analytical sensitivity of these products was evaluated using a range of reference materials including P. falciparum and Plasmodium vivax whole parasite samples as well as recombinant proteins. The best performing HRP2-based RDTs could detect all P. falciparum cultured samples at concentrations as low as 0.8 ng/mL of HRP2. The limit of detection of the best performing pLDH-based RDT specifically detecting P. vivax was 25 ng/mL of pLDH. The analytical sensitivity of P. vivax and Pan pLDH-based RDTs appears to vary considerably from product to product, and improvement of the limit-of-detection for P. vivax detecting RDTs is needed to match the performance of HRP2 and Pf pLDH-based RDTs for P. falciparum. Different assays using different reference materials produce different values for antigen concentration in a given specimen, highlighting the need to establish universal reference assays.

Effect of Using 2 mm Voxels on Observer Performance for PET Lesion Detection

NASA Astrophysics Data System (ADS)

Morey, A. M.; Noo, Frédéric; Kadrmas, Dan J.

2016-06-01

Positron emission tomography (PET) images are typically reconstructed with an in-plane pixel size of approximately 4 mm for cancer imaging. The objective of this work was to evaluate the effect of using smaller pixels on general oncologic lesion-detection. A series of observer studies was performed using experimental phantom data from the Utah PET Lesion Detection Database, which modeled whole-body FDG PET cancer imaging of a 92 kg patient. The data comprised 24 scans over 4 days on a Biograph mCT time-of-flight (TOF) PET/CT scanner, with up to 23 lesions (diam. 6-16 mm) distributed throughout the phantom each day. Images were reconstructed with 2.036 mm and 4.073 mm pixels using ordered-subsets expectation-maximization (OSEM) both with and without point spread function (PSF) modeling and TOF. Detection performance was assessed using the channelized non-prewhitened numerical observer with localization receiver operating characteristic (LROC) analysis. Tumor localization performance and the area under the LROC curve were then analyzed as functions of the pixel size. In all cases, the images with 2 mm pixels provided higher detection performance than those with 4 mm pixels. The degree of improvement from the smaller pixels was larger than that offered by PSF modeling for these data, and provided roughly half the benefit of using TOF. Key results were confirmed by two human observers, who read subsets of the test data. This study suggests that a significant improvement in tumor detection performance for PET can be attained by using smaller voxel sizes than commonly used at many centers. The primary drawback is a 4-fold increase in reconstruction time and data storage requirements.

The Effects of Compensatory Scanning Training on Mobility in Patients with Homonymous Visual Field Defects: Further Support, Predictive Variables and Follow-Up

PubMed Central

Melis-Dankers, Bart J. M.; Brouwer, Wiebo H.; Tucha, Oliver; Heutink, Joost

2016-01-01

Introduction People with homonymous visual field defects (HVFD) often report difficulty detecting obstacles in the periphery on their blind side in time when moving around. Recently, a randomized controlled trial showed that the InSight-Hemianopia Compensatory Scanning Training (IH-CST) specifically improved detection of peripheral stimuli and avoiding obstacles when moving around, especially in dual task situations. Method The within-group training effects of the previously reported IH-CST are examined in an extended patient group. Performance of patients with HVFD on a pre-assessment, post-assessment and follow-up assessment and performance of a healthy control group are compared. Furthermore, it is examined whether training effects can be predicted by demographic characteristics, variables related to the visual disorder, and neuropsychological test results. Results Performance on both subjective and objective measures of mobility-related scanning was improved after training, while no evidence was found for improvement in visual functions (including visual fields), reading, visual search and dot counting. Self-reported improvement did not correlate with improvement in objective mobility performance. According to the participants, the positive effects were still present six to ten months after training. No demographic characteristics, variables related to the visual disorder, and neuropsychological test results were found to predict the size of training effect, although some inconclusive evidence was found for more improvement in patients with left-sided HVFD than in patients with right-sided HFVD. Conclusion Further support was found for a positive effect of IH-CST on detection of visual stimuli during mobility-related activities specifically. Based on the reports given by patients, these effects appear to be long-term effects. However, no conclusions can be drawn on the objective long-term training effects. PMID:27935973

A Joint Optimization Criterion for Blind DS-CDMA Detection

NASA Astrophysics Data System (ADS)

Durán-Díaz, Iván; Cruces-Alvarez, Sergio A.

2006-12-01

This paper addresses the problem of the blind detection of a desired user in an asynchronous DS-CDMA communications system with multipath propagation channels. Starting from the inverse filter criterion introduced by Tugnait and Li in 2001, we propose to tackle the problem in the context of the blind signal extraction methods for ICA. In order to improve the performance of the detector, we present a criterion based on the joint optimization of several higher-order statistics of the outputs. An algorithm that optimizes the proposed criterion is described, and its improved performance and robustness with respect to the near-far problem are corroborated through simulations. Additionally, a simulation using measurements on a real software-radio platform at 5 GHz has also been performed.

Two-view information fusion for improvement of computer-aided detection (CAD) of breast masses on mammograms

NASA Astrophysics Data System (ADS)

Wei, Jun; Sahiner, Berkman; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Zhou, Chuan; Ge, Jun; Zhang, Yiheng

2006-03-01

We are developing a two-view information fusion method to improve the performance of our CAD system for mass detection. Mass candidates on each mammogram were first detected with our single-view CAD system. Potential object pairs on the two-view mammograms were then identified by using the distance between the object and the nipple. Morphological features, Hessian feature, correlation coefficients between the two paired objects and texture features were used as input to train a similarity classifier that estimated a similarity scores for each pair. Finally, a linear discriminant analysis (LDA) classifier was used to fuse the score from the single-view CAD system and the similarity score. A data set of 475 patients containing 972 mammograms with 475 biopsy-proven masses was used to train and test the CAD system. All cases contained the CC view and the MLO or LM view. We randomly divided the data set into two independent sets of 243 cases and 232 cases. The training and testing were performed using the 2-fold cross validation method. The detection performance of the CAD system was assessed by free response receiver operating characteristic (FROC) analysis. The average test FROC curve was obtained from averaging the FP rates at the same sensitivity along the two corresponding test FROC curves from the 2-fold cross validation. At the case-based sensitivities of 90%, 85% and 80% on the test set, the single-view CAD system achieved an FP rate of 2.0, 1.5, and 1.2 FPs/image, respectively. With the two-view fusion system, the FP rates were reduced to 1.7, 1.3, and 1.0 FPs/image, respectively, at the corresponding sensitivities. The improvement was found to be statistically significant (p<0.05) by the AFROC method. Our results indicate that the two-view fusion scheme can improve the performance of mass detection on mammograms.

Photon-number-resolving SSPDs with system detection efficiency over 50% at telecom range

NASA Astrophysics Data System (ADS)

Zolotov, P.; Divochiy, A.; Vakhtomin, Yu.; Moshkova, M.; Morozov, P.; Seleznev, V.; Smirnov, K.

2018-02-01

We used technology of making high-efficiency superconducting single-photon detectors as a basis for improvement of photon-number-resolving devices. By adding optical cavity and using an improved NbN superconducting film, we enhanced previously reported system detection efficiency at telecom range for such detectors. Our results show that implementation of optical cavity helps to develop four-section device with quantum efficiency over 50% at 1.55 µm. Performed experimental studies of detecting multi-photon optical pulses showed irregularities over defining multi-photon through single-photon quantum efficiency.

Robust multiperson detection and tracking for mobile service and social robots.

PubMed

Li, Liyuan; Yan, Shuicheng; Yu, Xinguo; Tan, Yeow Kee; Li, Haizhou

2012-10-01

This paper proposes an efficient system which integrates multiple vision models for robust multiperson detection and tracking for mobile service and social robots in public environments. The core technique is a novel maximum likelihood (ML)-based algorithm which combines the multimodel detections in mean-shift tracking. First, a likelihood probability which integrates detections and similarity to local appearance is defined. Then, an expectation-maximization (EM)-like mean-shift algorithm is derived under the ML framework. In each iteration, the E-step estimates the associations to the detections, and the M-step locates the new position according to the ML criterion. To be robust to the complex crowded scenarios for multiperson tracking, an improved sequential strategy to perform the mean-shift tracking is proposed. Under this strategy, human objects are tracked sequentially according to their priority order. To balance the efficiency and robustness for real-time performance, at each stage, the first two objects from the list of the priority order are tested, and the one with the higher score is selected. The proposed method has been successfully implemented on real-world service and social robots. The vision system integrates stereo-based and histograms-of-oriented-gradients-based human detections, occlusion reasoning, and sequential mean-shift tracking. Various examples to show the advantages and robustness of the proposed system for multiperson tracking from mobile robots are presented. Quantitative evaluations on the performance of multiperson tracking are also performed. Experimental results indicate that significant improvements have been achieved by using the proposed method.

An automatic fall detection framework using data fusion of Doppler radar and motion sensor network.

PubMed

Liu, Liang; Popescu, Mihail; Skubic, Marjorie; Rantz, Marilyn

2014-01-01

This paper describes the ongoing work of detecting falls in independent living senior apartments. We have developed a fall detection system with Doppler radar sensor and implemented ceiling radar in real senior apartments. However, the detection accuracy on real world data is affected by false alarms inherent in the real living environment, such as motions from visitors. To solve this issue, this paper proposes an improved framework by fusing the Doppler radar sensor result with a motion sensor network. As a result, performance is significantly improved after the data fusion by discarding the false alarms generated by visitors. The improvement of this new method is tested on one week of continuous data from an actual elderly person who frequently falls while living in her senior home.

Hand-held optical imager (Gen-2): improved instrumentation and target detectability

PubMed Central

Gonzalez, Jean; DeCerce, Joseph; Erickson, Sarah J.; Martinez, Sergio L.; Nunez, Annie; Roman, Manuela; Traub, Barbara; Flores, Cecilia A.; Roberts, Seigbeh M.; Hernandez, Estrella; Aguirre, Wenceslao; Kiszonas, Richard

2012-01-01

Abstract. Hand-held optical imagers are developed by various researchers towards reflectance-based spectroscopic imaging of breast cancer. Recently, a Gen-1 handheld optical imager was developed with capabilities to perform two-dimensional (2-D) spectroscopic as well as three-dimensional (3-D) tomographic imaging studies. However, the imager was bulky with poor surface contact (∼30%) along curved tissues, and limited sensitivity to detect targets consistently. Herein, a Gen-2 hand-held optical imager that overcame the above limitations of the Gen-1 imager has been developed and the instrumentation described. The Gen-2 hand-held imager is less bulky, portable, and has improved surface contact (∼86%) on curved tissues. Additionally, the forked probe head design is capable of simultaneous bilateral reflectance imaging of both breast tissues, and also transillumination imaging of a single breast tissue. Experimental studies were performed on tissue phantoms to demonstrate the improved sensitivity in detecting targets using the Gen-2 imager. The improved instrumentation of the Gen-2 imager allowed detection of targets independent of their location with respect to the illumination points, unlike in Gen-1 imager. The developed imager has potential for future clinical breast imaging with enhanced sensitivity, via both reflectance and transillumination imaging. PMID:23224163

A Dual Frequency Carrier Phase Error Difference Checking Algorithm for the GNSS Compass.

PubMed

Liu, Shuo; Zhang, Lei; Li, Jian

2016-11-24

The performance of the Global Navigation Satellite System (GNSS) compass is related to the quality of carrier phase measurement. How to process the carrier phase error properly is important to improve the GNSS compass accuracy. In this work, we propose a dual frequency carrier phase error difference checking algorithm for the GNSS compass. The algorithm aims at eliminating large carrier phase error in dual frequency double differenced carrier phase measurement according to the error difference between two frequencies. The advantage of the proposed algorithm is that it does not need additional environment information and has a good performance on multiple large errors compared with previous research. The core of the proposed algorithm is removing the geographical distance from the dual frequency carrier phase measurement, then the carrier phase error is separated and detectable. We generate the Double Differenced Geometry-Free (DDGF) measurement according to the characteristic that the different frequency carrier phase measurements contain the same geometrical distance. Then, we propose the DDGF detection to detect the large carrier phase error difference between two frequencies. The theoretical performance of the proposed DDGF detection is analyzed. An open sky test, a manmade multipath test and an urban vehicle test were carried out to evaluate the performance of the proposed algorithm. The result shows that the proposed DDGF detection is able to detect large error in dual frequency carrier phase measurement by checking the error difference between two frequencies. After the DDGF detection, the accuracy of the baseline vector is improved in the GNSS compass.

Detecting persons concealed in a vehicle

DOEpatents

Tucker, Jr., Raymond W.

2005-03-29

An improved method for detecting the presence of humans or animals concealed within in a vehicle uses a combination of the continuous wavelet transform and a ratio-based energy calculation to determine whether the motion detected using seismic sensors placed on the vehicle is due to the presence of a heartbeat within the vehicle or is the result of motion caused by external factors such as the wind. The method performs well in the presence of light to moderate ambient wind levels, producing far fewer false alarm indications. The new method significantly improves the range of ambient environmental conditions under which human presence detection systems can reliably operate.

Moving target detection method based on improved Gaussian mixture model

NASA Astrophysics Data System (ADS)

Ma, J. Y.; Jie, F. R.; Hu, Y. J.

2017-07-01

Gaussian Mixture Model is often employed to build background model in background difference methods for moving target detection. This paper puts forward an adaptive moving target detection algorithm based on improved Gaussian Mixture Model. According to the graylevel convergence for each pixel, adaptively choose the number of Gaussian distribution to learn and update background model. Morphological reconstruction method is adopted to eliminate the shadow.. Experiment proved that the proposed method not only has good robustness and detection effect, but also has good adaptability. Even for the special cases when the grayscale changes greatly and so on, the proposed method can also make outstanding performance.

Diagnosis of glaucoma and detection of glaucoma progression using spectral domain optical coherence tomography.

PubMed

Grewal, Dilraj S; Tanna, Angelo P

2013-03-01

With the rapid adoption of spectral domain optical coherence tomography (SDOCT) in clinical practice and the recent advances in software technology, there is a need for a review of the literature on glaucoma detection and progression analysis algorithms designed for the commercially available instruments. Peripapillary retinal nerve fiber layer (RNFL) thickness and macular thickness, including segmental macular thickness calculation algorithms, have been demonstrated to be repeatable and reproducible, and have a high degree of diagnostic sensitivity and specificity in discriminating between healthy and glaucomatous eyes across the glaucoma continuum. Newer software capabilities such as glaucoma progression detection algorithms provide an objective analysis of longitudinally obtained structural data that enhances our ability to detect glaucomatous progression. RNFL measurements obtained with SDOCT appear more sensitive than time domain OCT (TDOCT) for glaucoma progression detection; however, agreement with the assessments of visual field progression is poor. Over the last few years, several studies have been performed to assess the diagnostic performance of SDOCT structural imaging and its validity in assessing glaucoma progression. Most evidence suggests that SDOCT performs similarly to TDOCT for glaucoma diagnosis; however, SDOCT may be superior for the detection of early stage disease. With respect to progression detection, SDOCT represents an important technological advance because of its improved resolution and repeatability. Advancements in RNFL thickness quantification, segmental macular thickness calculation and progression detection algorithms, when used correctly, may help to improve our ability to diagnose and manage glaucoma.

Improved performance comparisons of radioxenon systems for low level releases in nuclear explosion monitoring.

PubMed

Haas, Derek A; Eslinger, Paul W; Bowyer, Theodore W; Cameron, Ian M; Hayes, James C; Lowrey, Justin D; Miley, Harry S

2017-11-01

The Comprehensive Nuclear-Test-Ban Treaty bans all nuclear tests and mandates development of verification measures to detect treaty violations. One verification measure is detection of radioactive xenon isotopes produced in the fission of actinides. The International Monitoring System (IMS) currently deploys automated radioxenon systems that can detect four radioxenon isotopes. Radioxenon systems with lower detection limits are currently in development. Historically, the sensitivity of radioxenon systems was measured by the minimum detectable concentration for each isotope. In this paper we analyze the response of radioxenon systems using rigorous metrics in conjunction with hypothetical representative releases indicative of an underground nuclear explosion instead of using only minimum detectable concentrations. Our analyses incorporate the impact of potential spectral interferences on detection limits and the importance of measuring isotopic ratios of the relevant radioxenon isotopes in order to improve discrimination from background sources particularly for low-level releases. To provide a sufficient data set for analysis, hypothetical representative releases are simulated every day from the same location for an entire year. The performance of three types of samplers are evaluated assuming they are located at 15 IMS radionuclide stations in the region of the release point. The performance of two IMS-deployed samplers and a next-generation system is compared with proposed metrics for detection and discrimination using representative releases from the nuclear test site used by the Democratic People's Republic of Korea. Copyright © 2017 Elsevier Ltd. All rights reserved.

Acoustic-based Technology to Detect Buried Pipes

DOT National Transportation Integrated Search

2011-07-29

The objective of this project is to build a pre-commercial device, improve its performance to detect multiple buried pipes, and evaluate the pre-commercial device at utility sites. In the past, Gas Technology Institute (GTI) and SoniVerse Inc. (SVI) ...

Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study.

PubMed

Li, Feng

2015-07-01

This review paper is based on our research experience in the past 30 years. The importance of radiologists' role is discussed in the development or evaluation of new medical images and of computer-aided detection (CAD) schemes in chest radiology. The four main topics include (1) introducing what diseases can be included in a research database for different imaging techniques or CAD systems and what imaging database can be built by radiologists, (2) understanding how radiologists' subjective judgment can be combined with technical objective features to improve CAD performance, (3) sharing our experience in the design of successful observer performance studies, and (4) finally, discussing whether the new images and CAD systems can improve radiologists' diagnostic ability in chest radiology. In conclusion, advanced imaging techniques and detection/classification of CAD systems have a potential clinical impact on improvement of radiologists' diagnostic ability, for both the detection and the differential diagnosis of various lung diseases, in chest radiology.

Image based book cover recognition and retrieval

NASA Astrophysics Data System (ADS)

Sukhadan, Kalyani; Vijayarajan, V.; Krishnamoorthi, A.; Bessie Amali, D. Geraldine

2017-11-01

In this we are developing a graphical user interface using MATLAB for the users to check the information related to books in real time. We are taking the photos of the book cover using GUI, then by using MSER algorithm it will automatically detect all the features from the input image, after this it will filter bifurcate non-text features which will be based on morphological difference between text and non-text regions. We implemented a text character alignment algorithm which will improve the accuracy of the original text detection. We will also have a look upon the built in MATLAB OCR recognition algorithm and an open source OCR which is commonly used to perform better detection results, post detection algorithm is implemented and natural language processing to perform word correction and false detection inhibition. Finally, the detection result will be linked to internet to perform online matching. More than 86% accuracy can be obtained by this algorithm.

Kernel parameter variation-based selective ensemble support vector data description for oil spill detection on the ocean via hyperspectral imaging

NASA Astrophysics Data System (ADS)

Uslu, Faruk Sukru

2017-07-01

Oil spills on the ocean surface cause serious environmental, political, and economic problems. Therefore, these catastrophic threats to marine ecosystems require detection and monitoring. Hyperspectral sensors are powerful optical sensors used for oil spill detection with the help of detailed spectral information of materials. However, huge amounts of data in hyperspectral imaging (HSI) require fast and accurate computation methods for detection problems. Support vector data description (SVDD) is one of the most suitable methods for detection, especially for large data sets. Nevertheless, the selection of kernel parameters is one of the main problems in SVDD. This paper presents a method, inspired by ensemble learning, for improving performance of SVDD without tuning its kernel parameters. Additionally, a classifier selection technique is proposed to get more gain. The proposed approach also aims to solve the small sample size problem, which is very important for processing high-dimensional data in HSI. The algorithm is applied to two HSI data sets for detection problems. In the first HSI data set, various targets are detected; in the second HSI data set, oil spill detection in situ is realized. The experimental results demonstrate the feasibility and performance improvement of the proposed algorithm for oil spill detection problems.

Vehicle parts detection based on Faster - RCNN with location constraints of vehicle parts feature point

NASA Astrophysics Data System (ADS)

Yang, Liqin; Sang, Nong; Gao, Changxin

2018-03-01

Vehicle parts detection plays an important role in public transportation safety and mobility. The detection of vehicle parts is to detect the position of each vehicle part. We propose a new approach by combining Faster RCNN and three level cascaded convolutional neural network (DCNN). The output of Faster RCNN is a series of bounding boxes with coordinate information, from which we can locate vehicle parts. DCNN can precisely predict feature point position, which is the center of vehicle part. We design an output strategy by combining these two results. There are two advantages for this. The quality of the bounding boxes are greatly improved, which means vehicle parts feature point position can be located more precise. Meanwhile we preserve the position relationship between vehicle parts and effectively improve the validity and reliability of the result. By using our algorithm, the performance of the vehicle parts detection improve obviously compared with Faster RCNN.

4D numerical observer for lesion detection in respiratory-gated PET

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

Lorsakul, Auranuch; Li, Quanzheng; Ouyang, Jinsong

2014-10-15

Purpose: Respiratory-gated positron emission tomography (PET)/computed tomography protocols reduce lesion smearing and improve lesion detection through a synchronized acquisition of emission data. However, an objective assessment of image quality of the improvement gained from respiratory-gated PET is mainly limited to a three-dimensional (3D) approach. This work proposes a 4D numerical observer that incorporates both spatial and temporal informations for detection tasks in pulmonary oncology. Methods: The authors propose a 4D numerical observer constructed with a 3D channelized Hotelling observer for the spatial domain followed by a Hotelling observer for the temporal domain. Realistic {sup 18}F-fluorodeoxyglucose activity distributions were simulated usingmore » a 4D extended cardiac torso anthropomorphic phantom including 12 spherical lesions at different anatomical locations (lower, upper, anterior, and posterior) within the lungs. Simulated data based on Monte Carlo simulation were obtained using GEANT4 application for tomographic emission (GATE). Fifty noise realizations of six respiratory-gated PET frames were simulated by GATE using a model of the Siemens Biograph mMR scanner geometry. PET sinograms of the thorax background and pulmonary lesions that were simulated separately were merged to generate different conditions of the lesions to the background (e.g., lesion contrast and motion). A conventional ordered subset expectation maximization (OSEM) reconstruction (5 iterations and 6 subsets) was used to obtain: (1) gated, (2) nongated, and (3) motion-corrected image volumes (a total of 3200 subimage volumes: 2400 gated, 400 nongated, and 400 motion-corrected). Lesion-detection signal-to-noise ratios (SNRs) were measured in different lesion-to-background contrast levels (3.5, 8.0, 9.0, and 20.0), lesion diameters (10.0, 13.0, and 16.0 mm), and respiratory motion displacements (17.6–31.3 mm). The proposed 4D numerical observer applied on multiple-gated images was compared to the conventional 3D approach applied on the nongated and motion-corrected images. Results: On average, the proposed 4D numerical observer improved the detection SNR by 48.6% (p < 0.005), whereas the 3D methods on motion-corrected images improved by 31.0% (p < 0.005) as compared to the nongated method. For all different conditions of the lesions, the relative SNR measurement (Gain = SNR{sub Observed}/SNR{sub Nongated}) of the 4D method was significantly higher than one from the motion-corrected 3D method by 13.8% (p < 0.02), where Gain{sub 4D} was 1.49 ± 0.21 and Gain{sub 3D} was 1.31 ± 0.15. For the lesion with the highest amplitude of motion, the 4D numerical observer yielded the highest observer-performance improvement (176%). For the lesion undergoing the smallest motion amplitude, the 4D method provided superior lesion detectability compared with the 3D method, which provided a detection SNR close to the nongated method. The investigation on a structure of the 4D numerical observer showed that a Laguerre–Gaussian channel matrix with a volumetric 3D function yielded higher lesion-detection performance than one with a 2D-stack-channelized function, whereas a different kind of channels that have the ability to mimic the human visual system, i.e., difference-of-Gaussian, showed similar performance in detecting uniform and spherical lesions. The investigation of the detection performance when increasing noise levels yielded decreasing detection SNR by 27.6% and 41.5% for the nongated and gated methods, respectively. The investigation of lesion contrast and diameter showed that the proposed 4D observer preserved the linearity property of an optimal-linear observer while the motion was present. Furthermore, the investigation of the iteration and subset numbers of the OSEM algorithm demonstrated that these parameters had impact on the lesion detectability and the selection of the optimal parameters could provide the maximum lesion-detection performance. The proposed 4D numerical observer outperformed the other observers for the lesion-detection task in various lesion conditions and motions. Conclusions: The 4D numerical observer shows substantial improvement in lesion detectability over the 3D observer method. The proposed 4D approach could potentially provide a more reliable objective assessment of the impact of respiratory-gated PET improvement for lesion-detection tasks. On the other hand, the 4D approach may be used as an upper bound to investigate the performance of the motion correction method. In future work, the authors will validate the proposed 4D approach on clinical data for detection tasks in pulmonary oncology.« less

Passive IR polarization sensors: a new technology for mine detection

NASA Astrophysics Data System (ADS)

Barbour, Blair A.; Jones, Michael W.; Barnes, Howard B.; Lewis, Charles P.

1998-09-01

The problem of mine and minefield detection continues to provide a significant challenge to sensor systems. Although the various sensor technologies (infrared, ground penetrating radar, etc.) may excel in certain situations there does not exist a single sensor technology that can adequately detect mines in all conditions such as time of day, weather, buried or surface laid, etc. A truly robust mine detection system will likely require the fusion of data from multiple sensor technologies. The performance of these systems, however, will ultimately depend on the performance of the individual sensors. Infrared (IR) polarimetry is a new and innovative sensor technology that adds substantial capabilities to the detection of mines. IR polarimetry improves on basic IR imaging by providing improved spatial resolution of the target, an inherent ability to suppress clutter, and the capability for zero (Delta) T imaging. Nichols Research Corporation (Nichols) is currently evaluating the effectiveness of IR polarization for mine detection. This study is partially funded by the U.S. Army Night Vision & Electronic Sensors Directorate (NVESD). The goal of the study is to demonstrate, through phenomenology studies and limited field trials, that IR polarizaton outperforms conventional IR imaging in the mine detection arena.

An improved AE detection method of rail defect based on multi-level ANC with VSS-LMS

NASA Astrophysics Data System (ADS)

Zhang, Xin; Cui, Yiming; Wang, Yan; Sun, Mingjian; Hu, Hengshan

2018-01-01

In order to ensure the safety and reliability of railway system, Acoustic Emission (AE) method is employed to investigate rail defect detection. However, little attention has been paid to the defect detection at high speed, especially for noise interference suppression. Based on AE technology, this paper presents an improved rail defect detection method by multi-level ANC with VSS-LMS. Multi-level noise cancellation based on SANC and ANC is utilized to eliminate complex noises at high speed, and tongue-shaped curve with index adjustment factor is proposed to enhance the performance of variable step-size algorithm. Defect signals and reference signals are acquired by the rail-wheel test rig. The features of noise signals and defect signals are analyzed for effective detection. The effectiveness of the proposed method is demonstrated by comparing with the previous study, and different filter lengths are investigated to obtain a better noise suppression performance. Meanwhile, the detection ability of the proposed method is verified at the top speed of the test rig. The results clearly illustrate that the proposed method is effective in detecting rail defects at high speed, especially for noise interference suppression.

Pharmaceutical Composition for Improving Physical Working Capacity.

PubMed

Baulin, S I; Rogacheva, S M; Afanaseva, S V; Zabanova, E V; Karagaycheva, Yu V

2015-11-01

For development of a pharmaceutical composition improving physical performance, effects of various drugs and their combinations on forced swimming test performance were studied on laboratory rats. Maximum increase in animal performance was produced by a 3-component composition asparcam+mildronate+metaprote in proportion of 5.0, 10.7, and 14.3 mg/kg, respectively. No changes in blood serum biochemistry and morphological composition of the peripheral blood were detected after single intragastric administration of the composition.

Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

NASA Astrophysics Data System (ADS)

Li, Hong; Ding, Xue

2017-03-01

This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

Detection of Bovine IgG Isotypes in a PPA-ELISA for Johne's Disease Diagnosis in Infected Herds

PubMed Central

Fernández, Bárbara; Gilardoni, Liliana Rosa; Jolly, Ana; Colavecchia, Silvia Beatriz; Paolicchi, Fernando Alberto; Mundo, Silvia Leonor

2012-01-01

Johne's Disease or Paratuberculosis is a chronic granulomatous enteritis disease affecting ruminants. Detection of subclinically infected animals is difficult, hampering the control of this disease. The aim of this work was to evaluate the performance of detection of IgG isotypes in a PPA-ELISA to improve the recognition of cattle naturally infected with Map in different stages. A total of 108 animals from Tuberculosis-free herds were grouped as follows: exposed (n = 30), subclinically infected (n = 26), clinically infected (n = 14), and healthy controls (n = 38). Receiver-operating characteristic (ROC) curves of isotypes/PPA-ELISAs were constructed and areas under the curves were compared to evaluate the performance of each test. Our study demonstrated that the conventional PPA-ELISA (detecting IgG) is the best to identify clinically infected animals with high sensitivity (92.9%) and specificity (100%). Meanwhile, IgG2/PPA-ELISA improved the number of subclinically infected cattle detected as compared with conventional IgG/PPA-ELISA (53.8 versus 23.1%). In addition, it had the maximum sensitivity (65.0%, taking into account all Map-infected cattle). In conclusion, the combination of IgG and IgG2/PPA-ELISAs may improve the identification of Map-infected cattle in different stages of disease. The usefulness of IgG2 detection in serological tests for Johne's Disease diagnosis should be further evaluated. PMID:22792511

Detection of Bovine IgG Isotypes in a PPA-ELISA for Johne's Disease Diagnosis in Infected Herds.

PubMed

Fernández, Bárbara; Gilardoni, Liliana Rosa; Jolly, Ana; Colavecchia, Silvia Beatriz; Paolicchi, Fernando Alberto; Mundo, Silvia Leonor

2012-01-01

Johne's Disease or Paratuberculosis is a chronic granulomatous enteritis disease affecting ruminants. Detection of subclinically infected animals is difficult, hampering the control of this disease. The aim of this work was to evaluate the performance of detection of IgG isotypes in a PPA-ELISA to improve the recognition of cattle naturally infected with Map in different stages. A total of 108 animals from Tuberculosis-free herds were grouped as follows: exposed (n = 30), subclinically infected (n = 26), clinically infected (n = 14), and healthy controls (n = 38). Receiver-operating characteristic (ROC) curves of isotypes/PPA-ELISAs were constructed and areas under the curves were compared to evaluate the performance of each test. Our study demonstrated that the conventional PPA-ELISA (detecting IgG) is the best to identify clinically infected animals with high sensitivity (92.9%) and specificity (100%). Meanwhile, IgG2/PPA-ELISA improved the number of subclinically infected cattle detected as compared with conventional IgG/PPA-ELISA (53.8 versus 23.1%). In addition, it had the maximum sensitivity (65.0%, taking into account all Map-infected cattle). In conclusion, the combination of IgG and IgG2/PPA-ELISAs may improve the identification of Map-infected cattle in different stages of disease. The usefulness of IgG2 detection in serological tests for Johne's Disease diagnosis should be further evaluated.

Improving Fall Detection Using an On-Wrist Wearable Accelerometer

PubMed Central

Chira, Camelia; González, Víctor M.; de la Cal, Enrique

2018-01-01

Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The feature extraction is extended in order to balance the dataset for the minority class. Alternative models have been analyzed to reduce the computational constraints so the solution can be embedded in smart-phones or smart wristbands. Several published datasets have been used in the Materials and Methods section. Although these datasets do not include data from real falls of elderly people, a complete comparison study of fall-related datasets shows statistical differences between the simulated falls and real falls from participants suffering from impairment diseases. Given the obtained results, the rule-based systems represent a promising research line as they perform similarly to neural networks, but with a reduced computational cost. Furthermore, support vector machines performed with a high specificity. However, further research to validate the proposal in real on-line scenarios is needed. Furthermore, a slight improvement should be made to reduce the number of false alarms. PMID:29701721

a Single-Exposure Dual-Energy Computed Radiography Technique for Improved Nodule Detection and Classification in Chest Imaging

NASA Astrophysics Data System (ADS)

Zink, Frank Edward

The detection and classification of pulmonary nodules is of great interest in chest radiography. Nodules are often indicative of primary cancer, and their detection is particularly important in asymptomatic patients. The ability to classify nodules as calcified or non-calcified is important because calcification is a positive indicator that the nodule is benign. Dual-energy methods offer the potential to improve both the detection and classification of nodules by allowing the formation of material-selective images. Tissue-selective images can improve detection by virtue of the elimination of obscuring rib structure. Bone -selective images are essentially calcium images, allowing classification of the nodule. A dual-energy technique is introduced which uses a computed radiography system to acquire dual-energy chest radiographs in a single-exposure. All aspects of the dual-energy technique are described, with particular emphasis on scatter-correction, beam-hardening correction, and noise-reduction algorithms. The adaptive noise-reduction algorithm employed improves material-selective signal-to-noise ratio by up to a factor of seven with minimal sacrifice in selectivity. A clinical comparison study is described, undertaken to compare the dual-energy technique to conventional chest radiography for the tasks of nodule detection and classification. Observer performance data were collected using the Free Response Observer Characteristic (FROC) method and the bi-normal Alternative FROC (AFROC) performance model. Results of the comparison study, analyzed using two common multiple observer statistical models, showed that the dual-energy technique was superior to conventional chest radiography for detection of nodules at a statistically significant level (p < .05). Discussion of the comparison study emphasizes the unique combination of data collection and analysis techniques employed, as well as the limitations of comparison techniques in the larger context of technology assessment.

Improved relocatable over-the-horizon radar detection and tracking using the maximum likelihood adaptive neural system algorithm

NASA Astrophysics Data System (ADS)

Perlovsky, Leonid I.; Webb, Virgil H.; Bradley, Scott R.; Hansen, Christopher A.

1998-07-01

An advanced detection and tracking system is being developed for the U.S. Navy's Relocatable Over-the-Horizon Radar (ROTHR) to provide improved tracking performance against small aircraft typically used in drug-smuggling activities. The development is based on the Maximum Likelihood Adaptive Neural System (MLANS), a model-based neural network that combines advantages of neural network and model-based algorithmic approaches. The objective of the MLANS tracker development effort is to address user requirements for increased detection and tracking capability in clutter and improved track position, heading, and speed accuracy. The MLANS tracker is expected to outperform other approaches to detection and tracking for the following reasons. It incorporates adaptive internal models of target return signals, target tracks and maneuvers, and clutter signals, which leads to concurrent clutter suppression, detection, and tracking (track-before-detect). It is not combinatorial and thus does not require any thresholding or peak picking and can track in low signal-to-noise conditions. It incorporates superresolution spectrum estimation techniques exceeding the performance of conventional maximum likelihood and maximum entropy methods. The unique spectrum estimation method is based on the Einsteinian interpretation of the ROTHR received energy spectrum as a probability density of signal frequency. The MLANS neural architecture and learning mechanism are founded on spectrum models and maximization of the "Einsteinian" likelihood, allowing knowledge of the physical behavior of both targets and clutter to be injected into the tracker algorithms. The paper describes the addressed requirements and expected improvements, theoretical foundations, engineering methodology, and results of the development effort to date.

Automatic threshold optimization in nonlinear energy operator based spike detection.

PubMed

Malik, Muhammad H; Saeed, Maryam; Kamboh, Awais M

2016-08-01

In neural spike sorting systems, the performance of the spike detector has to be maximized because it affects the performance of all subsequent blocks. Non-linear energy operator (NEO), is a popular spike detector due to its detection accuracy and its hardware friendly architecture. However, it involves a thresholding stage, whose value is usually approximated and is thus not optimal. This approximation deteriorates the performance in real-time systems where signal to noise ratio (SNR) estimation is a challenge, especially at lower SNRs. In this paper, we propose an automatic and robust threshold calculation method using an empirical gradient technique. The method is tested on two different datasets. The results show that our optimized threshold improves the detection accuracy in both high SNR and low SNR signals. Boxplots are presented that provide a statistical analysis of improvements in accuracy, for instance, the 75th percentile was at 98.7% and 93.5% for the optimized NEO threshold and traditional NEO threshold, respectively.

Continuous high PRF waveforms for challenging environments

NASA Astrophysics Data System (ADS)

Jaroszewski, Steven; Corbeil, Allan; Ryland, Robert; Sobota, David

2017-05-01

Current airborne radar systems segment the available time-on-target during each beam dwell into multiple Coherent Processing Intervals (CPIs) in order to eliminate range eclipsing, solve for unambiguous range, and increase the detection performance against larger Radar Cross Section (RCS) targets. As a consequence, these radars do not realize the full Signal-to-Noise Ratio (SNR) increase and detection performance improvement that is possible. Continuous High Pulse Repetition Frequency (HPRF) waveforms and processing enables the coherent integration of all available radar data over the full time-on-target. This can greatly increase the SNR for air targets at long range and/or with weak radar returns and significantly improve the detection performance against such targets. TSC worked with its partner KeyW to implement a Continuous HPRF waveform in their Sahara radar testbed and obtained measured radar data on both a ground vehicle target and an airborne target of opportunity. This experimental data was processed by TSC to validate the expected benefits of Continuous HPRF waveforms.

Amphetamine improves mouse and human attention in the 5-choice continuous performance test.

PubMed

MacQueen, David A; Minassian, Arpi; Kenton, Johnny A; Geyer, Mark A; Perry, William; Brigman, Jonathan L; Young, Jared W

2018-05-31

Non-medical use of prescription stimulants amongst college students is common, with claims of cognitive and academic benefits. The mechanism, magnitude, and pervasiveness of the cognitive enhancing effects of stimulants in healthy adults remain poorly understood however. The present study determined the effects of dextroamphetamine (D-amp) on the 5-choice continuous performance test (5C-CPT) of attention in healthy young adult humans and mice. A mixed gender sample received placebo (n = 29), 10 (n = 17) or 20 mg D-amp (n = 25) in a double-blind fashion before 5C-CPT testing. In addition, male C57BL/6J mice were trained on a touchscreen adaptation of the 5C-CPT and tested after receiving saline or D-amp (0.1, 0.3, 1.0 mg/kg; n = 8/dose). In humans, D-amp significantly improved 5C-CPT performance. Both doses improved signal detection driven by increased hit rate (reduced omissions). Both doses also improved response accuracy and reduced hit reaction time (HRT) variability. In mice, similar effects (improved signal detection, hit rate, and response accuracy) were observed at the moderate dose (0.3 mg/kg). In contrast to human participants however, no effect on HRT variability was detected in mice, with no effect on HRT in either species. Human 5C-CPT performance was consistent with prior studies and consistent with alternative CPT paradigms. The performance of C57BL/6J mice on the touchscreen 5C-CPT mirrored performance of this strain on 5-hole operant chambers. Importantly, comparable facilitation of attention with D-amp was observed in both species. The 5C-CPT provides a cross-species paradigm by which the cognitive enhancing properties of stimulants and the neural underpinnings of attention can be assessed. Copyright © 2018. Published by Elsevier Ltd.

Dim target detection method based on salient graph fusion

NASA Astrophysics Data System (ADS)

Hu, Ruo-lan; Shen, Yi-yan; Jiang, Jun

2018-02-01

Dim target detection is one key problem in digital image processing field. With development of multi-spectrum imaging sensor, it becomes a trend to improve the performance of dim target detection by fusing the information from different spectral images. In this paper, one dim target detection method based on salient graph fusion was proposed. In the method, Gabor filter with multi-direction and contrast filter with multi-scale were combined to construct salient graph from digital image. And then, the maximum salience fusion strategy was designed to fuse the salient graph from different spectral images. Top-hat filter was used to detect dim target from the fusion salient graph. Experimental results show that proposal method improved the probability of target detection and reduced the probability of false alarm on clutter background images.

A light and faster regional convolutional neural network for object detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Ding, Peng; Zhang, Ye; Deng, Wei-Jian; Jia, Ping; Kuijper, Arjan

2018-07-01

Detection of objects from satellite optical remote sensing images is very important for many commercial and governmental applications. With the development of deep convolutional neural networks (deep CNNs), the field of object detection has seen tremendous advances. Currently, objects in satellite remote sensing images can be detected using deep CNNs. In general, optical remote sensing images contain many dense and small objects, and the use of the original Faster Regional CNN framework does not yield a suitably high precision. Therefore, after careful analysis we adopt dense convoluted networks, a multi-scale representation and various combinations of improvement schemes to enhance the structure of the base VGG16-Net for improving the precision. We propose an approach to reduce the test-time (detection time) and memory requirements. To validate the effectiveness of our approach, we perform experiments using satellite remote sensing image datasets of aircraft and automobiles. The results show that the improved network structure can detect objects in satellite optical remote sensing images more accurately and efficiently.

An Improved Time-Frequency Analysis Method in Interference Detection for GNSS Receivers

PubMed Central

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-01-01

In this paper, an improved joint time-frequency (TF) analysis method based on a reassigned smoothed pseudo Wigner–Ville distribution (RSPWVD) has been proposed in interference detection for Global Navigation Satellite System (GNSS) receivers. In the RSPWVD, the two-dimensional low-pass filtering smoothing function is introduced to eliminate the cross-terms present in the quadratic TF distribution, and at the same time, the reassignment method is adopted to improve the TF concentration properties of the auto-terms of the signal components. This proposed interference detection method is evaluated by experiments on GPS L1 signals in the disturbing scenarios compared to the state-of-the-art interference detection approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-terms problem and also preserves good TF localization properties, which has been proven to be effective and valid to enhance the interference detection performance of the GNSS receivers, particularly in the jamming environments. PMID:25905704

An improved algorithm of laser spot center detection in strong noise background

NASA Astrophysics Data System (ADS)

Zhang, Le; Wang, Qianqian; Cui, Xutai; Zhao, Yu; Peng, Zhong

2018-01-01

Laser spot center detection is demanded in many applications. The common algorithms for laser spot center detection such as centroid and Hough transform method have poor anti-interference ability and low detection accuracy in the condition of strong background noise. In this paper, firstly, the median filtering was used to remove the noise while preserving the edge details of the image. Secondly, the binarization of the laser facula image was carried out to extract target image from background. Then the morphological filtering was performed to eliminate the noise points inside and outside the spot. At last, the edge of pretreated facula image was extracted and the laser spot center was obtained by using the circle fitting method. In the foundation of the circle fitting algorithm, the improved algorithm added median filtering, morphological filtering and other processing methods. This method could effectively filter background noise through theoretical analysis and experimental verification, which enhanced the anti-interference ability of laser spot center detection and also improved the detection accuracy.

Colonoscopy audit over 10 years--what can be learnt?

PubMed

Fraser, Alan G; Gamble, Greg D; Rose, Toby R; Dunn, John P

2013-09-13

The goals of colonoscopy are changing over time and it is important to regularly determine if endoscopists are achieving key performance indicators. Data on key performance indicators were recorded independently by nursing staff for all colonoscopies performed during a 10-year period. The results were discussed at regular meetings and feedback given to endoscopists. Audit data was recorded for 67,570 procedures. The key performance indicators (time to caecum, withdrawal time, adjusted caecal intubation rate and polyp detection rate) all improved over the audit period (p<0.0001 for trend). For each endoscopist the mean withdrawal time was highly variable ranging from 3.1 mins (95%CI 3.0; 3.1) to 11.2 mins (11.0; 11.3). For each endoscopist mean polyp detection rate varied from 29% (CI 26, 31%) to 69% (CI 68, 70%). There was a significant correlation between mean withdrawal time and mean polyp detection rate for each endoscopist (r=0.42; p=0.03). The polyp detection rate improved from 29% in 1999 to 49% in 2010. The proportion of procedures with more than 2 polyps increased from 22% in 2001 to 33% in 2010. There was a significant association of patient discomfort with time to caecum and also to level of consciousness, p<0.0001. There was a significant decrease in the proportion with significant discomfort over the audit period, p<0.0001. Colonoscopy audit as a routine process with data collection by endoscopy nurses over several years may be able to improve key performance indicators by the process of regular feedback to endoscopists. Audit should be encouraged as a routine process rather than simply as a research tool for a limited period.

Designing Performance Measurement For Supply Chain's Actors And Regulator Using Scale Balanced Scorecard And Data Envelopment Analysis

NASA Astrophysics Data System (ADS)

Kusrini, Elisa; Subagyo; Aini Masruroh, Nur

2016-01-01

This research is a sequel of the author's earlier conducted researches in the fields of designing of integrated performance measurement between supply chain's actors and regulator. In the previous paper, the design of performance measurement is done by combining Balanced Scorecard - Supply Chain Operation Reference - Regulator Contribution model and Data Envelopment Analysis. This model referred as B-S-Rc-DEA model. The combination has the disadvantage that all the performance variables have the same weight. This paper investigates whether by giving weight to performance variables will produce more sensitive performance measurement in detecting performance improvement. Therefore, this paper discusses the development of the model B-S-Rc-DEA by giving weight to its performance'variables. This model referred as Scale B-S-Rc-DEA model. To illustrate the model of development, some samples from small medium enterprises of leather craft industry supply chain in province of Yogyakarta, Indonesia are used in this research. It is found that Scale B-S-Rc-DEA model is more sensitive to detecting performance improvement than B-S- Rc-DEA model.

Research on the strategy of underwater united detection fusion and communication using multi-sensor

NASA Astrophysics Data System (ADS)

Xu, Zhenhua; Huang, Jianguo; Huang, Hai; Zhang, Qunfei

2011-09-01

In order to solve the distributed detection fusion problem of underwater target detection, when the signal to noise ratio (SNR) of the acoustic channel is low, a new strategy for united detection fusion and communication using multiple sensors was proposed. The performance of detection fusion was studied and compared based on the Neyman-Pearson principle when the binary phase shift keying (BPSK) and on-off keying (OOK) modes were used by the local sensors. The comparative simulation and analysis between the optimal likelihood ratio test and the proposed strategy was completed, and both the theoretical analysis and simulation indicate that using the proposed new strategy could improve the detection performance effectively. In theory, the proposed strategy of united detection fusion and communication is of great significance to the establishment of an underwater target detection system.

Change detection of medical images using dictionary learning techniques and principal component analysis.

PubMed

Nika, Varvara; Babyn, Paul; Zhu, Hongmei

2014-07-01

Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of magnetic resonance imaging (MRI) scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are being used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. We present an improved version of the EigenBlockCD algorithm, named the EigenBlockCD-2. The EigenBlockCD-2 algorithm performs an initial global registration and identifies the changes between serial MR images of the brain. Blocks of pixels from a baseline scan are used to train local dictionaries to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between [Formula: see text] and [Formula: see text] norms as two possible similarity measures in the improved EigenBlockCD-2 algorithm. We show the advantages of the [Formula: see text] norm over the [Formula: see text] norm both theoretically and numerically. We also demonstrate the performance of the new EigenBlockCD-2 algorithm for detecting changes of MR images and compare our results with those provided in the recent literature. Experimental results with both simulated and real MRI scans show that our improved EigenBlockCD-2 algorithm outperforms the previous methods. It detects clinical changes while ignoring the changes due to the patient's position and other acquisition artifacts.

Effect of respiratory muscle training on exercise performance in healthy individuals: a systematic review and meta-analysis.

PubMed

Illi, Sabine K; Held, Ulrike; Frank, Irène; Spengler, Christina M

2012-08-01

Two distinct types of specific respiratory muscle training (RMT), i.e. respiratory muscle strength (resistive/threshold) and endurance (hyperpnoea) training, have been established to improve the endurance performance of healthy individuals. We performed a systematic review and meta-analysis in order to determine the factors that affect the change in endurance performance after RMT in healthy subjects. A computerized search was performed without language restriction in MEDLINE, EMBASE and CINAHL and references of original studies and reviews were searched for further relevant studies. RMT studies with healthy individuals assessing changes in endurance exercise performance by maximal tests (constant load, time trial, intermittent incremental, conventional [non-intermittent] incremental) were screened and abstracted by two independent investigators. A multiple linear regression model was used to identify effects of subjects' fitness, type of RMT (inspiratory or combined inspiratory/expiratory muscle strength training, respiratory muscle endurance training), type of exercise test, test duration and type of sport (rowing, running, swimming, cycling) on changes in performance after RMT. In addition, a meta-analysis was performed to determine the effect of RMT on endurance performance in those studies providing the necessary data. The multiple linear regression analysis including 46 original studies revealed that less fit subjects benefit more from RMT than highly trained athletes (6.0% per 10 mL · kg⁻¹ · min⁻¹ decrease in maximal oxygen uptake, 95% confidence interval [CI] 1.8, 10.2%; p = 0.005) and that improvements do not differ significantly between inspiratory muscle strength and respiratory muscle endurance training (p = 0.208), while combined inspiratory and expiratory muscle strength training seems to be superior in improving performance, although based on only 6 studies (+12.8% compared with inspiratory muscle strength training, 95% CI 3.6, 22.0%; p = 0.006). Furthermore, constant load tests (+16%, 95% CI 10.2, 22.9%) and intermittent incremental tests (+18.5%, 95% CI 10.8, 26.3%) detect changes in endurance performance better than conventional incremental tests (both p < 0.001) with no difference between time trials and conventional incremental tests (p = 0.286). With increasing test duration, improvements in performance are greater (+0.4% per minute test duration, 95% CI 0.1, 0.6%; p = 0.011) and the type of sport does not influence the magnitude of improvements (all p > 0.05). The meta-analysis, performed on eight controlled trials revealed a significant improvement in performance after RMT, which was detected by constant load tests, time trials and intermittent incremental tests, but not by conventional incremental tests. RMT improves endurance exercise performance in healthy individuals with greater improvements in less fit individuals and in sports of longer durations. The two most common types of RMT (inspiratory muscle strength and respiratory muscle endurance training) do not differ significantly in their effect, while combined inspiratory/expiratory strength training might be superior. Improvements are similar between different types of sports. Changes in performance can be detected by constant load tests, time trials and intermittent incremental tests only. Thus, all types of RMT can be used to improve exercise performance in healthy subjects but care must be taken regarding the test used to investigate the improvements.

Task-based statistical image reconstruction for high-quality cone-beam CT

NASA Astrophysics Data System (ADS)

Dang, Hao; Webster Stayman, J.; Xu, Jennifer; Zbijewski, Wojciech; Sisniega, Alejandro; Mow, Michael; Wang, Xiaohui; Foos, David H.; Aygun, Nafi; Koliatsos, Vassilis E.; Siewerdsen, Jeffrey H.

2017-11-01

Task-based analysis of medical imaging performance underlies many ongoing efforts in the development of new imaging systems. In statistical image reconstruction, regularization is often formulated in terms to encourage smoothness and/or sharpness (e.g. a linear, quadratic, or Huber penalty) but without explicit formulation of the task. We propose an alternative regularization approach in which a spatially varying penalty is determined that maximizes task-based imaging performance at every location in a 3D image. We apply the method to model-based image reconstruction (MBIR—viz., penalized weighted least-squares, PWLS) in cone-beam CT (CBCT) of the head, focusing on the task of detecting a small, low-contrast intracranial hemorrhage (ICH), and we test the performance of the algorithm in the context of a recently developed CBCT prototype for point-of-care imaging of brain injury. Theoretical predictions of local spatial resolution and noise are computed via an optimization by which regularization (specifically, the quadratic penalty strength) is allowed to vary throughout the image to maximize local task-based detectability index ({{d}\\prime} ). Simulation studies and test-bench experiments were performed using an anthropomorphic head phantom. Three PWLS implementations were tested: conventional (constant) penalty; a certainty-based penalty derived to enforce constant point-spread function, PSF; and the task-based penalty derived to maximize local detectability at each location. Conventional (constant) regularization exhibited a fairly strong degree of spatial variation in {{d}\\prime} , and the certainty-based method achieved uniform PSF, but each exhibited a reduction in detectability compared to the task-based method, which improved detectability up to ~15%. The improvement was strongest in areas of high attenuation (skull base), where the conventional and certainty-based methods tended to over-smooth the data. The task-driven reconstruction method presents a promising regularization method in MBIR by explicitly incorporating task-based imaging performance as the objective. The results demonstrate improved ICH conspicuity and support the development of high-quality CBCT systems.

High-sensitivity high-selectivity detection of CWAs and TICs using tunable laser photoacoustic spectroscopy

NASA Astrophysics Data System (ADS)

Pushkarsky, Michael; Webber, Michael; Patel, C. Kumar N.

2005-03-01

We provide a general technique for evaluating the performance of an optical sensor for the detection of chemical warfare agents (CWAs) in realistic environments and present data from a simulation model based on a field deployed discretely tunable 13CO2 laser photoacoustic spectrometer (L-PAS). Results of our calculations show the sensor performance in terms of usable sensor sensitivity as a function of probability of false positives (PFP). The false positives arise from the presence of many other gases in the ambient air that could be interferents. Using the L-PAS as it exists today, we can achieve a detection threshold of about 4 ppb for the CWAs while maintaining a PFP of less than 1:106. Our simulation permits us to vary a number of parameters in the model to provide guidance for performance improvement. We find that by using a larger density of laser lines (such as those obtained through the use of tunable semiconductor lasers), improving the detector noise and maintaining the accuracy of laser frequency determination, optical detection schemes can make possible CWA sensors having sub-ppb detection capability with <1:108 PFP. We also describe the results of a preliminary experiment that verifies the results of the simulation model. Finally, we discuss the use of continuously tunable quantum cascade lasers in L-PAS for CWA and TIC detection.

A study on real-time low-quality content detection on Twitter from the users' perspective.

PubMed

Chen, Weiling; Yeo, Chai Kiat; Lau, Chiew Tong; Lee, Bu Sung

2017-01-01

Detection techniques of malicious content such as spam and phishing on Online Social Networks (OSN) are common with little attention paid to other types of low-quality content which actually impacts users' content browsing experience most. The aim of our work is to detect low-quality content from the users' perspective in real time. To define low-quality content comprehensibly, Expectation Maximization (EM) algorithm is first used to coarsely classify low-quality tweets into four categories. Based on this preliminary study, a survey is carefully designed to gather users' opinions on different categories of low-quality content. Both direct and indirect features including newly proposed features are identified to characterize all types of low-quality content. We then further combine word level analysis with the identified features and build a keyword blacklist dictionary to improve the detection performance. We manually label an extensive Twitter dataset of 100,000 tweets and perform low-quality content detection in real time based on the characterized significant features and word level analysis. The results of our research show that our method has a high accuracy of 0.9711 and a good F1 of 0.8379 based on a random forest classifier with real time performance in the detection of low-quality content in tweets. Our work therefore achieves a positive impact in improving user experience in browsing social media content.

DeepFruits: A Fruit Detection System Using Deep Neural Networks

PubMed Central

Sa, Inkyu; Ge, Zongyuan; Dayoub, Feras; Upcroft, Ben; Perez, Tristan; McCool, Chris

2016-01-01

This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit. PMID:27527168

DeepFruits: A Fruit Detection System Using Deep Neural Networks.

PubMed

Sa, Inkyu; Ge, Zongyuan; Dayoub, Feras; Upcroft, Ben; Perez, Tristan; McCool, Chris

2016-08-03

This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0 . 807 to 0 . 838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.

A study on real-time low-quality content detection on Twitter from the users’ perspective

PubMed Central

Yeo, Chai Kiat; Lau, Chiew Tong; Lee, Bu Sung

2017-01-01

Detection techniques of malicious content such as spam and phishing on Online Social Networks (OSN) are common with little attention paid to other types of low-quality content which actually impacts users’ content browsing experience most. The aim of our work is to detect low-quality content from the users’ perspective in real time. To define low-quality content comprehensibly, Expectation Maximization (EM) algorithm is first used to coarsely classify low-quality tweets into four categories. Based on this preliminary study, a survey is carefully designed to gather users’ opinions on different categories of low-quality content. Both direct and indirect features including newly proposed features are identified to characterize all types of low-quality content. We then further combine word level analysis with the identified features and build a keyword blacklist dictionary to improve the detection performance. We manually label an extensive Twitter dataset of 100,000 tweets and perform low-quality content detection in real time based on the characterized significant features and word level analysis. The results of our research show that our method has a high accuracy of 0.9711 and a good F1 of 0.8379 based on a random forest classifier with real time performance in the detection of low-quality content in tweets. Our work therefore achieves a positive impact in improving user experience in browsing social media content. PMID:28793347

Improved Conflict Detection for Reducing Operational Errors in Air Traffic Control

NASA Technical Reports Server (NTRS)

Paielli, Russell A.; Erzberger, Hainz

2003-01-01

An operational error is an incident in which an air traffic controller allows the separation between two aircraft to fall below the minimum separation standard. The rates of such errors in the US have increased significantly over the past few years. This paper proposes new detection methods that can help correct this trend by improving on the performance of Conflict Alert, the existing software in the Host Computer System that is intended to detect and warn controllers of imminent conflicts. In addition to the usual trajectory based on the flight plan, a "dead-reckoning" trajectory (current velocity projection) is also generated for each aircraft and checked for conflicts. Filters for reducing common types of false alerts were implemented. The new detection methods were tested in three different ways. First, a simple flightpath command language was developed t o generate precisely controlled encounters for the purpose of testing the detection software. Second, written reports and tracking data were obtained for actual operational errors that occurred in the field, and these were "replayed" to test the new detection algorithms. Finally, the detection methods were used to shadow live traffic, and performance was analysed, particularly with regard to the false-alert rate. The results indicate that the new detection methods can provide timely warnings of imminent conflicts more consistently than Conflict Alert.

On the performance of energy detection-based CR with SC diversity over IG channel

NASA Astrophysics Data System (ADS)

Verma, Pappu Kumar; Soni, Sanjay Kumar; Jain, Priyanka

2017-12-01

Cognitive radio (CR) is a viable 5G technology to address the scarcity of the spectrum. Energy detection-based sensing is known to be the simplest method as far as hardware complexity is concerned. In this paper, the performance of spectrum sensing-based energy detection technique in CR networks over inverse Gaussian channel for selection combining diversity technique is analysed. More specifically, accurate analytical expressions for the average detection probability under different detection scenarios such as single channel (no diversity) and with diversity reception are derived and evaluated. Further, the detection threshold parameter is optimised by minimising the probability of error over several diversity branches. The results clearly show the significant improvement in the probability of detection when optimised threshold parameter is applied. The impact of shadowing parameters on the performance of energy detector is studied in terms of complimentary receiver operating characteristic curve. To verify the correctness of our analysis, the derived analytical expressions are corroborated via exact result and Monte Carlo simulations.

Effect of agitation and terminal subcultures on yield and speed of detection of the Oxoid Signal blood culture system versus the BACTEC radiometric system

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

Weinstein, M.P.; Mirrett, S.; Reimer, L.G.

1989-03-01

In an initial evaluation, we found the Oxoid Signal blood culture system inferior to the BACTEC radiometric system for detection of some microorganisms causing septicemia. To determine whether modified processing of the Oxoid Signal blood culture system could improve its yield and speed of detecting positive cultures relative to the BACTEC radiometric system, we agitated all Oxoid bottles during the first 24 to 48 h of incubation and performed aerobic and anaerobic subcultures of all Oxoid bottles negative after 7 days of incubation. These modifications improved the overall performance of the Oxoid system, particularly with regard to the yield ofmore » streptococci, members of the family Enterobacteriaceae, and Haemophilus, Neisseria, and Acinetobacter spp. The speed of detecting positive cultures also was improved, especially within the first 24 h of incubation. However, the BACTEC system still detected more positive cultures (P less than 0.005), especially of obligate aerobes such as Pseudomonas aeruginosa (P less than 0.05) and yeasts (P less than 0.005). The BACTEC system also detected positive cultures earlier than the Oxoid system (e.g., at 24 h of incubation, 70.5% of BACTEC positive cultures detected versus 62.1% of Oxoid positive cultures detected). Further modifications of the Oxoid system which might include a revised medium, additional processing modifications, altered headspace atmosphere, or a complementary second broth medium should be considered, since the system is attractive in concept and is easy to use in the clinical laboratory.« less

A Metric for Reducing False Positives in the Computer-Aided Detection of Breast Cancer from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Screening Examinations of High-Risk Women.

PubMed

Levman, Jacob E D; Gallego-Ortiz, Cristina; Warner, Ellen; Causer, Petrina; Martel, Anne L

2016-02-01

Magnetic resonance imaging (MRI)-enabled cancer screening has been shown to be a highly sensitive method for the early detection of breast cancer. Computer-aided detection systems have the potential to improve the screening process by standardizing radiologists to a high level of diagnostic accuracy. This retrospective study was approved by the institutional review board of Sunnybrook Health Sciences Centre. This study compares the performance of a proposed method for computer-aided detection (based on the second-order spatial derivative of the relative signal intensity) with the signal enhancement ratio (SER) on MRI-based breast screening examinations. Comparison is performed using receiver operating characteristic (ROC) curve analysis as well as free-response receiver operating characteristic (FROC) curve analysis. A modified computer-aided detection system combining the proposed approach with the SER method is also presented. The proposed method provides improvements in the rates of false positive markings over the SER method in the detection of breast cancer (as assessed by FROC analysis). The modified computer-aided detection system that incorporates both the proposed method and the SER method yields ROC results equal to that produced by SER while simultaneously providing improvements over the SER method in terms of false positives per noncancerous exam. The proposed method for identifying malignancies outperforms the SER method in terms of false positives on a challenging dataset containing many small lesions and may play a useful role in breast cancer screening by MRI as part of a computer-aided detection system.

Detection of Early Ischemic Changes in Noncontrast CT Head Improved with "Stroke Windows".

PubMed

Mainali, Shraddha; Wahba, Mervat; Elijovich, Lucas

2014-01-01

Introduction. Noncontrast head CT (NCCT) is the standard radiologic test for patients presenting with acute stroke. Early ischemic changes (EIC) are often overlooked on initial NCCT. We determine the sensitivity and specificity of improved EIC detection by a standardized method of image evaluation (Stroke Windows). Methods. We performed a retrospective chart review to identify patients with acute ischemic stroke who had NCCT at presentation. EIC was defined by the presence of hyperdense MCA/basilar artery sign; sulcal effacement; basal ganglia/subcortical hypodensity; and loss of cortical gray-white differentiation. NCCT was reviewed with standard window settings and with specialized Stroke Windows. Results. Fifty patients (42% females, 58% males) with a mean NIHSS of 13.4 were identified. EIC was detected in 9 patients with standard windows, while EIC was detected using Stroke Windows in 35 patients (18% versus 70%; P < 0.0001). Hyperdense MCA sign was the most commonly reported EIC; it was better detected with Stroke Windows (14% and 36%; P < 0.0198). Detection of the remaining EIC also improved with Stroke Windows (6% and 46%; P < 0.0001). Conclusions. Detection of EIC has important implications in diagnosis and treatment of acute ischemic stroke. Utilization of Stroke Windows significantly improved detection of EIC.

Investigating prior probabilities in a multiple hypothesis test for use in space domain awareness

NASA Astrophysics Data System (ADS)

Hardy, Tyler J.; Cain, Stephen C.

2016-05-01

The goal of this research effort is to improve Space Domain Awareness (SDA) capabilities of current telescope systems through improved detection algorithms. Ground-based optical SDA telescopes are often spatially under-sampled, or aliased. This fact negatively impacts the detection performance of traditionally proposed binary and correlation-based detection algorithms. A Multiple Hypothesis Test (MHT) algorithm has been previously developed to mitigate the effects of spatial aliasing. This is done by testing potential Resident Space Objects (RSOs) against several sub-pixel shifted Point Spread Functions (PSFs). A MHT has been shown to increase detection performance for the same false alarm rate. In this paper, the assumption of a priori probability used in a MHT algorithm is investigated. First, an analysis of the pixel decision space is completed to determine alternate hypothesis prior probabilities. These probabilities are then implemented into a MHT algorithm, and the algorithm is then tested against previous MHT algorithms using simulated RSO data. Results are reported with Receiver Operating Characteristic (ROC) curves and probability of detection, Pd, analysis.

The energy ratio mapping algorithm: a tool to improve the energy-based detection of odontocete echolocation clicks.

PubMed

Klinck, Holger; Mellinger, David K

2011-04-01

The energy ratio mapping algorithm (ERMA) was developed to improve the performance of energy-based detection of odontocete echolocation clicks, especially for application in environments with limited computational power and energy such as acoustic gliders. ERMA systematically evaluates many frequency bands for energy ratio-based detection of echolocation clicks produced by a target species in the presence of the species mix in a given geographic area. To evaluate the performance of ERMA, a Teager-Kaiser energy operator was applied to the series of energy ratios as derived by ERMA. A noise-adaptive threshold was then applied to the Teager-Kaiser function to identify clicks in data sets. The method was tested for detecting clicks of Blainville's beaked whales while rejecting echolocation clicks of Risso's dolphins and pilot whales. Results showed that the ERMA-based detector correctly identified 81.6% of the beaked whale clicks in an extended evaluation data set. Average false-positive detection rate was 6.3% (3.4% for Risso's dolphins and 2.9% for pilot whales).

Flash memory management system and method utilizing multiple block list windows

NASA Technical Reports Server (NTRS)

Chow, James (Inventor); Gender, Thomas K. (Inventor)

2005-01-01

The present invention provides a flash memory management system and method with increased performance. The flash memory management system provides the ability to efficiently manage and allocate flash memory use in a way that improves reliability and longevity, while maintaining good performance levels. The flash memory management system includes a free block mechanism, a disk maintenance mechanism, and a bad block detection mechanism. The free block mechanism provides efficient sorting of free blocks to facilitate selecting low use blocks for writing. The disk maintenance mechanism provides for the ability to efficiently clean flash memory blocks during processor idle times. The bad block detection mechanism provides the ability to better detect when a block of flash memory is likely to go bad. The flash status mechanism stores information in fast access memory that describes the content and status of the data in the flash disk. The new bank detection mechanism provides the ability to automatically detect when new banks of flash memory are added to the system. Together, these mechanisms provide a flash memory management system that can improve the operational efficiency of systems that utilize flash memory.

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings

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

Frank, Stephen; Heaney, Michael; Jin, Xin

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energymore » models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.« less

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

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

Frank, Stephen; Heaney, Michael; Jin, Xin

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energymore » models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.« less

Improvement of the Xpert Carba-R Kit for the Detection of Carbapenemase-Producing Enterobacteriaceae

PubMed Central

Fusaro, Mathieu

2016-01-01

The Xpert Carba-R kit, version 2 (v2), which has been improved for the efficient detection of blaOXA-181 and blaOXA-232 genes, was tested on a collection of 150 well-characterized enterobacterial isolates that had a reduced susceptibility to carbapenems. The performance of the Xpert Carba-R v2 was high, as it was able to detect the five major carbapenemases (NDM, VIM, IMP, KPC, and OXA-48). Thus, it is now well adapted to the carbapenemase-producing Enterobacteriaceae epidemiology of many countries worldwide. PMID:27021332

The role of fetal-maternal microchimerism as a natural-born healer in integrity improvement of maternal damaged kidney.

PubMed

Kajbafzadeh, Abdol-Mohammad; Sabetkish, Shabnam; Sabetkish, Nastaran

2018-01-01

To identify the fetal stem cell (FSC) response to maternal renal injury with emphasis on renal integrity improvement and Y chromosome detection in damaged maternal kidney. Eight non-green fluorescent protein (GFP) transgenic Sprague- Dawley rats were mated with GFP-positive transgenic male rats. Renal damage was induced on the right kidney at gestational day 11. The same procedure was performed in eight non-pregnant rats as control group. Three months after delivery, right nephrectomy was performed in order to evaluate the injured kidney. The fresh perfused kidneys were stained with anti-GFP antibody. Polymerase chain reaction (PCR) assay was also performed for the Y chromosome detection. Cell culture was performed to detect the GFP-positive cells. Technetium-99m-DMSA renal scan and single-photon emission computed tomography (SPECT) were performed after renal damage induction and 3 months later to evaluate the improvement of renal integrity. The presence of FSCs was confirmed by immune histochemical staining as well as immunofluorescent imaging of the damaged part. Gradient PCR of female rat purified DNA demonstrated the presence of Y-chromosome in the damaged maternal kidney. Moreover, the culture of kidney cells showed GPF- positive cells by immunofluorescence microscopy. The acute renal scar was repaired and the integrity of damaged kidney reached to near normal levels in experimental group as shown in DMSA scan. However, no significant improvement was observed in control group. FSC seems to be the main mechanism in repairing of the maternal renal injury during pregnancy as indicated by Y chromosome and GFP-positive cells in the sub-cultured medium. Copyright® by the International Brazilian Journal of Urology.

Automatic Building Detection based on Supervised Classification using High Resolution Google Earth Images

NASA Astrophysics Data System (ADS)

Ghaffarian, S.; Ghaffarian, S.

2014-08-01

This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervised classification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions. Therefore, the methodology begins with the detection and masking out the shadow areas using luminance component of the LAB color space, which indicates the lightness of the image, and a novel double thresholding technique. Further, the training areas for supervised classification are selected by automatically determining a buffer zone on each building whose shadow is detected by using the shadow shape and the sun illumination direction. Thereafter, by calculating the statistic values of each buffer zone which is collected from the building areas the Improved Parallelepiped Supervised Classification is executed to detect the buildings. Standard deviation thresholding applied to the Parallelepiped classification method to improve its accuracy. Finally, simple morphological operations conducted for releasing the noises and increasing the accuracy of the results. The experiments were performed on set of high resolution Google Earth images. The performance of the proposed approach was assessed by comparing the results of the proposed approach with the reference data by using well-known quality measurements (Precision, Recall and F1-score) to evaluate the pixel-based and object-based performances of the proposed approach. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.4 % and 853 % overall pixel-based and object-based precision performances, respectively.

Can we say: There is a <5% chance a new fish has invaded the St. Louis River? Evolving aquatic invasive species early detection

EPA Science Inventory

The Great Lakes Water Quality Agreement, Annex 6 calls for a U.S.-Canada, basin-wide aquatic invasive species early detection network by 2015. The objective of our research is to explore survey design strategies that can improve detection efficiency, and to develop performance me...

Rotor Smoothing and Vibration Monitoring Results for the US Army VMEP

DTIC Science & Technology

2009-06-01

individual component CI detection thresholds, and development of models for diagnostics, prognostics , and anomaly detection . Figure 16 VMEP Server...and prognostics are of current interest. Development of those systems requires large amounts of data (collection, monitoring , manipulation) to capture...development of automated systems and for continuous updating of algorithms to improve detection , classification, and prognostic performance. A test

[Current status and prospects of gene doping detection].

PubMed

Wang, Wenjun; Zhang, Sichun; Xu, Jingjuan; Xia, Xinghua; Tian, Yaping; Zhang, Xinrong; Chen, Hong-Yuan

2008-07-01

The fast development of biotechnology promotes the development of doping. From recombinant protein to gene doping, there is a great challenge to their detection. The improvement of gene therapy and potential to enhance athletic performance open the door for gene doping. After a brief introduction of the concept of gene doping, the current status and prospects of gene doping detection are reviewed.

Hands-on resonance-enhanced photoacoustic detection

NASA Astrophysics Data System (ADS)

Euler, Manfred

2001-10-01

The design of an improved photoacoustic converter cell using kitchen equipment is described. It operates by changing manually the Helmholtz resonance frequency of bottles by adjusting the distance between the bottleneck and the outer ear. The experiment helps to gain insights in ear performance, in photoacoustic detection methods, in resonance phenomena and their role for detecting small periodic signals in the presence of noise.

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data.

PubMed

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-05-15

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection.

A Knowledge-Based Approach to Automatic Detection of Equipment Alarm Sounds in a Neonatal Intensive Care Unit Environment.

PubMed

Raboshchuk, Ganna; Nadeu, Climent; Jancovic, Peter; Lilja, Alex Peiro; Kokuer, Munevver; Munoz Mahamud, Blanca; Riverola De Veciana, Ana

2018-01-01

A large number of alarm sounds triggered by biomedical equipment occur frequently in the noisy environment of a neonatal intensive care unit (NICU) and play a key role in providing healthcare. In this paper, our work on the development of an automatic system for detection of acoustic alarms in that difficult environment is presented. Such automatic detection system is needed for the investigation of how a preterm infant reacts to auditory stimuli of the NICU environment and for an improved real-time patient monitoring. The approach presented in this paper consists of using the available knowledge about each alarm class in the design of the detection system. The information about the frequency structure is used in the feature extraction stage, and the time structure knowledge is incorporated at the post-processing stage. Several alternative methods are compared for feature extraction, modeling, and post-processing. The detection performance is evaluated with real data recorded in the NICU of the hospital, and by using both frame-level and period-level metrics. The experimental results show that the inclusion of both spectral and temporal information allows to improve the baseline detection performance by more than 60%.

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data

PubMed Central

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-01-01

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection. PMID:28505135

An incremental anomaly detection model for virtual machines.

PubMed

Zhang, Hancui; Chen, Shuyu; Liu, Jun; Zhou, Zhen; Wu, Tianshu

2017-01-01

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.

An incremental anomaly detection model for virtual machines

PubMed Central

Zhang, Hancui; Chen, Shuyu; Liu, Jun; Zhou, Zhen; Wu, Tianshu

2017-01-01

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform. PMID:29117245

A Knowledge-Based Approach to Automatic Detection of Equipment Alarm Sounds in a Neonatal Intensive Care Unit Environment

PubMed Central

Nadeu, Climent; Jančovič, Peter; Lilja, Alex Peiró; Köküer, Münevver; Muñoz Mahamud, Blanca; Riverola De Veciana, Ana

2018-01-01

A large number of alarm sounds triggered by biomedical equipment occur frequently in the noisy environment of a neonatal intensive care unit (NICU) and play a key role in providing healthcare. In this paper, our work on the development of an automatic system for detection of acoustic alarms in that difficult environment is presented. Such automatic detection system is needed for the investigation of how a preterm infant reacts to auditory stimuli of the NICU environment and for an improved real-time patient monitoring. The approach presented in this paper consists of using the available knowledge about each alarm class in the design of the detection system. The information about the frequency structure is used in the feature extraction stage, and the time structure knowledge is incorporated at the post-processing stage. Several alternative methods are compared for feature extraction, modeling, and post-processing. The detection performance is evaluated with real data recorded in the NICU of the hospital, and by using both frame-level and period-level metrics. The experimental results show that the inclusion of both spectral and temporal information allows to improve the baseline detection performance by more than 60%. PMID:29404227

Feasibility of fast neutron analysis for the detection of explosives buried in soil

NASA Astrophysics Data System (ADS)

Faust, A. A.; McFee, J. E.; Bowman, C. L.; Mosquera, C.; Andrews, H. R.; Kovaltchouk, V. D.; Ing, H.

2011-12-01

A commercialized thermal neutron analysis (TNA) sensor has been developed to confirm the presence of buried bulk explosives as part of a multi-sensor anti-tank landmine detection system. Continuing improvements to the TNA system have included the use of an electronic pulsed neutron generator that offers the possibility of applying fast neutron analysis (FNA) methods to improve the system's detection capability. This paper describes an investigation into the use of FNA as a complementary component in such a TNA system. The results of a modeling study using simple geometries and a full model of the TNA sensor head are presented, as well as preliminary results from an experimental associated particle imaging (API) system that supports the modeling study results. The investigation has concluded that the pulsed beam FNA approach would not improve the detection performance of a TNA system for landmine or buried IED detection in a confirmation role, and could not be made into a practical stand-alone detection system for buried anti-tank landmines. Detection of buried landmines and IEDs by FNA remains a possibility, however, through the use of the API technique.

A novel rail defect detection method based on undecimated lifting wavelet packet transform and Shannon entropy-improved adaptive line enhancer

NASA Astrophysics Data System (ADS)

Hao, Qiushi; Zhang, Xin; Wang, Yan; Shen, Yi; Makis, Viliam

2018-07-01

Acoustic emission (AE) technology is sensitive to subliminal rail defects, however strong wheel-rail contact rolling noise under high-speed condition has gravely impeded detecting of rail defects using traditional denoising methods. In this context, the paper develops an adaptive detection method for rail cracks, which combines multiresolution analysis with an improved adaptive line enhancer (ALE). To obtain elaborate multiresolution information of transient crack signals with low computational cost, lifting scheme-based undecimated wavelet packet transform is adopted. In order to feature the impulsive property of crack signals, a Shannon entropy-improved ALE is proposed as a signal enhancing approach, where Shannon entropy is introduced to improve the cost function. Then a rail defect detection plan based on the proposed method for high-speed condition is put forward. From theoretical analysis and experimental verification, it is demonstrated that the proposed method has superior performance in enhancing the rail defect AE signal and reducing the strong background noise, offering an effective multiresolution approach for rail defect detection under high-speed and strong-noise condition.

Network anomaly detection system with optimized DS evidence theory.

PubMed

Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

2014-01-01

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network-complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each sensor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.

Detection of nuclear resonance signals: modification of the receiver operating characteristics using feedback.

PubMed

Blauch, A J; Schiano, J L; Ginsberg, M D

2000-06-01

The performance of a nuclear resonance detection system can be quantified using binary detection theory. Within this framework, signal averaging increases the probability of a correct detection and decreases the probability of a false alarm by reducing the variance of the noise in the average signal. In conjunction with signal averaging, we propose another method based on feedback control concepts that further improves detection performance. By maximizing the nuclear resonance signal amplitude, feedback raises the probability of correct detection. Furthermore, information generated by the feedback algorithm can be used to reduce the probability of false alarm. We discuss the advantages afforded by feedback that cannot be obtained using signal averaging. As an example, we show how this method is applicable to the detection of explosives using nuclear quadrupole resonance. Copyright 2000 Academic Press.

Network Anomaly Detection System with Optimized DS Evidence Theory

PubMed Central

Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

2014-01-01

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly. PMID:25254258

Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection.

PubMed

van Zelst, J C M; Tan, T; Platel, B; de Jong, M; Steenbakkers, A; Mourits, M; Grivegnee, A; Borelli, C; Karssemeijer, N; Mann, R M

2017-04-01

To investigate the effect of dedicated Computer Aided Detection (CAD) software for automated breast ultrasound (ABUS) on the performance of radiologists screening for breast cancer. 90 ABUS views of 90 patients were randomly selected from a multi-institutional archive of cases collected between 2010 and 2013. This dataset included normal cases (n=40) with >1year of follow up, benign (n=30) lesions that were either biopsied or remained stable, and malignant lesions (n=20). Six readers evaluated all cases with and without CAD in two sessions. CAD-software included conventional CAD-marks and an intelligent minimum intensity projection of the breast tissue. Readers reported using a likelihood-of-malignancy scale from 0 to 100. Alternative free-response ROC analysis was used to measure the performance. Without CAD, the average area-under-the-curve (AUC) of the readers was 0.77 and significantly improved with CAD to 0.84 (p=0.001). Sensitivity of all readers improved (range 5.2-10.6%) by using CAD but specificity decreased in four out of six readers (range 1.4-5.7%). No significant difference was observed in the AUC between experienced radiologists and residents both with and without CAD. Dedicated CAD-software for ABUS has the potential to improve the cancer detection rates of radiologists screening for breast cancer. Copyright © 2017 Elsevier B.V. All rights reserved.

Studies of recognition with multitemporal remote sensor data

NASA Technical Reports Server (NTRS)

Malila, W. A.; Hieber, R. H.; Cicone, R. C.

1975-01-01

Characteristics of multitemporal data and their use in recognition processing were investigated. Principal emphasis was on satellite data collected by the LANDSAT multispectral scanner and on temporal changes throughout a growing season. The effects of spatial misregistration on recognition performance with multitemporal data were examined. A capability to compute probabilities of detection and false alarm was developed and used with simulated distributions for misregistered pixels. Wheat detection was found to be degraded and false alarms increased by misregistration effects. Multitemporal signature characteristics and multitemporal recognition processing were studied to gain insights into problems associated with this approach and possible improvements. Recognition performance with one multitemporal data set displayed marked improvements over results from single-time data.

Simulations of coronagraphy with a dynamic hologram for the direct detection of exo-planets

NASA Astrophysics Data System (ADS)

Ricci, Davide; Le Coroller, Hervé; Labeyrie, Antoine; Piron, Pierre

2010-07-01

In a previous paper,1 we discussed an original solution to improve the performances of coronagraphs by adding, in the optical scheme, an adaptive hologram removing most of the residual speckle starlight. In our simulations, the detection limit in the flux ratio between a host star and a very near planet (5λ/D) improves over a factor 1000 (resp. 10000) when equipped with a hologram for cases of wavefront bumpiness imperfections of λ/20 (resp. λ/100). We derive, in this paper, the transmission accuracy required on the hologram pixels to achieve such goals. We show that preliminary tests could be performed on the basis of existing technologies.

"Dip-and-read" paper-based analytical devices using distance-based detection with color screening.

PubMed

Yamada, Kentaro; Citterio, Daniel; Henry, Charles S

2018-05-15

An improved paper-based analytical device (PAD) using color screening to enhance device performance is described. Current detection methods for PADs relying on the distance-based signalling motif can be slow due to the assay time being limited by capillary flow rates that wick fluid through the detection zone. For traditional distance-based detection motifs, analysis can take up to 45 min for a channel length of 5 cm. By using a color screening method, quantification with a distance-based PAD can be achieved in minutes through a "dip-and-read" approach. A colorimetric indicator line deposited onto a paper substrate using inkjet-printing undergoes a concentration-dependent colorimetric response for a given analyte. This color intensity-based response has been converted to a distance-based signal by overlaying a color filter with a continuous color intensity gradient matching the color of the developed indicator line. As a proof-of-concept, Ni quantification in welding fume was performed as a model assay. The results of multiple independent user testing gave mean absolute percentage error and average relative standard deviations of 10.5% and 11.2% respectively, which were an improvement over analysis based on simple visual color comparison with a read guide (12.2%, 14.9%). In addition to the analytical performance comparison, an interference study and a shelf life investigation were performed to further demonstrate practical utility. The developed system demonstrates an alternative detection approach for distance-based PADs enabling fast (∼10 min), quantitative, and straightforward assays.

Applying model abstraction techniques to optimize monitoring networks for detecting subsurface contaminant transport

USDA-ARS?s Scientific Manuscript database

Improving strategies for monitoring subsurface contaminant transport includes performance comparison of competing models, developed independently or obtained via model abstraction. Model comparison and parameter discrimination involve specific performance indicators selected to better understand s...

Efficacy of Stochastic Vestibular Stimulation to Improve Locomotor Performance in a Discordant Sensory Environment

NASA Technical Reports Server (NTRS)

Temple, D. R.; De Dios, Y. E.; Layne, C. S.; Bloomberg, J. J.; Mulavara, A. P.

2016-01-01

Astronauts exposed to microgravity face sensorimotor challenges incurred when readapting to a gravitational environment. Sensorimotor Adaptability (SA) training has been proposed as a countermeasure to improve locomotor performance during re-adaptation, and it is suggested that the benefits of SA training may be further enhanced by improving detection of weak sensory signals via mechanisms such as stochastic resonance when a non-zero level of stochastic white noise based electrical stimulation is applied to the vestibular system (stochastic vestibular stimulation, SVS). The purpose of this study was to test the efficacy of using SVS to improve short-term adaptation in a sensory discordant environment during performance of a locomotor task.

Automated Detection of Sepsis Using Electronic Medical Record Data: A Systematic Review.

PubMed

Despins, Laurel A

Severe sepsis and septic shock are global issues with high mortality rates. Early recognition and intervention are essential to optimize patient outcomes. Automated detection using electronic medical record (EMR) data can assist this process. This review describes automated sepsis detection using EMR data. PubMed retrieved publications between January 1, 2005 and January 31, 2015. Thirteen studies met study criteria: described an automated detection approach with the potential to detect sepsis or sepsis-related deterioration in real or near-real time; focused on emergency department and hospitalized neonatal, pediatric, or adult patients; and provided performance measures or results indicating the impact of automated sepsis detection. Detection algorithms incorporated systemic inflammatory response and organ dysfunction criteria. Systems in nine studies generated study or care team alerts. Care team alerts did not consistently lead to earlier interventions. Earlier interventions did not consistently translate to improved patient outcomes. Performance measures were inconsistent. Automated sepsis detection is potentially a means to enable early sepsis-related therapy but current performance variability highlights the need for further research.

An electromagnetic induction method for underground target detection and characterization

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

Bartel, L.C.; Cress, D.H.

1997-01-01

An improved capability for subsurface structure detection is needed to support military and nonproliferation requirements for inspection and for surveillance of activities of threatening nations. As part of the DOE/NN-20 program to apply geophysical methods to detect and characterize underground facilities, Sandia National Laboratories (SNL) initiated an electromagnetic induction (EMI) project to evaluate low frequency electromagnetic (EM) techniques for subsurface structure detection. Low frequency, in this case, extended from kilohertz to hundreds of kilohertz. An EMI survey procedure had already been developed for borehole imaging of coal seams and had successfully been applied in a surface mode to detect amore » drug smuggling tunnel. The SNL project has focused on building upon the success of that procedure and applying it to surface and low altitude airborne platforms. Part of SNL`s work has focused on improving that technology through improved hardware and data processing. The improved hardware development has been performed utilizing Laboratory Directed Research and Development (LDRD) funding. In addition, SNL`s effort focused on: (1) improvements in modeling of the basic geophysics of the illuminating electromagnetic field and its coupling to the underground target (partially funded using LDRD funds) and (2) development of techniques for phase-based and multi-frequency processing and spatial processing to support subsurface target detection and characterization. The products of this project are: (1) an evaluation of an improved EM gradiometer, (2) an improved gradiometer concept for possible future development, (3) an improved modeling capability, (4) demonstration of an EM wave migration method for target recognition, and a demonstration that the technology is capable of detecting targets to depths exceeding 25 meters.« less

Immunodiagnosis of Human Fascioliasis: An Update of Concepts and Performances of the Serological Assays

PubMed Central

Khabisi, Samaneh Abdolahi

2017-01-01

Human Fascioliasis (HF) is a foodborne neglected parasitic disease caused by Fasciola hepatica and Fasciola gigantica. New epidemiological data suggest that the endemic areas of the disease are expanding and HF is being reported from areas where it was previously not observed. Diagnosis of HF is challenging. Performances of parasitological approaches, based on the detection of parasite’s egg in the stool, are not satisfactory. Currently serological methods for the diagnosis of HF are mainly based on detection of anti-Fasciola antibodies in serum. Although, there have been some improvement in the development of immunological diagnostic tests for the diagnosis of HF, yet these tests suffer from insufficiency in sensitivity or/and specificity. Detection of antigens, rather than antibodies, seems to be a suitable approach in the diagnosis of HF. Antigen can be detected in sera or stool of the fascioliasis patients. Circulating antigen in serum disappears within a short time and most of the circulating antigens are in immune complex forms which are not freely available to be detected. Therefore, antigenemia might not be an appropriate method for the diagnosis of HF. Detection of antigen in stool (coproantigens) seems to be a suitable alternative method for the diagnosis of HF. Recent data provided convincing evidence that detection of coproantigen improved and simplified the diagnosis of HF. The present review highlights the new achievements in designing and improvement of diagnostic approaches for the immunodiagnosis of HF. Moreover, current status of the available immunodiagnostic techniques for the diagnosis of HF, their strengths and weaknesses has been discussed. PMID:28764235

A systematic review: Performance of RDTs for the detection of Plasmodium knowlesi, Plasmodium malariae, and Plasmodium ovale mono-infections in human blood.

PubMed

Yerlikaya, Seda; Campillo, Ana; Gonzalez, Iveth J

2018-03-15

Despite the increased use and worldwide distribution of malaria rapid diagnostic tests (RDTs) which distinguish between Plasmodium falciparum and non-falciparum species, little is known about their performance for detecting Plasmodium knowlesi (Pk), Plasmodium malariae (Pm), and Plasmodium ovale (Po). The objective of this review is to analyze results of published studies evaluating the diagnostic accuracy of malaria RDTs in detecting Pk, Pm and Po mono-infections.MEDLINE, EMBASE, Web of Science and CENTRAL databases were systematically searched to identify studies which reported on the performance of RDTs in detecting Pk, Pm,Po mono-infections.Among 40 studies included in the review, three reported on Pk, eight on Pm, five on Po, one on Pk and Pm, and 23 on Pm and Po infections. In the meta-analysis, estimates of sensitivities of RDTs in detecting Pk infections ranged from 2% to 48%. Test performances for Pm and Po infections were less accurate and highly heterogeneous, mainly due to the small number of samples tested.Limited data available suggest that malaria RDTs show suboptimal performance for detecting Pk, Pm,Po infections. New improved RDTs as well as appropriately designed, cross-sectional studies to demonstrate their usefulness in the detection of neglected Plasmodium species, are urgently needed.

Blood doping: risks to athletes' health and strategies for detection.

PubMed

Oliveira, Carolina Dizioli Rodrigues de; Bairros, André Valle de; Yonamine, Mauricio

2014-07-01

Blood doping has been defined as the misuse of substances or certain techniques to optimize oxygen delivery to muscles with the aim to increase performance in sports activities. It includes blood transfusion, administration of erythropoiesis-stimulating agents or blood substitutes, and gene manipulations. The main reasons for the widespread use of blood doping include: its availability for athletes (erythropoiesis-stimulating agents and blood transfusions), its efficiency in improving performance, and its difficult detection. This article reviews and discusses the blood doping substances and methods used for in sports, the adverse effects related to this practice, and current strategies for its detection.

Performance comparison of single and dual-excitation-wavelength resonance-Raman explosives detectors

NASA Astrophysics Data System (ADS)

Yellampalle, Balakishore; Martin, Robert; Witt, Kenneth; McCormick, William; Wu, Hai-Shan; Sluch, Mikhail; Ice, Robert; Lemoff, Brian

2017-05-01

Deep-ultraviolet Raman spectroscopy is a very useful approach for standoff detection of explosive traces. Using two simultaneous excitation wavelengths improves the specificity and sensitivity to standoff explosive detection. The High Technology Foundation developed a highly compact prototype of resonance Raman explosives detector. In this work, we discuss the relative performance of a dual-excitation sensor compared to a single-excitation sensor. We present trade space analysis comparing three representative Raman systems with similar size, weight, and power. The analysis takes into account, cost, spectral resolution, detection/identification time and the overall system benefit.

Deep belief networks for false alarm rejection in forward-looking ground-penetrating radar

NASA Astrophysics Data System (ADS)

Becker, John; Havens, Timothy C.; Pinar, Anthony; Schulz, Timothy J.

2015-05-01

Explosive hazards are one of the most deadly threats in modern conflicts. The U.S. Army is interested in a reliable way to detect these hazards at range. A promising way of accomplishing this task is using a forward-looking ground-penetrating radar (FLGPR) system. Recently, the Army has been testing a system that utilizes both L-band and X-band radar arrays on a vehicle mounted platform. Using data from this system, we sought to improve the performance of a constant false-alarm-rate (CFAR) prescreener through the use of a deep belief network (DBN). DBNs have also been shown to perform exceptionally well at generalized anomaly detection. They combine unsupervised pre-training with supervised fine-tuning to generate low-dimensional representations of high-dimensional input data. We seek to take advantage of these two properties by training a DBN on the features of the CFAR prescreener's false alarms (FAs) and then use that DBN to separate FAs from true positives. Our analysis shows that this method improves the detection statistics significantly. By training the DBN on a combination of image features, we were able to significantly increase the probability of detection while maintaining a nominal number of false alarms per square meter. Our research shows that DBNs are a good candidate for improving detection rates in FLGPR systems.

Relationships Between Long-Range Lightning Networks and TRMM/LIS Observations

NASA Technical Reports Server (NTRS)

Rudlosky, Scott D.; Holzworth, Robert H.; Carey, Lawrence D.; Schultz, Chris J.; Bateman, Monte; Cummins, Kenneth L.; Cummins, Kenneth L.; Blakeslee, Richard J.; Goodman, Steven J.

2012-01-01

Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. The present study intercompares long-range lightning data with observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study examines network detection efficiency and location accuracy relative to LIS observations, describes spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by the long-range networks. Improved knowledge of relationships between these datasets will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM).

Application of Microwave Irradiation and Heat to Improve Gliadin Detection and Ricin ELISA Throughput with Food Samples.

PubMed

Garber, Eric A E; Thole, Joseph

2015-06-11

The utility of microwave irradiation to accelerate the onset of equilibrium and improve ELISA performance was examined using ELISAs for the detection of the plant toxin ricin and gliadin. The ricin ELISA normally requires several one hour incubations at 37 °C, a total assay time of approximately five hours, and employs a complex buffer containing PBS, Tween-20®, and non-fat milk. Different energy levels and pulse designs were compared to the use of abbreviated incubation times at 37 °C for the detection of ricin in food. The use of microwave irradiation had no significant advantage over the application of heat using an oven incubator and performed worse with some foods. In contrast, a gliadin ELISA that relied on 30 min incubation steps at room temperature and a salt-based buffer performed better upon irradiation but also displayed improvement upon incubating the microtiter plate at 37 °C. Whether microwave irradiation was advantageous compared to incubation in an oven was inconclusive. However, by abbreviating the incubation time of the ricin ELISA, it was possible to cut the assay time to less than 2 hours and still display LOD values < 10 ppb and recoveries of 78%-98%.

Face detection on distorted images using perceptual quality-aware features

NASA Astrophysics Data System (ADS)

Gunasekar, Suriya; Ghosh, Joydeep; Bovik, Alan C.

2014-02-01

We quantify the degradation in performance of a popular and effective face detector when human-perceived image quality is degraded by distortions due to additive white gaussian noise, gaussian blur or JPEG compression. It is observed that, within a certain range of perceived image quality, a modest increase in image quality can drastically improve face detection performance. These results can be used to guide resource or bandwidth allocation in a communication/delivery system that is associated with face detection tasks. A new face detector based on QualHOG features is also proposed that augments face-indicative HOG features with perceptual quality-aware spatial Natural Scene Statistics (NSS) features, yielding improved tolerance against image distortions. The new detector provides statistically significant improvements over a strong baseline on a large database of face images representing a wide range of distortions. To facilitate this study, we created a new Distorted Face Database, containing face and non-face patches from images impaired by a variety of common distortion types and levels. This new dataset is available for download and further experimentation at www.ideal.ece.utexas.edu/˜suriya/DFD/.

Object form discontinuity facilitates displacement discrimination across saccades.

PubMed

Demeyer, Maarten; De Graef, Peter; Wagemans, Johan; Verfaillie, Karl

2010-06-01

Stimulus displacements coinciding with a saccadic eye movement are poorly detected by human observers. In recent years, converging evidence has shown that this phenomenon does not result from poor transsaccadic retention of presaccadic stimulus position information, but from the visual system's efforts to spatially align presaccadic and postsaccadic perception on the basis of visual landmarks. It is known that this process can be disrupted, and transsaccadic displacement detection performance can be improved, by briefly blanking the stimulus display during and immediately after the saccade. In the present study, we investigated whether this improvement could also follow from a discontinuity in the task-irrelevant form of the displaced stimulus. We observed this to be the case: Subjects more accurately identified the direction of intrasaccadic displacements when the displaced stimulus simultaneously changed form, compared to conditions without a form change. However, larger improvements were still observed under blanking conditions. In a second experiment, we show that facilitation induced by form changes and blanks can combine. We conclude that a strong assumption of visual stability underlies the suppression of transsaccadic change detection performance, the rejection of which generalizes from stimulus form to stimulus position.

Research of the absorbance detection and fluorescence detection for multifunctional nutrition analyzer

NASA Astrophysics Data System (ADS)

Ni, Zhengyuan; Yan, Huimin; Ni, Xuxiang; Zhang, Xiuda

2017-10-01

The research of the multifunctional analyzer which integrates absorbance detection, fluorescence detection, time-resolved fluorescence detection, biochemical luminescence detection methods, can make efficient detection and analysis for a variety of human body nutrients. This article focuses on the absorbance detection and fluorescence detection system. The two systems are modular in design and controlled by embedded system, to achieve automatic measurement according to user settings. In the optical path design, the application of confocal design can improve the optical signal acquisition capability, and reduce the interference. A photon counter is used for detection, and a high performance counter module is designed to measure the output of photon counter. In the experiment, we use neutral density filters and potassium dichromate solution to test the absorbance detection system, and use fluorescein isothiocyanate FITC for fluorescence detection system performance test. The experimental results show that the absorbance detection system has a detection range of 0 4OD, and has good linearity in the detection range, while the fluorescence detection system has a high sensitivity of 1pmol/L concentration.

The Detection and Photometric Redshift Determination of Distant Galaxies using SIRTF's Infrared Array Camera

NASA Technical Reports Server (NTRS)

Simpson, C.; Eisenhardt, P.

1998-01-01

We investigate the ability of the Space Infrared Telescope Facility's Infrared Array Camera to detect distant (z3) galaxies and measure their photometric redshifts. Our analysis shows that changing the original long wavelength filter specifications provides significant improvements in performance in this and other areas.

Drug drug interaction extraction from the literature using a recursive neural network

PubMed Central

Lim, Sangrak; Lee, Kyubum

2018-01-01

Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI information from the literature is a laborious task. However, text mining can be used to find DDIs in the biomedical literature. Among the recently developed neural networks, we use a Recursive Neural Network to improve the performance of DDI extraction. Our recursive neural network model uses a position feature, a subtree containment feature, and an ensemble method to improve the performance of DDI extraction. Compared with the state-of-the-art models, the DDI detection and type classifiers of our model performed 4.4% and 2.8% better, respectively, on the DDIExtraction Challenge’13 test data. We also validated our model on the PK DDI corpus that consists of two types of DDIs data: in vivo DDI and in vitro DDI. Compared with the existing model, our detection classifier performed 2.3% and 6.7% better on in vivo and in vitro data respectively. The results of our validation demonstrate that our model can automatically extract DDIs better than existing models. PMID:29373599

Determining the Requisite Components of Visual Threat Detection to Improve Operational Performance

DTIC Science & Technology

2014-04-01

cognitive processes, and may be enhanced by focusing training development on the principle components such as causal reasoning. The second report will...discuss the development and evaluation of a research-based training exemplar. Visual threat detection pervades many military contexts, but is also... developing computer-controlled exercises to study the primary components of visual threat detection. Similarly, civilian law enforcement officers were

Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection.

PubMed

Bogaarts, J G; Hilkman, D M W; Gommer, E D; van Kranen-Mastenbroek, V H J M; Reulen, J P H

2016-12-01

Continuous electroencephalographic monitoring of critically ill patients is an established procedure in intensive care units. Seizure detection algorithms, such as support vector machines (SVM), play a prominent role in this procedure. To correct for inter-human differences in EEG characteristics, as well as for intra-human EEG variability over time, dynamic EEG feature normalization is essential. Recently, the median decaying memory (MDM) approach was determined to be the best method of normalization. MDM uses a sliding baseline buffer of EEG epochs to calculate feature normalization constants. However, while this method does include non-seizure EEG epochs, it also includes EEG activity that can have a detrimental effect on the normalization and subsequent seizure detection performance. In this study, EEG data that is to be incorporated into the baseline buffer are automatically selected based on a novelty detection algorithm (Novelty-MDM). Performance of an SVM-based seizure detection framework is evaluated in 17 long-term ICU registrations using the area under the sensitivity-specificity ROC curve. This evaluation compares three different EEG normalization methods, namely a fixed baseline buffer (FB), the median decaying memory (MDM) approach, and our novelty median decaying memory (Novelty-MDM) method. It is demonstrated that MDM did not improve overall performance compared to FB (p < 0.27), partly because seizure like episodes were included in the baseline. More importantly, Novelty-MDM significantly outperforms both FB (p = 0.015) and MDM (p = 0.0065).

Improved space object detection using short-exposure image data with daylight background.

PubMed

Becker, David; Cain, Stephen

2018-05-10

Space object detection is of great importance in the highly dependent yet competitive and congested space domain. The detection algorithms employed play a crucial role in fulfilling the detection component in the space situational awareness mission to detect, track, characterize, and catalog unknown space objects. Many current space detection algorithms use a matched filter or a spatial correlator on long-exposure data to make a detection decision at a single pixel point of a spatial image based on the assumption that the data follow a Gaussian distribution. Long-exposure imaging is critical to detection performance in these algorithms; however, for imaging under daylight conditions, it becomes necessary to create a long-exposure image as the sum of many short-exposure images. This paper explores the potential for increasing detection capabilities for small and dim space objects in a stack of short-exposure images dominated by a bright background. The algorithm proposed in this paper improves the traditional stack and average method of forming a long-exposure image by selectively removing short-exposure frames of data that do not positively contribute to the overall signal-to-noise ratio of the averaged image. The performance of the algorithm is compared to a traditional matched filter detector using data generated in MATLAB as well as laboratory-collected data. The results are illustrated on a receiver operating characteristic curve to highlight the increased probability of detection associated with the proposed algorithm.

[Detection of endpoint for segmentation between consonants and vowels in aphasia rehabilitation software based on artificial intelligence scheduling].

PubMed

Deng, Xingjuan; Chen, Ji; Shuai, Jie

2009-08-01

For the purpose of improving the efficiency of aphasia rehabilitation training, artificial intelligence-scheduling function is added in the aphasia rehabilitation software, and the software's performance is improved. With the characteristics of aphasia patient's voice as well as with the need of artificial intelligence-scheduling functions under consideration, the present authors have designed a set of endpoint detection algorithm. It determines the reference endpoints, then extracts every word and ensures the reasonable segmentation points between consonants and vowels, using the reference endpoints. The results of experiments show that the algorithm is able to attain the objects of detection at a higher accuracy rate. Therefore, it is applicable to the detection of endpoint on aphasia-patient's voice.

Ergonomics for enhancing detection of machine abnormalities.

PubMed

Illankoon, Prasanna; Abeysekera, John; Singh, Sarbjeet

2016-10-17

Detecting abnormal machine conditions is of great importance in an autonomous maintenance environment. Ergonomic aspects can be invaluable when detection of machine abnormalities using human senses is examined. This research outlines the ergonomic issues involved in detecting machine abnormalities and suggests how ergonomics would improve such detections. Cognitive Task Analysis was performed in a plant in Sri Lanka where Total Productive Maintenance is being implemented to identify sensory types that would be used to detect machine abnormalities and relevant Ergonomic characteristics. As the outcome of this research, a methodology comprising of an Ergonomic Gap Analysis Matrix for machine abnormality detection is presented.

Toward detection of marine vehicles on horizon from buoy camera

NASA Astrophysics Data System (ADS)

Fefilatyev, Sergiy; Goldgof, Dmitry B.; Langebrake, Lawrence

2007-10-01

This paper presents a new technique for automatic detection of marine vehicles in open sea from a buoy camera system using computer vision approach. Users of such system include border guards, military, port safety and flow management, sanctuary protection personnel. The system is intended to work autonomously, taking images of the surrounding ocean surface and analyzing them on the subject of presence of marine vehicles. The goal of the system is to detect an approximate window around the ship and prepare the small image for transmission and human evaluation. The proposed computer vision-based algorithm combines horizon detection method with edge detection and post-processing. The dataset of 100 images is used to evaluate the performance of proposed technique. We discuss promising results of ship detection and suggest necessary improvements for achieving better performance.

Efficient coding and detection of ultra-long IDs for visible light positioning systems.

PubMed

Zhang, Hualong; Yang, Chuanchuan

2018-05-14

Visible light positioning (VLP) is a promising technique to complement Global Navigation Satellite System (GNSS) such as Global positioning system (GPS) and BeiDou Navigation Satellite System (BDS) which features the advantage of low-cost and high accuracy. The situation becomes even more crucial for indoor environments, where satellite signals are weak or even unavailable. For large-scale application of VLP, there would be a considerable number of Light emitting diode (LED) IDs, which bring forward the demand of long LED ID detection. In particular, to provision indoor localization globally, a convenient way is to program a unique ID into each LED during manufacture. This poses a big challenge for image sensors, such as the CMOS camera in everybody's hands since the long ID covers the span of multiple frames. In this paper, we investigate the detection of ultra-long ID using rolling shutter cameras. By analyzing the pattern of data loss in each frame, we proposed a novel coding technique to improve the efficiency of LED ID detection. We studied the performance of Reed-Solomon (RS) code in this system and designed a new coding method which considered the trade-off between performance and decoding complexity. Coding technique decreases the number of frames needed in data processing, significantly reduces the detection time, and improves the accuracy of detection. Numerical and experimental results show that the detected LED ID can be much longer with the coding technique. Besides, our proposed coding method is proved to achieve a performance close to that of RS code while the decoding complexity is much lower.

Enhanced thermal and pyroelectric properties in 0-3 TGS:PVDF composites doped with graphene for infrared application

NASA Astrophysics Data System (ADS)

Feng, Xiaodong; Wang, Minqiang; Li, Le; Yang, Zhi; Cao, Minghui; Cheng, Z.-Y.

Pyroelectric composites of triglycine sulfate (TGS)-polyvinylidene difluoride (PVDF) doped with graphene are studied. It is found that the graphene can effectively improve the polling efficiency and thermal property of the composites so that the infrared detective performance can be significantly improved. For example, by adding about 0.83 wt.% of graphene, the infrared detective property can be improved by more than 30%. It is also found that the size of the graphene plays a critical role on the property improvement. For example, the small-sized graphene prepared by ultrasonic exfoliation (UE) method is more effective than the big-sized graphene prepared by electrochemical exfoliation (EE) method.

Improvement of the Xpert Carba-R Kit for the Detection of Carbapenemase-Producing Enterobacteriaceae.

PubMed

Dortet, Laurent; Fusaro, Mathieu; Naas, Thierry

2016-06-01

The Xpert Carba-R kit, version 2 (v2), which has been improved for the efficient detection of blaOXA-181 and blaOXA-232 genes, was tested on a collection of 150 well-characterized enterobacterial isolates that had a reduced susceptibility to carbapenems. The performance of the Xpert Carba-R v2 was high, as it was able to detect the five major carbapenemases (NDM, VIM, IMP, KPC, and OXA-48). Thus, it is now well adapted to the carbapenemase-producing Enterobacteriaceae epidemiology of many countries worldwide. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Transcranial alternating current stimulation modulates auditory temporal resolution in elderly people.

PubMed

Baltus, Alina; Vosskuhl, Johannes; Boetzel, Cindy; Herrmann, Christoph Siegfried

2018-05-13

Recent research provides evidence for a functional role of brain oscillations for perception. For example, auditory temporal resolution seems to be linked to individual gamma frequency of auditory cortex. Individual gamma frequency not only correlates with performance in between-channel gap detection tasks but can be modulated via auditory transcranial alternating current stimulation. Modulation of individual gamma frequency is accompanied by an improvement in gap detection performance. Aging changes electrophysiological frequency components and sensory processing mechanisms. Therefore, we conducted a study to investigate the link between individual gamma frequency and gap detection performance in elderly people using auditory transcranial alternating current stimulation. In a within-subject design, twelve participants were electrically stimulated with two individualized transcranial alternating current stimulation frequencies: 3 Hz above their individual gamma frequency (experimental condition) and 4 Hz below their individual gamma frequency (control condition) while they were performing a between-channel gap detection task. As expected, individual gamma frequencies correlated significantly with gap detection performance at baseline and in the experimental condition, transcranial alternating current stimulation modulated gap detection performance. In the control condition, stimulation did not modulate gap detection performance. In addition, in elderly, the effect of transcranial alternating current stimulation on auditory temporal resolution seems to be dependent on endogenous frequencies in auditory cortex: elderlies with slower individual gamma frequencies and lower auditory temporal resolution profit from auditory transcranial alternating current stimulation and show increased gap detection performance during stimulation. Our results strongly suggest individualized transcranial alternating current stimulation protocols for successful modulation of performance. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

NASA Astrophysics Data System (ADS)

Sa, Qila; Wang, Zhihui

2018-03-01

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

A Comparative Study of Unsupervised Anomaly Detection Techniques Using Honeypot Data

NASA Astrophysics Data System (ADS)

Song, Jungsuk; Takakura, Hiroki; Okabe, Yasuo; Inoue, Daisuke; Eto, Masashi; Nakao, Koji

Intrusion Detection Systems (IDS) have been received considerable attention among the network security researchers as one of the most promising countermeasures to defend our crucial computer systems or networks against attackers on the Internet. Over the past few years, many machine learning techniques have been applied to IDSs so as to improve their performance and to construct them with low cost and effort. Especially, unsupervised anomaly detection techniques have a significant advantage in their capability to identify unforeseen attacks, i.e., 0-day attacks, and to build intrusion detection models without any labeled (i.e., pre-classified) training data in an automated manner. In this paper, we conduct a set of experiments to evaluate and analyze performance of the major unsupervised anomaly detection techniques using real traffic data which are obtained at our honeypots deployed inside and outside of the campus network of Kyoto University, and using various evaluation criteria, i.e., performance evaluation by similarity measurements and the size of training data, overall performance, detection ability for unknown attacks, and time complexity. Our experimental results give some practical and useful guidelines to IDS researchers and operators, so that they can acquire insight to apply these techniques to the area of intrusion detection, and devise more effective intrusion detection models.

Enhancing Community Detection By Affinity-based Edge Weighting Scheme

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

Yoo, Andy; Sanders, Geoffrey; Henson, Van

Community detection refers to an important graph analytics problem of finding a set of densely-connected subgraphs in a graph and has gained a great deal of interest recently. The performance of current community detection algorithms is limited by an inherent constraint of unweighted graphs that offer very little information on their internal community structures. In this paper, we propose a new scheme to address this issue that weights the edges in a given graph based on recently proposed vertex affinity. The vertex affinity quantifies the proximity between two vertices in terms of their clustering strength, and therefore, it is idealmore » for graph analytics applications such as community detection. We also demonstrate that the affinity-based edge weighting scheme can improve the performance of community detection algorithms significantly.« less

Performances of Four Helicobacter pylori Serological Detection Kits Using Stool Antigen Test as Gold Standard.

PubMed

Biranjia-Hurdoyal, Susheela D; Seetulsingh-Goorah, Sharmila P

2016-01-01

The aim was to determine the performances of four Helicobacter pylori serological detection kits in different target groups, using Amplified IDEIA™ Hp StAR™ as gold standard. Kits studied were Rapid Immunochromatoghraphic Hexagon, Helicoblot 2.1, an EIA IgG kit and EIA IgA kit. Stool and blood samples were collected from 162 apparently healthy participants (control) and 60 Type 2 diabetes mellitus (T2DM) patients. The performances of the four serological detection kits were found to be affected by gender, age, health status and ethnicity of the participants. In the control group, the Helicoblot 2.1 kit had the best performance (AUC = 0.85; p<0.05, accuracy = 86.4%), followed by EIA IgG (AUC = 0.75; p<0.05, accuracy = 75.2%). The Rapid Hexagon and EIA IgA kits had relatively poor performances. In the T2DM subgroup, the kits H2.1 and EIA IgG had best performances, with accuracies of 96.5% and 93.1% respectively. The performance of EIA IgG improved with adjustment of its cut-off value. The performances of the detection kits were affected by various factors which should be taken into consideration.

Benefits of Stimulus Exposure: Developmental Learning Independent of Task Performance

PubMed Central

Green, David B.; Ohlemacher, Jocelyn; Rosen, Merri J.

2016-01-01

Perceptual learning (training-induced performance improvement) can be elicited by task-irrelevant stimulus exposure in humans. In contrast, task-irrelevant stimulus exposure in animals typically disrupts perception in juveniles while causing little to no effect in adults. This may be due to the extent of exposure, which is brief in humans while chronic in animals. Here we assessed the effects of short bouts of passive stimulus exposure on learning during development in gerbils, compared with non-passive stimulus exposure (i.e., during testing). We used prepulse inhibition of the acoustic startle response, a method that can be applied at any age, to measure gap detection thresholds across four age groups, spanning development. First, we showed that both gap detection thresholds and gap detection learning across sessions displayed a long developmental trajectory, improving throughout the juvenile period. Additionally, we demonstrated larger within- and across-animal performance variability in younger animals. These results are generally consistent with results in humans, where there are extended developmental trajectories for both the perception of temporally-varying signals, and the effects of perceptual training, as well as increased variability and poorer performance consistency in children. We then chose an age (mid-juveniles) that displayed clear learning over sessions in order to assess effects of brief passive stimulus exposure on this learning. We compared learning in mid-juveniles exposed to either gap detection testing (gaps paired with startles) or equivalent gap exposure without testing (gaps alone) for three sessions. Learning was equivalent in both these groups and better than both naïve age-matched animals and controls receiving no gap exposure but only startle testing. Thus, short bouts of exposure to gaps independent of task performance is sufficient to induce learning at this age, and is as effective as gap detection testing. PMID:27378837

Direct Detection Electron Energy-Loss Spectroscopy: A Method to Push the Limits of Resolution and Sensitivity.

PubMed

Hart, James L; Lang, Andrew C; Leff, Asher C; Longo, Paolo; Trevor, Colin; Twesten, Ray D; Taheri, Mitra L

2017-08-15

In many cases, electron counting with direct detection sensors offers improved resolution, lower noise, and higher pixel density compared to conventional, indirect detection sensors for electron microscopy applications. Direct detection technology has previously been utilized, with great success, for imaging and diffraction, but potential advantages for spectroscopy remain unexplored. Here we compare the performance of a direct detection sensor operated in counting mode and an indirect detection sensor (scintillator/fiber-optic/CCD) for electron energy-loss spectroscopy. Clear improvements in measured detective quantum efficiency and combined energy resolution/energy field-of-view are offered by counting mode direct detection, showing promise for efficient spectrum imaging, low-dose mapping of beam-sensitive specimens, trace element analysis, and time-resolved spectroscopy. Despite the limited counting rate imposed by the readout electronics, we show that both core-loss and low-loss spectral acquisition are practical. These developments will benefit biologists, chemists, physicists, and materials scientists alike.

Assessment of mass detection performance in contrast enhanced digital mammography

NASA Astrophysics Data System (ADS)

Carton, Ann-Katherine; de Carvalho, Pablo M.; Li, Zhijin; Dromain, Clarisse; Muller, Serge

2015-03-01

We address the detectability of contrast-agent enhancing masses for contrast-agent enhanced spectral mammography (CESM), a dual-energy technique providing functional projection images of breast tissue perfusion and vascularity using simulated CESM images. First, the realism of simulated CESM images from anthropomorphic breast software phantoms generated with a software X-ray imaging platform was validated. Breast texture was characterized by power-law coefficients calculated in data sets of real clinical and simulated images. We also performed a 2-alternative forced choice (2-AFC) psychophysical experiment whereby simulated and real images were presented side-by-side to an experienced radiologist to test if real images could be distinguished from the simulated images. It was found that texture in our simulated CESM images has a fairly realistic appearance. Next, the relative performance of human readers and previously developed mathematical observers was assessed for the detection of iodine-enhancing mass lesions containing different contrast agent concentrations. A four alternative-forced-choice (4 AFC) task was designed; the task for the model and human observer was to detect which one of the four simulated DE recombined images contained an iodineenhancing mass. Our results showed that the NPW and NPWE models largely outperform human performance. After introduction of an internal noise component, both observers approached human performance. The CHO observer performs slightly worse than the average human observer. There is still work to be done in improving model observers as predictors of human-observer performance. Larger trials could also improve our test statistics. We hope that in the future, this framework of software breast phantoms, virtual image acquisition and processing, and mathematical observers can be beneficial to optimize CESM imaging techniques.

UWB radar technique for arc detection in coaxial cables and waveguides

NASA Astrophysics Data System (ADS)

Maggiora, R.; Salvador, S.

2009-11-01

As spread spectrum technology has revolutionized the communications industry, Ultra Wide Band (UWB) technology is dramatically improving radar performances. These advanced signal processing techniques have been adapted to coaxial cables and waveguides to provide new features and enhanced performance on arc detection. UWB signals constituted by a sequence of chips (properly chosen to reduce side lobes and to improve detection accuracy) are transmitted along the transmission lines at a specified Pulse Repetition Frequency (PRF) and their echoes are received by means of directional couplers. The core of the receiver is an ultra high-speed correlator implemented in a Digital Signal Processor (DSP). When a target (arc) is detected, its position and its "radar cross section" are calculated to be able to provide the arc position along the transmission line and to be able to classify the type of detected arc. The "background scattering" is routinely extracted from the received signal at any pulse. This permits to be resilient to the background structure of transmission lines (bends, junctions, windows, etc.). Thanks to the localization feature, segmentation is also possible for creating sensed and non-sensed zones (for example, to be insensitive to antenna load variations).

Sweet-spot training for early esophageal cancer detection

NASA Astrophysics Data System (ADS)

van der Sommen, Fons; Zinger, Svitlana; Schoon, Erik J.; de With, Peter H. N.

2016-03-01

Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled physicians to visually inspect the intestinal tissue for early signs of malignant lesions. Besides this, recent studies show the feasibility of supportive image analysis for endoscopists, but the analysis problem is typically approached as a segmentation task where binary ground truth is employed. In this study, we show that the detection of early cancerous tissue in the gastrointestinal tract cannot be approached as a binary segmentation problem and it is crucial and clinically relevant to involve multiple experts for annotating early lesions. By employing the so-called sweet spot for training purposes as a metric, a much better detection performance can be achieved. Furthermore, a multi-expert-based ground truth, i.e. a golden standard, enables an improved validation of the resulting delineations. For this purpose, besides the sweet spot we also propose another novel metric, the Jaccard Golden Standard (JIGS) that can handle multiple ground-truth annotations. Our experiments involving these new metrics and based on the golden standard show that the performance of a detection algorithm of early neoplastic lesions in Barrett's esophagus can be increased significantly, demonstrating a 10 percent point increase in the resulting F1 detection score.

Performance of the unique-word-reverse-modulation type demodulator for mobile satellite communications

NASA Technical Reports Server (NTRS)

Dohi, Tomohiro; Nitta, Kazumasa; Ueda, Takashi

1993-01-01

This paper proposes a new type of coherent demodulator, the unique-word (UW)-reverse-modulation type demodulator, for burst signal controlled by voice operated transmitter (VOX) in mobile satellite communication channels. The demodulator has three individual circuits: a pre-detection signal combiner, a pre-detection UW detector, and a UW-reverse-modulation type demodulator. The pre-detection signal combiner combines signal sequences received by two antennas and improves bit energy-to-noise power density ratio (E(sub b)/N(sub 0)) 2.5 dB to yield 10(exp -3) average bit error rate (BER) when carrier power-to-multipath power ratio (CMR) is 15 dB. The pre-detection UW detector improves UW detection probability when the frequency offset is large. The UW-reverse-modulation type demodulator realizes a maximum pull-in frequency of 3.9 kHz, the pull-in time is 2.4 seconds and frequency error is less than 20 Hz. The performances of this demodulator are confirmed through computer simulations and its effect is clarified in real-time experiments at a bit rate of 16.8 kbps using a digital signal processor (DSP).

A comparative analysis of frequency modulation threshold extension techniques

NASA Technical Reports Server (NTRS)

Arndt, G. D.; Loch, F. J.

1970-01-01

FM threshold extension for system performance improvement, comparing impulse noise elimination, correlation detection and delta modulation signal processing techniques implemented at demodulator output

Changes in cerebro-cerebellar interaction during response inhibition after performance improvement.

PubMed

Hirose, Satoshi; Jimura, Koji; Kunimatsu, Akira; Abe, Osamu; Ohtomo, Kuni; Miyashita, Yasushi; Konishi, Seiki

2014-10-01

It has been demonstrated that motor learning is supported by the cerebellum and the cerebro-cerebellar interaction. Response inhibition involves motor responses and the higher-order inhibition that controls the motor responses. In this functional MRI study, we measured the cerebro-cerebellar interaction during response inhibition in two separate days of task performance, and detected the changes in the interaction following performance improvement. Behaviorally, performance improved in the second day, compared to the first day. The psycho-physiological interaction (PPI) analysis revealed the interaction decrease from the right inferior frontal cortex (rIFC) to the cerebellum (lobule VII or VI). It was also revealed that the interaction increased from the same cerebellar region to the primary motor area. These results suggest the involvement of the cerebellum in response inhibition, and raise the possibility that the performance improvement was supported by the changes in the cerebro-cerebellar interaction. Copyright © 2014 Elsevier Inc. All rights reserved.

Development and validation of trauma surgical skills metrics: Preliminary assessment of performance after training.

PubMed

Shackelford, Stacy; Garofalo, Evan; Shalin, Valerie; Pugh, Kristy; Chen, Hegang; Pasley, Jason; Sarani, Babak; Henry, Sharon; Bowyer, Mark; Mackenzie, Colin F

2015-07-01

Maintaining trauma-specific surgical skills is an ongoing challenge for surgical training programs. An objective assessment of surgical skills is needed. We hypothesized that a validated surgical performance assessment tool could detect differences following a training intervention. We developed surgical performance assessment metrics based on discussion with expert trauma surgeons, video review of 10 experts and 10 novice surgeons performing three vascular exposure procedures and lower extremity fasciotomy on cadavers, and validated the metrics with interrater reliability testing by five reviewers blinded to level of expertise and a consensus conference. We tested these performance metrics in 12 surgical residents (Year 3-7) before and 2 weeks after vascular exposure skills training in the Advanced Surgical Skills for Exposure in Trauma (ASSET) course. Performance was assessed in three areas as follows: knowledge (anatomic, management), procedure steps, and technical skills. Time to completion of procedures was recorded, and these metrics were combined into a single performance score, the Trauma Readiness Index (TRI). Wilcoxon matched-pairs signed-ranks test compared pretraining/posttraining effects. Mean time to complete procedures decreased by 4.3 minutes (from 13.4 minutes to 9.1 minutes). The performance component most improved by the 1-day skills training was procedure steps, completion of which increased by 21%. Technical skill scores improved by 12%. Overall knowledge improved by 3%, with 18% improvement in anatomic knowledge. TRI increased significantly from 50% to 64% with ASSET training. Interrater reliability of the surgical performance assessment metrics was validated with single intraclass correlation coefficient of 0.7 to 0.98. A trauma-relevant surgical performance assessment detected improvements in specific procedure steps and anatomic knowledge taught during a 1-day course, quantified by the TRI. ASSET training reduced time to complete vascular control by one third. Future applications include assessing specific skills in a larger surgeon cohort, assessing military surgical readiness, and quantifying skill degradation with time since training.

Quality improvement project in cervical cancer screening: practical measures for monitoring laboratory performance.

PubMed

Tarkkanen, Jussi; Geagea, Antoine; Nieminen, Pekka; Anttila, Ahti

2003-01-01

We conducted a quality improvement project in a cervical cancer screening programme in Helsinki in order to see if detection of precancerous lesions could be influenced by external (participation rate) and internal (laboratory praxis) quality measures. In order to increase the participation rate, a second personal invitation to Pap-test was mailed to nonparticipants of the first call. In order to improve the quality of screening, the cytotechnicians monitored their performance longitudinally by recording the number of slides reviewed per day, the pick-up rate of abnormal smears, the report of the consulting cytopathologist, and the number of histologically verified lesions detected from the cases that they had screened. Regular sessions were held to compare the histological findings with the cytological findings of all cases referred for colposcopy. No pressure was applied on the cytotechnicians to ensure that they felt comfortable with their daily workload. A total of 110 000 smears were screened for cervical cancer at the Helsinki City Hospital during 1996-99. Initially, the overall participation rate increased from 62% to 71%. The number of histologically confirmed precancerous lesions (CIN 1-3) more than doubled and their detection rate increased from 0.32% to 0.72%. Continuous education and feedback from daily work performance were important, yet rather inexpensive means in increasing laboratory performance. Additional measures are needed to further increase the participation rate. Impact of the quality measures on cancer incidence needs to be assessed later on.

Performance evaluation of the Abbott RealTime HCV Genotype II for hepatitis C virus genotyping.

PubMed

Sohn, Yong-Hak; Ko, Sun-Young; Kim, Myeong Hee; Oh, Heung-Bum

2010-04-01

The Abbott RealTime hepatitis C virus (HCV) Genotype II (Abbott Molecular Inc.) for HCV genotyping, which uses real-time PCR technology, has recently been developed. Accuracy and sensitivity of detection were assessed using the HCV RNA PHW202 performance panel (SeraCare Life Sciences). Consistency with restriction fragment mass polymorphism (RFMP) data, cross-reactivity with other viruses, and the ability to detect minor strains in mixtures of genotypes 1 and 2 were evaluated using clinical samples. All performance panel viruses were correctly genotyped at levels of >500 IU/mL. Results were 100% concordant with RFMP genotypic data (66/66). However, 5% (3/66) of the samples examined displayed probable genotypic cross reactivity. No cross reactivity with other viruses was evident. Minor strains in the mixtures were not effectively distinguished, even at quantities higher than the detection limit. The Abbott RealTime HCV Genotype II assay was very accurate and yielded results consistent with RFMP data. Although the assay has the advantages of automation and short turnaround time, we suggest that further improvements are necessary before it is used routinely in clinical practice. Efforts are needed to decrease cross reactivity among genotypes and to improve the ability to detect minor genotypes in mixed infections.

Neuroimaging Evidence for 2 Types of Plasticity in Association with Visual Perceptual Learning.

PubMed

Shibata, Kazuhisa; Sasaki, Yuka; Kawato, Mitsuo; Watanabe, Takeo

2016-09-01

Visual perceptual learning (VPL) is long-term performance improvement as a result of perceptual experience. It is unclear whether VPL is associated with refinement in representations of the trained feature (feature-based plasticity), improvement in processing of the trained task (task-based plasticity), or both. Here, we provide empirical evidence that VPL of motion detection is associated with both types of plasticity which occur predominantly in different brain areas. Before and after training on a motion detection task, subjects' neural responses to the trained motion stimuli were measured using functional magnetic resonance imaging. In V3A, significant response changes after training were observed specifically to the trained motion stimulus but independently of whether subjects performed the trained task. This suggests that the response changes in V3A represent feature-based plasticity in VPL of motion detection. In V1 and the intraparietal sulcus, significant response changes were found only when subjects performed the trained task on the trained motion stimulus. This suggests that the response changes in these areas reflect task-based plasticity. These results collectively suggest that VPL of motion detection is associated with the 2 types of plasticity, which occur in different areas and therefore have separate mechanisms at least to some degree. © The Author 2016. Published by Oxford University Press.

External quality assessment for enterovirus 71 and coxsackievirus A16 detection by reverse transcription-PCR using armored RNA as a virus surrogate.

PubMed

Song, Liqiong; Sun, Shipeng; Li, Bo; Pan, Yang; Li, Wenli; Zhang, Kuo; Li, Jinming

2011-10-01

Three armored RNAs (virus-like particles [VLPs]) containing target sequences from enterovirus 71 (EV71) and coxsackievirus A16 (CA16) and a pan-enterovirus (pan-EV) sequence were constructed and used in an external quality assessment (EQA) to determine the performance of laboratories in the detection of EV71 and CA16. The EQA panel, which consisted of 20 samples, including 14 positive samples with different concentrations of EV and either EV71 or CA16 armored RNAs, 2 samples with all 3 armored RNAs, and 4 negative-control samples (NaN(3)-preserved minimal essential medium [MEM] without VLPs), was distributed to 54 laboratories that perform molecular diagnosis of hand, foot, and mouth disease (HFMD) virus infections. A total of 41 data sets from 41 participants were returned; 5 (12.2%) were generated using conventional in-house reverse transcription-PCR (RT-PCR) assays, and 36 (87.8%) were generated using commercial real-time RT-PCR assays. Performance assessments of laboratories differed; 12 (29.3%) showed a need for improvement. Surprisingly, 4 laboratories were unable to detect EV71 RNA in any samples, even those containing the highest concentration of 10(7) IU/ml. Furthermore, the detection sensitivity for EV71 among all laboratories (82.1%) was substantially lower than that for EV (97.4%) or CA16 (95.1%). Overall, the results of the present study indicate that EQA should be performed periodically to help laboratories monitor their ability to detect HFMD viruses and to improve the comparability of results from different laboratories.

Automatic Earthquake Detection by Active Learning

NASA Astrophysics Data System (ADS)

Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

Learning to detect and combine the features of an object

PubMed Central

Suchow, Jordan W.; Pelli, Denis G.

2013-01-01

To recognize an object, it is widely supposed that we first detect and then combine its features. Familiar objects are recognized effortlessly, but unfamiliar objects—like new faces or foreign-language letters—are hard to distinguish and must be learned through practice. Here, we describe a method that separates detection and combination and reveals how each improves as the observer learns. We dissociate the steps by two independent manipulations: For each step, we do or do not provide a bionic crutch that performs it optimally. Thus, the two steps may be performed solely by the human, solely by the crutches, or cooperatively, when the human takes one step and a crutch takes the other. The crutches reveal a double dissociation between detecting and combining. Relative to the two-step ideal, the human observer’s overall efficiency for unconstrained identification equals the product of the efficiencies with which the human performs the steps separately. The two-step strategy is inefficient: Constraining the ideal to take two steps roughly halves its identification efficiency. In contrast, we find that humans constrained to take two steps perform just as well as when unconstrained, which suggests that they normally take two steps. Measuring threshold contrast (the faintness of a barely identifiable letter) as it improves with practice, we find that detection is inefficient and learned slowly. Combining is learned at a rate that is 4× higher and, after 1,000 trials, 7× more efficient. This difference explains much of the diversity of rates reported in perceptual learning studies, including effects of complexity and familiarity. PMID:23267067

External Quality Assessment for Enterovirus 71 and Coxsackievirus A16 Detection by Reverse Transcription-PCR Using Armored RNA as a Virus Surrogate▿†

PubMed Central

Song, Liqiong; Sun, Shipeng; Li, Bo; Pan, Yang; Li, Wenli; Zhang, Kuo; Li, Jinming

2011-01-01

Three armored RNAs (virus-like particles [VLPs]) containing target sequences from enterovirus 71 (EV71) and coxsackievirus A16 (CA16) and a pan-enterovirus (pan-EV) sequence were constructed and used in an external quality assessment (EQA) to determine the performance of laboratories in the detection of EV71 and CA16. The EQA panel, which consisted of 20 samples, including 14 positive samples with different concentrations of EV and either EV71 or CA16 armored RNAs, 2 samples with all 3 armored RNAs, and 4 negative-control samples (NaN3-preserved minimal essential medium [MEM] without VLPs), was distributed to 54 laboratories that perform molecular diagnosis of hand, foot, and mouth disease (HFMD) virus infections. A total of 41 data sets from 41 participants were returned; 5 (12.2%) were generated using conventional in-house reverse transcription-PCR (RT-PCR) assays, and 36 (87.8%) were generated using commercial real-time RT-PCR assays. Performance assessments of laboratories differed; 12 (29.3%) showed a need for improvement. Surprisingly, 4 laboratories were unable to detect EV71 RNA in any samples, even those containing the highest concentration of 107 IU/ml. Furthermore, the detection sensitivity for EV71 among all laboratories (82.1%) was substantially lower than that for EV (97.4%) or CA16 (95.1%). Overall, the results of the present study indicate that EQA should be performed periodically to help laboratories monitor their ability to detect HFMD viruses and to improve the comparability of results from different laboratories. PMID:21865426

Characterizing Perceptual Learning with External Noise

ERIC Educational Resources Information Center

Gold, Jason M.; Sekuler, Allison B.; Bennett, Partrick J.

2004-01-01

Performance in perceptual tasks often improves with practice. This effect is known as "perceptual learning," and it has been the source of a great deal of interest and debate over the course of the last century. Here, we consider the effects of perceptual learning within the context of signal detection theory. According to signal detection theory,…

Determination of the Potential Benefit of Time-Frequency Gain Manipulation

PubMed Central

Anzalone, Michael C.; Calandruccio, Lauren; Doherty, Karen A.; Carney, Laurel H.

2008-01-01

Objective The purpose of this study was to determine the maximum benefit provided by a time-frequency gain-manipulation algorithm for noise-reduction (NR) based on an ideal detector of speech energy. The amount of detected energy necessary to show benefit using this type of NR algorithm was examined, as well as the necessary speed and frequency resolution of the gain manipulation. Design NR was performed using time-frequency gain manipulation, wherein the gains of individual frequency bands depended on the absence or presence of speech energy within each band. Three different experiments were performed: (1) NR using ideal detectors, (2) NR with nonideal detectors, and (3) NR with ideal detectors and different processing speeds and frequency resolutions. All experiments were performed using the Hearing-in-Noise test (HINT). A total of 6 listeners with normal hearing and 14 listeners with hearing loss were tested. Results HINT thresholds improved for all listeners with NR based on the ideal detectors used in Experiment I. The nonideal detectors of Experiment II required detection of at least 90% of the speech energy before an improvement was seen in HINT thresholds. The results of Experiment III demonstrated that relatively high temporal resolution (<100 msec) was required by the NR algorithm to improve HINT thresholds. Conclusions The results indicated that a single-microphone NR system based on time-frequency gain manipulation improved the HINT thresholds of listeners. However, to obtain benefit in speech intelligibility, the detectors used in such a strategy were required to detect an unrealistically high percentage of the speech energy and to perform the gain manipulations on a fast temporal basis. PMID:16957499

Effectiveness of computer aided detection for solitary pulmonary nodules

NASA Astrophysics Data System (ADS)

Yan, Jiayong; Li, Wenjie; Du, Xiangying; Lu, Huihai; Xu, Jianrong; Xu, Mantao; Rong, Dongdong

2009-02-01

This study is to investigate the incremental effect of using a high performance computer-aided detection (CAD) system in detection of solitary pulmonary nodules in chest radiographs. The Kodak Chest CAD system was evaluated by a panel of six radiologists at different levels of experience. The observer study consisted of two independent phases: readings without CAD and readings with assistance of CAD. The study was conducted over a set of chest radiographs comprising 150 cancer cases and 150 cancer-free cases. The actual sensitivity of the CAD system is 72% with 3.7 false positives per case. Receiver operating characteristic (ROC) analysis was used to assess the overall observer performance. The AUZ (area under ROC curve) showed a significantly improvement (P=0.0001) from 0.844 to 0.884 after using CAD. The ROC analysis was also applied for observer performances on nodules in different sizes and visibilities. The average AUZs are improved from 0.798 to 0.835 (P=0.0003) for 5-10mm nodules, 0.853 to 0.907 (P=0.001) for 10-15mm nodules, 0.864 to 0.897 (P=0.051) for 15-20 mm nodules and 0.859 to 0.896 (P=0.0342) for 20-30mm nodules, respectively. For different visibilities, the average AUZs are improved from 0.886 to 0.915 (P=0.0337), 0.803 to 0.840 (P=0.063), 0.830 to 0.893 (P=0.0001), and 0.813 to 0.847 (P=0.152), for nodules clearly visible, hidden by ribs, partially overlap with ribs, and overlap with other structures, respectively. These results showed that observer performance could be greatly improved when the CAD system is employed as a second reader, especially for small nodules and nodules occluded by ribs.

Dry immunochemical sensor for the detection of PETN vapor

NASA Astrophysics Data System (ADS)

Lukens, Herbert Richard

1992-05-01

Giaever (1973) showed that an indium semi-mirror coated with a monolayer of a substance would undergo reduced optical reflectance after incubation with a solution of the substance's antibody. He was able to see the effect with the naked eye. Lukens and Williams (1977) reversed the process by first attaching the target substance's antibody to the semi-mirror, after which the device would show a decline in optical density when exposed to a solution of the target substance. Lukens and Williams (1982) subsequently found that the device could also be used as an immunochemical film badge (IFB) to detect an airborne target substance. Early efforts to develop the IFB for the detection of airborne substances were plagued by such a high degree of performance variability that there were doubts in some quarters that an airborne target substance could bind to its antibody until Lukens (1990) demonstrated such binding in experiments with radiolabeled cocaine and morphine. Recently improved semi-mirrors and densitometry have been obtained and have led to improved performance of IFB's. As shown in this paper, IFB's can now be constructed that detect PETN vapor in a few seconds.

A Hypergraph and Arithmetic Residue-based Probabilistic Neural Network for classification in Intrusion Detection Systems.

PubMed

Raman, M R Gauthama; Somu, Nivethitha; Kirthivasan, Kannan; Sriram, V S Shankar

2017-08-01

Over the past few decades, the design of an intelligent Intrusion Detection System (IDS) remains an open challenge to the research community. Continuous efforts by the researchers have resulted in the development of several learning models based on Artificial Neural Network (ANN) to improve the performance of the IDSs. However, there exists a tradeoff with respect to the stability of ANN architecture and the detection rate for less frequent attacks. This paper presents a novel approach based on Helly property of Hypergraph and Arithmetic Residue-based Probabilistic Neural Network (HG AR-PNN) to address the classification problem in IDS. The Helly property of Hypergraph was exploited for the identification of the optimal feature subset and the arithmetic residue of the optimal feature subset was used to train the PNN. The performance of HG AR-PNN was evaluated using KDD CUP 1999 intrusion dataset. Experimental results prove the dominance of HG AR-PNN classifier over the existing classifiers with respect to the stability and improved detection rate for less frequent attacks. Copyright © 2017 Elsevier Ltd. All rights reserved.

Linear Phase Sharp Transition BPF to Detect Noninvasive Maternal and Fetal Heart Rate.

PubMed

Marchon, Niyan; Naik, Gourish; Pai, K R

2018-01-01

Fetal heart rate (FHR) detection can be monitored using either direct fetal scalp electrode recording (invasive) or by indirect noninvasive technique. Weeks before delivery, the invasive method poses a risk factor to the fetus, while the latter provides accurate fetal ECG (FECG) information which can help diagnose fetal's well-being. Our technique employs variable order linear phase sharp transition (LPST) FIR band-pass filter which shows improved stopband attenuation at higher filter orders. The fetal frequency fiduciary edges form the band edges of the filter characterized by varying amounts of overlap of maternal ECG (MECG) spectrum. The one with the minimum maternal spectrum overlap was found to be optimum with no power line interference and maximum fetal heart beats being detected. The improved filtering is reflected in the enhancement of the performance of the fetal QRS detector (FQRS). The improvement has also occurred in fetal heart rate obtained using our algorithm which is in close agreement with the true reference (i.e., invasive fetal scalp ECG). The performance parameters of the FQRS detector such as sensitivity (Se), positive predictive value (PPV), and accuracy (F 1 ) were found to improve even for lower filter order. The same technique was extended to evaluate maternal QRS detector (MQRS) and found to yield satisfactory maternal heart rate (MHR) results.

Improvement in QEPAS system utilizing a second harmonic based wavelength calibration technique

NASA Astrophysics Data System (ADS)

Zhang, Qinduan; Chang, Jun; Wang, Fupeng; Wang, Zongliang; Xie, Yulei; Gong, Weihua

2018-05-01

A simple laser wavelength calibration technique, based on second harmonic signal, is demonstrated in this paper to improve the performance of quartz enhanced photoacoustic spectroscopy (QEPAS) gas sensing system, e.g. improving the signal to noise ratio (SNR), detection limit and long-term stability. Constant current, corresponding to the gas absorption line, combining f/2 frequency sinusoidal signal are used to drive the laser (constant driving mode), a software based real-time wavelength calibration technique is developed to eliminate the wavelength drift due to ambient fluctuations. Compared to conventional wavelength modulation spectroscopy (WMS), this method allows lower filtering bandwidth and averaging algorithm applied to QEPAS system, improving SNR and detection limit. In addition, the real-time wavelength calibration technique guarantees the laser output is modulated steadily at gas absorption line. Water vapor is chosen as an objective gas to evaluate its performance compared to constant driving mode and conventional WMS system. The water vapor sensor was designed insensitive to the incoherent external acoustic noise by the numerical averaging technique. As a result, the SNR increases 12.87 times in wavelength calibration technique based system compared to conventional WMS system. The new system achieved a better linear response (R2 = 0 . 9995) in concentration range from 300 to 2000 ppmv, and achieved a minimum detection limit (MDL) of 630 ppbv.

The Critical Power Model as a Potential Tool for Anti-doping

PubMed Central

Puchowicz, Michael J.; Mizelman, Eliran; Yogev, Assaf; Koehle, Michael S.; Townsend, Nathan E.; Clarke, David C.

2018-01-01

Existing doping detection strategies rely on direct and indirect biochemical measurement methods focused on detecting banned substances, their metabolites, or biomarkers related to their use. However, the goal of doping is to improve performance, and yet evidence from performance data is not considered by these strategies. The emergence of portable sensors for measuring exercise intensities and of player tracking technologies may enable the widespread collection of performance data. How these data should be used for doping detection is an open question. Herein, we review the basis by which performance models could be used for doping detection, followed by critically reviewing the potential of the critical power (CP) model as a prototypical performance model that could be used in this regard. Performance models are mathematical representations of performance data specific to the athlete. Some models feature parameters with physiological interpretations, changes to which may provide clues regarding the specific doping method. The CP model is a simple model of the power-duration curve and features two physiologically interpretable parameters, CP and W′. We argue that the CP model could be useful for doping detection mainly based on the predictable sensitivities of its parameters to ergogenic aids and other performance-enhancing interventions. However, our argument is counterbalanced by the existence of important limitations and unresolved questions that need to be addressed before the model is used for doping detection. We conclude by providing a simple worked example showing how it could be used and propose recommendations for its implementation. PMID:29928234

Variable Effect during Polymerization

ERIC Educational Resources Information Center

Lunsford, S. K.

2005-01-01

An experiment performing the polymerization of 3-methylthiophene(P-3MT) onto the conditions for the selective electrode to determine the catechol by using cyclic voltammetry was performed. The P-3MT formed under optimized conditions improved electrochemical reversibility, selectivity and reproducibility for the detection of the catechol.

Case base classification on digital mammograms: improving the performance of case base classifier

NASA Astrophysics Data System (ADS)

Raman, Valliappan; Then, H. H.; Sumari, Putra; Venkatesa Mohan, N.

2011-10-01

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. The aim of the research presented here is in twofold. First stage of research involves machine learning techniques, which segments and extracts features from the mass of digital mammograms. Second level is on problem solving approach which includes classification of mass by performance based case base classifier. In this paper we build a case-based Classifier in order to diagnose mammographic images. We explain different methods and behaviors that have been added to the classifier to improve the performance of the classifier. Currently the initial Performance base Classifier with Bagging is proposed in the paper and it's been implemented and it shows an improvement in specificity and sensitivity.

Automatic sentence extraction for the detection of scientific paper relations

NASA Astrophysics Data System (ADS)

Sibaroni, Y.; Prasetiyowati, S. S.; Miftachudin, M.

2018-03-01

The relations between scientific papers are very useful for researchers to see the interconnection between scientific papers quickly. By observing the inter-article relationships, researchers can identify, among others, the weaknesses of existing research, performance improvements achieved to date, and tools or data typically used in research in specific fields. So far, methods that have been developed to detect paper relations include machine learning and rule-based methods. However, a problem still arises in the process of sentence extraction from scientific paper documents, which is still done manually. This manual process causes the detection of scientific paper relations longer and inefficient. To overcome this problem, this study performs an automatic sentences extraction while the paper relations are identified based on the citation sentence. The performance of the built system is then compared with that of the manual extraction system. The analysis results suggested that the automatic sentence extraction indicates a very high level of performance in the detection of paper relations, which is close to that of manual sentence extraction.

Rapid road repair vehicle

DOEpatents

Mara, Leo M.

1999-01-01

Disclosed are improvments to a rapid road repair vehicle comprising an improved cleaning device arrangement, two dispensing arrays for filling defects more rapidly and efficiently, an array of pre-heaters to heat the road way surface in order to help the repair material better bond to the repaired surface, a means for detecting, measuring, and computing the number, location and volume of each of the detected surface imperfection, and a computer means schema for controlling the operation of the plurality of vehicle subsystems. The improved vehicle is, therefore, better able to perform its intended function of filling surface imperfections while moving over those surfaces at near normal traffic speeds.

Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System

PubMed Central

Jung, Jaehoon; Yoon, Inhye; Paik, Joonki

2016-01-01

This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i) automatic camera calibration using both moving objects and a background structure; (ii) object depth estimation; and (iii) detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB) camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems. PMID:27347978

Domain Adaptation Methods for Improving Lab-to-field Generalization of Cocaine Detection using Wearable ECG.

PubMed

Natarajan, Annamalai; Angarita, Gustavo; Gaiser, Edward; Malison, Robert; Ganesan, Deepak; Marlin, Benjamin M

2016-09-01

Mobile health research on illicit drug use detection typically involves a two-stage study design where data to learn detectors is first collected in lab-based trials, followed by a deployment to subjects in a free-living environment to assess detector performance. While recent work has demonstrated the feasibility of wearable sensors for illicit drug use detection in the lab setting, several key problems can limit lab-to-field generalization performance. For example, lab-based data collection often has low ecological validity, the ground-truth event labels collected in the lab may not be available at the same level of temporal granularity in the field, and there can be significant variability between subjects. In this paper, we present domain adaptation methods for assessing and mitigating potential sources of performance loss in lab-to-field generalization and apply them to the problem of cocaine use detection from wearable electrocardiogram sensor data.

CRISPR Recognition Tool (CRT): a tool for automatic detection ofclustered regularly interspaced palindromic repeats

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

Bland, Charles; Ramsey, Teresa L.; Sabree, Fareedah

Clustered Regularly Interspaced Palindromic Repeats (CRISPRs) are a novel type of direct repeat found in a wide range of bacteria and archaea. CRISPRs are beginning to attract attention because of their proposed mechanism; that is, defending their hosts against invading extrachromosomal elements such as viruses. Existing repeat detection tools do a poor job of identifying CRISPRs due to the presence of unique spacer sequences separating the repeats. In this study, a new tool, CRT, is introduced that rapidly and accurately identifies CRISPRs in large DNA strings, such as genomes and metagenomes. CRT was compared to CRISPR detection tools, Patscan andmore » Pilercr. In terms of correctness, CRT was shown to be very reliable, demonstrating significant improvements over Patscan for measures precision, recall and quality. When compared to Pilercr, CRT showed improved performance for recall and quality. In terms of speed, CRT also demonstrated superior performance, especially for genomes containing large numbers of repeats. In this paper a new tool was introduced for the automatic detection of CRISPR elements. This tool, CRT, was shown to be a significant improvement over the current techniques for CRISPR identification. CRT's approach to detecting repetitive sequences is straightforward. It uses a simple sequential scan of a DNA sequence and detects repeats directly without any major conversion or preprocessing of the input. This leads to a program that is easy to describe and understand; yet it is very accurate, fast and memory efficient, being O(n) in space and O(nm/l) in time.« less

Integrated Remote Sensing Modalities for Classification at a Legacy Test Site

NASA Astrophysics Data System (ADS)

Lee, D. J.; Anderson, D.; Craven, J.

2016-12-01

Detecting, locating, and characterizing suspected underground nuclear test sites is of interest to the worldwide nonproliferation monitoring community. Remote sensing provides both cultural and surface geological information over a large search area in a non-intrusive manner. We have characterized a legacy nuclear test site at the Nevada National Security Site (NNSS) using an aerial system based on RGB imagery, light detection and ranging, and hyperspectral imaging. We integrate these different remote sensing modalities to perform pattern recognition and classification tasks on the test site. These tasks include detecting cultural artifacts and exotic materials. We evaluate if the integration of different remote sensing modalities improves classification performance.

Probabilistic double guarantee kidnapping detection in SLAM.

PubMed

Tian, Yang; Ma, Shugen

2016-01-01

For determining whether kidnapping has happened and which type of kidnapping it is while a robot performs autonomous tasks in an unknown environment, a double guarantee kidnapping detection (DGKD) method has been proposed. The good performance of DGKD in a relative small environment is shown. However, a limitation of DGKD is found in a large-scale environment by our recent work. In order to increase the adaptability of DGKD in a large-scale environment, an improved method called probabilistic double guarantee kidnapping detection is proposed in this paper to combine probability of features' positions and the robot's posture. Simulation results demonstrate the validity and accuracy of the proposed method.

Detection of respiratory viruses and bacteria in children using a twenty-two target reverse-transcription real-time PCR (RT-qPCR) panel.

PubMed

Ellis, Chelsey; Misir, Amita; Hui, Charles; Jabbour, Mona; Barrowman, Nicholas; Langill, Jonathan; Bowes, Jennifer; Slinger, Robert

2016-05-01

Rapid detection of the wide range of viruses and bacteria that cause respiratory infection in children is important for patient care and antibiotic stewardship. We therefore designed and evaluated a ready-to-use 22 target respiratory infection reverse-transcription real-time polymerase chain reaction (RT-qPCR) panel to determine if this would improve detection of these agents at our pediatric hospital. RT-qPCR assays for twenty-two target organisms were dried-down in individual wells of 96 well plates and saved at room temperature. Targets included 18 respiratory viruses and 4 bacteria. After automated nucleic acid extraction of nasopharyngeal aspirate (NPA) samples, rapid qPCR was performed. RT-qPCR results were compared with those obtained by the testing methods used at our hospital laboratories. One hundred fifty-nine pediatric NPA samples were tested with the RT-qPCR panel. One or more respiratory pathogens were detected in 132/159 (83%) samples. This was significantly higher than the detection rate of standard methods (94/159, 59%) (P<0.001). This difference was mainly due to improved RT-qPCR detection of rhinoviruses, parainfluenza viruses, bocavirus, and coronaviruses. The panel internal control assay performance remained stable at room temperature storage over a two-month testing period. The RT-qPCR panel was able to identify pathogens in a high proportion of respiratory samples. The panel detected more positive specimens than the methods in use at our hospital. The pre-made panel format was easy to use and rapid, with results available in approximately 90 minutes. We now plan to determine if use of this panel improves patient care and antibiotic stewardship.

Improved PCR-Based Detection of Soil Transmitted Helminth Infections Using a Next-Generation Sequencing Approach to Assay Design.

PubMed

Pilotte, Nils; Papaiakovou, Marina; Grant, Jessica R; Bierwert, Lou Ann; Llewellyn, Stacey; McCarthy, James S; Williams, Steven A

2016-03-01

The soil transmitted helminths are a group of parasitic worms responsible for extensive morbidity in many of the world's most economically depressed locations. With growing emphasis on disease mapping and eradication, the availability of accurate and cost-effective diagnostic measures is of paramount importance to global control and elimination efforts. While real-time PCR-based molecular detection assays have shown great promise, to date, these assays have utilized sub-optimal targets. By performing next-generation sequencing-based repeat analyses, we have identified high copy-number, non-coding DNA sequences from a series of soil transmitted pathogens. We have used these repetitive DNA elements as targets in the development of novel, multi-parallel, PCR-based diagnostic assays. Utilizing next-generation sequencing and the Galaxy-based RepeatExplorer web server, we performed repeat DNA analysis on five species of soil transmitted helminths (Necator americanus, Ancylostoma duodenale, Trichuris trichiura, Ascaris lumbricoides, and Strongyloides stercoralis). Employing high copy-number, non-coding repeat DNA sequences as targets, novel real-time PCR assays were designed, and assays were tested against established molecular detection methods. Each assay provided consistent detection of genomic DNA at quantities of 2 fg or less, demonstrated species-specificity, and showed an improved limit of detection over the existing, proven PCR-based assay. The utilization of next-generation sequencing-based repeat DNA analysis methodologies for the identification of molecular diagnostic targets has the ability to improve assay species-specificity and limits of detection. By exploiting such high copy-number repeat sequences, the assays described here will facilitate soil transmitted helminth diagnostic efforts. We recommend similar analyses when designing PCR-based diagnostic tests for the detection of other eukaryotic pathogens.

Computed-aided diagnosis (CAD) in the detection of breast cancer.

PubMed

Dromain, C; Boyer, B; Ferré, R; Canale, S; Delaloge, S; Balleyguier, C

2013-03-01

Computer-aided detection (CAD) systems have been developed for interpretation to improve mammographic detection of breast cancer at screening by reducing the number of false-negative interpretation that can be caused by subtle findings, radiologist distraction and complex architecture. They use a digitized mammographic image that can be obtained from both screen-film mammography and full field digital mammography. Its performance in breast cancer detection is dependent on the performance of the CAD itself, the population to which it is applied and the radiologists who use it. There is a clear benefit to the use of CAD in less experienced radiologist and in detecting breast carcinomas presenting as microcalcifications. This review gives a detailed description CAD systems used in mammography and their performance in assistance of reading in screening mammography and as an alternative to double reading. Other CAD systems developed for MRI and ultrasound are also presented and discussed. Copyright © 2012. Published by Elsevier Ireland Ltd.

Cumulative detection probabilities and range accuracy of a pulsed Geiger-mode avalanche photodiode laser ranging system

NASA Astrophysics Data System (ADS)

Luo, Hanjun; Ouyang, Zhengbiao; Liu, Qiang; Chen, Zhiliang; Lu, Hualan

2017-10-01

Cumulative pulses detection with appropriate cumulative pulses number and threshold has the ability to improve the detection performance of the pulsed laser ranging system with GM-APD. In this paper, based on Poisson statistics and multi-pulses cumulative process, the cumulative detection probabilities and their influence factors are investigated. With the normalized probability distribution of each time bin, the theoretical model of the range accuracy and precision is established, and the factors limiting the range accuracy and precision are discussed. The results show that the cumulative pulses detection can produce higher target detection probability and lower false alarm probability. However, for a heavy noise level and extremely weak echo intensity, the false alarm suppression performance of the cumulative pulses detection deteriorates quickly. The range accuracy and precision is another important parameter evaluating the detection performance, the echo intensity and pulse width are main influence factors on the range accuracy and precision, and higher range accuracy and precision is acquired with stronger echo intensity and narrower echo pulse width, for 5-ns echo pulse width, when the echo intensity is larger than 10, the range accuracy and precision lower than 7.5 cm can be achieved.

Improving the performance of US Environmental Protection Agency Method 300.1 for monitoring drinking water compliance.

PubMed

Wagner, Herbert P; Pepich, Barry V; Hautman, Daniel P; Munch, David J

2003-09-05

In 1998, the United States Environmental Protection Agency (EPA) promulgated the maximum contaminant level (MCL) for bromate in drinking water at 10 microg/l, and the method for compliance monitoring of bromate in drinking water was established under Stage 1 of the Disinfectants/Disinfection By-Products Rule (D/DBP) as EPA Method 300.1. In January 2002, the United States Food and Drug Administration (FDA) regulated the bromate concentration in bottled waters at 10 microg/l. EPA anticipates proposing additional methods, which have improved performance for bromate monitoring, in addition to EPA Method 300.1, in the Stage 2 DBP Rule. Until the Stage 2 Rule is promulgated, EPA Method 300.1 will continue to be the only method approved for compliance monitoring of bromate. This manuscript describes the work completed at EPA's Technical Support Center (TSC) to assess the performance of recently developed suppressor technologies toward improving the trace level performance of EPA Method 300.1, specifically for the analysis of trace levels of bromate in high ionic matrices. Three different types of Dionex suppressors were evaluated. The baseline noise, return to baseline after the water dip, detection limits, precision and accuracy, and advantages/disadvantages of each suppressor are discussed. Performance data for the three different suppressors indicates that chemical suppression of the eluent, using the AMMS III suppressor, is the most effective means to reduce baseline noise, resulting in the best resolution and the lowest bromate detection limits, even when a high ionic matrix is analyzed. Incorporation of the AMMS III suppressor improves the performance of EPA Method 300.1 at and below 5.0 microg/l and is a quick way for laboratories to improve their bromate compliance monitoring.

How to Improve the Quality of Screening Endoscopy in Korea: National Endoscopy Quality Improvement Program.

PubMed

Cho, Yu Kyung

2016-07-01

In Korea, gastric cancer screening, either esophagogastroduodenoscopy or upper gastrointestinal series (UGIS), is performed biennially for adults aged 40 years or older. Screening endoscopy has been shown to be associated with localized cancer detection and better than UGIS. However, the diagnostic sensitivity of detecting cancer is not satisfactory. The National Endoscopy Quality Improvement (QI) program was initiated in 2009 to enhance the quality of medical institutions and improve the effectiveness of the National Cancer Screening Program (NCSP). The Korean Society of Gastrointestinal Endoscopy developed quality standards through a broad systematic review of other endoscopic quality guidelines and discussions with experts. The standards comprise five domains: qualifications of endoscopists, endoscopic unit facilities and equipment, endoscopic procedure, endoscopy outcomes, and endoscopic reprocessing. After 5 years of the QI program, feedback surveys showed that the perception of QI and endoscopic practice improved substantially in all domains of quality, but the quality standards need to be revised. How to avoid missing cancer in endoscopic procedures in daily practice was reviewed, which can be applied to the mass screening endoscopy. To improve the quality and effectiveness of NCSP, key performance indicators, acceptable quality standards, regular audit, and appropriate reimbursement are necessary.

Optimum outlier model for potential improvement of environmental cleaning and disinfection.

PubMed

Rupp, Mark E; Huerta, Tomas; Cavalieri, R J; Lyden, Elizabeth; Van Schooneveld, Trevor; Carling, Philip; Smith, Philip W

2014-06-01

The effectiveness and efficiency of 17 housekeepers in terminal cleaning 292 hospital rooms was evaluated through adenosine triphosphate detection. A subgroup of housekeepers was identified who were significantly more effective and efficient than their coworkers. These optimum outliers may be used in performance improvement to optimize environmental cleaning.

DETECTION OF FAST RADIO TRANSIENTS WITH MULTIPLE STATIONS: A CASE STUDY USING THE VERY LONG BASELINE ARRAY

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

Thompson, David R.; Wagstaff, Kiri L.; Majid, Walid A.

2011-07-10

Recent investigations reveal an important new class of transient radio phenomena that occur on submillisecond timescales. Often, transient surveys' data volumes are too large to archive exhaustively. Instead, an online automatic system must excise impulsive interference and detect candidate events in real time. This work presents a case study using data from multiple geographically distributed stations to perform simultaneous interference excision and transient detection. We present several algorithms that incorporate dedispersed data from multiple sites, and report experiments with a commensal real-time transient detection system on the Very Long Baseline Array. We test the system using observations of pulsar B0329+54.more » The multiple-station algorithms enhanced sensitivity for detection of individual pulses. These strategies could improve detection performance for a future generation of geographically distributed arrays such as the Australian Square Kilometre Array Pathfinder and the Square Kilometre Array.« less

Virtual reality and live simulation: a comparison between two simulation tools for assessing mass casualty triage skills.

PubMed

Luigi Ingrassia, Pier; Ragazzoni, Luca; Carenzo, Luca; Colombo, Davide; Ripoll Gallardo, Alba; Della Corte, Francesco

2015-04-01

This study tested the hypothesis that virtual reality simulation is equivalent to live simulation for testing naive medical students' abilities to perform mass casualty triage using the Simple Triage and Rapid Treatment (START) algorithm in a simulated disaster scenario and to detect the improvement in these skills after a teaching session. Fifty-six students in their last year of medical school were randomized into two groups (A and B). The same scenario, a car accident, was developed identically on the two simulation methodologies: virtual reality and live simulation. On day 1, group A was exposed to the live scenario and group B was exposed to the virtual reality scenario, aiming to triage 10 victims. On day 2, all students attended a 2-h lecture on mass casualty triage, specifically the START triage method. On day 3, groups A and B were crossed over. The groups' abilities to perform mass casualty triage in terms of triage accuracy, intervention correctness, and speed in the scenarios were assessed. Triage and lifesaving treatment scores were assessed equally by virtual reality and live simulation on day 1 and on day 3. Both simulation methodologies detected an improvement in triage accuracy and treatment correctness from day 1 to day 3 (P<0.001). The time to complete each scenario and its decrease from day 1 to day 3 were detected equally in the two groups (P<0.05). Virtual reality simulation proved to be a valuable tool, equivalent to live simulation, to test medical students' abilities to perform mass casualty triage and to detect improvement in such skills.

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.

A computerized scheme for lung nodule detection in multiprojection chest radiography

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

Guo Wei; Li Qiang; Boyce, Sarah J.

2012-04-15

Purpose: Our previous study indicated that multiprojection chest radiography could significantly improve radiologists' performance for lung nodule detection in clinical practice. In this study, the authors further verify that multiprojection chest radiography can greatly improve the performance of a computer-aided diagnostic (CAD) scheme. Methods: Our database consisted of 59 subjects, including 43 subjects with 45 nodules and 16 subjects without nodules. The 45 nodules included 7 real and 38 simulated ones. The authors developed a conventional CAD scheme and a new fusion CAD scheme to detect lung nodules. The conventional CAD scheme consisted of four steps for (1) identification ofmore » initial nodule candidates inside lungs, (2) nodule candidate segmentation based on dynamic programming, (3) extraction of 33 features from nodule candidates, and (4) false positive reduction using a piecewise linear classifier. The conventional CAD scheme processed each of the three projection images of a subject independently and discarded the correlation information between the three images. The fusion CAD scheme included the four steps in the conventional CAD scheme and two additional steps for (5) registration of all candidates in the three images of a subject, and (6) integration of correlation information between the registered candidates in the three images. The integration step retained all candidates detected at least twice in the three images of a subject and removed those detected only once in the three images as false positives. A leave-one-subject-out testing method was used for evaluation of the performance levels of the two CAD schemes. Results: At the sensitivities of 70%, 65%, and 60%, our conventional CAD scheme reported 14.7, 11.3, and 8.6 false positives per image, respectively, whereas our fusion CAD scheme reported 3.9, 1.9, and 1.2 false positives per image, and 5.5, 2.8, and 1.7 false positives per patient, respectively. The low performance of the conventional CAD scheme may be attributed to the high noise level in chest radiography, and the small size and low contrast of most nodules. Conclusions: This study indicated that the fusion of correlation information in multiprojection chest radiography can markedly improve the performance of CAD scheme for lung nodule detection.« less

SIRE: a MIMO radar for landmine/IED detection

NASA Astrophysics Data System (ADS)

Ojowu, Ode; Wu, Yue; Li, Jian; Nguyen, Lam

2013-05-01

Multiple-input multiple-output (MIMO) radar systems have been shown to have significant performance improvements over their single-input multiple-output (SIMO) counterparts. For transmit and receive elements that are collocated, the waveform diversity afforded by this radar is exploited for performance improvements. These improvements include but are not limited to improved target detection, improved parameter identifiability and better resolvability. In this paper, we present the Synchronous Impulse Reconstruction Radar (SIRE) Ultra-wideband (UWB) radar designed by the Army Research Lab (ARL) for landmine and improvised explosive device (IED) detection as a 2 by 16 MIMO radar (with collocated antennas). Its improvement over its SIMO counterpart in terms of beampattern/cross range resolution are discussed and demonstrated using simulated data herein. The limitations of this radar for Radio Frequency Interference (RFI) suppression are also discussed in this paper. A relaxation method (RELAX) combined with averaging of multiple realizations of the measured data is presented for RFI suppression; results show no noticeable target signature distortion after suppression. In this paper, the back-projection (delay and sum) data independent method is used for generating SAR images. A side-lobe minimization technique called recursive side-lobe minimization (RSM) is also discussed for reducing side-lobes in this data independent approach. We introduce a data-dependent sparsity based spectral estimation technique called Sparse Learning via Iterative Minimization (SLIM) as well as a data-dependent CLEAN approach for generating SAR images for the SIRE radar. These data-adaptive techniques show improvement in side-lobe reduction and resolution for simulated data for the SIRE radar.

A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

NASA Astrophysics Data System (ADS)

Shuxin, Li; Zhilong, Zhang; Biao, Li

2018-01-01

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

Advanced Atmospheric Water Vapor DIAL Detection System

NASA Technical Reports Server (NTRS)

Refaat, Tamer F.; Elsayed-Ali, Hani E.; DeYoung, Russell J. (Technical Monitor)

2000-01-01

Measurement of atmospheric water vapor is very important for understanding the Earth's climate and water cycle. The remote sensing Differential Absorption Lidar (DIAL) technique is a powerful method to perform such measurement from aircraft and space. This thesis describes a new advanced detection system, which incorporates major improvements regarding sensitivity and size. These improvements include a low noise advanced avalanche photodiode detector, a custom analog circuit, a 14-bit digitizer, a microcontroller for on board averaging and finally a fast computer interface. This thesis describes the design and validation of this new water vapor DIAL detection system which was integrated onto a small Printed Circuit Board (PCB) with minimal weight and power consumption. Comparing its measurements to an existing DIAL system for aerosol and water vapor profiling validated the detection system.

Multi-Object Tracking with Correlation Filter for Autonomous Vehicle.

PubMed

Zhao, Dawei; Fu, Hao; Xiao, Liang; Wu, Tao; Dai, Bin

2018-06-22

Multi-object tracking is a crucial problem for autonomous vehicle. Most state-of-the-art approaches adopt the tracking-by-detection strategy, which is a two-step procedure consisting of the detection module and the tracking module. In this paper, we improve both steps. We improve the detection module by incorporating the temporal information, which is beneficial for detecting small objects. For the tracking module, we propose a novel compressed deep Convolutional Neural Network (CNN) feature based Correlation Filter tracker. By carefully integrating these two modules, the proposed multi-object tracking approach has the ability of re-identification (ReID) once the tracked object gets lost. Extensive experiments were performed on the KITTI and MOT2015 tracking benchmarks. Results indicate that our approach outperforms most state-of-the-art tracking approaches.

Can Technology Improve the Quality of Colonoscopy?

PubMed

Thirumurthi, Selvi; Ross, William A; Raju, Gottumukkala S

2016-07-01

In order for screening colonoscopy to be an effective tool in reducing colon cancer incidence, exams must be performed in a high-quality manner. Quality metrics have been presented by gastroenterology societies and now include higher adenoma detection rate targets than in the past. In many cases, the quality of colonoscopy can often be improved with simple low-cost interventions such as improved procedure technique, implementing split-dose bowel prep, and monitoring individuals' performances. Emerging technology has expanded our field of view and image quality during colonoscopy. We will critically review several technological advances in the context of quality metrics and discuss if technology can really improve the quality of colonoscopy.

Multi-Complementary Model for Long-Term Tracking

PubMed Central

Zhang, Deng; Zhang, Junchang; Xia, Chenyang

2018-01-01

In recent years, video target tracking algorithms have been widely used. However, many tracking algorithms do not achieve satisfactory performance, especially when dealing with problems such as object occlusions, background clutters, motion blur, low illumination color images, and sudden illumination changes in real scenes. In this paper, we incorporate an object model based on contour information into a Staple tracker that combines the correlation filter model and color model to greatly improve the tracking robustness. Since each model is responsible for tracking specific features, the three complementary models combine for more robust tracking. In addition, we propose an efficient object detection model with contour and color histogram features, which has good detection performance and better detection efficiency compared to the traditional target detection algorithm. Finally, we optimize the traditional scale calculation, which greatly improves the tracking execution speed. We evaluate our tracker on the Object Tracking Benchmarks 2013 (OTB-13) and Object Tracking Benchmarks 2015 (OTB-15) benchmark datasets. With the OTB-13 benchmark datasets, our algorithm is improved by 4.8%, 9.6%, and 10.9% on the success plots of OPE, TRE and SRE, respectively, in contrast to another classic LCT (Long-term Correlation Tracking) algorithm. On the OTB-15 benchmark datasets, when compared with the LCT algorithm, our algorithm achieves 10.4%, 12.5%, and 16.1% improvement on the success plots of OPE, TRE, and SRE, respectively. At the same time, it needs to be emphasized that, due to the high computational efficiency of the color model and the object detection model using efficient data structures, and the speed advantage of the correlation filters, our tracking algorithm could still achieve good tracking speed. PMID:29425170

Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment

PubMed Central

Guttmann, Aline; Li, Xinran; Feschet, Fabien; Gaudart, Jean; Demongeot, Jacques; Boire, Jean-Yves; Ouchchane, Lemlih

2015-01-01

In cluster detection of disease, the use of local cluster detection tests (CDTs) is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC) is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps. PMID:26086911

Investigation on iterative multiuser detection physical layer network coding in two-way relay free-space optical links with turbulences and pointing errors.

PubMed

Abu-Almaalie, Zina; Ghassemlooy, Zabih; Bhatnagar, Manav R; Le-Minh, Hoa; Aslam, Nauman; Liaw, Shien-Kuei; Lee, It Ee

2016-11-20

Physical layer network coding (PNC) improves the throughput in wireless networks by enabling two nodes to exchange information using a minimum number of time slots. The PNC technique is proposed for two-way relay channel free space optical (TWR-FSO) communications with the aim of maximizing the utilization of network resources. The multipair TWR-FSO is considered in this paper, where a single antenna on each pair seeks to communicate via a common receiver aperture at the relay. Therefore, chip interleaving is adopted as a technique to separate the different transmitted signals at the relay node to perform PNC mapping. Accordingly, this scheme relies on the iterative multiuser technique for detection of users at the receiver. The bit error rate (BER) performance of the proposed system is examined under the combined influences of atmospheric loss, turbulence-induced channel fading, and pointing errors (PEs). By adopting the joint PNC mapping with interleaving and multiuser detection techniques, the BER results show that the proposed scheme can achieve a significant performance improvement against the degrading effects of turbulences and PEs. It is also demonstrated that a larger number of simultaneous users can be supported with this new scheme in establishing a communication link between multiple pairs of nodes in two time slots, thereby improving the channel capacity.

Learning-based image preprocessing for robust computer-aided detection

NASA Astrophysics Data System (ADS)

Raghupathi, Laks; Devarakota, Pandu R.; Wolf, Matthias

2013-03-01

Recent studies have shown that low dose computed tomography (LDCT) can be an effective screening tool to reduce lung cancer mortality. Computer-aided detection (CAD) would be a beneficial second reader for radiologists in such cases. Studies demonstrate that while iterative reconstructions (IR) improve LDCT diagnostic quality, it however degrades CAD performance significantly (increased false positives) when applied directly. For improving CAD performance, solutions such as retraining with newer data or applying a standard preprocessing technique may not be suffice due to high prevalence of CT scanners and non-uniform acquisition protocols. Here, we present a learning-based framework that can adaptively transform a wide variety of input data to boost an existing CAD performance. This not only enhances their robustness but also their applicability in clinical workflows. Our solution consists of applying a suitable pre-processing filter automatically on the given image based on its characteristics. This requires the preparation of ground truth (GT) of choosing an appropriate filter resulting in improved CAD performance. Accordingly, we propose an efficient consolidation process with a novel metric. Using key anatomical landmarks, we then derive consistent feature descriptors for the classification scheme that then uses a priority mechanism to automatically choose an optimal preprocessing filter. We demonstrate CAD prototype∗ performance improvement using hospital-scale datasets acquired from North America, Europe and Asia. Though we demonstrated our results for a lung nodule CAD, this scheme is straightforward to extend to other post-processing tools dedicated to other organs and modalities.

Preliminary Measures of Instructor Learning in Teaching Junctional Tourniquet Users.

PubMed

Kragh, John F; Aden, James K; Shackelford, Stacy; Dubick, Michael A

2016-01-01

The objective of the present study was to assess the effect of instructor learning on student performance in use of junctional tourniquets. From a convenience sample of data available after another study, we used a manikin for assessment of control of bleeding from a right groin gunshot wound. Blood loss was measured by the instructor while training users. The data set represented a group of 30 persons taught one at a time. The first measure was a plot of mean blood loss volumes for the sequential users. The second measure was a plot of the cumulative sum (CUSUM) of mean blood loss (BL) volumes for users. Mean blood loss trended down as the instructor gained experience with each newly instructed user. User performance continually improved as the instructor gained more experience with teaching. No plateau effect was observed within the 30 users. The CUSUM plot illustrated a turning point or cusp at the seventh user. The prior portion of the plot (users 1-7) had the greatest improvement; performance did not improve as much thereafter. The improvement after the seventh user was the only change detected in the instructor's trend of performance. The instructor's teaching experience appeared to directly affect user performance; in a model of junctional hemorrhage, the volume of blood loss from the manikin during junctional tourniquet placement was a useful metric of instructor learning. The CUSUM technique detected a small but meaningful change in trend where the instructor learning curve was greatest while working with the first seven users. 2016.

Coast Guard Surface Vessel Radar Detection Performance

DTIC Science & Technology

1982-04-01

conjunction with two vis-, ual detection experiments in 1980 and 1981 and a dedicated electronic detection experiment in 1981 conducted by the U.S.C.G. R&D...Center. These are partof 4 series of experiments designed to improve search planning guidance contained in the National Search and Rescue Manual. Eighty...BACKGROUND .................... 1-. 1.1 SCOPE . . 1.2 AN/SPS-64(V) AND AN/SPS-66 SYSTEM DESCRIPTIONS . . . . . . 1-1 1.3 DESCRIPTION OF THE EXPERIMENTS

Laser heterodyne detection techniques. [for atmospheric monitoring applications

NASA Technical Reports Server (NTRS)

Menzies, R. T.

1976-01-01

The principles of heterodyne radiometry are examined, taking into account thermal radiation, the Dicke microwave radiometer, photomixing in the infrared, and signal-to-noise considerations. The passive heterodyne radiometer is considered and a description is presented of heterodyne techniques in active monitoring systems. Attention is given to gas emissivities in the infrared, component requirements, experimental heterodyne detection of gases, a comparison of the passive heterodyne radiometer with the Michelson interferometer-spectrometer, airborne monitoring applications, turbulence effects on passive heterodyne radiometry, sensitivity improvements with heterodyning, atmosphere-induced degradation of bistatic system performance, pollutant detection experiments with a bistatic system, and the airborne laser absorption spectrometer. Future improvements in spectral flexibility are also discussed.

Sensor integration of multiple tripolar concentric ring electrodes improves pentylenetetrazole-induced seizure onset detection in rats.

PubMed

Makeyev, Oleksandr; Ding, Quan; Kay, Steven M; Besio, Walter G

2012-01-01

As epilepsy affects approximately one percent of the world population, electrical stimulation of the brain has recently shown potential for additive seizure control therapy. Previously, we applied noninvasive transcranial focal stimulation via tripolar concentric ring electrodes on the scalp of rats after inducing seizures with pentylenetetrazole. We developed a system to detect seizures and automatically trigger the stimulation and evaluated the system on the electrographic activity from rats. In this preliminary study we propose and validate a novel seizure onset detection algorithm based on exponentially embedded family. Unlike the previously proposed approach it integrates the data from multiple electrodes allowing an improvement of the detector performance.

A New Reassigned Spectrogram Method in Interference Detection for GNSS Receivers.

PubMed

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-09-02

Interference detection is very important for Global Navigation Satellite System (GNSS) receivers. Current work on interference detection in GNSS receivers has mainly focused on time-frequency (TF) analysis techniques, such as spectrogram and Wigner-Ville distribution (WVD), where the spectrogram approach presents the TF resolution trade-off problem, since the analysis window is used, and the WVD method suffers from the very serious cross-term problem, due to its quadratic TF distribution nature. In order to solve the cross-term problem and to preserve good TF resolution in the TF plane at the same time, in this paper, a new TF distribution by using a reassigned spectrogram has been proposed in interference detection for GNSS receivers. This proposed reassigned spectrogram method efficiently combines the elimination of the cross-term provided by the spectrogram itself according to its inherent nature and the improvement of the TF aggregation property achieved by the reassignment method. Moreover, a notch filter has been adopted in interference mitigation for GNSS receivers, where receiver operating characteristics (ROCs) are used as metrics for the characterization of interference mitigation performance. The proposed interference detection method by using a reassigned spectrogram is evaluated by experiments on GPS L1 signals in the disturbing scenarios in comparison to the state-of-the-art TF analysis approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-term problem and also keeps good TF localization properties, which has been proven to be valid and effective to enhance the interference Sensors 2015, 15 22168 detection performance; in addition, the adoption of the notch filter in interference mitigation has shown a significant acquisition performance improvement in terms of ROC curves for GNSS receivers in jamming environments.

A New Reassigned Spectrogram Method in Interference Detection for GNSS Receivers

PubMed Central

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-01-01

Interference detection is very important for Global Navigation Satellite System (GNSS) receivers. Current work on interference detection in GNSS receivers has mainly focused on time-frequency (TF) analysis techniques, such as spectrogram and Wigner–Ville distribution (WVD), where the spectrogram approach presents the TF resolution trade-off problem, since the analysis window is used, and the WVD method suffers from the very serious cross-term problem, due to its quadratic TF distribution nature. In order to solve the cross-term problem and to preserve good TF resolution in the TF plane at the same time, in this paper, a new TF distribution by using a reassigned spectrogram has been proposed in interference detection for GNSS receivers. This proposed reassigned spectrogram method efficiently combines the elimination of the cross-term provided by the spectrogram itself according to its inherent nature and the improvement of the TF aggregation property achieved by the reassignment method. Moreover, a notch filter has been adopted in interference mitigation for GNSS receivers, where receiver operating characteristics (ROCs) are used as metrics for the characterization of interference mitigation performance. The proposed interference detection method by using a reassigned spectrogram is evaluated by experiments on GPS L1 signals in the disturbing scenarios in comparison to the state-of-the-art TF analysis approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-term problem and also keeps good TF localization properties, which has been proven to be valid and effective to enhance the interference detection performance; in addition, the adoption of the notch filter in interference mitigation has shown a significant acquisition performance improvement in terms of ROC curves for GNSS receivers in jamming environments. PMID:26364637

EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures.

PubMed

Wang, Lei; Long, Xi; Arends, Johan B A M; Aarts, Ronald M

2017-10-01

The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed. A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG. A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FD t /h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FD t /h of 1.4s). The proposed VGS-based features can help improve seizure detection for ID patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Experimental Detection and Characterization of Void using Time-Domain Reflection Wave

NASA Astrophysics Data System (ADS)

Zahari, M. N. H.; Madun, A.; Dahlan, S. H.; Joret, A.; Zainal Abidin, M. H.; Mohammad, A. H.; Omar, A. H.

2018-04-01

Recent technologies in engineering views have brought the significant improvement in terms of performance and precision. One of those improvements is in geophysics studies for underground detection. Reflection method has been demonstrated to able to detect and locate subsurface anomalies in previous studies, including voids. Conventional method merely involves field testing only for limited areas. This may lead to undiscovered of the void position. Problems arose when the voids were not recognised in early stage and thus, causing hazards, costs increment, and can lead to serious accidents and structural damages. Therefore, to achieve better certainty of the site investigation, a dynamic approach is needed to be implemented. To estimate and characterize the anomalies signal in a better way, an attempt has been made to model air-filled void as experimental testing at site. Robust detection and characterization of voids through inexpensive cost using reflection method are proposed to improve the detectability and characterization of the void. The result shows 2-Dimensional and 3-Dimensional analyses of void based on reflection data with P-waves velocity at 454.54 m/s.

Visual performance for trip hazard detection when using incandescent and led miner cap lamps.

PubMed

Sammarco, John J; Gallagher, Sean; Reyes, Miguel

2010-04-01

Accident data for 2003-2007 indicate that slip, trip, and falls (STFs) are the second leading accident class (17.8%, n=2,441) of lost-time injuries in underground mining. Proper lighting plays a critical role in enabling miners to detect STF hazards in this environment. Often, the only lighting available to the miner is from a cap lamp worn on the miner's helmet. The focus of this research was to determine if the spectral content of light from light-emitting diode (LED) cap lamps enabled visual performance improvements for the detection of tripping hazards as compared to incandescent cap lamps that are traditionally used in underground mining. A secondary objective was to determine the effects of aging on visual performance. The visual performance of 30 subjects was quantified by measuring each subject's speed and accuracy in detecting objects positioned on the floor both in the near field, at 1.83 meters, and far field, at 3.66 meters. Near field objects were positioned at 0 degrees and +/-20 degrees off axis, while far field objects were positioned at 0 degrees and +/-10 degrees off axis. Three age groups were designated: group A consisted of subjects 18 to 25 years old, group B consisted of subjects 40 to 50 years old, and group C consisted of subjects 51 years and older. Results of the visual performance comparison for a commercially available LED, a prototype LED, and an incandescent cap lamp indicate that the location of objects on the floor, the type of cap lamp used, and subject age all had significant influences on the time required to identify potential trip hazards. The LED-based cap lamps enabled detection times that were an average of 0.96 seconds faster compared to the incandescent cap lamp. Use of the LED cap lamps resulted in average detection times that were about 13.6% faster than those recorded for the incandescent cap lamp. The visual performance differences between the commercially available LED and prototype LED cap lamp were not statistically significant. It can be inferred from this data that the spectral content from LED-based cap lamps could enable significant visual performance improvements for miners in the detection of trip hazards. Published by Elsevier Ltd.

Improvement in detection of small wildfires

NASA Astrophysics Data System (ADS)

Sleigh, William J.

1991-12-01

Detecting and imaging small wildfires with an Airborne Scanner is done against generally high background levels. The Airborne Scanner System used is a two-channel thermal IR scanner, with one channel selected for imaging the terrain and the other channel sensitive to hotter targets. If a relationship can be determined between the two channels that quantifies the background signal for hotter targets, then an algorithm can be determined that removes the background signal in that channel leaving only the fire signal. The relationship can be determined anywhere between various points in the signal processing of the radiometric data from the radiometric input to the quantized output of the system. As long as only linear operations are performed on the signal, the relationship will only depend on the system gain and offsets within the range of interest. The algorithm can be implemented either by using a look-up table or performing the calculation in the system computer. The current presentation will describe the algorithm, its derivation, and its implementation in the Firefly Wildfire Detection System by means of an off-the-shelf commercial scanner. Improvement over the previous algorithm used and the margin gained for improving the imaging of the terrain will be demonstrated.

Improvement in detection of small wildfires

NASA Technical Reports Server (NTRS)

Sleigh, William J.

1991-01-01

Detecting and imaging small wildfires with an Airborne Scanner is done against generally high background levels. The Airborne Scanner System used is a two-channel thermal IR scanner, with one channel selected for imaging the terrain and the other channel sensitive to hotter targets. If a relationship can be determined between the two channels that quantifies the background signal for hotter targets, then an algorithm can be determined that removes the background signal in that channel leaving only the fire signal. The relationship can be determined anywhere between various points in the signal processing of the radiometric data from the radiometric input to the quantized output of the system. As long as only linear operations are performed on the signal, the relationship will only depend on the system gain and offsets within the range of interest. The algorithm can be implemented either by using a look-up table or performing the calculation in the system computer. The current presentation will describe the algorithm, its derivation, and its implementation in the Firefly Wildfire Detection System by means of an off-the-shelf commercial scanner. Improvement over the previous algorithm used and the margin gained for improving the imaging of the terrain will be demonstrated.

Detection performance of three different lightning location networks in Beijing area based on accurate fast antenna records

NASA Astrophysics Data System (ADS)

Srivastava, A.; Tian, Y.; Wang, D.; Yuan, S.; Chen, Z.; Sun, Z.; Qie, X.

2016-12-01

Scientists have developed the regional and worldwide lightning location network to study the lightning physics and locating the lightning stroke. One of the key issue in all the networks; to recognize the performance of the network. The performance of each network would be different based on the regional geographic conditions and the instrumental limitation. To improve the performance of the network. it is necessary to know the ground truth of the network and to discuss about the detection efficiency (DE) and location accuracy (LA). A comparative study has been discussed among World Wide Lightning Location Network (WWLLN), ADvanced TOA and Direction system (ADTD) and Beijing Lightning NETwork (BLNET) lightning detection network in Beijing area. WWLLN locate the cloud to ground (CG) and strong inter cloud (IC) globally without demonstrating any differences. ADTD locate the CG strokes in the entire China as regional. Both these networks are long range detection system that does not provide the focused details of a thunderstorm. BLNET can locate the CG and IC and is focused on thunderstorm detection. The waveform of fast antenna checked manually and the relative DE among the three networks has been obtained based on the CG strokes. The relative LA has been obtained using the matched flashes among these networks as well as LA obtained using the strike on the tower. The relative DE of BLNET is much higher than the ADTD and WWLLN as these networks has approximately similar relative DE. The relative LA of WWLLN and ADTD location is eastward and northward respectively from the BLNET. The LA based on tower observation is relatively high-quality in favor of BLNET. The ground truth of WWLLN, ADTD and BLNET has been obtained and found the performance of BLNET network is much better. This study is helpful to improve the performance of the networks and to provide a belief of LA that can follow the thunderstorm path with the prediction and forecasting of thunderstorm and lightning.

Glider communications and controls for the sea sentry mission.

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

Feddema, John Todd; Dohner, Jeffrey Lynn

2005-03-01

This report describes a system level study on the use of a swarm of sea gliders to detect, confirm and kill littoral submarine threats. The report begins with a description of the problem and derives the probability of detecting a constant speed threat without networking. It was concluded that glider motion does little to improve this probability unless the speed of a glider is greater than the speed of the threat. Therefore, before detection, the optimal character for a swarm of gliders is simply to lie in wait for the detection of a threat. The report proceeds by describing themore » effect of noise on the localization of a threat once initial detection is achieved. This noise is estimated as a function of threat location relative to the glider and is temporally reduced through the use of an information or Kalman filtering. In the next section, the swarm probability of confirming and killing a threat is formulated. Results are compared to a collection of stationary sensors. These results show that once a glider has the ability to move faster than the threat, the performance of the swarm is equal to the performance of a stationary swarm of gliders with confirmation and kill ranges equal to detection range. Moreover, at glider speeds greater than the speed of the threat, swarm performance becomes a weak function of speed. At these speeds swarm performance is dominated by detection range. Therefore, to future enhance swarm performance or to reduce the number of gliders required for a given performance, detection range must be increased. Communications latency is also examined. It was found that relatively large communication delays did little to change swarm performance. Thus gliders may come to the surface and use SATCOMS to effectively communicate in this application.« less

Automatic Detection of Frontal Face Midline by Chain-coded Merlin-Farber Hough Trasform

NASA Astrophysics Data System (ADS)

Okamoto, Daichi; Ohyama, Wataru; Wakabayashi, Tetsushi; Kimura, Fumitaka

We propose a novel approach for detection of the facial midline (facial symmetry axis) from a frontal face image. The facial midline has several applications, for instance reducing computational cost required for facial feature extraction (FFE) and postoperative assessment for cosmetic or dental surgery. The proposed method detects the facial midline of a frontal face from an edge image as the symmetry axis using the Merlin-Faber Hough transformation. And a new performance improvement scheme for midline detection by MFHT is present. The main concept of the proposed scheme is suppression of redundant vote on the Hough parameter space by introducing chain code representation for the binary edge image. Experimental results on the image dataset containing 2409 images from FERET database indicate that the proposed algorithm can improve the accuracy of midline detection from 89.9% to 95.1 % for face images with different scales and rotation.

Probabilistic monitoring in intrusion detection module for energy efficiency in mobile ad hoc networks

NASA Astrophysics Data System (ADS)

De Rango, Floriano; Lupia, Andrea

2016-05-01

MANETs allow mobile nodes communicating to each other using the wireless medium. A key aspect of these kind of networks is the security, because their setup is done without an infrastructure, so external nodes could interfere in the communication. Mobile nodes could be compromised, misbehaving during the multi-hop transmission of data, or they could have a selfish behavior to save energy, which is another important constraint in MANETs. The detection of these behaviors need a framework that takes into account the latest interactions among nodes, so malicious or selfish nodes could be detected also if their behavior is changed over time. The monitoring activity increases the energy consumption, so our proposal takes into account this issue reducing the energy required by the monitoring system, keeping the effectiveness of the intrusion detection system. The results show an improvement in the saved energy, improving the detection performance too.

Detection of forced oscillations in power systems with multichannel methods

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

Follum, James D.

2015-09-30

The increasing availability of high fidelity, geographically dispersed measurements in power systems improves the ability of researchers and engineers to study dynamic behaviors in the grid. One such behavior that is garnering increased attention is the presence of forced oscillations. Power system engineers are interested in forced oscillations because they are often symptomatic of the malfunction or misoperation of equipment. Though the resulting oscillation is not always large in amplitude, the root cause may be serious. In this report, multi-channel forced oscillation detection methods are developed. These methods leverage previously developed detection approaches based on the periodogram and spectral-coherence. Makingmore » use of geographically distributed channels of data is shown to improved detection performance and shorten the delay before an oscillation can be detected in the online environment. Results from simulated and measured power system data are presented.« less

An Algorithm to Improve Test Answer Copying Detection Using the Omega Statistic

ERIC Educational Resources Information Center

Maeda, Hotaka; Zhang, Bo

2017-01-01

The omega (?) statistic is reputed to be one of the best indices for detecting answer copying on multiple choice tests, but its performance relies on the accurate estimation of copier ability, which is challenging because responses from the copiers may have been contaminated. We propose an algorithm that aims to identify and delete the suspected…

Infiltrated photonic crystal cavity as a highly sensitive platform for glucose concentration detection

NASA Astrophysics Data System (ADS)

Arafa, Safia; Bouchemat, Mohamed; Bouchemat, Touraya; Benmerkhi, Ahlem; Hocini, Abdesselam

2017-02-01

A Bio-sensing platform based on an infiltrated photonic crystal ring shaped holes cavity-coupled waveguide system is proposed for glucose concentration detection. Considering silicon-on-insulator (SOI) technology, it has been demonstrated that the ring shaped holes configuration provides an excellent optical confinement within the cavity region, which further enhances the light-matter interactions at the precise location of the analyte medium. Thus, the sensitivity and the quality factor (Q) can be significantly improved. The transmission characteristics of light in the biosensor under different refractive indices that correspond to the change in the analyte glucose concentration are analyzed by performing finite-difference time-domain (FDTD) simulations. Accordingly, an improved sensitivity of 462 nm/RIU and a Q factor as high as 1.11х105 have been achieved, resulting in a detection limit of 3.03х10-6 RIU. Such combination of attributes makes the designed structure a promising element for performing label-free biosensing in medical diagnosis and environmental monitoring.

Knock detection system to improve petrol engine performance, using microphone sensor

NASA Astrophysics Data System (ADS)

Sujono, Agus; Santoso, Budi; Juwana, Wibawa Endra

2017-01-01

An increase of power and efficiency of spark ignition engines (petrol engines) are always faced with the problem of knock. Even the characteristics of the engine itself are always determined from the occurrence of knock. Until today, this knocking problem has not been solved completely. Knock is caused by principal factors that are influenced by the engine rotation, the load or opening the throttle and spark advance (ignition timing). In this research, the engine is mounted on the engine test bed (ETB) which is equipped with the necessary sensors. Knock detection using a new method, which is based on pattern recognition, which through the knock sound detection by using a microphone sensor, active filter, the regression of the normalized envelope function, and the calculation of the Euclidean distance is used for identifying knock. This system is implemented with a microcontroller which uses fuzzy logic controller ignition (FLIC), which aims to set proper spark advance, in accordance with operating conditions. This system can improve the engine performance for approximately 15%.

Optimization of OT-MACH Filter Generation for Target Recognition

NASA Technical Reports Server (NTRS)

Johnson, Oliver C.; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

2009-01-01

An automatic Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter generator for use in a gray-scale optical correlator (GOC) has been developed for improved target detection at JPL. While the OT-MACH filter has been shown to be an optimal filter for target detection, actually solving for the optimum is too computationally intensive for multiple targets. Instead, an adaptive step gradient descent method was tested to iteratively optimize the three OT-MACH parameters, alpha, beta, and gamma. The feedback for the gradient descent method was a composite of the performance measures, correlation peak height and peak to side lobe ratio. The automated method generated and tested multiple filters in order to approach the optimal filter quicker and more reliably than the current manual method. Initial usage and testing has shown preliminary success at finding an approximation of the optimal filter, in terms of alpha, beta, gamma values. This corresponded to a substantial improvement in detection performance where the true positive rate increased for the same average false positives per image.

Intermolecular G-quadruplex structure-based fluorescent DNA detection system.

PubMed

Zhou, Hui; Wu, Zai-Sheng; Shen, Guo-Li; Yu, Ru-Qin

2013-03-15

Adopting multi-donors to pair with one acceptor could improve the performance of fluorogenic detection probes. However, common dyes (e.g., fluorescein) in close proximity to each other would self-quench the fluorescence, and the fluorescence is difficult to restore. In this contribution, we constructed a novel "multi-donors-to-one acceptor" fluorescent DNA detection system by means of the intermolecular G-quadruplex (IGQ) structure-based fluorescence signal enhancement combined with the hairpin oligonucleotide. The novel IGQ-hairpin system was characterized using the p53 gene as the model target DNA. The proposed system showed an improved assay performance due to the introduction of IGQ-structure into fluorescent signaling probes, which could inhibit the background fluorescence and increase fluorescence restoration amplitude of fluoresceins upon target DNA hybridization. The proof-of-concept scheme is expected to provide new insight into the potential of G-quadruplex structure and promote the application of fluorescent oligonucleotide probes in fundamental research, diagnosis, and treatment of genetic diseases. Copyright © 2012 Elsevier B.V. All rights reserved.

Improvement of retinal blood vessel detection by spur removal and Gaussian matched filtering compensation

NASA Astrophysics Data System (ADS)

Xiao, Di; Vignarajan, Janardhan; An, Dong; Tay-Kearney, Mei-Ling; Kanagasingam, Yogi

2016-03-01

Retinal photography is a non-invasive and well-accepted clinical diagnosis of ocular diseases. Qualitative and quantitative assessment of retinal images is crucial in ocular diseases related clinical application. In this paper, we proposed approaches for improving the quality of blood vessel detection based on our initial blood vessel detection methods. A blood vessel spur pruning method has been developed for removing the blood vessel spurs both on vessel medial lines and binary vessel masks, which are caused by artifacts and side-effect of Gaussian matched vessel enhancement. A Gaussian matched filtering compensation method has been developed for removing incorrect vessel branches in the areas of low illumination. The proposed approaches were applied and tested on the color fundus images from one publicly available database and our diabetic retinopathy screening dataset. A preliminary result has demonstrated the robustness and good performance of the proposed approaches and their potential application for improving retinal blood vessel detection.

Hypoglycemia alarm enhancement using data fusion.

PubMed

Skladnev, Victor N; Tarnavskii, Stanislav; McGregor, Thomas; Ghevondian, Nejhdeh; Gourlay, Steve; Jones, Timothy W

2010-01-01

The acceptance of closed-loop blood glucose (BG) control using continuous glucose monitoring systems (CGMS) is likely to improve with enhanced performance of their integral hypoglycemia alarms. This article presents an in silico analysis (based on clinical data) of a modeled CGMS alarm system with trained thresholds on type 1 diabetes mellitus (T1DM) patients that is augmented by sensor fusion from a prototype hypoglycemia alarm system (HypoMon). This prototype alarm system is based on largely independent autonomic nervous system (ANS) response features. Alarm performance was modeled using overnight BG profiles recorded previously on 98 T1DM volunteers. These data included the corresponding ANS response features detected by HypoMon (AiMedics Pty. Ltd.) systems. CGMS data and alarms were simulated by applying a probabilistic model to these overnight BG profiles. The probabilistic model developed used a mean response delay of 7.1 minutes, measurement error offsets on each sample of +/- standard deviation (SD) = 4.5 mg/dl (0.25 mmol/liter), and vertical shifts (calibration offsets) of +/- SD = 19.8 mg/dl (1.1 mmol/liter). Modeling produced 90 to 100 simulated measurements per patient. Alarm systems for all analyses were optimized on a training set of 46 patients and evaluated on the test set of 56 patients. The split between the sets was based on enrollment dates. Optimization was based on detection accuracy but not time to detection for these analyses. The contribution of this form of data fusion to hypoglycemia alarm performance was evaluated by comparing the performance of the trained CGMS and fused data algorithms on the test set under the same evaluation conditions. The simulated addition of HypoMon data produced an improvement in CGMS hypoglycemia alarm performance of 10% at equal specificity. Sensitivity improved from 87% (CGMS as stand-alone measurement) to 97% for the enhanced alarm system. Specificity was maintained constant at 85%. Positive predictive values on the test set improved from 61 to 66% with negative predictive values improving from 96 to 99%. These enhancements were stable within sensitivity analyses. Sensitivity analyses also suggested larger performance increases at lower CGMS alarm performance levels. Autonomic nervous system response features provide complementary information suitable for fusion with CGMS data to enhance nocturnal hypoglycemia alarms. 2010 Diabetes Technology Society.

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.

From Pacemaker to Wearable: Techniques for ECG Detection Systems.

PubMed

Kumar, Ashish; Komaragiri, Rama; Kumar, Manjeet

2018-01-11

With the alarming rise in the deaths due to cardiovascular diseases (CVD), present medical research scenario places notable importance on techniques and methods to detect CVDs. As adduced by world health organization, technological proceeds in the field of cardiac function assessment have become the nucleus and heart of all leading research studies in CVDs in which electrocardiogram (ECG) analysis is the most functional and convenient tool used to test the range of heart-related irregularities. Most of the approaches present in the literature of ECG signal analysis consider noise removal, rhythm-based analysis, and heartbeat detection to improve the performance of a cardiac pacemaker. Advancements achieved in the field of ECG segments detection and beat classification have a limited evaluation and still require clinical approvals. In this paper, approaches on techniques to implement on-chip ECG detector for a cardiac pacemaker system are discussed. Moreover, different challenges regarding the ECG signal morphology analysis deriving from medical literature is extensively reviewed. It is found that robustness to noise, wavelet parameter choice, numerical efficiency, and detection performance are essential performance indicators required by a state-of-the-art ECG detector. Furthermore, many algorithms described in the existing literature are not verified using ECG data from the standard databases. Some ECG detection algorithms show very high detection performance with the total number of detected QRS complexes. However, the high detection performance of the algorithm is verified using only a few datasets. Finally, gaps in current advancements and testing are identified, and the primary challenge remains to be implementing bullseye test for morphology analysis evaluation.

Explosive detection using high-volume vapor sampling and analysis by trained canines and ultra-trace detection equipment

NASA Astrophysics Data System (ADS)

Fisher, Mark; Sikes, John; Prather, Mark

2004-09-01

The dog's nose is an effective, highly-mobile sampling system, while the canine olfactory organs are an extremely sensitive detector. Having been trained to detect a wide variety of substances with exceptional results, canines are widely regarded as the 'gold standard' in chemical vapor detection. Historically, attempts to mimic the ability of dogs to detect vapors of explosives using electronic 'dogs noses' has proven difficult. However, recent advances in technology have resulted in development of detection (i.e., sampling and sensor) systems with performance that is rapidly approaching that of trained canines. The Nomadics Fido was the first sensor to demonstrate under field conditions the detection of landmines with performance approaching that of canines. More recently, comparative testing of Fido against canines has revealed that electronic vapor detection, when coupled with effective sampling methods, can produce results comparable to that of highly-trained canines. The results of these comparative tests will be presented, as will recent test results in which explosives hidden in cargo were detected using Fido with a high-volume sampling technique. Finally, the use of canines along with electronic sensors will be discussed as a means of improving the performance and expanding the capabilities of both methods.

Optimization of strand displacement amplification-sensitized G-quadruplex DNAzyme-based sensing system and its application in activity detection of uracil-DNA glycosylase.

PubMed

Du, Yi-Chen; Jiang, Hong-Xin; Huo, Yan-Fang; Han, Gui-Mei; Kong, De-Ming

2016-03-15

As an isothermal nucleic acid amplification technique, strand displacement amplification (SDA) reaction has been introduced in G-quadruplex DNAzyme-based sensing system to improve the sensing performance. To further provide useful information for the design of SDA-amplified G-quadruplex DNAzyme-based sensors, the effects of nicking site number in SDA template DNA were investigated. With the increase of the nicking site number from 1 to 2, enrichment of G-quadruplex DNAzyme by SDA is changed from a linear amplification to an exponential amplification, thus greatly increasing the amplification efficiency and subsequently improving the sensing performance of corresponding sensing system. The nicking site number cannot be further increased because more nicking sites might result in high background signals. However, we demonstrated that G-quadruplex DNAzyme enrichment efficiency could be further improved by introducing a cross-triggered SDA strategy, in which two templates each has two nicking sites are used. To validate the proposed cross-triggered SDA strategy, we used it to develop a sensing platform for the detection of uracil-DNA glycosylase (UDG) activity. The sensor enables sensitive detection of UDG activity in the range of 1 × 10(-4)-1 U/mL with a detection limit of 1 × 10(-4)U/mL. Copyright © 2015 Elsevier B.V. All rights reserved.

Algorithm improvement program nuclide identification algorithm scoring criteria and scoring application.

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

Enghauser, Michael

2016-02-01

The goal of the Domestic Nuclear Detection Office (DNDO) Algorithm Improvement Program (AIP) is to facilitate gamma-radiation detector nuclide identification algorithm development, improvement, and validation. Accordingly, scoring criteria have been developed to objectively assess the performance of nuclide identification algorithms. In addition, a Microsoft Excel spreadsheet application for automated nuclide identification scoring has been developed. This report provides an overview of the equations, nuclide weighting factors, nuclide equivalencies, and configuration weighting factors used by the application for scoring nuclide identification algorithm performance. Furthermore, this report presents a general overview of the nuclide identification algorithm scoring application including illustrative examples.

Improved pressurized Marinelli beaker measurements of radioactive xenon in air.

PubMed

Robinson, Troy; Mann, Nick; Houghton, Tracy; Watrous, Matthew; Peterson, John; Fabian, Paul; Hipp, Pat; Reavis, Mark; Fernandez, Francisco

2017-08-01

INL has shown that a Marinelli beaker geometry can be used for the measurement of radioactive xenon in air using an aluminum Marinelli. A carbon fiber Marinelli was designed and constructed to improve overall performance. This composite Marinelli can withstand sample pressures of 276bar and achieve approximately a 4x performance improvement in the minimum detectable concentrations (MDCs) and concentration uncertainties. The MDCs obtained during a 24h assay for 133 Xe, 131m Xe, and 135 Xe are: 1.4, 13, and 0.35Bq/m 3 . Copyright © 2016. Published by Elsevier Ltd.

Improving detection of low SNR targets using moment-based detection

NASA Astrophysics Data System (ADS)

Young, Shannon R.; Steward, Bryan J.; Hawks, Michael; Gross, Kevin C.

2016-05-01

Increases in the number of cameras deployed, frame rate, and detector array sizes have led to a dramatic increase in the volume of motion imagery data that is collected. Without a corresponding increase in analytical manpower, much of the data is not analyzed to full potential. This creates a need for fast, automated, and robust methods for detecting signals of interest. Current approaches fall into two categories: detect-before-track (DBT), which are fast but often poor at detecting dim targets, and track-before-detect (TBD) methods which can offer better performance but are typically much slower. This research seeks to contribute to the near real time detection of low SNR, unresolved moving targets through an extension of earlier work on higher order moments anomaly detection, a method that exploits both spatial and temporal information but is still computationally efficient and massively parallelizable. It was found that intelligent selection of parameters can improve probability of detection by as much as 25% compared to earlier work with higherorder moments. The present method can reduce detection thresholds by 40% compared to the Reed-Xiaoli anomaly detector for low SNR targets (for a given probability of detection and false alarm).

Using transfer learning to detect galaxy mergers

NASA Astrophysics Data System (ADS)

Ackermann, Sandro; Schawinksi, Kevin; Zhang, Ce; Weigel, Anna K.; Turp, M. Dennis

2018-05-01

We investigate the use of deep convolutional neural networks (deep CNNs) for automatic visual detection of galaxy mergers. Moreover, we investigate the use of transfer learning in conjunction with CNNs, by retraining networks first trained on pictures of everyday objects. We test the hypothesis that transfer learning is useful for improving classification performance for small training sets. This would make transfer learning useful for finding rare objects in astronomical imaging datasets. We find that these deep learning methods perform significantly better than current state-of-the-art merger detection methods based on nonparametric systems like CAS and GM20. Our method is end-to-end and robust to image noise and distortions; it can be applied directly without image preprocessing. We also find that transfer learning can act as a regulariser in some cases, leading to better overall classification accuracy (p = 0.02). Transfer learning on our full training set leads to a lowered error rate from 0.0381 down to 0.0321, a relative improvement of 15%. Finally, we perform a basic sanity-check by creating a merger sample with our method, and comparing with an already existing, manually created merger catalogue in terms of colour-mass distribution and stellar mass function.

Radar fall detection using principal component analysis

NASA Astrophysics Data System (ADS)

Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

2016-05-01

Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

Potential fault region detection in TFDS images based on convolutional neural network

NASA Astrophysics Data System (ADS)

Sun, Junhua; Xiao, Zhongwen

2016-10-01

In recent years, more than 300 sets of Trouble of Running Freight Train Detection System (TFDS) have been installed on railway to monitor the safety of running freight trains in China. However, TFDS is simply responsible for capturing, transmitting, and storing images, and fails to recognize faults automatically due to some difficulties such as such as the diversity and complexity of faults and some low quality images. To improve the performance of automatic fault recognition, it is of great importance to locate the potential fault areas. In this paper, we first introduce a convolutional neural network (CNN) model to TFDS and propose a potential fault region detection system (PFRDS) for simultaneously detecting four typical types of potential fault regions (PFRs). The experimental results show that this system has a higher performance of image detection to PFRs in TFDS. An average detection recall of 98.95% and precision of 100% are obtained, demonstrating the high detection ability and robustness against various poor imaging situations.

Research on capability of detecting ballistic missile by near space infrared system

NASA Astrophysics Data System (ADS)

Lu, Li; Sheng, Wen; Jiang, Wei; Jiang, Feng

2018-01-01

The infrared detection technology of ballistic missile based on near space platform can effectively make up the shortcomings of high-cost of traditional early warning satellites and the limited earth curvature of ground-based early warning radar. In terms of target detection capability, aiming at the problem that the formula of the action distance based on contrast performance ignores the background emissivity in the calculation process and the formula is only valid for the monochromatic light, an improved formula of the detecting range based on contrast performance is proposed. The near space infrared imaging system parameters are introduced, the expression of the contrastive action distance formula based on the target detection of the near space platform is deduced. The detection range of the near space infrared system for the booster stage ballistic missile skin, the tail nozzle and the tail flame is calculated. The simulation results show that the near-space infrared system has the best effect on the detection of tail-flame radiation.

Development of a Machine Vision Method for the Monitoring of Laying Hens and Detection of Multiple Nest Occupations.

PubMed

Zaninelli, Mauro; Redaelli, Veronica; Luzi, Fabio; Mitchell, Malcolm; Bontempo, Valentino; Cattaneo, Donata; Dell'Orto, Vittorio; Savoini, Giovanni

2018-01-05

Free range systems can improve the welfare of laying hens. However, the access to environmental resources can be partially limited by social interactions, feeding of hens, and productivity, can be not stable and damaging behaviors, or negative events, can be observed more frequently than in conventional housing systems. In order to reach a real improvement of the hens' welfare the study of their laying performances and behaviors is necessary. With this purpose, many systems have been developed. However, most of them do not detect a multiple occupation of the nest negatively affecting the accuracy of data collected. To overcome this issue, a new "nest-usage-sensor" was developed and tested. It was based on the evaluation of thermografic images, as acquired by a thermo-camera, and the performing of patter recognitions on images acquired from the nest interior. The sensor was setup with a "Multiple Nest Occupation Threshold" of 796 colored pixels and a template of triangular shape and sizes of 43 × 33 pixels (high per base). It was tested through an experimental nesting system where 10 hens were reared for a month. Results showed that the evaluation of thermografic images could increase the detection performance of a multiple occupation of the nest and to apply an image pattern recognition technique could allow for counting the number of hens in the nest in case of a multiple occupation. As a consequence, the accuracy of data collected in studies on laying performances and behaviors of hens, reared in a free-range housing system, could result to be improved.

Augmenting intracortical brain-machine interface with neurally driven error detectors

NASA Astrophysics Data System (ADS)

Even-Chen, Nir; Stavisky, Sergey D.; Kao, Jonathan C.; Ryu, Stephen I.; Shenoy, Krishna V.

2017-12-01

Objective. Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs. Approach. We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task. Main results. We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error ‘detect-and-act’ system that attempts to automatically ‘undo’ or ‘prevent’ mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF). Significance. Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks.

PubMed

Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-11-24

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed.

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks

PubMed Central

Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-01-01

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed. PMID:29186756

CONEDEP: COnvolutional Neural network based Earthquake DEtection and Phase Picking

NASA Astrophysics Data System (ADS)

Zhou, Y.; Huang, Y.; Yue, H.; Zhou, S.; An, S.; Yun, N.

2017-12-01

We developed an automatic local earthquake detection and phase picking algorithm based on Fully Convolutional Neural network (FCN). The FCN algorithm detects and segments certain features (phases) in 3 component seismograms to realize efficient picking. We use STA/LTA algorithm and template matching algorithm to construct the training set from seismograms recorded 1 month before and after the Wenchuan earthquake. Precise P and S phases are identified and labeled to construct the training set. Noise data are produced by combining back-ground noise and artificial synthetic noise to form the equivalent scale of noise set as the signal set. Training is performed on GPUs to achieve efficient convergence. Our algorithm has significantly improved performance in terms of the detection rate and precision in comparison with STA/LTA and template matching algorithms.

A normal incidence X-ray telescope

NASA Technical Reports Server (NTRS)

Golub, Leon

1987-01-01

The postflight performance evaluation of the X-ray telescope was summarized. All payload systems and subsystems performed well within acceptable limits, with the sole exception of the light-blocking prefilters. Launch, flight and recovery were performed in a fully satisfactory manner. The payload was recovered in a timely manner and in excellent condition. The prefilter performance analysis showed that no X-ray images were detected on the processed flight film. Recommendations for improved performance are listed.

Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.

PubMed

Mansouri, Majdi; Nounou, Mohamed N; Nounou, Hazem N

2017-09-01

In our previous work, we have demonstrated the effectiveness of the linear multiscale principal component analysis (PCA)-based moving window (MW)-generalized likelihood ratio test (GLRT) technique over the classical PCA and multiscale principal component analysis (MSPCA)-based GLRT methods. The developed fault detection algorithm provided optimal properties by maximizing the detection probability for a particular false alarm rate (FAR) with different values of windows, and however, most real systems are nonlinear, which make the linear PCA method not able to tackle the issue of non-linearity to a great extent. Thus, in this paper, first, we apply a nonlinear PCA to obtain an accurate principal component of a set of data and handle a wide range of nonlinearities using the kernel principal component analysis (KPCA) model. The KPCA is among the most popular nonlinear statistical methods. Second, we extend the MW-GLRT technique to one that utilizes exponential weights to residuals in the moving window (instead of equal weightage) as it might be able to further improve fault detection performance by reducing the FAR using exponentially weighed moving average (EWMA). The developed detection method, which is called EWMA-GLRT, provides improved properties, such as smaller missed detection and FARs and smaller average run length. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. This provides a more accurate estimation of the GLRT statistic and provides a stronger memory that will enable better decision making with respect to fault detection. Therefore, in this paper, a KPCA-based EWMA-GLRT method is developed and utilized in practice to improve fault detection in biological phenomena modeled by S-systems and to enhance monitoring process mean. The idea behind a KPCA-based EWMA-GLRT fault detection algorithm is to combine the advantages brought forward by the proposed EWMA-GLRT fault detection chart with the KPCA model. Thus, it is used to enhance fault detection of the Cad System in E. coli model through monitoring some of the key variables involved in this model such as enzymes, transport proteins, regulatory proteins, lysine, and cadaverine. The results demonstrate the effectiveness of the proposed KPCA-based EWMA-GLRT method over Q , GLRT, EWMA, Shewhart, and moving window-GLRT methods. The detection performance is assessed and evaluated in terms of FAR, missed detection rates, and average run length (ARL 1 ) values.

A fast automatic target detection method for detecting ships in infrared scenes

NASA Astrophysics Data System (ADS)

Özertem, Kemal Arda

2016-05-01

Automatic target detection in infrared scenes is a vital task for many application areas like defense, security and border surveillance. For anti-ship missiles, having a fast and robust ship detection algorithm is crucial for overall system performance. In this paper, a straight-forward yet effective ship detection method for infrared scenes is introduced. First, morphological grayscale reconstruction is applied to the input image, followed by an automatic thresholding onto the suppressed image. For the segmentation step, connected component analysis is employed to obtain target candidate regions. At this point, it can be realized that the detection is defenseless to outliers like small objects with relatively high intensity values or the clouds. To deal with this drawback, a post-processing stage is introduced. For the post-processing stage, two different methods are used. First, noisy detection results are rejected with respect to target size. Second, the waterline is detected by using Hough transform and the detection results that are located above the waterline with a small margin are rejected. After post-processing stage, there are still undesired holes remaining, which cause to detect one object as multi objects or not to detect an object as a whole. To improve the detection performance, another automatic thresholding is implemented only to target candidate regions. Finally, two detection results are fused and post-processing stage is repeated to obtain final detection result. The performance of overall methodology is tested with real world infrared test data.

Investigation of statistical iterative reconstruction for dedicated breast CT

PubMed Central

Makeev, Andrey; Glick, Stephen J.

2013-01-01

Purpose: Dedicated breast CT has great potential for improving the detection and diagnosis of breast cancer. Statistical iterative reconstruction (SIR) in dedicated breast CT is a promising alternative to traditional filtered backprojection (FBP). One of the difficulties in using SIR is the presence of free parameters in the algorithm that control the appearance of the resulting image. These parameters require tuning in order to achieve high quality reconstructions. In this study, the authors investigated the penalized maximum likelihood (PML) method with two commonly used types of roughness penalty functions: hyperbolic potential and anisotropic total variation (TV) norm. Reconstructed images were compared with images obtained using standard FBP. Optimal parameters for PML with the hyperbolic prior are reported for the task of detecting microcalcifications embedded in breast tissue. Methods: Computer simulations were used to acquire projections in a half-cone beam geometry. The modeled setup describes a realistic breast CT benchtop system, with an x-ray spectra produced by a point source and an a-Si, CsI:Tl flat-panel detector. A voxelized anthropomorphic breast phantom with 280 μm microcalcification spheres embedded in it was used to model attenuation properties of the uncompressed woman's breast in a pendant position. The reconstruction of 3D images was performed using the separable paraboloidal surrogates algorithm with ordered subsets. Task performance was assessed with the ideal observer detectability index to determine optimal PML parameters. Results: The authors' findings suggest that there is a preferred range of values of the roughness penalty weight and the edge preservation threshold in the penalized objective function with the hyperbolic potential, which resulted in low noise images with high contrast microcalcifications preserved. In terms of numerical observer detectability index, the PML method with optimal parameters yielded substantially improved performance (by a factor of greater than 10) compared to FBP. The hyperbolic prior was also observed to be superior to the TV norm. A few of the best-performing parameter pairs for the PML method also demonstrated superior performance for various radiation doses. In fact, using PML with certain parameter values results in better images, acquired using 2 mGy dose, than FBP-reconstructed images acquired using 6 mGy dose. Conclusions: A range of optimal free parameters for the PML algorithm with hyperbolic and TV norm-based potentials is presented for the microcalcification detection task, in dedicated breast CT. The reported values can be used as starting values of the free parameters, when SIR techniques are used for image reconstruction. Significant improvement in image quality can be achieved by using PML with optimal combination of parameters, as compared to FBP. Importantly, these results suggest improved detection of microcalcifications can be obtained by using PML with lower radiation dose to the patient, than using FBP with higher dose. PMID:23927318

Circuit weight training and cardiac morphology: a trial with magnetic resonance imaging.

PubMed

Camargo, M D; Stein, R; Ribeiro, J P; Schvartzman, P R; Rizzatti, M O; Schaan, B D

2008-02-01

Aerobic training (AT) and circuit weight training (CWT) improve peak oxygen uptake (VO(2)peak). During CWT the circulatory system is exposed to higher pressure, which could induce left ventricle morphological adaptations, possibly distinct from those derived from aerobic training. To compare the effects of aerobic training and CWT upon morphological and functional cardiac adaptations detected by magnetic resonance imaging. Twenty healthy sedentary individuals were randomly assigned to participate in a 12-week programme of aerobic training (n = 6), CWR (n = 7) or no intervention (n = 7, controls). Training programmes consisted of 36 sessions, 35 min each, 3 times per week, at 70% of maximal heart rate, and CWT included series of resistance exercises performed at 60% of 1 maximal repetition. Cardiopulmonary exercise testing and cardiac magnetic resonance imaging were performed before and after the intervention. There was a similar improvement in VO(2)peak following aerobic training (mean (SD) increment: 12 (4)%) and CWT (12 (4)%), while there was no change in the control group. Aerobic training (12 (6)%) and CWT (16 (5)%) improved strength in the lower limbs, and only CWT resulted in improvement of 13 (4)% in the strength of the upper limbs. However, there were no detectable changes in left ventricular mass, end-diastolic volume, stroke volume or ejection fraction. In previously sedentary individuals, short-term CWT and aerobic training induce similar improvement in functional capacity without any adaptation in cardiac morphology detectable by cardiac magnetic resonance imaging.

A Viola-Jones based hybrid face detection framework

NASA Astrophysics Data System (ADS)

Murphy, Thomas M.; Broussard, Randy; Schultz, Robert; Rakvic, Ryan; Ngo, Hau

2013-12-01

Improvements in face detection performance would benefit many applications. The OpenCV library implements a standard solution, the Viola-Jones detector, with a statistically boosted rejection cascade of binary classifiers. Empirical evidence has shown that Viola-Jones underdetects in some instances. This research shows that a truncated cascade augmented by a neural network could recover these undetected faces. A hybrid framework is constructed, with a truncated Viola-Jones cascade followed by an artificial neural network, used to refine the face decision. Optimally, a truncation stage that captured all faces and allowed the neural network to remove the false alarms is selected. A feedforward backpropagation network with one hidden layer is trained to discriminate faces based upon the thresholding (detection) values of intermediate stages of the full rejection cascade. A clustering algorithm is used as a precursor to the neural network, to group significant overlappings. Evaluated on the CMU/VASC Image Database, comparison with an unmodified OpenCV approach shows: (1) a 37% increase in detection rates if constrained by the requirement of no increase in false alarms, (2) a 48% increase in detection rates if some additional false alarms are tolerated, and (3) an 82% reduction in false alarms with no reduction in detection rates. These results demonstrate improved face detection and could address the need for such improvement in various applications.

Background Adjusted Alignment-Free Dissimilarity Measures Improve the Detection of Horizontal Gene Transfer.

PubMed

Tang, Kujin; Lu, Yang Young; Sun, Fengzhu

2018-01-01

Horizontal gene transfer (HGT) plays an important role in the evolution of microbial organisms including bacteria. Alignment-free methods based on single genome compositional information have been used to detect HGT. Currently, Manhattan and Euclidean distances based on tetranucleotide frequencies are the most commonly used alignment-free dissimilarity measures to detect HGT. By testing on simulated bacterial sequences and real data sets with known horizontal transferred genomic regions, we found that more advanced alignment-free dissimilarity measures such as CVTree and [Formula: see text] that take into account the background Markov sequences can solve HGT detection problems with significantly improved performance. We also studied the influence of different factors such as evolutionary distance between host and donor sequences, size of sliding window, and host genome composition on the performances of alignment-free methods to detect HGT. Our study showed that alignment-free methods can predict HGT accurately when host and donor genomes are in different order levels. Among all methods, CVTree with word length of 3, [Formula: see text] with word length 3, Markov order 1 and [Formula: see text] with word length 4, Markov order 1 outperform others in terms of their highest F 1 -score and their robustness under the influence of different factors.

Detecting bugweed (Solanum mauritianum) abundance in plantation forestry using multisource remote sensing

NASA Astrophysics Data System (ADS)

Peerbhay, Kabir; Mutanga, Onisimo; Lottering, Romano; Bangamwabo, Victor; Ismail, Riyad

2016-11-01

The invasive weed Solanum mauritianum (bugweed) has infested large areas of plantation forests in KwaZulu-Natal, South Africa. Bugweed often forms dense infestations and rapidly capitalises on available natural resources hindering the production of forest resources. Precise assessment of bugweed canopy cover, especially at low abundance cover, is essential to an effective weed management strategy. In this study, the utility of AISA Eagle airborne hyperspectral data (393-994 nm) with the new generation Worldview-2 multispectral sensor (427-908 nm) was compared to detect the abundance of bugweed cover within the Hodgsons Sappi forest plantation. Using sparse partial least squares discriminant analysis (SPLS-DA), the best detection results were obtained when performing discrimination using the remotely sensing images combined with LiDAR. Overall classification accuracies subsequently improved by 10% and 11.67% for AISA and Worldview-2 respectively, with improved detection accuracies for bugweed cover densities as low as 5%. The incorporation of LiDAR worked well within the SPLS-DA framework for detecting the abundance of bugweed cover using remotely sensed data. In addition, the algorithm performed simultaneous dimension reduction and variable selection successfully whereby wavelengths in the visible (393-670 nm) and red-edge regions (725-734 nm) of the spectrum were the most effective.

Automatic Data Traffic Control on DSM Architecture

NASA Technical Reports Server (NTRS)

Frumkin, Michael; Jin, Hao-Qiang; Yan, Jerry; Kwak, Dochan (Technical Monitor)

2000-01-01

We study data traffic on distributed shared memory machines and conclude that data placement and grouping improve performance of scientific codes. We present several methods which user can employ to improve data traffic in his code. We report on implementation of a tool which detects the code fragments causing data congestions and advises user on improvements of data routing in these fragments. The capabilities of the tool include deduction of data alignment and affinity from the source code; detection of the code constructs having abnormally high cache or TLB misses; generation of data placement constructs. We demonstrate the capabilities of the tool on experiments with NAS parallel benchmarks and with a simple computational fluid dynamics application ARC3D.

Detection of neovascularization based on fractal and texture analysis with interaction effects in diabetic retinopathy.

PubMed

Lee, Jack; Zee, Benny Chung Ying; Li, Qing

2013-01-01

Diabetic retinopathy is a major cause of blindness. Proliferative diabetic retinopathy is a result of severe vascular complication and is visible as neovascularization of the retina. Automatic detection of such new vessels would be useful for the severity grading of diabetic retinopathy, and it is an important part of screening process to identify those who may require immediate treatment for their diabetic retinopathy. We proposed a novel new vessels detection method including statistical texture analysis (STA), high order spectrum analysis (HOS), fractal analysis (FA), and most importantly we have shown that by incorporating their associated interactions the accuracy of new vessels detection can be greatly improved. To assess its performance, the sensitivity, specificity and accuracy (AUC) are obtained. They are 96.3%, 99.1% and 98.5% (99.3%), respectively. It is found that the proposed method can improve the accuracy of new vessels detection significantly over previous methods. The algorithm can be automated and is valuable to detect relatively severe cases of diabetic retinopathy among diabetes patients.

Recent Advances on Luminescent Enhancement-Based Porous Silicon Biosensors.

PubMed

Jenie, S N Aisyiyah; Plush, Sally E; Voelcker, Nicolas H

2016-10-01

Luminescence-based detection paradigms have key advantages over other optical platforms such as absorbance, reflectance or interferometric based detection. However, autofluorescence, low quantum yield and lack of photostability of the fluorophore or emitting molecule are still performance-limiting factors. Recent research has shown the need for enhanced luminescence-based detection to overcome these drawbacks while at the same time improving the sensitivity, selectivity and reducing the detection limits of optical sensors and biosensors. Nanostructures have been reported to significantly improve the spectral properties of the emitting molecules. These structures offer unique electrical, optic and magnetic properties which may be used to tailor the surrounding electrical field of the emitter. Here, the main principles behind luminescence and luminescence enhancement-based detections are reviewed, with an emphasis on europium complexes as the emitting molecule. An overview of the optical porous silicon microcavity (pSiMC) as a biosensing platform and recent proof-of-concept examples on enhanced luminescence-based detection using pSiMCs are provided and discussed.

Survey of Fire Detection Technologies and System Evaluation/Certification Methodologies and Their Suitability for Aircraft Cargo Compartments

NASA Technical Reports Server (NTRS)

Cleary, T.; Grosshandler, W.

1999-01-01

As part of the National Aeronautics and Space Administration (NASA) initiated program on global civil aviation, NIST is assisting Federal Aviation Administration in its research to improve fire detection in aircraft cargo compartments. Aircraft cargo compartment detection certification methods have been reviewed. The Fire Emulator-Detector Evaluator (FE/DE) has been designed to evaluate fire detection technologies such as new sensors, multi-element detectors, and detectors that employ complex algorithms. The FE/DE is a flow tunnel that can reproduce velocity, temperature, smoke, and Combustion gas levels to which a detector might be exposed during a fire. A scientific literature survey and patent search have been conducted relating to existing and emerging fire detection technologies, and the potential use of new fire detection strategies in cargo compartment areas has been assessed. In the near term, improved detector signal processing and multi-sensor detectors based on combinations of smoke measurements, combustion gases and temperature are envisioned as significantly impacting detector system performance.

Technological advances in diagnostic testing for von Willebrand disease: new approaches and challenges.

PubMed

Hayward, C P M; Moffat, K A; Graf, L

2014-06-01

Diagnostic tests for von Willebrand disease (VWD) are important for the assessment of VWD, which is a commonly encountered bleeding disorder worldwide. Technical innovations have been applied to improve the precision and lower limit of detection of von Willebrand factor (VWF) assays, including the ristocetin cofactor activity assay (VWF:RCo) that uses the antibiotic ristocetin to induce plasma VWF binding to glycoprotein (GP) IbIXV on target platelets. VWF-collagen-binding assays, depending on the type of collagen used, can improve the detection of forms of VWD with high molecular weight VWF multimer loss, although the best method is debatable. A number of innovations have been applied to VWF:RCo (which is commonly performed on an aggregometer), including replacing the target platelets with immobilized GPIbα, and quantification by an enzyme-linked immunosorbent assay (ELISA), immunoturbidimetric, or chemiluminescent end-point. Some common polymorphisms in the VWF gene that do not cause bleeding are associated with falsely low VWF activity by ristocetin-dependent methods. To overcome the need for ristocetin, some new VWF activity assays use gain-of-function GPIbα mutants that bind VWF without the need for ristocetin, with an improved precision and lower limit of detection than measuring VWF:RCo by aggregometry. ELISA of VWF binding to mutated GPIbα shows promise as a method to identify gain-of-function defects from type 2B VWD. The performance characteristics of many new VWF activity assays suggest that the detection of VWD, and monitoring of VWD therapy, by clinical laboratories could be improved through adopting newer generation VWF assays. © 2014 John Wiley & Sons Ltd.

Investigations related to evaluation of ultramicrofluorometer

NASA Technical Reports Server (NTRS)

Whitcomb, B.

1981-01-01

High resolution emission and excitation fluorescent spectra were obtained for several samples in an effort to determine the optimum operational design for the instrument. The instrument was used to determine the required nature of a sample which could be detected, and in so doing, several different sample preparation techniques were considered. Numerous experiments were performed to determine the capabilities of the instrument with regard to the detection of suitably prepared virus specimens. Significant results were obtained in several areas. The fluorescent spectra indicated that substantial changes in the laser might be used advantageously to greatly improve the performance of the instrument. In the existing configuration, the instrument was shown to be capable of detecting the presence of suitably prepared virus samples.

Pancreatic Reference Set Application: Ivan Blasutig-University of Toronto (2014) — EDRN Public Portal

Cancer.gov

The primary objective of this study is to independently validate a panel of serum biomarkers for the early detection of pancreatic ductal adenocarcinoma (PDAC). The biomarkers were identified in various discovery studies performed in our laboratory1-6. We hypothesize that our candidate biomarkers can be used as a panel that will perform better than CA19.9 alone for the early detection of PDAC. Such a panel has the potential to lead to improved patient outcomes by enabling patients to receive treatment as early as possible.

Clinical Impact of Time-of-Flight and Point Response Modeling in PET Reconstructions: A Lesion Detection Study

PubMed Central

Schaefferkoetter, Joshua; Casey, Michael; Townsend, David; Fakhri, Georges El

2013-01-01

Time-of-flight (TOF) and point spread function (PSF) modeling have been shown to improve PET reconstructions, but the impact on physicians in the clinical setting has not been thoroughly investigated. A lesion detection and localization study was performed using simulated lesions in real patient images. Four reconstruction schemes were considered: ordinary Poisson OSEM (OP) alone and combined with TOF, PSF, and TOF+PSF. The images were presented to physicians experienced in reading PET images, and the performance of each was quantified using localization receiver operating characteristic (LROC). Numerical observers (non-prewhitening and Hotelling) were used to identify optimal reconstruction parameters, and observer SNR was compared to the performance of the physicians. The numerical models showed good agreement with human performance, and best performance was achieved by both when using TOF+PSF. These findings suggest a large potential benefit of TOF+PSF for oncology PET studies, especially in the detection of small, low-intensity, focal disease in larger patients. PMID:23403399

Photometric redshifts for the next generation of deep radio continuum surveys - I. Template fitting

NASA Astrophysics Data System (ADS)

Duncan, Kenneth J.; Brown, Michael J. I.; Williams, Wendy L.; Best, Philip N.; Buat, Veronique; Burgarella, Denis; Jarvis, Matt J.; Małek, Katarzyna; Oliver, S. J.; Röttgering, Huub J. A.; Smith, Daniel J. B.

2018-01-01

We present a study of photometric redshift performance for galaxies and active galactic nuclei detected in deep radio continuum surveys. Using two multiwavelength data sets, over the NOAO Deep Wide Field Survey Boötes and COSMOS fields, we assess photometric redshift (photo-z) performance for a sample of ∼4500 radio continuum sources with spectroscopic redshifts relative to those of ∼63 000 non-radio-detected sources in the same fields. We investigate the performance of three photometric redshift template sets as a function of redshift, radio luminosity and infrared/X-ray properties. We find that no single template library is able to provide the best performance across all subsets of the radio-detected population, with variation in the optimum template set both between subsets and between fields. Through a hierarchical Bayesian combination of the photo-z estimates from all three template sets, we are able to produce a consensus photo-z estimate that equals or improves upon the performance of any individual template set.

Clinical impact of time-of-flight and point response modeling in PET reconstructions: a lesion detection study

NASA Astrophysics Data System (ADS)

Schaefferkoetter, Joshua; Casey, Michael; Townsend, David; El Fakhri, Georges

2013-03-01

Time-of-flight (TOF) and point spread function (PSF) modeling have been shown to improve PET reconstructions, but the impact on physicians in the clinical setting has not been thoroughly investigated. A lesion detection and localization study was performed using simulated lesions in real patient images. Four reconstruction schemes were considered: ordinary Poisson OSEM (OP) alone and combined with TOF, PSF, and TOF + PSF. The images were presented to physicians experienced in reading PET images, and the performance of each was quantified using localization receiver operating characteristic. Numerical observers (non-prewhitening and Hotelling) were used to identify optimal reconstruction parameters, and observer SNR was compared to the performance of the physicians. The numerical models showed good agreement with human performance, and best performance was achieved by both when using TOF + PSF. These findings suggest a large potential benefit of TOF + PSF for oncology PET studies, especially in the detection of small, low-intensity, focal disease in larger patients.

Impedimetric detection of bacteria by using a microfluidic chip and silver nanoparticle based signal enhancement.

PubMed

Wang, Renjie; Xu, Yi; Sors, Thomas; Irudayaraj, Joseph; Ren, Wen; Wang, Rong

2018-02-19

The authors describe a method that can significantly improve the performance of impedimetric detection of bacteria. A multifunctional microfluidic chip was designed consisting of interdigitated microelectrodes and a micro-mixing zone with a Tesla structure. This maximizes the coating of bacterial surfaces with nanoparticles and results in improved impedimetric detection. The method was applied to the detection of Escherichia coli O157:H7 (E. coli). Silver enhancement was accomplished by coating E.coli with the cationic polymer diallyldimethylammonium chloride (PDDA) to form positively charged E. coli/PDDA complexes. Then, gold nanoparticles (AuNPs) were added, and the resulting E. coli/PDDA/AuNPs complexes were collected at interdigitated electrodes via positive dielectrophoresis (pDEP). A silver adduct was then formed on the E. coli/PDDA/AuNP complexes by using silver enhancement solutions and by using the AuNPs as catalysts. The combination of pDEP based capture and of using silver adducts reduces impedance by increasing the conductivity of the solution and the double layer capacitance around the microelectrodes. Impedance decreases linearly in the 2 × 10 3 -2 × 10 5  cfu·mL -1 E. coli concentration range, with a 500 cfu·mL -1 detection limit. Egg shell wash samples and tap water spiked with E. coli were successfully used for validation, and this demonstrates the practical application of this method. Graphical abstract Schematic representation of the AuNP@Ag enhancement method integrated with multifunctional microfluidic chip platform for impedimetric quantitation of bacteria. The method significantly improves the performance of impedimetric detection of bacteria.

Postmortem computed tomography as an adjunct to autopsy for analyzing fatal motor vehicle crash injuries: results of a pilot study.

PubMed

Sochor, Mark R; Trowbridge, Matthew J; Boscak, Alexis; Maino, John C; Maio, Ronald F

2008-09-01

Detailed fatal injury data after fatal motor vehicle crashes (MVC) are necessary to improve occupant safety and promote injury prevention. Autopsy remains the principle source of detailed fatal injury data. However, procedure rates are declining because of a range of technical, ethical, and religious concerns. Postmortem computed tomography (PMCT) is a potential alternative or adjunct to autopsy which is increasingly used by forensic researchers. However, there are only limited data regarding the utility of PMCT for analysis of fatal MVC injuries. We performed whole body PMCT and autopsy on six subjects fatally injured in MVC in a single county in Michigan. All injuries detected by either method were coded using the Abbreviated Injury Scale (AIS). Severe injuries, defined as AIS 3 or higher (AIS 3+), were tallied for each forensic procedure to allow a comparison of relative diagnostic performance. A total of 46 AIS 3+ injuries were identified by autopsy and PMCT for these cases. The addition of PMCT to autopsy increased overall detection of AIS 3+ injuries (all types) by 28%. PMCT detected 27% more AIS 3+ skeletal injuries than autopsy but 25% less soft tissue injuries. Use of PMCT improves the detection of AIS 3+ injuries after fatal MVC compared with isolated use of autopsy and also produces a highly detailed permanent objective record. PMCT appears to improve detection of skeletal injury compared with autopsy but is less sensitive than autopsy for the detection of AIS 3+ soft tissue injuries. Neither autopsy nor PMCT identified all AIS 3+ injuries revealed by the combination of the two methodologies. This suggests that PMCT should be used as an adjunct to autopsy rather than a replacement whenever feasible.

Improved close-in detection for the mine hunter/killer system

NASA Astrophysics Data System (ADS)

Bishop, Steven S.; Campana, Stephen B.; Duston, Brian M.; Lang, David A.; Wiggins, Carl M.

2001-10-01

The Close-In Detector (CID) is the vehicle-mounted multi-sensor anti-tank landmine detection technology for the Army CECOM Night Vision Electronic Sensors Directorate (NVESD) Mine Hunter-Killer (MH/K) Program. The CID includes two down-looking sensor arrays: a 20-antenna ground-penetrating radar (GPR) and a 16-coil metal detector (MD). These arrays span 3-meters in front of a high mobility, multipurpose wheeled vehicle (HMMWV). The CID also includes a roof-mounted, forward looking infrared (FLIR) camera that images a trapezoidal area of the road ahead of the vehicle. Signals from each of the three sensors are processed separately to detect and localize objects of interest. Features of candidate objects are integrated in a processor that uses them to discriminates between anti-tank (AT) mines and clutter and produces a list of suspected mine locations which are passed to the neutralization subsystem of MH/K. This paper reviews the current design and performance of the CID based on field test results on dirt and gravel mine test lanes. Improvements in CID performance for probability of detection, false alarm rate, target positional accuracy and system rate of advance over the past year and a half that meet most of the program goals are described. Sensor performances are compared, and the effectiveness of six different sensor fusion approaches are measured and compared.

The significance and robustness of a plasma free amino acid (PFAA) profile-based multiplex function for detecting lung cancer

PubMed Central

2013-01-01

Background We have recently reported on the changes in plasma free amino acid (PFAA) profiles in lung cancer patients and the efficacy of a PFAA-based, multivariate discrimination index for the early detection of lung cancer. In this study, we aimed to verify the usefulness and robustness of PFAA profiling for detecting lung cancer using new test samples. Methods Plasma samples were collected from 171 lung cancer patients and 3849 controls without apparent cancer. PFAA levels were measured by high-performance liquid chromatography (HPLC)–electrospray ionization (ESI)–mass spectrometry (MS). Results High reproducibility was observed for both the change in the PFAA profiles in the lung cancer patients and the discriminating performance for lung cancer patients compared to previously reported results. Furthermore, multivariate discriminating functions obtained in previous studies clearly distinguished the lung cancer patients from the controls based on the area under the receiver-operator characteristics curve (AUC of ROC = 0.731 ~ 0.806), strongly suggesting the robustness of the methodology for clinical use. Moreover, the results suggested that the combinatorial use of this classifier and tumor markers improves the clinical performance of tumor markers. Conclusions These findings suggest that PFAA profiling, which involves a relatively simple plasma assay and imposes a low physical burden on subjects, has great potential for improving early detection of lung cancer. PMID:23409863

Quantitative three-dimensional transrectal ultrasound (TRUS) for prostate imaging

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Aarnink, Rene G.; de la Rosette, Jean J.; Chalana, Vikram; Wijkstra, Hessel; Haynor, David R.; Debruyne, Frans M. J.; Kim, Yongmin

1998-06-01

With the number of men seeking medical care for prostate diseases rising steadily, the need of a fast and accurate prostate boundary detection and volume estimation tool is being increasingly experienced by the clinicians. Currently, these measurements are made manually, which results in a large examination time. A possible solution is to improve the efficiency by automating the boundary detection and volume estimation process with minimal involvement from the human experts. In this paper, we present an algorithm based on SNAKES to detect the boundaries. Our approach is to selectively enhance the contrast along the edges using an algorithm called sticks and integrate it with a SNAKES model. This integrated algorithm requires an initial curve for each ultrasound image to initiate the boundary detection process. We have used different schemes to generate the curves with a varying degree of automation and evaluated its effects on the algorithm performance. After the boundaries are identified, the prostate volume is calculated using planimetric volumetry. We have tested our algorithm on 6 different prostate volumes and compared the performance against the volumes manually measured by 3 experts. With the increase in the user inputs, the algorithm performance improved as expected. The results demonstrate that given an initial contour reasonably close to the prostate boundaries, the algorithm successfully delineates the prostate boundaries in an image, and the resulting volume measurements are in close agreement with those made by the human experts.

An Energy-Efficient Multi-Tier Architecture for Fall Detection Using Smartphones.

PubMed

Guvensan, M Amac; Kansiz, A Oguz; Camgoz, N Cihan; Turkmen, H Irem; Yavuz, A Gokhan; Karsligil, M Elif

2017-06-23

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

Detection of mitotic nuclei in breast histopathology images using localized ACM and Random Kitchen Sink based classifier.

PubMed

Beevi, K Sabeena; Nair, Madhu S; Bindu, G R

2016-08-01

The exact measure of mitotic nuclei is a crucial parameter in breast cancer grading and prognosis. This can be achieved by improving the mitotic detection accuracy by careful design of segmentation and classification techniques. In this paper, segmentation of nuclei from breast histopathology images are carried out by Localized Active Contour Model (LACM) utilizing bio-inspired optimization techniques in the detection stage, in order to handle diffused intensities present along object boundaries. Further, the application of a new optimal machine learning algorithm capable of classifying strong non-linear data such as Random Kitchen Sink (RKS), shows improved classification performance. The proposed method has been tested on Mitosis detection in breast cancer histological images (MITOS) dataset provided for MITOS-ATYPIA CONTEST 2014. The proposed framework achieved 95% recall, 98% precision and 96% F-score.

Target Detection of Quantum Illumination Receiver Based on Photon-subtracted Entanglement State

NASA Astrophysics Data System (ADS)

Chi, Jiao; Liu, HongJun; Huang, Nan; Wang, ZhaoLu

2017-12-01

We theoretically propose a quantum illumination receiver based on the ideal photon-subtracted two-mode squeezed state (PSTMSS) to efficiently detect the noise-hidden target. This receiver is generated by applying an optical parametric amplifier (OPA) to the cross correlation detection. With analyzing the output performance, it is found that OPA as a preposition technology of the receiver can contribute to the PSTMSS by significantly reducing the error probability than that of the general two-mode squeezed state (TMSS). Comparing with TMSS, the signal-to-noise ratio of quantum illumination based on ideal PSTMSS and OPA is improved more than 4 dB under an optimal gain of OPA. This work may provide a potential improvement in the application of accurate target detection when two kinds of resource have the identical real squeezing parameter.

Improved imaging algorithm for bridge crack detection

NASA Astrophysics Data System (ADS)

Lu, Jingxiao; Song, Pingli; Han, Kaihong

2012-04-01

This paper present an improved imaging algorithm for bridge crack detection, through optimizing the eight-direction Sobel edge detection operator, making the positioning of edge points more accurate than without the optimization, and effectively reducing the false edges information, so as to facilitate follow-up treatment. In calculating the crack geometry characteristics, we use the method of extracting skeleton on single crack length. In order to calculate crack area, we construct the template of area by making logical bitwise AND operation of the crack image. After experiment, the results show errors of the crack detection method and actual manual measurement are within an acceptable range, meet the needs of engineering applications. This algorithm is high-speed and effective for automated crack measurement, it can provide more valid data for proper planning and appropriate performance of the maintenance and rehabilitation processes of bridge.

Automatic Constraint Detection for 2D Layout Regularization.

PubMed

Jiang, Haiyong; Nan, Liangliang; Yan, Dong-Ming; Dong, Weiming; Zhang, Xiaopeng; Wonka, Peter

2016-08-01

In this paper, we address the problem of constraint detection for layout regularization. The layout we consider is a set of two-dimensional elements where each element is represented by its bounding box. Layout regularization is important in digitizing plans or images, such as floor plans and facade images, and in the improvement of user-created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing alignment, size, and distance constraints between layout elements. Similar to previous work, we formulate layout regularization as a quadratic programming problem. In addition, we propose a novel optimization algorithm that automatically detects constraints. We evaluate the proposed framework using a variety of input layouts from different applications. Our results demonstrate that our method has superior performance to the state of the art.

Development efforts to improve curved-channel microchannel plates

NASA Technical Reports Server (NTRS)

Corbett, M. B.; Feller, W. B.; Laprade, B. N.; Cochran, R.; Bybee, R.; Danks, A.; Joseph, C.

1993-01-01

Curved-channel microchannel plate (C-plate) improvements resulting from an ongoing NASA STIS microchannel plate (MCP) development program are described. Performance limitations of previous C-plates led to a development program in support of the STIS MAMA UV photon counter, a second generation instrument on the Hubble Space Telescope. C-plate gain, quantum detection efficiency, dark noise, and imaging distortion, which are influenced by channel curvature non-uniformities, have all been improved through use of a new centrifuge fabrication technique. This technique will be described, along with efforts to improve older, more conventional shearing methods. Process optimization methods used to attain targeted C-plate performance goals will be briefly characterized. Newly developed diagnostic measurement techniques to study image distortion, gain uniformity, input bias angle, channel curvature, and ion feedback, will be described. Performance characteristics and initial test results of the improved C-plates will be reported. Future work and applications will also be discussed.

Underwater electric field detection system based on weakly electric fish

NASA Astrophysics Data System (ADS)

Xue, Wei; Wang, Tianyu; Wang, Qi

2018-04-01

Weakly electric fish sense their surroundings in complete darkness by their active electric field detection system. However, due to the insufficient detection capacity of the electric field, the detection distance is not enough, and the detection accuracy is not high. In this paper, a method of underwater detection based on rotating current field theory is proposed to improve the performance of underwater electric field detection system. First of all, we built underwater detection system based on the theory of the spin current field mathematical model with the help of the results of previous researchers. Then we completed the principle prototype and finished the metal objects in the water environment detection experiments, laid the foundation for the further experiments.

Triton Hodge Test: Improved Protocol for Modified Hodge Test for Enhanced Detection of NDM and Other Carbapenemase Producers

PubMed Central

Pasteran, Fernando; Gonzalez, Lisandro J.; Albornoz, Ezequiel; Bahr, Guillermo; Vila, Alejandro J.

2015-01-01

Accurate detection of carbapenemase-producing Gram-negative bacilli is of utmost importance for the control of nosocomial spread and the initiation of appropriate antimicrobial therapy. The modified Hodge test (MHT), a carbapenem inactivation assay, has shown poor sensitivity in detecting the worldwide spread of New Delhi metallo-β-lactamase (NDM). Recent studies demonstrated that NDM is a lipoprotein anchored to the outer membrane in Gram-negative bacteria, unlike all other known carbapenemases. Here we report that membrane anchoring of β-lactamases precludes detection of carbapenemase activity by the MHT. We also show that this limitation can be overcome by the addition of Triton X-100 during the test, which allows detection of NDM. We propose an improved version of the assay, called the Triton Hodge test (THT), which allows detection of membrane-bound carbapenemases with the addition of this nonionic surfactant. This test was challenged with a panel of 185 clinical isolates (145 carrying known carbapenemase-encoding genes and 40 carbapenemase nonproducers). The THT displayed test sensitivity of >90% against NDM-producing clinical isolates, while improving performance against other carbapenemases. Ertapenem provided the highest sensitivity (97 to 100%, depending on the type of carbapenemase), followed by meropenem (92.5 to 100%). Test specificity was not affected by the addition of Triton (87.5% and 92.5% with ertapenem and meropenem, respectively). This simple inexpensive test confers a large improvement to the sensitivity of the MHT for the detection of NDM and other carbapenemases. PMID:26719442

Effect of Radiologists’ Diagnostic Work-up Volume on Interpretive Performance

PubMed Central

Anderson, Melissa L.; Smith, Robert A.; Carney, Patricia A.; Miglioretti, Diana L.; Monsees, Barbara S.; Sickles, Edward A.; Taplin, Stephen H.; Geller, Berta M.; Yankaskas, Bonnie C.; Onega, Tracy L.

2014-01-01

Purpose To examine radiologists’ screening performance in relation to the number of diagnostic work-ups performed after abnormal findings are discovered at screening mammography by the same radiologist or by different radiologists. Materials and Methods In an institutional review board–approved HIPAA-compliant study, the authors linked 651 671 screening mammograms interpreted from 2002 to 2006 by 96 radiologists in the Breast Cancer Surveillance Consortium to cancer registries (standard of reference) to evaluate the performance of screening mammography (sensitivity, false-positive rate [FPRfalse-positive rate], and cancer detection rate [CDRcancer detection rate]). Logistic regression was used to assess the association between the volume of recalled screening mammograms (“own” mammograms, where the radiologist who interpreted the diagnostic image was the same radiologist who had interpreted the screening image, and “any” mammograms, where the radiologist who interpreted the diagnostic image may or may not have been the radiologist who interpreted the screening image) and screening performance and whether the association between total annual volume and performance differed according to the volume of diagnostic work-up. Results Annually, 38% of radiologists performed the diagnostic work-up for 25 or fewer of their own recalled screening mammograms, 24% performed the work-up for 0–50, and 39% performed the work-up for more than 50. For the work-up of recalled screening mammograms from any radiologist, 24% of radiologists performed the work-up for 0–50 mammograms, 32% performed the work-up for 51–125, and 44% performed the work-up for more than 125. With increasing numbers of radiologist work-ups for their own recalled mammograms, the sensitivity (P = .039), FPRfalse-positive rate (P = .004), and CDRcancer detection rate (P < .001) of screening mammography increased, yielding a stepped increase in women recalled per cancer detected from 17.4 for 25 or fewer mammograms to 24.6 for more than 50 mammograms. Increases in work-ups for any radiologist yielded significant increases in FPRfalse-positive rate (P = .011) and CDRcancer detection rate (P = .001) and a nonsignificant increase in sensitivity (P = .15). Radiologists with a lower annual volume of any work-ups had consistently lower FPRfalse-positive rate, sensitivity, and CDRcancer detection rate at all annual interpretive volumes. Conclusion These findings support the hypothesis that radiologists may improve their screening performance by performing the diagnostic work-up for their own recalled screening mammograms and directly receiving feedback afforded by means of the outcomes associated with their initial decision to recall. Arranging for radiologists to work up a minimum number of their own recalled cases could improve screening performance but would need systems to facilitate this workflow. © RSNA, 2014 Online supplemental material is available for this article. PMID:24960110

Effect of radiologists' diagnostic work-up volume on interpretive performance.

PubMed

Buist, Diana S M; Anderson, Melissa L; Smith, Robert A; Carney, Patricia A; Miglioretti, Diana L; Monsees, Barbara S; Sickles, Edward A; Taplin, Stephen H; Geller, Berta M; Yankaskas, Bonnie C; Onega, Tracy L

2014-11-01

To examine radiologists' screening performance in relation to the number of diagnostic work-ups performed after abnormal findings are discovered at screening mammography by the same radiologist or by different radiologists. In an institutional review board-approved HIPAA-compliant study, the authors linked 651 671 screening mammograms interpreted from 2002 to 2006 by 96 radiologists in the Breast Cancer Surveillance Consortium to cancer registries (standard of reference) to evaluate the performance of screening mammography (sensitivity, false-positive rate [ FPR false-positive rate ], and cancer detection rate [ CDR cancer detection rate ]). Logistic regression was used to assess the association between the volume of recalled screening mammograms ("own" mammograms, where the radiologist who interpreted the diagnostic image was the same radiologist who had interpreted the screening image, and "any" mammograms, where the radiologist who interpreted the diagnostic image may or may not have been the radiologist who interpreted the screening image) and screening performance and whether the association between total annual volume and performance differed according to the volume of diagnostic work-up. Annually, 38% of radiologists performed the diagnostic work-up for 25 or fewer of their own recalled screening mammograms, 24% performed the work-up for 0-50, and 39% performed the work-up for more than 50. For the work-up of recalled screening mammograms from any radiologist, 24% of radiologists performed the work-up for 0-50 mammograms, 32% performed the work-up for 51-125, and 44% performed the work-up for more than 125. With increasing numbers of radiologist work-ups for their own recalled mammograms, the sensitivity (P = .039), FPR false-positive rate (P = .004), and CDR cancer detection rate (P < .001) of screening mammography increased, yielding a stepped increase in women recalled per cancer detected from 17.4 for 25 or fewer mammograms to 24.6 for more than 50 mammograms. Increases in work-ups for any radiologist yielded significant increases in FPR false-positive rate (P = .011) and CDR cancer detection rate (P = .001) and a nonsignificant increase in sensitivity (P = .15). Radiologists with a lower annual volume of any work-ups had consistently lower FPR false-positive rate , sensitivity, and CDR cancer detection rate at all annual interpretive volumes. These findings support the hypothesis that radiologists may improve their screening performance by performing the diagnostic work-up for their own recalled screening mammograms and directly receiving feedback afforded by means of the outcomes associated with their initial decision to recall. Arranging for radiologists to work up a minimum number of their own recalled cases could improve screening performance but would need systems to facilitate this workflow.

Apparatus having reduced background for measuring radiation activity in aerosol particles

DOEpatents

Rodgers, John C.; McFarland, Andrew R.; Oritz, Carlos A.; Marlow, William H.

1992-01-01

Apparatus having reduced background for measuring radiation activity in aerosol particles. A continuous air monitoring sampler is described for use in detecting the presence of alpha-emitting aerosol particles. An inlet fractionating screen has been demonstrated to remove about 95% of freshly formed radon progeny from the aerosol sample, and approximately 33% of partially aged progeny. Addition of an electrical condenser and a modified dichotomous virtual impactor are expected to produce considerable improvement in these numbers, the goal being to enrich the transuranic (TRU) fraction of the aerosols. This offers the possibility of improving the signal-to-noise ratio for the detected alpha-particle energy spectrum in the region of interest for detecting TRU materials associated with aerosols, thereby enhancing the performance of background-compensation algorithms for improving the quality of alarm signals intended to warn personnel of potentially harmful quantities of TRU materials in the ambient air.

Microcontroller-based real-time QRS detection.

PubMed

Sun, Y; Suppappola, S; Wrublewski, T A

1992-01-01

The authors describe the design of a system for real-time detection of QRS complexes in the electrocardiogram based on a single-chip microcontroller (Motorola 68HC811). A systematic analysis of the instrumentation requirements for QRS detection and of the various design techniques is also given. Detection algorithms using different nonlinear transforms for the enhancement of QRS complexes are evaluated by using the ECG database of the American Heart Association. The results show that the nonlinear transform involving multiplication of three adjacent, sign-consistent differences in the time domain gives a good performance and a quick response. When implemented with an appropriate sampling rate, this algorithm is also capable of rejecting pacemaker spikes. The eight-bit single-chip microcontroller provides sufficient throughput and shows a satisfactory performance. Implementation of multiple detection algorithms in the same system improves flexibility and reliability. The low chip count in the design also favors maintainability and cost-effectiveness.

Prescreening with FOBT Improves Yield and Is Cost-Effective in Colorectal Screening in the Elderly

PubMed Central

Changela, Kinesh; Mathur, Siddharth; Reddy, Sridhar; Momeni, Mojdeh; Krishnaiah, Mahesh; Anand, Sury

2014-01-01

Background. Utilization of colonoscopy for routine colorectal cancer (CRC) screening in the elderly (patients over 75) is controversial. This study was designed to evaluate if using fecal occult blood test (FOBT) to select patients for colonoscopy can improve yield and be a cost- effective approach for the elderly. Methods. Records of 10,908 subjects who had colonoscopy during the study period were reviewed. 1496 (13.7%) were ≥75 years. In 118 of these subjects, a colonoscopy was performed to evaluate a positive FOBT. Outcomes were compared between +FOBT group (F-Group) and the asymptomatic screening group (AS-Group). The cost-effectiveness was also calculated using a median estimated standardized worldwide colonoscopy and FOBT cost (rounded to closest whole numbers) of 1000 US $ and 10 US $, respectively. Results. 118/1496 (7.9%) colonoscopies were performed for evaluation of +FOBT. 464/1496 (31%) colonoscopies were performed in AS-Group. In F-Group, high risk adenoma detection rate (HR-ADR) was 15.2%, and 11.9% had 1-2 tubular adenomas. In comparison, the control AS-Group had HR-ADR of 19.2% and 17.7% had 1-2 tubular adenomas. In the FOBT+ group, CRC was detected in 5.1% which was significantly higher than the AS-Group in which CRC was detected in 1.7% (P = 0.03). On cost-effectiveness analysis, cost per CRC detected was significantly lower, that is, 19,666 US $ in F-Group in comparison to AS-Group 58,000 US $ (P < 0.05). There were no significant differences in other parameters among groups. Conclusion. Prescreening with FOBT to select elderly for colonoscopy seems to improve the yield and can be a cost-effective CRC screening approach in this subset. The benefit in the risk benefit analysis of screening the elderly appears improved by prescreening with an inexpensive tool. PMID:25101179

Prescreening with FOBT Improves Yield and Is Cost-Effective in Colorectal Screening in the Elderly.

PubMed

Singhal, Shashideep; Changela, Kinesh; Basi, Puneet; Mathur, Siddharth; Reddy, Sridhar; Momeni, Mojdeh; Krishnaiah, Mahesh; Anand, Sury

2014-01-01

Background. Utilization of colonoscopy for routine colorectal cancer (CRC) screening in the elderly (patients over 75) is controversial. This study was designed to evaluate if using fecal occult blood test (FOBT) to select patients for colonoscopy can improve yield and be a cost- effective approach for the elderly. Methods. Records of 10,908 subjects who had colonoscopy during the study period were reviewed. 1496 (13.7%) were ≥75 years. In 118 of these subjects, a colonoscopy was performed to evaluate a positive FOBT. Outcomes were compared between +FOBT group (F-Group) and the asymptomatic screening group (AS-Group). The cost-effectiveness was also calculated using a median estimated standardized worldwide colonoscopy and FOBT cost (rounded to closest whole numbers) of 1000 US $ and 10 US $, respectively. Results. 118/1496 (7.9%) colonoscopies were performed for evaluation of +FOBT. 464/1496 (31%) colonoscopies were performed in AS-Group. In F-Group, high risk adenoma detection rate (HR-ADR) was 15.2%, and 11.9% had 1-2 tubular adenomas. In comparison, the control AS-Group had HR-ADR of 19.2% and 17.7% had 1-2 tubular adenomas. In the FOBT+ group, CRC was detected in 5.1% which was significantly higher than the AS-Group in which CRC was detected in 1.7% (P = 0.03). On cost-effectiveness analysis, cost per CRC detected was significantly lower, that is, 19,666 US $ in F-Group in comparison to AS-Group 58,000 US $ (P < 0.05). There were no significant differences in other parameters among groups. Conclusion. Prescreening with FOBT to select elderly for colonoscopy seems to improve the yield and can be a cost-effective CRC screening approach in this subset. The benefit in the risk benefit analysis of screening the elderly appears improved by prescreening with an inexpensive tool.

Adaptive Trajectory Prediction Algorithm for Climbing Flights

NASA Technical Reports Server (NTRS)

Schultz, Charles Alexander; Thipphavong, David P.; Erzberger, Heinz

2012-01-01

Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced features for NextGen. The algorithm described in this paper improves climb trajectory prediction accuracy by adjusting trajectory predictions based on observed track data. It utilizes rate-of-climb and airspeed measurements derived from position data to dynamically adjust the aircraft weight modeled for trajectory predictions. In simulations with weight uncertainty, the algorithm is able to adapt to within 3 percent of the actual gross weight within two minutes of the initial adaptation. The root-mean-square of altitude errors for five-minute predictions was reduced by 73 percent. Conflict detection performance also improved, with a 15 percent reduction in missed alerts and a 10 percent reduction in false alerts. In a simulation with climb speed capture intent and weight uncertainty, the algorithm improved climb trajectory prediction accuracy by up to 30 percent and conflict detection performance, reducing missed and false alerts by up to 10 percent.

Improvements in Space Surveillance Processing for Wide Field of View Optical Sensors

NASA Astrophysics Data System (ADS)

Sydney, P.; Wetterer, C.

2014-09-01

For more than a decade, an autonomous satellite tracking system at the Air Force Maui Optical and Supercomputing (AMOS) observatory has been generating routine astrometric measurements of Earth-orbiting Resident Space Objects (RSOs) using small commercial telescopes and sensors. Recent work has focused on developing an improved processing system, enhancing measurement performance and response while supporting other sensor systems and missions. This paper will outline improved techniques in scheduling, detection, astrometric and photometric measurements, and catalog maintenance. The processing system now integrates with Special Perturbation (SP) based astrodynamics algorithms, allowing covariance-based scheduling and more precise orbital estimates and object identification. A merit-based scheduling algorithm provides a global optimization framework to support diverse collection tasks and missions. The detection algorithms support a range of target tracking and camera acquisition rates. New comprehensive star catalogs allow for more precise astrometric and photometric calibrations including differential photometry for monitoring environmental changes. This paper will also examine measurement performance with varying tracking rates and acquisition parameters.

Delayed entanglement echo for individual control of a large number of nuclear spins

PubMed Central

Wang, Zhen-Yu; Casanova, Jorge; Plenio, Martin B.

2017-01-01

Methods to selectively detect and manipulate nuclear spins by single electrons of solid-state defects play a central role for quantum information processing and nanoscale nuclear magnetic resonance (NMR). However, with standard techniques, no more than eight nuclear spins have been resolved by a single defect centre. Here we develop a method that improves significantly the ability to detect, address and manipulate nuclear spins unambiguously and individually in a broad frequency band by using a nitrogen-vacancy (NV) centre as model system. On the basis of delayed entanglement control, a technique combining microwave and radio frequency fields, our method allows to selectively perform robust high-fidelity entangling gates between hardly resolved nuclear spins and the NV electron. Long-lived qubit memories can be naturally incorporated to our method for improved performance. The application of our ideas will increase the number of useful register qubits accessible to a defect centre and improve the signal of nanoscale NMR. PMID:28256508

Delayed entanglement echo for individual control of a large number of nuclear spins.

PubMed

Wang, Zhen-Yu; Casanova, Jorge; Plenio, Martin B

2017-03-03

Methods to selectively detect and manipulate nuclear spins by single electrons of solid-state defects play a central role for quantum information processing and nanoscale nuclear magnetic resonance (NMR). However, with standard techniques, no more than eight nuclear spins have been resolved by a single defect centre. Here we develop a method that improves significantly the ability to detect, address and manipulate nuclear spins unambiguously and individually in a broad frequency band by using a nitrogen-vacancy (NV) centre as model system. On the basis of delayed entanglement control, a technique combining microwave and radio frequency fields, our method allows to selectively perform robust high-fidelity entangling gates between hardly resolved nuclear spins and the NV electron. Long-lived qubit memories can be naturally incorporated to our method for improved performance. The application of our ideas will increase the number of useful register qubits accessible to a defect centre and improve the signal of nanoscale NMR.

Alpha-ray detection with a MgB 2 transition edge sensor

NASA Astrophysics Data System (ADS)

Okayasu, S.; Katagiri, M.; Hojou, K.; Morii, Y.; Miki, S.; Shimakage, H.; Wang, Z.; Ishida, T.

2008-09-01

We have been investigating for neutron detection with the MgB 2 transition edge sensor (TES). For the purpose, we have been developing a low noise measurement system for the detection. To confirm the performance of the detecting sensor, alpha ray detection from an americium-241 ( 241Am) alpha-ray source was achieved. A short microfabricated sample with 10 μm length and 1 μm width is used to improve the S/N ratio. The detection is achieved under a constant current condition in the range between 1 and 6 μA bias current, and the resistivity changes at the sample due to the alpha ray irradiation is detected just on the transition edge.

A novel method for quantification of beam's-eye-view tumor tracking performance.

PubMed

Hu, Yue-Houng; Myronakis, Marios; Rottmann, Joerg; Wang, Adam; Morf, Daniel; Shedlock, Daniel; Baturin, Paul; Star-Lack, Josh; Berbeco, Ross

2017-11-01

In-treatment imaging using an electronic portal imaging device (EPID) can be used to confirm patient and tumor positioning. Real-time tumor tracking performance using current digital megavolt (MV) imagers is hindered by poor image quality. Novel EPID designs may help to improve quantum noise response, while also preserving the high spatial resolution of the current clinical detector. Recently investigated EPID design improvements include but are not limited to multi-layer imager (MLI) architecture, thick crystalline and amorphous scintillators, and phosphor pixilation and focusing. The goal of the present study was to provide a method of quantitating improvement in tracking performance as well as to reveal the physical underpinnings of detector design that impact tracking quality. The study employs a generalizable ideal observer methodology for the quantification of tumor tracking performance. The analysis is applied to study both the effect of increasing scintillator thickness on a standard, single-layer imager (SLI) design as well as the effect of MLI architecture on tracking performance. The present study uses the ideal observer signal-to-noise ratio (d') as a surrogate for tracking performance. We employ functions which model clinically relevant tasks and generalized frequency-domain imaging metrics to connect image quality with tumor tracking. A detection task for relevant Cartesian shapes (i.e., spheres and cylinders) was used to quantitate trackability of cases employing fiducial markers. Automated lung tumor tracking algorithms often leverage the differences in benign and malignant lung tissue textures. These types of algorithms (e.g., soft-tissue localization - STiL) were simulated by designing a discrimination task, which quantifies the differentiation of tissue textures, measured experimentally and fit as a power-law in trend (with exponent β) using a cohort of MV images of patient lungs. The modeled MTF and NPS were used to investigate the effect of scintillator thickness and MLI architecture on tumor tracking performance. Quantification of MV images of lung tissue as an inverse power-law with respect to frequency yields exponent values of β = 3.11 and 3.29 for benign and malignant tissues, respectively. Tracking performance with and without fiducials was found to be generally limited by quantum noise, a factor dominated by quantum detective efficiency (QDE). For generic SLI construction, increasing the scintillator thickness (gadolinium oxysulfide - GOS) from a standard 290 μm to 1720 μm reduces noise to about 10%. However, 81% of this reduction is appreciated between 290 and 1000 μm. In comparing MLI and SLI detectors of equivalent individual GOS layer thickness, the improvement in noise is equal to the number of layers in the detector (i.e., 4) with almost no difference in MTF. Further, improvement in tracking performance was slightly less than the square-root of the reduction in noise, approximately 84-90%. In comparing an MLI detector with an SLI with a GOS scintillator of equivalent total thickness, improvement in object detectability is approximately 34-39%. We have presented a novel method for quantification of tumor tracking quality and have applied this model to evaluate the performance of SLI and MLI EPID designs. We showed that improved tracking quality is primarily limited by improvements in NPS. When compared to very thick scintillator SLI, employing MLI architecture exhibits the same gains in QDE, but by mitigating the effect of optical Swank noise, results in more dramatic improvements in tracking performance. © 2017 American Association of Physicists in Medicine.

Towards Enhanced Underwater Lidar Detection via Source Separation

NASA Astrophysics Data System (ADS)

Illig, David W.

Interest in underwater optical sensors has grown as technologies enabling autonomous underwater vehicles have been developed. Propagation of light through water is complicated by the dual challenges of absorption and scattering. While absorption can be reduced by operating in the blue-green region of the visible spectrum, reducing scattering is a more significant challenge. Collection of scattered light negatively impacts underwater optical ranging, imaging, and communications applications. This thesis concentrates on the ranging application, where scattering reduces operating range as well as range accuracy. The focus of this thesis is on the problem of backscatter, which can create a "clutter" return that may obscure submerged target(s) of interest. The main contributions of this thesis are explorations of signal processing approaches to increase the separation between the target and backscatter returns. Increasing this separation allows detection of weak targets in the presence of strong scatter, increasing both operating range and range accuracy. Simulation and experimental results will be presented for a variety of approaches as functions of water clarity and target position. This work provides several novel contributions to the underwater lidar field: 1. Quantification of temporal separation approaches: While temporal separation has been studied extensively, this work provides a quantitative assessment of the extent to which both high frequency modulation and spatial filter approaches improve the separation between target and backscatter. 2. Development and assessment of frequency separation: This work includes the first frequency-based separation approach for underwater lidar, in which the channel frequency response is measured with a wideband waveform. Transforming to the time-domain gives a channel impulse response, in which target and backscatter returns may appear in unique range bins and thus be separated. 3. Development and assessment of statistical separation: The first investigations of statistical separation approaches for underwater lidar are presented. By demonstrating that target and backscatter returns have different statistical properties, a new separation axis is opened. This work investigates and quantifies performance of three statistical separation approaches. 4. Application of detection theory to underwater lidar: While many similar applications use detection theory to assess performance, less development has occurred in the underwater lidar field. This work applies these concepts to statistical separation approaches, providing another perspective in which to assess performance. In addition, by using detection theory approaches, statistical metrics can be used to associate a level of confidence in each ranging measurement. 5. Preliminary investigation of forward scatter suppression: If backscatter is sufficiently suppressed, forward scattering becomes a performance-limiting factor. This work presents a proof-of-concept demonstration of the potential for statistical separation approaches to suppress both forward and backward scatter. These results provide a demonstration of the capability that signal processing has to improve separation between target and backscatter. Separation capability improves in the transition from temporal to frequency to statistical separation approaches, with the statistical separation approaches improving target detection sensitivity by as much as 30 dB. Ranging and detection results demonstrate the enhanced performance this would allow in ranging applications. This increased performance is an important step in moving underwater lidar capability towards the requirements of the next generation of sensors.

The Electrooculogram and a New Blink Detection Algorithm

DTIC Science & Technology

2015-10-30

applications, and physiological monitoring has proven quite helpful with this assessment. One such physiological signal , the electrooculogram ( EOG ...significantly improve performance. One such physiological signal , the electrooculogram ( EOG ), can provide blink rate and blink duration measures. Blink...that such variability substantiates the need for blink detection algorithms, using the EOG signal , that are robust to noise, artifacts, and intra- and

A Pilot Evaluation of On-Road Detection Performance by Drivers with Hemianopia Using Oblique Peripheral Prisms

PubMed Central

Bowers, Alex R.; Tant, Mark; Peli, Eli

2012-01-01

Aims. Homonymous hemianopia (HH), a severe visual consequence of stroke, causes difficulties in detecting obstacles on the nonseeing (blind) side. We conducted a pilot study to evaluate the effects of oblique peripheral prisms, a novel development in optical treatments for HH, on detection of unexpected hazards when driving. Methods. Twelve people with complete HH (median 49 years, range 29–68) completed road tests with sham oblique prism glasses (SP) and real oblique prism glasses (RP). A masked evaluator rated driving performance along the 25 km routes on busy streets in Ghent, Belgium. Results. The proportion of satisfactory responses to unexpected hazards on the blind side was higher in the RP than the SP drive (80% versus 30%; P = 0.001), but similar for unexpected hazards on the seeing side. Conclusions. These pilot data suggest that oblique peripheral prisms may improve responses of people with HH to blindside hazards when driving and provide the basis for a future, larger-sample clinical trial. Testing responses to unexpected hazards in areas of heavy vehicle and pedestrian traffic appears promising as a real-world outcome measure for future evaluations of HH rehabilitation interventions aimed at improving detection when driving. PMID:23316415

Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions

PubMed Central

Tan, Maxine; Aghaei, Faranak; Wang, Yunzhi; Zheng, Bin

2017-01-01

The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography (FFDM) images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a “scoring fusion” artificial neural network (ANN) classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC = 0.793±0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions. PMID:27997380

Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms

PubMed Central

Liu, Min-Yin; Huang, Adam; Huang, Norden E.

2017-01-01

Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz) measured by electroencephalography (EEG) mostly during non-rapid eye movement (NREM) stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1) the lack of common benchmark databases, and (2) the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA), the Strength Pareto Evolutionary Algorithm (SPEA2), to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT), and two Hilbert-Huang transform (HHT) based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737. PMID:28572762

Bedside Ultrasound in the Emergency Department to Detect Hydronephrosis for the Evaluation of Suspected Ureteric Colic.

PubMed

Shrestha, R; Shakya, R M; Khan A, A

2016-01-01

Background Renal colic is a common emergency department presentation. Hydronephrosis is indirect sign of urinary obstruction which may be due to obstructing ureteric calculus and can be detected easily by bedside ultrasound with minimal training. Objective To compare the accuracy of detection of hydronephrosis performed by the emergency physician with that of radiologist's in suspected renal colic cases. Method This was a prospective observational study performed over a period of 6 months. Patients >8 years with provisional diagnosis of renal colic with both the bedside ultrasound and the formal ultrasound performed were included. Presence of hydronephrosis in both ultrasounds and size and location of ureteric stone if present in formal ultrasound was recorded. The accuracy of the emergency physician detection of hydronephrosis was determined using the scan reported by the radiologists as the "gold standard" as computed tomography was unavailable. Statistical analysis was executed using SPSS 17.0. Result Among the 111 included patients, 56.7% had ureteric stone detected in formal ultrasound. The overall sensitivity, specificity, positive predictive value and negative predictive value of bedside ultrasound performed by emergency physician for detection of hydronephrosis with that of formal ultrasound performed by radiologist was 90.8%., 78.3%, 85.5% and 85.7% respectively. Bedside ultrasound and formal ultrasound both detected hydronephrosis more often in patients with larger stones and the difference was statistically significant (p=.000). Conclusion Bedside ultrasound can be potentially used as an important tool in detecting clinically significant hydronephrosis in emergency to evaluate suspected ureteric colic. Focused training in ultrasound could greatly improve the emergency management of these patients.

Mapping Health Data: Improved Privacy Protection With Donut Method Geomasking

PubMed Central

Hampton, Kristen H.; Fitch, Molly K.; Allshouse, William B.; Doherty, Irene A.; Gesink, Dionne C.; Leone, Peter A.; Serre, Marc L.; Miller, William C.

2010-01-01

A major challenge in mapping health data is protecting patient privacy while maintaining the spatial resolution necessary for spatial surveillance and outbreak identification. A new adaptive geomasking technique, referred to as the donut method, extends current methods of random displacement by ensuring a user-defined minimum level of geoprivacy. In donut method geomasking, each geocoded address is relocated in a random direction by at least a minimum distance, but less than a maximum distance. The authors compared the donut method with current methods of random perturbation and aggregation regarding measures of privacy protection and cluster detection performance by masking multiple disease field simulations under a range of parameters. Both the donut method and random perturbation performed better than aggregation in cluster detection measures. The performance of the donut method in geoprivacy measures was at least 42.7% higher and in cluster detection measures was less than 4.8% lower than that of random perturbation. Results show that the donut method provides a consistently higher level of privacy protection with a minimal decrease in cluster detection performance, especially in areas where the risk to individual geoprivacy is greatest. PMID:20817785

Mapping health data: improved privacy protection with donut method geomasking.

PubMed

Hampton, Kristen H; Fitch, Molly K; Allshouse, William B; Doherty, Irene A; Gesink, Dionne C; Leone, Peter A; Serre, Marc L; Miller, William C

2010-11-01

A major challenge in mapping health data is protecting patient privacy while maintaining the spatial resolution necessary for spatial surveillance and outbreak identification. A new adaptive geomasking technique, referred to as the donut method, extends current methods of random displacement by ensuring a user-defined minimum level of geoprivacy. In donut method geomasking, each geocoded address is relocated in a random direction by at least a minimum distance, but less than a maximum distance. The authors compared the donut method with current methods of random perturbation and aggregation regarding measures of privacy protection and cluster detection performance by masking multiple disease field simulations under a range of parameters. Both the donut method and random perturbation performed better than aggregation in cluster detection measures. The performance of the donut method in geoprivacy measures was at least 42.7% higher and in cluster detection measures was less than 4.8% lower than that of random perturbation. Results show that the donut method provides a consistently higher level of privacy protection with a minimal decrease in cluster detection performance, especially in areas where the risk to individual geoprivacy is greatest.

Enhanced biosensor performance using an avidin-biotin bridge for antibody immobilization

NASA Astrophysics Data System (ADS)

Narang, Upvan; Anderson, George P.; King, Keeley D.; Liss, Heidi S.; Ligler, Frances S.

1997-05-01

Maintaining antibody function after immobilization is critical to the performance of a biosensor. The conventional methods to immobilize antibodies onto surfaces are via covalent attachment using a crosslinker or by adsorption. Often, these methods of immobilization result in partial denaturation of the antibody and conformational changes leading to a reduced activity of the antibody. In this paper, we report on the immobilization of antibodies onto the surface of an optical fiber through an avidin-biotin bridge for the detection of ricin, ovalbumin, and Bacillus globigii (Bg). The assays are performed in a sandwich format. First, a capture antibody is immobilized, followed by the addition of the analyte. Finally, a fluorophore- labeled antibody is added for the specific detection of the analyte. The evanescent wave-induced fluorescence is coupled back through the same fiber to be detected using a photodiode. In all cases, we observe an improved performance of the biosensor, i.e., lower limit of detection and wide linear dynamic range, for the assays in which the antibody is immobilized via avidin-biotin bridges compared to covalent attachment method.

Optical filtering in directly modulated/detected OOFDM systems.

PubMed

Sánchez, C; Ortega, B; Wei, J L; Capmany, J

2013-12-16

This work presents a theoretical investigation on the performance of directly modulated/detected (DM/DD) optical orthogonal frequency division multiplexed (OOFDM) systems subject to optical filtering. The impact of both linear and nonlinear distortion effects are taken into account to calculate the effective signal-to-noise ratio of each subcarrier. These results are then employed to optimize the design parameters of two simple optical filtering structures: a Mach Zehnder interferometer and a uniform fiber Bragg grating, leading to a significant optical power budget improvement given by 3.3 and 3dB, respectively. These can be further increased to 5.5 and 4.2dB respectively when balanced detection configurations are employed. We find as well that this improvement is highly dependent on the clipping ratio.

Evaluation of the efficacy of the four tests (p16 immunochemistry, PCR, DNA and RNA In situ Hybridization) to evaluate a Human Papillomavirus infection in head and neck cancers: a cohort of 348 French squamous cell carcinomas.

PubMed

Augustin, Jérémy; Outh-Gauer, Sophie; Mandavit, Marion; Gasne, Cassandre; Grard, Ophélie; Denize, Thomas; Nervo, Marine; Mirghani, Haïtham; Laccourreye, Ollivier; Bonfils, Pierre; Bruneval, Patrick; Veyer, David; Péré, Hélène; Tartour, Eric; Badoual, Cécile

2018-04-20

It is now established that HPV plays a role in the development of a subset of head and neck squamous cell carcinomas (HNSCCs), notably oropharyngeal squamous cell carcinomas (SCCs). However, it is not clear which test one should use to detect HPV in oropharyngeal (OP) and non-OP SCCs. In this study, using 348 HNSCCs (126 OP SCCs and 222 non-OP SCCs), we evaluated diagnostic performances of different HPV tests in OP and non-OP SCCs: PCR, p16 immunostaining, in situ hybridization targeting DNA (DNA-CISH) and RNA (RNA-CISH), combined p16 + DNA-CISH, and combined p16 + RNA-CISH. HPV DNA (PCR) was detected in 26% of all tumors (44% of OP SCCs and 17% of non-OP SCCs). For OP SCCs, RNA-CISH was the most sensitive standalone test (88%), but p16 + RNA-CISH was even more sensitive (95%). Specificities were the same for RNA-CISH and DNA-CISH (97%) but it was better for p16 + RNA-CISH (100%). For non-OP SCCs, all tests had sensitivities below 50%, and RNA-CISH, DNA-CISH and p16 + DNA-CISH had respectively 100%, 97% and 99% specificities. As a standalone test, RNA-CISH is the most performant assay to detect HPV in OP SCCs, and combined p16 + RNA-CISH test slightly improves its performances. However, RNA-CISH has the advantage of being one single test. Like p16 and DNA-CISH, RNA-CISH performances are poor in non-OP SCCs to detect HPV, and combining tests does not improve performances. Copyright © 2018. Published by Elsevier Inc.

Computer-aided detection of breast masses: Four-view strategy for screening mammography

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

Wei Jun; Chan Heangping; Zhou Chuan

2011-04-15

Purpose: To improve the performance of a computer-aided detection (CAD) system for mass detection by using four-view information in screening mammography. Methods: The authors developed a four-view CAD system that emulates radiologists' reading by using the craniocaudal and mediolateral oblique views of the ipsilateral breast to reduce false positives (FPs) and the corresponding views of the contralateral breast to detect asymmetry. The CAD system consists of four major components: (1) Initial detection of breast masses on individual views, (2) information fusion of the ipsilateral views of the breast (referred to as two-view analysis), (3) information fusion of the corresponding viewsmore » of the contralateral breast (referred to as bilateral analysis), and (4) fusion of the four-view information with a decision tree. The authors collected two data sets for training and testing of the CAD system: A mass set containing 389 patients with 389 biopsy-proven masses and a normal set containing 200 normal subjects. All cases had four-view mammograms. The true locations of the masses on the mammograms were identified by an experienced MQSA radiologist. The authors randomly divided the mass set into two independent sets for cross validation training and testing. The overall test performance was assessed by averaging the free response receiver operating characteristic (FROC) curves of the two test subsets. The FP rates during the FROC analysis were estimated by using the normal set only. The jackknife free-response ROC (JAFROC) method was used to estimate the statistical significance of the difference between the test FROC curves obtained with the single-view and the four-view CAD systems. Results: Using the single-view CAD system, the breast-based test sensitivities were 58% and 77% at the FP rates of 0.5 and 1.0 per image, respectively. With the four-view CAD system, the breast-based test sensitivities were improved to 76% and 87% at the corresponding FP rates, respectively. The improvement was found to be statistically significant (p<0.0001) by JAFROC analysis. Conclusions: The four-view information fusion approach that emulates radiologists' reading strategy significantly improves the performance of breast mass detection of the CAD system in comparison with the single-view approach.« less

Assessment and improvement of statistical tools for comparative proteomics analysis of sparse data sets with few experimental replicates.

PubMed

Schwämmle, Veit; León, Ileana Rodríguez; Jensen, Ole Nørregaard

2013-09-06

Large-scale quantitative analyses of biological systems are often performed with few replicate experiments, leading to multiple nonidentical data sets due to missing values. For example, mass spectrometry driven proteomics experiments are frequently performed with few biological or technical replicates due to sample-scarcity or due to duty-cycle or sensitivity constraints, or limited capacity of the available instrumentation, leading to incomplete results where detection of significant feature changes becomes a challenge. This problem is further exacerbated for the detection of significant changes on the peptide level, for example, in phospho-proteomics experiments. In order to assess the extent of this problem and the implications for large-scale proteome analysis, we investigated and optimized the performance of three statistical approaches by using simulated and experimental data sets with varying numbers of missing values. We applied three tools, including standard t test, moderated t test, also known as limma, and rank products for the detection of significantly changing features in simulated and experimental proteomics data sets with missing values. The rank product method was improved to work with data sets containing missing values. Extensive analysis of simulated and experimental data sets revealed that the performance of the statistical analysis tools depended on simple properties of the data sets. High-confidence results were obtained by using the limma and rank products methods for analyses of triplicate data sets that exhibited more than 1000 features and more than 50% missing values. The maximum number of differentially represented features was identified by using limma and rank products methods in a complementary manner. We therefore recommend combined usage of these methods as a novel and optimal way to detect significantly changing features in these data sets. This approach is suitable for large quantitative data sets from stable isotope labeling and mass spectrometry experiments and should be applicable to large data sets of any type. An R script that implements the improved rank products algorithm and the combined analysis is available.

Method of wavefront tilt correction for optical heterodyne detection systems under strong turbulence

NASA Astrophysics Data System (ADS)

Xiang, Jing-song; Tian, Xin; Pan, Le-chun

2014-07-01

Atmospheric turbulence decreases the heterodyne mixing efficiency of the optical heterodyne detection systems. Wavefront tilt correction is often used to improve the optical heterodyne mixing efficiency. But the performance of traditional centroid tracking tilt correction is poor under strong turbulence conditions. In this paper, a tilt correction method which tracking the peak value of laser spot on focal plane is proposed. Simulation results show that, under strong turbulence conditions, the performance of peak value tracking tilt correction is distinctly better than that of traditional centroid tracking tilt correction method, and the phenomenon of large antenna's performance inferior to small antenna's performance which may be occurred in centroid tracking tilt correction method can also be avoid in peak value tracking tilt correction method.

Performance evaluation of Bragg coherent diffraction imaging

NASA Astrophysics Data System (ADS)

Öztürk, H.; Huang, X.; Yan, H.; Robinson, I. K.; Noyan, I. C.; Chu, Y. S.

2017-10-01

In this study, we present a numerical framework for modeling three-dimensional (3D) diffraction data in Bragg coherent diffraction imaging (Bragg CDI) experiments and evaluating the quality of obtained 3D complex-valued real-space images recovered by reconstruction algorithms under controlled conditions. The approach is used to systematically explore the performance and the detection limit of this phase-retrieval-based microscopy tool. The numerical investigation suggests that the superb performance of Bragg CDI is achieved with an oversampling ratio above 30 and a detection dynamic range above 6 orders. The observed performance degradation subject to the data binning processes is also studied. This numerical tool can be used to optimize experimental parameters and has the potential to significantly improve the throughput of Bragg CDI method.

A feasibility study of automatic lung nodule detection in chest digital tomosynthesis with machine learning based on support vector machine

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Kim, Ye-seul; Choi, Sunghoon; Lee, Haenghwa; Jo, Byungdu; Choi, Seungyeon; Shin, Jungwook; Kim, Hee-Joung

2017-03-01

The chest digital tomosynthesis(CDT) is recently developed medical device that has several advantage for diagnosing lung disease. For example, CDT provides depth information with relatively low radiation dose compared to computed tomography (CT). However, a major problem with CDT is the image artifacts associated with data incompleteness resulting from limited angle data acquisition in CDT geometry. For this reason, the sensitivity of lung disease was not clear compared to CT. In this study, to improve sensitivity of lung disease detection in CDT, we developed computer aided diagnosis (CAD) systems based on machine learning. For design CAD systems, we used 100 cases of lung nodules cropped images and 100 cases of normal lesion cropped images acquired by lung man phantoms and proto type CDT. We used machine learning techniques based on support vector machine and Gabor filter. The Gabor filter was used for extracting characteristics of lung nodules and we compared performance of feature extraction of Gabor filter with various scale and orientation parameters. We used 3, 4, 5 scales and 4, 6, 8 orientations. After extracting features, support vector machine (SVM) was used for classifying feature of lesions. The linear, polynomial and Gaussian kernels of SVM were compared to decide the best SVM conditions for CDT reconstruction images. The results of CAD system with machine learning showed the capability of automatically lung lesion detection. Furthermore detection performance was the best when Gabor filter with 5 scale and 8 orientation and SVM with Gaussian kernel were used. In conclusion, our suggested CAD system showed improving sensitivity of lung lesion detection in CDT and decide Gabor filter and SVM conditions to achieve higher detection performance of our developed CAD system for CDT.

Comparison of detectability in step-and-shoot mode and continuous mode digital tomosynthesis systems

NASA Astrophysics Data System (ADS)

Lee, Changwoo; Han, Minah; Baek, Jongduk

2017-03-01

Digital tomosynthesis system has been widely used in chest, dental, and breast imaging. Since the digital tomosynthesis system provides volumetric images from multiple projection data, structural noise inherent in X-ray radiograph can be reduced, and thus signal detection performance is improved. Currently, tomosynthesis system uses two data acquisition modes: step-and-shoot mode and continuous mode. Several studies have been conducted to compare the system performance of two acquisition modes with respect to spatial resolution and contrast. In this work, we focus on signal detectability in step-and-shoot mode and continuous mode. For evaluation, uniform background is considered, and eight spherical objects with diameters of 0.5, 0.8, 1, 2, 3, 5, 8, 10 mm are used as signals. Projection data with and without spherical objects are acquired in step-and-shoot mode and continuous mode, respectively, and quantum noise are added. Then, noisy projection data are reconstructed by FDK algorithm. To compare the detection performance of two acquisition modes, we calculate task signal-to-noise ratio (SNR) of channelized Hotelling observer with Laguerre-Gauss channels for each spherical object. While the task-SNR values of two acquisition modes are similar for spherical objects larger than 1 mm diameter, step-and-shoot mode yields higher detectability for small signal sizes. The main reason of this behavior is that small signal is more affected by X-ray tube motion blur than large signal. Our results indicate that it is beneficial to use step-and-shoot data acquisition mode to improve the detectability of small signals (i.e., less than 1 mm diameter) in digital tomosynthesis systems.

New developments in supra-threshold perimetry.

PubMed

Henson, David B; Artes, Paul H

2002-09-01

To describe a series of recent enhancements to supra-threshold perimetry. Computer simulations were used to develop an improved algorithm (HEART) for the setting of the supra-threshold test intensity at the beginning of a field test, and to evaluate the relationship between various pass/fail criteria and the test's performance (sensitivity and specificity) and how they compare with modern threshold perimetry. Data were collected in optometric practices to evaluate HEART and to assess how the patient's response times can be analysed to detect false positive response errors in visual field test results. The HEART algorithm shows improved performance (reduced between-eye differences) over current algorithms. A pass/fail criterion of '3 stimuli seen of 3-5 presentations' at each test location reduces test/retest variability and combines high sensitivity and specificity. A large percentage of false positive responses can be detected by comparing their latencies to the average response time of a patient. Optimised supra-threshold visual field tests can perform as well as modern threshold techniques. Such tests may be easier to perform for novice patients, compared with the more demanding threshold tests.

Object tracking via background subtraction for monitoring illegal activity in crossroad

NASA Astrophysics Data System (ADS)

Ghimire, Deepak; Jeong, Sunghwan; Park, Sang Hyun; Lee, Joonwhoan

2016-07-01

In the field of intelligent transportation system a great number of vision-based techniques have been proposed to prevent pedestrians from being hit by vehicles. This paper presents a system that can perform pedestrian and vehicle detection and monitoring of illegal activity in zebra crossings. In zebra crossing, according to the traffic light status, to fully avoid a collision, a driver or pedestrian should be warned earlier if they possess any illegal moves. In this research, at first, we detect the traffic light status of pedestrian and monitor the crossroad for vehicle pedestrian moves. The background subtraction based object detection and tracking is performed to detect pedestrian and vehicles in crossroads. Shadow removal, blob segmentation, trajectory analysis etc. are used to improve the object detection and classification performance. We demonstrate the experiment in several video sequences which are recorded in different time and environment such as day time and night time, sunny and raining environment. Our experimental results show that such simple and efficient technique can be used successfully as a traffic surveillance system to prevent accidents in zebra crossings.

Environmental enrichment enhances cognitive flexibility in C57BL/6 mice on a touchscreen reversal learning task.

PubMed

Zeleznikow-Johnston, Ariel; Burrows, Emma L; Renoir, Thibault; Hannan, Anthony J

2017-05-01

Environmental enrichment (EE) is any positive modification of the 'standard housing' (SH) conditions in which laboratory animals are typically held, usually involving increased opportunity for cognitive stimulation and physical activity. EE has been reported to enhance baseline performance of wild-type animals on traditional cognitive behavioural tasks. Recently, touchscreen operant testing chambers have emerged as a way of performing rodent cognitive assays, providing greater reproducibility, translatability and automatability. Cognitive tests in touchscreen chambers are performed over numerous trials and thus experimenters have the power to detect subtle enhancements in performance. We used touchscreens to analyse the effects of EE on reversal learning, visual discrimination and hippocampal-dependent spatial pattern separation and working memory. We hypothesized that EE would enhance the performance of mice on cognitive touchscreen tasks. Our hypothesis was partially supported in that EE induced enhancements in cognitive flexibility as observed in visual discrimination and reversal learning improvements. However, no other significant effects of EE on cognitive performance were observed. EE decreased the activity level of mice in the touchscreen chambers, which may influence the enrichment level of the animals. Although we did not see enhancements on all hypothesized parameters, our testing paradigm is capable of detecting EE-induced improved cognitive flexibility in mice, which has implications for both understanding the mechanisms of EE and improving screening of putative cognitive-enhancing therapeutics. Copyright © 2017 Elsevier Ltd. All rights reserved.

Temporal trends and variability of colonoscopy performance in a gastroenterology practice.

PubMed

le Clercq, Chantal M C; Mooi, Rick J; Winkens, Bjorn; Salden, Bouke N H; Bakker, C Minke; van Nunen, Annick B; Keulen, Eric P T; de Ridder, Rogier J; Masclee, Ad A M; Sanduleanu, Silvia

2016-03-01

Quality measures for colonoscopy are operator dependent and vary. It is unclear whether quality measures change over time. In this study, time-dependent variation in colonoscopy performance was examined in a gastroenterology practice. Colonoscopy and histopathology records that were collected at three hospitals (one university and two non-university hospitals) over three time periods (2007, 2010, and 2013) were reviewed. Data from colonoscopists performing at least 100 procedures per year were analyzed. Inter-colonoscopist variation in performance (i. e. adjusted cecal intubation rate [aCIR], adenoma detection rate [ADR], advanced ADR, mean adenomas per procedure [MAP], proximal ADR, nonpolypoid ADR, and serrated polyp detection rate) were examined using coefficients of variation. Logistic regression analyses were also performed, adjusting for covariates. A total of 23 colonoscopists performing 6400 procedures were included. Overall, the mean aCIR, ADR, MAP, and proximal ADR improved significantly over time, from 91.9 %, 22.5 %, 0.37, and 10.2 % in 2007 to 95.3 %, 25.8 %, 0.45, and 13.4 %, respectively, in 2013 (P < 0.05). The inter-colonoscopist variation in ADR decreased from 37 % in 2007 to 15 % in 2013 (P < 0.05). In the non-university hospitals, mean values for quality measures increased significantly over time, whereas they remained stable in the university hospital. Variability in performance among colonoscopists decreased significantly within the gastroenterology clinical practice. Core quality measures improved over time, mainly through improvement of the lower performers. Measurement of inter-colonoscopist variation in performance helps to identify factors that stimulate or hinder performance, and forms the basis for interventions. http://www.trialregister.nl. © Georg Thieme Verlag KG Stuttgart · New York.

Two-species occupancy modeling accounting for species misidentification and nondetection

USGS Publications Warehouse

Chambert, Thierry; Grant, Evan H. Campbell; Miller, David A. W.; Nichols, James; Mulder, Kevin P.; Brand, Adrianne B,

2018-01-01

In occupancy studies, species misidentification can lead to false‐positive detections, which can cause severe estimator biases. Currently, all models that account for false‐positive errors only consider omnibus sources of false detections and are limited to single‐species occupancy.However, false detections for a given species often occur because of the misidentification with another, closely related species. To exploit this explicit source of false‐positive detection error, we develop a two‐species occupancy model that accounts for misidentifications between two species of interest. As with other false‐positive models, identifiability is greatly improved by the availability of unambiguous detections at a subset of site x occasions. Here, we consider the case where some of the field observations can be confirmed using laboratory or other independent identification methods (“confirmatory data”).We performed three simulation studies to (1) assess the model's performance under various realistic scenarios, (2) investigate the influence of the proportion of confirmatory data on estimator accuracy and (3) compare the performance of this two‐species model with that of the single‐species false‐positive model. The model shows good performance under all scenarios, even when only small proportions of detections are confirmed (e.g. 5%). It also clearly outperforms the single‐species model.We illustrate application of this model using a 4‐year dataset on two sympatric species of lungless salamanders: the US federally endangered Shenandoah salamander Plethodon shenandoah, and its presumed competitor, the red‐backed salamander Plethodon cinereus. Occupancy of red‐backed salamanders appeared very stable across the 4 years of study, whereas the Shenandoah salamander displayed substantial turnover in occupancy of forest habitats among years.Given the extent of species misidentification issues in occupancy studies, this modelling approach should help improve the reliability of estimates of species distribution, which is the goal of many studies and monitoring programmes. Further developments, to account for different forms of state uncertainty, can be readily undertaken under our general approach.

Active link selection for efficient semi-supervised community detection

NASA Astrophysics Data System (ADS)

Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

2015-03-01

Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches.

Detection Performance of Upgraded "Polished Panel" Optical Receiver Concept on the Deep-Space Network's 34 Meter Research Antenna

NASA Technical Reports Server (NTRS)

Vilnrotter, Victor A.

2012-01-01

The development and demonstration of a "polished panel" optical receiver concept on the 34 meter research antenna of the Deep Space Network (DSN) has been the subject of recent papers. This concept would enable simultaneous reception of optical and microwave signals by retaining the original shape of the main reflector for microwave reception, but with the aluminum panels polished to high reflectivity to enable focusing of optical signal energy as well. A test setup has been installed on the DSN's 34 meter research antenna at Deep Space Station 13 (DSS-13) of NASA's Goldstone Communications Complex in California, and preliminary experimental results have been obtained. This paper describes the results of our latest efforts to improve the point-spread function (PSF) generated by a custom polished panel, in an attempt to reduce the dimensions of the PSF, thus enabling more precise tracking and improved detection performance. The design of the new mechanical support structure and its operation are described, and the results quantified in terms of improvements in collected signal energy and optical communications performance, based on data obtained while tracking the planet Jupiter with the 34 meter research antenna at DSS-13.

Active link selection for efficient semi-supervised community detection

PubMed Central

Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

2015-01-01

Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches. PMID:25761385

Anticipatory detection of turning in humans for intuitive control of robotic mobility assistance.

PubMed

Farkhatdinov, Ildar; Roehri, Nicolas; Burdet, Etienne

2017-09-26

Many wearable lower-limb robots for walking assistance have been developed in recent years. However, it remains unclear how they can be commanded in an intuitive and efficient way by their user. In particular, providing robotic assistance to neurologically impaired individuals in turning remains a significant challenge. The control should be safe to the users and their environment, yet yield sufficient performance and enable natural human-machine interaction. Here, we propose using the head and trunk anticipatory behaviour in order to detect the intention to turn in a natural, non-intrusive way, and use it for triggering turning movement in a robot for walking assistance. We therefore study head and trunk orientation during locomotion of healthy adults, and investigate upper body anticipatory behaviour during turning. The collected walking and turning kinematics data are clustered using the k-means algorithm and cross-validation tests and k-nearest neighbours method are used to evaluate the performance of turning detection during locomotion. Tests with seven subjects exhibited accurate turning detection. Head anticipated turning by more than 400-500 ms in average across all subjects. Overall, the proposed method detected turning 300 ms after its initiation and 1230 ms before the turning movement was completed. Using head anticipatory behaviour enabled to detect turning faster by about 100 ms, compared to turning detection using only pelvis orientation measurements. Finally, it was demonstrated that the proposed turning detection can improve the quality of human-robot interaction by improving the control accuracy and transparency.

OPAD-EDIFIS Real-Time Processing

NASA Technical Reports Server (NTRS)

Katsinis, Constantine

1997-01-01

The Optical Plume Anomaly Detection (OPAD) detects engine hardware degradation of flight vehicles through identification and quantification of elemental species found in the plume by analyzing the plume emission spectra in a real-time mode. Real-time performance of OPAD relies on extensive software which must report metal amounts in the plume faster than once every 0.5 sec. OPAD software previously written by NASA scientists performed most necessary functions at speeds which were far below what is needed for real-time operation. The research presented in this report improved the execution speed of the software by optimizing the code without changing the algorithms and converting it into a parallelized form which is executed in a shared-memory multiprocessor system. The resulting code was subjected to extensive timing analysis. The report also provides suggestions for further performance improvement by (1) identifying areas of algorithm optimization, (2) recommending commercially available multiprocessor architectures and operating systems to support real-time execution and (3) presenting an initial study of fault-tolerance requirements.

Applications of Kalman filtering to real-time trace gas concentration measurements

NASA Technical Reports Server (NTRS)

Leleux, D. P.; Claps, R.; Chen, W.; Tittel, F. K.; Harman, T. L.

2002-01-01

A Kalman filtering technique is applied to the simultaneous detection of NH3 and CO2 with a diode-laser-based sensor operating at 1.53 micrometers. This technique is developed for improving the sensitivity and precision of trace gas concentration levels based on direct overtone laser absorption spectroscopy in the presence of various sensor noise sources. Filter performance is demonstrated to be adaptive to real-time noise and data statistics. Additionally, filter operation is successfully performed with dynamic ranges differing by three orders of magnitude. Details of Kalman filter theory applied to the acquired spectroscopic data are discussed. The effectiveness of this technique is evaluated by performing NH3 and CO2 concentration measurements and utilizing it to monitor varying ammonia and carbon dioxide levels in a bioreactor for water reprocessing, located at the NASA-Johnson Space Center. Results indicate a sensitivity enhancement of six times, in terms of improved minimum detectable absorption by the gas sensor.

Comparison of digital signal-signal beat interference compensation techniques in direct-detection subcarrier modulation systems.

PubMed

Li, Zhe; Erkilinc, M Sezer; Galdino, Lidia; Shi, Kai; Thomsen, Benn C; Bayvel, Polina; Killey, Robert I

2016-12-12

Single-polarization direct-detection transceivers may offer advantages compared to digital coherent technology for some metro, back-haul, access and inter-data center applications since they offer low-cost and complexity solutions. However, a direct-detection receiver introduces nonlinearity upon photo detection, since it is a square-law device, which results in signal distortion due to signal-signal beat interference (SSBI). Consequently, it is desirable to develop effective and low-cost SSBI compensation techniques to improve the performance of such transceivers. In this paper, we compare the performance of a number of recently proposed digital signal processing-based SSBI compensation schemes, including the use of single- and two-stage linearization filters, an iterative linearization filter and a SSBI estimation and cancellation technique. Their performance is assessed experimentally using a 7 × 25 Gb/s wavelength division multiplexed (WDM) single-sideband 16-QAM Nyquist-subcarrier modulation system operating at a net information spectral density of 2.3 (b/s)/Hz.

Design and performance investigation of LDPC-coded upstream transmission systems in IM/DD OFDM-PONs

NASA Astrophysics Data System (ADS)

Gong, Xiaoxue; Guo, Lei; Wu, Jingjing; Ning, Zhaolong

2016-12-01

In Intensity-Modulation Direct-Detection (IM/DD) Orthogonal Frequency Division Multiplexing Passive Optical Networks (OFDM-PONs), aside from Subcarrier-to-Subcarrier Intermixing Interferences (SSII) induced by square-law detection, the same laser frequency for data sending from Optical Network Units (ONUs) results in ONU-to-ONU Beating Interferences (OOBI) at the receiver. To mitigate those interferences, we design a Low-Density Parity Check (LDPC)-coded and spectrum-efficient upstream transmission system. A theoretical channel model is also derived, in order to analyze the detrimental factors influencing system performances. Simulation results demonstrate that the receiver sensitivity is improved 3.4 dB and 2.5 dB under QPSK and 8QAM, respectively, after 100 km Standard Single-Mode Fiber (SSMF) transmission. Furthermore, the spectrum efficiency can be improved by about 50%.

Implementation of Multipattern String Matching Accelerated with GPU for Intrusion Detection System

NASA Astrophysics Data System (ADS)

Nehemia, Rangga; Lim, Charles; Galinium, Maulahikmah; Rinaldi Widianto, Ahmad

2017-04-01

As Internet-related security threats continue to increase in terms of volume and sophistication, existing Intrusion Detection System is also being challenged to cope with the current Internet development. Multi Pattern String Matching algorithm accelerated with Graphical Processing Unit is being utilized to improve the packet scanning performance of the IDS. This paper implements a Multi Pattern String Matching algorithm, also called Parallel Failureless Aho Corasick accelerated with GPU to improve the performance of IDS. OpenCL library is used to allow the IDS to support various GPU, including popular GPU such as NVIDIA and AMD, used in our research. The experiment result shows that the application of Multi Pattern String Matching using GPU accelerated platform provides a speed up, by up to 141% in term of throughput compared to the previous research.

Learning representations for the early detection of sepsis with deep neural networks.

PubMed

Kam, Hye Jin; Kim, Ha Young

2017-10-01

Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

Weak wide-band signal detection method based on small-scale periodic state of Duffing oscillator

NASA Astrophysics Data System (ADS)

Hou, Jian; Yan, Xiao-peng; Li, Ping; Hao, Xin-hong

2018-03-01

The conventional Duffing oscillator weak signal detection method, which is based on a strong reference signal, has inherent deficiencies. To address these issues, the characteristics of the Duffing oscillatorʼs phase trajectory in a small-scale periodic state are analyzed by introducing the theory of stopping oscillation system. Based on this approach, a novel Duffing oscillator weak wide-band signal detection method is proposed. In this novel method, the reference signal is discarded, and the to-be-detected signal is directly used as a driving force. By calculating the cosine function of a phase space angle, a single Duffing oscillator can be used for weak wide-band signal detection instead of an array of uncoupled Duffing oscillators. Simulation results indicate that, compared with the conventional Duffing oscillator detection method, this approach performs better in frequency detection intervals, and reduces the signal-to-noise ratio detection threshold, while improving the real-time performance of the system. Project supported by the National Natural Science Foundation of China (Grant No. 61673066).

Study of dual-polarization OQAM-OFDM PON with direct detection

NASA Astrophysics Data System (ADS)

Luo, Qing-long; Feng, Min; Bai, Cheng-lin; Hu, Wei-sheng

2016-01-01

An offset quadrature amplitude modulation orthogonal frequency-division multiplexing (OQAM-OFDM) passive optical network (PON) architecture with direct detection is brought up to increase the transmission range and improve the system performance. In optical line terminal (OLT), OQAM-OFDM signals at 40 Gbit/s are transmitted as downstream. At each optical network unit (ONU), the optical OQAM-OFDM signal is demodulated with direct detection. The results show that the transmission distance can exceed 20 km with negligible penalty under the experimental conditions.

Attention Modifies Spatial Resolution According to Task Demands.

PubMed

Barbot, Antoine; Carrasco, Marisa

2017-03-01

How does visual attention affect spatial resolution? In texture-segmentation tasks, exogenous (involuntary) attention automatically increases resolution at the attended location, which improves performance where resolution is too low (at the periphery) but impairs performance where resolution is already too high (at central locations). Conversely, endogenous (voluntary) attention improves performance at all eccentricities, which suggests a more flexible mechanism. Here, using selective adaptation to spatial frequency, we investigated the mechanism by which endogenous attention benefits performance in resolution tasks. Participants detected a texture target that could appear at several eccentricities. Adapting to high or low spatial frequencies selectively affected performance in a manner consistent with changes in resolution. Moreover, adapting to high, but not low, frequencies mitigated the attentional benefit at central locations where resolution was too high; this shows that attention can improve performance by decreasing resolution. Altogether, our results indicate that endogenous attention benefits performance by modulating the contribution of high-frequency information in order to flexibly adjust spatial resolution according to task demands.

Attention Modifies Spatial Resolution According to Task Demands

PubMed Central

Barbot, Antoine; Carrasco, Marisa

2017-01-01

How does visual attention affect spatial resolution? In texture-segmentation tasks, exogenous (involuntary) attention automatically increases resolution at the attended location, which improves performance where resolution is too low (at the periphery) but impairs performance where resolution is already too high (at central locations). Conversely, endogenous (voluntary) attention improves performance at all eccentricities, which suggests a more flexible mechanism. Here, using selective adaptation to spatial frequency, we investigated the mechanism by which endogenous attention benefits performance in resolution tasks. Participants detected a texture target that could appear at several eccentricities. Adapting to high or low spatial frequencies selectively affected performance in a manner consistent with changes in resolution. Moreover, adapting to high, but not low, frequencies mitigated the attentional benefit at central locations where resolution was too high; this shows that attention can improve performance by decreasing resolution. Altogether, our results indicate that endogenous attention benefits performance by modulating the contribution of high-frequency information in order to flexibly adjust spatial resolution according to task demands. PMID:28118103

The Practical Relevance of Accountability Systems for School Improvement: A Descriptive Analysis of California Schools. CSE Report 713

ERIC Educational Resources Information Center

Mintrop, Heinrich; Trujillo, Tina

2007-01-01

In search for the practical relevance of accountability systems for school improvement, we ask whether practitioners traveling between the worlds of system-designated high and low-performing schools would detect tangible differences by observing concrete behaviors, looking at student work, or inquiring about teacher, administrator, or student…

Development of a candidate reference material for adventitious virus detection in vaccine and biologicals manufacturing by deep sequencing

PubMed Central

Mee, Edward T.; Preston, Mark D.; Minor, Philip D.; Schepelmann, Silke; Huang, Xuening; Nguyen, Jenny; Wall, David; Hargrove, Stacey; Fu, Thomas; Xu, George; Li, Li; Cote, Colette; Delwart, Eric; Li, Linlin; Hewlett, Indira; Simonyan, Vahan; Ragupathy, Viswanath; Alin, Voskanian-Kordi; Mermod, Nicolas; Hill, Christiane; Ottenwälder, Birgit; Richter, Daniel C.; Tehrani, Arman; Jacqueline, Weber-Lehmann; Cassart, Jean-Pol; Letellier, Carine; Vandeputte, Olivier; Ruelle, Jean-Louis; Deyati, Avisek; La Neve, Fabio; Modena, Chiara; Mee, Edward; Schepelmann, Silke; Preston, Mark; Minor, Philip; Eloit, Marc; Muth, Erika; Lamamy, Arnaud; Jagorel, Florence; Cheval, Justine; Anscombe, Catherine; Misra, Raju; Wooldridge, David; Gharbia, Saheer; Rose, Graham; Ng, Siemon H.S.; Charlebois, Robert L.; Gisonni-Lex, Lucy; Mallet, Laurent; Dorange, Fabien; Chiu, Charles; Naccache, Samia; Kellam, Paul; van der Hoek, Lia; Cotten, Matt; Mitchell, Christine; Baier, Brian S.; Sun, Wenping; Malicki, Heather D.

2016-01-01

Background Unbiased deep sequencing offers the potential for improved adventitious virus screening in vaccines and biotherapeutics. Successful implementation of such assays will require appropriate control materials to confirm assay performance and sensitivity. Methods A common reference material containing 25 target viruses was produced and 16 laboratories were invited to process it using their preferred adventitious virus detection assay. Results Fifteen laboratories returned results, obtained using a wide range of wet-lab and informatics methods. Six of 25 target viruses were detected by all laboratories, with the remaining viruses detected by 4–14 laboratories. Six non-target viruses were detected by three or more laboratories. Conclusion The study demonstrated that a wide range of methods are currently used for adventitious virus detection screening in biological products by deep sequencing and that they can yield significantly different results. This underscores the need for common reference materials to ensure satisfactory assay performance and enable comparisons between laboratories. PMID:26709640

Can a totally different approach to soft tissue computer aided detection (CADe) result in affecting radiologists' decisions?

NASA Astrophysics Data System (ADS)

Gur, David

2018-03-01

We tested whether a case based CADe scheme, developed only on negatively interpreted screening mammograms, has predictive value for cancer detection during subsequent screening and how this approach may affect radiologists' performances when alerting them to a small subset ( 15%) of exams on which radiologists tend to miss cancers. A series of six parameters case based CADe schemes, using 200 negative mammograms (800 images 100 women with breast cancer at subsequent screening and 100 women who remained negative), carefully matched by age and breast density, were optimized. CADe alone schemes performed at AUC=0.68 (+/- 0.01). Five radiologists and 4 residents interpreted the same cases and performed at AUC =0.71 (experienced radiologists) and AUC= 0.61 (residents). With the "CADe warnings" shown to the interpreters only if they did not recall one of 24 highest CADe scoring cases, assisted performance of radiologists and residents respectively, were 0.71 and 0.63 (p>0.05). However, when the CADe alone performance was raised to an AUC=0.78, by artificially increasing the number of possible warnings from 16 to 24, radiologists' performances significantly improved from an AUC of 0.68 to 0.72 (p<0.05). In conclusion, the use case based information other than breast density could highlight a small fraction of women whose cancers are more likely to be missed by radiologists and later detected during subsequent mammograms, thereby, leading to an assisted approach that improves radiologists' performances. However, to be effective, the performance of the CADe alone should be substantially higher (e.g. ΔAUC >=0.07) than that of the un-assisted radiologist.

Automatic Censoring CFAR Detector Based on Ordered Data Difference for Low-Flying Helicopter Safety

PubMed Central

Jiang, Wen; Huang, Yulin; Yang, Jianyu

2016-01-01

Being equipped with a millimeter-wave radar allows a low-flying helicopter to sense the surroundings in real time, which significantly increases its safety. However, nonhomogeneous clutter environments, such as a multiple target situation and a clutter edge environment, can dramatically affect the radar signal detection performance. In order to improve the radar signal detection performance in nonhomogeneous clutter environments, this paper proposes a new automatic censored cell averaging CFAR detector. The proposed CFAR detector does not require any prior information about the background environment and uses the hypothesis test of the first-order difference (FOD) result of ordered data to reject the unwanted samples in the reference window. After censoring the unwanted ranked cells, the remaining samples are combined to form an estimate of the background power level, thus getting better radar signal detection performance. The simulation results show that the FOD-CFAR detector provides low loss CFAR performance in a homogeneous environment and also performs robustly in nonhomogeneous environments. Furthermore, the measured results of a low-flying helicopter validate the basic performance of the proposed method. PMID:27399714

Investing in improved performance of national tuberculosis programs reduces the tuberculosis burden: analysis of 22 high-burden countries, 2002-2009.

PubMed

Akachi, Yoko; Zumla, Alimuddin; Atun, Rifat

2012-05-15

To assess the impact of investment in national tuberculosis programs (NTPs) on NTP performance and tuberculosis burden in 22 high-burden countries, as determined by the World Health Organization (WHO). Estimates of annual tuberculosis burden and NTP performance indicators and control variables during 2002-2009 were obtained from the Organization for Economic Cooperation and Development, the WHO, the World Bank, and the Penn World Table for the 22 high-burden countries. Panel data analysis was performed using the outcome variables tuberculosis incidence, prevalence, and mortality and the key explanatory variables Partnership case detection rate and treatment success rate, controlling for gross domestic product per capita, population structure, and human immunodeficiency virus (HIV) prevalence. A $1 per capita (general population) higher NTP budget (including domestic and external sources) was associated with a 1.9% (95% confidence interval, .12%-3.6%) higher estimated case detection rate the following year for the 22 high-burden countries between 2002 and 2009. In the final models, which corrected for autocorrelation and heteroskedasticity, achieving the STOP TB Partnership case detection rate target of >70% was associated with significantly (P < .01) lower tuberculosis incidence, prevalence, and mortality the following year, even when controlling for general economic development and HIV prevalence as potential confounding variables. Increased investment in NTPs was significantly associated with improved performance and with a downward trend in the tuberculosis burden in the 22 high-burden countries during 2002-2009.

Multiple symbol partially coherent detection of MPSK

NASA Technical Reports Server (NTRS)

Simon, M. K.; Divsalar, D.

1992-01-01

It is shown that by using the known (or estimated) value of carrier tracking loop signal to noise ratio (SNR) in the decision metric, it is possible to improve the error probability performance of a partially coherent multiple phase-shift-keying (MPSK) system relative to that corresponding to the commonly used ideal coherent decision rule. Using a maximum-likeihood approach, an optimum decision metric is derived and shown to take the form of a weighted sum of the ideal coherent decision metric (i.e., correlation) and the noncoherent decision metric which is optimum for differential detection of MPSK. The performance of a receiver based on this optimum decision rule is derived and shown to provide continued improvement with increasing length of observation interval (data symbol sequence length). Unfortunately, increasing the observation length does not eliminate the error floor associated with the finite loop SNR. Nevertheless, in the limit of infinite observation length, the average error probability performance approaches the algebraic sum of the error floor and the performance of ideal coherent detection, i.e., at any error probability above the error floor, there is no degradation due to the partial coherence. It is shown that this limiting behavior is virtually achievable with practical size observation lengths. Furthermore, the performance is quite insensitive to mismatch between the estimate of loop SNR (e.g., obtained from measurement) fed to the decision metric and its true value. These results may be of use in low-cost Earth-orbiting or deep-space missions employing coded modulations.

Magnetic recording performance of keepered media

NASA Astrophysics Data System (ADS)

Coughlin, T. M.; Tang, Y. S.; Velu, E. M. T.; Lairson, B.

1997-04-01

Using low flying and proximity inductive heads, keepered media show improved on- and off-track performance leading us to conclude that a greater than 20% areal density improvement is possible with a keeper layer over the magnetic storage layer. For Sendust keeper layers there is an optimal range of thickness and an optimal bias point for best performance. There are both amplitude and timing asymmetries that are functions of the read-back bias. For a peak detect channel the best performance corresponds to the minimum timing asymmetry although this is not the point where the pulses are narrowest. Keepered media may have an advantage in total jitter and partial erasure. NLTS is almost identical for keepered versus unkeepered media.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning.

PubMed

Lim, Kwangyong; Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).

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.

Emotion and anxiety potentiate the way attention alters visual appearance.

PubMed

Barbot, Antoine; Carrasco, Marisa

2018-04-12

The ability to swiftly detect and prioritize the processing of relevant information around us is critical for the way we interact with our environment. Selective attention is a key mechanism that serves this purpose, improving performance in numerous visual tasks. Reflexively attending to sudden information helps detect impeding threat or danger, a possible reason why emotion modulates the way selective attention affects perception. For instance, the sudden appearance of a fearful face potentiates the effects of exogenous (involuntary, stimulus-driven) attention on performance. Internal states such as trait anxiety can also modulate the impact of attention on early visual processing. However, attention does not only improve performance; it also alters the way visual information appears to us, e.g. by enhancing perceived contrast. Here we show that emotion potentiates the effects of exogenous attention on both performance and perceived contrast. Moreover, we found that trait anxiety mediates these effects, with stronger influences of attention and emotion in anxious observers. Finally, changes in performance and appearance correlated with each other, likely reflecting common attentional modulations. Altogether, our findings show that emotion and anxiety interact with selective attention to truly alter how we see.

Effects of the performance management information system in improving performance: an empirical study in Shanghai Ninth People's Hospital.

PubMed

Cui, Yinghui; Wu, Zhengyi; Lu, Yao; Jin, Wenzhong; Dai, Xing; Bai, Jinxi

2016-01-01

Improving the performance of clinical departments is not only the significant content of the healthcare system reform in China, but also the essential approach to better satisfying the Chinese growing demand for medical services. Performance management is vital and meaningful to public hospitals in China. Several studies are conducted in hospital internal performance management, but almost none of them consider the effects of informational tools. Therefore, we carried out an empirical study on effects of using performance management information system in Shanghai Ninth People's Hospital. The main feature of the system is that it provides a real-time query platform for users to analyze and dynamically monitor the key performance indexes, timely detect problems and make adjustments. We collected pivotal medical data on 35 clinical departments of this hospital from January 2013 until December 2014, 1 year before and after applying the performance management information system. Comparative analysis was conducted by statistical methods. The results show that the system is beneficial to improve performance scores of clinical departments and lower the proportion of drug expenses, meanwhile, shorten the average hospitalized days and increase the bed turnover rate. That is to say, with the increasing medical services, the quality and efficiency is greatly improved. In a word, application of the performance management information system has a positive effect on improving performance of clinical departments.

Progress in molecular imaging in endoscopy and endomicroscopy for cancer imaging

PubMed Central

Khondee, Supang; Wang, Thomas D.

2014-01-01

Imaging is an essential tool for effective cancer management. Endoscopes are important medical instruments for performing in vivo imaging in hollow organs. Early detection of cancer can be achieved with surveillance using endoscopy, and has been shown to reduce mortality and to improve outcomes. Recently, great advancements have been made in endoscopic instruments, including new developments in optical designs, light sources, optical fibers, miniature scanners, and multimodal systems, allowing for improved resolution, greater tissue penetration, and multispectral imaging. In addition, progress has been made in the development of highly-specific optical probes, allowing for improved specificity for molecular targets. Integration of these new endoscopic instruments with molecular probes provides a unique opportunity for significantly improving patient outcomes and has potential to further improve early detection, image guided therapy, targeted therapy, and personalized medicine. This work summarizes current and evolving endoscopic technologies, and provides an overview of various promising optical molecular probes. PMID:23502247

Algorithm Improvement Program Nuclide Identification Algorithm Scoring Criteria And Scoring Application - DNDO.

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

Enghauser, Michael

2015-02-01

The goal of the Domestic Nuclear Detection Office (DNDO) Algorithm Improvement Program (AIP) is to facilitate gamma-radiation detector nuclide identification algorithm development, improvement, and validation. Accordingly, scoring criteria have been developed to objectively assess the performance of nuclide identification algorithms. In addition, a Microsoft Excel spreadsheet application for automated nuclide identification scoring has been developed. This report provides an overview of the equations, nuclide weighting factors, nuclide equivalencies, and configuration weighting factors used by the application for scoring nuclide identification algorithm performance. Furthermore, this report presents a general overview of the nuclide identification algorithm scoring application including illustrative examples.

Mass type-specific sparse representation for mass classification in computer-aided detection on mammograms

PubMed Central

2013-01-01

Background Breast cancer is the leading cause of both incidence and mortality in women population. For this reason, much research effort has been devoted to develop Computer-Aided Detection (CAD) systems for early detection of the breast cancers on mammograms. In this paper, we propose a new and novel dictionary configuration underpinning sparse representation based classification (SRC). The key idea of the proposed algorithm is to improve the sparsity in terms of mass margins for the purpose of improving classification performance in CAD systems. Methods The aim of the proposed SRC framework is to construct separate dictionaries according to the types of mass margins. The underlying idea behind our method is that the separated dictionaries can enhance the sparsity of mass class (true-positive), leading to an improved performance for differentiating mammographic masses from normal tissues (false-positive). When a mass sample is given for classification, the sparse solutions based on corresponding dictionaries are separately solved and combined at score level. Experiments have been performed on both database (DB) named as Digital Database for Screening Mammography (DDSM) and clinical Full Field Digital Mammogram (FFDM) DBs. In our experiments, sparsity concentration in the true class (SCTC) and area under the Receiver operating characteristic (ROC) curve (AUC) were measured for the comparison between the proposed method and a conventional single dictionary based approach. In addition, a support vector machine (SVM) was used for comparing our method with state-of-the-arts classifier extensively used for mass classification. Results Comparing with the conventional single dictionary configuration, the proposed approach is able to improve SCTC of up to 13.9% and 23.6% on DDSM and FFDM DBs, respectively. Moreover, the proposed method is able to improve AUC with 8.2% and 22.1% on DDSM and FFDM DBs, respectively. Comparing to SVM classifier, the proposed method improves AUC with 2.9% and 11.6% on DDSM and FFDM DBs, respectively. Conclusions The proposed dictionary configuration is found to well improve the sparsity of dictionaries, resulting in an enhanced classification performance. Moreover, the results show that the proposed method is better than conventional SVM classifier for classifying breast masses subject to various margins from normal tissues. PMID:24564973

First signal from a broadband cryogenic preamplifier cooled by circulating liquid nitrogen in a 7 T Fourier transform ion cyclotron resonance mass spectrometer.

PubMed

Choi, Myoung Choul; Lee, Jeong Min; Lee, Se Gyu; Choi, Sang Hwan; Choi, Yeon Suk; Lee, Kyung Jae; Kim, SeungYong; Kim, Hyun Sik; Stahl, Stefan

2012-12-18

Despite the outstanding performance of Fourier transform ion cyclotron/mass spectrometry (FTICR/MS), the complexity of the cellular proteome or natural compounds presents considerable challenges. Sensitivity is a key performance parameter of a FTICR mass spectrometer. By improving this parameter, the dynamic range of the instrument can be increased to improve the detection signal of low-abundance compounds or fragment ion peaks. In order to improve sensitivity, a cryogenic detection system was developed by the KBSI (Korean Basic Science Institute) in collaboration with Stahl-Electronics (Mettenheim, Germany). A simple, efficient liquid circulation cooling system was designed and a cryogenic preamplifier implemented inside a FTICR mass spectrometer. This cooling system circulates a cryoliquid from a Dewar to the "liquid circulation unit" through a CF flange to cool a copper block and a cryopreamplifier; the cooling medium is subsequently exhausted into the air. The cryopreamplifier can be operated over a very wide temperature range, from room temperature to low temperature environments (4.2 K). First, ion signals detected by the cryopreamplifier using a circulating liquid nitrogen cooling system were observed and showed a signal-to-noise ratio (S/N) about 130% better than that obtained at room temperature.

[Audit system on quality of breast cancer diagnosis and treatment: results of quality indicators on screen-detected lesions in Italy, 2010].

PubMed

Ponti, Antonio; Mano, Maria Piera; Tomatis, Mariano; Baiocchi, Diego; Barca, Alessandra; Berti, Rosa; Bisanti, Luigi; Casella, Denise; Deandrea, Silvia; Delrio, Daria; Donati, Giovanni; Falcini, Fabio; Frammartino, Brunella; Frigerio, Alfonso; Mantellini, Paola; Naldoni, Carlo; Orzalesi, Lorenzo; Pagano, Giovanni; Pietribiasi, Francesca; Ravaioli, Alessandra; Sedda, Maria Laura; Taffurelli, Mario; Cataliotti, Luigi; Segnan, Nereo

2012-01-01

This survey, conducted by the Italian breast screening network (GISMa), collects yearly individual data on diagnosis and treatment on about 50% of all screen-detected, operated lesions in Italy. The 2010 results show good overall quality and an improving trend over time. Critical issues were identified, including waiting times and compliance with the recommendations on not performing frozen section examination on small lesions. Preoperative diagnosis improved constantly over the years, but there is still a large variation between regions and programmes. For almost 90% of screen-detected invasive cancers the sentinel lymph node technique (SLN) was performed on the axilla, avoiding a large number of potentially harmful dissections. On the other hand, potential overuse of SLN for ductal carcinoma in situ deserves further investigation. The detailed results have been distributed, also by means of a web data warehouse, to regional and local screening programmes in order to allow multidisciplinary discussion and identification of the appropriate solutions to any issues documented by the data. It should be assigned priority to the problem of waiting times. Specialist Breast Units with adequate case volume and enough resources would provide the best setting for making monitoring effective in producing quality improvements with shorter waiting times.

Improving the Efficiency and Effectiveness of Community Detection via Prior-Induced Equivalent Super-Network.

PubMed

Yang, Liang; Jin, Di; He, Dongxiao; Fu, Huazhu; Cao, Xiaochun; Fogelman-Soulie, Francoise

2017-03-29

Due to the importance of community structure in understanding network and a surge of interest aroused on community detectability, how to improve the community identification performance with pairwise prior information becomes a hot topic. However, most existing semi-supervised community detection algorithms only focus on improving the accuracy but ignore the impacts of priors on speeding detection. Besides, they always require to tune additional parameters and cannot guarantee pairwise constraints. To address these drawbacks, we propose a general, high-speed, effective and parameter-free semi-supervised community detection framework. By constructing the indivisible super-nodes according to the connected subgraph of the must-link constraints and by forming the weighted super-edge based on network topology and cannot-link constraints, our new framework transforms the original network into an equivalent but much smaller Super-Network. Super-Network perfectly ensures the must-link constraints and effectively encodes cannot-link constraints. Furthermore, the time complexity of super-network construction process is linear in the original network size, which makes it efficient. Meanwhile, since the constructed super-network is much smaller than the original one, any existing community detection algorithm is much faster when using our framework. Besides, the overall process will not introduce any additional parameters, making it more practical.

Time series analysis of infrared satellite data for detecting thermal anomalies: a hybrid approach

NASA Astrophysics Data System (ADS)

Koeppen, W. C.; Pilger, E.; Wright, R.

2011-07-01

We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust Satellite Techniques (RST) algorithm. Using test sites at Anatahan and Kīlauea volcanoes, the hybrid time series approach detected ~15% more thermal anomalies than MODVOLC with very few, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf of Mexico as an end-member scenario representing very persistent thermal anomalies. At Cantarell, the hybrid algorithm showed only a slight improvement, but it did identify flares that were undetected by MODVOLC. We estimate that at least 80 MODIS images for each calendar month are required to create good reference images necessary for the time series analysis of the hybrid algorithm. The improved performance of the new algorithm over MODVOLC will result in the detection of low temperature thermal anomalies that will be useful in improving our ability to document Earth's volcanic eruptions, as well as detecting low temperature thermal precursors to larger eruptions.

Ultrahigh-Sensitivity Piezoresistive Pressure Sensors for Detection of Tiny Pressure.

PubMed

Li, Hongwei; Wu, Kunjie; Xu, Zeyang; Wang, Zhongwu; Meng, Yancheng; Li, Liqiang

2018-06-20

High-sensitivity pressure sensors are crucial for the ultrasensitive touch technology and E-skin, especially at the tiny-pressure range below 100 Pa. However, it is highly challenging to substantially promote sensitivity beyond the current level at several to 200 kPa -1 and to improve the detection limit lower than 0.1 Pa, which is significant for the development of pressure sensors toward ultrasensitive and highly precise detection. Here, we develop an efficient strategy to greatly improve the sensitivity near to 2000 kPa -1 using short-channel coplanar device structure and sharp microstructure, which is systematically proposed for the first time and rationalized by the mathematic calculation and analysis. Significantly, benefiting from the ultrahigh sensitivity, the detection limit is improved to be as small as 0.075 Pa. The sensitivity and detection limit are both superior to the current levels and far surpass the function of human skin. Furthermore, the sensor shows fast response time (50 μs), excellent reproducibility and stability, and low power consumption. Remarkably, the sensor shows excellent detection capacity in the tiny-pressure range, including light-emitting diode switching with a pressure of 7 Pa, ringtone (2-20 Pa) recognition, and ultrasensitive (0.1 Pa) electronic glove. This work represents a performance and strategic progress in the field of pressure sensing.

Market Assessment of Forward-Looking Turbulence Sensing Systems

NASA Technical Reports Server (NTRS)

Kauffmann, Paul; Sousa-Poza, Andres

2001-01-01

In recognition of the importance of turbulence mitigation as a tool to improve aviation safety, NASA's Aviation Safety Program developed a Turbulence Detection and Mitigation Sub-element. The objective of this effort is to develop highly reliable turbulence detection technologies for commercial transport aircraft to sense dangerous turbulence with sufficient time warning so that defensive measures can be implemented and prevent passenger and crew injuries. Current research involves three forward sensing products to improve the cockpit awareness of possible turbulence hazards. X-band radar enhancements will improve the capabilities of current weather radar to detect turbulence associated with convective activity. LIDAR (Light Detection and Ranging) is a laser-based technology that is capable of detecting turbulence in clear air. Finally, a possible Radar-LIDAR hybrid sensor is envisioned to detect the full range of convective and clear air turbulence. To support decisions relating to the development of these three forward-looking turbulence sensor technologies, the objective of this study was defined as examination of cost and implementation metrics. Tasks performed included the identification of cost factors and certification issues, the development and application of an implementation model, and the development of cost budget/targets for installing the turbulence sensor and associated software devices into the commercial transport fleet.

Multispectral photoacoustic tomography for detection of small tumors inside biological tissues

NASA Astrophysics Data System (ADS)

Hirasawa, Takeshi; Okawa, Shinpei; Tsujita, Kazuhiro; Kushibiki, Toshihiro; Fujita, Masanori; Urano, Yasuteru; Ishihara, Miya

2018-02-01

Visualization of small tumors inside biological tissue is important in cancer treatment because that promotes accurate surgical resection and enables therapeutic effect monitoring. For sensitive detection of tumor, we have been developing photoacoustic (PA) imaging technique to visualize tumor-specific contrast agents, and have already succeeded to image a subcutaneous tumor of a mouse using the contrast agents. To image tumors inside biological tissues, extension of imaging depth and improvement of sensitivity were required. In this study, to extend imaging depth, we developed a PA tomography (PAT) system that can image entire cross section of mice. To improve sensitivity, we discussed the use of the P(VDF-TrFE) linear array acoustic sensor that can detect PA signals with wide ranges of frequencies. Because PA signals produced from low absorbance optical absorbers shifts to low frequency, we hypothesized that the detection of low frequency PA signals improves sensitivity to low absorbance optical absorbers. We developed a PAT system with both a PZT linear array acoustic sensor and the P(VDF-TrFE) sensor, and performed experiment using tissue-mimicking phantoms to evaluate lower detection limits of absorbance. As a result, PAT images calculated from low frequency components of PA signals detected by the P(VDF-TrFE) sensor could visualize optical absorbers with lower absorbance.

Organic electrochemical transistor based immunosensor for prostate specific antigen (PSA) detection using gold nanoparticles for signal amplification.

PubMed

Kim, Duck-Jin; Lee, Nae-Eung; Park, Joon-Shik; Park, In-Jun; Kim, Jung-Gu; Cho, Hyoung J

2010-07-15

We demonstrated a highly sensitive organic electrochemical transistor (OECT) based immunosensor with a low detection limit for prostate specific antigen/alpha1-antichymotrypsin (PSA-ACT) complex. The poly(styrenesulfonate) doped poly(3,4-ethylenedioxythiophene) (PEDOT:PSS) based OECT with secondary antibody conjugated gold nanoparticles (AuNPs) provided a detection limit of the PSA-ACT complex as low as 1pg/ml, as well as improved sensitivity and a dynamic range, due to the role of AuNPs in the signal amplification. The sensor performances were particularly improved in the lower concentration range where the detection is clinically important for the preoperative diagnosis and screening of prostate cancer. This result shows that the OECT-based immunosensor can be used as a transducer platform acceptable to the point-of-care (POC) diagnostic systems and demonstrates adaptability of organic electronics to clinical applications. Copyright (c) 2010 Elsevier B.V. All rights reserved.

ProtDec-LTR2.0: an improved method for protein remote homology detection by combining pseudo protein and supervised Learning to Rank.

PubMed

Chen, Junjie; Guo, Mingyue; Li, Shumin; Liu, Bin

2017-11-01

As one of the most important tasks in protein sequence analysis, protein remote homology detection is critical for both basic research and practical applications. Here, we present an effective web server for protein remote homology detection called ProtDec-LTR2.0 by combining ProtDec-Learning to Rank (LTR) and pseudo protein representation. Experimental results showed that the detection performance is obviously improved. The web server provides a user-friendly interface to explore the sequence and structure information of candidate proteins and find their conserved domains by launching a multiple sequence alignment tool. The web server is free and open to all users with no login requirement at http://bioinformatics.hitsz.edu.cn/ProtDec-LTR2.0/. bliu@hit.edu.cn. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Rapid surface defect detection based on singular value decomposition using steel strips as an example

NASA Astrophysics Data System (ADS)

Sun, Qianlai; Wang, Yin; Sun, Zhiyi

2018-05-01

For most surface defect detection methods based on image processing, image segmentation is a prerequisite for determining and locating the defect. In our previous work, a method based on singular value decomposition (SVD) was used to determine and approximately locate surface defects on steel strips without image segmentation. For the SVD-based method, the image to be inspected was projected onto its first left and right singular vectors respectively. If there were defects in the image, there would be sharp changes in the projections. Then the defects may be determined and located according sharp changes in the projections of each image to be inspected. This method was simple and practical but the SVD should be performed for each image to be inspected. Owing to the high time complexity of SVD itself, it did not have a significant advantage in terms of time consumption over image segmentation-based methods. Here, we present an improved SVD-based method. In the improved method, a defect-free image is considered as the reference image which is acquired under the same environment as the image to be inspected. The singular vectors of each image to be inspected are replaced by the singular vectors of the reference image, and SVD is performed only once for the reference image off-line before detecting of the defects, thus greatly reducing the time required. The improved method is more conducive to real-time defect detection. Experimental results confirm its validity.

Applications of gold nanoparticles in the detection and identification of infectious diseases and biothreats.

PubMed

Lin, Meihua; Pei, Hao; Yang, Fan; Fan, Chunhai; Zuo, Xiaolei

2013-07-05

The situation of infectious diseases and biothreats all over the world remains serious. The effective identification of such diseases plays a very important role. In recent years, gold nanoparticles have been widely used in biosensor design to improve the performance for the detection of infectious diseases and biothreats. Here, recent advances of gold-nanoparticle-based biosensors in this field are summarized.

A Novel Technique for Detecting Antibiotic-Resistant Typhoid from Rapid Diagnostic Tests

PubMed Central

Nic Fhogartaigh, Caoimhe; Dance, David A. B.; Davong, Viengmon; Tann, Pisey; Phetsouvanh, Rattanaphone; Turner, Paul; Newton, Paul N.

2015-01-01

Fluoroquinolone-resistant typhoid is increasing. An antigen-detecting rapid diagnotic test (RDT) can rapidly diagnose typhoid from blood cultures. A simple, inexpensive molecular technique performed with DNA from positive RDTs accurately identified gyrA mutations consistent with phenotypic susceptibility testing results. Field diagnosis combined with centralized molecular resistance testing could improve typhoid management and surveillance in low-resource settings. PMID:25762768

Validation of an improved anaplasma antibody cELISA kit for detection of anaplasma ovis antibody in domestic sheep at the U.S. Sheep Experiment Station in Dubois, ID

USDA-ARS?s Scientific Manuscript database

An accurate and simple-to-perform new version of a competitive ELISA (cELISA) kit that became commercially available in 2015 for testing of cattle for antibody to Anaplasma marginale was validated for detection of Anaplasma ovis antibody in domestic sheep. True positives and negatives were identifie...

A controlled comparison of the BacT/ALERT® 3D and VIRTUO™ microbial detection systems.

PubMed

Totty, H; Ullery, M; Spontak, J; Viray, J; Adamik, M; Katzin, B; Dunne, W M; Deol, P

2017-10-01

The performance of the next-generation BacT/ALERT® VIRTUO™ Microbial Detection System (VIRTUO™, bioMérieux Inc., Hazelwood, MO) was compared to the BacT/ALERT® 3D Microbial Detection System (3D, bioMérieux Inc., Durham, NC) using BacT/ALERT® FA Plus (FA Plus), BacT/ALERT® PF Plus (PF Plus), BacT/ALERT® FN Plus (FN Plus), BacT/ALERT® Standard Aerobic (SA), and BacT/ALERT® Standard Anaerobic (SN) blood culture bottles (bioMérieux Inc., Durham, NC). A seeded limit of detection (LoD) study was performed for each bottle type in both systems. The LoD studies demonstrated that both systems were capable of detecting organisms at nearly identical levels [<10 colony-forming units (CFU) per bottle], with no significant difference. Following LoD determination, a seeded study was performed to compare the time to detection (TTD) between the systems using a panel of clinically relevant microorganisms inoculated at or near the LoD with 0, 4, or 10 mL of healthy human blood. VIRTUO™ exhibited a faster TTD by an average of 3.5 h, as well as demonstrated a significantly improved detection rate of 99.9% compared to 98.8% with 3D (p-value <0.05).

Unattended Sensor System With CLYC Detectors

NASA Astrophysics Data System (ADS)

Myjak, Mitchell J.; Becker, Eric M.; Gilbert, Andrew J.; Hoff, Jonathan E.; Knudson, Christa K.; Landgren, Peter C.; Lee, Samantha F.; McDonald, Benjamin S.; Pfund, David M.; Redding, Rebecca L.; Smart, John E.; Taubman, Matthew S.; Torres-Torres, Carlos R.; Wiseman, Clinton G.

2016-06-01

We have developed an unattended sensor for detecting anomalous radiation sources. The system combines several technologies to reduce size and weight, increase battery lifetime, and improve decision-making capabilities. Sixteen Cs2LiYCl6:Ce (CLYC) scintillators allow for gamma-ray spectroscopy and neutron detection in the same volume. Low-power electronics for readout, high voltage bias, and digital processing reduce the total operating power to 1.7 W. Computationally efficient analysis algorithms perform spectral anomaly detection and isotope identification. When an alarm occurs, the system transmits alarm information over a cellular modem. In this paper, we describe the overall design of the unattended sensor, present characterization results, and compare the performance to stock NaI:Tl and 3He detectors.

Analysis of 6-mercaptopurine in human plasma with a high-performance liquid chromatographic method including post-column derivatization and fluorimetric detection.

PubMed

Jonkers, R E; Oosterhuis, B; ten Berge, R J; van Boxtel, C J

1982-12-10

A relatively simple assay with improved reliability and sensitivity for measuring levels of 6-mercaptopurine in human plasma is presented. After extraction of the compound and the added internal standard with phenyl mercury acetate, samples were separated by ion-pair reversed-phase high-performance liquid chromatography. On-line the analytes were oxidized to fluorescent products and detected in a flow-fluorimeter. The within-day coefficient of variation was 3.8% at a concentration of 25 ng/ml. The lower detection limit was 2 ng/ml when 1.0 ml of plasma was used. Mercaptopurine concentration versus time curves of two subjects after a single oral dose of azathioprine are shown.

Unattended Sensor System With CLYC Detectors

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

Myjak, Mitchell J.; Becker, Eric M.; Gilbert, Andrew J.

2016-06-01

We have developed a next-generation unattended sensor for detecting anomalous radiation sources. The system combines several technologies to reduce size and weight, increase battery lifetime, and improve decision-making capabilities. Sixteen Cs2LiYCl6:Ce (CLYC) scintillators allow for gamma-ray spectroscopy and neutron detection in the same volume. Low-power electronics for readout, high voltage bias, and digital processing reduce the total operating power to 1.3 W. Computationally efficient analysis algorithms perform spectral anomaly detection and isotope identification. When an alarm occurs, the system transmits alarm information over a cellular modem. In this paper, we describe the overall design of the unattended sensor, present characterizationmore » results, and compare the performance to stock NaI:Tl and 3He detectors.« less

Improved FastICA algorithm in fMRI data analysis using the sparsity property of the sources.

PubMed

Ge, Ruiyang; Wang, Yubao; Zhang, Jipeng; Yao, Li; Zhang, Hang; Long, Zhiying

2016-04-01

As a blind source separation technique, independent component analysis (ICA) has many applications in functional magnetic resonance imaging (fMRI). Although either temporal or spatial prior information has been introduced into the constrained ICA and semi-blind ICA methods to improve the performance of ICA in fMRI data analysis, certain types of additional prior information, such as the sparsity, has seldom been added to the ICA algorithms as constraints. In this study, we proposed a SparseFastICA method by adding the source sparsity as a constraint to the FastICA algorithm to improve the performance of the widely used FastICA. The source sparsity is estimated through a smoothed ℓ0 norm method. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of SparseFastICA and made a performance comparison between SparseFastICA, FastICA and Infomax ICA. Results of the simulated and real fMRI data demonstrated the feasibility and robustness of SparseFastICA for the source separation in fMRI data. Both the simulated and real fMRI experimental results showed that SparseFastICA has better robustness to noise and better spatial detection power than FastICA. Although the spatial detection power of SparseFastICA and Infomax did not show significant difference, SparseFastICA had faster computation speed than Infomax. SparseFastICA was comparable to the Infomax algorithm with a faster computation speed. More importantly, SparseFastICA outperformed FastICA in robustness and spatial detection power and can be used to identify more accurate brain networks than FastICA algorithm. Copyright © 2016 Elsevier B.V. All rights reserved.

Development of a Machine Vision Method for the Monitoring of Laying Hens and Detection of Multiple Nest Occupations

PubMed Central

Redaelli, Veronica; Luzi, Fabio; Mitchell, Malcolm; Bontempo, Valentino; Cattaneo, Donata; Dell’Orto, Vittorio; Savoini, Giovanni

2018-01-01

Free range systems can improve the welfare of laying hens. However, the access to environmental resources can be partially limited by social interactions, feeding of hens, and productivity, can be not stable and damaging behaviors, or negative events, can be observed more frequently than in conventional housing systems. In order to reach a real improvement of the hens’ welfare the study of their laying performances and behaviors is necessary. With this purpose, many systems have been developed. However, most of them do not detect a multiple occupation of the nest negatively affecting the accuracy of data collected. To overcome this issue, a new “nest-usage-sensor” was developed and tested. It was based on the evaluation of thermografic images, as acquired by a thermo-camera, and the performing of patter recognitions on images acquired from the nest interior. The sensor was setup with a “Multiple Nest Occupation Threshold” of 796 colored pixels and a template of triangular shape and sizes of 43 × 33 pixels (high per base). It was tested through an experimental nesting system where 10 hens were reared for a month. Results showed that the evaluation of thermografic images could increase the detection performance of a multiple occupation of the nest and to apply an image pattern recognition technique could allow for counting the number of hens in the nest in case of a multiple occupation. As a consequence, the accuracy of data collected in studies on laying performances and behaviors of hens, reared in a free-range housing system, could result to be improved. PMID:29303981

Multi-Target Tracking Using an Improved Gaussian Mixture CPHD Filter.

PubMed

Si, Weijian; Wang, Liwei; Qu, Zhiyu

2016-11-23

The cardinalized probability hypothesis density (CPHD) filter is an alternative approximation to the full multi-target Bayesian filter for tracking multiple targets. However, although the joint propagation of the posterior intensity and cardinality distribution in its recursion allows more reliable estimates of the target number than the PHD filter, the CPHD filter suffers from the spooky effect where there exists arbitrary PHD mass shifting in the presence of missed detections. To address this issue in the Gaussian mixture (GM) implementation of the CPHD filter, this paper presents an improved GM-CPHD filter, which incorporates a weight redistribution scheme into the filtering process to modify the updated weights of the Gaussian components when missed detections occur. In addition, an efficient gating strategy that can adaptively adjust the gate sizes according to the number of missed detections of each Gaussian component is also presented to further improve the computational efficiency of the proposed filter. Simulation results demonstrate that the proposed method offers favorable performance in terms of both estimation accuracy and robustness to clutter and detection uncertainty over the existing methods.

Automated Fall Detection With Quality Improvement “Rewind” to Reduce Falls in Hospital Rooms

PubMed Central

Rantz, Marilyn J.; Banerjee, Tanvi S.; Cattoor, Erin; Scott, Susan D.; Skubic, Marjorie; Popescu, Mihail

2014-01-01

The purpose of this study was to test the implementation of a fall detection and “rewind” privacy-protecting technique using the Microsoft® Kinect™ to not only detect but prevent falls from occurring in hospitalized patients. Kinect sensors were placed in six hospital rooms in a step-down unit and data were continuously logged. Prior to implementation with patients, three researchers performed a total of 18 falls (walking and then falling down or falling from the bed) and 17 non-fall events (crouching down, stooping down to tie shoe laces, and lying on the floor). All falls and non-falls were correctly identified using automated algorithms to process Kinect sensor data. During the first 8 months of data collection, processing methods were perfected to manage data and provide a “rewind” method to view events that led to falls for post-fall quality improvement process analyses. Preliminary data from this feasibility study show that using the Microsoft Kinect sensors provides detection of falls, fall risks, and facilitates quality improvement after falls in real hospital environments unobtrusively, while taking into account patient privacy. PMID:24296567

Quantum Limits of Space-to-Ground Optical Communications

NASA Technical Reports Server (NTRS)

Hemmati, H.; Dolinar, S.

2012-01-01

For a pure loss channel, the ultimate capacity can be achieved with classical coherent states (i.e., ideal laser light): (1) Capacity-achieving receiver (measurement) is yet to be determined. (2) Heterodyne detection approaches the ultimate capacity at high mean photon numbers. (3) Photon-counting approaches the ultimate capacity at low mean photon numbers. A number of current technology limits drive the achievable performance of free-space communication links. Approaching fundamental limits in the bandwidth-limited regime: (1) Heterodyne detection with high-order coherent-state modulation approaches ultimate limits. SOA improvements to laser phase noise, adaptive optics systems for atmospheric transmission would help. (2) High-order intensity modulation and photon-counting can approach heterodyne detection within approximately a factor of 2. This may have advantages over coherent detection in the presence of turbulence. Approaching fundamental limits in the photon-limited regime (1) Low-duty cycle binary coherent-state modulation (OOK, PPM) approaches ultimate limits. SOA improvements to laser extinction ratio, receiver dark noise, jitter, and blocking would help. (2) In some link geometries (near field links) number-state transmission could improve over coherent-state transmission

Quality Assurance Through Quality Improvement and Professional Development in the National Breast and Cervical Cancer Early Detection Program

PubMed Central

Siegl, Elvira J.; Miller, Jacqueline W.; Khan, Kris; Harris, Susan E.

2015-01-01

Quality assurance (QA) is the process of providing evidence that the outcome meets the established standards. Quality improvement (QI), by contrast, is the act of methodically developing ways to meet acceptable quality standards and evaluating current processes to improve overall performance. In the case of the National Breast and Cervical Cancer Early Detection Program (NBCCEDP), the desired outcome is the delivery of quality health care services to program clients. The NBCCEDP provides professional development to ensure that participating providers have current knowledge of evidence-based clinical standards regarding breast and cervical cancer screening and diagnosis and are monitoring women with abnormal screening results for timely follow-up. To assess the quality of clinical care provided to NBCCEDP clients, performance data are collected by NBCCEDP grantees and compared against predetermined Centers for Disease Control and Prevention (CDC) benchmarks known as Data Quality Indicator Guides. In this article, the authors describe 1) the development and use of indicators for QI in the NBCCEDP and 2) the professional development activities implemented to improve clinical outcomes. QA identifies problems, whereas QI systematically corrects them. The quality of service delivery and improved patient outcomes among NBCCEDP grantees has enhanced significantly because of continuous monitoring of performance and professional development. By using QA, NBCCEDP grantees can maximize the quality of patient screening, diagnostic services, and follow-up. Examples of grantee activities to maintain quality of care are also described in this report. PMID:25099901

Study to develop improved methods to detect leakage in fluid systems, phase 2

NASA Technical Reports Server (NTRS)

Janus, J. C.; Cimerman, I.

1971-01-01

An ultrasonic contact sensor engineering prototype leak detection system was developed and its capabilities under cryogenic operations demonstrated. The results from tests indicate that the transducer performed well on liquid hydrogen plumbing, that flow and valve actuation could be monitored, and that the phase change from gaseous to liquid hydrogen could be detected by the externally mounted transducers. Tests also demonstrate the ability of the system to detect internal leaks past valve seats and to function as a flow meter. Such a system demonstrates that it is not necessary to break into welded systems to locate internal leaks.

Border-oriented post-processing refinement on detected vehicle bounding box for ADAS

NASA Astrophysics Data System (ADS)

Chen, Xinyuan; Zhang, Zhaoning; Li, Minne; Li, Dongsheng

2018-04-01

We investigate a new approach for improving localization accuracy of detected vehicles for object detection in advanced driver assistance systems(ADAS). Specifically, we implement a bounding box refinement as a post-processing of the state-of-the-art object detectors (Faster R-CNN, YOLOv2, etc.). The bounding box refinement is achieved by individually adjusting each border of the detected bounding box to its target location using a regression method. We use HOG features which perform well on the edge detection of vehicles to train the regressor and the regressor is independent of the CNN-based object detectors. Experiment results on the KITTI 2012 benchmark show that we can achieve up to 6% improvements over YOLOv2 and Faster R-CNN object detectors on the IoU threshold of 0.8. Also, the proposed refinement framework is computationally light, allowing for processing one bounding box within a few milliseconds on CPU. Further, this refinement method can be added to any object detectors, especially those with high speed but less accuracy.

Capillary electrophoresis with laser-induced fluorescence detection: a sensitive method for monitoring extracellular concentrations of amino acids in the periaqueductal grey matter.

PubMed

Bergquist, J; Vona, M J; Stiller, C O; O'Connor, W T; Falkenberg, T; Ekman, R

1996-03-01

The use of capillary electrophoresis with laser-induced fluorescence detection (CE-LIF) for the analysis of microdialysate samples from the periaqueductal grey matter (PAG) of freely moving rats is described. By employing 3-(4-carboxybenzoyl)-2-quinoline-carboxaldehyde (CBQCA) as a derivatization agent, we simultaneously monitored the concentrations of 8 amino acids (arginine, glutamine, valine, gamma-amino-n-butyric acid (GABA), alanine, glycine, glutamate, and aspartate), with nanomolar and subnanomolar detection limits. Two of the amino acids (GABA and glutamate) were analysed in parallel by conventional high-performance liquid chromatography (HPLC) in order to directly compare the two analytical methods. Other CE methods for analysis of microdialysate have been previously described, and this improved method offers greater sensitivity, ease of use, and the possibility to monitor several amino acids simultaneously. By using this technique together with an optimised form of microdialysis technique, the tiny sample consumption and the improved detection limits permit the detection of fast and transient transmitter changes.

A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals.

PubMed

Gold, Nathan; Frasch, Martin G; Herry, Christophe L; Richardson, Bryan S; Wang, Xiaogang

2017-01-01

Experimentally and clinically collected time series data are often contaminated with significant confounding noise, creating short, noisy time series. This noise, due to natural variability and measurement error, poses a challenge to conventional change point detection methods. We propose a novel and robust statistical method for change point detection for noisy biological time sequences. Our method is a significant improvement over traditional change point detection methods, which only examine a potential anomaly at a single time point. In contrast, our method considers all suspected anomaly points and considers the joint probability distribution of the number of change points and the elapsed time between two consecutive anomalies. We validate our method with three simulated time series, a widely accepted benchmark data set, two geological time series, a data set of ECG recordings, and a physiological data set of heart rate variability measurements of fetal sheep model of human labor, comparing it to three existing methods. Our method demonstrates significantly improved performance over the existing point-wise detection methods.

Biomimetic Sniffing Improves the Detection Performance of a 3D Printed Nose of a Dog and a Commercial Trace Vapor Detector

NASA Astrophysics Data System (ADS)

Staymates, Matthew E.; Maccrehan, William A.; Staymates, Jessica L.; Kunz, Roderick R.; Mendum, Thomas; Ong, Ta-Hsuan; Geurtsen, Geoffrey; Gillen, Greg J.; Craven, Brent A.

2016-12-01

Unlike current chemical trace detection technology, dogs actively sniff to acquire an odor sample. Flow visualization experiments with an anatomically-similar 3D printed dog’s nose revealed the external aerodynamics during canine sniffing, where ventral-laterally expired air jets entrain odorant-laden air toward the nose, thereby extending the “aerodynamic reach” for inspiration of otherwise inaccessible odors. Chemical sampling and detection experiments quantified two modes of operation with the artificial nose-active sniffing and continuous inspiration-and demonstrated an increase in odorant detection by a factor of up to 18 for active sniffing. A 16-fold improvement in detection was demonstrated with a commercially-available explosives detector by applying this bio-inspired design principle and making the device “sniff” like a dog. These lessons learned from the dog may benefit the next-generation of vapor samplers for explosives, narcotics, pathogens, or even cancer, and could inform future bio-inspired designs for optimized sampling of odor plumes.

Biomimetic Sniffing Improves the Detection Performance of a 3D Printed Nose of a Dog and a Commercial Trace Vapor Detector

PubMed Central

Staymates, Matthew E.; MacCrehan, William A.; Staymates, Jessica L.; Kunz, Roderick R.; Mendum, Thomas; Ong, Ta-Hsuan; Geurtsen, Geoffrey; Gillen, Greg J.; Craven, Brent A.

2016-01-01

Unlike current chemical trace detection technology, dogs actively sniff to acquire an odor sample. Flow visualization experiments with an anatomically-similar 3D printed dog’s nose revealed the external aerodynamics during canine sniffing, where ventral-laterally expired air jets entrain odorant-laden air toward the nose, thereby extending the “aerodynamic reach” for inspiration of otherwise inaccessible odors. Chemical sampling and detection experiments quantified two modes of operation with the artificial nose-active sniffing and continuous inspiration-and demonstrated an increase in odorant detection by a factor of up to 18 for active sniffing. A 16-fold improvement in detection was demonstrated with a commercially-available explosives detector by applying this bio-inspired design principle and making the device “sniff” like a dog. These lessons learned from the dog may benefit the next-generation of vapor samplers for explosives, narcotics, pathogens, or even cancer, and could inform future bio-inspired designs for optimized sampling of odor plumes. PMID:27906156

Community detection enhancement using non-negative matrix factorization with graph regularization

NASA Astrophysics Data System (ADS)

Liu, Xiao; Wei, Yi-Ming; Wang, Jian; Wang, Wen-Jun; He, Dong-Xiao; Song, Zhan-Jie

2016-06-01

Community detection is a meaningful task in the analysis of complex networks, which has received great concern in various domains. A plethora of exhaustive studies has made great effort and proposed many methods on community detection. Particularly, a kind of attractive one is the two-step method which first makes a preprocessing for the network and then identifies its communities. However, not all types of methods can achieve satisfactory results by using such preprocessing strategy, such as the non-negative matrix factorization (NMF) methods. In this paper, rather than using the above two-step method as most works did, we propose a graph regularized-based model to improve, specialized, the NMF-based methods for the detection of communities, namely NMFGR. In NMFGR, we introduce the similarity metric which contains both the global and local information of networks, to reflect the relationships between two nodes, so as to improve the accuracy of community detection. Experimental results on both artificial and real-world networks demonstrate the superior performance of NMFGR to some competing methods.

A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data.

PubMed

Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

2017-06-01

Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

Recent advances in micro-scale and nano-scale high-performance liquid-phase chromatography for proteome research.

PubMed

Tao, Dingyin; Zhang, Lihua; Shan, Yichu; Liang, Zhen; Zhang, Yukui

2011-01-01

High-performance liquid chromatography-electrospray ionization tandem mass spectrometry (HPLC-ESI-MS-MS) is regarded as one of the most powerful techniques for separation and identification of proteins. Recently, much effort has been made to improve the separation capacity, detection sensitivity, and analysis throughput of micro- and nano-HPLC, by increasing column length, reducing column internal diameter, and using integrated techniques. Development of HPLC columns has also been rapid, as a result of the use of submicrometer packing materials and monolithic columns. All these innovations result in clearly improved performance of micro- and nano-HPLC for proteome research.

Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms.

PubMed

Pisano, E D; Zong, S; Hemminger, B M; DeLuca, M; Johnston, R E; Muller, K; Braeuning, M P; Pizer, S M

1998-11-01

The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms. Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized at 50 micron pixels, 12 bits deep. Film images with no CLAHE applied were compared to film images with nine different combinations of clip levels and region sizes applied. A simulated spiculation was embedded in a background of dense breast tissue, with the orientation of the spiculation varied. The key variables involved in each trial included the orientation of the spiculation, contrast level of the spiculation and the CLAHE settings applied to the image. Combining the 10 CLAHE conditions, 4 contrast levels and 4 orientations gave 160 combinations. The trials were constructed by pairing 160 combinations of key variables with 40 backgrounds. Twenty student observers were asked to detect the orientation of the spiculation in the image. There was a statistically significant improvement in detection performance for spiculations with CLAHE over unenhanced images when the region size was set at 32 with a clip level of 2, and when the region size was set at 32 with a clip level of 4. The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved.

Performance of basic kinematic thresholds in the identification of crash and near-crash events within naturalistic driving data.

PubMed

Perez, Miguel A; Sudweeks, Jeremy D; Sears, Edie; Antin, Jonathan; Lee, Suzanne; Hankey, Jonathan M; Dingus, Thomas A

2017-06-01

Understanding causal factors for traffic safety-critical events (e.g., crashes and near-crashes) is an important step in reducing their frequency and severity. Naturalistic driving data offers unparalleled insight into these factors, but requires identification of situations where crashes are present within large volumes of data. Sensitivity and specificity of these identification approaches are key to minimizing the resources required to validate candidate crash events. This investigation used data from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) and the Canada Naturalistic Driving Study (CNDS) to develop and validate different kinematic thresholds that can be used to detect crash events. Results indicate that the sensitivity of many of these approaches can be quite low, but can be improved by selecting particular threshold levels based on detection performance. Additional improvements in these approaches are possible, and may involve leveraging combinations of different detection approaches, including advanced statistical techniques and artificial intelligence approaches, additional parameter modifications, and automation of validation processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Laser development for optimal helicopter obstacle warning system LADAR performance

NASA Astrophysics Data System (ADS)

Yaniv, A.; Krupkin, V.; Abitbol, A.; Stern, J.; Lurie, E.; German, A.; Solomonovich, S.; Lubashitz, B.; Harel, Y.; Engart, S.; Shimoni, Y.; Hezy, S.; Biltz, S.; Kaminetsky, E.; Goldberg, A.; Chocron, J.; Zuntz, N.; Zajdman, A.

2005-04-01

Low lying obstacles present immediate danger to both military and civilian helicopters performing low-altitude flight missions. A LADAR obstacle detection system is the natural solution for enhancing helicopter safety and improving the pilot situation awareness. Elop is currently developing an advanced Surveillance and Warning Obstacle Ranging and Display (SWORD) system for the Israeli Air Force. Several key factors and new concepts have contributed to system optimization. These include an adaptive FOV, data memorization, autonomous obstacle detection and warning algorithms and the use of an agile laser transmitter. In the present work we describe the laser design and performance and discuss some of the experimental results. Our eye-safe laser is characterized by its pulse energy, repetition rate and pulse length agility. By dynamically controlling these parameters, we are able to locally optimize the system"s obstacle detection range and scan density in accordance with the helicopter instantaneous maneuver.

Accessing long-term memory representations during visual change detection.

PubMed

Beck, Melissa R; van Lamsweerde, Amanda E

2011-04-01

In visual change detection tasks, providing a cue to the change location concurrent with the test image (post-cue) can improve performance, suggesting that, without a cue, not all encoded representations are automatically accessed. Our studies examined the possibility that post-cues can encourage the retrieval of representations stored in long-term memory (LTM). Participants detected changes in images composed of familiar objects. Performance was better when the cue directed attention to the post-change object. Supporting the role of LTM in the cue effect, the effect was similar regardless of whether the cue was presented during the inter-stimulus interval, concurrent with the onset of the test image, or after the onset of the test image. Furthermore, the post-cue effect and LTM performance were similarly influenced by encoding time. These findings demonstrate that monitoring the visual world for changes does not automatically engage LTM retrieval.

GPS/DR Error Estimation for Autonomous Vehicle Localization.

PubMed

Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In

2015-08-21

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.

GPS/DR Error Estimation for Autonomous Vehicle Localization

PubMed Central

Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In

2015-01-01

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level. PMID:26307997

ARGOS wavefront sensing: from detection to correction

NASA Astrophysics Data System (ADS)

Orban de Xivry, Gilles; Bonaglia, M.; Borelli, J.; Busoni, L.; Connot, C.; Esposito, S.; Gaessler, W.; Kulas, M.; Mazzoni, T.; Puglisi, A.; Rabien, S.; Storm, J.; Ziegleder, J.

2014-08-01

Argos is the ground-layer adaptive optics system for the Large Binocular Telescope. In order to perform its wide-field correction, Argos uses three laser guide stars which sample the atmospheric turbulence. To perform the correction, Argos has at disposal three different wavefront sensing measurements : its three laser guide stars, a NGS tip-tilt, and a third wavefront sensor. We present the wavefront sensing architecture and its individual components, in particular: the finalized Argos pnCCD camera detecting the 3 laser guide stars at 1kHz, high quantum efficiency and 4e- noise; the Argos tip-tilt sensor based on a quad-cell avalanche photo-diodes; and the Argos wavefront computer. Being in the middle of the commissioning, we present the first wavefront sensing configurations and operations performed at LBT, and discuss further improvements in the measurements of the 3 laser guide star slopes as detected by the pnCCD.

Application of Composite Indices for Improving Joint Detection Capabilities of Instrumented Roof Bolt Drills in Underground Mining and Construction

NASA Astrophysics Data System (ADS)

Liu, Wenpeng; Rostami, Jamal; Elsworth, Derek; Ray, Asok

2018-03-01

Roof bolts are the dominant method of ground support in mining and tunneling applications, and the concept of using drilling parameters from the bolter for ground characterization has been studied for a few decades. This refers to the use of drilling data to identify geological features in the ground including joints and voids, as well as rock classification. Rock mass properties, including distribution of joints/voids and strengths of rock layers, are critical factors for proper design of ground support to avoid instability. The goal of this research was to improve the capability and sensitivity of joint detection programs based on the updated pattern recognition algorithms in sensing joints with smaller than 3.175 mm (0.125 in.) aperture while reducing the number of false alarms, and discriminating rock layers with different strengths. A set of concrete blocks with different strengths were used to simulate various rock layers, where the gap between the blocks would represent the joints in laboratory tests. Data obtained from drilling through these blocks were analyzed to improve the reliability and precision of joint detection systems. While drilling parameters can be used to detect the gaps, due to low accuracy of the results, new composite indices have been introduced and used in the analysis to improve the detection rates. This paper briefly discusses ongoing research on joint detection by using drilling parameters collected from a roof bolter in a controlled environment. The performances of the new algorithms for joint detection are also examined by comparing their ability to identify existing joints and reducing false alarms.

Triton Hodge Test: Improved Protocol for Modified Hodge Test for Enhanced Detection of NDM and Other Carbapenemase Producers.

PubMed

Pasteran, Fernando; Gonzalez, Lisandro J; Albornoz, Ezequiel; Bahr, Guillermo; Vila, Alejandro J; Corso, Alejandra

2016-03-01

Accurate detection of carbapenemase-producing Gram-negative bacilli is of utmost importance for the control of nosocomial spread and the initiation of appropriate antimicrobial therapy. The modified Hodge test (MHT), a carbapenem inactivation assay, has shown poor sensitivity in detecting the worldwide spread of New Delhi metallo-β-lactamase (NDM). Recent studies demonstrated that NDM is a lipoprotein anchored to the outer membrane in Gram-negative bacteria, unlike all other known carbapenemases. Here we report that membrane anchoring of β-lactamases precludes detection of carbapenemase activity by the MHT. We also show that this limitation can be overcome by the addition of Triton X-100 during the test, which allows detection of NDM. We propose an improved version of the assay, called the Triton Hodge test (THT), which allows detection of membrane-bound carbapenemases with the addition of this nonionic surfactant. This test was challenged with a panel of 185 clinical isolates (145 carrying known carbapenemase-encoding genes and 40 carbapenemase nonproducers). The THT displayed test sensitivity of >90% against NDM-producing clinical isolates, while improving performance against other carbapenemases. Ertapenem provided the highest sensitivity (97 to 100%, depending on the type of carbapenemase), followed by meropenem (92.5 to 100%). Test specificity was not affected by the addition of Triton (87.5% and 92.5% with ertapenem and meropenem, respectively). This simple inexpensive test confers a large improvement to the sensitivity of the MHT for the detection of NDM and other carbapenemases. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Analysis of hybrid subcarrier multiplexing of OCDMA based on single photodiode detection

NASA Astrophysics Data System (ADS)

Ahmad, N. A. A.; Junita, M. N.; Aljunid, S. A.; Rashidi, C. B. M.; Endut, R.

2017-11-01

This paper analyzes the performance of subcarrier multiplexing (SCM) of spectral amplitude coding optical code multiple access (SAC-OCDMA) by applying Recursive Combinatorial (RC) code based on single photodiode detection (SPD). SPD is used in the receiver part to reduce the effect of multiple access interference (MAI) which contributes as a dominant noise in incoherent SAC-OCDMA systems. Results indicate that the SCM OCDMA network performance could be improved by using lower data rates and higher number of weight. Total number of users can also be enhanced by adding lower data rates and higher number of subcarriers.