Hedgehog spin-vortex crystal stabilized in a hole-doped iron-based superconductor

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

Meier, William R.; Ding, Qing-Ping; Kreyssig, Andreas

Magnetism is widely considered to be a key ingredient of unconventional superconductivity. In contrast to cuprate high-temperature superconductors, antiferromagnetism in most Fe-based superconductors (FeSCs) is characterized by a pair of magnetic propagation vectors, (π,0) and (0,π). Consequently, three different types of magnetic order are possible. Of these, only stripe-type spin-density wave (SSDW) and spin-charge-density wave (SCDW) orders have been observed. A realization of the proposed spin-vortex crystal (SVC) order is noticeably absent. We report a magnetic phase consistent with the hedgehog variation of SVC order in Ni-doped and Co-doped CaKFe 4As 4 based on thermodynamic, transport, structural and local magneticmore » probes combined with symmetry analysis. The exotic SVC phase is stabilized by the reduced symmetry of the CaKFe 4As 4 structure. Thus, our results suggest that the possible magnetic ground states in FeSCs have very similar energies, providing an enlarged configuration space for magnetic fluctuations to promote high-temperature superconductivity.« less

Development of a computer aided diagnosis model for prostate cancer classification on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Alfano, R.; Soetemans, D.; Bauman, G. S.; Gibson, E.; Gaed, M.; Moussa, M.; Gomez, J. A.; Chin, J. L.; Pautler, S.; Ward, A. D.

2018-02-01

Multi-parametric MRI (mp-MRI) is becoming a standard in contemporary prostate cancer screening and diagnosis, and has shown to aid physicians in cancer detection. It offers many advantages over traditional systematic biopsy, which has shown to have very high clinical false-negative rates of up to 23% at all stages of the disease. However beneficial, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a solution as they have the power to perform deterministic quantitative image analysis. We measured the accuracy of such a system validated using accurately co-registered whole-mount digitized histology. We trained a logistic linear classifier (LOGLC), support vector machine (SVC), k-nearest neighbour (KNN) and random forest classifier (RFC) in a four part ROI based experiment against: 1) cancer vs. non-cancer, 2) high-grade (Gleason score ≥4+3) vs. low-grade cancer (Gleason score <4+3), 3) high-grade vs. other tissue components and 4) high-grade vs. benign tissue by selecting the classifier with the highest AUC using 1-10 features from forward feature selection. The CAD model was able to classify malignant vs. benign tissue and detect high-grade cancer with high accuracy. Once fully validated, this work will form the basis for a tool that enhances the radiologist's ability to detect malignancies, potentially improving biopsy guidance, treatment selection, and focal therapy for prostate cancer patients, maximizing the potential for cure and increasing quality of life.

Effects of Anxiety and Depression on Arterial Elasticity of Subjects With Suboptimal Physical Health.

PubMed

Sun, Ningling; Xi, Yang; Zhu, Zhiming; Yin, Huijun; Tao, Qiushan; Wang, Hongyi; Wang, Luyan; Ma, Zhiyi; Chen, Yuanyuan; Yao, Dan

2015-10-01

The authors investigated the effects of suboptimal health status (SHS; high-normal blood pressure, blood glucose, and blood lipids) on arterial elasticity in subjects with or without anxiety or depression. Suboptimal physical health status and anxiety or depression increase the risk of cardiovascular diseases. This was a cross-sectional, observational, multicenter study. Among 1520 subjects who underwent physical examination between May 2009 and December 2012 in Beijing and Chongqing, China, 955 were included. All subjects completed anxiety and depression questionnaires. Systemic vascular compliance (SVC), systemic vascular resistance, and brachial artery distensibility (BAD) were measured during arterial elasticity evaluation. Of 955 participants, 633 were classified as having SHS and 322 were classified as healthy. Systemic vascular compliance and BAD were worse in SHS subjects than in healthy subjects (SVC: 1.23 ± 0.22 vs 1.29 ± 0.25 mL/mm Hg; BAD: 6.26 ± 1.32 vs 6.61 ± 1.24%/mm Hg, respectively; both P < 0.05). Of 955 subjects, 37.7% and 43.9% had anxiety and depression, respectively. Systemic vascular compliance and BAD in SHS subjects with concomitant anxiety or depression were significantly lower than in SHS subjects without anxiety or depression (SVC: 1.22 ± 0.23 vs 1.23 ± 0.20 mL/mm Hg; BAD: 6.10 ± 1.36 vs 6.33 ± 1.20 %/mm Hg, respectively; both P < 0.05) and even lower than in healthy subjects. Though anxiety and depression had less impact on arterial elasticity in a healthy population, they may be involved in pathogenesis of vascular damage in the population with SHS. © 2015 Wiley Periodicals, Inc.

A “Train-Track” Technique in Anatomic Reconstruction of SVC Bifurcation Complicated by Cardiac Tamponade: An Introspection

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

Karuppasamy, Karunakaravel, E-mail: karuppk@ccf.org; Al-Natour, Mohammed, E-mail: mnatour85@msn.com; Gurajala, Ram Kishore, E-mail: gurajar@ccf.org

This report describes a stenting technique used to anatomically reconstruct superior vena cava (SVC) bifurcation in a patient with benign SVC syndrome. After recanalizing the SVC bifurcation, we exchanged two 0.035-in. wires for two 0.018-in. wires, deployed the SVC stent over these two wires (“train-track” technique), and stented each innominate vein over one wire. However, our decisions to recanalize both innominate veins, use the “buddy-wire” technique for SVC dilation, and dilate the SVC to 16 mm before stent deployment likely contributed to SVC tear, which was managed by resuscitation, SVC stent placement, and pericardial drainage. Here, we describe the steps ofmore » the train-track technique, which can be adopted to reconstruct other bifurcations; we also discuss the controversial aspects of this case.« less

Defibrillation efficacy of different electrode placements in a human thorax model.

PubMed

de Jongh, A L; Entcheva, E G; Replogle, J A; Booker, R S; Kenknight, B H; Claydon, F J

1999-01-01

The objective of this study was to measure the defibrillation threshold (DFT) associated with different electrode placements using a three-dimensional anatomically realistic finite element model of the human thorax. Coil electrodes (Endotak DSP, model 125, Guidant/CPI) were placed in the RV apex along the lateral wall (RV), withdrawn 10 mm away from the RV apex along the lateral wall (RVprox), in the RV apex along the anterior septum (RVseptal), and in the SVC. An active pulse generator (can) was placed in the subcutaneous prepectoral space. Five electrode configurations were studied: RV-->SVC, RVprox-->SVC, RVSEPTAL-->SVC, RV-->Can, and RV-->SVC + Can. DFTs are defined as the energy required to produce a potential gradient of at least 5 V/cm in 95% of the ventricular myocardium. DFTs for RV-->SVC, RVprox-->SVC, RVseptal-->SVC, RV-->Can, and RV-->SVC + Can were 10, 16, 7, 9, and 6 J, respectively. The DFTs measured at each configuration fell within one standard deviation of the mean DFTs reported in clinical studies using the Endotak leads. The relative changes in DFT among electrode configurations also compared favorably. This computer model allows measurements of DFT or other defibrillation parameters with several different electrode configurations saving time and cost of clinical studies.

The difference between slow and forced vital capacity increases with increasing body mass index: a paradoxical difference in low and normal body mass indices.

PubMed

Fortis, Spyridon; Corazalla, Edward O; Wang, Qi; Kim, Hyun J

2015-01-01

Obesity reduces FVC, the most commonly used measurement of vital capacity (VC) and slow VC (SVC). It is unknown whether the difference between SVC and FVC is constant in different body mass indices (BMIs). We hypothesized that the difference between SVC and FVC increases as a function of BMI. We retrospectively reviewed pulmonary function tests (PFTs) that included spirometry and plethysmography and were performed in adults from January 2013 to August 2013. A total of 1,805 PFTs were enrolled. The non-parametric Wilcoxon signed-rank test was used to compare FVC with SVC, and to compare FEV1/FVC with FEV1/SVC ratio. Spearman correlation analysis was used to determine whether BMI has an effect on the discordance between FVC and SVC. Finally, we used the McNemar test for paired binary data to compare the prevalence rate of obstruction when using different measurements of VC. In individuals with BMI < 25 kg/m(2) and no evidence of obstruction in the PFTs, FVC was larger than SVC (P = .03), whereas in overweight and obese individuals, SVC was significantly larger than FVC. The difference between SVC and FVC was positively correlated with BMI (P < .001). One hundred thirty-one patients had a normal FEV1/FVC but low FEV1/SVC ratio. Fifty of these 131 individuals also had a normal FVC; the majority of them (46 of 50) had the PFTs for investigation of respiratory symptoms and had BMI > 25 kg/m(2) (42 of 50). Our results indicate that FVC is larger than SVC in patients with low and normal BMI and no evidence of obstruction in the PFTs, whereas FVC is smaller than SVC in overweight and obese individual. Our findings add to the existing literature that use of FEV1/FVC may lead to underdiagnosis of obstructive airway disease in overweight and obese individuals. Copyright © 2015 by Daedalus Enterprises.

Managing central venous obstruction in cystic fibrosis recipients--lung transplant considerations.

PubMed

Otani, Shinji; Westall, Glen P; Levvey, Bronwyn J; Marasco, Silvana; Lyon, Stuart; Snell, Gregory I

2015-03-01

The superior vena cava (SVC) syndrome in cystic fibrosis (CF) patients is rare, but presents unique challenges in the peri-transplant period. We reviewed our experience of SVC syndrome in CF recipients undergoing lung transplantation. This is a retrospective case series from a single center chart-review. SVC obstruction is defined by clinically significant stenosis or obstruction of the SVC as detected by contrast studies. We identified SVC obstruction in seven post-transplant cases and one pre-transplant case. All eight patients had previous or current history of indwelling central venous catheters. Three recipients experienced operative complications. Five of the seven recipients suffered at least one episode of post-operative SVC obstruction or bleeding despite prophylactic anticoagulation. At a median follow-up of 29 months, six of the seven patients transplanted are well. Strategies are available to minimize the risks of intra/peri-operative acute life-threatening SVC obstruction in CF patients. Copyright © 2014 European Cystic Fibrosis Society. Published by Elsevier B.V. All rights reserved.

Assessing consumption of bioactive micro-particles by filter-feeding Asian carp

USGS Publications Warehouse

Jensen, Nathan R.; Amberg, Jon J.; Luoma, James A.; Walleser, Liza R.; Gaikowski, Mark P.

2012-01-01

Silver carp Hypophthalmichthys molitrix (SVC) and bighead carp H. nobilis (BHC) have impacted waters in the US since their escape. Current chemical controls for aquatic nuisance species are non-selective. Development of a bioactive micro-particle that exploits filter-feeding habits of SVC or BHC could result in a new control tool. It is not fully understood if SVC or BHC will consume bioactive micro-particles. Two discrete trials were performed to: 1) evaluate if SVC and BHC consume the candidate micro-particle formulation; 2) determine what size they consume; 3) establish methods to evaluate consumption of filter-feeders for future experiments. Both SVC and BHC were exposed to small (50-100 μm) and large (150-200 μm) micro-particles in two 24-h trials. Particles in water were counted electronically and manually (microscopy). Particles on gill rakers were counted manually and intestinal tracts inspected for the presence of micro-particles. In Trial 1, both manual and electronic count data confirmed reductions of both size particles; SVC appeared to remove more small particles than large; more BHC consumed particles; SVC had fewer overall particles in their gill rakers than BHC. In Trial 2, electronic counts confirmed reductions of both size particles; both SVC and BHC consumed particles, yet more SVC consumed micro-particles compared to BHC. Of the fish that ate micro-particles, SVC consumed more than BHC. It is recommended to use multiple metrics to assess consumption of candidate micro-particles by filter-feeders when attempting to distinguish differential particle consumption. This study has implications for developing micro-particles for species-specific delivery of bioactive controls to help fisheries, provides some methods for further experiments with bioactive micro-particles, and may also have applications in aquaculture.

CMMI for Services (CMMI-SVC) Overview Presentation

DTIC Science & Technology

2008-10-01

processes 5 CMMI for Services (CMMI-SVC) Overview Forrester, 2008 © 2008 Carnegie Mellon University Why is the CMMI-SVC needed? Service providers...Project Management (IPM) • Project Monitoring and Control (PMC) • Project Planning (PP) • Requirements Management (REQM) • Risk Management (RSKM...contact, and your resume reflecting 5 or more years of service experience to cmmi- svc-app@sei.cmu.edu . 2. Fill out the sponsorship form here: http

Temporary bypass for superior vena cava reconstruction with Anthron bypass tubeTM

PubMed Central

Yamasaki, Naoya; Tsuchiya, Tomoshi; Miyazaki, Takuro; Kamohara, Ryotaro; Hatachi, Go; Nagayasu, Takeshi

2017-01-01

Total superior vena cava (SVC) clamping for SVC replacement or repair can be used in thoracic surgery. A bypass technique is an option to avoid hemodynamic instability and cerebral venous hypertension and hypoperfusion. The present report describes a venous bypass technique using Anthron bypass tubeTM for total SVC clamping. Indications for this procedure include the need for a temporary bypass between the brachiocephalic vein and atrium for complete tumor resection. This procedure allows the surgeons sufficient time to complete replacement of SVC or partial resection of SVC without adverse effects. Further, it is a relatively simple procedure requiring minimal time. PMID:28840027

The perfusion index derived from a pulse oximeter for predicting low superior vena cava flow in very low birth weight infants.

PubMed

Takahashi, S; Kakiuchi, S; Nanba, Y; Tsukamoto, K; Nakamura, T; Ito, Y

2010-04-01

Superior vena cava (SVC) flow is used as an index for evaluating systemic blood flow in neonates. Thus far, several reports have shown that low SVC flow is a risk factor for intraventricular hemorrhage (IVH) in the preterm infant. Therefore, it is likely to be a useful index in the management of the preterm infant. The perfusion index (PI) derived from a pulse oximeter is a marker that allows noninvasive and continuous monitoring of peripheral perfusion. The objective of this paper was to determine the accuracy of the PI for detecting low SVC flow in very low birth weight infants born before 32 weeks of gestation. We studied the correlation between PI and SVC flow 0 to 72 h after birth in very low birth weight infants born before 32 weeks of gestation. The best cut-off value for low SVC flow was calculated from the respective receiver-operating characteristic curves. A positive correlation was found between the PI and SVC flow (r=0.509, P<0.001). The best cut-off value for the PI to detect low SVC flow was 0.44 (sensitivity 87.5%, specificity 86.3%, positive predictive value 38.9%, negative predictive value 98.6%). This study found that the PI was associated with SVC flow, and it was a useful index for detecting low SVC flow in very low birth weight infants born before 32 weeks of gestation. Therefore, use of the PI should be evaluated in the cardiovascular management of the preterm infant.

Endovascular recanalization of a port catheter-associated superior vena cava syndrome.

PubMed

Tonak, Julia; Fetscher, Sebastian; Barkhausen, Jörg; Goltz, Jan Peter

2015-01-01

Superior vena cava (SVC) syndrome owing to benign etiology is rare and endovascular techniques have been advocated as the treatment of choice. We report a case of endovascular revascularization of a port catheter-associated complete occlusion of the SVC with reversed flow in the azygos vein. In this setting using a sheath in combination with its dilatator to pass the occlusion of the SVC after neither a diagnostic catheter nor a PTA balloon would pass the lesion may be a valid option. A dual venous approach was established using the right common femoral vein and an indwelling port catheter in the right cephalic vein to dilate and stent the lesion. Finally, a port may be implanted after the revascularization had been successful. Passage through the port catheter-associated occlusion of the SVC was only possible by use of the sheath in combination with its dilatator. A dual venous access by the femoral approach and the indwelling central catheter is helpful in treating a SVC occlusion. Long-term central venous catheters may cause SVC syndrome, especially with a catheter tip located too far cranially. An endovascular revascularization of a complete occlusion of the SVC represents the therapy of choice.

Bleeding 'downhill' esophageal varices associated with benign superior vena cava obstruction: case report and literature review.

PubMed

Loudin, Michael; Anderson, Sharon; Schlansky, Barry

2016-10-24

Proximal or 'downhill' esophageal varices are a rare cause of upper gastrointestinal hemorrhage. Unlike the much more common distal esophageal varices, which are most commonly a result of portal hypertension, downhill esophageal varices result from vascular obstruction of the superior vena cava (SVC). While SVC obstruction is most commonly secondary to malignant causes, our review of the literature suggests that benign causes of SVC obstruction are the most common cause actual bleeding from downhill varices. Given the alternative pathophysiology of downhill varices, they require a unique approach to management. Variceal band ligation may be used to temporize acute variceal bleeding, and should be applied on the proximal end of the varix. Relief of the underlying SVC obstruction is the cornerstone of definitive treatment of downhill varices. A young woman with a benign superior vena cava stenosis due to a tunneled internal jugular vein dialysis catheter presented with hematemesis and melena. Urgent upper endoscopy revealed multiple 'downhill' esophageal varices with stigmata of recent hemorrhage. As there was no active bleeding, no endoscopic intervention was performed. CT angiography demonstrated stenosis of the SVC surrounding the distal tip of her indwelling hemodialysis catheter. The patient underwent balloon angioplasty of the stenotic SVC segment with resolution of her bleeding and clinical stabilization. Downhill esophageal varices are a distinct entity from the more common distal esophageal varices. Endoscopic therapies have a role in temporizing active variceal bleeding, but relief of the underlying SVC obstruction is the cornerstone of treatment and should be pursued as rapidly as possible. It is unknown why benign, as opposed to malignant, causes of SVC obstruction result in bleeding from downhill varices at such a high rate, despite being a less common etiology of SVC obstruction.

Kernel PLS-SVC for Linear and Nonlinear Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Trejo, Leonard J.; Matthews, Bryan

2003-01-01

A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.

Public Evaluation of a Community College.

ERIC Educational Resources Information Center

Garner, W. Harold; Shapton, Karen

A marketing study was conducted in the Sauk Valley College (SVC) Illinois district. This public comprehensive community college conducted a survey to determine public perceptions of the scope and quality of its programs; extent of public involvement with SVC; prospective market for SVC; and primary information sources used by the public concerning…

9 CFR 93.901 - General restrictions; exceptions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... cultures of SVC virus, preserved SVC virus viral RNA or DNA, tissue samples containing viable SVC virus, or... live fish, fertilized eggs, and gametes are handled as follows: (1) They are maintained under... with paragraph (b)(4) of this section as adequate to prevent the spread within the United States of any...

9 CFR 93.901 - General restrictions; exceptions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... cultures of SVC virus, preserved SVC virus viral RNA or DNA, tissue samples containing viable SVC virus, or... live fish, fertilized eggs, and gametes are handled as follows: (1) They are maintained under... with paragraph (b)(4) of this section as adequate to prevent the spread within the United States of any...

9 CFR 93.901 - General restrictions; exceptions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... cultures of SVC virus, preserved SVC virus viral RNA or DNA, tissue samples containing viable SVC virus, or... live fish, fertilized eggs, and gametes are handled as follows: (1) They are maintained under... with paragraph (b)(4) of this section as adequate to prevent the spread within the United States of any...

9 CFR 93.901 - General restrictions; exceptions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... cultures of SVC virus, preserved SVC virus viral RNA or DNA, tissue samples containing viable SVC virus, or... live fish, fertilized eggs, and gametes are handled as follows: (1) They are maintained under... with paragraph (b)(4) of this section as adequate to prevent the spread within the United States of any...

9 CFR 93.901 - General restrictions; exceptions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... cultures of SVC virus, preserved SVC virus viral RNA or DNA, tissue samples containing viable SVC virus, or... live fish, fertilized eggs, and gametes are handled as follows: (1) They are maintained under... with paragraph (b)(4) of this section as adequate to prevent the spread within the United States of any...

Spring viraemia of carp (SVC) in the UK: the road to freedom.

PubMed

Taylor, N G H; Peeler, E J; Denham, K L; Crane, C N; Thrush, M A; Dixon, P F; Stone, D M; Way, K; Oidtmann, B C

2013-08-01

Spring viraemia of carp (SVC) is a disease of international importance that predominantly affects cyprinid fish and can cause significant mortality. In the United Kingdom (UK), SVC was first detected in 1977 with further cases occurring in fisheries, farms, wholesale and retail establishments throughout England and Wales (but not Scotland, where few cyprinid populations exist, nor Northern Ireland where SVC has never been detected) over the subsequent 30 years. Following a control and eradication programme for the disease initiated in 2005, the UK was recognised free of the disease in 2010. This study compiles historic records of SVC cases in England and Wales with a view to understanding its routes of introduction and spread, and assessing the effectiveness of the control and eradication programme in order to improve contingency plans to prevent and control future disease incursions in the cyprinid fish sectors. Between 1977 and 2010 the presence of SVC was confirmed on 108 occasions, with 65 of the cases occurring in sport fisheries and the majority of the remainder occurring in the ornamental fish sector. The study found that throughout the history of SVC in the UK, though cases were widely distributed, their occurrence was sporadic and the virus did not become endemic. All evidence indicates that SVC was not able to persist under UK environmental conditions, suggesting that the majority of cases were a result of new introductions to the UK as opposed to within-country spread. The control and eradication programme adopted in 2005 was highly effective and two years after its implementation cases of SVC ceased. Given the non-persistent nature of the pathogen the most important aspect of the control programme focused on preventing re-introduction of the virus to the UK. Despite the effectiveness of these controls against SVC, this approach is likely to be less effective against more persistent pathogens such as koi herpesvirus, which are likely to require more stringent measures to prevent within-country spread. Crown Copyright © 2013. Published by Elsevier B.V. All rights reserved.

Rapid guiding catheter swapping for management of rupture during percutaneous venoplasty for idiopathic occlusion of superior vena cava.

PubMed

Pandit, Bhagya Narayan; Chaturvedi, Vivek; Parakh, Neeraj; Gade, Sandeep; Trehan, Vijay

2015-04-01

Treatment for superior vena cava syndrome (SVCS) by percutaneous interventions has become established as a definitive therapy. However, there is a significant risk of rupture during SVC intervention. We describe an uncommon case that developed SVC rupture during percutaneous intervention for idiopathic SVCS. This was managed successfully with pericardiocentesis and rapid implantation of covered stent in SVC by rapid guiding catheter swapping technique. This, however, led to inadvertent obstruction of left innominate vein which was successfully treated by kissing balloon inflation. At 18-month follow-up, he is asymptomatic with a well apposed patent stent-graft in the SVC.

Correlation between Forced Vital Capacity and Slow Vital Capacity for the assessment of respiratory involvement in Amyotrophic Lateral Sclerosis: a prospective study.

PubMed

Pinto, Susana; de Carvalho, Mamede

2017-02-01

Slow vital capacity (SVC) and forced vital capacity (FVC) are the most frequent used tests evaluating respiratory function in amyotrophic lateral sclerosis (ALS). No previous study has determined their interchangeability. To evaluate SVC-FVC correlation in ALS. Consecutive definite/probable ALS and primary lateral sclerosis (PLS) patients (2000-2014) in whom respiratory tests were performed at baseline/4-6months later were included. All were evaluated with revised ALS functional rating scale, the ALSFRS respiratory (R-subscore) and bulbar subscores, SVC, FVC, maximal inspiratory (MIP) and expiratory (MEP) pressures. SVC-FVC correlation was analysed by Pearson product-moment correlation test. Paired t-test compared baseline/follow-up values. Multilinear regression analysis modelled the relationship between tested variables. We included 592 ALS (332 men, mean onset age 62.6 ± 11.8 years, mean disease duration 15.4 ± 15 months) and 19 PLS (11 men, median age 54 years, median disease duration 5.5 years) patients. SVC and FVC predicted values decreased 2.15%/month and 2.08%/month, respectively. FVC and SVC were strongly correlated. Both were strongly correlated with MIP and MEP and moderately correlated with R-subscore for the all population and spinal-onset patients, but weakly correlated for bulbar-onset patients. FVC and SVC were strongly correlated and declined similarly. This correlation was preserved in bulbar-onset ALS and in spastic PLS patients.

Predicting preload responsiveness using simultaneous recordings of inferior and superior vena cavae diameters.

PubMed

Charbonneau, Hélène; Riu, Béatrice; Faron, Matthieu; Mari, Arnaud; Kurrek, Matt M; Ruiz, Jean; Geeraerts, Thomas; Fourcade, Olivier; Genestal, Michèle; Silva, Stein

2014-09-05

Echocardiographic indices based on respiratory variations of superior and inferior vena cavae diameters (ΔSVC and ΔIVC, respectively) have been proposed as predictors of fluid responsiveness in mechanically ventilated patients, but they have never been compared simultaneously in the same patient sample. The aim of this study was to compare the predictive value of these echocardiographic indices when concomitantly recorded in mechanically ventilated septic patients. Septic shock patients requiring hemodynamic monitoring were prospectively enrolled over a 1-year period in a mixed medical surgical ICU of a university teaching hospital (Toulouse, France). All patients were mechanically ventilated. Predictive indices were obtained by transesophageal and transthoracic echocardiography and were calculated as follows: (Dmax - Dmin)/Dmax for ΔSVC and (Dmax - Dmin)/Dmin for ΔIVC, where Dmax and Dmin are the maximal and minimal diameters of SVC and IVC. Measurements were performed at baseline and after a 7-ml/kg volume expansion using a plasma expander. Patients were separated into responders (increase in cardiac index ≥15%) and nonresponders (increase in cardiac index <15%). Among 44 included patients, 26 (59%) patients were responders (R). ΔSVC was significantly more accurate than ΔIVC in predicting fluid responsiveness. The areas under the receiver operating characteristic curves for ΔSVC and ΔIVC regarding assessment of fluid responsiveness were significantly different (0.74 (95% confidence interval (CI): 0.59 to 0.88) and 0.43 (95% CI: 0.25 to 0.61), respectively (P = 0.012)). No significant correlation between ΔSVC and ΔIVC was found (r = 0.005, P = 0.98). The best threshold values for discriminating R from NR was 29% for ΔSVC, with 54% sensitivity and 89% specificity, and 21% for ΔIVC, with 38% sensitivity and 61% specificity. ΔSVC was better than ΔIVC in predicting fluid responsiveness in our cohort. It is worth noting that the sensitivity and specificity values of ΔSVC and ΔIVC for predicting fluid responsiveness were lower than those reported in the literature, highlighting the limits of using these indices in a heterogeneous sample of medical and surgical septic patients.

Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.

PubMed

Hettige, Nuwan C; Nguyen, Thai Binh; Yuan, Chen; Rajakulendran, Thanara; Baddour, Jermeen; Bhagwat, Nikhil; Bani-Fatemi, Ali; Voineskos, Aristotle N; Mallar Chakravarty, M; De Luca, Vincenzo

2017-07-01

Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remains a complex problem for psychiatric interventions. Machine learning models allow for the integration of many risk factors in order to build an algorithm that predicts which patients are likely to attempt suicide. Currently it is unclear how to integrate previously identified risk factors into a clinically relevant predictive tool to estimate the probability of a patient with schizophrenia for attempting suicide. We conducted a cross-sectional assessment on a sample of 345 participants diagnosed with schizophrenia spectrum disorders. Suicide attempters and non-attempters were clearly identified using the Columbia Suicide Severity Rating Scale (C-SSRS) and the Beck Suicide Ideation Scale (BSS). We developed four classification algorithms using a regularized regression, random forest, elastic net and support vector machine models with sociocultural and clinical variables as features to train the models. All classification models performed similarly in identifying suicide attempters and non-attempters. Our regularized logistic regression model demonstrated an accuracy of 67% and an area under the curve (AUC) of 0.71, while the random forest model demonstrated 66% accuracy and an AUC of 0.67. Support vector classifier (SVC) model demonstrated an accuracy of 67% and an AUC of 0.70, and the elastic net model demonstrated and accuracy of 65% and an AUC of 0.71. Machine learning algorithms offer a relatively successful method for incorporating many clinical features to predict individuals at risk for future suicide attempts. Increased performance of these models using clinically relevant variables offers the potential to facilitate early treatment and intervention to prevent future suicide attempts. Copyright © 2017 Elsevier Inc. All rights reserved.

Currency crisis indication by using ensembles of support vector machine classifiers

NASA Astrophysics Data System (ADS)

Ramli, Nor Azuana; Ismail, Mohd Tahir; Wooi, Hooy Chee

2014-07-01

There are many methods that had been experimented in the analysis of currency crisis. However, not all methods could provide accurate indications. This paper introduces an ensemble of classifiers by using Support Vector Machine that's never been applied in analyses involving currency crisis before with the aim of increasing the indication accuracy. The proposed ensemble classifiers' performances are measured using percentage of accuracy, root mean squared error (RMSE), area under the Receiver Operating Characteristics (ROC) curve and Type II error. The performances of an ensemble of Support Vector Machine classifiers are compared with the single Support Vector Machine classifier and both of classifiers are tested on the data set from 27 countries with 12 macroeconomic indicators for each country. From our analyses, the results show that the ensemble of Support Vector Machine classifiers outperforms single Support Vector Machine classifier on the problem involving indicating a currency crisis in terms of a range of standard measures for comparing the performance of classifiers.

Vascular anatomy in children with univentricular hearts regarding transcatheter bidirectional Glenn anastomosis.

PubMed

Sizarov, Aleksander; Raimondi, Francesca; Bonnet, Damien; Boudjemline, Younes

2017-04-01

Transcatheter stent-secured Glenn anastomosis, aiming to reduce the invasiveness of palliation in patients with univentricular heart defects, has been reported in large experimental animals. The advent of biodegradable stents and tissue-engineered vascular grafts will make this procedure a reality in human patients. However, the relationship between the superior vena cava (SVC) and the right pulmonary artery (RPA) is different in humans. To characterise vascular anatomy in children with univentricular hearts, regarding technical aspects and device design for this procedure. Retrospective analysis of 35 thoracic computed tomography angiograms at a mean age of 18.1±22.4 months. Two types of arrangement between the SVC and the RPA were identified: anatomy convenient for immediate wire passage and stent deployment between the two vessels (60%); and pattern of early RPA branching, requiring the perforation wire to traverse the intervascular space to avoid entrance into the upper RPA branch (40%). In patients with the convenient vascular arrangement, the vessels were nearly perpendicular, having immediate contact, with the posterior SVC aspect partially "wrapping" the adjacent RPA in most patients. In patients with early RPA branching, the mean shortest SVC-to-central RPA distance was 4.3±2.7mm. For the total population, the mean length of proximal SVC that allowed stent deployment without covering the brachiocephalic vein was 15.6±5.1mm. A trumpet-shaped covered stent in a craniocaudal orientation reaching from the SVC into the prebranching RPA seems most suitable for achieving bidirectional Glenn anastomosis percutaneously in humans. However, the short length of the proximal SVC and the presence of early RPA branching pose challenges for optimal design of the dedicated device. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

A Concentration-Dependent Insulin Immobilization Behavior of Alkyl-Modified Silica Vesicles: The Impact of Alkyl Chain Length.

PubMed

Zhang, Jun; Zhang, Long; Lei, Chang; Huang, Xiaodan; Yang, Yannan; Yu, Chengzhong

2018-05-01

The insulin immobilization behaviors of silica vesicles (SV) before and after modification with hydrophobic alkyl -C 8 and -C 18 groups have been studied and correlated to the grafted alkyl chain length. In order to minimize the influence from the other structural parameters, monolayered -C 8 or -C 18 groups are grafted onto SV with controlled density. The insulin immobilization capacity of SV is dependent on the initial insulin concentrations (IIC). At high IIC (2.6-3.0 mg/mL), the trend of insulin immobilization capacity of SV is SV-OH > SV-C 8 > SV-C 18 , which is determined mainly by the surface area of SV. At medium IIC (0.6-1.9 mg/mL), the trend changes to SV-C 8 ≥ SV-C 18 > SV-OH as both the surface area and alkyl chain length contribute to the insulin immobilization. At an extremely low IIC, the hydrophobic-hydrophobic interaction between the alkyl group and insulin molecules plays the most significant role. Consequently, SV-C 18 with longer alkyl groups and the highest hydrophobicity show the best insulin enrichment performance compared to SV-C 8 and SV-OH, as evidenced by an insulin detection limit of 0.001 ng/mL in phosphate buffered saline (PBS) and 0.05 ng/mL in artficial urine determined by mass spectrometry (MS).

Dynamic full-scalability conversion in scalable video coding

NASA Astrophysics Data System (ADS)

Lee, Dong Su; Bae, Tae Meon; Thang, Truong Cong; Ro, Yong Man

2007-02-01

For outstanding coding efficiency with scalability functions, SVC (Scalable Video Coding) is being standardized. SVC can support spatial, temporal and SNR scalability and these scalabilities are useful to provide a smooth video streaming service even in a time varying network such as a mobile environment. But current SVC is insufficient to support dynamic video conversion with scalability, thereby the adaptation of bitrate to meet a fluctuating network condition is limited. In this paper, we propose dynamic full-scalability conversion methods for QoS adaptive video streaming in SVC. To accomplish full scalability dynamic conversion, we develop corresponding bitstream extraction, encoding and decoding schemes. At the encoder, we insert the IDR NAL periodically to solve the problems of spatial scalability conversion. At the extractor, we analyze the SVC bitstream to get the information which enable dynamic extraction. Real time extraction is achieved by using this information. Finally, we develop the decoder so that it can manage the changing scalability. Experimental results showed that dynamic full-scalability conversion was verified and it was necessary for time varying network condition.

Intra- and Inter-rater Agreement of Superior Vena Cava Flow and Right Ventricular Outflow Measurements in Late Preterm and Term Neonates.

PubMed

Mahoney, Liam; Fernandez-Alvarez, Jose R; Rojas-Anaya, Hector; Aiton, Neil; Wertheim, David; Seddon, Paul; Rabe, Heike

2018-02-24

To explore the intra- and inter-rater agreement of superior vena cava (SVC) flow and right ventricular (RV) outflow in healthy and unwell late preterm neonates (33-37 weeks' gestational age), term neonates (≥37 weeks' gestational age), and neonates receiving total-body cooling. The intra- and inter-rater agreement (n = 25 and 41 neonates, respectively) rates for SVC flow and RV outflow were determined by echocardiography in healthy and unwell late preterm and term neonates with the use of Bland-Altman plots, the repeatability coefficient, the repeatability index, and intraclass correlation coefficients. The intra-rater repeatability index values were 41% for SVC flow and 31% for RV outflow, with intraclass correlation coefficients indicating good agreement for both measures. The inter-rater repeatability index values for SVC flow and RV outflow were 63% and 51%, respectively, with intraclass correlation coefficients indicating moderate agreement for both measures. If SVC flow or RV outflow is used in the hemodynamic treatment of neonates, sequential measurements should ideally be performed by the same clinician to reduce potential variability. © 2018 by the American Institute of Ultrasound in Medicine.

Analysis of SVC’s Impact on Out-of-step Oscillation Based on Direct Method Considering Admittance Effect

NASA Astrophysics Data System (ADS)

He, Jing-bo; Ding, Jian; Feng, Li; Ren, Jian-wen; Tang, Wei; Yang, Cheng; Wang, Jing-jin; Song, Yun-ting

2017-05-01

The widely employment of power electronic equipment in modern power system, may affect grid structure and system operation because of their diverse dynamic characteristics. In this paper, the impact of the static var compensators (SVC) on out-of-step oscillation is investigated based on the equal area criterion by considering SVC’s admittance effect. Firstly, the variation pattern of bus voltage which is connected to SVC is concluded. Then the derivation of equation considering the admittance effect is given, which explains the ability of SVC to suppress out-of-step oscillation. SVC’s impact on migration of out-of-step oscillation centre (OSOC) is discussed based on the expression of OSOC’s electrical location. Moreover, the influence of SVC’s response speed and capacity on its effect are presented by qualitative analysis. Finally, simulations on a two-end equivalent test system are carried out to verify the correctness of the theoretical analysis. It is found that the capacity and a response speed of SVC have significant effect on the out-of-step oscillation, while SVC have no d istinct influence on location of OSOC.

Twin Valve Caval Stent for Functional Replacement of Incompetent Tricuspid Valve: A Feasibility Animal Study

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

Sochman, Jan, E-mail: jan.sochman@medicon.cz; Peregrin, Jan H., E-mail: jape@medicon.cz; Pavcnik, Dusan, E-mail: pavcnikd@ohsu.edu

Objective: To evaluate feasibility of a twin valve caval stent (TVCS) for functional replacement of an incompetent tricuspid valve (TV) in an acute animal study. Methods: One swine and three sheep were used in the study. TVCS placement was tested in a swine with a normal TV. TVCS function was tested in three sheep with TV regurgitation created by papillary muscle avulsion. Cardiac angiograms and pressure measurements were used to evaluate TVCS function. Two sheep were studied after fluid overload. Results: TVCS was percutaneously placed properly at the central portions of the superior vena cava (SVC) and inferior vena cavamore » (IVC) in the swine. Papillary muscle avulsion in three sheep caused significant tricuspid regurgitation with massive reflux into the right atrium (RA) and partial reflux into the SVC and IVC. TVCS placement eliminated reflux into the SVC and IVC. After fluid overload, there was enlargement of the right ventricle and RA and significant increase in right ventricle, RA, SVC, and IVC pressures, but no reflux into the IVC and SVC. Conclusion: The results of this feasibility study justify detailed evaluation of TVCS insertion for functional chronic replacement of incompetent TV.« less

Comparison of tricuspid inflow and superior vena caval Doppler velocities in acute simulated hypovolemia: new non-invasive indices for evaluating right ventricular preload.

PubMed

Liu, Jie; Cao, Tie-Sheng; Yuan, Li-Jun; Duan, Yun-You; Yang, Yi-Lin

2006-05-16

Assessment of cardiac preload is important for clinical management of some emergencies related to hypovolemia. Effects of acute simulated hypovolemia on Doppler blood flow velocity indices of tricuspid valve (TV) and superior vena cava (SVC) were investigated in order to find sensitive Doppler indices for predicting right ventricular preload. Doppler flow patterns of SVC and TV in 12 healthy young men were examined by transthoracic echocardiography (TTE) during graded lower body negative pressure (LBNP) of up to -60 mm Hg which simulated acute hypovolemia. Peak velocities of all waves and their related ratios (SVC S/D and tricuspid E/A) were measured, calculated and statistically analyzed. Except for the velocity of tricuspid A wave, velocities of all waves and their related ratios declined during volume decentralization. Of all indices measured, the peak velocities of S wave and AR wave in SVC correlated most strongly with levels of LBNP (r = -0.744 and -0.771, p < 0.001). The S and AR velocities are of good values in assessing right ventricular preload. Monitoring SVC flow may provide a relatively noninvasive means to assess direct changes in right ventricular preload.

Neutropenia is independently associated with sub-therapeutic serum concentration of vancomycin.

PubMed

Choi, Min Hyuk; Choe, Yeon Hwa; Lee, Sang-Guk; Jeong, Seok Hoon; Kim, Jeong-Ho

2017-02-01

We aimed to identify the impact of the presence of neutropenia on serum vancomycin concentration (SVC). A retrospective study was conducted from January 2005 to December 2015. The study population was comprised of adult patients who were performed serum concentration of vancomycin. Patients with renal failure or using non-conventional dosages of vancomycin were excluded. A total of 1307 adult patients were included in this study, of whom 163 (12.4%) were neutropenic. Patients with neutropenia presented significantly lower SVCs than non-neutropenic patients (P<0.0001). Multiple linear regressions showed significant association between neutropenia and trough SVC (beta coefficients, -2.351; P=0.004). Multiple logistic regression analysis also revealed a significant association between sub-therapeutic vancomycin concentrations (trough SVC values<10mg/l) and neutropenia (odds ratio, 1.75, P=0.029) CONCLUSIONS: The presence of neutropenia is significantly associated with low SVC, even after adjusting for other variables. Therefore, neutropenic patients had a higher risk of sub-therapeutic SVC compared with non-neutropenic patients. We recommended that vancomycin therapy should be monitored with TDM-guided optimization of dosage and intervals, especially in neutropenic patients. Copyright © 2016 Elsevier B.V. All rights reserved.

Life-threatening Cerebral Edema Caused by Acute Occlusion of a Superior Vena Cava Stent

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

Sofue, Keitaro, E-mail: keitarosofue@yahoo.co.jp; Takeuchi, Yoshito, E-mail: yotake62@qg8.so-net.ne.jp; Arai, Yasuaki, E-mail: arai-y3111@mvh.biglobe.ne.jp

A71-year-old man with advanced lung cancer developed a life-threatening cerebral edema caused by the acute occlusion of a superior vena cava (SVC) stent and was successfully treated by an additional stent placement. Although stent occlusion is a common early complication, no life-threatening situations have been reported until now. Our experience highlights the fact that acute stent occlusion can potentially lead to the complete venous shutdown of the SVC, resulting in life-threatening cerebral edema, after SVC stent placement. Immediate diagnosis and countermeasures are required.

CMMI (registered trademark) for Services (CMMI-SVC) Overview for Workshop

DTIC Science & Technology

2008-08-01

is from “DoD throws light on how it buys services [GCN 2006].” GAO data is from GAO report GAO-07-20. 5 CMMI for Services (CMMI-SVC) Forrester...Service Addition PAs 3 5 1 22 % of CMMI-DEV PAs are reused; % of Corporate Investments are potentially r usable! CMMI-DEV CMMI-ACQ CMMI-SVC 77...Service Modifications: •  21 amplification in 7 PAs •  5 added references •  1 modified PA (REQM) •  1 specific goal •  2 specific practices

Voltage stability index based optimal placement of static VAR compensator and sizing using Cuckoo search algorithm

NASA Astrophysics Data System (ADS)

Venkateswara Rao, B.; Kumar, G. V. Nagesh; Chowdary, D. Deepak; Bharathi, M. Aruna; Patra, Stutee

2017-07-01

This paper furnish the new Metaheuristic algorithm called Cuckoo Search Algorithm (CSA) for solving optimal power flow (OPF) problem with minimization of real power generation cost. The CSA is found to be the most efficient algorithm for solving single objective optimal power flow problems. The CSA performance is tested on IEEE 57 bus test system with real power generation cost minimization as objective function. Static VAR Compensator (SVC) is one of the best shunt connected device in the Flexible Alternating Current Transmission System (FACTS) family. It has capable of controlling the voltage magnitudes of buses by injecting the reactive power to system. In this paper SVC is integrated in CSA based Optimal Power Flow to optimize the real power generation cost. SVC is used to improve the voltage profile of the system. CSA gives better results as compared to genetic algorithm (GA) in both without and with SVC conditions.

Evaluation of serial urine viral cultures for the diagnosis of cytomegalovirus infection in neonates and infants.

PubMed

Chisholm, Karen M; Aziz, Natali; McDowell, Michal; Guo, Frances P; Srinivas, Nivedita; Benitz, William E; Norton, Mary E; Gutierrez, Kathleen; Folkins, Ann K; Pinsky, Benjamin A

2014-01-01

Cytomegalovirus (CMV) is the most common cause of congenital infection worldwide. Urine viral culture is the standard for CMV diagnosis in neonates and infants. The objectives of this study were to compare the performance of serial paired rapid shell vial cultures (SVC) and routine viral cultures (RVC), and to determine the optimal number of cultures needed to detect positive cases. From 2001 to 2011, all paired CMV SVC and RVC performed on neonates and infants less than 100 days of age were recorded. Testing episodes were defined as sets of cultures performed within 7 days of one another. A total of 1264 neonates and infants underwent 1478 testing episodes; 68 (5.4%) had at least one episode with a positive CMV culture. In episodes where CMV was detected before day 21 of life, the first specimen was positive in 100% (16/16) of cases. When testing occurred after 21 days of life, the first specimen was positive in 82.7% (43/52) of cases, requiring three cultures to reach 100% detection. The SVC was more prone to assay failure than RVC. Overall, when RVC was compared to SVC, there was 86.0% positive agreement and 99.9% negative agreement. In conclusion, three serial urine samples are necessary for detection of CMV in specimens collected between day of life 22 and 99, while one sample may be sufficient on or before day of life 21. Though SVC was more sensitive than RVC, the risk of SVC failure supports the use of multimodality testing to optimize detection.

The second virial coefficient of bounded Mie potentials

NASA Astrophysics Data System (ADS)

Heyes, D. M.; Pereira de Vasconcelos, T.

2017-12-01

The second virial coefficient (SVC) of bounded generalizations of the Mie m:n potential ϕ (r ) =λ [1 /(aq+rq ) m /q-1 /(aq+rq ) n /q ] , where λ, a, q, m, and n are constants (a ≥ 0), is explored. The particle separation distance is r. This potential could be used as an effective interaction between polymeric dispersed colloidal particles of various degrees of interpenetrability. The SVC is negative for all temperatures for a, greater than a critical value, ac, which coincides with the range of a, where the system is thermodynamically unstable. The Boyle temperature and the temperature at which the SVC is a maximum diverge to +∞ as a → ac from below. Various series expansion expressions for the SVC are derived following on from those derived for the Mie potential itself (i.e., a = 0) in the study of Heyes et al. [J. Chem. Phys. 145, 084505 (2016)]. Formulas based on an expansion of the exponential in the Mayer function definition of the SVC are formally convergent, but pose numerical problems for the useful range of a < 1. High temperature expansion (HTE) formulas extending those in the previous publication are derived, which in contrast converge rapidly for the full a range. The HTE formulas derived in this work could be useful in guiding the choice of nucleation and growth experimental conditions for dispersed soft polymeric particles. Inter alia, the SVC of the inverse power special case of the Bounded Mie potential, i .e ., ϕ (r ) =1 /(aq+rq ) m /q, are also derived.

Human aortic allograft: an excellent conduit choice for superior vena cava reconstruction

PubMed Central

2014-01-01

Superior vena cava (SVC) reconstruction is occasionally required in the treatment of benign and malignant conditions. We report a patient with symptomatic SVC obstruction secondary to mediastinal fibrosis successfully reconstructed with an aortic allograft. PMID:24428914

Superior Vena Cava Stent Migration into the Pulmonary Artery Causing Fatal Pulmonary Infarction

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

Anand, Girija, E-mail: gijanandm@hotmail.com; Lewanski, Conrad R.; Cowman, Steven A.

2011-02-15

Migration of superior vena cava (SVC) stents is a well-recognised complication of their deployment, and numerous strategies exist for their retrieval. To our knowledge, only three cases of migration of an SVC stent to the pulmonary vasculature have previously been reported. None of these patients developed complications that resulted in death. We report a case of SVC stent migration to the pulmonary vasculature with delayed pulmonary artery thrombosis and death from pulmonary infarction. We conclude that early retrieval of migrated stents should be performed to decrease the risk of serious complications.

Application of Static Var Compensator (SVC) With PI Controller for Grid Integration of Wind Farm Using Harmony Search

NASA Astrophysics Data System (ADS)

Keshta, H. E.; Ali, A. A.; Saied, E. M.; Bendary, F. M.

2016-10-01

Large-scale integration of wind turbine generators (WTGs) may have significant impacts on power system operation with respect to system frequency and bus voltages. This paper studies the effect of Static Var Compensator (SVC) connected to wind energy conversion system (WECS) on voltage profile and the power generated from the induction generator (IG) in wind farm. Also paper presents, a dynamic reactive power compensation using Static Var Compensator (SVC) at the a point of interconnection of wind farm while static compensation (Fixed Capacitor Bank) is unable to prevent voltage collapse. Moreover, this paper shows that using advanced optimization techniques based on artificial intelligence (AI) such as Harmony Search Algorithm (HS) and Self-Adaptive Global Harmony Search Algorithm (SGHS) instead of a Conventional Control Method to tune the parameters of PI controller for SVC and pitch angle. Also paper illustrates that the performance of the system with controllers based on AI is improved under different operating conditions. MATLAB/Simulink based simulation is utilized to demonstrate the application of SVC in wind farm integration. It is also carried out to investigate the enhancement in performance of the WECS achieved with a PI Controller tuned by Harmony Search Algorithm as compared to a Conventional Control Method.

Estimating the potential refolding yield of recombinant proteins expressed as inclusion bodies.

PubMed

Ho, Jason G S; Middelberg, Anton P J

2004-09-05

Recombinant protein production in bacteria is efficient except that insoluble inclusion bodies form when some gene sequences are expressed. Such proteins must undergo renaturation, which is an inefficient process due to protein aggregation on dilution from concentrated denaturant. In this study, the protein-protein interactions of eight distinct inclusion-body proteins are quantified, in different solution conditions, by measurement of protein second virial coefficients (SVCs). Protein solubility is shown to decrease as the SVC is reduced (i.e., as protein interactions become more attractive). Plots of SVC versus denaturant concentration demonstrate two clear groupings of proteins: a more aggregative group and a group having higher SVC and better solubility. A correlation of the measured SVC with protein molecular weight and hydropathicity, that is able to predict which group each of the eight proteins falls into, is presented. The inclusion of additives known to inhibit aggregation during renaturation improves solubility and increases the SVC of both protein groups. Furthermore, an estimate of maximum refolding yield (or solubility) using high-performance liquid chromatography was obtained for each protein tested, under different environmental conditions, enabling a relationship between "yield" and SVC to be demonstrated. Combined, the results enable an approximate estimation of the maximum refolding yield that is attainable for each of the eight proteins examined, under a selected chemical environment. Although the correlations must be tested with a far larger set of protein sequences, this work represents a significant move beyond empirical approaches for optimizing renaturation conditions. The approach moves toward the ideal of predicting maximum refolding yield using simple bioinformatic metrics that can be estimated from the gene sequence. Such a capability could potentially "screen," in silico, those sequences suitable for expression in bacteria from those that must be expressed in more complex hosts.

Prospective Evaluation of Electromyography-Guided Phrenic Nerve Monitoring During Superior Vena Cava Isolation to Anticipate Phrenic Nerve Injury.

PubMed

Miyazaki, Shinsuke; Ichihara, Noboru; Nakamura, Hiroaki; Taniguchi, Hiroshi; Hachiya, Hitoshi; Araki, Makoto; Takagi, Takamitsu; Iwasawa, Jin; Kuroi, Akio; Hirao, Kenzo; Iesaka, Yoshito

2016-04-01

Right phrenic nerve injury (PNI) is a major concern during superior vena cava (SVC) isolation due to the anatomical close proximity. The functional and histological severity of PNI parallels the degree of the reduction in the compound motor action potential (CMAP) amplitude. This study aimed to evaluate the feasibility of monitoring CMAPs during SVC isolation to anticipate PNI during atrial fibrillation (AF) ablation. Thirty-nine paroxysmal AF patients were prospectively enrolled. Radiofrequency energy was delivered point-by-point for 30 seconds with 20 W until eliminating all SVC potentials after the pulmonary vein isolation. Right diaphragmatic CMAPs were obtained from modified surface electrodes by pacing from the right subclavian vein. Radiofrequency applications were applied without fluoroscopy under CMAP monitoring at sites with phrenic nerve capture by high output pacing. Electrical SVC isolation was successfully achieved with a mean of 9.4 ± 3.3 applications in all patients. In 3 (7.5%) patients, the SVC was isolated without radiofrequency delivery at phrenic nerve capture sites. Among a total of 346 applications in the remaining 36 patients, 71 (20.5%) were delivered while monitoring CMAPs. In 1 (1.4%) application, the RF application was interrupted due to a decrease in the CMAP amplitude. However, no PNI was detected on fluoroscopy, and the decreased amplitude recovered spontaneously. The remaining 70 (98.6%) applications exhibited no significant changes in the CMAP amplitude throughout the applications (from 1.01 ± 0.47 to 0.98 ± 0.45 mV, P = 0.383). Stable right diaphragmatic CMAPs could be obtained, and monitoring CMAPs might be useful for anticipating right PNI during SVC isolation. © 2016 Wiley Periodicals, Inc.

Plasmonic enhancement of a silicon-vacancy center in a nanodiamond crystal

NASA Astrophysics Data System (ADS)

Meng, Xiang; Liu, Shang; Dadap, Jerry I.; Osgood, Richard M.

2017-06-01

This work reports a rigorous and comprehensive three-dimensional electromagnetic computation to investigate and design photoluminescence enhancement from a single silicon-vacancy center (SVC) in a nanodiamond crystal embedded in various metallic nanoantennae, each having a different geometry. The study demonstrates how each antenna design enhances the photoluminescence of SVCs in diamond. In particular, our report discusses how the 2D or 3D curvature of the nanoantenna and the control of the local environment of the SVC can lead to significant field enhancement of its optical field. Our calculated optimal photoluminescence for each design enhances the emission intensity by 15 -300 × that of a single SVC without antenna. The enhancement mechanisms are investigated using four representative structures that can be fabricated under feasible and realistic growth conditions, i.e., spherical-, nanorod-, nanodisk-dimer, and bow-tie nanoantennae. These results demonstrate a method for rationally designing arbitrary metallic nanoantenna/emitter assemblies to achieve optimal SVC photoluminescence.

Association of Microcirculation, Macrocirculation, and Severity of Illness in Septic Shock: A Prospective Observational Study to Identify Microcirculatory Targets Potentially Suitable for Guidance of Hemodynamic Therapy.

PubMed

Sturm, Timo; Leiblein, Julia; Schneider-Lindner, Verena; Kirschning, Thomas; Thiel, Manfred

2018-04-01

Clinically unapparent microcirculatory impairment is common and has a negative impact on septic shock, but specific therapy is not established so far. This prospective observational study aimed at identifying candidate parameters for microcirculatory-guided hemodynamic therapy. ClinicalTrials.gov : NCT01530932. Microcirculatory flow and postcapillary venous oxygen saturation were detected during vaso-occlusive testing (VOT) on days 1 (T0), 2 (T24), and 4 (T72) in 20 patients with septic shock at a surgical intensive care unit using a laser Doppler spectrophotometry system (O2C). Reperfusional maximal venous capillary oxygen saturation (SvcO 2 max) showed negative correlations with Simplified Acute Physiology Score II (SAPSII)/Sequential Organ Failure Assessment (SOFA) score, norepinephrine dosage, and lactate concentration and showed positive correlations with cardiac index (CI). At T24 and T72, SvcO 2 max was also inversely linked to fluid balance. With respect to any predictive value, SvcO 2 max and CI determined on day 1 (T0) were negatively correlated with SAPS II/SOFA on day 4 (T72). Moreover, SvcO 2 max measured on day 1 or day 2 was negatively correlated with cumulated fluid balance on day 4 ( r= -.472, P < .05 and r = -.829, P < .001). By contrast, CI neither on day 1 nor on day 2 was correlated with cumulated fluid balance on day 4 ( r = -.343, P = .17 and r = -.365, P = .15). In patients with septic shock, microcirculatory reserve as assessed by SvcO 2 max following VOT was impaired and negatively correlated with severity of illness and fluid balance. In contrast to CI, SvcO 2 max determined on day 1 or day 2 was significantly negatively correlated with cumulative fluid balance on day 4. Therefore, early microcirculatory measurement of SvcO 2 max might be superior to CI in guidance of sepsis therapy to avoid fluid overload. This has to be addressed in future clinical studies.

Testing of the Support Vector Machine for Binary-Class Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew

2011-01-01

The Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Support Vector Machines implemented in this research were used as classifiers for the final stage in a Multistage Autonomous Target Recognition system. A single kernel SVM known as SVMlight, and a modified version known as a Support Vector Machine with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SMV as a method for classification. From trial to trial, SVM produces consistent results

Surgical repair of an unusual type of supra-cardiac total anomalous pulmonary venous connection to the superior vena cava.

PubMed

Perri, Gianluigi; Filippelli, Sergio; Kirk, Richard; Hasan, Asif; Griselli, Massimo

2012-05-01

Anomalies of the pulmonary venous drainage vary widely in their anatomic spectrum and clinical presentation. We describe an unusual case of supra-cardiac total anomalous pulmonary venous connection (TAPVC), where the pulmonary veins drained directly in the posterior aspect of proximal right superior vena cava (SVC) through separate ostia. The veins were re-routed with a patch to the left atrium via the secundum atrial septal defect (ASD). The continuity between distal SVC and right atrium was re-established by re-implanting the SVC to the right atrial appendage (Warden Procedure). © 2012 Wiley Periodicals, Inc.

Association Between Decline in Slow Vital Capacity and Respiratory Insufficiency, Use of Assisted Ventilation, Tracheostomy, or Death in Patients With Amyotrophic Lateral Sclerosis

PubMed Central

Meng, Lisa; Kulke, Sarah F.; Rudnicki, Stacy A.; Wolff, Andrew A.; Bozik, Michael E.; Malik, Fady I.; Shefner, Jeremy M.

2017-01-01

Importance The prognostic value of slow vital capacity (SVC) in relation to respiratory function decline and disease progression in patients with amyotrophic lateral sclerosis (ALS) is not well understood. Objective To investigate the rate of decline in percentage predicted SVC and its association with respiratory-related clinical events and mortality in patients with ALS. Design, Setting, and Participants This retrospective study included 893 placebo-treated patients from 2 large clinical trials (EMPOWER and BENEFIT-ALS, conducted from March 28, 2011, to November 1, 2012, and from October 23, 2012, to March 21, 2014, respectively) and an ALS trial database (PRO-ACT, containing studies completed between 1990 and 2010) to investigate the rate of decline in SVC. Data from the EMPOWER trial (which enrolled adults with possible, probable, or definite ALS; symptom onset within 24 months before screening; and upright SVC at least 65% of predicted value for age, height, and sex) were used to assess the relationship of SVC to respiratory-related clinical events; 456 patients randomized to placebo were used in this analysis. The 2 clinical trials included patients from North America, Australia, and Europe. Main Outcomes and Measures Clinical events included the earlier of time to death or time to decline in the Amyotrophic Lateral Sclerosis Functional Rating Scale–Revised (ALSFRS-R) respiratory subdomain, time to onset of respiratory insufficiency, time to tracheostomy, and all-cause mortality. Results Among 893 placebo-treated patients with ALS, the mean (SD) patient age was 56.7 (11.2) years, and the mean (SD) SVC was 90.5% (17.1%) at baseline; 65.5% (585 of 893) were male, and 20.5% (183 of 893) had bulbar-onset ALS. In EMPOWER, average decline of SVC from baseline through 1.5-year follow-up was −2.7 percentage points per month. Steeper declines were found in patients older than 65 years (−3.6 percentage points per month [P = .005 vs <50 years and P = .007 vs 50-65 years) and in patients with an ALSFRS-R total score of 39 or less at baseline (−3.1 percentage points per month [P < .001 vs >39]). When the rate of decline of SVC was slower by 1.5 percentage points per month in the first 6 months, risk reductions for events after 6 months were 19% for decline in the ALSFRS-R respiratory subdomain or death after 6 months, 22% for first onset of respiratory insufficiency or death after 6 months, 23% for first occurrence of tracheostomy or death after 6 months, and 23% for death at any time after 6 months (P < .001 for all). Conclusions and Relevance The rate of decline in SVC is associated with meaningful clinical events in ALS, including respiratory failure, tracheostomy, or death, suggesting that it is an important indicator of clinical progression. PMID:29181534

Observations of Subvisible Cirrus Clouds and Gravity Waves at the Tropical Tropopause

NASA Technical Reports Server (NTRS)

Pfister, Leonhard; Browell, E. V.; Hipskind, R. Stephen (Technical Monitor)

1998-01-01

Thin, subvisible cirrus (SVC) clouds at the tropical tropopause have been observed by a number of methods in a variety of observational programs, including in situ sampling and aircraft and space-based lidar. Modeling studies suggest that these clouds play an important role in dehydrating tropospheric air as it enters the stratosphere. This is because particles large enough to have significant fall speeds can form under the conditions of slow cooling that are implied by the large horizontal extent of the SVC sheets. The IR radiation that these clouds absorb, and the upward vertical motion this implies, also makes them candidates for a tropical troposphere-to-stratosphere mass transfer mechanism. They may also play a role in the earth's radiation budget. These sheets were observed on five flights during the Tropical Ozone Transport Experiment (TOTE) by the NASA Langley DIAL lidar aboard NASA's DC-8 research aircraft operating during December 1995 and February 1996 south of Hawaii. The relationship of the SVC's observed during TOTE to convection was not a simple one. One class of SVC's are within 1000 km of the persistent strong convection near 15S (the SPCZ). Trajectory analyses indicated that the SVC air masses have in fact passed through the SPCZ within a few days of observation. These clouds are very close to the tropopause, with maximum potential temperatures not much higher than 370K, consistent with in situ water and total water measurements near the tropopause made during the Stratosphere Troposphere Exchange Project in January 1987 at Darwin, Australia. A second class of SVC's are not immediately downstream of convection. These clouds tend to be higher, reaching potential temperatures of 390K or more. Trajectory analyses indicate that the air in these SVC's originates either in the equatorial western Pacific or along the subtropical jet. In any case, the warm temperatures the SVC air masses encounter just prior to the observation time along the back trajectory imply that the clouds cannot be residual particles from cirrus blowoff, but must form locally as the air move upward and equatorward south of Hawaii. Since all the parcels have encountered colder temperatures than those at the time of observation early in their history, subsynoptic scale temperatures colder than the analysis temperatures appear to be required to explain the formation of ice particles. In fact, the sloping shapes of the SVC's do suggest that they are gravity or inertia-gravity waves. In situ meteorological measurements made by the ER-2 within a day of the DC-8 remote lidar observations show a gravity wave structure near the equator with an estimated period of about 30 hours. This is sufficiently long to allow large particles to form and fall out (thus allowing dehydration). Other ER-2 flights south of Hawaii at other times of year show gravity and inertia-gravity waves with a poleward wavenumber component and significant (5 degrees peak to peak) temperature perturbation.

9 CFR 53.10 - Claims not allowed.

Code of Federal Regulations, 2014 CFR

2014-01-01

... destruction of fish due to infectious salmon anemia (ISA) unless the claimants have agreed in writing to... water and are free of wild carp and any other SVC-susceptible species; and (iii) Prevented the migration of wild carp and any other wild SVC-susceptible species into their farming establishment. (Approved...

9 CFR 53.10 - Claims not allowed.

Code of Federal Regulations, 2011 CFR

2011-01-01

... destruction of fish due to infectious salmon anemia (ISA) unless the claimants have agreed in writing to... water and are free of wild carp and any other SVC-susceptible species; and (iii) Prevented the migration of wild carp and any other wild SVC-susceptible species into their farming establishment. (Approved...

9 CFR 53.10 - Claims not allowed.

Code of Federal Regulations, 2013 CFR

2013-01-01

... destruction of fish due to infectious salmon anemia (ISA) unless the claimants have agreed in writing to... water and are free of wild carp and any other SVC-susceptible species; and (iii) Prevented the migration of wild carp and any other wild SVC-susceptible species into their farming establishment. (Approved...

9 CFR 53.10 - Claims not allowed.

Code of Federal Regulations, 2010 CFR

2010-01-01

... destruction of fish due to infectious salmon anemia (ISA) unless the claimants have agreed in writing to... water and are free of wild carp and any other SVC-susceptible species; and (iii) Prevented the migration of wild carp and any other wild SVC-susceptible species into their farming establishment. (Approved...

9 CFR 53.10 - Claims not allowed.

Code of Federal Regulations, 2012 CFR

2012-01-01

... destruction of fish due to infectious salmon anemia (ISA) unless the claimants have agreed in writing to... water and are free of wild carp and any other SVC-susceptible species; and (iii) Prevented the migration of wild carp and any other wild SVC-susceptible species into their farming establishment. (Approved...

The mean-square error optimal linear discriminant function and its application to incomplete data vectors

NASA Technical Reports Server (NTRS)

Walker, H. F.

1979-01-01

In many pattern recognition problems, data vectors are classified although one or more of the data vector elements are missing. This problem occurs in remote sensing when the ground is obscured by clouds. Optimal linear discrimination procedures for classifying imcomplete data vectors are discussed.

9 CFR 93.903 - Import permits for live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2010 CFR

2010-01-01

..., fertilized eggs, and gametes. 93.903 Section 93.903 Animals and Animal Products ANIMAL AND PLANT HEALTH... General Provisions for Svc-Regulated Fish Species § 93.903 Import permits for live fish, fertilized eggs, and gametes. (a) Live fish, fertilized eggs, or gametes of SVC-susceptible species imported into the...

9 CFR 93.903 - Import permits for live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2011 CFR

2011-01-01

..., fertilized eggs, and gametes. 93.903 Section 93.903 Animals and Animal Products ANIMAL AND PLANT HEALTH... General Provisions for Svc-Regulated Fish Species § 93.903 Import permits for live fish, fertilized eggs, and gametes. (a) Live fish, fertilized eggs, or gametes of SVC-susceptible species imported into the...

Integer-Linear-Programing Optimization in Scalable Video Multicast with Adaptive Modulation and Coding in Wireless Networks

PubMed Central

Lee, Chaewoo

2014-01-01

The advancement in wideband wireless network supports real time services such as IPTV and live video streaming. However, because of the sharing nature of the wireless medium, efficient resource allocation has been studied to achieve a high level of acceptability and proliferation of wireless multimedia. Scalable video coding (SVC) with adaptive modulation and coding (AMC) provides an excellent solution for wireless video streaming. By assigning different modulation and coding schemes (MCSs) to video layers, SVC can provide good video quality to users in good channel conditions and also basic video quality to users in bad channel conditions. For optimal resource allocation, a key issue in applying SVC in the wireless multicast service is how to assign MCSs and the time resources to each SVC layer in the heterogeneous channel condition. We formulate this problem with integer linear programming (ILP) and provide numerical results to show the performance under 802.16 m environment. The result shows that our methodology enhances the overall system throughput compared to an existing algorithm. PMID:25276862

Comparison of superior vena caval and inferior vena caval access using a radioisotope technique during normal perfusion and cardiopulmonary resuscitation

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

Dalsey, W.C.; Barsan, W.G.; Joyce, S.M.

1984-10-01

Recent studies of thoracic pressure changes during external cardiopulmonary resuscitation (CPR) suggest that there may be a significant difference in the rate of delivery of intravenous drugs when they are administered through the extrathoracic inferior vena cava (IVC) rather than the intrathoracic superior vena cava (SVC). Comparison of delivery of a radionuclide given using superior and inferior vena caval access sites was made during normal blood flow and during CPR. Mean times from injection to peak emission count in each ventricle were determined. There were no significant differences between mean peak times for SVC or IVC routes during normal flowmore » or CPR. When peak times were corrected for variations in cardiac output, there were no significant differences between IVC and SVC peak times during normal flow. During CPR, however, mean left ventricular peak time, when corrected for cardiac output, was significantly shorter (P less than .05) when the SVC route was used. The mean time for the counts to reach half the ventricular peak was statistically shorter (P less than .05) in both ventricles with the SVC route during the low flow of CPR. This suggests that during CPR, increased drug dispersion may occur when drugs are infused by the IVC route and thus may modify the anticipated effect of the drug bolus. These results suggest that during CPR, both the cardiac output and the choice of venous access are important variables for drug delivery.« less

The assisted bidirectional Glenn: a novel surgical approach for first-stage single-ventricle heart palliation.

PubMed

Esmaily-Moghadam, Mahdi; Hsia, Tain-Yen; Marsden, Alison L

2015-03-01

Outcomes after a modified Blalock-Taussig shunt (mBTS) in neonates with single-ventricle physiology remain unsatisfactory. However, initial palliation with a superior cavopulmonary connection, such as a bidirectional Glenn (BDG), is discouraged, owing to potential for inadequate pulmonary blood flow (PBF). We tested the feasibility of a novel surgical approach, adopting the engineering concept of an ejector pump, whereby the flow in the BDG is "assisted" by injection of a high-energy flow stream from the systemic circulation. Realistic 3-dimensional models of the neonatal mBTS and BDG circulations were created. The "assisted" bidirectional Glenn (ABG) consisted of a shunt between the right innominate artery and the superior vena cava (SVC), with a 1.5-mm clip near the SVC anastomosis to create a Venturi effect. The 3 models were coupled to a validated hydraulic circulation model, and 2 pulmonary vascular resistance (PVR) values (7 and 2.3 Wood units) were simulated. The ABG provided the highest systemic oxygen saturation and oxygen delivery at both PVR levels. In addition to achieving higher PBF than the BDG, the ABG produced a lower single-ventricular workload than mBTS. SVC pressure was highest in the ABG model (ABG: 15; Glenn: 11; mBTS: 3 mm Hg; PVR = 7 Wood units), but at low PVR, the SVC pressure was significantly lower (ABG: 8; Glenn: 6; mBTS: <3 mm Hg). Adopting the principle of an ejector pump, with additional flow directed into the SVC in a BDG, the ABG appears to increase PBF with a modest increase in SVC and pulmonary arterial pressure. Although multiscale modeling results demonstrate the conceptual feasibility of the ABG circulation, further technical refinement and investigations are necessary, especially in an appropriate animal model. Copyright © 2015 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Ultrasound confirmation of central venous catheter position via a right supraclavicular fossa view using a microconvex probe: an observational pilot study.

PubMed

Kim, Se-Chan; Heinze, Ingo; Schmiedel, Alexandra; Baumgarten, Georg; Knuefermann, Pascal; Hoeft, Andreas; Weber, Stefan

2015-01-01

Visualisation of a central venous catheter (CVC) with ultrasound is restricted to the internal jugular vein (IJV). CVC tip position is confirmed by chest radiography, intracardiac ECG or transoesophageal/transthoracic echocardiography (TEE/TTE). We explored the feasibility, safety and accuracy of a right supraclavicular view for visualisation of the lower superior vena cava (SVC) and the right pulmonary artery (RPA) as an ultrasound landmark for real-time ultrasound-guided CVC tip positioning via the right IJV. Ultrasound was then compared with chest radiography. An observational pilot study. Bonn, University Hospital, Germany. From July to October 2012. Fifty-one patients scheduled for elective surgery. Reasons for exclusion were emergency procedure, thrombosis or small IJV lumen and mechanical obstacle to guidewire advancement. In 48 patients, CVC insertion via the right IJV and progress of the guidewire into the lower SVC were continuously guided by an ultrasound transducer in the right supraclavicular fossa. CVC tip position in lower SVC and tip-to-carina distance were assessed with chest radiography as a reference method and additionally with TEE in cardiothoracic patients. Insertion depth was compared with intracardiac ECG and body-height formula. The guidewire tip was seen in the SVC of all patients. In four patients, the tip was not visible in proximity of the RPA. Chest radiography and TEE confirmed CVC tip position in the lower SVC (zone A). Bland-Altman analysis revealed an average of difference of 1.6 cm for ultrasound versus ECG (95% limit of agreement -2 to 5 cm) and an average of difference of 1 cm for ultrasound versus body-height formula (95% limit of agreement -2 to 4 cm). Ultrasound via a right supraclavicular view is a feasible, well tolerated and accurate approach and should be further explored. Chest radiography confirmed CVC position in the lower SVC.

A prospective comparison of phakic collamer lenses and wavefront-optimized laser-assisted in situ keratomileusis for correction of myopia

PubMed Central

Parkhurst, Gregory D

2016-01-01

Purpose The aim of this study was to evaluate and compare night vision and low-luminance contrast sensitivity (CS) in patients undergoing implantation of phakic collamer lenses or wavefront-optimized laser-assisted in situ keratomileusis (LASIK). Patients and methods This is a nonrandomized, prospective study, in which 48 military personnel were recruited. Rabin Super Vision Test was used to compare the visual acuity and CS of Visian implantable collamer lens (ICL) and LASIK groups under normal and low light conditions, using a filter for simulated vision through night vision goggles. Results Preoperative mean spherical equivalent was −6.10 D in the ICL group and −6.04 D in the LASIK group (P=0.863). Three months postoperatively, super vision acuity (SVa), super vision acuity with (low-luminance) goggles (SVaG), super vision contrast (SVc), and super vision contrast with (low luminance) goggles (SVcG) significantly improved in the ICL and LASIK groups (P<0.001). Mean improvement in SVaG at 3 months postoperatively was statistically significantly greater in the ICL group than in the LASIK group (mean change [logarithm of the minimum angle of resolution, LogMAR]: ICL =−0.134, LASIK =−0.085; P=0.032). Mean improvements in SVc and SVcG were also statistically significantly greater in the ICL group than in the LASIK group (SVc mean change [logarithm of the CS, LogCS]: ICL =0.356, LASIK =0.209; P=0.018 and SVcG mean change [LogCS]: ICL =0.390, LASIK =0.259; P=0.024). Mean improvement in SVa at 3 months was comparable in both groups (P=0.154). Conclusion Simulated night vision improved with both ICL implantation and wavefront-optimized LASIK, but improvements were significantly greater with ICLs. These differences may be important in a military setting and may also affect satisfaction with civilian vision correction. PMID:27418804

A prospective comparison of phakic collamer lenses and wavefront-optimized laser-assisted in situ keratomileusis for correction of myopia.

PubMed

Parkhurst, Gregory D

2016-01-01

The aim of this study was to evaluate and compare night vision and low-luminance contrast sensitivity (CS) in patients undergoing implantation of phakic collamer lenses or wavefront-optimized laser-assisted in situ keratomileusis (LASIK). This is a nonrandomized, prospective study, in which 48 military personnel were recruited. Rabin Super Vision Test was used to compare the visual acuity and CS of Visian implantable collamer lens (ICL) and LASIK groups under normal and low light conditions, using a filter for simulated vision through night vision goggles. Preoperative mean spherical equivalent was -6.10 D in the ICL group and -6.04 D in the LASIK group (P=0.863). Three months postoperatively, super vision acuity (SVa), super vision acuity with (low-luminance) goggles (SVaG), super vision contrast (SVc), and super vision contrast with (low luminance) goggles (SVcG) significantly improved in the ICL and LASIK groups (P<0.001). Mean improvement in SVaG at 3 months postoperatively was statistically significantly greater in the ICL group than in the LASIK group (mean change [logarithm of the minimum angle of resolution, LogMAR]: ICL =-0.134, LASIK =-0.085; P=0.032). Mean improvements in SVc and SVcG were also statistically significantly greater in the ICL group than in the LASIK group (SVc mean change [logarithm of the CS, LogCS]: ICL =0.356, LASIK =0.209; P=0.018 and SVcG mean change [LogCS]: ICL =0.390, LASIK =0.259; P=0.024). Mean improvement in SVa at 3 months was comparable in both groups (P=0.154). Simulated night vision improved with both ICL implantation and wavefront-optimized LASIK, but improvements were significantly greater with ICLs. These differences may be important in a military setting and may also affect satisfaction with civilian vision correction.

Voter Participation in a Community College Referendum.

ERIC Educational Resources Information Center

Garner, W. Harold; Shapton, Karen

Between 1983 and 1984, tax referenda for Sauk Valley College (SVC) were on the ballot three times. Research conducted before and after the tax referendum failed to pass in November 1983 provided the basis for strategy enhancement that brought SVC closer to its goal in March 1984 and finally to the achievement of the goal in November. In October…

Highly predictive and interpretable models for PAMPA permeability.

PubMed

Sun, Hongmao; Nguyen, Kimloan; Kerns, Edward; Yan, Zhengyin; Yu, Kyeong Ri; Shah, Pranav; Jadhav, Ajit; Xu, Xin

2017-02-01

Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4071 compounds with quantitative data is able to predict the remaining 1364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimizing 18F-FDG PET/CT Imaging of Vessel Wall Inflammation –The Impact of 18F-FDG Circulation Time, Injected Dose, Uptake Parameters, and Fasting Blood Glucose Levels

PubMed Central

Bucerius, Jan; Mani, Venkatesh; Moncrieff, Colin; Machac, Josef; Fuster, Valentin; Farkouh, Michael E.; Tawakol, Ahmed; Rudd, James H. F.; Fayad, Zahi A.

2014-01-01

Purpose 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is increasingly used for imaging of vessel wall inflammation. However, limited data is available regarding the impact of methodological variables, i. e. patient’s pre-scan fasting glucose, the FDG circulation time, the injected FDG dose, and of different FDG uptake parameters, in vascular FDG-PET imaging. Methods 195 patients underwent vascular FDG-PET/CT of the aorta and the carotids. Arterial standard uptake values (meanSUVmax) as well as target-to-background-ratios (meanTBRmax) and the FDG blood pool activity in the superior vein cava (SVC) and the jugular veins (JV) were quantified. Vascular FDG uptake classified according to tertiles of patient’s pre-scan fasting glucose levels, the FDG circulation time, and the injected FDG dose was compared using ANOVA. Multivariate regression analyses were performed to identify the potential impact of all variables described on the arterial and blood pool FDG uptake. Results Tertile analyses revealed FDG circulation times of about 2.5 h and prescan glucose levels of less than 7.0 mmol/l showing favorable relations between the arterial and blood pool FDG uptake. FDG circulation times showed negative associations with the aortic meanSUVmax values as well as SVC- and JV FDG blood pool activity but a positive correlation with the aortic- and carotid meanTBRmax values. Pre-scan glucose was negatively associated with aortic- and carotid meanTBRmax and carotid meanSUVmax values, but correlated positively with the SVC blood pool uptake. Injected FDG dose failed to show any significant association with the vascular FDG uptake. Conclusion FDG circulation times and pre-scan blood glucose levels significantly impact FDG uptake within the aortic and carotid wall and may bias the results of image interpretation in patients undergoing vascular FDG-PET/CT. FDG dose injected was less critical. Therefore, circulation times of about 2.5 h and pre-scan glucose levels less than 7.0 mmol/l should be preferred in this setting. PMID:24271038

The genetic tale of a recovering lion population (Panthera leo) in the Savé Valley region (Zimbabwe): A better understanding of the history and managing the future

PubMed Central

Groom, Rosemary J.; Khuzwayo, Joy; Jansen van Vuuren, Bettine

2018-01-01

The rapid decline of the African lion (Panthera leo) has raised conservation concerns. In the Savé Valley Conservancy (SVC), in the Lowveld of Zimbabwe, lions were presumably reduced to approximately 5 to 10 individuals. After ten lions were reintroduced in 2005, the population has recovered to over 200 lions in 2016. Although the increase of lions in the SVC seems promising, a question remains whether the population is genetically viable, considering their small founding population. In this study, we document the genetic diversity in the SVC lion population using both mitochondrial and nuclear genetic markers, and compare our results to literature from other lion populations across Africa. We also tested whether genetic diversity is spatially structured between lion populations residing on several reserves in the Lowveld of Zimbabwe. A total of 42 lions were genotyped successfully for 11 microsatellite loci. We confirmed that the loss of allelic richness (probably resulting from genetic drift and small number of founders) has resulted in low genetic diversity and inbreeding. The SVC lion population was also found to be genetically differentiated from surrounding population, as a result of genetic drift and restricted natural dispersal due to anthropogenic barriers. From a conservation perspective, it is important to avoid further loss of genetic variability in the SVC lion population and maintain evolutionary potential required for future survival. Genetic restoration through the introduction of unrelated individuals is recommended, as this will increase genetic heterozygosity and improve survival and reproductive fitness in populations. PMID:29415031

The genetic tale of a recovering lion population (Panthera leo) in the Savé Valley region (Zimbabwe): A better understanding of the history and managing the future.

PubMed

Tensen, Laura; Groom, Rosemary J; Khuzwayo, Joy; Jansen van Vuuren, Bettine

2018-01-01

The rapid decline of the African lion (Panthera leo) has raised conservation concerns. In the Savé Valley Conservancy (SVC), in the Lowveld of Zimbabwe, lions were presumably reduced to approximately 5 to 10 individuals. After ten lions were reintroduced in 2005, the population has recovered to over 200 lions in 2016. Although the increase of lions in the SVC seems promising, a question remains whether the population is genetically viable, considering their small founding population. In this study, we document the genetic diversity in the SVC lion population using both mitochondrial and nuclear genetic markers, and compare our results to literature from other lion populations across Africa. We also tested whether genetic diversity is spatially structured between lion populations residing on several reserves in the Lowveld of Zimbabwe. A total of 42 lions were genotyped successfully for 11 microsatellite loci. We confirmed that the loss of allelic richness (probably resulting from genetic drift and small number of founders) has resulted in low genetic diversity and inbreeding. The SVC lion population was also found to be genetically differentiated from surrounding population, as a result of genetic drift and restricted natural dispersal due to anthropogenic barriers. From a conservation perspective, it is important to avoid further loss of genetic variability in the SVC lion population and maintain evolutionary potential required for future survival. Genetic restoration through the introduction of unrelated individuals is recommended, as this will increase genetic heterozygosity and improve survival and reproductive fitness in populations.

Outcomes of video-assisted thoracoscopic surgery for transvenous lead extraction.

PubMed

Dai, Mingyan; Joyce, David L; Blackmon, Shanda; Friedman, M P H Paul A; Espinosa, Raul; Osborn, Michael J; Huang, Congxin; Cha, Yong-Mei

2018-06-02

To evaluate the outcomes of video-assisted thoracoscopic surgery (VATS) during transvenous lead extractions (TLEs). Ninety-one high-risk patients who underwent TLE in the operating room from January 1, 2015, through March 31, 2017, were included in the study. Of these, 9 patients underwent VATS during TLE. Their clinical characteristics, indications for lead extraction, and complications associated with TLE in the 9 patients who had VATS were compared with those for the 82 patients who did not have VATS. The mean (SD) age of the study patients was 61 (17) years (64.8% were male). The lead dwell time, number of leads extracted, and clinical comorbidities were similar between the two groups. Superior vena cava (SVC) tear occurred in 2 of the 9 patients in VATS group and in 1 of the 82 in the non-VATS group (22.2% vs 1.2%, P = .03). Of the 2 patients in the VATS group who had SVC tears, in 1 the tear was visualized immediately and there was no hemodynamic compromise. In the other patient, the SVC tear was within the pericardium; the blood pressure recovered quickly after sternotomy and repair. Both patients had complete lead extraction and survived hospitalization. The patient in the non-VATS group who had an SVC tear had a successful repair but died of postoperative complications. Utilization of VATS to facilitate TLE is beneficial for early recognition of SVC tear and timely surgical repair in select high-risk patients. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Gateway to Healthcare Careers for Vulnerable Students: A New Approach to the Teaching of Anatomy and Physiology

ERIC Educational Resources Information Center

DeCiccio, Albert; Kenny, Tammy; Lippacher, Linda; Flanary, Barry

2011-01-01

At Southern Vermont College (SVC) and at the nation's other colleges and universities, Anatomy and Physiology I (A&PI) is the gateway course into healthcare careers. Disturbingly, at SVC and elsewhere, many first-year students interested in healthcare careers do not succeed in this course. They withdraw from the course or the institution, or…

The Student Voice Collaborative: An Effort to Systematize Student Participation in School and District Improvement

ERIC Educational Resources Information Center

Sussman, Ari

2015-01-01

This chapter recounts the first 3 years of the Student Voice Collaborative (SVC) in New York City, a district supported student leadership initiative that engages high school aged youth in school reform work at school and district levels. Based on his experiences developing and running the SVC, the author identifies nine design and implementation…

Profiles of digestive enzymes of two competing planktivores, silver carp and gizzard shad, differ

USGS Publications Warehouse

Amberg, Jon J.; Jensen, Nathan R.; Erickson, Richard A.; Sauey, Blake W.; Jackson, Craig

2018-01-01

Typically, studies in digestive physiology in fish focus on a few enzymes and provide insight into the specific processes of the enzyme in a targeted species. Comparative studies assessing a wide number of digestive enzymes on fishes that compete for food resources are lacking, especially in the context of an introduced species. It is generally thought that the invasive silver carp (SVC; Hypophthalmichthys molitrix) directly compete for food resources with the native gizzard shad (GZS; Dorosoma cepedianum) in waters where they coexist. We compared 19 digestive enzymes between SVC and GZS throughout a year and in two rivers in the Midwestern United States: Illinois River and Wabash River. All digestive enzymes analyzed were detected in both SVC and GZS in both rivers. However, the profiles of the digestive enzymes varied by species. Alkaline phosphatase, valine arylamidase, acid phosphatase, naphthol-AS-BI-phosphohydrolase and N-acetyl-β-glucosaminidase were all much higher in SVC than in GZS. Differences between digestive enzyme profiles were also observed between rivers and months. This study demonstrates the utility of using an ecological approach to compare physiological features in fishes.

NMR Study of the New Magnetic Superconductor CaK(Fe 0:951Ni0:049) 4As 4: Microscopic Coexistence of Hedgehog Spin-vortex Crystal and Superconductivity

DOE PAGES

Ding, Q. P.; Meier, W. R.; Bohmer, A. E.; ...

2017-12-29

Coexistence of a new-type antiferromagnetic (AFM) state, the so-called hedgehog spin-vortex crystal (SVC), and superconductivity (SC) is evidenced by 75As nuclear magnetic resonance study on single-crystalline CaK(Fe 0:951Ni0:049) 4As 4. The hedgehog SVC order is clearly demonstrated by the direct observation of the internal magnetic induction along the c axis at the As1 site (close to K) and a zero net internal magnetic induction at the As2 site (close to Ca) below an AFM ordering temperature T N ~ 52 K. The nuclear spin-lattice relaxation rate 1/T 1 shows a distinct decrease below T c ~ 10 K, providing alsomore » unambiguous evidence for the microscopic coexistence. Furthermore, based on the analysis of the 1/T 1 data, the hedgehog SVC-type spin correlations are found to be enhanced below T ~ 150 K in the paramagnetic state. Furthermore, these results indicate the hedgehog SVC-type spin correlations play an important role for the appearance of SC in the new magnetic superconductor.« less

Video traffic characteristics of modern encoding standards: H.264/AVC with SVC and MVC extensions and H.265/HEVC.

PubMed

Seeling, Patrick; Reisslein, Martin

2014-01-01

Video encoding for multimedia services over communication networks has significantly advanced in recent years with the development of the highly efficient and flexible H.264/AVC video coding standard and its SVC extension. The emerging H.265/HEVC video coding standard as well as 3D video coding further advance video coding for multimedia communications. This paper first gives an overview of these new video coding standards and then examines their implications for multimedia communications by studying the traffic characteristics of long videos encoded with the new coding standards. We review video coding advances from MPEG-2 and MPEG-4 Part 2 to H.264/AVC and its SVC and MVC extensions as well as H.265/HEVC. For single-layer (nonscalable) video, we compare H.265/HEVC and H.264/AVC in terms of video traffic and statistical multiplexing characteristics. Our study is the first to examine the H.265/HEVC traffic variability for long videos. We also illustrate the video traffic characteristics and statistical multiplexing of scalable video encoded with the SVC extension of H.264/AVC as well as 3D video encoded with the MVC extension of H.264/AVC.

Optimized bit extraction using distortion modeling in the scalable extension of H.264/AVC.

PubMed

Maani, Ehsan; Katsaggelos, Aggelos K

2009-09-01

The newly adopted scalable extension of H.264/AVC video coding standard (SVC) demonstrates significant improvements in coding efficiency in addition to an increased degree of supported scalability relative to the scalable profiles of prior video coding standards. Due to the complicated hierarchical prediction structure of the SVC and the concept of key pictures, content-aware rate adaptation of SVC bit streams to intermediate bit rates is a nontrivial task. The concept of quality layers has been introduced in the design of the SVC to allow for fast content-aware prioritized rate adaptation. However, existing quality layer assignment methods are suboptimal and do not consider all network abstraction layer (NAL) units from different layers for the optimization. In this paper, we first propose a technique to accurately and efficiently estimate the quality degradation resulting from discarding an arbitrary number of NAL units from multiple layers of a bitstream by properly taking drift into account. Then, we utilize this distortion estimation technique to assign quality layers to NAL units for a more efficient extraction. Experimental results show that a significant gain can be achieved by the proposed scheme.

Video Traffic Characteristics of Modern Encoding Standards: H.264/AVC with SVC and MVC Extensions and H.265/HEVC

PubMed Central

2014-01-01

Video encoding for multimedia services over communication networks has significantly advanced in recent years with the development of the highly efficient and flexible H.264/AVC video coding standard and its SVC extension. The emerging H.265/HEVC video coding standard as well as 3D video coding further advance video coding for multimedia communications. This paper first gives an overview of these new video coding standards and then examines their implications for multimedia communications by studying the traffic characteristics of long videos encoded with the new coding standards. We review video coding advances from MPEG-2 and MPEG-4 Part 2 to H.264/AVC and its SVC and MVC extensions as well as H.265/HEVC. For single-layer (nonscalable) video, we compare H.265/HEVC and H.264/AVC in terms of video traffic and statistical multiplexing characteristics. Our study is the first to examine the H.265/HEVC traffic variability for long videos. We also illustrate the video traffic characteristics and statistical multiplexing of scalable video encoded with the SVC extension of H.264/AVC as well as 3D video encoded with the MVC extension of H.264/AVC. PMID:24701145

Patency of cavopulmonary connection studied by single phase electron beam computed tomography.

PubMed

Choi, Byoung Wook; Park, Young Hwan; Lee, Jong Kyun; Kim, Dong Joon; Kim, Min Jung; Choe, Kyu Ok

2003-10-01

The shunt patency and anatomic alteration of central PA after cavopulmonary connection was assessed by one phase electron-beam computed tomography (EBCT) METHODS: Thirteen patients that received a bi-directional cavo-pulmonary shunt (BCPS, n = 7) or total cavo-pulmonary connection (TCPC, n = 6) were included. The patency of the shunt and the anatomy of intra-pericardial PA were evaluated by EBCT, and compared by angiography and echocardiography. EBCT accurately evaluated shunt patency and the anatomy of the intra-pericardial PA, except for the incorrect diagnosis of SVC-PA shunt patency and peripheral pulmonary stenosis in two TCPC patients. Both of these patients had bilateral SVC and received either bilateral BCPS or ligation of the left SVC respectively. The baffle between the IVC and PA was partly opacified through a fenestration of the baffle, but was not opacified in two patients without fenestration. EBCT accurately evaluated shunt patency and the anatomy of central PA, however, the accuracy was limited in two cases with bilateral SVC. The opacification of the intra-atrial baffle was insufficient in TCPC cases. Multi-phase CT angiography may overcome this limitation in this patient subset.

Studies of material and process compatibility in developing compact silicon vapor chambers

NASA Astrophysics Data System (ADS)

Cai, Qingjun; Bhunia, Avijit; Tsai, Chialun; Kendig, Martin W.; DeNatale, Jeffrey F.

2013-06-01

The performance and long-term reliability of a silicon vapor chamber (SVC) developed for thermal management of high-power electronics critically depend on compatibility of the component materials. A hermetically sealed SVC presented in this paper is composed of bulk silicon, glass-frit as a bonding agent, lead/tin solder as an interface sealant and a copper charging tube. These materials, in the presence of a water/vapor environment, may chemically react and release noncondensable gas (NCG), which can weaken structural strength and degrade the heat transfer performance with time. The present work reports detailed studies on chemical compatibility of the components and potential solutions to avoid the resulting thermal performance degradation. Silicon surface oxidation and purification of operating liquid are necessary steps to reduce performance degradation in the transient period. A lead-based solder with its low reflow temperature is found to be electrochemically stable in water/vapor environment. High glazing temperature solidifies molecular bonding in glass-frit and mitigates PbO precipitation. Numerous liquid flushes guarantee removal of chemical residual after the charging tube is soldered to SVC. With these improvements on the SVC material and process compatibility, high effective thermal conductivity and steady heat transfer performance are obtained.

NMR Study of the New Magnetic Superconductor CaK(Fe 0:951Ni0:049) 4As 4: Microscopic Coexistence of Hedgehog Spin-vortex Crystal and Superconductivity

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

Ding, Q. P.; Meier, W. R.; Bohmer, A. E.

Coexistence of a new-type antiferromagnetic (AFM) state, the so-called hedgehog spin-vortex crystal (SVC), and superconductivity (SC) is evidenced by 75As nuclear magnetic resonance study on single-crystalline CaK(Fe 0:951Ni0:049) 4As 4. The hedgehog SVC order is clearly demonstrated by the direct observation of the internal magnetic induction along the c axis at the As1 site (close to K) and a zero net internal magnetic induction at the As2 site (close to Ca) below an AFM ordering temperature T N ~ 52 K. The nuclear spin-lattice relaxation rate 1/T 1 shows a distinct decrease below T c ~ 10 K, providing alsomore » unambiguous evidence for the microscopic coexistence. Furthermore, based on the analysis of the 1/T 1 data, the hedgehog SVC-type spin correlations are found to be enhanced below T ~ 150 K in the paramagnetic state. Furthermore, these results indicate the hedgehog SVC-type spin correlations play an important role for the appearance of SC in the new magnetic superconductor.« less

Accuracy of Intravenous Electrocardiography Confirmation of Peripherally Inserted Central Catheter for Parenteral Nutrition.

PubMed

Mundi, Manpreet S; Edakkanambeth Varayil, Jithinraj; McMahon, Megan T; Okano, Akiko; Vallumsetla, Nishanth; Bonnes, Sara L; Andrews, James C; Hurt, Ryan T

2016-04-01

Parenteral nutrition (PN) is a life-saving therapy for patients with intestinal failure. Safe delivery of hyperosmotic solution requires a central venous catheter (CVC) with tip in the lower superior vena cava (SVC) or at the SVC-right atrium (RA) junction. To reduce cost and delay in use of CVC, new techniques such as intravascular electrocardiogram (ECG) are being used for tip confirmation in place of chest x-ray (CXR). The present study assessed for accuracy of ECG confirmation in home PN (HPN). Records for all patients consulted for HPN from December 17, 2014, to June 16, 2015, were reviewed for patient demographics, diagnosis leading to HPN initiation, and ECG and CXR confirmation. CXRs were subsequently reviewed by a radiologist to reassess location of the CVC tip and identify those that should be adjusted. Seventy-three patients were eligible, and after assessment for research authorization and postplacement CXR, 17 patients (30% male) with an age of 54 ± 14 years were reviewed. In all patients, postplacement intravascular ECG reading stated tip in the SVC. However, based on CXR, the location of the catheter tip was satisfactory (low SVC or SVC-RA junction) in 10 of 17 patients (59%). Due to the high osmolality of PN, CVC tip location is of paramount importance. After radiology review of CXR, we noted that 7 of 17 (41%) peripherally inserted central catheter lines were in an unsatisfactory position despite ECG confirmation. With current data available, intravenous ECG confirmation should not be used as the sole source of tip confirmation in patients receiving HPN. © 2016 American Society for Parenteral and Enteral Nutrition.

Superior vena cava syndrome with central venous catheter for chemotherapy treated successfully with fibrinolysis.

PubMed

Guijarro Escribano, J F; Antón, R F; Colmenarejo Rubio, A; Sáenz Cascos, L; Sainz González, F; Alguacil Rodríguez, R

2007-03-01

Recently, there has been an increase in the number of cases of superior vena cava (SVC) syndrome associated with chronic indwelling central venous catheters. Fibrinolytic therapy and endovascular treatment are currently achieving good results. We present a case history of a patient with SVC with a catheter used for chemotherapy, which was successfully treated with catheter-directed (intraclot) infusion thrombolytic therapy with urokinase.

Support vector machines

NASA Technical Reports Server (NTRS)

Garay, Michael J.; Mazzoni, Dominic; Davies, Roger; Wagstaff, Kiri

2004-01-01

Support Vector Machines (SVMs) are a type of supervised learning algorith,, other examples of which are Artificial Neural Networks (ANNs), Decision Trees, and Naive Bayesian Classifiers. Supervised learning algorithms are used to classify objects labled by a 'supervisor' - typically a human 'expert.'.

Lysine acetylation sites prediction using an ensemble of support vector machine classifiers.

PubMed

Xu, Yan; Wang, Xiao-Bo; Ding, Jun; Wu, Ling-Yun; Deng, Nai-Yang

2010-05-07

Lysine acetylation is an essentially reversible and high regulated post-translational modification which regulates diverse protein properties. Experimental identification of acetylation sites is laborious and expensive. Hence, there is significant interest in the development of computational methods for reliable prediction of acetylation sites from amino acid sequences. In this paper we use an ensemble of support vector machine classifiers to perform this work. The experimentally determined acetylation lysine sites are extracted from Swiss-Prot database and scientific literatures. Experiment results show that an ensemble of support vector machine classifiers outperforms single support vector machine classifier and other computational methods such as PAIL and LysAcet on the problem of predicting acetylation lysine sites. The resulting method has been implemented in EnsemblePail, a web server for lysine acetylation sites prediction available at http://www.aporc.org/EnsemblePail/. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

2014 Survivor Experience Survey: Report on Preliminary Results. Fiscal Year 2014, Quarter 4

DTIC Science & Technology

2014-12-01

contact with the survivor, nor an ability to “track” or determine the survivor’s identity. The challenge , given the limitations noted above, was...was done primarily through SARCs, with additional support from UVAs/VAs and Special Victims’ Legal Counsels/Victims’ Legal Counsels (SVC/ VLC ). These...and SVC/ VLC to recruit their assistance in notifying eligible survivors about the survey effort and steps to obtain a ticket number without requiring

Southern Vermont College (SVC) and Wheelock College (WC): 2010 Urban and Rural Healthcare Academy Program (HAP) for College Progress and Workforce Development

ERIC Educational Resources Information Center

DeCiccio, Albert C.

2010-01-01

(Purpose) This is a report about the Urban and Rural Healthcare Academy Pilot Program (HAP) that launched at Southern Vermont College (SVC) and Wheelock College (WC) in summer 2010. HAP enabled 18 vulnerable high school students to learn about how to progress to college, how to transition when they arrive on a college campus, and how to prepare…

Characteristics of the road and surrounding environment in metropolitan shopping strips: association with the frequency and severity of single-vehicle crashes.

PubMed

Stephan, Karen L; Newstead, Stuart V

2014-01-01

Modeling crash risk in urban areas is more complicated than in rural areas due to the complexity of the environment and the difficulty obtaining data to fully characterize the road and surrounding environment. Knowledge of factors that impact crash risk and severity in urban areas can be used for countermeasure development and the design of risk assessment tools for practitioners. This research aimed to identify the characteristics of the road and roadside, surrounding environment, and sociodemographic factors associated with single-vehicle crash (SVC) frequency and severity in complex urban environments, namely, strip shopping center road segments. A comprehensive evidence-based list of data required for measuring the influence of the road, roadside, and other factors on crash risk was developed. The data included a broader range of factors than those traditionally considered in accident prediction models. One hundred and forty-two strip shopping segments located on arterial roads in metropolitan Melbourne, Australia, were identified. Police-reported casualty data were used to determine how many SVC occurred on the segments between 2005 and 2009. Data describing segment characteristics were collected from a diverse range of sources; for example, administrative government databases (traffic volume, speed limit, pavement condition, sociodemographic data, liquor licensing), detailed maps, on-line image sources, and digital images of arterial roads collected for the Victorian state road authority. Regression models for count data were used to identify factors associated with SVC frequency. Logistic regression was used to determine factors associated with serious and fatal outcomes. One hundred and seventy SVC occurred on the 142 selected road segments during the 5-year study period. A range of factors including traffic exposure, road cross section (curves, presence of median), road type, requirement for sharing the road with other vehicle types (trams and bicycles), roadside poles, and local amenities were associated with SVC frequency. A different set of risk factors was associated with the odds of a crash leading to a severe outcome: segment length, road cross section (curves, carriageway width), pavement condition, local amenities and vehicle, and driver factors. The presence of curves was the only factor associated with both SVC frequency and severity. A range of risk factors were associated with SVC frequency and severity in complex urban areas (metropolitan shopping strips), including traditionally studied characteristics such as traffic density and road design but also less commonly studied characteristics such as local amenities. Future behavioral research is needed to further investigate how and why these factors change the risk and severity of crashes before effective countermeasures can be developed.

A Bag of Concepts Approach for Biomedical Document Classification Using Wikipedia Knowledge.

PubMed

Mouriño-García, Marcos A; Pérez-Rodríguez, Roberto; Anido-Rifón, Luis E

2017-01-01

The ability to efficiently review the existing literature is essential for the rapid progress of research. This paper describes a classifier of text documents, represented as vectors in spaces of Wikipedia concepts, and analyses its suitability for classification of Spanish biomedical documents when only English documents are available for training. We propose the cross-language concept matching (CLCM) technique, which relies on Wikipedia interlanguage links to convert concept vectors from the Spanish to the English space. The performance of the classifier is compared to several baselines: a classifier based on machine translation, a classifier that represents documents after performing Explicit Semantic Analysis (ESA), and a classifier that uses a domain-specific semantic an- notator (MetaMap). The corpus used for the experiments (Cross-Language UVigoMED) was purpose-built for this study, and it is composed of 12,832 English and 2,184 Spanish MEDLINE abstracts. The performance of our approach is superior to any other state-of-the art classifier in the benchmark, with performance increases up to: 124% over classical machine translation, 332% over MetaMap, and 60 times over the classifier based on ESA. The results have statistical significance, showing p-values < 0.0001. Using knowledge mined from Wikipedia to represent documents as vectors in a space of Wikipedia concepts and translating vectors between language-specific concept spaces, a cross-language classifier can be built, and it performs better than several state-of-the-art classifiers. Schattauer GmbH.

A Bag of Concepts Approach for Biomedical Document Classification Using Wikipedia Knowledge*. Spanish-English Cross-language Case Study.

PubMed

Mouriño-García, Marcos A; Pérez-Rodríguez, Roberto; Anido-Rifón, Luis E

2017-10-26

The ability to efficiently review the existing literature is essential for the rapid progress of research. This paper describes a classifier of text documents, represented as vectors in spaces of Wikipedia concepts, and analyses its suitability for classification of Spanish biomedical documents when only English documents are available for training. We propose the cross-language concept matching (CLCM) technique, which relies on Wikipedia interlanguage links to convert concept vectors from the Spanish to the English space. The performance of the classifier is compared to several baselines: a classifier based on machine translation, a classifier that represents documents after performing Explicit Semantic Analysis (ESA), and a classifier that uses a domain-specific semantic annotator (MetaMap). The corpus used for the experiments (Cross-Language UVigoMED) was purpose-built for this study, and it is composed of 12,832 English and 2,184 Spanish MEDLINE abstracts. The performance of our approach is superior to any other state-of-the art classifier in the benchmark, with performance increases up to: 124% over classical machine translation, 332% over MetaMap, and 60 times over the classifier based on ESA. The results have statistical significance, showing p-values < 0.0001. Using knowledge mined from Wikipedia to represent documents as vectors in a space of Wikipedia concepts and translating vectors between language-specific concept spaces, a cross-language classifier can be built, and it performs better than several state-of-the-art classifiers.

Application of an Adaptive Control Grid Interpolation Technique to Morphological Vascular Reconstruction: A Component of a Comprehensive Surgical Planning and Evaluation Tool

DTIC Science & Technology

2001-10-25

a lateral tunnel through the right atrium connecting the inferior vena cava with the RPA. The procedure results in a complete bypass of the right...IVC SVC RPA LPA SVC: superior vena cava IVC: inferior vena cava RPA: right pulmonary artery LPA: left pulmonary artery...Abstract – The total cavopulmonary connection (TCPC) is a palliative surgical repair performed on children with a single ventricle (SV

Real-time video streaming using H.264 scalable video coding (SVC) in multihomed mobile networks: a testbed approach

NASA Astrophysics Data System (ADS)

Nightingale, James; Wang, Qi; Grecos, Christos

2011-03-01

Users of the next generation wireless paradigm known as multihomed mobile networks expect satisfactory quality of service (QoS) when accessing streamed multimedia content. The recent H.264 Scalable Video Coding (SVC) extension to the Advanced Video Coding standard (AVC), offers the facility to adapt real-time video streams in response to the dynamic conditions of multiple network paths encountered in multihomed wireless mobile networks. Nevertheless, preexisting streaming algorithms were mainly proposed for AVC delivery over multipath wired networks and were evaluated by software simulation. This paper introduces a practical, hardware-based testbed upon which we implement and evaluate real-time H.264 SVC streaming algorithms in a realistic multihomed wireless mobile networks environment. We propose an optimised streaming algorithm with multi-fold technical contributions. Firstly, we extended the AVC packet prioritisation schemes to reflect the three-dimensional granularity of SVC. Secondly, we designed a mechanism for evaluating the effects of different streamer 'read ahead window' sizes on real-time performance. Thirdly, we took account of the previously unconsidered path switching and mobile networks tunnelling overheads encountered in real-world deployments. Finally, we implemented a path condition monitoring and reporting scheme to facilitate the intelligent path switching. The proposed system has been experimentally shown to offer a significant improvement in PSNR of the received stream compared with representative existing algorithms.

Preferential cephalic redistribution of left ventricular cardiac output during therapeutic hypothermia for perinatal hypoxic-ischemic encephalopathy.

PubMed

Hochwald, Ori; Jabr, Mohammad; Osiovich, Horacio; Miller, Steven P; McNamara, Patrick J; Lavoie, Pascal M

2014-05-01

To determine the relationship between left ventricular cardiac output (LVCO), superior vena cava (SVC) flow, and brain injury during whole-body therapeutic hypothermia. Sixteen newborns with moderate or severe hypoxic-ischemic encephalopathy were studied using echocardiography during and immediately after therapeutic hypothermia. Measures were also compared with 12 healthy newborns of similar postnatal age. Newborns undergoing therapeutic hypothermia also had cerebral magnetic resonance imaging as part of routine clinical care on postnatal day 3-4. LVCO was markedly reduced (mean ± SD 126 ± 38 mL/kg/min) during therapeutic hypothermia, whereas SVC flow was maintained within expected normal values (88 ± 27 mL/kg/min) such that SVC flow represented 70% of the LVCO. The reduction in LVCO during therapeutic hypothermia was mainly accounted by a reduction in heart rate (99 ± 13 vs 123 ± 17 beats/min; P < .001) compared with immediately postwarming in the context of myocardial dysfunction. Neonates with brain injury on magnetic resonance imaging had higher SVC flow prerewarming, compared with newborns without brain injury (P = .013). Newborns with perinatal hypoxic-ischemic encephalopathy showed a preferential systemic-to-cerebral redistribution of cardiac blood flow during whole-body therapeutic hypothermia, which may reflect a lack of cerebral vascular adaptation in newborns with more severe brain injury. Copyright © 2014 Elsevier Inc. All rights reserved.

Sharp Central Venous Recanalization in Hemodialysis Patients: A Single-Institution Experience

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

Arabi, Mohammad, E-mail: marabi2004@hotmail.com; Ahmed, Ishtiaq; Mat’hami, Abdulaziz

PurposeWe report our institutional experience with sharp central venous recanalization in chronic hemodialysis patients who failed standard techniques.Materials and MethodsSince January 2014, a series of seven consecutive patients (four males and three females), mean age 35 years (18–65 years), underwent sharp central venous recanalization. Indications included obtaining hemodialysis access (n = 6) and restoration of superior vena cava (SVC) patency to alleviate occlusion symptoms and restore fistula function (n = 1). The transseptal needle was used for sharp recanalization in six patients, while it could not be introduced in one patient due to total occlusion of the inferior vena cava. Instead, transmediastinal SVC access using Chibamore » needle was obtained.ResultsTechnical success was achieved in all cases. SVC recanalization achieved symptoms’ relief and restored fistula function in the symptomatic patient. One patient underwent arteriovenous fistula creation on the recanalized side 3 months after the procedure. The remaining catheters were functional at median follow-up time of 9 months (1–14 months). Two major complications occurred including a right hemothorax and a small hemopericardium, which were managed by covered stent placement across the perforated SVC.ConclusionSharp central venous recanalization using the transseptal needle is feasible technique in patients who failed standard recanalization procedures. The potential high risk of complications necessitates thorough awareness of anatomy and proper technical preparedness.« less

Comparison of right atrial pressure and central venous pressures measured at various anatomical locations in children.

PubMed

Lin, Ming-Chih; Fu, Yun-Ching; Jan, Sheng-Ling; Chen, Ying-Tsung; Chi, Ching-Shiang

2005-01-01

To compare the right atrial pressure to the central venous pressures measured at different points in spontaneously breathing children and try to find a formula to estimate right atrial pressure by central venous pressure measurement. Fifty-one children, aged 5 +/- 4.7 years, who underwent right heart catheterization were studied. All patients were sedated and breathed naturally. The mean pressure was the electronic mean of nine heart beats calculated by Philips BC4000 digital angiographic system. Mean pressure of the right atrium was compared to those measured at the high superior vena cava (SVC), low SVC, high inferior vena cava (IVC) (T10-11), middle IVC (L1-2), low IVC (L3-4), and iliac vein (L5-S1). Mean pressures of central veins were significantly higher than that of the right atrium (all p<0.01). Adjusted central venous pressures of SVC-0.5, high IVC-1.5, middle IVC-2, low IVC-2.5, and iliac vein-3 (mmHg) had a good agreement with the right atrial pressure. Central venous pressures are significantly higher than the right atrial pressure in spontaneously breathing children. Adjusted pressures of SVC-0.5, high IVC-1.5, middle IVC-2, low IVC-2.5, and iliac vein-3 (mmHg) can accurately reflect the right atrial pressure.

Risk Factors for Intracardiac Thrombosis in the Right Atrium and Superior Vena Cava in Critically Ill Neonates who Required the Installation of a Central Venous Catheter.

PubMed

Ulloa-Ricardez, Alfredo; Romero-Espinoza, Lizett; Estrada-Loza, María de Jesús; González-Cabello, Héctor Jaime; Núñez-Enríquez, Juan Carlos

2016-08-01

Central venous catheter (CVC) installation is essential for the treatment of critically ill neonates; however, it is associated with the development of neonatal intracardiac thrombosis, which is a complication that is associated with a poor prognosis. We aimed to identify specific risk factors for the development of intracardiac thrombosis in the right atrium (RA) and superior vena cava (SVC) related to the use of CVC in critically ill neonates. A case-control study was conducted at the tertiary referral neonatal intensive care unit of the Pediatric Hospital Siglo XXI in Mexico City, Mexico from 2008 to 2013. The included cases (n = 43) were de novo patients with intracardiac thrombosis in the RA and SVC diagnosed by echocardiography. The controls (n = 43) were neonates without intracardiac thrombosis or thrombosis at other sites. A logistic regression analysis was conducted, and odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated. The independent risk factors for intracardiac thrombosis in the RA and SVC were the surgical cut-down insertion technique (OR = 2.98; 95% CI: 1.18-9.10), a maternal history of gestational diabetes/diabetes mellitus (OR = 10.64; 95% CI: 1.13-121.41), Staphylococcus epidermidis infection (OR = 7.09; 95% CI: 1.09-45.92), and CVC placement in the SVC (OR = 5.77; 95% CI: 1.10-30.18). This study allowed us to identify several contributing factors to the development of intracardiac thrombosis in the RA and SVC related to the installation of a CVC in a subgroup of critically ill neonates. Multicenter and well-designed studies with a larger number of patients could help validate our findings and/or identify other risk factors that were not identified in the present study. Copyright © 2015. Published by Elsevier B.V.

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

Combating speckle in SAR images - Vector filtering and sequential classification based on a multiplicative noise model

NASA Technical Reports Server (NTRS)

Lin, Qian; Allebach, Jan P.

1990-01-01

An adaptive vector linear minimum mean-squared error (LMMSE) filter for multichannel images with multiplicative noise is presented. It is shown theoretically that the mean-squared error in the filter output is reduced by making use of the correlation between image bands. The vector and conventional scalar LMMSE filters are applied to a three-band SIR-B SAR, and their performance is compared. Based on a mutliplicative noise model, the per-pel maximum likelihood classifier was derived. The authors extend this to the design of sequential and robust classifiers. These classifiers are also applied to the three-band SIR-B SAR image.

An assessment of support vector machines for land cover classification

USGS Publications Warehouse

Huang, C.; Davis, L.S.; Townshend, J.R.G.

2002-01-01

The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.

Downhill oesophageal variceal bleeding: A rare complication in Behçet's disease-related superior vena cava syndrome.

PubMed

Ennaifer, Rym; B'chir Hamzaoui, Saloua; Larbi, Thara; Romdhane, Hayfa; Abdallah, Maya; Bel Hadj, Najet; M'rad, Sander

2015-03-01

Behçet's disease (BD) is a multisystemic disorder that involves vessels of all sizes. Superior vena cava (SVC) thrombosis is a rare complication that can lead to the development of various collateral pathways. A 31-year-old man presented with SVC syndrome. He had a history of recurrent genital aphthosis. Computed tomography revealed extensive thrombosis of the right internal jugular, axillary, and subclavian veins with collateral circulation. The patient was diagnosed with BD, and he was started on anticoagulation and immunosuppressive therapy. One week later, he presented with haematemesis. Upper gastrointestinal endoscopy disclosed varices in the upper third of the oesophagus with stigmata of recent bleeding. Portal hypertension was ruled out. Anticoagulation therapy was discontinued. He was discharged on immunosuppressive therapy. Bleeding from downhill oesophageal varices should be suspected in any patient presenting with upper gastrointestinal bleeding and a history of SVC syndrome due to BD. Copyright © 2015 Arab Journal of Gastroenterology. Published by Elsevier B.V. All rights reserved.

Hypocalcemic stimulation and nonselective venous sampling for localizing parathyroid adenomas: work in progress.

PubMed

Doppman, J L; Skarulis, M C; Chang, R; Alexander, H R; Bartlett, D; Libutti, S K; Marx, S J; Spiegel, A M

1998-07-01

To evaluate whether the release of parathyroid hormone (PTH) from parathyroid tumors during selective parathyroid arteriography can help localize the tumors. In 20 patients (six men, 14 women; age range, 24-72 years) with parathyroid tumors undergoing parathyroid arteriography after failed surgery, serial measurements of PTH were obtained during selective arteriography with nonionic contrast material. PTH levels were measured in the superior vena cava (SVC) before and at varying times from 20 to 120 seconds after arteriography. A 1.4-fold increase in the PTH level of the postarteriographic SVC samples enabled correct prediction of the site of adenoma in 13 of the 20 patients (65%). Of nine patients with positive arteriograms, eight had positive results of postarteriographic sampling. Of 11 patients with negative arteriograms, five had positive results of postarteriographic sampling. Sampling the SVC for PTH gradients after selective parathyroid arteriography correctly indicated the site of the adenoma in 13 of 20 patients (65%).

SCTP as scalable video coding transport

NASA Astrophysics Data System (ADS)

Ortiz, Jordi; Graciá, Eduardo Martínez; Skarmeta, Antonio F.

2013-12-01

This study presents an evaluation of the Stream Transmission Control Protocol (SCTP) for the transport of the scalable video codec (SVC), proposed by MPEG as an extension to H.264/AVC. Both technologies fit together properly. On the one hand, SVC permits to split easily the bitstream into substreams carrying different video layers, each with different importance for the reconstruction of the complete video sequence at the receiver end. On the other hand, SCTP includes features, such as the multi-streaming and multi-homing capabilities, that permit to transport robustly and efficiently the SVC layers. Several transmission strategies supported on baseline SCTP and its concurrent multipath transfer (CMT) extension are compared with the classical solutions based on the Transmission Control Protocol (TCP) and the Realtime Transmission Protocol (RTP). Using ns-2 simulations, it is shown that CMT-SCTP outperforms TCP and RTP in error-prone networking environments. The comparison is established according to several performance measurements, including delay, throughput, packet loss, and peak signal-to-noise ratio of the received video.

CT-Guided Superior Vena Cava Puncture: A Solution to Re-Establishing Access in Haemodialysis-Related Central Venous Occlusion Refractory to Conventional Endovascular Techniques

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

Khalifa, Mohamed, E-mail: mkhalifa@nhs.net; Patel, Neeral R., E-mail: neeral.patel06@gmail.com; Moser, Steven, E-mail: steven.moser@imperial.nhs.uk

PurposeThe purpose of this technical note is to demonstrate the novel use of CT-guided superior vena cava (SVC) puncture and subsequent tunnelled haemodialysis (HD) line placement in end-stage renal failure (ESRF) patients with central venous obstruction refractory to conventional percutaneous venoplasty (PTV) and wire transgression, thereby allowing resumption of HD.MethodsThree successive ESRF patients underwent CT-guided SVC puncture with subsequent tract recanalisation. Ultrasound-guided puncture of the right internal jugular vein was performed, the needle advanced to the patent SVC under CT guidance, with subsequent insertion of a stabilisation guidewire. Following appropriate tract angioplasty, twin-tunnelled HD catheters were inserted and HD resumed.ResultsNomore » immediate complications were identified. There was resumption of HD in all three patients with a 100 % success rate. One patient’s HD catheter remained in use for 2 years post-procedure, and another remains functional 1 year to the present day. One patient died 2 weeks after the procedure due to pancreatitis-related abdominal sepsis unrelated to the Tesio lines.ConclusionCT-guided SVC puncture and tunnelled HD line insertion in HD-related central venous occlusion (CVO) refractory to conventional recanalisation options can be performed safely, requires no extra equipment and lies within the skill set and resources of most interventional radiology departments involved in the management of HD patients.« less

Optimization of Support Vector Machine (SVM) for Object Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

Support vector machines classifiers of physical activities in preschoolers

USDA-ARS?s Scientific Manuscript database

The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a s...

Fabric wrinkle characterization and classification using modified wavelet coefficients and optimized support-vector-machine classifier

USDA-ARS?s Scientific Manuscript database

This paper presents a novel wrinkle evaluation method that uses modified wavelet coefficients and an optimized support-vector-machine (SVM) classification scheme to characterize and classify wrinkle appearance of fabric. Fabric images were decomposed with the wavelet transform (WT), and five parame...

An implementation of support vector machine on sentiment classification of movie reviews

NASA Astrophysics Data System (ADS)

Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

2018-03-01

With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

Feature detection in satellite images using neural network technology

NASA Technical Reports Server (NTRS)

Augusteijn, Marijke F.; Dimalanta, Arturo S.

1992-01-01

A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.

The use of portable video media vs standard verbal communication in the urological consent process: a multicentre, randomised controlled, crossover trial.

PubMed

Winter, Matthew; Kam, Jonathan; Nalavenkata, Sunny; Hardy, Ellen; Handmer, Marcus; Ainsworth, Hannah; Lee, Wai Gin; Louie-Johnsun, Mark

2016-11-01

To determine if portable video media (PVM) improves patient's knowledge and satisfaction acquired during the consent process for cystoscopy and insertion of a ureteric stent compared to standard verbal communication (SVC), as informed consent is a crucial component of patient care and PVM is an emerging technology that may help improve the consent process. In this multi-centre randomised controlled crossover trial, patients requiring cystoscopy and stent insertion were recruited from two major teaching hospitals in Australia over a 15-month period (July 2014-December 2015). Patient information delivery was via PVM and SVC. The PVM consisted of an audio-visual presentation with cartoon animation presented on an iPad. Patient satisfaction was assessed using the validated Client Satisfaction Questionnaire 8 (CSQ-8; maximum score 32) and knowledge was tested using a true/false questionnaire (maximum score 28). Questionnaires were completed after first intervention and after crossover. Scores were analysed using the independent samples t-test and Wilcoxon signed-rank test for the crossover analysis. In all, 88 patients were recruited. A significant 3.1 point (15.5%) increase in understanding was demonstrable favouring the use of PVM (P < 0.001). There was no difference in patient satisfaction between the groups as judged by the CSQ-8. A significant 3.6 point (17.8%) increase in knowledge score was seen when the SVC group were crossed over to the PVM arm. A total of 80.7% of patients preferred PVM and 19.3% preferred SVC. Limitations include the lack of a validated questionnaire to test knowledge acquired from the interventions. This study demonstrates patients' preference towards PVM in the urological consent process of cystoscopy and ureteric stent insertion. PVM improves patient's understanding compared with SVC and is a more effective means of content delivery to patients in terms of overall preference and knowledge gained during the consent process. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

Progressive Classification Using Support Vector Machines

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri; Kocurek, Michael

2009-01-01

An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user can halt this reclassification process at any point, thereby obtaining the best possible result for a given amount of computation time. Alternatively, the results can be displayed as they are generated, providing the user with real-time feedback about the current accuracy of classification.

Right Site, Wrong Route - Cannulating the Left Internal Jugular Vein.

PubMed

Paik, Peter; Arukala, Sanjay K; Sule, Anupam A

2018-01-09

Central venous catheters are placed in approximately five million patients annually in the US. The preferred site of insertion is one with fewer risks and easier access. Although the right internal jugular vein is preferred, on occasion, the left internal jugular may have to be accessed. A patient was admitted for septic shock, cerebrovascular accident, and non-ST-segment elevation myocardial infarction. A central venous line was needed for antibiotic and vasopressor administration. Due to trauma from a fall to the right side and previously failed catheterization attempts at the left subclavian and femoral veins, the left internal jugular vein was accessed. On chest radiography for confirmation, the left internal jugular central venous catheter was seen projecting down the left paraspinal region. It did not take the expected course across the midline toward the right and into the superior vena cava (SVC). A review of a computed tomography (CT) scan of the chest with contrast done on a prior admission revealed a duplicated SVC on the left side that had not been reported in the original CT scan interpretation. A left-sided SVC is present in approximately 0.3% to 0.5% of the population, with 90% of these draining into the coronary sinus. During placements of central venous lines and pacemakers, irritation of the coronary sinus may result in hypotension, arrhythmia, myocardial ischemia, or cardiac arrest. A widened mediastinum can be an indication of a duplicated SVC. When attempting a left internal jugular vein central venous catheter placement, it is important to be aware of venous anomalies in order to prevent complications.

Relationship between lung function and grip strength in older hospitalized patients: a pilot study

PubMed Central

Holmes, Sarah J; Allen, Stephen C; Roberts, Helen C

2017-01-01

Objective Older people with reduced respiratory muscle strength may be misclassified as having COPD on the basis of spirometric results. We aimed to evaluate the relationship between lung function and grip strength in older hospitalized patients without known airways disease. Methods Patients in acute medical wards were recruited who were aged ≥70 years; no history, symptoms, or signs of respiratory disease; Mini Mental State Examination ≥24; willing and able to consent to participate; and able to perform hand grip and forced spirometry. Data including lung function (forced expiratory volume in 1 second [FEV1], forced vital capacity [FVC], FEV1/FVC, peak expiratory flow rate [PEFR], and slow vital capacity [SVC]), grip strength, age, weight, and height were recorded. Data were analyzed using descriptive statistics and linear regression unadjusted and adjusted (for age, height, and weight). Results A total of 50 patients (20 men) were recruited. Stronger grip strength in men was significantly associated with greater FEV1, but this was attenuated by adjustment for age, height, and weight. Significant positive associations were found in women between grip strength and both PEFR and SVC, both of which remained robust to adjustment. Conclusion The association between grip strength and PEFR and SVC may reflect stronger patients generating higher intrathoracic pressure at the start of spirometry and pushing harder against thoracic cage recoil at end-expiration. Conversely, patients with weaker grip strength had lower PEFR and SVC. These patients may be misclassified as having COPD on the basis of spirometric results. PMID:28458532

Single-Coil Defibrillator Leads Yield Satisfactory Defibrillation Safety Margin in Hypertrophic Cardiomyopathy.

PubMed

Okamura, Hideo; Friedman, Paul A; Inoue, Yuko; Noda, Takashi; Aiba, Takeshi; Yasuda, Satoshi; Ogawa, Hisao; Kamakura, Shiro; Kusano, Kengo; Espinosa, Raul E

2016-09-23

Single-coil defibrillator leads have gained favor because of their potential ease of extraction. However, a high defibrillation threshold remains a concern in patients with hypertrophic cardiomyopathy (HCM), and in many cases, dual-coil leads have been used for this patient group. There is little data on using single-coil leads for HCM patients. We evaluated 20 patients with HCM who received an implantable cardioverter-defibrillator (ICD) on the left side in combination with a dual-coil lead. Two sets of defibrillation tests were performed in each patient, one with the superior vena cava (SVC) coil "on" and one with the SVC coil "off". ICDs were programmed to deliver 25 joules (J) for the first attempt followed by maximum energy (35 J or 40 J). Shock impedance and shock pulse width at 25 J in each setting as well as the results of the shock were analyzed. All 25-J shocks in both settings successfully terminated ventricular fibrillation. However, shock impedance and pulse width increased substantially with the SVC coil programmed "off" compared with "on" (66.4±6.1 ohm and 14.0±1.3 ms "off" vs. 41.9±5.0 ohm and 9.3±0.8 ms "on", P<0.0001 respectively). Biphasic 25-J shocks with the SVC coil 'off' successfully terminated ventricular fibrillation in HCM patients, indicating a satisfactory safety margin for 35-J devices. Single-coil leads appear appropriate for left-sided implantation in this patient group. (Circ J 2016; 80: 2199-2203).

Anatomic bifurcated reconstruction of chronic bilateral innominate-superior vena cava occlusion using the Y-stenting technique.

PubMed

Amin, Parth; Sharafuddin, Mel J; Laurich, Chad; Nicholson, Rachael M; Sun, Raphael C; Roh, Simon; Kresowik, Timothy F; Sharp, William J

2012-02-01

This article presents the case of a 42-year-old man who presented with superior vena cava (SVC) syndrome due to fibrosing mediastinitis with multiple failed attempts at recanalization. We initially treated him with unilateral sharp needle recanalization of the right innominate vein into the SVC stump followed by stenting. Although his symptoms improved immediately, they did not completely resolve. Six months later, he returned with worsening symptoms, and venography revealed in-stent restenosis. The patient requested simultaneous treatment on the left side. The right stent was dilated, and a 3-cm-long occlusion of the left innominate vein was recanalized, again using sharp needle technique, homing into the struts of the right-sided stent. Following fenestration of the stent, a second stent was deployed from the left side into the SVC, and the two Y limbs were sequentially dilated to allow a true bifurcation anatomy (figure). The patient had complete resolution of his symptoms and continues to do well 6 months later. Copyright © 2012 Annals of Vascular Surgery Inc. Published by Elsevier Inc. All rights reserved.

Superior Vena Cava as Gateway to Heart: Metastatic Breast Carcinoma Causing Ball in a Loop Metastasis to Right Atrium.

PubMed

Sandhu, Harpreet Singh; Mahendrakar, Sampath Kumar Mahadevappa; Ladhani, Sulaiman Sadruddin; Khan, Azizullah Hafizullah; Loya, Yunus Shafi

2017-07-01

Breast carcinoma is the most common invasive cancer in women worldwide. It metastasizes commonly to bone, lungs, regional lymph nodes and brain. Cardiac metastasis of lung and breast cancers is a known but rare complication of advanced disease with tumour metastasising to pericardium via the locoregional lymphatic system. Here we present a case of 59-year-old female presenting with right upper limb oedema, facial puffiness and features of Superior Vena Cava (SVC) syndrome 15 years after mastectomy and adjuvant chemotherapy, radiotherapy for carcinoma of the right breast. Further evaluation revealed extensive thrombus invading the right internal jugular vein, subclavian vein, SVC with intraluminal extension into right atrium causing ball in a loop obstruction at tricuspid valve. Whole body Positron emission tomography scan confirmed the diagnosis of extensive metastatic disease and patient was managed on palliative therapy. Haematogenous spread and intraluminal growth of metastatic deposits from breast carcinoma 15 years ago is rare and clinical presentation as SVC obstruction has not been reported in our review of literature.

Vision based nutrient deficiency classification in maize plants using multi class support vector machines

NASA Astrophysics Data System (ADS)

Leena, N.; Saju, K. K.

2018-04-01

Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

Effect of excess pore pressure on the long runout of debris flows over low gradient channels: A case study of the Dongyuege debris flow in Nu River, China

NASA Astrophysics Data System (ADS)

Zhou, Zhen-Hua; Ren, Zhe; Wang, Kun; Yang, Kui; Tang, Yong-Jun; Tian, Lin; Xu, Ze-Min

2018-05-01

Debris flows with long reaches are one of the major natural hazards to human life and property on alluvial fans, as shown by the debris flow that occurred in the Dongyuege (DYG) Gully in August 18, 2010, and caused 96 deaths. The travel distance and the runout distance of the DYG large-scale tragic debris flow were 11 km and 9 km, respectively. In particular, the runout distance over the low gradient channel (channel slope < 5°) upstream of the depositional fan apex reached up to 3.3 km. The build-up and maintenance of excess pore pressure in the debris-flow mass might have played a crucial role in the persistence and long runout of the bouldery viscous debris flow. Experiments to measure pore pressure and pore water escape have been carried out by reconstituting the debris flow bodies with the DYG debris flow deposit. The slurrying of the debris is governed by solid volumetric concentration (SVC), and the difference between the lower SVC limit and the upper SVC limit can be defined as debris flow index (Id). Peak value (Kp) and rate of dissipation (R) of relative excess pore pressure are dependent on SVC. Further, the SVC that gives the lowest rate of dissipation is regarded as the optimum SVC (Cvo). The dissipation response of excess pore pressure can be characterized by the R value under Cvo at a given moment (i.e., 0.5 h, 1 h or 2 h later after peak time). The results reveal that a relatively high level of excess pore pressure developed within the DYG debris-flow mass and had a strong persistence capability. Further research shows that the development, peak value and dissipation of excess pore pressure in a mixture of sediment and water are related to the maximum grain size (MGS), gradation and mineralogy of clay-size particles of the sediment. The layer-lattice silicates in clay particles can be the typical clay minerals, including kaolinite, montmorillonite and illite, and also the unrepresentative clay minerals such as muscovite and chlorite. Moreover, small woody debris can also contribute to the slurrying of sediments and maintenance of debris flows in well vegetated mountainous areas and the boulders suspended in debris flows can elevate excess pore pressure and extend debris-flow mobility. The parameters, including Id, Kp, R and etc., are affected by the intrinsic properties of debris. They, therefore, can reflect the slurrying susceptibility of sediments, and can also be applied to the research on the occurrence mechanisms and risk assessment of other debris flows.

Quantum Support Vector Machine for Big Data Classification

NASA Astrophysics Data System (ADS)

Rebentrost, Patrick; Mohseni, Masoud; Lloyd, Seth

2014-09-01

Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.

Extraction and classification of 3D objects from volumetric CT data

NASA Astrophysics Data System (ADS)

Song, Samuel M.; Kwon, Junghyun; Ely, Austin; Enyeart, John; Johnson, Chad; Lee, Jongkyu; Kim, Namho; Boyd, Douglas P.

2016-05-01

We propose an Automatic Threat Detection (ATD) algorithm for Explosive Detection System (EDS) using our multistage Segmentation Carving (SC) followed by Support Vector Machine (SVM) classifier. The multi-stage Segmentation and Carving (SC) step extracts all suspect 3-D objects. The feature vector is then constructed for all extracted objects and the feature vector is classified by the Support Vector Machine (SVM) previously learned using a set of ground truth threat and benign objects. The learned SVM classifier has shown to be effective in classification of different types of threat materials. The proposed ATD algorithm robustly deals with CT data that are prone to artifacts due to scatter, beam hardening as well as other systematic idiosyncrasies of the CT data. Furthermore, the proposed ATD algorithm is amenable for including newly emerging threat materials as well as for accommodating data from newly developing sensor technologies. Efficacy of the proposed ATD algorithm with the SVM classifier is demonstrated by the Receiver Operating Characteristics (ROC) curve that relates Probability of Detection (PD) as a function of Probability of False Alarm (PFA). The tests performed using CT data of passenger bags shows excellent performance characteristics.

Human action classification using procrustes shape theory

NASA Astrophysics Data System (ADS)

Cho, Wanhyun; Kim, Sangkyoon; Park, Soonyoung; Lee, Myungeun

2015-02-01

In this paper, we propose new method that can classify a human action using Procrustes shape theory. First, we extract a pre-shape configuration vector of landmarks from each frame of an image sequence representing an arbitrary human action, and then we have derived the Procrustes fit vector for pre-shape configuration vector. Second, we extract a set of pre-shape vectors from tanning sample stored at database, and we compute a Procrustes mean shape vector for these preshape vectors. Third, we extract a sequence of the pre-shape vectors from input video, and we project this sequence of pre-shape vectors on the tangent space with respect to the pole taking as a sequence of mean shape vectors corresponding with a target video. And we calculate the Procrustes distance between two sequences of the projection pre-shape vectors on the tangent space and the mean shape vectors. Finally, we classify the input video into the human action class with minimum Procrustes distance. We assess a performance of the proposed method using one public dataset, namely Weizmann human action dataset. Experimental results reveal that the proposed method performs very good on this dataset.

Fluoroscopy of spontaneous breathing is more sensitive than phrenic nerve stimulation for detection of right phrenic nerve injury during cryoballoon ablation of atrial fibrillation.

PubMed

Linhart, Markus; Nielson, Annika; Andrié, René P; Mittmann-Braun, Erica L; Stöckigt, Florian; Kreuz, Jens; Nickenig, Georg; Schrickel, Jan W; Lickfett, Lars M

2014-08-01

Right phrenic nerve palsy (PNP) is a typical complication of cryoballoon ablation of the right-sided pulmonary veins (PVs). Phrenic nerve function can be monitored by palpating the abdomen during phrenic nerve pacing from the superior vena cava (SVC pacing) or by fluoroscopy of spontaneous breathing. We sought to compare the sensitivity of these 2 techniques during cryoballoon ablation for detection of PNP. A total of 133 patients undergoing cryoballoon ablation were monitored with both SVC pacing and fluoroscopy of spontaneous breathing during ablation of the right superior PV. PNP occurred in 27/133 patients (20.0%). Most patients (89%) had spontaneous recovery of phrenic nerve function at the end of the procedure or on the following day. Three patients were discharged with persistent PNP. All PNP were detected first by fluoroscopic observation of diaphragm movement during spontaneous breathing, while diaphragm could still be stimulated by SVC pacing. In patients with no recovery until discharge, PNP occurred at a significantly earlier time (86 ± 34 seconds vs. 296 ± 159 seconds, P < 0.001). No recovery occurred in 2/4 patients who were ablated with a 23 mm cryoballoon as opposed to 1/23 patients with a 28 mm cryoballoon (P = 0.049). Fluoroscopic assessment of diaphragm movement during spontaneous breathing is more sensitive for detection PNP as compared to SVC pacing. PNP as assessed by fluoroscopy is frequent (20.0%) and carries a high rate of recovery (89%) until discharge. Early onset of PNP and use of 23 mm cryoballoon are associated with PNP persisting beyond hospital discharge. © 2014 Wiley Periodicals, Inc.

Evaluation of blood flow distribution asymmetry and vascular geometry in patients with Fontan circulation using 4-D flow MRI

PubMed Central

Jarvis, Kelly; Schnell, Susanne; Barker, Alex J.; Garcia, Julio; Lorenz, Ramona; Rose, Michael; Chowdhary, Varun; Carr, James; Robinson, Joshua D.; Rigsby, Cynthia K.; Markl, Michael

2016-01-01

Background Asymmetrical caval to pulmonary blood flow is suspected to cause complications in patients with Fontan circulation. The aim of this study was to test the feasibility of 4-D flow MRI for characterizing the relationship between 3-D blood flow distribution and vascular geometry. Objective We hypothesized that both flow distribution and geometry can be calculated with low interobserver variability and will detect a direct relationship between flow distribution and Fontan geometry. Materials and methods Four-dimensional flow MRI was acquired in 10 Fontan patients (age: 16±4 years [mean ± standard deviation; range 9–21 years]). The Fontan connection was isolated by 3-D segmentation to evaluate flow distribution from the inferior vena cava (IVC) and superior vena cava (SVC) to the left and right pulmonary arteries (LPA, RPA) and to characterize geometry (cross-sectional area, caval offset, vessel angle). Results Flow distribution results indicated SVC flow tended toward the RPA while IVC flow was more evenly distributed (SVC to RPA: 78%±28 [9–100], IVC to LPA: 54%±28 [4–98]). There was a significant relationship between pulmonary artery cross-sectional area and flow distribution (IVC to RPA: R2=0.50, P=0.02; SVC to LPA: R2=0.81, P=0.0004). Good agreement was found between observers and for flow distribution when compared to net flow values. Conclusion Four-dimensional (4-D) flow MRI was able to detect relationships between flow distribution and vessel geometry. Future studies are warranted to investigate the potential of patient specific hemodynamic analysis to improve diagnostic capability. PMID:27350377

A time-frequency classifier for human gait recognition

NASA Astrophysics Data System (ADS)

Mobasseri, Bijan G.; Amin, Moeness G.

2009-05-01

Radar has established itself as an effective all-weather, day or night sensor. Radar signals can penetrate walls and provide information on moving targets. Recently, radar has been used as an effective biometric sensor for classification of gait. The return from a coherent radar system contains a frequency offset in the carrier frequency, known as the Doppler Effect. The movements of arms and legs give rise to micro Doppler which can be clearly detailed in the time-frequency domain using traditional or modern time-frequency signal representation. In this paper we propose a gait classifier based on subspace learning using principal components analysis(PCA). The training set consists of feature vectors defined as either time or frequency snapshots taken from the spectrogram of radar backscatter. We show that gait signature is captured effectively in feature vectors. Feature vectors are then used in training a minimum distance classifier based on Mahalanobis distance metric. Results show that gait classification with high accuracy and short observation window is achievable using the proposed classifier.

Modeling, analysis, control and design application guidelines of Doubly Fed Induction Generator (DFIG) for wind power applications

NASA Astrophysics Data System (ADS)

Masaud, Tarek

Double Fed Induction Generators (DFIG) has been widely used for the past two decades in large wind farms. However, there are many open-ended problems yet to be solved before they can be implemented in some specific applications. This dissertation deals with the general analysis, modeling, control and applications of the DFIG for large wind farm applications. A detailed "d-q" model of DFIG along with other applications is simulated using the MATLAB/Simulink platform. The simulation results have been discussed in detail in both sub-synchronous and super-synchronous mode of operation. An improved vector control strategy based on the rotor flux oriented vector control has been proposed to control the active power output of the DFIG. The new vector control strategy is compared with the stator flux oriented vector control which is commonly used. It is observed that the new improved vector control method provides a better active power tracking accuracy compare with the stator flux oriented vector control. The behavior of the DFIG -based wind farm under the various grid disturbances is also studied in this dissertation. The implementation of the Flexible AC Transmission System devices (FACTS) to overcome the voltage stability issue for such applications is investigated. The study includes the implementation of both a static synchronous compensator (STATCOM), and the static VAR compensator (SVC) as dynamic reactive power compensators at the point of common coupling to support DFIG-based wind farm during disturbances. Integrating FACTS protect the grid connected DFIG-based wind farm from going offline during and after the disturbances. It is found that the both devices improve the transient performance and therefore helps the wind turbine generator system to remain in service during grid faults. A comparison between the performance of the two devices in terms of the amount of reactive power injected, time response and the application cost has been discussed in this dissertation. Finally, the integration of the battery energy storage system (BESS) into a grid connected DFIG- based wind turbine as a proposed solution to smooth out the output power during wind speed variations is also addressed.

Vertebral Uptake of Tc-99m Macroaggregated Albumin (MAA) with SPECT/CT Occurring in Superior Vena Cava Obstruction.

PubMed

Karls, Shawn; Hassoun, Hani; Derbekyan, Vilma

2016-09-01

A 67-year-old male presented with dyspnea for which lung scintigraphy was ordered to rule out pulmonary embolus. Planar images demonstrated abnormal midline uptake of Tc-99m macroaggregated albumin, which SPECT/CT localized to several thoracic vertebrae. Thoracic vertebral uptake on perfusion lung scintigraphy was previously described on planar imaging. Radionuclide venography and contrast-enhanced CT subsequently demonstrated superior vena cava (SVC) obstruction with collateralization through the azygous/hemiazygous system and vertebral venous plexus. SPECT/CT differentiated residual esophageal/tracheal ventilation activity, a clinically insignificant finding, from vertebral uptake indicative of SVC obstruction, a potentially life-threatening condition.

Bandwidth auction for SVC streaming in dynamic multi-overlay

NASA Astrophysics Data System (ADS)

Xiong, Yanting; Zou, Junni; Xiong, Hongkai

2010-07-01

In this paper, we study the optimal bandwidth allocation for scalable video coding (SVC) streaming in multiple overlays. We model the whole bandwidth request and distribution process as a set of decentralized auction games between the competing peers. For the upstream peer, a bandwidth allocation mechanism is introduced to maximize the aggregate revenue. For the downstream peer, a dynamic bidding strategy is proposed. It achieves maximum utility and efficient resource usage by collaborating with a content-aware layer dropping/adding strategy. Also, the convergence of the proposed auction games is theoretically proved. Experimental results show that the auction strategies can adapt to dynamic join of competing peers and video layers.

A Case of Multiple Cardiovascular and Tracheal Anomalies Presented with Wolff-Parkinson-White Syndrome in a Middle-aged Adult.

PubMed

Shi, Hyejin; Sohn, Sungmin; Wang, SungHo; Park, Sungrock; Lee, SangKi; Kim, Song Yi; Jeong, Sun Young; Kim, Changhwan

2017-12-01

Congenital cardiovascular anomalies, such as dextrocardia, persistent left superior vena cava (SVC), and pulmonary artery (PA) sling, are rare disorders. These congenital anomalies can occur alone, or coincide with other congenital malformations. In the majority of cases, congenital anomalies are detected early in life by certain signs and symptoms. A 56-year-old man with no previous medical history was admitted due to recurrent wide QRS complex tachycardia with hemodynamic collapse. A chest radiograph showed dextrocardia. After synchronized cardioversion, an electrocardiogram revealed Wolff-Parkinson-White (WPW) syndrome. Persistent left SVC, PA sling, and right tracheal bronchus were also detected by a chest computed tomography (CT) scan. He was diagnosed with paroxysmal supraventricular tachycardia (PSVT) associated with WPW syndrome, and underwent radiofrequency ablation. We reported the first case of situs solitus dextrocardia coexisting with persistent left SVC, PA sling and right tracheal bronchus presented with WPW and PSVT in a middle-aged adult. In patients with a cardiovascular anomaly, clinicians should consider thorough evaluation of possibly combined cardiovascular and airway malformations and cardiac dysrhythmia. © 2017 The Korean Academy of Medical Sciences.

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine.

PubMed

Wahba, Maram A; Ashour, Amira S; Napoleon, Sameh A; Abd Elnaby, Mustafa M; Guo, Yanhui

2017-12-01

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM). The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features. Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

Breast cancer risk assessment and diagnosis model using fuzzy support vector machine based expert system

NASA Astrophysics Data System (ADS)

Dheeba, J.; Jaya, T.; Singh, N. Albert

2017-09-01

Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.

Combination of support vector machine, artificial neural network and random forest for improving the classification of convective and stratiform rain using spectral features of SEVIRI data

NASA Astrophysics Data System (ADS)

Lazri, Mourad; Ameur, Soltane

2018-05-01

A model combining three classifiers, namely Support vector machine, Artificial neural network and Random forest (SAR) is designed for improving the classification of convective and stratiform rain. This model (SAR model) has been trained and then tested on a datasets derived from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager). Well-classified, mid-classified and misclassified pixels are determined from the combination of three classifiers. Mid-classified and misclassified pixels that are considered unreliable pixels are reclassified by using a novel training of the developed scheme. In this novel training, only the input data corresponding to the pixels in question to are used. This whole process is repeated a second time and applied to mid-classified and misclassified pixels separately. Learning and validation of the developed scheme are realized against co-located data observed by ground radar. The developed scheme outperformed different classifiers used separately and reached 97.40% of overall accuracy of classification.

Local Subspace Classifier with Transform-Invariance for Image Classification

NASA Astrophysics Data System (ADS)

Hotta, Seiji

A family of linear subspace classifiers called local subspace classifier (LSC) outperforms the k-nearest neighbor rule (kNN) and conventional subspace classifiers in handwritten digit classification. However, LSC suffers very high sensitivity to image transformations because it uses projection and the Euclidean distances for classification. In this paper, I present a combination of a local subspace classifier (LSC) and a tangent distance (TD) for improving accuracy of handwritten digit recognition. In this classification rule, we can deal with transform-invariance easily because we are able to use tangent vectors for approximation of transformations. However, we cannot use tangent vectors in other type of images such as color images. Hence, kernel LSC (KLSC) is proposed for incorporating transform-invariance into LSC via kernel mapping. The performance of the proposed methods is verified with the experiments on handwritten digit and color image classification.

Classifying low-grade and high-grade bladder cancer using label-free serum surface-enhanced Raman spectroscopy and support vector machine

NASA Astrophysics Data System (ADS)

Zhang, Yanjiao; Lai, Xiaoping; Zeng, Qiuyao; Li, Linfang; Lin, Lin; Li, Shaoxin; Liu, Zhiming; Su, Chengkang; Qi, Minni; Guo, Zhouyi

2018-03-01

This study aims to classify low-grade and high-grade bladder cancer (BC) patients using serum surface-enhanced Raman scattering (SERS) spectra and support vector machine (SVM) algorithms. Serum SERS spectra are acquired from 88 serum samples with silver nanoparticles as the SERS-active substrate. Diagnostic accuracies of 96.4% and 95.4% are obtained when differentiating the serum SERS spectra of all BC patients versus normal subjects and low-grade versus high-grade BC patients, respectively, with optimal SVM classifier models. This study demonstrates that the serum SERS technique combined with SVM has great potential to noninvasively detect and classify high-grade and low-grade BC patients.

A support vector machine approach for classification of welding defects from ultrasonic signals

NASA Astrophysics Data System (ADS)

Chen, Yuan; Ma, Hong-Wei; Zhang, Guang-Ming

2014-07-01

Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Discrimination of malignant lymphomas and leukemia using Radon transform based-higher order spectra

NASA Astrophysics Data System (ADS)

Luo, Yi; Celenk, Mehmet; Bejai, Prashanth

2006-03-01

A new algorithm that can be used to automatically recognize and classify malignant lymphomas and leukemia is proposed in this paper. The algorithm utilizes the morphological watersheds to obtain boundaries of cells from cell images and isolate them from the surrounding background. The areas of cells are extracted from cell images after background subtraction. The Radon transform and higher-order spectra (HOS) analysis are utilized as an image processing tool to generate class feature vectors of different type cells and to extract testing cells' feature vectors. The testing cells' feature vectors are then compared with the known class feature vectors for a possible match by computing the Euclidean distances. The cell in question is classified as belonging to one of the existing cell classes in the least Euclidean distance sense.

Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier.

PubMed

Amin, Morteza Moradi; Kermani, Saeed; Talebi, Ardeshir; Oghli, Mostafa Ghelich

2015-01-01

Acute lymphoblastic leukemia is the most common form of pediatric cancer which is categorized into three L1, L2, and L3 and could be detected through screening of blood and bone marrow smears by pathologists. Due to being time-consuming and tediousness of the procedure, a computer-based system is acquired for convenient detection of Acute lymphoblastic leukemia. Microscopic images are acquired from blood and bone marrow smears of patients with Acute lymphoblastic leukemia and normal cases. After applying image preprocessing, cells nuclei are segmented by k-means algorithm. Then geometric and statistical features are extracted from nuclei and finally these cells are classified to cancerous and noncancerous cells by means of support vector machine classifier with 10-fold cross validation. These cells are also classified into their sub-types by multi-Support vector machine classifier. Classifier is evaluated by these parameters: Sensitivity, specificity, and accuracy which values for cancerous and noncancerous cells 98%, 95%, and 97%, respectively. These parameters are also used for evaluation of cell sub-types which values in mean 84.3%, 97.3%, and 95.6%, respectively. The results show that proposed algorithm could achieve an acceptable performance for the diagnosis of Acute lymphoblastic leukemia and its sub-types and can be used as an assistant diagnostic tool for pathologists.

A Nearest Neighbor Classifier Employing Critical Boundary Vectors for Efficient On-Chip Template Reduction.

PubMed

Xia, Wenjun; Mita, Yoshio; Shibata, Tadashi

2016-05-01

Aiming at efficient data condensation and improving accuracy, this paper presents a hardware-friendly template reduction (TR) method for the nearest neighbor (NN) classifiers by introducing the concept of critical boundary vectors. A hardware system is also implemented to demonstrate the feasibility of using an field-programmable gate array (FPGA) to accelerate the proposed method. Initially, k -means centers are used as substitutes for the entire template set. Then, to enhance the classification performance, critical boundary vectors are selected by a novel learning algorithm, which is completed within a single iteration. Moreover, to remove noisy boundary vectors that can mislead the classification in a generalized manner, a global categorization scheme has been explored and applied to the algorithm. The global characterization automatically categorizes each classification problem and rapidly selects the boundary vectors according to the nature of the problem. Finally, only critical boundary vectors and k -means centers are used as the new template set for classification. Experimental results for 24 data sets show that the proposed algorithm can effectively reduce the number of template vectors for classification with a high learning speed. At the same time, it improves the accuracy by an average of 2.17% compared with the traditional NN classifiers and also shows greater accuracy than seven other TR methods. We have shown the feasibility of using a proof-of-concept FPGA system of 256 64-D vectors to accelerate the proposed method on hardware. At a 50-MHz clock frequency, the proposed system achieves a 3.86 times higher learning speed than on a 3.4-GHz PC, while consuming only 1% of the power of that used by the PC.

Classification and Compression of Multi-Resolution Vectors: A Tree Structured Vector Quantizer Approach

DTIC Science & Technology

2002-01-01

their expression profile and for classification of cells into tumerous and non- tumerous classes. Then we will present a parallel tree method for... cancerous cells. We will use the same dataset and use tree structured classifiers with multi-resolution analysis for classifying cancerous from non- cancerous ...cells. We have the expressions of 4096 genes from 98 different cell types. Of these 98, 72 are cancerous while 26 are non- cancerous . We are interested

Invariant object recognition based on the generalized discrete radon transform

NASA Astrophysics Data System (ADS)

Easley, Glenn R.; Colonna, Flavia

2004-04-01

We introduce a method for classifying objects based on special cases of the generalized discrete Radon transform. We adjust the transform and the corresponding ridgelet transform by means of circular shifting and a singular value decomposition (SVD) to obtain a translation, rotation and scaling invariant set of feature vectors. We then use a back-propagation neural network to classify the input feature vectors. We conclude with experimental results and compare these with other invariant recognition methods.

Improving P2P live-content delivery using SVC

NASA Astrophysics Data System (ADS)

Schierl, T.; Sánchez, Y.; Hellge, C.; Wiegand, T.

2010-07-01

P2P content delivery techniques for video transmission have become of high interest in the last years. With the involvement of client into the delivery process, P2P approaches can significantly reduce the load and cost on servers, especially for popular services. However, previous studies have already pointed out the unreliability of P2P-based live streaming approaches due to peer churn, where peers may ungracefully leave the P2P infrastructure, typically an overlay networks. Peers ungracefully leaving the system cause connection losses in the overlay, which require repair operations. During such repair operations, which typically take a few roundtrip times, no data is received from the lost connection. While taking low delay for fast-channel tune-in into account as a key feature for broadcast-like streaming applications, the P2P live streaming approach can only rely on a certain media pre-buffer during such repair operations. In this paper, multi-tree based Application Layer Multicast as a P2P overlay technique for live streaming is considered. The use of Flow Forwarding (FF), a.k.a. Retransmission, or Forward Error Correction (FEC) in combination with Scalable video Coding (SVC) for concealment during overlay repair operations is shown. Furthermore the benefits of using SVC over the use of AVC single layer transmission are presented.

Proper projective symmetry in LRS Bianchi type V spacetimes

NASA Astrophysics Data System (ADS)

Shabbir, Ghulam; Mahomed, K. S.; Mahomed, F. M.; Moitsheki, R. J.

2018-04-01

In this paper, we investigate proper projective vector fields of locally rotationally symmetric (LRS) Bianchi type V spacetimes using direct integration and algebraic techniques. Despite the non-degeneracy in the Riemann tensor eigenvalues, we classify proper Bianchi type V spacetimes and show that the above spacetimes do not admit proper projective vector fields. Here, in all the cases projective vector fields are Killing vector fields.

Bidirectional Glenn on cardiopulmonary bypass: A comparison of three techniques.

PubMed

Talwar, Sachin; Kumar, Manikala Vinod; Nehra, Ashima; Malhotra Kapoor, Poonam; Makhija, Neeti; Sreenivas, Vishnubhatla; Choudhary, Shiv Kumar; Airan, Balram

2017-05-01

To analyze the intraoperative and early results of the bidirectional Glenn (BDG) procedure performed on cardiopulmonary bypass (CPB) using three different techniques. Between September 2013 and June 2015, 75 consecutive patients (mean age 42 ± 34.4 months) undergoing BDG were randomly assigned to either technique I: open anastomosis or technique II: superior vena cava (SVC) cannulation or technique III: intermittent SVC clamping. We monitored the cerebral near infrared spectrophotometry (NIRS), SVC pressure, CPB time, intensive care unit (ICU) stay, and neurocognitive function. Patients in technique III had abnormal lower NIRS values during the procedure (57 ± 7.4) compared to techniques I and II (64 ± 7.5 and 61 ± 8.0, P = 0.01). Postoperative SVC pressure in technique III was higher than other two groups (17.6 ± 3.7 mmHg vs. 14.2 ± 3.5 mmHg and 15.3 ± 2.0 mmHg in techniques I and II, respectively = 0.0008). CPB time was highest in technique II (44 ± 18 min) compared to techniques I and III (29 ± 14 min and 38 ± 16 min, P = 0.006), respectively. ICU stay was longer in technique III (30 ± 15 h) compared to the other two techniques (22 ± 8.5 h and 27 ± 8.3 h in techniques I and II, respectively = 0.04). No patient experienced significant neurocognitive dysfunction. All techniques of BDG provided acceptable results. The open technique was faster and its use in smaller children merits consideration. The technique of intermittent clamping should be used as a last resort. © 2017 Wiley Periodicals, Inc.

Dual role of cerebral blood flow in regional brain temperature control in the healthy newborn infant.

PubMed

Iwata, Sachiko; Tachtsidis, Ilias; Takashima, Sachio; Matsuishi, Toyojiro; Robertson, Nicola J; Iwata, Osuke

2014-10-01

Small shifts in brain temperature after hypoxia-ischaemia affect cell viability. The main determinants of brain temperature are cerebral metabolism, which contributes to local heat production, and brain perfusion, which removes heat. However, few studies have addressed the effect of cerebral metabolism and perfusion on regional brain temperature in human neonates because of the lack of non-invasive cot-side monitors. This study aimed (i) to determine non-invasive monitoring tools of cerebral metabolism and perfusion by combining near-infrared spectroscopy and echocardiography, and (ii) to investigate the dependence of brain temperature on cerebral metabolism and perfusion in unsedated newborn infants. Thirty-two healthy newborn infants were recruited. They were studied with cerebral near-infrared spectroscopy, echocardiography, and a zero-heat flux tissue thermometer. A surrogate of cerebral blood flow (CBF) was measured using superior vena cava flow adjusted for cerebral volume (rSVC flow). The tissue oxygenation index, fractional oxygen extraction (FOE), and the cerebral metabolic rate of oxygen relative to rSVC flow (CMRO₂ index) were also estimated. A greater rSVC flow was positively associated with higher brain temperatures, particularly for superficial structures. The CMRO₂ index and rSVC flow were positively coupled. However, brain temperature was independent of FOE and the CMRO₂ index. A cooler ambient temperature was associated with a greater temperature gradient between the scalp surface and the body core. Cerebral oxygen metabolism and perfusion were monitored in newborn infants without using tracers. In these healthy newborn infants, cerebral perfusion and ambient temperature were significant independent variables of brain temperature. CBF has primarily been associated with heat removal from the brain. However, our results suggest that CBF is likely to deliver heat specifically to the superficial brain. Further studies are required to assess the effect of cerebral metabolism and perfusion on regional brain temperature in low-cardiac output conditions, fever, and with therapeutic hypothermia. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

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

Le Blanche, Alain F.; Pautas, Eric; Gouin, Isabelle

Purpose. To evaluate routine use of access sites in the arm for percutaneous caval filter placement (PCFP) in elderly patients. Neck arthritis, patient anxiety, access site thrombosis or fecal/urinary incontinence complicating jugular or femoral access may require alternative access sites in this population. Methods. Access via the right arm was chosen for PCFP (VenaTech LP). The indication for PCFP was deep vein thrombosis, a history of pulmonary embolism, and a contraindication to anticoagulant therapy. Ultrasound-guided puncture was performed after diameter measurement of the arm veins (O{sub AV}). The filter was inserted with standard imaging procedures. Procedural difficulty was graded andmore » compared with O{sub AV} and the angle from the arm vein to the superior vena cava ({alpha}{sub AV/SVC}). Results. Over 2 years, 16 patients (14 women, 2 men) with an average age of 90 years (range 79-97 years) were included in the study. The average O{sub AV} value of the basilic or brachial veins was 4.2 mm (range 3.0-5.1 mm). The minimal O{sub AV} for successful access was determined after the first 15 patients. No hematoma occurred at the puncture sites. The average {alpha}{sub AV/SVC} value was 62 deg. (range 29 deg. - 90 deg.). Arm access was possible in 12 of 16 patients (75%) with O{sub AV} {>=} 3.5 mm and {alpha}{sub AV/SVC} {>=} 29 deg. Every procedure via the arm was graded 'easy' by the operator, regardless of angulation values. Femoral access was used in one case due to the impossibility of traversing the heart (patient no. 2), and jugular access was used in 3 of 16 (19%) patients due to puncture failure (patient no. 4), small O{sub AV} (3 mm) (patient no. 6), and stenosis of the distal right subclavian vein (patient no.16), respectively. Conclusion. PCFP via the arm can be routinely accomplished in patients older than 75 years, provided O{sub AV} {>=} 3.5 mm, and {alpha}{sub AV/SVC} {>=} 200119 d.« less

The relationship between BNP, NTproBNP and echocardiographic measurements of systemic blood flow in very preterm infants.

PubMed

König, K; Guy, K J; Walsh, G; Drew, S M; Watkins, A; Barfield, C P

2014-04-01

Preterm infants are at risk of circulatory compromise following birth. Functional neonatal echocardiography including superior vena cava (SVC) flow is increasingly used in neonatal medicine, and low SVC flow has been associated with adverse outcome. However, echocardiography is not readily available in many neonatal units and B-type natriuretic peptides (BNPs) may be useful in guiding further cardiovascular assessment. This study investigated the relationship between BNP, N-terminal pro-BNP (NTproBNP) and echocardiographic measurements of systemic blood flow in very preterm infants. This is a prospective observational study. Sixty preterm infants <32 weeks gestational age were included after the treating neonatologist had requested an echocardiogram for suspected cardiovascular compromise. BNP and NTproBNP were sampled just before the echocardiogram. Echocardiographic examination included fractional shortening (FS), SVC flow, left and right ventricular output (LVO and RVO). Statistical analysis included simple linear regression of BNP and NTproBNP with echocardiographic measures and multiple regression including potential confounding variables. Mean (s.d.) gestational age at birth was 27(5) (2(1)) weeks, median (interquartile range, IQR) birth weight was 995 (845 to 1175) grams. Neither BNP nor NTproBNP correlated with SVC flow (BNP 95% confidence interval (CI) -0.0014 to 0.013, P=0.12; NTproBNP 95% CI -0.00069 to 0.01, P=0.085); LVO (BNP 95% CI -0.00078 to 0.0072, P=0.11; NTproBNP 95% CI -0.0034 to 0.0034, P=0.99); RVO (BNP 95% CI -0.00066 to 0.0058, P=0.12; NTproBNP 95% CI -0.0012 to 0.0044, P=0.25); or FS (BNP 95% CI -0.053 to 0.051, P=0.96; NTproBNP 95% CI -0.061 to 0.019, P=0.3). Multivariate linear regression did not significantly alter results. In this cohort of very preterm infants, BNP and NTproBNP did not correlate with echocardiographic measurements of systemic blood flow within the first 72 h of life.

Recognizing human activities using appearance metric feature and kinematics feature

NASA Astrophysics Data System (ADS)

Qian, Huimin; Zhou, Jun; Lu, Xinbiao; Wu, Xinye

2017-05-01

The problem of automatically recognizing human activities from videos through the fusion of the two most important cues, appearance metric feature and kinematics feature, is considered. And a system of two-dimensional (2-D) Poisson equations is introduced to extract the more discriminative appearance metric feature. Specifically, the moving human blobs are first detected out from the video by background subtraction technique to form a binary image sequence, from which the appearance feature designated as the motion accumulation image and the kinematics feature termed as centroid instantaneous velocity are extracted. Second, 2-D discrete Poisson equations are employed to reinterpret the motion accumulation image to produce a more differentiated Poisson silhouette image, from which the appearance feature vector is created through the dimension reduction technique called bidirectional 2-D principal component analysis, considering the balance between classification accuracy and time consumption. Finally, a cascaded classifier based on the nearest neighbor classifier and two directed acyclic graph support vector machine classifiers, integrated with the fusion of the appearance feature vector and centroid instantaneous velocity vector, is applied to recognize the human activities. Experimental results on the open databases and a homemade one confirm the recognition performance of the proposed algorithm.

SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

PubMed

Pirooznia, Mehdi; Deng, Youping

2006-12-12

Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image

PubMed Central

Zhong, Xiaomei; Li, Jianping; Dou, Huacheng; Deng, Shijun; Wang, Guofei; Jiang, Yu; Wang, Yongjie; Zhou, Zebing; Wang, Li; Yan, Fei

2013-01-01

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. PMID:23936016

Plausibility assessment of a 2-state self-paced mental task-based BCI using the no-control performance analysis.

PubMed

Faradji, Farhad; Ward, Rabab K; Birch, Gary E

2009-06-15

The feasibility of having a self-paced brain-computer interface (BCI) based on mental tasks is investigated. The EEG signals of four subjects performing five mental tasks each are used in the design of a 2-state self-paced BCI. The output of the BCI should only be activated when the subject performs a specific mental task and should remain inactive otherwise. For each subject and each task, the feature coefficient and the classifier that yield the best performance are selected, using the autoregressive coefficients as the features. The classifier with a zero false positive rate and the highest true positive rate is selected as the best classifier. The classifiers tested include: linear discriminant analysis, quadratic discriminant analysis, Mahalanobis discriminant analysis, support vector machine, and radial basis function neural network. The results show that: (1) some classifiers obtained the desired zero false positive rate; (2) the linear discriminant analysis classifier does not yield acceptable performance; (3) the quadratic discriminant analysis classifier outperforms the Mahalanobis discriminant analysis classifier and performs almost as well as the radial basis function neural network; and (4) the support vector machine classifier has the highest true positive rates but unfortunately has nonzero false positive rates in most cases.

Classifying features in CT imagery: accuracy for some single- and multiple-species classifiers

Treesearch

Daniel L. Schmoldt; Jing He; A. Lynn Abbott

1998-01-01

Our current approach to automatically label features in CT images of hardwood logs classifies each pixel of an image individually. These feature classifiers use a back-propagation artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this type of...

An ultra low power feature extraction and classification system for wearable seizure detection.

PubMed

Page, Adam; Pramod Tim Oates, Siddharth; Mohsenin, Tinoosh

2015-01-01

In this paper we explore the use of a variety of machine learning algorithms for designing a reliable and low-power, multi-channel EEG feature extractor and classifier for predicting seizures from electroencephalographic data (scalp EEG). Different machine learning classifiers including k-nearest neighbor, support vector machines, naïve Bayes, logistic regression, and neural networks are explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. The input to each machine learning classifier is a 198 feature vector containing 9 features for each of the 22 EEG channels obtained over 1-second windows. All classifiers were able to obtain F1 scores over 80% and onset sensitivity of 100% when tested on 10 patients. Among five different classifiers that were explored, logistic regression (LR) proved to have minimum hardware complexity while providing average F-1 score of 91%. Both ASIC and FPGA implementations of logistic regression are presented and show the smallest area, power consumption, and the lowest latency when compared to the previous work.

Power line identification of millimeter wave radar based on PCA-GS-SVM

NASA Astrophysics Data System (ADS)

Fang, Fang; Zhang, Guifeng; Cheng, Yansheng

2017-12-01

Aiming at the problem that the existing detection method can not effectively solve the security of UAV's ultra low altitude flight caused by power line, a power line recognition method based on grid search (GS) and the principal component analysis and support vector machine (PCA-SVM) is proposed. Firstly, the candidate line of Hough transform is reduced by PCA, and the main feature of candidate line is extracted. Then, upport vector machine (SVM is) optimized by grid search method (GS). Finally, using support vector machine classifier optimized parameters to classify the candidate line. MATLAB simulation results show that this method can effectively identify the power line and noise, and has high recognition accuracy and algorithm efficiency.

Leveraging Wikipedia knowledge to classify multilingual biomedical documents.

PubMed

Antonio Mouriño García, Marcos; Pérez Rodríguez, Roberto; Anido Rifón, Luis

2018-05-02

This article presents a classifier that leverages Wikipedia knowledge to represent documents as vectors of concepts weights, and analyses its suitability for classifying biomedical documents written in any language when it is trained only with English documents. We propose the cross-language concept matching technique, which relies on Wikipedia interlanguage links to convert concept vectors between languages. The performance of the classifier is compared to a classifier based on machine translation, and two classifiers based on MetaMap. To perform the experiments, we created two multilingual corpus. The first one, Multi-Lingual UVigoMED (ML-UVigoMED) is composed of 23,647 Wikipedia documents about biomedical topics written in English, German, French, Spanish, Italian, Galician, Romanian, and Icelandic. The second one, English-French-Spanish-German UVigoMED (EFSG-UVigoMED) is composed of 19,210 biomedical abstract extracted from MEDLINE written in English, French, Spanish, and German. The performance of the approach proposed is superior to any of the state-of-the art classifier in the benchmark. We conclude that leveraging Wikipedia knowledge is of great advantage in tasks of multilingual classification of biomedical documents. Copyright © 2018 Elsevier B.V. All rights reserved.

Preferential cephalic redistribution of left ventricular cardiac output during therapeutic hypothermia for perinatal hypoxic-ischemic encephalopathy

PubMed Central

Hochwald, Ori; Jabr, Mohammed; Osiovich, Horacio; Miller, Steven P.; McNamara, Patrick J.; Lavoie, Pascal M.

2015-01-01

Objective To determine the relationship between left ventricular cardiac output (LVCO), superior vena cava (SVC) flow, and brain injury during whole-body therapeutic hypothermia. Study design Sixteen newborns with moderate or severe hypoxic-ischemic encephalopathy were studied using echocardiography during and immediately after therapeutic hypothermia. Measures were also compared with 12 healthy newborns of similar postnatal age. Newborns undergoing therapeutic hypothermia also had a cerebral magnetic resonance imaging as part of routine clinical care on postnatal day 3–4. Results LVCO was markedly reduced (mean+/−SD: 126+/−38 mL/kg/min) during therapeutic hypothermia, whereas SVC flow was maintained within expected normal values (88+/− 27 mL/kg/min) such that it represented 70% of the LVCO. The reduction in LVCO during therapeutic hypothermia was mainly accounted by a reduction in heart rate (99 +/− 13 BPM versus 123 +/− 17 BPM; p<0.001) compared to immediately post-warming, in the context of myocardial dysfunction. Neonates with documented brain injury on MRI showed higher SVC flow pre-rewarming, compared to newborns without brain injury (p=0.013). Conclusion Newborns with perinatal hypoxic-ischemic encephalopathy showed a preferential systemic-to cerebral redistribution of cardiac blood flow during whole-body therapeutic hypothermia, which may reflect a lack of cerebral vascular adaptation in newborns with more severe brain injury. PMID:24582011

A rational design for hepatitis B virus X protein refolding and bioprocess development guided by second virial coefficient studies.

PubMed

Basu, Anindya; Chen, Wei Ning; Leong, Susanna Su Jan

2011-04-01

The hepatitis B virus X (HBx) protein is well known for its role in hepatitis B virus infection that often leads to hepatocellular carcinoma. Despite the clinical importance of HBx, there is little progress in anti-HBx drug development strategies due to shortage of HBx from native sources. Consistent expression of HBx as insoluble inclusion bodies within various expression systems has largely hindered HBx manufacturing via economical biosynthesis routes. Confronted by this roadblock, this study aims to quantitatively understand HBx protein behaviour in solution that will guide the rational development of a refolding-based bioprocess for HBx production. Second virial coefficient (SVC) measurements were employed to study the effects of varying physicochemical parameters on HBx intermolecular protein interaction. The SVC results suggest that covalent HBx aggregates play a key role in protein destabilisation during refolding. The use of an SVC-optimised refolding environment yielded bioactive and soluble HBx proteins from the denatured-reduced inclusion body state. This study provides new knowledge on HBx solubility behaviour in vitro, which is important in structure-function elucidation behaviour of this hydrophobic protein. Importantly, a rational refolding-based Escherichia coli bioprocess that can deliver purified and soluble HBx at large scale is successfully developed, which opens the way for rapid preparation of soluble HBx for further clinical and characterisation studies.

Pathogenesis of spring viremia of carp virus in emerald shiner Notropis atherinoides Rafinesque, fathead minnow Pimephales promelas Rafinesque and white sucker Catostomus commersonii (Lacepede).

PubMed

Misk, E; Garver, K; Nagy, E; Isaac, S; Tubbs, L; Huber, P; Al-Hussinee, L; Lumsden, J S

2016-06-01

Spring viremia of carp (SVC) is a reportable disease to the World Organization of Animal Health (OIE) as it is known to cause significant international economic impact. In Canada, the first and only isolation of SVC virus (SVCV) was in 2006, from common carp Cyprinus carpio L., at Hamilton Harbour, Lake Ontario. The susceptibility of fathead minnow Pimephales promelas Rafinesque, emerald shiner Notropis atherinoides Rafinesque and white sucker Catostomus commersonii (Lacepede) to intraperitoneal injection of the Canadian isolate (HHOcarp06) was evaluated using experimental infection, virus isolation, quantitative reverse transcription polymerase chain reaction (qRT-PCR), histopathology and immunohistochemistry (IHC). Emerald shiner and fathead minnow were most susceptible with 43 and 53% cumulative mortality, respectively, compared with koi at 33%. Quantitative RT-PCR demonstrated that koi had high viral loads throughout the experiment. At 34 days post-infection, SVCV was detected from sampled emerald shiner and white sucker in very low titre and was not detected from fathead minnow. Koi, fathead minnow and emerald shiner had gross lesions typical of SVC disease. The histopathological picture was mostly dominated by necrotic changes in kidney, spleen, liver, pancreas and intestine. IHC further confirmed SVCV infection, and staining was largely correlated with histological lesions. © 2015 John Wiley & Sons Ltd.

Evaluation of a central venous catheter tip placement for superior vena cava–subclavian central venous catheterization using a premeasured length

PubMed Central

Kwon, Hyun-Jung; Jeong, Young-Il; Jun, In-Gu; Moon, Young-Jin; Lee, Yu-Mi

2018-01-01

Abstract Subclavian central venous catheterization is a common procedure for which misplacement of the central venous catheter (CVC) is a frequent complication that can potentially be fatal. The carina is located in the mid-zone of the superior vena cava (SVC) and is considered a reliable landmark for CVC placement in chest radiographs. The C-length, defined as the distance from the edge of the right transverse process of the first thoracic spine to the carina, can be measured in posteroanterior chest radiographs using a picture archiving and communication system. To evaluate the placement of the tip of the CVC in subclavian central venous catheterizations using the C-length, we reviewed the medical records and chest radiographs of 122 adult patients in whom CVC catheterization was performed (from January 2012 to December 2014) via the right subclavian vein using the C-length. The tips of all subclavian CVCs were placed in the SVC using the C-length. No subclavian CVC entered the right atrium. Tip placement was not affected by demographic characteristics such as age, sex, height, weight, and body mass index. The evidence indicates that the C-length on chest radiographs can be used to determine the available insertion length and place the right subclavian CVC tip into the SVC. PMID:29480861

Applying machine-learning techniques to Twitter data for automatic hazard-event classification.

NASA Astrophysics Data System (ADS)

Filgueira, R.; Bee, E. J.; Diaz-Doce, D.; Poole, J., Sr.; Singh, A.

2017-12-01

The constant flow of information offered by tweets provides valuable information about all sorts of events at a high temporal and spatial resolution. Over the past year we have been analyzing in real-time geological hazards/phenomenon, such as earthquakes, volcanic eruptions, landslides, floods or the aurora, as part of the GeoSocial project, by geo-locating tweets filtered by keywords in a web-map. However, not all the filtered tweets are related with hazard/phenomenon events. This work explores two classification techniques for automatic hazard-event categorization based on tweets about the "Aurora". First, tweets were filtered using aurora-related keywords, removing stop words and selecting the ones written in English. For classifying the remaining between "aurora-event" or "no-aurora-event" categories, we compared two state-of-art techniques: Support Vector Machine (SVM) and Deep Convolutional Neural Networks (CNN) algorithms. Both approaches belong to the family of supervised learning algorithms, which make predictions based on labelled training dataset. Therefore, we created a training dataset by tagging 1200 tweets between both categories. The general form of SVM is used to separate two classes by a function (kernel). We compared the performance of four different kernels (Linear Regression, Logistic Regression, Multinomial Naïve Bayesian and Stochastic Gradient Descent) provided by Scikit-Learn library using our training dataset to build the SVM classifier. The results shown that the Logistic Regression (LR) gets the best accuracy (87%). So, we selected the SVM-LR classifier to categorise a large collection of tweets using the "dispel4py" framework.Later, we developed a CNN classifier, where the first layer embeds words into low-dimensional vectors. The next layer performs convolutions over the embedded word vectors. Results from the convolutional layer are max-pooled into a long feature vector, which is classified using a softmax layer. The CNN's accuracy is lower (83%) than the SVM-LR, since the algorithm needs a bigger training dataset to increase its accuracy. We used TensorFlow framework for applying CNN classifier to the same collection of tweets.In future we will modify both classifiers to work with other geo-hazards, use larger training datasets and apply them in real-time.

Breast Cancer Recognition Using a Novel Hybrid Intelligent Method

PubMed Central

Addeh, Jalil; Ebrahimzadeh, Ata

2012-01-01

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three main modules: the feature extraction module, the classifier module, and the optimization module. In the feature extraction module, fuzzy features are proposed as the efficient characteristic of the patterns. In the classifier module, because of the promising generalization capability of support vector machines (SVM), a SVM-based classifier is proposed. In support vector machine training, the hyperparameters have very important roles for its recognition accuracy. Therefore, in the optimization module, the bees algorithm (BA) is proposed for selecting appropriate parameters of the classifier. The proposed system is tested on Wisconsin Breast Cancer database and simulation results show that the recommended system has a high accuracy. PMID:23626945

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2012-09-30

floor 1176 Howell St Newport RI 02842 phone: (401) 832-5749 fax: (401) 832-4441 email: David.Moretti@navy.mil Steve W. Martin SPAWAR...multiclass support vector machine (SVM) classifier was previously developed ( Jarvis et al. 2008). This classifier both detects and classifies echolocation...whales. Here Moretti’s group, especially S. Jarvis , will improve the SVM classifier by resolving confusion between species whose clicks overlap in

Non-coaxial superposition of vector vortex beams.

PubMed

Aadhi, A; Vaity, Pravin; Chithrabhanu, P; Reddy, Salla Gangi; Prabakar, Shashi; Singh, R P

2016-02-10

Vector vortex beams are classified into four types depending upon spatial variation in their polarization vector. We have generated all four of these types of vector vortex beams by using a modified polarization Sagnac interferometer with a vortex lens. Further, we have studied the non-coaxial superposition of two vector vortex beams. It is observed that the superposition of two vector vortex beams with same polarization singularity leads to a beam with another kind of polarization singularity in their interaction region. The results may be of importance in ultrahigh security of the polarization-encrypted data that utilizes vector vortex beams and multiple optical trapping with non-coaxial superposition of vector vortex beams. We verified our experimental results with theory.

A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

NASA Astrophysics Data System (ADS)

Mohan, Dhanya; Kumar, C. Santhosh

2016-03-01

Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2013-09-30

N0001411WX21394 Steve W. Martin SPAWAR Systems Center Pacific 53366 Front St. San Diego, CA 92152-6551 phone: (619) 553-9882 email: Steve.W.Martin...multiclass support vector machine (SVM) classifier was previously developed ( Jarvis et al. 2008). This classifier both detects and classifies echolocation...whales. Here Moretti’s group, particularly S. Jarvis , will improve the SVM classifier by resolving confusion between species whose clicks overlap in

Portable Video Media Versus Standard Verbal Communication in Surgical Information Delivery to Nurses: A Prospective Multicenter, Randomized Controlled Crossover Trial.

PubMed

Kam, Jonathan; Ainsworth, Hannah; Handmer, Marcus; Louie-Johnsun, Mark; Winter, Matthew

2016-10-01

Continuing education of health professionals is important for delivery of quality health care. Surgical nurses are often required to understand surgical procedures. Nurses need to be aware of the expected outcomes and recognize potential complications of such procedures during their daily work. Traditional educational methods, such as conferences and tutorials or informal education at the bedside, have many drawbacks for delivery of this information in a universal, standardized, and timely manner. The rapid uptake of portable media devices makes portable video media (PVM) a potential alternative to current educational methods. To compare PVM to standard verbal communication (SVC) for surgical information delivery and educational training for nurses and evaluate its impact on knowledge acquisition and participant satisfaction. Prospective, multicenter, randomized controlled crossover trial. Two hospitals: Gosford District Hospital and Wyong Hospital. Seventy-two nursing staff (36 at each site). Information delivery via PVM--7-minute video compared to information delivered via SVC. Knowledge acquisition was measured by a 32-point questionnaire, and satisfaction with the method of education delivery was measured using the validated Client Satisfaction Questionnaire (CSQ-8). Knowledge acquisition was higher via PVM compared to SVC 25.9 (95% confidence interval [CI] 25.2-26.6) versus 24.3 (95% CI 23.5-25.1), p = .004. Participant satisfaction was higher with PVM 29.5 (95% CI 28.3-30.7) versus 26.5 (95% CI 25.1-27.9), p = .003. Following information delivery via SVC, participants had a 6% increase in knowledge scores, 24.3 (95% CI 23.5-25.1) versus 25.7 (95% CI 24.9-26.5) p = .001, and a 13% increase in satisfaction scores, 26.5 (95% CI 25.1-27.9) versus 29.9 (95% CI 28.8-31.0) p < .001, when they crossed-over to information delivery via PVM. PVM provides a novel method for providing education to nurses that improves knowledge retention and satisfaction with the educational process. © 2016 Sigma Theta Tau International.

Incremental classification learning for anomaly detection in medical images

NASA Astrophysics Data System (ADS)

Giritharan, Balathasan; Yuan, Xiaohui; Liu, Jianguo

2009-02-01

Computer-aided diagnosis usually screens thousands of instances to find only a few positive cases that indicate probable presence of disease.The amount of patient data increases consistently all the time. In diagnosis of new instances, disagreement occurs between a CAD system and physicians, which suggests inaccurate classifiers. Intuitively, misclassified instances and the previously acquired data should be used to retrain the classifier. This, however, is very time consuming and, in some cases where dataset is too large, becomes infeasible. In addition, among the patient data, only a small percentile shows positive sign, which is known as imbalanced data.We present an incremental Support Vector Machines(SVM) as a solution for the class imbalance problem in classification of anomaly in medical images. The support vectors provide a concise representation of the distribution of the training data. Here we use bootstrapping to identify potential candidate support vectors for future iterations. Experiments were conducted using images from endoscopy videos, and the sensitivity and specificity were close to that of SVM trained using all samples available at a given incremental step with significantly improved efficiency in training the classifier.

Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers

NASA Astrophysics Data System (ADS)

Maier, Oskar; Wilms, Matthias; von der Gablentz, Janina; Krämer, Ulrike; Handels, Heinz

2014-03-01

Automatic segmentation of ischemic stroke lesions in magnetic resonance (MR) images is important in clinical practice and for neuroscientific trials. The key problem is to detect largely inhomogeneous regions of varying sizes, shapes and locations. We present a stroke lesion segmentation method based on local features extracted from multi-spectral MR data that are selected to model a human observer's discrimination criteria. A support vector machine classifier is trained on expert-segmented examples and then used to classify formerly unseen images. Leave-one-out cross validation on eight datasets with lesions of varying appearances is performed, showing our method to compare favourably with other published approaches in terms of accuracy and robustness. Furthermore, we compare a number of feature selectors and closely examine each feature's and MR sequence's contribution.

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

PubMed

Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

2015-08-01

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

A hybrid approach to select features and classify diseases based on medical data

NASA Astrophysics Data System (ADS)

AbdelLatif, Hisham; Luo, Jiawei

2018-03-01

Feature selection is popular problem in the classification of diseases in clinical medicine. Here, we developing a hybrid methodology to classify diseases, based on three medical datasets, Arrhythmia, Breast cancer, and Hepatitis datasets. This methodology called k-means ANOVA Support Vector Machine (K-ANOVA-SVM) uses K-means cluster with ANOVA statistical to preprocessing data and selection the significant features, and Support Vector Machines in the classification process. To compare and evaluate the performance, we choice three classification algorithms, decision tree Naïve Bayes, Support Vector Machines and applied the medical datasets direct to these algorithms. Our methodology was a much better classification accuracy is given of 98% in Arrhythmia datasets, 92% in Breast cancer datasets and 88% in Hepatitis datasets, Compare to use the medical data directly with decision tree Naïve Bayes, and Support Vector Machines. Also, the ROC curve and precision with (K-ANOVA-SVM) Achieved best results than other algorithms

Recognition and Classification of Road Condition on the Basis of Friction Force by Using a Mobile Robot

NASA Astrophysics Data System (ADS)

Watanabe, Tatsuhito; Katsura, Seiichiro

A person operating a mobile robot in a remote environment receives realistic visual feedback about the condition of the road on which the robot is moving. The categorization of the road condition is necessary to evaluate the conditions for safe and comfortable driving. For this purpose, the mobile robot should be capable of recognizing and classifying the condition of the road surfaces. This paper proposes a method for recognizing the type of road surfaces on the basis of the friction between the mobile robot and the road surfaces. This friction is estimated by a disturbance observer, and a support vector machine is used to classify the surfaces. The support vector machine identifies the type of the road surface using feature vector, which is determined using the arithmetic average and variance derived from the torque values. Further, these feature vectors are mapped onto a higher dimensional space by using a kernel function. The validity of the proposed method is confirmed by experimental results.

Applying six classifiers to airborne hyperspectral imagery for detecting giant reed

USDA-ARS?s Scientific Manuscript database

This study evaluated and compared six different image classifiers, including minimum distance (MD), Mahalanobis distance (MAHD), maximum likelihood (ML), spectral angle mapper (SAM), mixture tuned matched filtering (MTMF) and support vector machine (SVM), for detecting and mapping giant reed (Arundo...

Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

PubMed

Alcaide-Leon, P; Dufort, P; Geraldo, A F; Alshafai, L; Maralani, P J; Spears, J; Bharatha, A

2017-06-01

Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma. Seventy-one adult patients with enhancing gliomas and 35 adult patients with primary central nervous system lymphomas were included. The tumors were manually contoured on contrast-enhanced T1WI, and the resulting volumes of interest were mined for textural features and subjected to a support vector machine-based machine-learning protocol. Three readers classified the tumors independently on contrast-enhanced T1WI. Areas under the receiver operating characteristic curves were estimated for each reader and for the support vector machine classifier. A noninferiority test for diagnostic accuracy based on paired areas under the receiver operating characteristic curve was performed with a noninferiority margin of 0.15. The mean areas under the receiver operating characteristic curve were 0.877 (95% CI, 0.798-0.955) for the support vector machine classifier; 0.878 (95% CI, 0.807-0.949) for reader 1; 0.899 (95% CI, 0.833-0.966) for reader 2; and 0.845 (95% CI, 0.757-0.933) for reader 3. The mean area under the receiver operating characteristic curve of the support vector machine classifier was significantly noninferior to the mean area under the curve of reader 1 ( P = .021), reader 2 ( P = .035), and reader 3 ( P = .007). Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma. © 2017 by American Journal of Neuroradiology.

On the use of feature selection to improve the detection of sea oil spills in SAR images

NASA Astrophysics Data System (ADS)

Mera, David; Bolon-Canedo, Veronica; Cotos, J. M.; Alonso-Betanzos, Amparo

2017-03-01

Fast and effective oil spill detection systems are crucial to ensure a proper response to environmental emergencies caused by hydrocarbon pollution on the ocean's surface. Typically, these systems uncover not only oil spills, but also a high number of look-alikes. The feature extraction is a critical and computationally intensive phase where each detected dark spot is independently examined. Traditionally, detection systems use an arbitrary set of features to discriminate between oil spills and look-alikes phenomena. However, Feature Selection (FS) methods based on Machine Learning (ML) have proved to be very useful in real domains for enhancing the generalization capabilities of the classifiers, while discarding the existing irrelevant features. In this work, we present a generic and systematic approach, based on FS methods, for choosing a concise and relevant set of features to improve the oil spill detection systems. We have compared five FS methods: Correlation-based feature selection (CFS), Consistency-based filter, Information Gain, ReliefF and Recursive Feature Elimination for Support Vector Machine (SVM-RFE). They were applied on a 141-input vector composed of features from a collection of outstanding studies. Selected features were validated via a Support Vector Machine (SVM) classifier and the results were compared with previous works. Test experiments revealed that the classifier trained with the 6-input feature vector proposed by SVM-RFE achieved the best accuracy and Cohen's kappa coefficient (87.1% and 74.06% respectively). This is a smaller feature combination with similar or even better classification accuracy than previous works. The presented finding allows to speed up the feature extraction phase without reducing the classifier accuracy. Experiments also confirmed the significance of the geometrical features since 75.0% of the different features selected by the applied FS methods as well as 66.67% of the proposed 6-input feature vector belong to this category.

SVC obstruction

MedlinePlus

The goal of treatment is to relieve the blockage. Diuretics (water pills) or steroids (anti-inflammtory drugs) may be used to temporarily relieve swelling . Other treatment options may include radiation or chemotherapy to shrink the tumor, or surgery to ...

Design of an H.264/SVC resilient watermarking scheme

NASA Astrophysics Data System (ADS)

Van Caenegem, Robrecht; Dooms, Ann; Barbarien, Joeri; Schelkens, Peter

2010-01-01

The rapid dissemination of media technologies has lead to an increase of unauthorized copying and distribution of digital media. Digital watermarking, i.e. embedding information in the multimedia signal in a robust and imperceptible manner, can tackle this problem. Recently, there has been a huge growth in the number of different terminals and connections that can be used to consume multimedia. To tackle the resulting distribution challenges, scalable coding is often employed. Scalable coding allows the adaptation of a single bit-stream to varying terminal and transmission characteristics. As a result of this evolution, watermarking techniques that are robust against scalable compression become essential in order to control illegal copying. In this paper, a watermarking technique resilient against scalable video compression using the state-of-the-art H.264/SVC codec is therefore proposed and evaluated.

Cannulation for veno-venous extracorporeal membrane oxygenation

PubMed Central

2018-01-01

Extracorporeal membrane oxygenation (ECMO) is described as a modified, smaller cardiopulmonary bypass circuit. The veno-venous (VV) ECMO circuit drains venous blood, oxygenate the blood, and pump the blood back into the same venous compartment. Draining and reinfusing in the same compartment means there are a risk of recirculation. The draining position within the venous system, ECMO pump flow, return flow position within the venous system and the patients cardiac output (CO) all have an impact on recirculation. Using two single lumen cannulas or one dual lumen cannula, but also the design of the venous cannula, can have an impact on where within the venous system the cannula is draining blood and will affect the efficiency of the ECMO circuit. VV ECMO can be performed with different cannulation strategies. The use of two single lumen cannulas draining in inferior vena cava (IVC) and reinfusing in superior vena cava (SVC) or draining in SVC and reinfusing in IVC, or one dual lumen cannula inserted in right jugular vein is all possible cannulation strategies. Independent of cannulation strategy there will be a risk of recirculation. Efficiency can be reasonable in either strategy if the cannulas are carefully positioned and monitored during the dynamic procedure of pulmonary disease. The disadvantage draining from IVC only occurs when there is a need for converting from VV to veno-arterial (VA) ECMO, reinfusing in the femoral artery. Then draining from SVC is the most efficient strategy, draining low saturated venous blood, and also means low risk of dual circulation. PMID:29732177

Outcome and management of pacemaker-induced superior vena cava syndrome.

PubMed

Fu, Hai-Xia; Huang, Xin-Miao; Zhong, Li; Osborn, Michael J; Bjarnason, Haraldur; Mulpuru, Siva; Zhao, Xian-Xian; Friedman, Paul A; Cha, Yong-Mei

2014-11-01

We aimed to determine the long-term outcomes of percutaneous lead extraction and stent placement in patients with pacemaker-induced superior vena cava (SVC) syndrome. The study retrospectively screened patients who underwent lead extraction followed by central vein stent implantation at Mayo Clinic (Rochester, MN, USA), from January 2005 to December 2012, to identify the patients with pacemaker-induced SVC syndrome. Demographic, clinical, and follow-up characteristics of those patients were collected from electronic medical records. Six cases were identified. The mean (standard deviation) age was 56 (15) years (male, 67%). All patients had permanent dual-chamber pacemakers, with a mean 11-year history of pacemaker placement. The entire device system was explanted in five patients; one patient had a 21-year-old pacemaker lead that could not be removed. Eight stents were implanted in six patients: five patients had one stent, one patient had three. A new pacemaker system was reimplanted through the stented vein in five patients. Technical success was achieved in all patients, without any complication. Symptoms rapidly resolved in all patients after stent deployment. The mean follow-up duration was 48 months (range, 10-100 months). Three patients remained symptom free. Reintervention with percutaneous balloon venoplasty was successful in three patients with symptom recurrence. Percutaneous stent implantation after lead removal followed by reimplantation of leads is a feasible alternative therapy for pacemaker-induced SVC syndrome, although some cases may require repeat intervention. ©2014 Wiley Periodicals, Inc.

A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine.

PubMed

Xie, Hong-Bo; Huang, Hu; Wu, Jianhua; Liu, Lei

2015-02-01

We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian kernel method which avoids some limitations of the support vector machine (SVM). However, RVM still suffers the difficulty of possible unclassifiable regions in multiclass problems. We propose two fuzzy membership function-based FRVM algorithms to solve such problems, based on experiments conducted on seven healthy subjects and two amputees with six hand motions. Two feature sets, namely, AR model coefficients and room mean square value (AR-RMS), and wavelet transform (WT) features, are extracted from the recorded sEMG signals. Fuzzy support vector machine (FSVM) analysis was also conducted for wide comparison in terms of accuracy, sparsity, training and testing time, as well as the effect of training sample sizes. FRVM yielded comparable classification accuracy with dramatically fewer support vectors in comparison with FSVM. Furthermore, the processing delay of FRVM was much less than that of FSVM, whilst training time of FSVM much faster than FRVM. The results indicate that FRVM classifier trained using sufficient samples can achieve comparable generalization capability as FSVM with significant sparsity in multi-channel sEMG classification, which is more suitable for sEMG-based real-time control applications.

Relevance Vector Machine and Support Vector Machine Classifier Analysis of Scanning Laser Polarimetry Retinal Nerve Fiber Layer Measurements

PubMed Central

Bowd, Christopher; Medeiros, Felipe A.; Zhang, Zuohua; Zangwill, Linda M.; Hao, Jiucang; Lee, Te-Won; Sejnowski, Terrence J.; Weinreb, Robert N.; Goldbaum, Michael H.

2010-01-01

Purpose To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP). Methods Seventy-two eyes of 72 healthy control subjects (average age = 64.3 ± 8.8 years, visual field mean deviation =−0.71 ± 1.2 dB) and 92 eyes of 92 patients with glaucoma (average age = 66.9 ± 8.9 years, visual field mean deviation =−5.32 ± 4.0 dB) were imaged with SLP with variable corneal compensation (GDx VCC; Laser Diagnostic Technologies, San Diego, CA). RVM and SVM learning classifiers were trained and tested on SLP-determined RNFL thickness measurements from 14 standard parameters and 64 sectors (approximately 5.6° each) obtained in the circumpapillary area under the instrument-defined measurement ellipse (total 78 parameters). Tenfold cross-validation was used to train and test RVM and SVM classifiers on unique subsets of the full 164-eye data set and areas under the receiver operating characteristic (AUROC) curve for the classification of eyes in the test set were generated. AUROC curve results from RVM and SVM were compared to those for 14 SLP software-generated global and regional RNFL thickness parameters. Also reported was the AUROC curve for the GDx VCC software-generated nerve fiber indicator (NFI). Results The AUROC curves for RVM and SVM were 0.90 and 0.91, respectively, and increased to 0.93 and 0.94 when the training sets were optimized with sequential forward and backward selection (resulting in reduced dimensional data sets). AUROC curves for optimized RVM and SVM were significantly larger than those for all individual SLP parameters. The AUROC curve for the NFI was 0.87. Conclusions Results from RVM and SVM trained on SLP RNFL thickness measurements are similar and provide accurate classification of glaucomatous and healthy eyes. RVM may be preferable to SVM, because it provides a Bayesian-derived probability of glaucoma as an output. These results suggest that these machine learning classifiers show good potential for glaucoma diagnosis. PMID:15790898

Identifying saltcedar with hyperspectral data and support vector machines

USDA-ARS?s Scientific Manuscript database

Saltcedar (Tamarix spp.) are a group of dense phreatophytic shrubs and trees that are invasive to riparian areas throughout the United States. This study determined the feasibility of using hyperspectral data and a support vector machine (SVM) classifier to discriminate saltcedar from other cover t...

Classification of a set of vectors using self-organizing map- and rule-based technique

NASA Astrophysics Data System (ADS)

Ae, Tadashi; Okaniwa, Kaishirou; Nosaka, Kenzaburou

2005-02-01

There exist various objects, such as pictures, music, texts, etc., around our environment. We have a view for these objects by looking, reading or listening. Our view is concerned with our behaviors deeply, and is very important to understand our behaviors. We have a view for an object, and decide the next action (data selection, etc.) with our view. Such a series of actions constructs a sequence. Therefore, we propose a method which acquires a view as a vector from several words for a view, and apply the vector to sequence generation. We focus on sequences of the data of which a user selects from a multimedia database containing pictures, music, movie, etc... These data cannot be stereotyped because user's view for them changes by each user. Therefore, we represent the structure of the multimedia database as the vector representing user's view and the stereotyped vector, and acquire sequences containing the structure as elements. Such a vector can be classified by SOM (Self-Organizing Map). Hidden Markov Model (HMM) is a method to generate sequences. Therefore, we use HMM of which a state corresponds to the representative vector of user's view, and acquire sequences containing the change of user's view. We call it Vector-state Markov Model (VMM). We introduce the rough set theory as a rule-base technique, which plays a role of classifying the sets of data such as the sets of "Tour".

Advanced Techniques for Scene Analysis

DTIC Science & Technology

2010-06-01

robustness prefers a bigger intergration window to handle larger motions. The advantage of pyramidal implementation is that, while each motion vector dL...labeled SAR images. Now the previous algorithm leads to a more dedicated classifier for the particular target; however, our algorithm trades generality for...accuracy is traded for generality. 7.3.2 I-RELIEF Feature weighting transforms the original feature vector x into a new feature vector x′ by assigning each

The Probability of Exceedance as a Nonparametric Person-Fit Statistic for Tests of Moderate Length

ERIC Educational Resources Information Center

Tendeiro, Jorge N.; Meijer, Rob R.

2013-01-01

To classify an item score pattern as not fitting a nonparametric item response theory (NIRT) model, the probability of exceedance (PE) of an observed response vector x can be determined as the sum of the probabilities of all response vectors that are, at most, as likely as x, conditional on the test's total score. Vector x is to be considered…

Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

NASA Astrophysics Data System (ADS)

Sanal Kumar, K. P.; Bhavani, R., Dr.

2017-08-01

Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

77 FR 4557 - Combined Notice of Filings #1

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-30

.... Applicants: Entergy Nuclear Generation Company, Entergy Nuclear Power Marketing, LLC, Entergy Nuclear Vermont... IFA and Svc Agmt with FPL Energy Green Power Wind, LLC to be effective 1/21/2012. Filed Date: 1/20/12...

Vector coding of wavelet-transformed images

NASA Astrophysics Data System (ADS)

Zhou, Jun; Zhi, Cheng; Zhou, Yuanhua

1998-09-01

Wavelet, as a brand new tool in signal processing, has got broad recognition. Using wavelet transform, we can get octave divided frequency band with specific orientation which combines well with the properties of Human Visual System. In this paper, we discuss the classified vector quantization method for multiresolution represented image.

A Prototype SSVEP Based Real Time BCI Gaming System

PubMed Central

Martišius, Ignas

2016-01-01

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel. PMID:27051414

A Prototype SSVEP Based Real Time BCI Gaming System.

PubMed

Martišius, Ignas; Damaševičius, Robertas

2016-01-01

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.

PubMed

Marafino, Ben J; Davies, Jason M; Bardach, Naomi S; Dean, Mitzi L; Dudley, R Adams

2014-01-01

Existing risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times. To develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment. We selected notes from 2001-2008 for 4191 neonatal ICU (NICU) and 2198 adult ICU patients from the MIMIC-II database from the Beth Israel Deaconess Medical Center. Using these notes, we developed an implementation of the SVM classifier to identify procedures (mechanical ventilation and phototherapy in NICU notes) and diagnoses (jaundice in NICU and intracranial hemorrhage (ICH) in adult ICU). On the jaundice classification task, we also compared classifier performance using n-gram features to unigrams with application of a negation algorithm (NegEx). Our classifier accurately identified mechanical ventilation (accuracy=0.982, F1=0.954) and phototherapy use (accuracy=0.940, F1=0.912), as well as jaundice (accuracy=0.898, F1=0.884) and ICH diagnoses (accuracy=0.938, F1=0.943). Including bigram features improved performance on the jaundice (accuracy=0.898 vs 0.865) and ICH (0.938 vs 0.927) tasks, and outperformed NegEx-derived unigram features (accuracy=0.898 vs 0.863) on the jaundice task. Overall, a classifier using n-gram support vectors displayed excellent performance characteristics. The classifier generalizes to diverse patient populations, diagnoses, and procedures. SVM-based classifiers can accurately identify procedure status and diagnoses among ICU patients, and including n-gram features improves performance, compared to existing methods. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Real-data comparison of data mining methods in prediction of diabetes in iran.

PubMed

Tapak, Lily; Mahjub, Hossein; Hamidi, Omid; Poorolajal, Jalal

2013-09-01

Diabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-mean, and random forests) to classify persons with and without diabetes. The data set used in this study included 6,500 subjects from the Iranian national non-communicable diseases risk factors surveillance obtained through a cross-sectional survey. The obtained sample was based on cluster sampling of the Iran population which was conducted in 2005-2009 to assess the prevalence of major non-communicable disease risk factors. Ten risk factors that are commonly associated with diabetes were selected to compare the performance of six classifiers in terms of sensitivity, specificity, total accuracy, and area under the receiver operating characteristic (ROC) curve criteria. Support vector machines showed the highest total accuracy (0.986) as well as area under the ROC (0.979). Also, this method showed high specificity (1.000) and sensitivity (0.820). All other methods produced total accuracy of more than 85%, but for all methods, the sensitivity values were very low (less than 0.350). The results of this study indicate that, in terms of sensitivity, specificity, and overall classification accuracy, the support vector machine model ranks first among all the classifiers tested in the prediction of diabetes. Therefore, this approach is a promising classifier for predicting diabetes, and it should be further investigated for the prediction of other diseases.

Adaptive Bayes classifiers for remotely sensed data

NASA Technical Reports Server (NTRS)

Raulston, H. S.; Pace, M. O.; Gonzalez, R. C.

1975-01-01

An algorithm is developed for a learning, adaptive, statistical pattern classifier for remotely sensed data. The estimation procedure consists of two steps: (1) an optimal stochastic approximation of the parameters of interest, and (2) a projection of the parameters in time and space. The results reported are for Gaussian data in which the mean vector of each class may vary with time or position after the classifier is trained.

Generative Models for Similarity-based Classification

DTIC Science & Technology

2007-01-01

NC), local nearest centroid (local NC), k-nearest neighbors ( kNN ), and condensed nearest neighbors (CNN) are all similarity-based classifiers which...vector machine to the k nearest neighbors of the test sample [80]. The SVM- KNN method was developed to address the robustness and dimensionality...concerns that afflict nearest neighbors and SVMs. Similarly to the nearest-means classifier, the SVM- KNN is a hybrid local and global classifier developed

Minimum distance classification in remote sensing

NASA Technical Reports Server (NTRS)

Wacker, A. G.; Landgrebe, D. A.

1972-01-01

The utilization of minimum distance classification methods in remote sensing problems, such as crop species identification, is considered. Literature concerning both minimum distance classification problems and distance measures is reviewed. Experimental results are presented for several examples. The objective of these examples is to: (a) compare the sample classification accuracy of a minimum distance classifier, with the vector classification accuracy of a maximum likelihood classifier, and (b) compare the accuracy of a parametric minimum distance classifier with that of a nonparametric one. Results show the minimum distance classifier performance is 5% to 10% better than that of the maximum likelihood classifier. The nonparametric classifier is only slightly better than the parametric version.

Comparison between Indirect Immunofluorescence Assay and Shell Vial Culture for Detection of Mumps Virus from Clinical Samples

PubMed Central

Reina, Jordi; Ballesteros, Francisca; Ruiz de Gopegui, Enrique; Munar, Maria; Mari, Margarita

2003-01-01

We report a prospective comparison of the efficacies of an indirect immunofluorescence assay (IFA) and shell vial culture (SVC) of throat swab and urine samples from patients with mumps. Throat swab samples were used for the IFA; the urine samples and throat swabs were inoculated into vials of Vero cells. We studied 62 patients by using 62 throat swabs and 50 urine samples (50 patients with both samples). Sixty (96.7%) throat samples were positive in the SVC, and 61 (98.3%) were positive in the IFA. For the 50 patients from whom both samples were available, the IFA was positive in 50 (100%) cases, the urine sample was positive in 49 (98%) cases, and the throat swab was positive in 48 (96%) cases (P > 0.05). This comparison of throat swabs and urine samples has shown that the two clinical samples are similar in efficacy. PMID:14605158

Research on Classification of Chinese Text Data Based on SVM

NASA Astrophysics Data System (ADS)

Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

2017-09-01

Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

Human action recognition with group lasso regularized-support vector machine

NASA Astrophysics Data System (ADS)

Luo, Huiwu; Lu, Huanzhang; Wu, Yabei; Zhao, Fei

2016-05-01

The bag-of-visual-words (BOVW) and Fisher kernel are two popular models in human action recognition, and support vector machine (SVM) is the most commonly used classifier for the two models. We show two kinds of group structures in the feature representation constructed by BOVW and Fisher kernel, respectively, since the structural information of feature representation can be seen as a prior for the classifier and can improve the performance of the classifier, which has been verified in several areas. However, the standard SVM employs L2-norm regularization in its learning procedure, which penalizes each variable individually and cannot express the structural information of feature representation. We replace the L2-norm regularization with group lasso regularization in standard SVM, and a group lasso regularized-support vector machine (GLRSVM) is proposed. Then, we embed the group structural information of feature representation into GLRSVM. Finally, we introduce an algorithm to solve the optimization problem of GLRSVM by alternating directions method of multipliers. The experiments evaluated on KTH, YouTube, and Hollywood2 datasets show that our method achieves promising results and improves the state-of-the-art methods on KTH and YouTube datasets.

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

PubMed Central

Thanh Noi, Phan; Kappas, Martin

2017-01-01

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km2 within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets. PMID:29271909

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery.

PubMed

Thanh Noi, Phan; Kappas, Martin

2017-12-22

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km² within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets.

Discontinuity Detection in the Shield Metal Arc Welding Process

PubMed Central

Cocota, José Alberto Naves; Garcia, Gabriel Carvalho; da Costa, Adilson Rodrigues; de Lima, Milton Sérgio Fernandes; Rocha, Filipe Augusto Santos; Freitas, Gustavo Medeiros

2017-01-01

This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors—a microphone and piezoelectric—that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system’s high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries. PMID:28489045

Discontinuity Detection in the Shield Metal Arc Welding Process.

PubMed

Cocota, José Alberto Naves; Garcia, Gabriel Carvalho; da Costa, Adilson Rodrigues; de Lima, Milton Sérgio Fernandes; Rocha, Filipe Augusto Santos; Freitas, Gustavo Medeiros

2017-05-10

This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors-a microphone and piezoelectric-that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system's high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries.

Detection of distorted frames in retinal video-sequences via machine learning

NASA Astrophysics Data System (ADS)

Kolar, Radim; Liberdova, Ivana; Odstrcilik, Jan; Hracho, Michal; Tornow, Ralf P.

2017-07-01

This paper describes detection of distorted frames in retinal sequences based on set of global features extracted from each frame. The feature vector is consequently used in classification step, in which three types of classifiers are tested. The best classification accuracy 96% has been achieved with support vector machine approach.

Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences.

PubMed

An, Ji-Yong; Meng, Fan-Rong; You, Zhu-Hong; Fang, Yu-Hong; Zhao, Yu-Jun; Zhang, Ming

2016-01-01

We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

Complement factor H is expressed in adipose tissue in association with insulin resistance.

PubMed

Moreno-Navarrete, José María; Martínez-Barricarte, Rubén; Catalán, Victoria; Sabater, Mònica; Gómez-Ambrosi, Javier; Ortega, Francisco José; Ricart, Wifredo; Blüher, Mathias; Frühbeck, Gema; Rodríguez de Cordoba, Santiago; Fernández-Real, José Manuel

2010-01-01

Activation of the alternative pathway of the complement system, in which factor H (fH; complement fH [CFH]) is a key regulatory component, has been suggested as a link between obesity and metabolic disorders. Our objective was to study the associations between circulating and adipose tissue gene expressions of CFH and complement factor B (fB; CFB) with obesity and insulin resistance. Circulating fH and fB were determined by enzyme-linked immunosorbent assay in 398 subjects. CFH and CFB gene expressions were evaluated in 76 adipose tissue samples, in isolated adipocytes, and in stromovascular cells (SVC) (n = 13). The effects of weight loss and rosiglitazone were investigated in independent cohorts. Both circulating fH and fB were associated positively with BMI, waist circumference, triglycerides, and inflammatory parameters and negatively with insulin sensitivity and HDL cholesterol. For the first time, CFH gene expression was detected in human adipose tissue (significantly increased in subcutaneous compared with omental fat). CFH gene expression in omental fat was significantly associated with insulin resistance. In contrast, CFB gene expression was significantly increased in omental fat but also in association with fasting glucose and triglycerides. The SVC fraction was responsible for these differences, although isolated adipocytes also expressed fB and fH at low levels. Both weight loss and rosiglitazone led to significantly decreased circulating fB and fH levels. Increased circulating fH and fB concentrations in subjects with altered glucose tolerance could reflect increased SVC-induced activation of the alternative pathway of complement in omental adipose tissue linked to insulin resistance and metabolic disturbances.

An Endovascular Approach to the Entrapped Central Venous Catheter After Cardiac Surgery

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

Desai, Shamit S., E-mail: shamit.desai@northwestern.edu; Konanur, Meghana; Foltz, Gretchen

PurposeEntrapment of central venous catheters (CVC) at the superior vena cava (SVC) cardiopulmonary bypass cannulation site by closing purse-string sutures is a rare complication of cardiac surgery. Historically, resternotomy has been required for suture release. An endovascular catheter release approach was developed.Materials and MethodsFour cases of CVC tethering against the SVC wall and associated resistance to removal, suggestive of entrapment, were encountered. In each case, catheter removal was achieved using a reverse catheter fluoroscopically guided over the suture fixation point between catheter and SVC wall, followed by the placement of a guidewire through the catheter. The guidewire was snared andmore » externalized to create a through-and-through access with the apex of the loop around the suture. A snare placed from the femoral venous access provided concurrent downward traction on the distal CVC during suture release maneuvers.ResultsIn the initial attempt, gentle traction freed the CVC, which fractured and was removed in two sections. In the subsequent three cases, traction alone did not release the CVC. Therefore, a cutting balloon was introduced over the guidewire and inflated. Gentle back-and-forth motion of the cutting balloon atherotomes successfully incised the suture in all three attempts. No significant postprocedural complications were encountered. During all cases, a cardiovascular surgeon was present in the interventional suite and prepared for emergent resternotomy, if necessary.ConclusionAn endovascular algorithm to the “entrapped CVC” is proposed, which likely reduces risks posed by resternotomy to cardiac surgery patients in the post-operative period.« less

Research on bearing fault diagnosis of large machinery based on mathematical morphology

NASA Astrophysics Data System (ADS)

Wang, Yu

2018-04-01

To study the automatic diagnosis of large machinery fault based on support vector machine, combining the four common faults of the large machinery, the support vector machine is used to classify and identify the fault. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of fault diagnosis in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.

Feature selection using a one dimensional naïve Bayes' classifier increases the accuracy of support vector machine classification of CDR3 repertoires.

PubMed

Cinelli, Mattia; Sun, Yuxin; Best, Katharine; Heather, James M; Reich-Zeliger, Shlomit; Shifrut, Eric; Friedman, Nir; Shawe-Taylor, John; Chain, Benny

2017-04-01

Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund's adjuvant (CFA) or CFA alone. The CDR3β sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave-one-out validation test reaching >90% in some cases. The study describes a novel two-stage classification strategy combining a one-dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund's Adjuvant. The sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893 . The Decombinator package is available at github.com/innate2adaptive/Decombinator . The R package e1071 is available at the CRAN repository https://cran.r-project.org/web/packages/e1071/index.html . b.chain@ucl.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

Walsh-Hadamard transform kernel-based feature vector for shot boundary detection.

PubMed

Lakshmi, Priya G G; Domnic, S

2014-12-01

Video shot boundary detection (SBD) is the first step of video analysis, summarization, indexing, and retrieval. In SBD process, videos are segmented into basic units called shots. In this paper, a new SBD method is proposed using color, edge, texture, and motion strength as vector of features (feature vector). Features are extracted by projecting the frames on selected basis vectors of Walsh-Hadamard transform (WHT) kernel and WHT matrix. After extracting the features, based on the significance of the features, weights are calculated. The weighted features are combined to form a single continuity signal, used as input for Procedure Based shot transition Identification process (PBI). Using the procedure, shot transitions are classified into abrupt and gradual transitions. Experimental results are examined using large-scale test sets provided by the TRECVID 2007, which has evaluated hard cut and gradual transition detection. To evaluate the robustness of the proposed method, the system evaluation is performed. The proposed method yields F1-Score of 97.4% for cut, 78% for gradual, and 96.1% for overall transitions. We have also evaluated the proposed feature vector with support vector machine classifier. The results show that WHT-based features can perform well than the other existing methods. In addition to this, few more video sequences are taken from the Openvideo project and the performance of the proposed method is compared with the recent existing SBD method.

HMM for hyperspectral spectrum representation and classification with endmember entropy vectors

NASA Astrophysics Data System (ADS)

Arabi, Samir Y. W.; Fernandes, David; Pizarro, Marco A.

2015-10-01

The Hyperspectral images due to its good spectral resolution are extensively used for classification, but its high number of bands requires a higher bandwidth in the transmission data, a higher data storage capability and a higher computational capability in processing systems. This work presents a new methodology for hyperspectral data classification that can work with a reduced number of spectral bands and achieve good results, comparable with processing methods that require all hyperspectral bands. The proposed method for hyperspectral spectra classification is based on the Hidden Markov Model (HMM) associated to each Endmember (EM) of a scene and the conditional probabilities of each EM belongs to each other EM. The EM conditional probability is transformed in EM vector entropy and those vectors are used as reference vectors for the classes in the scene. The conditional probability of a spectrum that will be classified is also transformed in a spectrum entropy vector, which is classified in a given class by the minimum ED (Euclidian Distance) among it and the EM entropy vectors. The methodology was tested with good results using AVIRIS spectra of a scene with 13 EM considering the full 209 bands and the reduced spectral bands of 128, 64 and 32. For the test area its show that can be used only 32 spectral bands instead of the original 209 bands, without significant loss in the classification process.

Support vector machine as a binary classifier for automated object detection in remotely sensed data

NASA Astrophysics Data System (ADS)

Wardaya, P. D.

2014-02-01

In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result.

Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine

PubMed Central

Yang, Zhutian; Wu, Zhilu; Yin, Zhendong; Quan, Taifan; Sun, Hongjian

2013-01-01

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, namely the primary signal recognition and the advanced signal recognition. In the former step, a novel rough k-means classifier, which comprises three regions, i.e., certain area, rough area and uncertain area, is proposed to cluster the samples of radar emitter signals. In the latter step, the samples within the rough boundary are used to train the relevance vector machine (RVM). Then RVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and presents lower computational complexity than traditional approaches. PMID:23344380

9 CFR 93.904 - Health certificate for live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2010 CFR

2010-01-01

... that are imported from any region of the world must be accompanied by a health certificate issued by a... viral assays by the competent authority. (3) SVC-susceptible fish populations in the region or...

Comparison of Covered Versus Uncovered Stents for Benign Superior Vena Cava (SVC) Obstruction.

PubMed

Haddad, Mustafa M; Simmons, Benjamin; McPhail, Ian R; Kalra, Manju; Neisen, Melissa J; Johnson, Matthew P; Stockland, Andrew H; Andrews, James C; Misra, Sanjay; Bjarnason, Haraldur

2018-05-01

To identify whether long-term symptom relief and stent patency vary with the use of covered versus uncovered stents for the treatment of benign SVC obstruction. We retrospectively identified all patients with benign SVC syndrome treated to stent placement between January 2003 and December 2015 (n = 59). Only cases with both clinical and imaging follow-up were included (n = 47). In 33 (70%) of the patients, the obstruction was due to a central line or pacemaker wires, and in 14 (30%), the cause was fibrosing mediastinitis. Covered stents were placed in 17 (36%) of the patients, and 30 (64%) patients had an uncovered stent. Clinical and treatment outcomes, complications, and the percent stenosis of each stent were evaluated. Technical success was achieved in all cases at first attempt. Average clinical and imaging follow-up in years was 2.7 (range 0.1-11.1) (covered) and 1.7 (range 0.2-10.5) (uncovered), respectively. There was a significant difference (p = 0.044) in the number of patients who reported a return of symptoms between the covered (5/17 or 29.4%) and uncovered (18/30 or 60%) groups. There was also a significant difference (p = < 0.001) in the mean percent stenosis after stent placement between the covered [17.9% (range 0-100) ± 26.2] and uncovered [48.3% (range 6.8-100) ± 33.5] groups. No significant difference (p = 0.227) was found in the time (days) between the date of the procedure and the date of clinical follow-up where a return of symptoms was reported [covered: 426.6 (range 28-1554) ± 633.9 and uncovered 778.1 (range 23-3851) ± 1066.8]. One patient in the uncovered group had non-endovascular surgical intervention (innominate to right atrial bypass), while none in the covered group required surgical intervention. One major complication (SIR grade C) occurred that consisted of a pericardial hemorrhagic effusion after angioplasty that required covered stent placement. There were no procedure-related deaths. Both covered and uncovered stents can be used for treating benign SVC syndrome. Covered stents, however, may be a more effective option at providing symptom relief and maintaining stent patency if validated by further studies.

Large thoracic tumor without superior vena cava syndrome.

PubMed

Garmpis, Nikolaos; Damaskos, Christos; Patelis, Nikolaos; Dimitroulis, Dimitrios; Spartalis, Eleftherios; Tomos, Ioannis; Garmpi, Anna; Spartalis, Michael; Antoniou, Efstathios A; Kontzoglou, Konstantinos; Tomos, Periklis

2017-04-10

A 62 year-old male with long-standing smoking history presented with hemoptysis. Plain chest x-ray showed abnormal findings proximate to the right pulmonary hilum. Bronchoscopy revealed a fragile exophytic tumor of the right wall of the lower third of the trachea, infiltrating the right main bronchus (75% stenosis) and the right upper lobar bronchus (near total occlusion). Contrast-enhanced chest CT demonstrated a 7.2x4.9 cm tumor contiguous to the above-mentioned structures, mediastinal lymph node pathology, and a vessel coursing inferiorly to the left of the aortic arch and anterior to the left hilum. Despite the tumor constricting the right superior vena cava, no signs of superior vena cava syndrome were present. In this case, the patient does not present with Superior Vena Cava (SVC) syndrome, as expected due to the constriction of the (right) SVC caused by the tumor, since head and neck veins drain through the Persistent Left Superior Vena Cava (PLSVC). PLSVC is the most common thoracic venous anomaly with an incidence of 0.3% to 0.5% of the general population and it is a congenital anomaly caused by the failure of the left anterior cardinal vein to regress and to consequently form the ligament of Marshall during fetal development. It is associated with absence of the left brachiocephalic vein and in 10 to 20% of cases the right SVC is absent. Two potential draining points of the PLSVC have been previously reported. In the majority of cases PLSVC drains directly into the coronary sinus, but less frequently it drains into the left atrium or the left superior pulmonary vein. In cases where the PLSVC drains into the coronary sinus, congenital heart defects are rare. The patient usually remains asymptomatic and PLSVC is an incidental finding during radiographic imaging or medical procedures. When the PLSVC drains into the left atrium or the left superior pulmonary vein, a right-to-left shunt is formed; a condition usually asymptomatic. In some reported cases this PLSVC variant presents with persistent, unexplained hypoxia or cyanosis and embolisation causing recurrent transient ischemic attacks and/or cerebral abscesses. This PLSVC variant is more often associated with absence of the right SVC and congenital heart abnormalities.

Secular variation between 5 and 10c CE in Japan: remeasurements of 2000 samples collected between 1960-70's from Sueki earthenware kilns in Osaka.

NASA Astrophysics Data System (ADS)

Shibuya, H.; Mochizuki, N.; Hatakeyama, T.

2015-12-01

In Japan, archeomagnetic measurements are vigorously developed for years, though it is not well known to paleomagnetism community in english. One of the works is massive archeomagnetic study of Suemura kiln group carried out in Osaka University in 1960's to early 70's. More than 500 kilns were excavated in Sakai city and vicinities, Osaka Prefecture, Japan. The kiln group is called as Suemura Kilns, and are for Sueki earthenware of 5c to 10c CE. About 300 kilns were sampled and most of the samples were measured at the time, and the results are reported in e.g. Hirooka (1971) and Shibuya (1980). However, the results have significant scatter in direction, which may be due to the limitation of old astatic magnetometer measurements and handwriting graphic determination of magnetic direction, and/or the lack of demagnetization. We recently inherited many of those samples and remeasured them with spinner magnetometer applying alternation field demagnetization (afd). The magnetizations are generally very stable, as usual as other archeomagnetic samples, and afd does not change the magnetic direction mostly. However, significant number of sites show large scatter in magnetic directions, which might be due to the wrong identification of kiln floor or disturbance at the time of collapsing or excavation. Taking kilns of α95<4o, we recovered 131 paleomagnetic directions. Although third of them are dated by pottery shape chronology, the range of each pottery style is not precisely known and the relation of the baked floor and the potteries excavated around kilns are not always clear. The carbon dating of those kilns are very scares. Thus we first try to draw secular variation curve in declination-inclination plot. With the rough ages of those kilns, it is pretty easy to draw the SVC. It is also numerically determined taking the distance of each direction from nearest point in SVC and the velocity change of the SVC as penalty function, within a couple of degrees in the error. The the age of each point is assigned to satisfy the archeological ages. This precise SVC in the far east will improve understanding the geomagnetic variations, as well as application to the archeological dating.

Improvements on ν-Twin Support Vector Machine.

PubMed

Khemchandani, Reshma; Saigal, Pooja; Chandra, Suresh

2016-07-01

In this paper, we propose two novel binary classifiers termed as "Improvements on ν-Twin Support Vector Machine: Iν-TWSVM and Iν-TWSVM (Fast)" that are motivated by ν-Twin Support Vector Machine (ν-TWSVM). Similar to ν-TWSVM, Iν-TWSVM determines two nonparallel hyperplanes such that they are closer to their respective classes and are at least ρ distance away from the other class. The significant advantage of Iν-TWSVM over ν-TWSVM is that Iν-TWSVM solves one smaller-sized Quadratic Programming Problem (QPP) and one Unconstrained Minimization Problem (UMP); as compared to solving two related QPPs in ν-TWSVM. Further, Iν-TWSVM (Fast) avoids solving a smaller sized QPP and transforms it as a unimodal function, which can be solved using line search methods and similar to Iν-TWSVM, the other problem is solved as a UMP. Due to their novel formulation, the proposed classifiers are faster than ν-TWSVM and have comparable generalization ability. Iν-TWSVM also implements structural risk minimization (SRM) principle by introducing a regularization term, along with minimizing the empirical risk. The other properties of Iν-TWSVM, related to support vectors (SVs), are similar to that of ν-TWSVM. To test the efficacy of the proposed method, experiments have been conducted on a wide range of UCI and a skewed variation of NDC datasets. We have also given the application of Iν-TWSVM as a binary classifier for pixel classification of color images. Copyright © 2016 Elsevier Ltd. All rights reserved.

Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.

PubMed

Cannon, Edward O; Amini, Ata; Bender, Andreas; Sternberg, Michael J E; Muggleton, Stephen H; Glen, Robert C; Mitchell, John B O

2007-05-01

We investigate the classification performance of circular fingerprints in combination with the Naive Bayes Classifier (MP2D), Inductive Logic Programming (ILP) and Support Vector Inductive Logic Programming (SVILP) on a standard molecular benchmark dataset comprising 11 activity classes and about 102,000 structures. The Naive Bayes Classifier treats features independently while ILP combines structural fragments, and then creates new features with higher predictive power. SVILP is a very recently presented method which adds a support vector machine after common ILP procedures. The performance of the methods is evaluated via a number of statistical measures, namely recall, specificity, precision, F-measure, Matthews Correlation Coefficient, area under the Receiver Operating Characteristic (ROC) curve and enrichment factor (EF). According to the F-measure, which takes both recall and precision into account, SVILP is for seven out of the 11 classes the superior method. The results show that the Bayes Classifier gives the best recall performance for eight of the 11 targets, but has a much lower precision, specificity and F-measure. The SVILP model on the other hand has the highest recall for only three of the 11 classes, but generally far superior specificity and precision. To evaluate the statistical significance of the SVILP superiority, we employ McNemar's test which shows that SVILP performs significantly (p < 5%) better than both other methods for six out of 11 activity classes, while being superior with less significance for three of the remaining classes. While previously the Bayes Classifier was shown to perform very well in molecular classification studies, these results suggest that SVILP is able to extract additional knowledge from the data, thus improving classification results further.

Quantitative change of EEG and respiration signals during mindfulness meditation.

PubMed

Ahani, Asieh; Wahbeh, Helane; Nezamfar, Hooman; Miller, Meghan; Erdogmus, Deniz; Oken, Barry

2014-05-14

This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing. EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation. Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%). Support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies.

Quantitative change of EEG and respiration signals during mindfulness meditation

PubMed Central

2014-01-01

Background This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing. Methods EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation. Results Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%). Conclusion Support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies. PMID:24939519

Hierarchical human action recognition around sleeping using obscured posture information

NASA Astrophysics Data System (ADS)

Kudo, Yuta; Sashida, Takehiko; Aoki, Yoshimitsu

2015-04-01

This paper presents a new approach for human action recognition around sleeping with the human body parts locations and the positional relationship between human and sleeping environment. Body parts are estimated from the depth image obtained by a time-of-flight (TOF) sensor using oriented 3D normal vector. Issues in action recognition of sleeping situation are the demand of availability in darkness, and hiding of the human body by duvets. Therefore, the extraction of image features is difficult since color and edge features are obscured by covers. Thus, first in our method, positions of four parts of the body (head, torso, thigh, and lower leg) are estimated by using the shape model of bodily surface constructed by oriented 3D normal vector. This shape model can represent the surface shape of rough body, and is effective in robust posture estimation of the body hidden with duvets. Then, action descriptor is extracted from the position of each body part. The descriptor includes temporal variation of each part of the body and spatial vector of position of the parts and the bed. Furthermore, this paper proposes hierarchical action classes and classifiers to improve the indistinct action classification. Classifiers are composed of two layers, and recognize human action by using the action descriptor. First layer focuses on spatial descriptor and classifies action roughly. Second layer focuses on temporal descriptor and classifies action finely. This approach achieves a robust recognition of obscured human by using the posture information and the hierarchical action recognition.

Bayes estimation on parameters of the single-class classifier. [for remotely sensed crop data

NASA Technical Reports Server (NTRS)

Lin, G. C.; Minter, T. C.

1976-01-01

Normal procedures used for designing a Bayes classifier to classify wheat as the major crop of interest require not only training samples of wheat but also those of nonwheat. Therefore, ground truth must be available for the class of interest plus all confusion classes. The single-class Bayes classifier classifies data into the class of interest or the class 'other' but requires training samples only from the class of interest. This paper will present a procedure for Bayes estimation on the mean vector, covariance matrix, and a priori probability of the single-class classifier using labeled samples from the class of interest and unlabeled samples drawn from the mixture density function.

Probable autochthonous introduced malaria cases in Italy in 2009-2011 and the risk of local vector-borne transmission.

PubMed

Romi, R; Boccolini, D; Menegon, M; Rezza, G

2012-11-29

We describe two cases of probable autochthonous introduced Plasmodium vivax malaria that occurred in 2009 and 2011 in two sites of South-Central Italy. Although the sources of the infections were not detected, local transmission could not be disproved and therefore the cases were classified as autochthonous. Sporadic P. vivax cases transmitted by indigenous vectors may be considered possible in some areas of the country where vector abundance and environmental conditions are favourable to malaria transmission.

A best-fit model for concept vectors in biomedical research grants.

PubMed

Johnson, Calvin; Lau, William; Bhandari, Archna; Hays, Timothy

2008-11-06

The Research, Condition, and Disease Categorization (RCDC) project was created to standardize budget reporting by research topic. Text mining techniques have been implemented to classify NIH grant applications into proper research and disease categories. A best-fit model is shown to achieve classification performance rivaling that of concept vectors produced by human experts.

Co-Labeling for Multi-View Weakly Labeled Learning.

PubMed

Xu, Xinxing; Li, Wen; Xu, Dong; Tsang, Ivor W

2016-06-01

It is often expensive and time consuming to collect labeled training samples in many real-world applications. To reduce human effort on annotating training samples, many machine learning techniques (e.g., semi-supervised learning (SSL), multi-instance learning (MIL), etc.) have been studied to exploit weakly labeled training samples. Meanwhile, when the training data is represented with multiple types of features, many multi-view learning methods have shown that classifiers trained on different views can help each other to better utilize the unlabeled training samples for the SSL task. In this paper, we study a new learning problem called multi-view weakly labeled learning, in which we aim to develop a unified approach to learn robust classifiers by effectively utilizing different types of weakly labeled multi-view data from a broad range of tasks including SSL, MIL and relative outlier detection (ROD). We propose an effective approach called co-labeling to solve the multi-view weakly labeled learning problem. Specifically, we model the learning problem on each view as a weakly labeled learning problem, which aims to learn an optimal classifier from a set of pseudo-label vectors generated by using the classifiers trained from other views. Unlike traditional co-training approaches using a single pseudo-label vector for training each classifier, our co-labeling approach explores different strategies to utilize the predictions from different views, biases and iterations for generating the pseudo-label vectors, making our approach more robust for real-world applications. Moreover, to further improve the weakly labeled learning on each view, we also exploit the inherent group structure in the pseudo-label vectors generated from different strategies, which leads to a new multi-layer multiple kernel learning problem. Promising results for text-based image retrieval on the NUS-WIDE dataset as well as news classification and text categorization on several real-world multi-view datasets clearly demonstrate that our proposed co-labeling approach achieves state-of-the-art performance for various multi-view weakly labeled learning problems including multi-view SSL, multi-view MIL and multi-view ROD.

Speech sound classification and detection of articulation disorders with support vector machines and wavelets.

PubMed

Georgoulas, George; Georgopoulos, Voula C; Stylios, Chrysostomos D

2006-01-01

This paper proposes a novel integrated methodology to extract features and classify speech sounds with intent to detect the possible existence of a speech articulation disorder in a speaker. Articulation, in effect, is the specific and characteristic way that an individual produces the speech sounds. A methodology to process the speech signal, extract features and finally classify the signal and detect articulation problems in a speaker is presented. The use of support vector machines (SVMs), for the classification of speech sounds and detection of articulation disorders is introduced. The proposed method is implemented on a data set where different sets of features and different schemes of SVMs are tested leading to satisfactory performance.

Ensemble of classifiers for ontology enrichment

NASA Astrophysics Data System (ADS)

Semenova, A. V.; Kureichik, V. M.

2018-05-01

A classifier is a basis of ontology learning systems. Classification of text documents is used in many applications, such as information retrieval, information extraction, definition of spam. A new ensemble of classifiers based on SVM (a method of support vectors), LSTM (neural network) and word embedding are suggested. An experiment was conducted on open data, which allows us to conclude that the proposed classification method is promising. The implementation of the proposed classifier is performed in the Matlab using the functions of the Text Analytics Toolbox. The principal difference between the proposed ensembles of classifiers is the high quality of classification of data at acceptable time costs.

Arrogance analysis of several typical pattern recognition classifiers

NASA Astrophysics Data System (ADS)

Jing, Chen; Xia, Shengping; Hu, Weidong

2007-04-01

Various kinds of classification methods have been developed. However, most of these classical methods, such as Back-Propagation (BP), Bayesian method, Support Vector Machine(SVM), Self-Organizing Map (SOM) are arrogant. A so-called arrogance, for a human, means that his decision, which even is a mistake, overstates his actual experience. Accordingly, we say that he is a arrogant if he frequently makes arrogant decisions. Likewise, some classical pattern classifiers represent the similar characteristic of arrogance. Given an input feature vector, we say a classifier is arrogant in its classification if its veracity is high yet its experience is low. Typically, for a new sample which is distinguishable from original training samples, traditional classifiers recognize it as one of the known targets. Clearly, arrogance in classification is an undesirable attribute. Conversely, a classifier is non-arrogant in its classification if there is a reasonable balance between its veracity and its experience. Inquisitiveness is, in many ways, the opposite of arrogance. In nature, inquisitiveness is an eagerness for knowledge characterized by the drive to question, to seek a deeper understanding. The human capacity to doubt present beliefs allows us to acquire new experiences and to learn from our mistakes. Within the discrete world of computers, inquisitive pattern recognition is the constructive investigation and exploitation of conflict in information. Thus, we quantify this balance and discuss new techniques that will detect arrogance in a classifier.

LDA boost classification: boosting by topics

NASA Astrophysics Data System (ADS)

Lei, La; Qiao, Guo; Qimin, Cao; Qitao, Li

2012-12-01

AdaBoost is an efficacious classification algorithm especially in text categorization (TC) tasks. The methodology of setting up a classifier committee and voting on the documents for classification can achieve high categorization precision. However, traditional Vector Space Model can easily lead to the curse of dimensionality and feature sparsity problems; so it affects classification performance seriously. This article proposed a novel classification algorithm called LDABoost based on boosting ideology which uses Latent Dirichlet Allocation (LDA) to modeling the feature space. Instead of using words or phrase, LDABoost use latent topics as the features. In this way, the feature dimension is significantly reduced. Improved Naïve Bayes (NB) is designed as the weaker classifier which keeps the efficiency advantage of classic NB algorithm and has higher precision. Moreover, a two-stage iterative weighted method called Cute Integration in this article is proposed for improving the accuracy by integrating weak classifiers into strong classifier in a more rational way. Mutual Information is used as metrics of weights allocation. The voting information and the categorization decision made by basis classifiers are fully utilized for generating the strong classifier. Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms. Moreover, its runtime consumption is lower than different versions of AdaBoost, TC algorithms based on support vector machine and Neural Networks.

Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine

PubMed Central

Mourão-Miranda, Janaina; Hardoon, David R.; Hahn, Tim; Marquand, Andre F.; Williams, Steve C.R.; Shawe-Taylor, John; Brammer, Michael

2011-01-01

Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers. PMID:21723950

Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

PubMed Central

Burgansky-Eliash, Zvia; Wollstein, Gadi; Chu, Tianjiao; Ramsey, Joseph D.; Glymour, Clark; Noecker, Robert J.; Ishikawa, Hiroshi; Schuman, Joel S.

2007-01-01

Purpose Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection. Methods Forty-seven patients with glaucoma (47 eyes) and 42 healthy subjects (42 eyes) were included in this cross-sectional study. Of the glaucoma patients, 27 had early disease (visual field mean deviation [MD] ≥ −6 dB) and 20 had advanced glaucoma (MD < −6 dB). Machine-learning classifiers were trained to discriminate between glaucomatous and healthy eyes using parameters derived from OCT output. The classifiers were trained with all 38 parameters as well as with only 8 parameters that correlated best with the visual field MD. Five classifiers were tested: linear discriminant analysis, support vector machine, recursive partitioning and regression tree, generalized linear model, and generalized additive model. For the last two classifiers, a backward feature selection was used to find the minimal number of parameters that resulted in the best and most simple prediction. The cross-validated receiver operating characteristic (ROC) curve and accuracies were calculated. Results The largest area under the ROC curve (AROC) for glaucoma detection was achieved with the support vector machine using eight parameters (0.981). The sensitivity at 80% and 95% specificity was 97.9% and 92.5%, respectively. This classifier also performed best when judged by cross-validated accuracy (0.966). The best classification between early glaucoma and advanced glaucoma was obtained with the generalized additive model using only three parameters (AROC = 0.854). Conclusions Automated machine classifiers of OCT data might be useful for enhancing the utility of this technology for detecting glaucomatous abnormality. PMID:16249492

A novel approach for dimension reduction of microarray.

PubMed

Aziz, Rabia; Verma, C K; Srivastava, Namita

2017-12-01

This paper proposes a new hybrid search technique for feature (gene) selection (FS) using Independent component analysis (ICA) and Artificial Bee Colony (ABC) called ICA+ABC, to select informative genes based on a Naïve Bayes (NB) algorithm. An important trait of this technique is the optimization of ICA feature vector using ABC. ICA+ABC is a hybrid search algorithm that combines the benefits of extraction approach, to reduce the size of data and wrapper approach, to optimize the reduced feature vectors. This hybrid search technique is facilitated by evaluating the performance of ICA+ABC on six standard gene expression datasets of classification. Extensive experiments were conducted to compare the performance of ICA+ABC with the results obtained from recently published Minimum Redundancy Maximum Relevance (mRMR) +ABC algorithm for NB classifier. Also to check the performance that how ICA+ABC works as feature selection with NB classifier, compared the combination of ICA with popular filter techniques and with other similar bio inspired algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result shows that ICA+ABC has a significant ability to generate small subsets of genes from the ICA feature vector, that significantly improve the classification accuracy of NB classifier compared to other previously suggested methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System.

PubMed

Jaya, T; Dheeba, J; Singh, N Albert

2015-12-01

Diabetic retinopathy is a major cause of vision loss in diabetic patients. Currently, there is a need for making decisions using intelligent computer algorithms when screening a large volume of data. This paper presents an expert decision-making system designed using a fuzzy support vector machine (FSVM) classifier to detect hard exudates in fundus images. The optic discs in the colour fundus images are segmented to avoid false alarms using morphological operations and based on circular Hough transform. To discriminate between the exudates and the non-exudates pixels, colour and texture features are extracted from the images. These features are given as input to the FSVM classifier. The classifier analysed 200 retinal images collected from diabetic retinopathy screening programmes. The tests made on the retinal images show that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and features sets, the area under the receiver operating characteristic curve reached 0.9606, which corresponds to a sensitivity of 94.1% with a specificity of 90.0%. The results suggest that detecting hard exudates using FSVM contribute to computer-assisted detection of diabetic retinopathy and as a decision support system for ophthalmologists.

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

PubMed Central

Kim, Jongin; Park, Hyeong-jun

2016-01-01

The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems. PMID:28097128

Lamb wave based damage detection using Matching Pursuit and Support Vector Machine classifier

NASA Astrophysics Data System (ADS)

Agarwal, Sushant; Mitra, Mira

2014-03-01

In this paper, the suitability of using Matching Pursuit (MP) and Support Vector Machine (SVM) for damage detection using Lamb wave response of thin aluminium plate is explored. Lamb wave response of thin aluminium plate with or without damage is simulated using finite element. Simulations are carried out at different frequencies for various kinds of damage. The procedure is divided into two parts - signal processing and machine learning. Firstly, MP is used for denoising and to maintain the sparsity of the dataset. In this study, MP is extended by using a combination of time-frequency functions as the dictionary and is deployed in two stages. Selection of a particular type of atoms lead to extraction of important features while maintaining the sparsity of the waveform. The resultant waveform is then passed as input data for SVM classifier. SVM is used to detect the location of the potential damage from the reduced data. The study demonstrates that SVM is a robust classifier in presence of noise and more efficient as compared to Artificial Neural Network (ANN). Out-of-sample data is used for the validation of the trained and tested classifier. Trained classifiers are found successful in detection of the damage with more than 95% detection rate.

Complement Factor H Is Expressed in Adipose Tissue in Association With Insulin Resistance

PubMed Central

Moreno-Navarrete, José María; Martínez-Barricarte, Rubén; Catalán, Victoria; Sabater, Mònica; Gómez-Ambrosi, Javier; Ortega, Francisco José; Ricart, Wifredo; Blüher, Mathias; Frühbeck, Gema; Rodríguez de Cordoba, Santiago; Fernández-Real, José Manuel

2010-01-01

OBJECTIVE Activation of the alternative pathway of the complement system, in which factor H (fH; complement fH [CFH]) is a key regulatory component, has been suggested as a link between obesity and metabolic disorders. Our objective was to study the associations between circulating and adipose tissue gene expressions of CFH and complement factor B (fB; CFB) with obesity and insulin resistance. RESEARCH DESIGN AND METHODS Circulating fH and fB were determined by enzyme-linked immunosorbent assay in 398 subjects. CFH and CFB gene expressions were evaluated in 76 adipose tissue samples, in isolated adipocytes, and in stromovascular cells (SVC) (n = 13). The effects of weight loss and rosiglitazone were investigated in independent cohorts. RESULTS Both circulating fH and fB were associated positively with BMI, waist circumference, triglycerides, and inflammatory parameters and negatively with insulin sensitivity and HDL cholesterol. For the first time, CFH gene expression was detected in human adipose tissue (significantly increased in subcutaneous compared with omental fat). CFH gene expression in omental fat was significantly associated with insulin resistance. In contrast, CFB gene expression was significantly increased in omental fat but also in association with fasting glucose and triglycerides. The SVC fraction was responsible for these differences, although isolated adipocytes also expressed fB and fH at low levels. Both weight loss and rosiglitazone led to significantly decreased circulating fB and fH levels. CONCLUSIONS Increased circulating fH and fB concentrations in subjects with altered glucose tolerance could reflect increased SVC-induced activation of the alternative pathway of complement in omental adipose tissue linked to insulin resistance and metabolic disturbances. PMID:19833879

Subclavian vein pacing and venous pressure waveform measurement for phrenic nerve monitoring during cryoballoon ablation of atrial fibrillation.

PubMed

Ghosh, Justin; Singarayar, Suresh; Kabunga, Peter; McGuire, Mark A

2015-06-01

The phrenic nerves may be damaged during catheter ablation of atrial fibrillation. Phrenic nerve function is routinely monitored during ablation by stimulating the right phrenic nerve from a site in the superior vena cava (SVC) and manually assessing the strength of diaphragmatic contraction. However the optimal stimulation site, method of assessing diaphragmatic contraction, and techniques for monitoring the left phrenic nerve have not been established. We assessed novel techniques to monitor phrenic nerve function during cryoablation procedures. Pacing threshold and stability of phrenic nerve capture were assessed when pacing from the SVC, left and right subclavian veins. Femoral venous pressure waveforms were used to monitor the strength of diaphragmatic contraction. Stable capture of the left phrenic nerve by stimulation in the left subclavian vein was achieved in 96 of 100 patients, with a median capture threshold of 2.5 mA [inter-quartile range (IQR) 1.4-5.0 mA]. Stimulation of the right phrenic nerve from the subclavian vein was superior to stimulation from the SVC with lower pacing thresholds (1.8 mA IQR 1.4-3.3 vs. 6.0 mA IQR 3.4-8.0, P < 0.001). Venous pressure waveforms were obtained in all patients and attenuation of the waveform was always observed prior to onset of phrenic nerve palsy. The left phrenic nerve can be stimulated from the left subclavian vein. The subclavian veins are the optimal sites for phrenic nerve stimulation. Monitoring the femoral venous pressure waveform is a novel technique for detecting impending phrenic nerve damage. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2014. For permissions please email: journals.permissions@oup.com.

Sinus venosus syndrome: atrial septal defect or anomalous venous connection? A multiplane transoesophageal approach.

PubMed

Oliver, J M; Gallego, P; Gonzalez, A; Dominguez, F J; Aroca, A; Mesa, J M

2002-12-01

To discuss the anatomical features of sinus venosus atrial defect on the basis of a comprehensive transoesophageal echocardiography (TOE) examination and its relation to surgical data. 24 patients (13 men, 11 women, mean (SD) age 37 (17) years, range 17-73 years) with a posterior interatrial communication closely related to the entrance of the superior (SVC) or inferior vena cava (IVC) who underwent TOE before surgical repair. Records of these patients were retrospectively reviewed and compared with surgical assessments. In 13 patients, TOE showed a deficiency in the extraseptal wall that normally separates the left atrium and right upper pulmonary vein from the SVC and right atrium. This deficiency unroofed the right upper pulmonary vein, compelling it to drain into the SVC, which overrode the intact atrial septum. In three patients, TOE examination showed a defect in the wall of the IVC, which continued directly into the posterior border of the left atrium. Thus, the intact muscular border of the atrial septum was overridden by the mouth of the IVC, which presented a biatrial connection. In the remaining eight patients, the defect was located in the muscular posterior border of the fossa ovalis. A residuum of atrial septum was visualised in the superior margin of the defect. Neither caval vein overriding nor anomalous pulmonary vein drainage was present. Sinus venosus syndrome should be regarded as an anomalous venous connection with an interatrial communication outside the confines of the atrial septum, in the unfolding wall that normally separates the left atrium from either caval vein. It results in overriding of the caval veins across the intact atrial septum and partial pulmonary vein anomalous drainage. It should be differentiated from posterior atrial septal defect without overriding or anomalous venous connections.

Sinus venosus syndrome: atrial septal defect or anomalous venous connection? A multiplane transoesophageal approach

PubMed Central

Oliver, J M; Gallego, P; Gonzalez, A; Dominguez, F J; Aroca, A; Mesa, J M

2002-01-01

Objective: To discuss the anatomical features of sinus venosus atrial defect on the basis of a comprehensive transoesophageal echocardiography (TOE) examination and its relation to surgical data. Methods: 24 patients (13 men, 11 women, mean (SD) age 37 (17) years, range 17–73 years) with a posterior interatrial communication closely related to the entrance of the superior (SVC) or inferior vena cava (IVC) who underwent TOE before surgical repair. Records of these patients were retrospectively reviewed and compared with surgical assessments. Results: In 13 patients, TOE showed a deficiency in the extraseptal wall that normally separates the left atrium and right upper pulmonary vein from the SVC and right atrium. This deficiency unroofed the right upper pulmonary vein, compelling it to drain into the SVC, which overrode the intact atrial septum. In three patients, TOE examination showed a defect in the wall of the IVC, which continued directly into the posterior border of the left atrium. Thus, the intact muscular border of the atrial septum was overridden by the mouth of the IVC, which presented a biatrial connection. In the remaining eight patients, the defect was located in the muscular posterior border of the fossa ovalis. A residuum of atrial septum was visualised in the superior margin of the defect. Neither caval vein overriding nor anomalous pulmonary vein drainage was present. Conclusions: Sinus venosus syndrome should be regarded as an anomalous venous connection with an interatrial communication outside the confines of the atrial septum, in the unfolding wall that normally separates the left atrium from either caval vein. It results in overriding of the caval veins across the intact atrial septum and partial pulmonary vein anomalous drainage. It should be differentiated from posterior atrial septal defect without overriding or anomalous venous connections. PMID:12433899

[Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

PubMed

Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

2015-01-01

In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

PubMed

Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

2018-02-19

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

Spring viremia of carp

USGS Publications Warehouse

Ahne, W.; Bjorklund, H.V.; Essbauer, S.; Fijan, N.; Kurath, G.; Winton, J.R.

2002-01-01

pring viremia of carp (SVC) is an important disease affecting cyprinids, mainly common carp Cyprinus carpio. The disease is widespread in European carp culture, where it causes significant morbidity and mortality. Designated a notifiable disease by the Office International des Epizooties, SVC is caused by a rhabdovirus, spring viremia of carp virus (SVCV). Affected fish show destruction of tissues in the kidney, spleen and liver, leading to hemorrhage, loss of water-salt balance and impairment of immune response. High mortality occurs at water temperatures of 10 to 17°C, typically in spring. At higher temperatures, infected carp develop humoral antibodies that can neutralize the spread of virus and such carp are protected against re-infection by solid immunity. The virus is shed mostly with the feces and urine of clinically infected fish and by carriers. Waterborne transmission is believed to be the primary route of infection, but bloodsucking parasites like leeches and the carp louse may serve as mechanical vectors of SVCV. The genome of SVCV is composed of a single molecule of linear, negative-sense, single-stranded RNA containing 5 genes in the order 3¹-NPMGL-5¹ coding for the viral nucleoprotein, phosphoprotein, matrix protein, glycoprotein, and polymerase, respectively. Polyacrylamide gel electrophoresis of the viral proteins, and sequence homologies between the genes and gene junctions of SVCV and vesicular stomatitis viruses, have led to the placement of the virus as a tentative member of the genus Vesiculovirus in the family Rhabdoviridae. These methods also revealed that SVCV is not related to fish rhabdoviruses of the genus Novirhabdovirus. In vitro replication of SVCV takes place in the cytoplasm of cultured cells of fish, bird and mammalian origin at temperatures of 4 to 31°C, with an optimum of about 20°C. Spring viremia of carp can be diagnosed by clinical signs, isolation of virus in cell culture and molecular methods. Antibodies directed against SVCV react with the homologous virus in serum neutralization, immunofluorescence, immunoperoxidase, or enzyme-linked immunosorbent assays, but they cross-react to various degrees with the pike fry rhabdovirus (PFR), suggesting the 2 viruses are closely related. However, SVCV and PFR can be distinguished by certain serological tests and molecular methods such as the ribonuclease protection assay.

A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

PubMed

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

Three learning phases for radial-basis-function networks.

PubMed

Schwenker, F; Kestler, H A; Palm, G

2001-05-01

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Estimation of Teacher Practices Based on Text Transcripts of Teacher Speech Using a Support Vector Machine Algorithm

ERIC Educational Resources Information Center

Araya, Roberto; Plana, Francisco; Dartnell, Pablo; Soto-Andrade, Jorge; Luci, Gina; Salinas, Elena; Araya, Marylen

2012-01-01

Teacher practice is normally assessed by observers who watch classes or videos of classes. Here, we analyse an alternative strategy that uses text transcripts and a support vector machine classifier. For each one of the 710 videos of mathematics classes from the 2005 Chilean National Teacher Assessment Programme, a single 4-minute slice was…

Associative Pattern Recognition In Analog VLSI Circuits

NASA Technical Reports Server (NTRS)

Tawel, Raoul

1995-01-01

Winner-take-all circuit selects best-match stored pattern. Prototype cascadable very-large-scale integrated (VLSI) circuit chips built and tested to demonstrate concept of electronic associative pattern recognition. Based on low-power, sub-threshold analog complementary oxide/semiconductor (CMOS) VLSI circuitry, each chip can store 128 sets (vectors) of 16 analog values (vector components), vectors representing known patterns as diverse as spectra, histograms, graphs, or brightnesses of pixels in images. Chips exploit parallel nature of vector quantization architecture to implement highly parallel processing in relatively simple computational cells. Through collective action, cells classify input pattern in fraction of microsecond while consuming power of few microwatts.

An ensemble of SVM classifiers based on gene pairs.

PubMed

Tong, Muchenxuan; Liu, Kun-Hong; Xu, Chungui; Ju, Wenbin

2013-07-01

In this paper, a genetic algorithm (GA) based ensemble support vector machine (SVM) classifier built on gene pairs (GA-ESP) is proposed. The SVMs (base classifiers of the ensemble system) are trained on different informative gene pairs. These gene pairs are selected by the top scoring pair (TSP) criterion. Each of these pairs projects the original microarray expression onto a 2-D space. Extensive permutation of gene pairs may reveal more useful information and potentially lead to an ensemble classifier with satisfactory accuracy and interpretability. GA is further applied to select an optimized combination of base classifiers. The effectiveness of the GA-ESP classifier is evaluated on both binary-class and multi-class datasets. Copyright © 2013 Elsevier Ltd. All rights reserved.

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

PubMed

Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

2016-01-01

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

Method of Menu Selection by Gaze Movement Using AC EOG Signals

NASA Astrophysics Data System (ADS)

Kanoh, Shin'ichiro; Futami, Ryoko; Yoshinobu, Tatsuo; Hoshimiya, Nozomu

A method to detect the direction and the distance of voluntary eye gaze movement from EOG (electrooculogram) signals was proposed and tested. In this method, AC-amplified vertical and horizontal transient EOG signals were classified into 8-class directions and 2-class distances of voluntary eye gaze movements. A horizontal and a vertical EOGs during eye gaze movement at each sampling time were treated as a two-dimensional vector, and the center of gravity of the sample vectors whose norms were more than 80% of the maximum norm was used as a feature vector to be classified. By the classification using the k-nearest neighbor algorithm, it was shown that the averaged correct detection rates on each subject were 98.9%, 98.7%, 94.4%, respectively. This method can avoid strict EOG-based eye tracking which requires DC amplification of very small signal. It would be useful to develop robust human interfacing systems based on menu selection for severely paralyzed patients.

Using Support Vector Machines to Automatically Extract Open Water Signatures from POLDER Multi-Angle Data Over Boreal Regions

NASA Technical Reports Server (NTRS)

Pierce, J.; Diaz-Barrios, M.; Pinzon, J.; Ustin, S. L.; Shih, P.; Tournois, S.; Zarco-Tejada, P. J.; Vanderbilt, V. C.; Perry, G. L.; Brass, James A. (Technical Monitor)

2002-01-01

This study used Support Vector Machines to classify multiangle POLDER data. Boreal wetland ecosystems cover an estimated 90 x 10(exp 6) ha, about 36% of global wetlands, and are a major source of trace gases emissions to the atmosphere. Four to 20 percent of the global emission of methane to the atmosphere comes from wetlands north of 4 degrees N latitude. Large uncertainties in emissions exist because of large spatial and temporal variation in the production and consumption of methane. Accurate knowledge of the areal extent of open water and inundated vegetation is critical to estimating magnitudes of trace gas emissions. Improvements in land cover mapping have been sought using physical-modeling approaches, neural networks, and active microwave, examples that demonstrate the difficulties of separating open water, inundated vegetation and dry upland vegetation. Here we examine the feasibility of using a support vector machine to classify POLDER data representing open water, inundated vegetation and dry upland vegetation.

Color image segmentation with support vector machines: applications to road signs detection.

PubMed

Cyganek, Bogusław

2008-08-01

In this paper we propose efficient color segmentation method which is based on the Support Vector Machine classifier operating in a one-class mode. The method has been developed especially for the road signs recognition system, although it can be used in other applications. The main advantage of the proposed method comes from the fact that the segmentation of characteristic colors is performed not in the original but in the higher dimensional feature space. By this a better data encapsulation with a linear hypersphere can be usually achieved. Moreover, the classifier does not try to capture the whole distribution of the input data which is often difficult to achieve. Instead, the characteristic data samples, called support vectors, are selected which allow construction of the tightest hypersphere that encloses majority of the input data. Then classification of a test data simply consists in a measurement of its distance to a centre of the found hypersphere. The experimental results show high accuracy and speed of the proposed method.

Performance optimization of a hybrid micro-grid based on double-loop MPPT and SVC-MERS

NASA Astrophysics Data System (ADS)

Wei, Yewen; Hou, Xilun; Zhang, Xiang; Xiong, Shengnan; Peng, Fei

2018-02-01

With ever-increasing concerns on environmental pollution and energy shortage, the development of renewable resource has attracted a lot of attention. This paper first reviews both the wind and photovoltaic (PV) generation techniques and approaches of micro-grid voltage control. Then, a novel islanded micro-grid, which consists of wind & PV generation and hybrid-energy storage device, is built for application to remote and isolated areas. For the PV power generation branch, a double- maximum power point tracking (MPPT) technique is developed to trace the sunlight and regulate the tilt angle of PV panels. For wind-power generation branch, squirrel cage induction generator (SCIG) is used as its simple structure, robustness and less cost. In order to stabilize the output voltage of SCIGs, a new Static Var Compensator named magnetic energy recovery switch (SVC-MERS) is applied. Finally, experimental results confirm that both of the proposed methods can improve the efficiency of PV power generation and voltage stability of the micro-grid, respectively.

Treatment of Superior Vena Cava (SVC) Syndrome and Inferior Vena Cava (IVC) Thrombosis in a Patient with Colorectal Cancer: Combination of SVC Stenting and IVC Filter Placement to Palliate Symptoms and Pave the Way for Port Implantation

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

Sauter, Alexander; Triller, Juergen; Schmidt, Felix

Thrombosis of the inferior vena cava is a life-threatening complication in cancer patients leading to pulmonary embolism. These patients can also be affected by superior vena cava syndrome causing dyspnea followed by trunk or extremity swelling. We report the case of a 61-year-old female suffering from an extended colorectal tumor who became affected by both of the mentioned complications. Due to thrombus formation within the right vena jugularis interna, thrombosis of the inferior vena cava, and superior vena cava syndrome, a combined interventional procedure via a left jugular access with stenting of the superior vena cava and filter placement intomore » the inferior vena cava was performed As a consequence, relief of the patient's symptoms, prevention of pulmonary embolism, and paving of the way for further venous chemotherapy were achieved.« less

Automated implantable cardioverter defibrillator lead infection in a patient with previous superior vena cava thrombosis.

PubMed

Connelly, Tara; Siddiqui, Sadiq; Kolcow, Walenty; Veerasingam, Dave

2015-11-04

We present a case of a 44-year-old woman who presented with cough, pleuritic chest pain and fever leading to a diagnosis of pneumonia±pulmonary embolism. She had a history of familial hypertrophic obstructive cardiomyopathy (HOCM), for which an automated implantable cardioverter defibrillator (AICD) had been implanted, and a subsequent superior vena cava (SVC) thrombus, for which she was anticoagulated with warfarin. On admission, blood cultures grew a coagulase-negative Staphylococcus. CT pulmonary angiogram and transoesophageal echocardiography (TOE) were performed and revealed large vegetations adherent to the AICD leads with complete occlusion of the SVC. The infected leads were the source of sepsis. Open surgery was planned. For cardiopulmonary bypass, the venous cannula was inserted in the inferior vena cava (IVC) and a completely bloodless field was obtained in the right atrium allowing for the extraction of the AICD leads completely, along with the adherent vegetations from within. 2015 BMJ Publishing Group Ltd.

Using support vector machines to improve elemental ion identification in macromolecular crystal structures

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

Morshed, Nader; Lawrence Berkeley National Laboratory, Berkeley, CA 94720; Echols, Nathaniel, E-mail: nechols@lbl.gov

2015-05-01

A method to automatically identify possible elemental ions in X-ray crystal structures has been extended to use support vector machine (SVM) classifiers trained on selected structures in the PDB, with significantly improved sensitivity over manually encoded heuristics. In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here,more » the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalous diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.« less

LBP and SIFT based facial expression recognition

NASA Astrophysics Data System (ADS)

Sumer, Omer; Gunes, Ece O.

2015-02-01

This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.

Analysis of spectrally resolved autofluorescence images by support vector machines

NASA Astrophysics Data System (ADS)

Mateasik, A.; Chorvat, D.; Chorvatova, A.

2013-02-01

Spectral analysis of the autofluorescence images of isolated cardiac cells was performed to evaluate and to classify the metabolic state of the cells in respect to the responses to metabolic modulators. The classification was done using machine learning approach based on support vector machine with the set of the automatically calculated features from recorded spectral profile of spectral autofluorescence images. This classification method was compared with the classical approach where the individual spectral components contributing to cell autofluorescence were estimated by spectral analysis, namely by blind source separation using non-negative matrix factorization. Comparison of both methods showed that machine learning can effectively classify the spectrally resolved autofluorescence images without the need of detailed knowledge about the sources of autofluorescence and their spectral properties.

Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images

PubMed Central

Srinivasan, Pratul P.; Kim, Leo A.; Mettu, Priyatham S.; Cousins, Scott W.; Comer, Grant M.; Izatt, Joseph A.; Farsiu, Sina

2014-01-01

We present a novel fully automated algorithm for the detection of retinal diseases via optical coherence tomography (OCT) imaging. Our algorithm utilizes multiscale histograms of oriented gradient descriptors as feature vectors of a support vector machine based classifier. The spectral domain OCT data sets used for cross-validation consisted of volumetric scans acquired from 45 subjects: 15 normal subjects, 15 patients with dry age-related macular degeneration (AMD), and 15 patients with diabetic macular edema (DME). Our classifier correctly identified 100% of cases with AMD, 100% cases with DME, and 86.67% cases of normal subjects. This algorithm is a potentially impactful tool for the remote diagnosis of ophthalmic diseases. PMID:25360373

Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors.

PubMed

Rodriguez Gutierrez, D; Awwad, A; Meijer, L; Manita, M; Jaspan, T; Dineen, R A; Grundy, R G; Auer, D P

2014-05-01

Qualitative radiologic MR imaging review affords limited differentiation among types of pediatric posterior fossa brain tumors and cannot detect histologic or molecular subtypes, which could help to stratify treatment. This study aimed to improve current posterior fossa discrimination of histologic tumor type by using support vector machine classifiers on quantitative MR imaging features. This retrospective study included preoperative MRI in 40 children with posterior fossa tumors (17 medulloblastomas, 16 pilocytic astrocytomas, and 7 ependymomas). Shape, histogram, and textural features were computed from contrast-enhanced T2WI and T1WI and diffusivity (ADC) maps. Combinations of features were used to train tumor-type-specific classifiers for medulloblastoma, pilocytic astrocytoma, and ependymoma types in separation and as a joint posterior fossa classifier. A tumor-subtype classifier was also produced for classic medulloblastoma. The performance of different classifiers was assessed and compared by using randomly selected subsets of training and test data. ADC histogram features (25th and 75th percentiles and skewness) yielded the best classification of tumor type (on average >95.8% of medulloblastomas, >96.9% of pilocytic astrocytomas, and >94.3% of ependymomas by using 8 training samples). The resulting joint posterior fossa classifier correctly assigned >91.4% of the posterior fossa tumors. For subtype classification, 89.4% of classic medulloblastomas were correctly classified on the basis of ADC texture features extracted from the Gray-Level Co-Occurence Matrix. Support vector machine-based classifiers using ADC histogram features yielded very good discrimination among pediatric posterior fossa tumor types, and ADC textural features show promise for further subtype discrimination. These findings suggest an added diagnostic value of quantitative feature analysis of diffusion MR imaging in pediatric neuro-oncology. © 2014 by American Journal of Neuroradiology.

Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines.

PubMed

Zhang, Ming-Huan; Ma, Jun-Shan; Shen, Ying; Chen, Ying

2016-09-01

This study aimed to investigate the optimal support vector machines (SVM)-based classifier of duchenne muscular dystrophy (DMD) magnetic resonance imaging (MRI) images. T1-weighted (T1W) and T2-weighted (T2W) images of the 15 boys with DMD and 15 normal controls were obtained. Textural features of the images were extracted and wavelet decomposed, and then, principal features were selected. Scale transform was then performed for MRI images. Afterward, SVM-based classifiers of MRI images were analyzed based on the radical basis function and decomposition levels. The cost (C) parameter and kernel parameter [Formula: see text] were used for classification. Then, the optimal SVM-based classifier, expressed as [Formula: see text]), was identified by performance evaluation (sensitivity, specificity and accuracy). Eight of 12 textural features were selected as principal features (eigenvalues [Formula: see text]). The 16 SVM-based classifiers were obtained using combination of (C, [Formula: see text]), and those with lower C and [Formula: see text] values showed higher performances, especially classifier of [Formula: see text]). The SVM-based classifiers of T1W images showed higher performance than T1W images at the same decomposition level. The T1W images in classifier of [Formula: see text]) at level 2 decomposition showed the highest performance of all, and its overall correct sensitivity, specificity, and accuracy reached 96.9, 97.3, and 97.1 %, respectively. The T1W images in SVM-based classifier [Formula: see text] at level 2 decomposition showed the highest performance of all, demonstrating that it was the optimal classification for the diagnosis of DMD.

Combined data mining/NIR spectroscopy for purity assessment of lime juice

NASA Astrophysics Data System (ADS)

Shafiee, Sahameh; Minaei, Saeid

2018-06-01

This paper reports the data mining study on the NIR spectrum of lime juice samples to determine their purity (natural or synthetic). NIR spectra for 72 pure and synthetic lime juice samples were recorded in reflectance mode. Sample outliers were removed using PCA analysis. Different data mining techniques for feature selection (Genetic Algorithm (GA)) and classification (including the radial basis function (RBF) network, Support Vector Machine (SVM), and Random Forest (RF) tree) were employed. Based on the results, SVM proved to be the most accurate classifier as it achieved the highest accuracy (97%) using the raw spectrum information. The classifier accuracy dropped to 93% when selected feature vector by GA search method was applied as classifier input. It can be concluded that some relevant features which produce good performance with the SVM classifier are removed by feature selection. Also, reduced spectra using PCA do not show acceptable performance (total accuracy of 66% by RBFNN), which indicates that dimensional reduction methods such as PCA do not always lead to more accurate results. These findings demonstrate the potential of data mining combination with near-infrared spectroscopy for monitoring lime juice quality in terms of natural or synthetic nature.

A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier.

PubMed

Zhou, Shenghan; Qian, Silin; Chang, Wenbing; Xiao, Yiyong; Cheng, Yang

2018-06-14

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

Application of machine learning on brain cancer multiclass classification

NASA Astrophysics Data System (ADS)

Panca, V.; Rustam, Z.

2017-07-01

Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

Conformal Galilei algebras, symmetric polynomials and singular vectors

NASA Astrophysics Data System (ADS)

Křižka, Libor; Somberg, Petr

2018-01-01

We classify and explicitly describe homomorphisms of Verma modules for conformal Galilei algebras cga_ℓ (d,C) with d=1 for any integer value ℓ \\in N. The homomorphisms are uniquely determined by singular vectors as solutions of certain differential operators of flag type and identified with specific polynomials arising as coefficients in the expansion of a parametric family of symmetric polynomials into power sum symmetric polynomials.

The FKMM-invariant in low dimension

NASA Astrophysics Data System (ADS)

De Nittis, Giuseppe; Gomi, Kiyonori

2018-05-01

In this paper, we investigate the problem of the cohomological classification of "Quaternionic" vector bundles in low dimension (d≤slant 3). We show that there exists a characteristic class κ , called the FKMM-invariant, which takes value in the relative equivariant Borel cohomology and completely classifies "Quaternionic" vector bundles in low dimension. The main subject of the paper concerns a discussion about the surjectivity of κ.

Feature selection using a one dimensional naïve Bayes’ classifier increases the accuracy of support vector machine classification of CDR3 repertoires

PubMed Central

Cinelli, Mattia; Sun, , Yuxin; Best, Katharine; Heather, James M.; Reich-Zeliger, Shlomit; Shifrut, Eric; Friedman, Nir; Shawe-Taylor, John; Chain, Benny

2017-01-01

Abstract Motivation: Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund’s adjuvant (CFA) or CFA alone. Results: The CDR3β sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave-one-out validation test reaching >90% in some cases. Summary: The study describes a novel two-stage classification strategy combining a one-dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund’s Adjuvant. Availability and implementation: The sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893. The Decombinator package is available at github.com/innate2adaptive/Decombinator. The R package e1071 is available at the CRAN repository https://cran.r-project.org/web/packages/e1071/index.html. Contact: b.chain@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28073756

Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield.

PubMed

Hassanpour, Saeed; Langlotz, Curtis P; Amrhein, Timothy J; Befera, Nicholas T; Lungren, Matthew P

2017-04-01

The purpose of this study is to evaluate the performance of a natural language processing (NLP) system in classifying a database of free-text knee MRI reports at two separate academic radiology practices. An NLP system that uses terms and patterns in manually classified narrative knee MRI reports was constructed. The NLP system was trained and tested on expert-classified knee MRI reports from two major health care organizations. Radiology reports were modeled in the training set as vectors, and a support vector machine framework was used to train the classifier. A separate test set from each organization was used to evaluate the performance of the system. We evaluated the performance of the system both within and across organizations. Standard evaluation metrics, such as accuracy, precision, recall, and F1 score (i.e., the weighted average of the precision and recall), and their respective 95% CIs were used to measure the efficacy of our classification system. The accuracy for radiology reports that belonged to the model's clinically significant concept classes after training data from the same institution was good, yielding an F1 score greater than 90% (95% CI, 84.6-97.3%). Performance of the classifier on cross-institutional application without institution-specific training data yielded F1 scores of 77.6% (95% CI, 69.5-85.7%) and 90.2% (95% CI, 84.5-95.9%) at the two organizations studied. The results show excellent accuracy by the NLP machine learning classifier in classifying free-text knee MRI reports, supporting the institution-independent reproducibility of knee MRI report classification. Furthermore, the machine learning classifier performed well on free-text knee MRI reports from another institution. These data support the feasibility of multiinstitutional classification of radiologic imaging text reports with a single machine learning classifier without requiring institution-specific training data.

Vaxvec: The first web-based recombinant vaccine vector database and its data analysis

PubMed Central

Deng, Shunzhou; Martin, Carly; Patil, Rasika; Zhu, Felix; Zhao, Bin; Xiang, Zuoshuang; He, Yongqun

2015-01-01

A recombinant vector vaccine uses an attenuated virus, bacterium, or parasite as the carrier to express a heterologous antigen(s). Many recombinant vaccine vectors and related vaccines have been developed and extensively investigated. To compare and better understand recombinant vectors and vaccines, we have generated Vaxvec (http://www.violinet.org/vaxvec), the first web-based database that stores various recombinant vaccine vectors and those experimentally verified vaccines that use these vectors. Vaxvec has now included 59 vaccine vectors that have been used in 196 recombinant vector vaccines against 66 pathogens and cancers. These vectors are classified to 41 viral vectors, 15 bacterial vectors, 1 parasitic vector, and 1 fungal vector. The most commonly used viral vaccine vectors are double-stranded DNA viruses, including herpesviruses, adenoviruses, and poxviruses. For example, Vaxvec includes 63 poxvirus-based recombinant vaccines for over 20 pathogens and cancers. Vaxvec collects 30 recombinant vector influenza vaccines that use 17 recombinant vectors and were experimentally tested in 7 animal models. In addition, over 60 protective antigens used in recombinant vector vaccines are annotated and analyzed. User-friendly web-interfaces are available for querying various data in Vaxvec. To support data exchange, the information of vaccine vectors, vaccines, and related information is stored in the Vaccine Ontology (VO). Vaxvec is a timely and vital source of vaccine vector database and facilitates efficient vaccine vector research and development. PMID:26403370

Classification of subsurface objects using singular values derived from signal frames

DOEpatents

Chambers, David H; Paglieroni, David W

2014-05-06

The classification system represents a detected object with a feature vector derived from the return signals acquired by an array of N transceivers operating in multistatic mode. The classification system generates the feature vector by transforming the real-valued return signals into complex-valued spectra, using, for example, a Fast Fourier Transform. The classification system then generates a feature vector of singular values for each user-designated spectral sub-band by applying a singular value decomposition (SVD) to the N.times.N square complex-valued matrix formed from sub-band samples associated with all possible transmitter-receiver pairs. The resulting feature vector of singular values may be transformed into a feature vector of singular value likelihoods and then subjected to a multi-category linear or neural network classifier for object classification.

Ship localization in Santa Barbara Channel using machine learning classifiers.

PubMed

Niu, Haiqiang; Ozanich, Emma; Gerstoft, Peter

2017-11-01

Machine learning classifiers are shown to outperform conventional matched field processing for a deep water (600 m depth) ocean acoustic-based ship range estimation problem in the Santa Barbara Channel Experiment when limited environmental information is known. Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources. The classifiers perform well up to 10 km range whereas the conventional matched field processing fails at about 4 km range without accurate environmental information.

A Method of Neighbor Classes Based SVM Classification for Optical Printed Chinese Character Recognition

PubMed Central

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR. PMID:23536777

Onboard Classifiers for Science Event Detection on a Remote Sensing Spacecraft

NASA Technical Reports Server (NTRS)

Castano, Rebecca; Mazzoni, Dominic; Tang, Nghia; Greeley, Ron; Doggett, Thomas; Cichy, Ben; Chien, Steve; Davies, Ashley

2006-01-01

Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a spacecraft to identify high priority data for downlink to Earth, providing a method for maximizing the use of a potentially bandwidth limited downlink channel. Onboard analysis can also enable rapid reaction to dynamic events, such as flooding, volcanic eruptions or sea ice break-up. Four classifiers were developed to identify cryosphere events using hyperspectral images. These classifiers include a manually constructed classifier, a Support Vector Machine (SVM), a Decision Tree and a classifier derived by searching over combinations of thresholded band ratios. Each of the classifiers was designed to run in the computationally constrained operating environment of the spacecraft. A set of scenes was hand-labeled to provide training and testing data. Performance results on the test data indicate that the SVM and manual classifiers outperformed the Decision Tree and band-ratio classifiers with the SVM yielding slightly better classifications than the manual classifier.

On multi-site damage identification using single-site training data

NASA Astrophysics Data System (ADS)

Barthorpe, R. J.; Manson, G.; Worden, K.

2017-11-01

This paper proposes a methodology for developing multi-site damage location systems for engineering structures that can be trained using single-site damaged state data only. The methodology involves training a sequence of binary classifiers based upon single-site damage data and combining the developed classifiers into a robust multi-class damage locator. In this way, the multi-site damage identification problem may be decomposed into a sequence of binary decisions. In this paper Support Vector Classifiers are adopted as the means of making these binary decisions. The proposed methodology represents an advancement on the state of the art in the field of multi-site damage identification which require either: (1) full damaged state data from single- and multi-site damage cases or (2) the development of a physics-based model to make multi-site model predictions. The potential benefit of the proposed methodology is that a significantly reduced number of recorded damage states may be required in order to train a multi-site damage locator without recourse to physics-based model predictions. In this paper it is first demonstrated that Support Vector Classification represents an appropriate approach to the multi-site damage location problem, with methods for combining binary classifiers discussed. Next, the proposed methodology is demonstrated and evaluated through application to a real engineering structure - a Piper Tomahawk trainer aircraft wing - with its performance compared to classifiers trained using the full damaged-state dataset.

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

77 FR 30002 - Primary Power, LLC v. PJM Interconnection, LLC; Notice of Complaint

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-21

... Respondent) for the Respondent's failure to designate the Complainant to construct, own, and finance two static VAR compensator (``SVC'') projects sponsored by the Complainant that have been included in the PJM Regional Transmission Expansion Plan. The Complainant requests that the Commission grant emergency, interim...

78 FR 13760 - Proposed Collection of Information: Application Form for U.S. Department of the Treasury Stored...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-28

... U.S. Department of the Treasury Stored Value Card (SVC) Program AGENCY: Financial Management Service, Fiscal Service, Treasury. ACTION: Notice and request for comments. SUMMARY: The Financial Management... written comments to Financial Management Service, Records and Information Management Branch, Room 135...

9 CFR 93.907-93.909 - [Reserved

Code of Federal Regulations, 2010 CFR

2010-01-01

... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false [Reserved] 93.907-93.909 Section 93.907-93.909 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE, DEPARTMENT OF... FOR MEANS OF CONVEYANCE AND SHIPPING CONTAINERS Aquatic Animal Species General Provisions for Svc...

9 CFR 93.907-93.909 - [Reserved

Code of Federal Regulations, 2011 CFR

2011-01-01

... 9 Animals and Animal Products 1 2011-01-01 2011-01-01 false [Reserved] 93.907-93.909 Section 93.907-93.909 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE, DEPARTMENT OF... FOR MEANS OF CONVEYANCE AND SHIPPING CONTAINERS Aquatic Animal Species General Provisions for Svc...

Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

NASA Astrophysics Data System (ADS)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qian, Wei; Zheng, Bin

2018-03-01

Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p < 0.05) and odds ratio was 4.60 with a 95% confidence interval of [3.16, 6.70]. Study demonstrated that this new LPP-based feature regeneration approach enabled to produce an optimal feature vector and yield improved performance in assisting to predict risk of women having breast cancer detected in the next subsequent mammography screening.

Activity recognition using dynamic multiple sensor fusion in body sensor networks.

PubMed

Gao, Lei; Bourke, Alan K; Nelson, John

2012-01-01

Multiple sensor fusion is a main research direction for activity recognition. However, there are two challenges in those systems: the energy consumption due to the wireless transmission and the classifier design because of the dynamic feature vector. This paper proposes a multi-sensor fusion framework, which consists of the sensor selection module and the hierarchical classifier. The sensor selection module adopts the convex optimization to select the sensor subset in real time. The hierarchical classifier combines the Decision Tree classifier with the Naïve Bayes classifier. The dataset collected from 8 subjects, who performed 8 scenario activities, was used to evaluate the proposed system. The results show that the proposed system can obviously reduce the energy consumption while guaranteeing the recognition accuracy.

Sound Processing Features for Speaker-Dependent and Phrase-Independent Emotion Recognition in Berlin Database

NASA Astrophysics Data System (ADS)

Anagnostopoulos, Christos Nikolaos; Vovoli, Eftichia

An emotion recognition framework based on sound processing could improve services in human-computer interaction. Various quantitative speech features obtained from sound processing of acting speech were tested, as to whether they are sufficient or not to discriminate between seven emotions. Multilayered perceptrons were trained to classify gender and emotions on the basis of a 24-input vector, which provide information about the prosody of the speaker over the entire sentence using statistics of sound features. Several experiments were performed and the results were presented analytically. Emotion recognition was successful when speakers and utterances were “known” to the classifier. However, severe misclassifications occurred during the utterance-independent framework. At least, the proposed feature vector achieved promising results for utterance-independent recognition of high- and low-arousal emotions.

A comparative study of machine learning models for ethnicity classification

NASA Astrophysics Data System (ADS)

Trivedi, Advait; Bessie Amali, D. Geraldine

2017-11-01

This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

Black light - How sensors filter spectral variation of the illuminant

NASA Technical Reports Server (NTRS)

Brainard, David H.; Wandell, Brian A.; Cowan, William B.

1989-01-01

Visual sensor responses may be used to classify objects on the basis of their surface reflectance functions. In a color image, the image data are represented as a vector of sensor responses at each point in the image. This vector depends both on the surface reflectance functions and on the spectral power distribution of the ambient illumination. Algorithms designed to classify objects on the basis of their surface reflectance functions typically attempt to overcome the dependence of the sensor responses on the illuminant by integrating sensor data collected from multiple surfaces. In machine vision applications, it is shown that it is often possible to design the sensor spectral responsivities so that the vector direction of the sensor responses does not depend upon the illuminant. The conditions under which this is possible are given and an illustrative calculation is performed. In biological systems, where the sensor responsivities are fixed, it is shown that some changes in the illumination cause no change in the sensor responses. Such changes in illuminant are called black illuminants. It is possible to express any illuminant as the sum of two unique components. One component is a black illuminant. The second component is called the visible component. The visible component of an illuminant completely characterizes the effect of the illuminant on the vector of sensor responses.

Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.

PubMed

Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Li, Li-Ping; Huang, De-Shuang; Yan, Gui-Ying; Nie, Ru; Huang, Yu-An

2017-04-04

Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era. In this article, we report a novel computational model combining our newly developed discriminative vector machine classifier (DVM) and an improved Weber local descriptor (IWLD) for the prediction of PPIs. Two components, differential excitation and orientation, are exploited to build evolutionary features for each protein sequence. The main characteristics of the proposed method lies in introducing an effective feature descriptor IWLD which can capture highly discriminative evolutionary information from position-specific scoring matrixes (PSSM) of protein data, and employing the powerful and robust DVM classifier. When applying the proposed method to Yeast and H. pylori data sets, we obtained excellent prediction accuracies as high as 96.52% and 91.80%, respectively, which are significantly better than the previous methods. Extensive experiments were then performed for predicting cross-species PPIs and the predictive results were also pretty promising. To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set. The experimental results obtained indicate that our method is highly effective for PPIs prediction and can be taken as a supplementary tool for future proteomics research.

[A research on real-time ventricular QRS classification methods for single-chip-microcomputers].

PubMed

Peng, L; Yang, Z; Li, L; Chen, H; Chen, E; Lin, J

1997-05-01

Ventricular QRS classification is key technique of ventricular arrhythmias detection in single-chip-microcomputer based dynamic electrocardiogram real-time analyser. This paper adopts morphological feature vector including QRS amplitude, interval information to reveal QRS morphology. After studying the distribution of QRS morphology feature vector of MIT/BIH DB ventricular arrhythmia files, we use morphological feature vector cluster to classify multi-morphology QRS. Based on the method, morphological feature parameters changing method which is suitable to catch occasional ventricular arrhythmias is presented. Clinical experiments verify missed ventricular arrhythmia is less than 1% by this method.

An Improved TA-SVM Method Without Matrix Inversion and Its Fast Implementation for Nonstationary Datasets.

PubMed

Shi, Yingzhong; Chung, Fu-Lai; Wang, Shitong

2015-09-01

Recently, a time-adaptive support vector machine (TA-SVM) is proposed for handling nonstationary datasets. While attractive performance has been reported and the new classifier is distinctive in simultaneously solving several SVM subclassifiers locally and globally by using an elegant SVM formulation in an alternative kernel space, the coupling of subclassifiers brings in the computation of matrix inversion, thus resulting to suffer from high computational burden in large nonstationary dataset applications. To overcome this shortcoming, an improved TA-SVM (ITA-SVM) is proposed using a common vector shared by all the SVM subclassifiers involved. ITA-SVM not only keeps an SVM formulation, but also avoids the computation of matrix inversion. Thus, we can realize its fast version, that is, improved time-adaptive core vector machine (ITA-CVM) for large nonstationary datasets by using the CVM technique. ITA-CVM has the merit of asymptotic linear time complexity for large nonstationary datasets as well as inherits the advantage of TA-SVM. The effectiveness of the proposed classifiers ITA-SVM and ITA-CVM is also experimentally confirmed.

Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2016-12-01

In classification, prior knowledge is incorporated in a Bayesian framework by assuming that the feature-label distribution belongs to an uncertainty class of feature-label distributions governed by a prior distribution. A posterior distribution is then derived from the prior and the sample data. An optimal Bayesian classifier (OBC) minimizes the expected misclassification error relative to the posterior distribution. From an application perspective, prior construction is critical. The prior distribution is formed by mapping a set of mathematical relations among the features and labels, the prior knowledge, into a distribution governing the probability mass across the uncertainty class. In this paper, we consider prior knowledge in the form of stochastic differential equations (SDEs). We consider a vector SDE in integral form involving a drift vector and dispersion matrix. Having constructed the prior, we develop the optimal Bayesian classifier between two models and examine, via synthetic experiments, the effects of uncertainty in the drift vector and dispersion matrix. We apply the theory to a set of SDEs for the purpose of differentiating the evolutionary history between two species.

Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images.

PubMed

Alexandridis, Thomas K; Tamouridou, Afroditi Alexandra; Pantazi, Xanthoula Eirini; Lagopodi, Anastasia L; Kashefi, Javid; Ovakoglou, Georgios; Polychronos, Vassilios; Moshou, Dimitrios

2017-09-01

In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

Vaxvec: The first web-based recombinant vaccine vector database and its data analysis.

PubMed

Deng, Shunzhou; Martin, Carly; Patil, Rasika; Zhu, Felix; Zhao, Bin; Xiang, Zuoshuang; He, Yongqun

2015-11-27

A recombinant vector vaccine uses an attenuated virus, bacterium, or parasite as the carrier to express a heterologous antigen(s). Many recombinant vaccine vectors and related vaccines have been developed and extensively investigated. To compare and better understand recombinant vectors and vaccines, we have generated Vaxvec (http://www.violinet.org/vaxvec), the first web-based database that stores various recombinant vaccine vectors and those experimentally verified vaccines that use these vectors. Vaxvec has now included 59 vaccine vectors that have been used in 196 recombinant vector vaccines against 66 pathogens and cancers. These vectors are classified to 41 viral vectors, 15 bacterial vectors, 1 parasitic vector, and 1 fungal vector. The most commonly used viral vaccine vectors are double-stranded DNA viruses, including herpesviruses, adenoviruses, and poxviruses. For example, Vaxvec includes 63 poxvirus-based recombinant vaccines for over 20 pathogens and cancers. Vaxvec collects 30 recombinant vector influenza vaccines that use 17 recombinant vectors and were experimentally tested in 7 animal models. In addition, over 60 protective antigens used in recombinant vector vaccines are annotated and analyzed. User-friendly web-interfaces are available for querying various data in Vaxvec. To support data exchange, the information of vaccine vectors, vaccines, and related information is stored in the Vaccine Ontology (VO). Vaxvec is a timely and vital source of vaccine vector database and facilitates efficient vaccine vector research and development. Copyright © 2015 Elsevier Ltd. All rights reserved.

Comparison of Classifiers for Decoding Sensory and Cognitive Information from Prefrontal Neuronal Populations

PubMed Central

Astrand, Elaine; Enel, Pierre; Ibos, Guilhem; Dominey, Peter Ford; Baraduc, Pierre; Ben Hamed, Suliann

2014-01-01

Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF): the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders. PMID:24466019

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2011-09-30

Newport RI 02842 phone: (401) 832-5749 fax: (401) 832-4441 email: David.Moretti@navy.mil Steve W. Martin SPAWAR Systems Center Pacific...APPROACH Odontocete click detection and classification. A multiclass support vector machine (SVM) classifier was previously developed ( Jarvis et...beaked whales, Risso’s dolphins, short-finned pilot whales, and sperm whales. Here Moretti’s group, especially S. Jarvis , will improve the SVM classifier

A Generic multi-dimensional feature extraction method using multiobjective genetic programming.

PubMed

Zhang, Yang; Rockett, Peter I

2009-01-01

In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.

Hidden Markov Model and Support Vector Machine based decoding of finger movements using Electrocorticography

PubMed Central

Wissel, Tobias; Pfeiffer, Tim; Frysch, Robert; Knight, Robert T.; Chang, Edward F.; Hinrichs, Hermann; Rieger, Jochem W.; Rose, Georg

2013-01-01

Objective Support Vector Machines (SVM) have developed into a gold standard for accurate classification in Brain-Computer-Interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of Hidden Markov Models (HMM)for online BCIs and discuss strategies to improve their performance. Approach We compare the SVM, serving as a reference, and HMMs for classifying discrete finger movements obtained from the Electrocorticograms of four subjects doing a finger tapping experiment. The classifier decisions are based on a subset of low-frequency time domain and high gamma oscillation features. Main results We show that decoding optimization between the two approaches is due to the way features are extracted and selected and less dependent on the classifier. An additional gain in HMM performance of up to 6% was obtained by introducing model constraints. Comparable accuracies of up to 90% were achieved with both SVM and HMM with the high gamma cortical response providing the most important decoding information for both techniques. Significance We discuss technical HMM characteristics and adaptations in the context of the presented data as well as for general BCI applications. Our findings suggest that HMMs and their characteristics are promising for efficient online brain-computer interfaces. PMID:24045504

Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA

PubMed Central

Ma, Xiaoqi

2015-01-01

A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time. PMID:26543867

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

PubMed Central

Ma, Suliang; Wu, Jianwen; Wang, Yuhao; Jia, Bowen; Jiang, Yuan

2018-01-01

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods. PMID:29659548

Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

NASA Astrophysics Data System (ADS)

Löw, Fabian; Schorcht, Gunther; Michel, Ulrich; Dech, Stefan; Conrad, Christopher

2012-10-01

Accurate crop identification and crop area estimation are important for studies on irrigated agricultural systems, yield and water demand modeling, and agrarian policy development. In this study a novel combination of Random Forest (RF) and Support Vector Machine (SVM) classifiers is presented that (i) enhances crop classification accuracy and (ii) provides spatial information on map uncertainty. The methodology was implemented over four distinct irrigated sites in Middle Asia using RapidEye time series data. The RF feature importance statistics was used as feature-selection strategy for the SVM to assess possible negative effects on classification accuracy caused by an oversized feature space. The results of the individual RF and SVM classifications were combined with rules based on posterior classification probability and estimates of classification probability entropy. SVM classification performance was increased by feature selection through RF. Further experimental results indicate that the hybrid classifier improves overall classification accuracy in comparison to the single classifiers as well as useŕs and produceŕs accuracy.

NATO/CCMS Pilot Study Evaluation of Demonstrated and ...

EPA Pesticide Factsheets

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77 FR 31642 - Hawker Beech Craft Defense Company, LLC, Also Known As Hawker Beechcraft Corporation, Also Known...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-29

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-74,901] Hawker Beech Craft Defense Company, LLC, Also Known As Hawker Beechcraft Corporation, Also Known As Hawker Beechcraft International SVC, Also Known As Rapid Surplus Parts, Also Known As Hawker Beechcraft Svcs, Also Known As Travel...

9 CFR 93.903 - Import permits for live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 9 Animals and Animal Products 1 2014-01-01 2014-01-01 false Import permits for live fish... ANIMAL PRODUCTS IMPORTATION OF CERTAIN ANIMALS, BIRDS, FISH, AND POULTRY, AND CERTAIN ANIMAL, BIRD, AND... General Provisions for Svc-Regulated Fish Species § 93.903 Import permits for live fish, fertilized eggs...

9 CFR 93.904 - Health certificate for live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 9 Animals and Animal Products 1 2014-01-01 2014-01-01 false Health certificate for live fish... ANIMAL PRODUCTS IMPORTATION OF CERTAIN ANIMALS, BIRDS, FISH, AND POULTRY, AND CERTAIN ANIMAL, BIRD, AND... General Provisions for Svc-Regulated Fish Species § 93.904 Health certificate for live fish, fertilized...

9 CFR 93.903 - Import permits for live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 9 Animals and Animal Products 1 2012-01-01 2012-01-01 false Import permits for live fish... ANIMAL PRODUCTS IMPORTATION OF CERTAIN ANIMALS, BIRDS, FISH, AND POULTRY, AND CERTAIN ANIMAL, BIRD, AND... General Provisions for Svc-Regulated Fish Species § 93.903 Import permits for live fish, fertilized eggs...

9 CFR 93.903 - Import permits for live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 9 Animals and Animal Products 1 2013-01-01 2013-01-01 false Import permits for live fish... ANIMAL PRODUCTS IMPORTATION OF CERTAIN ANIMALS, BIRDS, FISH, AND POULTRY, AND CERTAIN ANIMAL, BIRD, AND... General Provisions for Svc-Regulated Fish Species § 93.903 Import permits for live fish, fertilized eggs...

75 FR 62424 - EDS, an HP Company (Re-Branded as HP-Enterprise Services) Including On-Site Workers From: Abel...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-08

... Floyd and Company, Trinity Government SYS a Private Co, Verizon Network Integration Corp, Vision... Provider Inc., Teksystems, The Experts Inc., TM Floyd and Company, Trinity Government SYS a Private Co... Compuware Corp Comsys Information Technology SVC, Diversified Systems Inc., E- Corn LLC, Farrington...

9 CFR 93.904 - Health certificate for live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2011 CFR

2011-01-01

..., fertilized eggs, and gametes. 93.904 Section 93.904 Animals and Animal Products ANIMAL AND PLANT HEALTH... eggs, and gametes. (a) General. All live fish, fertilized eggs, and gametes of SVC-susceptible species... from the date of issuance. The health certificate for the live fish, fertilized eggs, or gametes must...

9 CFR 93.906 - Inspection at the port of entry.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 9 Animals and Animal Products 1 2011-01-01 2011-01-01 false Inspection at the port of entry. 93.906 Section 93.906 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE, DEPARTMENT...; REQUIREMENTS FOR MEANS OF CONVEYANCE AND SHIPPING CONTAINERS Aquatic Animal Species General Provisions for Svc...

9 CFR 93.906 - Inspection at the port of entry.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Inspection at the port of entry. 93.906 Section 93.906 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE, DEPARTMENT...; REQUIREMENTS FOR MEANS OF CONVEYANCE AND SHIPPING CONTAINERS Aquatic Animal Species General Provisions for Svc...

Exo- and endocytotic trafficking of SCAMP2.

PubMed

Toyooka, Kiminori; Matsuoka, Ken

2009-12-01

Exo- and endocytotic membrane trafficking is an essential process for transport of secretory proteins, extracellular glycans, transporters and lipids in plant cells. Using secretory carrier membrane protein 2 (SCAMP2) as a marker for secretory vesicles and tobacco BY-2 cells as a model system, we recently demonstrated that SCAMP2 positive structures containing secretory materials are transported from the Golgi apparatus to the plasma membrane (PM) and/or cell plate. This structure is consisted with clustered vesicles and was thus named the secretory vesicle cluster (SVC). Here, we have utilized the reversible photoswitching fluorescent protein Dronpa1 to trace the movement of SCAMP2 on the PM and cell plate. Activated SCAMP2-Dronpa fluorescence on the PM and cell plate moved into the BY-2 cells within several minutes, but did not spread around PM. This is consistent with recycling of SCAMP2 among endomembrane compartments such as the TGN, PM and cell plate. The relationship between SVC-mediated trafficking and exo- and endocytosis of plant cells is discussed taking into account this new data and knowledge provided by recent reports.

Secure transport and adaptation of MC-EZBC video utilizing H.264-based transport protocols☆

PubMed Central

Hellwagner, Hermann; Hofbauer, Heinz; Kuschnig, Robert; Stütz, Thomas; Uhl, Andreas

2012-01-01

Universal Multimedia Access (UMA) calls for solutions where content is created once and subsequently adapted to given requirements. With regard to UMA and scalability, which is required often due to a wide variety of end clients, the best suited codecs are wavelet based (like the MC-EZBC) due to their inherent high number of scaling options. However, most transport technologies for delivering videos to end clients are targeted toward the H.264/AVC standard or, if scalability is required, the H.264/SVC. In this paper we will introduce a mapping of the MC-EZBC bitstream to existing H.264/SVC based streaming and scaling protocols. This enables the use of highly scalable wavelet based codecs on the one hand and the utilization of already existing network technologies without accruing high implementation costs on the other hand. Furthermore, we will evaluate different scaling options in order to choose the best option for given requirements. Additionally, we will evaluate different encryption options based on transport and bitstream encryption for use cases where digital rights management is required. PMID:26869746

Mediastinal germ cell tumour causing superior vena cava tumour thrombosis.

PubMed

Karanth, Suman S; Vaid, Ashok K; Batra, Sandeep; Sharma, Devender

2015-03-25

We report a rare case of a 35-year-old man who presented with a 1-week history of retrosternal chest pain of moderate intensity. A positron emission tomography CT (PET-CT) showed a large fluorodeoxy-glucose (FDG)-avid heterogeneously enhancing necrotic mass in the anterosuperior mediastinum with a focal FDG-avid thrombosis of the superior vena cava (SVC) suggestive of tumour thrombus and vascular invasion. α-Fetoprotein levels were raised (5690 IU/L). Image guided biopsy of the mediastinal mass was suggestive of non-seminomatous germ cell tumour (NSGCT). The patient received four cycles of BEP (bleomycin, etoposide and cisplatin) along with therapeutic anticoagulation with low-molecular-weight heparin. Follow-up whole body PET-CT revealed complete resolution of mediastinal mass and SVC tumour thrombosis. The documentation of FDG-PET-avid tumour thrombus resolving with chemotherapy supports the concept of circulating tumour cells being important not only in common solid tumours such as breast and colon cancer but also in relatively less common tumours such as NSGCT. The detection of circulating tumour cells could help deploy aggressive regimens upfront. 2015 BMJ Publishing Group Ltd.

TLBO based Voltage Stable Environment Friendly Economic Dispatch Considering Real and Reactive Power Constraints

NASA Astrophysics Data System (ADS)

Verma, H. K.; Mafidar, P.

2013-09-01

In view of growing concern towards environment, power system engineers are forced to generate quality green energy. Hence the economic dispatch (ED) aims at the power generation to meet the load demand at minimum fuel cost with environmental and voltage constraints along with essential constraints on real and reactive power. The emission control which reduces the negative impact on environment is achieved by including the additional constraints in ED problem. Presently, the power system mostly operates near its stability limits, therefore with increased demand the system faces voltage problem. The bus voltages are brought within limit in the present work by placement of static var compensator (SVC) at weak bus which is identified from bus participation factor. The optimal size of SVC is determined by univariate search method. This paper presents the use of Teaching Learning based Optimization (TLBO) algorithm for voltage stable environment friendly ED problem with real and reactive power constraints. The computational effectiveness of TLBO is established through test results over particle swarm optimization (PSO) and Big Bang-Big Crunch (BB-BC) algorithms for the ED problem.

Application of polynomial control to design a robust oscillation-damping controller in a multimachine power system.

PubMed

Hasanvand, Hamed; Mozafari, Babak; Arvan, Mohammad R; Amraee, Turaj

2015-11-01

This paper addresses the application of a static Var compensator (SVC) to improve the damping of interarea oscillations. Optimal location and size of SVC are defined using bifurcation and modal analysis to satisfy its primary application. Furthermore, the best-input signal for damping controller is selected using Hankel singular values and right half plane-zeros. The proposed approach is aimed to design a robust PI controller based on interval plants and Kharitonov's theorem. The objective here is to determine the stability region to attain robust stability, the desired phase margin, gain margin, and bandwidth. The intersection of the resulting stability regions yields the set of kp-ki parameters. In addition, optimal multiobjective design of PI controller using particle swarm optimization (PSO) algorithm is presented. The effectiveness of the suggested controllers in damping of local and interarea oscillation modes of a multimachine power system, over a wide range of loading conditions and system configurations, is confirmed through eigenvalue analysis and nonlinear time domain simulation. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

New vision in fractional radiofrequency technology with switching, vacuum and cooling.

PubMed

Elman, Monica; Gauthier, Nelly; Belenky, Inna

2015-04-01

Since the introduction of fractional technology, various systems were launched to the market. The first generation of fractional RF systems created epidermal ablation with coagulative/necrosis of the dermis with sufficient clinical outcomes, but with some limitations. The aim of this study was to evaluate the efficacy and safety of SVC technology, based on the principle of separate biological responses. Fifty-two patients were treated for 3-6 sessions using fractional RF handpiece and eight patients received combination treatments with non-invasive RF handpiece. All volunteers showed notable to significant improvement in the photoageing symptoms, without any significant complications or adverse events. Due to its wide spectrum of parameters, the SVC technology can promote different biological responses. Owing to the "Switching" technology, the control of energy depth penetration enables delivery of the necessary thermal dose to the targeted skin layer. In addition, this novel technology includes the "Vacuum" and "Cooling" mechanisms, each contributing to the safety of the treatment. The Smart Heat function reduces the necessary energy levels and thereby reduces the pain level and risks for side effects.

Classifier utility modeling and analysis of hypersonic inlet start/unstart considering training data costs

NASA Astrophysics Data System (ADS)

Chang, Juntao; Hu, Qinghua; Yu, Daren; Bao, Wen

2011-11-01

Start/unstart detection is one of the most important issues of hypersonic inlets and is also the foundation of protection control of scramjet. The inlet start/unstart detection can be attributed to a standard pattern classification problem, and the training sample costs have to be considered for the classifier modeling as the CFD numerical simulations and wind tunnel experiments of hypersonic inlets both cost time and money. To solve this problem, the CFD simulation of inlet is studied at first step, and the simulation results could provide the training data for pattern classification of hypersonic inlet start/unstart. Then the classifier modeling technology and maximum classifier utility theories are introduced to analyze the effect of training data cost on classifier utility. In conclusion, it is useful to introduce support vector machine algorithms to acquire the classifier model of hypersonic inlet start/unstart, and the minimum total cost of hypersonic inlet start/unstart classifier can be obtained by the maximum classifier utility theories.

Prediction of cell penetrating peptides by support vector machines.

PubMed

Sanders, William S; Johnston, C Ian; Bridges, Susan M; Burgess, Shane C; Willeford, Kenneth O

2011-07-01

Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating.

Integrating support vector machines and random forests to classify crops in time series of Worldview-2 images

NASA Astrophysics Data System (ADS)

Zafari, A.; Zurita-Milla, R.; Izquierdo-Verdiguier, E.

2017-10-01

Crop maps are essential inputs for the agricultural planning done at various governmental and agribusinesses agencies. Remote sensing offers timely and costs efficient technologies to identify and map crop types over large areas. Among the plethora of classification methods, Support Vector Machine (SVM) and Random Forest (RF) are widely used because of their proven performance. In this work, we study the synergic use of both methods by introducing a random forest kernel (RFK) in an SVM classifier. A time series of multispectral WorldView-2 images acquired over Mali (West Africa) in 2014 was used to develop our case study. Ground truth containing five common crop classes (cotton, maize, millet, peanut, and sorghum) were collected at 45 farms and used to train and test the classifiers. An SVM with the standard Radial Basis Function (RBF) kernel, a RF, and an SVM-RFK were trained and tested over 10 random training and test subsets generated from the ground data. Results show that the newly proposed SVM-RFK classifier can compete with both RF and SVM-RBF. The overall accuracies based on the spectral bands only are of 83, 82 and 83% respectively. Adding vegetation indices to the analysis result in the classification accuracy of 82, 81 and 84% for SVM-RFK, RF, and SVM-RBF respectively. Overall, it can be observed that the newly tested RFK can compete with SVM-RBF and RF classifiers in terms of classification accuracy.

A machine learning approach for classification of anatomical coverage in CT

NASA Astrophysics Data System (ADS)

Wang, Xiaoyong; Lo, Pechin; Ramakrishna, Bharath; Goldin, Johnathan; Brown, Matthew

2016-03-01

Automatic classification of anatomical coverage of medical images is critical for big data mining and as a pre-processing step to automatically trigger specific computer aided diagnosis systems. The traditional way to identify scans through DICOM headers has various limitations due to manual entry of series descriptions and non-standardized naming conventions. In this study, we present a machine learning approach where multiple binary classifiers were used to classify different anatomical coverages of CT scans. A one-vs-rest strategy was applied. For a given training set, a template scan was selected from the positive samples and all other scans were registered to it. Each registered scan was then evenly split into k × k × k non-overlapping blocks and for each block the mean intensity was computed. This resulted in a 1 × k3 feature vector for each scan. The feature vectors were then used to train a SVM based classifier. In this feasibility study, four classifiers were built to identify anatomic coverages of brain, chest, abdomen-pelvis, and chest-abdomen-pelvis CT scans. Each classifier was trained and tested using a set of 300 scans from different subjects, composed of 150 positive samples and 150 negative samples. Area under the ROC curve (AUC) of the testing set was measured to evaluate the performance in a two-fold cross validation setting. Our results showed good classification performance with an average AUC of 0.96.

Classification of burn wounds using support vector machines

NASA Astrophysics Data System (ADS)

Acha, Begona; Serrano, Carmen; Palencia, Sergio; Murillo, Juan Jose

2004-05-01

The purpose of this work is to improve a previous method developed by the authors for the classification of burn wounds into their depths. The inputs of the system are color and texture information, as these are the characteristics observed by physicians in order to give a diagnosis. Our previous work consisted in segmenting the burn wound from the rest of the image and classifying the burn into its depth. In this paper we focus on the classification problem only. We already proposed to use a Fuzzy-ARTMAP neural network (NN). However, we may take advantage of new powerful classification tools such as Support Vector Machines (SVM). We apply the five-folded cross validation scheme to divide the database into training and validating sets. Then, we apply a feature selection method for each classifier, which will give us the set of features that yields the smallest classification error for each classifier. Features used to classify are first-order statistical parameters extracted from the L*, u* and v* color components of the image. The feature selection algorithms used are the Sequential Forward Selection (SFS) and the Sequential Backward Selection (SBS) methods. As data of the problem faced here are not linearly separable, the SVM was trained using some different kernels. The validating process shows that the SVM method, when using a Gaussian kernel of variance 1, outperforms classification results obtained with the rest of the classifiers, yielding an error classification rate of 0.7% whereas the Fuzzy-ARTMAP NN attained 1.6 %.

Real-time object-to-features vectorisation via Siamese neural networks

NASA Astrophysics Data System (ADS)

Fedorenko, Fedor; Usilin, Sergey

2017-03-01

Object-to-features vectorisation is a hard problem to solve for objects that can be hard to distinguish. Siamese and Triplet neural networks are one of the more recent tools used for such task. However, most networks used are very deep networks that prove to be hard to compute in the Internet of Things setting. In this paper, a computationally efficient neural network is proposed for real-time object-to-features vectorisation into a Euclidean metric space. We use L2 distance to reflect feature vector similarity during both training and testing. In this way, feature vectors we develop can be easily classified using K-Nearest Neighbours classifier. Such approach can be used to train networks to vectorise such "problematic" objects like images of human faces, keypoint image patches, like keypoints on Arctic maps and surrounding marine areas.

Decision support system for diabetic retinopathy using discrete wavelet transform.

PubMed

Noronha, K; Acharya, U R; Nayak, K P; Kamath, S; Bhandary, S V

2013-03-01

Prolonged duration of the diabetes may affect the tiny blood vessels of the retina causing diabetic retinopathy. Routine eye screening of patients with diabetes helps to detect diabetic retinopathy at the early stage. It is very laborious and time-consuming for the doctors to go through many fundus images continuously. Therefore, decision support system for diabetic retinopathy detection can reduce the burden of the ophthalmologists. In this work, we have used discrete wavelet transform and support vector machine classifier for automated detection of normal and diabetic retinopathy classes. The wavelet-based decomposition was performed up to the second level, and eight energy features were extracted. Two energy features from the approximation coefficients of two levels and six energy values from the details in three orientations (horizontal, vertical and diagonal) were evaluated. These features were fed to the support vector machine classifier with various kernel functions (linear, radial basis function, polynomial of orders 2 and 3) to evaluate the highest classification accuracy. We obtained the highest average classification accuracy, sensitivity and specificity of more than 99% with support vector machine classifier (polynomial kernel of order 3) using three discrete wavelet transform features. We have also proposed an integrated index called Diabetic Retinopathy Risk Index using clinically significant wavelet energy features to identify normal and diabetic retinopathy classes using just one number. We believe that this (Diabetic Retinopathy Risk Index) can be used as an adjunct tool by the doctors during the eye screening to cross-check their diagnosis.

Video Coaching as an Efficient Teaching Method for Surgical Residents-A Randomized Controlled Trial.

PubMed

Soucisse, Mikael L; Boulva, Kerianne; Sideris, Lucas; Drolet, Pierre; Morin, Michel; Dubé, Pierre

As surgical training is evolving and operative exposure is decreasing, new, effective, and experiential learning methods are needed to ensure surgical competency and patient safety. Video coaching is an emerging concept in surgery that needs further investigation. In this randomized controlled trial conducted at a single teaching hospital, participating residents were filmed performing a side-to-side intestinal anastomosis on cadaveric dog bowel for baseline assessment. The Surgical Video Coaching (SVC) group then participated in a one-on-one video playback coaching and debriefing session with a surgeon, during which constructive feedback was given. The control group went on with their normal clinical duties without coaching or debriefing. All participants were filmed making a second intestinal anastomosis. This was compared to their first anastomosis using a 7-category-validated technical skill global rating scale, the Objective Structured Assessment of Technical Skills. A single independent surgeon who did not participate in coaching or debriefing to the SVC group reviewed all videos. A satisfaction survey was then sent to the residents in the coaching group. Department of Surgery, HôpitalMaisonneuve-Rosemont, tertiary teaching hospital affiliated to the University of Montreal, Canada. General surgery residents from University of Montreal were recruited to take part in this trial. A total of 28 residents were randomized and completed the study. After intervention, the SVC group (n = 14) significantly increased their Objective Structured Assessment of Technical Skills score (mean of differences 3.36, [1.09-5.63], p = 0.007) when compared to the control group (n = 14) (mean of differences 0.29, p = 0.759). All residents agreed or strongly agreed that video coaching was a time-efficient teaching method. Video coaching is an effective and efficient teaching intervention to improve surgical residents' technical skills. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

Transoesophageal echocardiographic evaluation of central venous catheter positioning using Peres' formula or a radiological landmark-based approach: a prospective randomized single-centre study.

PubMed

Ahn, J H; Kim, I S; Yang, J H; Lee, I G; Seo, D H; Kim, S P

2017-02-01

The lower superior vena cava (SVC), near its junction with the right atrium (RA), is considered the ideal location for the central venous catheter tip to ensure proper function and prevent injuries. We determined catheter insertion depth with a new formula using the sternoclavicular joint and the carina as radiological landmarks, with a 1.5 cm safety margin. The accuracy of tip positioning with the radiological landmark-based technique (R) and Peres' formula (P) was compared using transoesophageal echocardiography. Real-time ultrasound-guided central venous catheter insertion was done through the right internal jugular or subclavian vein. Patients were randomly assigned to either the P group (n=93) or the R group (n=95). Optimal catheter tip position was considered to be within 2 cm above and 1 cm below the RA-SVC junction. Catheter tip position, abutment, angle to the vascular wall, and flow stream were evaluated on a bicaval view. The distance from the skin insertion point to the RA-SVC junction and determined depth of catheter insertion were more strongly correlated in the R group [17.4 (1.2) and 16.7 (1.5) cm; r=0.821, P<0.001] than in the P group [17.3 (1.2) and 16.4 (1.1) cm; r=0.517, P<0.001], with z=3.96 (P<0.001). More tips were correctly positioned in the R group than in the P group (74 vs 93%, P=0.001). Abutment, tip angle to the lateral wall >40°, and disrupted flow stream were comparable. Catheter tip position was more accurate with a radiological landmark-based technique than with Peres' formula. Clinical Trial Registry of Korea: https://cris.nih.go.kr/cris/index.jsp KCT0001937. © The Author 2017. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Endovascular stent-based revascularization of malignant superior vena cava syndrome with concomitant implantation of a port device using a dual venous approach.

PubMed

Anton, Susanne; Oechtering, T; Stahlberg, E; Jacob, F; Kleemann, M; Barkhausen, J; Goltz, J P

2018-06-01

The aim of this paper is to evaluate the safety and efficacy of endovascular revascularization of malignant superior vena cava syndrome (SVCS) and simultaneous implantation of a totally implantable venous access port (TIVAP) using a dual venous approach. Retrospectively, 31 patients (mean age 67 ± 8 years) with malignant CVO who had undergone revascularization by implantation of a self-expanding stent into the superior vena cava (SVC) (Sinus XL®, OptiMed, Germany; n = 11 [Group1] and Protégé ™ EverFlex, Covidien, Ireland; n = 20 [Group 2]) via a transfemoral access were identified. Simultaneously, percutaneous access via a subclavian vein was used to (a) probe the lesion from above, (b) facilitate a through-and-through maneuver, and (c) implant a TIVAP. Primary endpoints with regard to the SVC syndrome were technical (residual stenosis < 30%) and clinical (relief of symptoms) success; with regard to TIVAP implantation technical success was defined as positioning of the functional catheter within the SVC. Secondary endpoints were complications as well as stent and TIVAP patency. Technical and clinical success rate were 100% for revascularization of the SVS and 100% for implantation of the TIVAP. One access site hematoma (minor complication, day 2) and one port-catheter-associated sepsis (major complication, day 18) were identified. Mean catheter days were 313 ± 370 days. Mean imaging follow-up was 184 ± 172 days. Estimated patency rates at 3, 6, and 12 months were 100% in Group 1 and 84, 84, and 56% in Group 2 (p = 0.338). Stent-based revascularization of malignant SVCS with concomitant implantation of a port device using a dual venous approach appears to be safe and effective.

An ensemble of dissimilarity based classifiers for Mackerel gender determination

NASA Astrophysics Data System (ADS)

Blanco, A.; Rodriguez, R.; Martinez-Maranon, I.

2014-03-01

Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity.

Carbon Nanotube Growth Rate Regression using Support Vector Machines and Artificial Neural Networks

DTIC Science & Technology

2014-03-27

intensity D peak. Reprinted with permission from [38]. The SVM classifier is trained using custom written Java code leveraging the Sequential Minimal...Society Encog is a machine learning framework for Java , C++ and .Net applications that supports Bayesian Networks, Hidden Markov Models, SVMs and ANNs [13...SVM classifiers are trained using Weka libraries and leveraging custom written Java code. The data set is created as an Attribute Relationship File

Obstacle detection by recognizing binary expansion patterns

NASA Technical Reports Server (NTRS)

Baram, Yoram; Barniv, Yair

1993-01-01

This paper describes a technique for obstacle detection, based on the expansion of the image-plane projection of a textured object, as its distance from the sensor decreases. Information is conveyed by vectors whose components represent first-order temporal and spatial derivatives of the image intensity, which are related to the time to collision through the local divergence. Such vectors may be characterized as patterns corresponding to 'safe' or 'dangerous' situations. We show that essential information is conveyed by single-bit vector components, representing the signs of the relevant derivatives. We use two recently developed, high capacity classifiers, employing neural learning techniques, to recognize the imminence of collision from such patterns.

Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform.

PubMed

Ben Salem, Samira; Bacha, Khmais; Chaari, Abdelkader

2012-09-01

In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line.

PubMed

Huang, Nantian; Qi, Jiajin; Li, Fuqing; Yang, Dongfeng; Cai, Guowei; Huang, Guilin; Zheng, Jian; Li, Zhenxin

2017-09-16

In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF₂) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach.

Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line

PubMed Central

Huang, Nantian; Qi, Jiajin; Li, Fuqing; Yang, Dongfeng; Cai, Guowei; Huang, Guilin; Zheng, Jian; Li, Zhenxin

2017-01-01

In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF2) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach. PMID:28926953

Leafhopper viral pathogens

USDA-ARS?s Scientific Manuscript database

Four newly discovered viral pathogens in leafhopper vectors of Pierce’s disease of grapes, have been shown to replicate in sharpshooter leafhoppers; the glassy-winged sharpshooter, GWSS, Homalodisca vitripennis, and Oncometopia nigricans (Hemiptera: Cicadellidae). The viruses were classified as memb...

Spatial and temporal abundance of three sylvatic yellow fever vectors in the influence area of the Manso hydroelectric power plant, Mato Grosso, Brazil.

PubMed

Ribeiro, A L M; Miyazaki, R D; Silva, M; Zeilhofer, P

2012-01-01

Human biting catches of sylvatic yellow fever (SYF) vectors were conducted at eight stations in the influence area of the Manso hydroelectric power plant (Central Brazil) in sampling campaigns every 2 mo from July 2000 to November 2001. In total, 206 individuals were captured and classified as one of three species important for the transmission of SYF in Mato Grosso state: Haemagogus (Haemagogus) janthinomys (Dyar, 1921); Haemagogus (Conopostegus) leucocelaenus (Dyar & Shannon, 1924); and Sabethes (Sabethoides) chloropterus (Humboldt, 1819). The highest vector abundance was observed during the rainy season (November through March) and SYF vectors were present in all sampling points throughout the year, mainly in riparian and shadowed transitional forests at shadowed ramps.

Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

NASA Astrophysics Data System (ADS)

Khehra, Baljit Singh; Pharwaha, Amar Partap Singh

2017-04-01

Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

Improving Classification of Cancer and Mining Biomarkers from Gene Expression Profiles Using Hybrid Optimization Algorithms and Fuzzy Support Vector Machine

PubMed Central

Moteghaed, Niloofar Yousefi; Maghooli, Keivan; Garshasbi, Masoud

2018-01-01

Background: Gene expression data are characteristically high dimensional with a small sample size in contrast to the feature size and variability inherent in biological processes that contribute to difficulties in analysis. Selection of highly discriminative features decreases the computational cost and complexity of the classifier and improves its reliability for prediction of a new class of samples. Methods: The present study used hybrid particle swarm optimization and genetic algorithms for gene selection and a fuzzy support vector machine (SVM) as the classifier. Fuzzy logic is used to infer the importance of each sample in the training phase and decrease the outlier sensitivity of the system to increase the ability to generalize the classifier. A decision-tree algorithm was applied to the most frequent genes to develop a set of rules for each type of cancer. This improved the abilities of the algorithm by finding the best parameters for the classifier during the training phase without the need for trial-and-error by the user. The proposed approach was tested on four benchmark gene expression profiles. Results: Good results have been demonstrated for the proposed algorithm. The classification accuracy for leukemia data is 100%, for colon cancer is 96.67% and for breast cancer is 98%. The results show that the best kernel used in training the SVM classifier is the radial basis function. Conclusions: The experimental results show that the proposed algorithm can decrease the dimensionality of the dataset, determine the most informative gene subset, and improve classification accuracy using the optimal parameters of the classifier with no user interface. PMID:29535919

9 CFR 93.902 - Ports designated for the importation of live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2010 CFR

2010-01-01

... of live fish, fertilized eggs, and gametes. 93.902 Section 93.902 Animals and Animal Products ANIMAL... importation of live fish, fertilized eggs, and gametes. (a) The following ports are designated as ports of entry for live fish, fertilized eggs, and gametes of SVC-susceptible species imported under this subpart...

Alterations in Upper Extremity Motor Function in Soldiers during Acute High Altitude Exposure,

DTIC Science & Technology

1988-03-01

sensitive or controversial items. a°. Encl ALLEN CYMERIAN, Ph.D. c, ARD . SGRD-UEZ ( ) - THRU Chief, Admin Svc Br FROM Commander DATE A p odk CMT 2 TO ak 2...Function orientation and familiarization with all the tests and procedures. Prior to actual sea-level collections , each subject underwent two UEMA

9 CFR 93.902 - Ports designated for the importation of live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2013 CFR

2013-01-01

... of live fish, fertilized eggs, and gametes. 93.902 Section 93.902 Animals and Animal Products ANIMAL... (INCLUDING POULTRY) AND ANIMAL PRODUCTS IMPORTATION OF CERTAIN ANIMALS, BIRDS, FISH, AND POULTRY, AND CERTAIN... Animal Species General Provisions for Svc-Regulated Fish Species § 93.902 Ports designated for the...

9 CFR 93.902 - Ports designated for the importation of live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2012 CFR

2012-01-01

... of live fish, fertilized eggs, and gametes. 93.902 Section 93.902 Animals and Animal Products ANIMAL... (INCLUDING POULTRY) AND ANIMAL PRODUCTS IMPORTATION OF CERTAIN ANIMALS, BIRDS, FISH, AND POULTRY, AND CERTAIN... Animal Species General Provisions for Svc-Regulated Fish Species § 93.902 Ports designated for the...

9 CFR 93.902 - Ports designated for the importation of live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2014 CFR

2014-01-01

... of live fish, fertilized eggs, and gametes. 93.902 Section 93.902 Animals and Animal Products ANIMAL... (INCLUDING POULTRY) AND ANIMAL PRODUCTS IMPORTATION OF CERTAIN ANIMALS, BIRDS, FISH, AND POULTRY, AND CERTAIN... Animal Species General Provisions for Svc-Regulated Fish Species § 93.902 Ports designated for the...

9 CFR 93.902 - Ports designated for the importation of live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2011 CFR

2011-01-01

... of live fish, fertilized eggs, and gametes. 93.902 Section 93.902 Animals and Animal Products ANIMAL... importation of live fish, fertilized eggs, and gametes. (a) The following ports are designated as ports of entry for live fish, fertilized eggs, and gametes of SVC-susceptible species imported under this subpart...

Feature weighting using particle swarm optimization for learning vector quantization classifier

NASA Astrophysics Data System (ADS)

Dongoran, A.; Rahmadani, S.; Zarlis, M.; Zakarias

2018-03-01

This paper discusses and proposes a method of feature weighting in classification assignments on competitive learning artificial neural network LVQ. The weighting feature method is the search for the weight of an attribute using the PSO so as to give effect to the resulting output. This method is then applied to the LVQ-Classifier and tested on the 3 datasets obtained from the UCI Machine Learning repository. Then an accuracy analysis will be generated by two approaches. The first approach using LVQ1, referred to as LVQ-Classifier and the second approach referred to as PSOFW-LVQ, is a proposed model. The result shows that the PSO algorithm is capable of finding attribute weights that increase LVQ-classifier accuracy.

Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble

PubMed Central

Liu, Hang; Chu, Renzhi; Tang, Zhenan

2015-01-01

Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for multi-class classifiers that consist of combinations of pairwise classifiers, such as support vector machines. We compare the performance of the strategy with those of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the two-dimensional ensemble outperforms the other methods considered. Furthermore, we propose a pre-aging process inspired by that applied to the sensors to improve the stability of the classifier ensemble. The experimental results demonstrate that the weight of each multi-class classifier model in the ensemble remains fairly static before and after the addition of new classifier models to the ensemble, when a pre-aging procedure is applied. PMID:25942640

Invariant-feature-based adaptive automatic target recognition in obscured 3D point clouds

NASA Astrophysics Data System (ADS)

Khuon, Timothy; Kershner, Charles; Mattei, Enrico; Alverio, Arnel; Rand, Robert

2014-06-01

Target recognition and classification in a 3D point cloud is a non-trivial process due to the nature of the data collected from a sensor system. The signal can be corrupted by noise from the environment, electronic system, A/D converter, etc. Therefore, an adaptive system with a desired tolerance is required to perform classification and recognition optimally. The feature-based pattern recognition algorithm architecture as described below is particularly devised for solving a single-sensor classification non-parametrically. Feature set is extracted from an input point cloud, normalized, and classifier a neural network classifier. For instance, automatic target recognition in an urban area would require different feature sets from one in a dense foliage area. The figure above (see manuscript) illustrates the architecture of the feature based adaptive signature extraction of 3D point cloud including LIDAR, RADAR, and electro-optical data. This network takes a 3D cluster and classifies it into a specific class. The algorithm is a supervised and adaptive classifier with two modes: the training mode and the performing mode. For the training mode, a number of novel patterns are selected from actual or artificial data. A particular 3D cluster is input to the network as shown above for the decision class output. The network consists of three sequential functional modules. The first module is for feature extraction that extracts the input cluster into a set of singular value features or feature vector. Then the feature vector is input into the feature normalization module to normalize and balance it before being fed to the neural net classifier for the classification. The neural net can be trained by actual or artificial novel data until each trained output reaches the declared output within the defined tolerance. In case new novel data is added after the neural net has been learned, the training is then resumed until the neural net has incrementally learned with the new novel data. The associative memory capability of the neural net enables the incremental learning. The back propagation algorithm or support vector machine can be utilized for the classification and recognition.

Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods.

PubMed

Polat, Huseyin; Danaei Mehr, Homay; Cetin, Aydin

2017-04-01

As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

PubMed

Ibrahim, Wisam; Abadeh, Mohammad Saniee

2017-05-21

Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Semantic classification of business images

NASA Astrophysics Data System (ADS)

Erol, Berna; Hull, Jonathan J.

2006-01-01

Digital cameras are becoming increasingly common for capturing information in business settings. In this paper, we describe a novel method for classifying images into the following semantic classes: document, whiteboard, business card, slide, and regular images. Our method is based on combining low-level image features, such as text color, layout, and handwriting features with high-level OCR output analysis. Several Support Vector Machine Classifiers are combined for multi-class classification of input images. The system yields 95% accuracy in classification.

Thermography based diagnosis of ruptured anterior cruciate ligament (ACL) in canines

NASA Astrophysics Data System (ADS)

Lama, Norsang; Umbaugh, Scott E.; Mishra, Deependra; Dahal, Rohini; Marino, Dominic J.; Sackman, Joseph

2016-09-01

Anterior cruciate ligament (ACL) rupture in canines is a common orthopedic injury in veterinary medicine. Veterinarians use both imaging and non-imaging methods to diagnose the disease. Common imaging methods such as radiography, computed tomography (CT scan) and magnetic resonance imaging (MRI) have some disadvantages: expensive setup, high dose of radiation, and time-consuming. In this paper, we present an alternative diagnostic method based on feature extraction and pattern classification (FEPC) to diagnose abnormal patterns in ACL thermograms. The proposed method was experimented with a total of 30 thermograms for each camera view (anterior, lateral and posterior) including 14 disease and 16 non-disease cases provided from Long Island Veterinary Specialists. The normal and abnormal patterns in thermograms are analyzed in two steps: feature extraction and pattern classification. Texture features based on gray level co-occurrence matrices (GLCM), histogram features and spectral features are extracted from the color normalized thermograms and the computed feature vectors are applied to Nearest Neighbor (NN) classifier, K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM) classifier with leave-one-out validation method. The algorithm gives the best classification success rate of 86.67% with a sensitivity of 85.71% and a specificity of 87.5% in ACL rupture detection using NN classifier for the lateral view and Norm-RGB-Lum color normalization method. Our results show that the proposed method has the potential to detect ACL rupture in canines.

Diagnosis of Tempromandibular Disorders Using Local Binary Patterns.

PubMed

Haghnegahdar, A A; Kolahi, S; Khojastepour, L; Tajeripour, F

2018-03-01

Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment. CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis. K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity. We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages.

Unresolved Galaxy Classifier for ESA/Gaia mission: Support Vector Machines approach

NASA Astrophysics Data System (ADS)

Bellas-Velidis, Ioannis; Kontizas, Mary; Dapergolas, Anastasios; Livanou, Evdokia; Kontizas, Evangelos; Karampelas, Antonios

A software package Unresolved Galaxy Classifier (UGC) is being developed for the ground-based pipeline of ESA's Gaia mission. It aims to provide an automated taxonomic classification and specific parameters estimation analyzing Gaia BP/RP instrument low-dispersion spectra of unresolved galaxies. The UGC algorithm is based on a supervised learning technique, the Support Vector Machines (SVM). The software is implemented in Java as two separate modules. An offline learning module provides functions for SVM-models training. Once trained, the set of models can be repeatedly applied to unknown galaxy spectra by the pipeline's application module. A library of galaxy models synthetic spectra, simulated for the BP/RP instrument, is used to train and test the modules. Science tests show a very good classification performance of UGC and relatively good regression performance, except for some of the parameters. Possible approaches to improve the performance are discussed.

Deep learning of support vector machines with class probability output networks.

PubMed

Kim, Sangwook; Yu, Zhibin; Kil, Rhee Man; Lee, Minho

2015-04-01

Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated. Copyright © 2014 Elsevier Ltd. All rights reserved.

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

PubMed Central

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J.; Raboso, Mariano

2015-01-01

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements. PMID:26091392

Support Vector Machines to improve physiologic hot flash measures: application to the ambulatory setting.

PubMed

Thurston, Rebecca C; Hernandez, Javier; Del Rio, Jose M; De La Torre, Fernando

2011-07-01

Most midlife women have hot flashes. The conventional criterion (≥2 μmho rise/30 s) for classifying hot flashes physiologically has shown poor performance. We improved this performance in the laboratory with Support Vector Machines (SVMs), a pattern classification method. We aimed to compare conventional to SVM methods to classify hot flashes in the ambulatory setting. Thirty-one women with hot flashes underwent 24 h of ambulatory sternal skin conductance monitoring. Hot flashes were quantified with conventional (≥2 μmho/30 s) and SVM methods. Conventional methods had low sensitivity (sensitivity=.57, specificity=.98, positive predictive value (PPV)=.91, negative predictive value (NPV)=.90, F1=.60), with performance lower with higher body mass index (BMI). SVMs improved this performance (sensitivity=.87, specificity=.97, PPV=.90, NPV=.96, F1=.88) and reduced BMI variation. SVMs can improve ambulatory physiologic hot flash measures. Copyright © 2010 Society for Psychophysiological Research.

Support vector machines-based fault diagnosis for turbo-pump rotor

NASA Astrophysics Data System (ADS)

Yuan, Sheng-Fa; Chu, Fu-Lei

2006-05-01

Most artificial intelligence methods used in fault diagnosis are based on empirical risk minimisation principle and have poor generalisation when fault samples are few. Support vector machines (SVM) is a new general machine-learning tool based on structural risk minimisation principle that exhibits good generalisation even when fault samples are few. Fault diagnosis based on SVM is discussed. Since basic SVM is originally designed for two-class classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification of SVM named 'one to others' algorithm is presented to solve the multi-class recognition problems. It is a binary tree classifier composed of several two-class classifiers organised by fault priority, which is simple, and has little repeated training amount, and the rate of training and recognition is expedited. The effectiveness of the method is verified by the application to the fault diagnosis for turbo pump rotor.

Predicting Flavonoid UGT Regioselectivity

PubMed Central

Jackson, Rhydon; Knisley, Debra; McIntosh, Cecilia; Pfeiffer, Phillip

2011-01-01

Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Novel indices characterizing graphical models of residues were proposed and found to be widely distributed among existing amino acid indices and to cluster residues appropriately. UGT subsequences biochemically linked to regioselectivity were modeled as sets of index sequences. Several learning techniques incorporating these UGT models were compared with classifications based on standard sequence alignment scores. These techniques included an application of time series distance functions to protein classification. Time series distances defined on the index sequences were used in nearest neighbor and support vector machine classifiers. Additionally, Bayesian neural network classifiers were applied to the index sequences. The experiments identified improvements over the nearest neighbor and support vector machine classifications relying on standard alignment similarity scores, as well as strong correlations between specific subsequences and regioselectivities. PMID:21747849

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

PubMed

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano

2015-06-17

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

Estimation from incomplete multinomial data. Ph.D. Thesis - Harvard Univ.

NASA Technical Reports Server (NTRS)

Credeur, K. R.

1978-01-01

The vector of multinomial cell probabilities was estimated from incomplete data, incomplete in that it contains partially classified observations. Each such partially classified observation was observed to fall in one of two or more selected categories but was not classified further into a single category. The data were assumed to be incomplete at random. The estimation criterion was minimization of risk for quadratic loss. The estimators were the classical maximum likelihood estimate, the Bayesian posterior mode, and the posterior mean. An approximation was developed for the posterior mean. The Dirichlet, the conjugate prior for the multinomial distribution, was assumed for the prior distribution.

MISR Level 2 TOA/Cloud Classifier parameters (MIL2TCCL_V3)

NASA Technical Reports Server (NTRS)

Diner, David J. (Principal Investigator)

The TOA/Cloud Classifiers contain the Angular Signature Cloud Mask (ASCM), a scene classifier calculated using support vector machine technology (SVM) both of which are on a 1.1 km grid, and cloud fractions at 17.6 km resolution that are available in different height bins (low, middle, high) and are also calculated on an angle-by-angle basis. [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1.1 km; Longitude_Resolution=1.1 km; Temporal_Resolution=about 15 orbits/day].

Automatic Recognition of Acute Myelogenous Leukemia in Blood Microscopic Images Using K-means Clustering and Support Vector Machine.

PubMed

Kazemi, Fatemeh; Najafabadi, Tooraj Abbasian; Araabi, Babak Nadjar

2016-01-01

Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significant diagnostic methodology for initial AML screening and considered as the first step toward diagnosis. It is time-consuming and due to the elusive nature of the signs and symptoms of AML; wrong diagnosis may occur by pathologists. Therefore, the need for automation of leukemia detection has arisen. In this paper, an automatic technique for identification and detection of AML and its prevalent subtypes, i.e., M2-M5 is presented. At first, microscopic images are acquired from blood smears of patients with AML and normal cases. After applying image preprocessing, color segmentation strategy is applied for segmenting white blood cells from other blood components and then discriminative features, i.e., irregularity, nucleus-cytoplasm ratio, Hausdorff dimension, shape, color, and texture features are extracted from the entire nucleus in the whole images containing multiple nuclei. Images are classified to cancerous and noncancerous images by binary support vector machine (SVM) classifier with 10-fold cross validation technique. Classifier performance is evaluated by three parameters, i.e., sensitivity, specificity, and accuracy. Cancerous images are also classified into their prevalent subtypes by multi-SVM classifier. The results show that the proposed algorithm has achieved an acceptable performance for diagnosis of AML and its common subtypes. Therefore, it can be used as an assistant diagnostic tool for pathologists.

Differentiation of several interstitial lung disease patterns in HRCT images using support vector machine: role of databases on performance

NASA Astrophysics Data System (ADS)

Kale, Mandar; Mukhopadhyay, Sudipta; Dash, Jatindra K.; Garg, Mandeep; Khandelwal, Niranjan

2016-03-01

Interstitial lung disease (ILD) is complicated group of pulmonary disorders. High Resolution Computed Tomography (HRCT) considered to be best imaging technique for analysis of different pulmonary disorders. HRCT findings can be categorised in several patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Nodular, Normal etc. based on their texture like appearance. Clinician often find it difficult to diagnosis these pattern because of their complex nature. In such scenario computer-aided diagnosis system could help clinician to identify patterns. Several approaches had been proposed for classification of ILD patterns. This includes computation of textural feature and training /testing of classifier such as artificial neural network (ANN), support vector machine (SVM) etc. In this paper, wavelet features are calculated from two different ILD database, publically available MedGIFT ILD database and private ILD database, followed by performance evaluation of ANN and SVM classifiers in terms of average accuracy. It is found that average classification accuracy by SVM is greater than ANN where trained and tested on same database. Investigation continued further to test variation in accuracy of classifier when training and testing is performed with alternate database and training and testing of classifier with database formed by merging samples from same class from two individual databases. The average classification accuracy drops when two independent databases used for training and testing respectively. There is significant improvement in average accuracy when classifiers are trained and tested with merged database. It infers dependency of classification accuracy on training data. It is observed that SVM outperforms ANN when same database is used for training and testing.

Balanced VS Imbalanced Training Data: Classifying Rapideye Data with Support Vector Machines

NASA Astrophysics Data System (ADS)

Ustuner, M.; Sanli, F. B.; Abdikan, S.

2016-06-01

The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

Classification of THz pulse signals using two-dimensional cross-correlation feature extraction and non-linear classifiers.

PubMed

Siuly; Yin, Xiaoxia; Hadjiloucas, Sillas; Zhang, Yanchun

2016-04-01

This work provides a performance comparison of four different machine learning classifiers: multinomial logistic regression with ridge estimators (MLR) classifier, k-nearest neighbours (KNN), support vector machine (SVM) and naïve Bayes (NB) as applied to terahertz (THz) transient time domain sequences associated with pixelated images of different powder samples. The six substances considered, although have similar optical properties, their complex insertion loss at the THz part of the spectrum is significantly different because of differences in both their frequency dependent THz extinction coefficient as well as differences in their refractive index and scattering properties. As scattering can be unquantifiable in many spectroscopic experiments, classification solely on differences in complex insertion loss can be inconclusive. The problem is addressed using two-dimensional (2-D) cross-correlations between background and sample interferograms, these ensure good noise suppression of the datasets and provide a range of statistical features that are subsequently used as inputs to the above classifiers. A cross-validation procedure is adopted to assess the performance of the classifiers. Firstly the measurements related to samples that had thicknesses of 2mm were classified, then samples at thicknesses of 4mm, and after that 3mm were classified and the success rate and consistency of each classifier was recorded. In addition, mixtures having thicknesses of 2 and 4mm as well as mixtures of 2, 3 and 4mm were presented simultaneously to all classifiers. This approach provided further cross-validation of the classification consistency of each algorithm. The results confirm the superiority in classification accuracy and robustness of the MLR (least accuracy 88.24%) and KNN (least accuracy 90.19%) algorithms which consistently outperformed the SVM (least accuracy 74.51%) and NB (least accuracy 56.86%) classifiers for the same number of feature vectors across all studies. The work establishes a general methodology for assessing the performance of other hyperspectral dataset classifiers on the basis of 2-D cross-correlations in far-infrared spectroscopy or other parts of the electromagnetic spectrum. It also advances the wider proliferation of automated THz imaging systems across new application areas e.g., biomedical imaging, industrial processing and quality control where interpretation of hyperspectral images is still under development. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Chaotic particle swarm optimization with mutation for classification.

PubMed

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.

[MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

PubMed

Chen, Zhiru; Hong, Wenxue

2016-02-01

Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.

Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning.

PubMed

Whiteside, David; Cant, Olivia; Connolly, Molly; Reid, Machar

2017-10-01

Quantifying external workload is fundamental to training prescription in sport. In tennis, global positioning data are imprecise and fail to capture hitting loads. The current gold standard (manual notation) is time intensive and often not possible given players' heavy travel schedules. To develop an automated stroke-classification system to help quantify hitting load in tennis. Nineteen athletes wore an inertial measurement unit (IMU) on their wrist during 66 video-recorded training sessions. Video footage was manually notated such that known shot type (serve, rally forehand, slice forehand, forehand volley, rally backhand, slice backhand, backhand volley, smash, or false positive) was associated with the corresponding IMU data for 28,582 shots. Six types of machine-learning models were then constructed to classify true shot type from the IMU signals. Across 10-fold cross-validation, a cubic-kernel support vector machine classified binned shots (overhead, forehand, or backhand) with an accuracy of 97.4%. A second cubic-kernel support vector machine achieved 93.2% accuracy when classifying all 9 shot types. With a view to monitoring external load, the combination of miniature inertial sensors and machine learning offers a practical and automated method of quantifying shot counts and discriminating shot types in elite tennis players.

Structural analysis of online handwritten mathematical symbols based on support vector machines

NASA Astrophysics Data System (ADS)

Simistira, Foteini; Papavassiliou, Vassilis; Katsouros, Vassilis; Carayannis, George

2013-01-01

Mathematical expression recognition is still a very challenging task for the research community mainly because of the two-dimensional (2d) structure of mathematical expressions (MEs). In this paper, we present a novel approach for the structural analysis between two on-line handwritten mathematical symbols of a ME, based on spatial features of the symbols. We introduce six features to represent the spatial affinity of the symbols and compare two multi-class classification methods that employ support vector machines (SVMs): one based on the "one-against-one" technique and one based on the "one-against-all", in identifying the relation between a pair of symbols (i.e. subscript, numerator, etc). A dataset containing 1906 spatial relations derived from the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2012 training dataset is constructed to evaluate the classifiers and compare them with the rule-based classifier of the ILSP-1 system participated in the contest. The experimental results give an overall mean error rate of 2.61% for the "one-against-one" SVM approach, 6.57% for the "one-against-all" SVM technique and 12.31% error rate for the ILSP-1 classifier.

A Novel Signal Modeling Approach for Classification of Seizure and Seizure-Free EEG Signals.

PubMed

Gupta, Anubha; Singh, Pushpendra; Karlekar, Mandar

2018-05-01

This paper presents a signal modeling-based new methodology of automatic seizure detection in EEG signals. The proposed method consists of three stages. First, a multirate filterbank structure is proposed that is constructed using the basis vectors of discrete cosine transform. The proposed filterbank decomposes EEG signals into its respective brain rhythms: delta, theta, alpha, beta, and gamma. Second, these brain rhythms are statistically modeled with the class of self-similar Gaussian random processes, namely, fractional Brownian motion and fractional Gaussian noises. The statistics of these processes are modeled using a single parameter called the Hurst exponent. In the last stage, the value of Hurst exponent and autoregressive moving average parameters are used as features to design a binary support vector machine classifier to classify pre-ictal, inter-ictal (epileptic with seizure free interval), and ictal (seizure) EEG segments. The performance of the classifier is assessed via extensive analysis on two widely used data set and is observed to provide good accuracy on both the data set. Thus, this paper proposes a novel signal model for EEG data that best captures the attributes of these signals and hence, allows to boost the classification accuracy of seizure and seizure-free epochs.

Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

PubMed

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-08-01

Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy. We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting). Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Classification of Alzheimer's disease patients with hippocampal shape wrapper-based feature selection and support vector machine

NASA Astrophysics Data System (ADS)

Young, Jonathan; Ridgway, Gerard; Leung, Kelvin; Ourselin, Sebastien

2012-02-01

It is well known that hippocampal atrophy is a marker of the onset of Alzheimer's disease (AD) and as a result hippocampal volumetry has been used in a number of studies to provide early diagnosis of AD and predict conversion of mild cognitive impairment patients to AD. However, rates of atrophy are not uniform across the hippocampus making shape analysis a potentially more accurate biomarker. This study studies the hippocampi from 226 healthy controls, 148 AD patients and 330 MCI patients obtained from T1 weighted structural MRI images from the ADNI database. The hippocampi are anatomically segmented using the MAPS multi-atlas segmentation method, and the resulting binary images are then processed with SPHARM software to decompose their shapes as a weighted sum of spherical harmonic basis functions. The resulting parameterizations are then used as feature vectors in Support Vector Machine (SVM) classification. A wrapper based feature selection method was used as this considers the utility of features in discriminating classes in combination, fully exploiting the multivariate nature of the data and optimizing the selected set of features for the type of classifier that is used. The leave-one-out cross validated accuracy obtained on training data is 88.6% for classifying AD vs controls and 74% for classifying MCI-converters vs MCI-stable with very compact feature sets, showing that this is a highly promising method. There is currently a considerable fall in accuracy on unseen data indicating that the feature selection is sensitive to the data used, however feature ensemble methods may overcome this.

Low Energy Multi-Stage Atrial Defibrillation Therapy Terminates Atrial Fibrillation with Less Energy than a Single Shock

PubMed Central

Li, Wenwen; Janardhan, Ajit H.; Fedorov, Vadim V.; Sha, Qun; Schuessler, Richard B.; Efimov, Igor R.

2011-01-01

Background Implantable device therapy of atrial fibrillation (AF) is limited by pain from high-energy shocks. We developed a low-energy multi-stage defibrillation therapy and tested it in a canine model of AF. Methods and Results AF was induced by burst pacing during vagus nerve stimulation. Our novel defibrillation therapy consisted of three stages: ST1 (1-4 low energy biphasic shocks), ST2 (6-10 ultra-low energy monophasic shocks), and ST3 (anti-tachycardia pacing). Firstly, ST1 testing compared single or multiple monophasic (MP) and biphasic (BP) shocks. Secondly, several multi-stage therapies were tested: ST1 versus ST1+ST3 versus ST1+ST2+ST3. Thirdly, three shock vectors were compared: superior vena cava to distal coronary sinus (SVC>CSd), proximal coronary sinus to left atrial appendage (CSp>LAA) and right atrial appendage to left atrial appendage (RAA>LAA). The atrial defibrillation threshold (DFT) of 1BP shock was less than 1MP shock (0.55 ± 0.1 versus 1.38 ± 0.31 J; p =0.003). 2-3 BP shocks terminated AF with lower peak voltage than 1BP or 1MP shock and with lower atrial DFT than 4 BP shocks. Compared to ST1 therapy alone, ST1+ST3 lowered the atrial DFT moderately (0.51 ± 0.46 versus 0.95 ± 0.32 J; p = 0.036) while a three-stage therapy, ST1+ST2+ST3, dramatically lowered the atrial DFT (0.19 ± 0.12 J versus 0.95 ± 0.32 J for ST1 alone, p=0.0012). Finally, the three-stage therapy ST1+ST2+ST3 was equally effective for all studied vectors. Conclusions Three-stage electrotherapy significantly reduces the AF defibrillation threshold and opens the door to low energy atrial defibrillation at or below the pain threshold. PMID:21980076

Fault Detection of Bearing Systems through EEMD and Optimization Algorithm

PubMed Central

Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

2017-01-01

This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. PMID:29143772

Clinical Investigation Program Annual Progress Report.

DTIC Science & Technology

1985-09-30

027 78/114 In Vitro Effect of Minoxidil on Collagen Produc- tion by Normal and Scleroderma Fibroblasts (C) (PR...effect of minoxidil on collagen production Dy normal and scleroderma fibroblasts. Previously titled: The use of minoxidil in treating progressive...Svc: (tO) Assoc Investigators: (11) Key Words: scleroderma, minoxidil Thomas P. O’Barr PhD, DAC fibroblasts, collagen Ellen Swanson MS, DAC Don

76 FR 53162 - Acceptance of Public Submissions Regarding the Study of Stable Value Contracts

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-25

... the risk of a run on a SVF? To the extent that SVC providers use value-at-risk (``VaR'') models, do such VaR models adequately assess the risk of loss resulting from such events or other possible but extremely unlikely events? Do other loss models more adequately assess the risk of loss, such as the...

9 CFR 93.905 - Declaration and other documents for live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2014 CFR

2014-01-01

... Animal Species General Provisions for Svc-Regulated Fish Species § 93.905 Declaration and other documents... entry, the name and address of the importer, the name and address of the broker, the origin of the live fish, fertilized eggs, or gametes, the number, species, and the purpose of the importation, the name of...

9 CFR 93.905 - Declaration and other documents for live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2012 CFR

2012-01-01

... Animal Species General Provisions for Svc-Regulated Fish Species § 93.905 Declaration and other documents... entry, the name and address of the importer, the name and address of the broker, the origin of the live fish, fertilized eggs, or gametes, the number, species, and the purpose of the importation, the name of...

9 CFR 93.905 - Declaration and other documents for live fish, fertilized eggs, and gametes.

Code of Federal Regulations, 2013 CFR

2013-01-01

... Animal Species General Provisions for Svc-Regulated Fish Species § 93.905 Declaration and other documents... entry, the name and address of the importer, the name and address of the broker, the origin of the live fish, fertilized eggs, or gametes, the number, species, and the purpose of the importation, the name of...

Tele-Proximity: Tele-Community of Inquiry Model. Facial Cues for Social, Cognitive, and Teacher Presence in Distance Education

ERIC Educational Resources Information Center

Themeli, Chryssa; Bougia, Anna

2016-01-01

Distance education is expanding in all continents, and the use of video has dominated internet. Synchronous Video Communication (SVC) has not been an option thoroughly investigated and practitioners, who use and design synchronous learning scenarios, are in urgent need of guidance. Distant learners face many barriers, and as a result, they drop…

Changing Our Selves, Our Schools, and Our School System: Students Take on the New York City Quality Review Process

ERIC Educational Resources Information Center

Parkham, Shamika; McBroom, Aravis

2015-01-01

In this chapter, two student members of the Student Voice Collaborative (SVC) describe their experiences as "Student Shadows" during the annual Quality Review process, used throughout the New York Department of Education to evaluate how well schools are organized to support student achievement. They chronicle how this experience enhanced…

Scalable Heuristics for Planning, Placement and Sizing of Flexible AC Transmission System Devices

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

Frolov, Vladmir; Backhaus, Scott N.; Chertkov, Michael

Aiming to relieve transmission grid congestion and improve or extend feasibility domain of the operations, we build optimization heuristics, generalizing standard AC Optimal Power Flow (OPF), for placement and sizing of Flexible Alternating Current Transmission System (FACTS) devices of the Series Compensation (SC) and Static VAR Compensation (SVC) type. One use of these devices is in resolving the case when the AC OPF solution does not exist because of congestion. Another application is developing a long-term investment strategy for placement and sizing of the SC and SVC devices to reduce operational cost and improve power system operation. SC and SVCmore » devices are represented by modification of the transmission line inductances and reactive power nodal corrections respectively. We find one placement and sizing of FACTs devices for multiple scenarios and optimal settings for each scenario simultaneously. Our solution of the nonlinear and nonconvex generalized AC-OPF consists of building a convergent sequence of convex optimizations containing only linear constraints and shows good computational scaling to larger systems. The approach is illustrated on single- and multi-scenario examples of the Matpower case-30 model.« less

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

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

Jiang, Huaiguang; Dai, Xiaoxiao; Gao, David Wenzhong

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized inmore » the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.« less

A simple method to accurately position Port-A-Cath without the aid of intraoperative fluoroscopy or other localizing devices.

PubMed

Horng, Huann-Cheng; Yuan, Chiou-Chung; Chao, Kuan-Chong; Cheng, Ming-Huei; Wang, Peng-Hui

2007-06-01

To evaluate the efficacy and acceptability of the Port-A-Cath (PAC) insertion method with (conventional group as II) and without (modified group as I) the aid of intraoperative fluoroscopy or other localizing devices. A total of 158 women with various kinds of gynecological cancers warranting PAC insertion (n = 86 in group I and n = 72 in group II, respectively) were evaluated. Data for analyses included patient age, main disease, dislocation site, surgical time, complications, and catheter outcome. There was no statistical difference between the two groups in terms of age, main disease, complications, and the experiencing of patent catheters. However, appropriate positioning (100% in group I, and 82% in group II) in the superior vena cava (SVC) showed statistical differences between the two groups (P = 0.001). In addition, the surgical time in group I was statistically shorter than that in group II (P < 0.001). The modified method for inserting the PAC offered the following benefits: including avoiding X-ray exposure for both the operator and the patient, defining the appropriate position in the SVC, and less surgical time. (c) 2007 Wiley-Liss, Inc.

Comparison Analysis of Recognition Algorithms of Forest-Cover Objects on Hyperspectral Air-Borne and Space-Borne Images

NASA Astrophysics Data System (ADS)

Kozoderov, V. V.; Kondranin, T. V.; Dmitriev, E. V.

2017-12-01

The basic model for the recognition of natural and anthropogenic objects using their spectral and textural features is described in the problem of hyperspectral air-borne and space-borne imagery processing. The model is based on improvements of the Bayesian classifier that is a computational procedure of statistical decision making in machine-learning methods of pattern recognition. The principal component method is implemented to decompose the hyperspectral measurements on the basis of empirical orthogonal functions. Application examples are shown of various modifications of the Bayesian classifier and Support Vector Machine method. Examples are provided of comparing these classifiers and a metrical classifier that operates on finding the minimal Euclidean distance between different points and sets in the multidimensional feature space. A comparison is also carried out with the " K-weighted neighbors" method that is close to the nonparametric Bayesian classifier.

Fall Detection Using Smartphone Audio Features.

PubMed

Cheffena, Michael

2016-07-01

An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs), linear predictive coding (LPC), and matching pursuit (MP) features of different fall and no-fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k-nearest neighbor classifier (k-NN), support vector machine (SVM), least squares method (LSM), and artificial neural network (ANN) are investigated for distinguishing between fall and no-fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy, and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity, and accuracy all above 98%. The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user.

An expert support system for breast cancer diagnosis using color wavelet features.

PubMed

Issac Niwas, S; Palanisamy, P; Chibbar, Rajni; Zhang, W J

2012-10-01

Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of tissue samples are investigated after decomposition by means of the Log-Gabor wavelet on HSV color domain and an algorithm is developed to compute the color wavelet features. These features are used for breast cancer diagnosis using Support Vector Machine (SVM) classifier algorithm. The ability of properly trained SVM is to correctly classify patterns and make them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The results are compared with other multivariate classifiers such as Naïves Bayes classifier and Artificial Neural Network. The overall accuracy of the proposed method using SVM classifier will be further useful for automation in cancer diagnosis.

E-Nose Vapor Identification Based on Dempster-Shafer Fusion of Multiple Classifiers

NASA Technical Reports Server (NTRS)

Li, Winston; Leung, Henry; Kwan, Chiman; Linnell, Bruce R.

2005-01-01

Electronic nose (e-nose) vapor identification is an efficient approach to monitor air contaminants in space stations and shuttles in order to ensure the health and safety of astronauts. Data preprocessing (measurement denoising and feature extraction) and pattern classification are important components of an e-nose system. In this paper, a wavelet-based denoising method is applied to filter the noisy sensor measurements. Transient-state features are then extracted from the denoised sensor measurements, and are used to train multiple classifiers such as multi-layer perceptions (MLP), support vector machines (SVM), k nearest neighbor (KNN), and Parzen classifier. The Dempster-Shafer (DS) technique is used at the end to fuse the results of the multiple classifiers to get the final classification. Experimental analysis based on real vapor data shows that the wavelet denoising method can remove both random noise and outliers successfully, and the classification rate can be improved by using classifier fusion.

Method and means for separating and classifying superconductive particles

DOEpatents

Park, Jin Y.; Kearney, Robert J.

1991-01-01

The specification and drawings describe a series of devices and methods for classifying and separating superconductive particles. The superconductive particles may be separated from non-superconductive particles, and the superconductive particles may be separated by degrees of susceptibility to the Meissner effect force. The particles may also be simultaneously separated by size or volume and mass to obtain substantially homogeneous groups of particles. The separation techniques include levitation, preferential sedimentation and preferential concentration. Multiple separation vector forces are disclosed.

Overview of existing algorithms for emotion classification. Uncertainties in evaluations of accuracies.

NASA Astrophysics Data System (ADS)

Avetisyan, H.; Bruna, O.; Holub, J.

2016-11-01

A numerous techniques and algorithms are dedicated to extract emotions from input data. In our investigation it was stated that emotion-detection approaches can be classified into 3 following types: Keyword based / lexical-based, learning based, and hybrid. The most commonly used techniques, such as keyword-spotting method, Support Vector Machines, Naïve Bayes Classifier, Hidden Markov Model and hybrid algorithms, have impressive results in this sphere and can reach more than 90% determining accuracy.

Machine Learning in Intrusion Detection

DTIC Science & Technology

2005-07-01

machine learning tasks. Anomaly detection provides the core technology for a broad spectrum of security-centric applications. In this dissertation, we examine various aspects of anomaly based intrusion detection in computer security. First, we present a new approach to learn program behavior for intrusion detection. Text categorization techniques are adopted to convert each process to a vector and calculate the similarity between two program activities. Then the k-nearest neighbor classifier is employed to classify program behavior as normal or intrusive. We demonstrate

Anytime query-tuned kernel machine classifiers via Cholesky factorization

NASA Technical Reports Server (NTRS)

DeCoste, D.

2002-01-01

We recently demonstrated 2 to 64-fold query-time speedups of Support Vector Machine and Kernel Fisher classifiers via a new computational geometry method for anytime output bounds (DeCoste,2002). This new paper refines our approach in two key ways. First, we introduce a simple linear algebra formulation based on Cholesky factorization, yielding simpler equations and lower computational overhead. Second, this new formulation suggests new methods for achieving additional speedups, including tuning on query samples. We demonstrate effectiveness on benchmark datasets.

HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.

PubMed

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2014-01-01

Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.

Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

PubMed Central

Arshad, Sannia; Rho, Seungmin

2014-01-01

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes. PMID:25295302

Robust framework to combine diverse classifiers assigning distributed confidence to individual classifiers at class level.

PubMed

Khalid, Shehzad; Arshad, Sannia; Jabbar, Sohail; Rho, Seungmin

2014-01-01

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.

Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations

PubMed Central

2016-01-01

Differentiation between ischaemic and non-ischaemic transient ST segment events of long term ambulatory electrocardiograms is a persisting weakness in present ischaemia detection systems. Traditional ST segment level measuring is not a sufficiently precise technique due to the single point of measurement and severe noise which is often present. We developed a robust noise resistant orthogonal-transformation based delineation method, which allows tracing the shape of transient ST segment morphology changes from the entire ST segment in terms of diagnostic and morphologic feature-vector time series, and also allows further analysis. For these purposes, we developed a new Legendre Polynomials based Transformation (LPT) of ST segment. Its basis functions have similar shapes to typical transient changes of ST segment morphology categories during myocardial ischaemia (level, slope and scooping), thus providing direct insight into the types of time domain morphology changes through the LPT feature-vector space. We also generated new Karhunen and Lo ève Transformation (KLT) ST segment basis functions using a robust covariance matrix constructed from the ST segment pattern vectors derived from the Long Term ST Database (LTST DB). As for the delineation of significant transient ischaemic and non-ischaemic ST segment episodes, we present a study on the representation of transient ST segment morphology categories, and an evaluation study on the classification power of the KLT- and LPT-based feature vectors to classify between ischaemic and non-ischaemic ST segment episodes of the LTST DB. Classification accuracy using the KLT and LPT feature vectors was 90% and 82%, respectively, when using the k-Nearest Neighbors (k = 3) classifier and 10-fold cross-validation. New sets of feature-vector time series for both transformations were derived for the records of the LTST DB which is freely available on the PhysioNet website and were contributed to the LTST DB. The KLT and LPT present new possibilities for human-expert diagnostics, and for automated ischaemia detection. PMID:26863140

Discrimination thresholds of normal and anomalous trichromats: Model of senescent changes in ocular media density on the Cambridge Colour Test

PubMed Central

Shinomori, Keizo; Panorgias, Athanasios; Werner, John S.

2017-01-01

Age-related changes in chromatic discrimination along dichromatic confusion lines were measured with the Cambridge Colour Test (CCT). One hundred and sixty-two individuals (16 to 88 years old) with normal Rayleigh matches were the major focus of this paper. An additional 32 anomalous trichromats classified by their Rayleigh matches were also tested. All subjects were screened to rule out abnormalities of the anterior and posterior segments. Thresholds on all three chromatic vectors measured with the CCT showed age-related increases. Protan and deutan vector thresholds increased linearly with age while the tritan vector threshold was described with a bilinear model. Analysis and modeling demonstrated that the nominal vectors of the CCT are shifted by senescent changes in ocular media density, and a method for correcting the CCT vectors is demonstrated. A correction for these shifts indicates that classification among individuals of different ages is unaffected. New vector thresholds for elderly observers and for all age groups are suggested based on calculated tolerance limits. PMID:26974943

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

NASA Astrophysics Data System (ADS)

Ai, Shihui; Lu, Tongwei; Xiong, Yudian

2018-03-01

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

Management of end-stage central venous access in children referred for possible small bowel transplantation.

PubMed

Rodrigues, A F; van Mourik, I D M; Sharif, K; Barron, D J; de Giovanni, J V; Bennett, J; Bromley, P; Protheroe, S; John, P; de Ville de Goyet, J; Beath, S V

2006-04-01

The 3-year survival after small bowel transplantation (SBTx) has improved to between 73% and 88%. Impaired venous access for parenteral nutrition can be an indication for SBTx in children with chronic intestinal failure. To report our experience in management of children with extreme end-stage venous access. The study consisted of 6 children (all boys), median age of assessment 27 months (range, 13-52 months), diagnosed with total intestinal aganglionosis (1), protracted diarrhea (1), and short bowel syndrome (4), of which gastroschisis (2) and malrotation with midgut volvulus (2) were the causes. All had a documented history of more than 10 central venous catheter insertions previously. All had venograms, and 1 child additionally had a magnetic resonance angiogram to evaluate venous access. Five of 6 presented with thrombosis of the superior vena cava (SVC) and/or inferior vena cava. Venous access was reestablished as follows: transhepatic venous catheters (5), direct intra-atrial catheter via midline sternotomy (4), azygous venous catheters (2), dilatation of left subclavian vein after passage of a guide wire and then placing a catheter to reach the right atrium (1), radiological recanalization of the SVC and placement of a central venous catheter in situ (1), and direct puncture of SVC stump(1). Complications included serous pleural effusion after direct intra-atrial line insertion, which resolved after chest drain insertion (1), displacement of transhepatic catheter needing repositioning (2), and SVC stent narrowing requiring repeated balloon dilatation. Four children with permanent intestinal failure on assessment were offered SBTx, 3 of which were transplanted and were established on full enteral nutrition; the family of 1 child declined the procedure. In the remaining 2 children in whom bowel adaptation was still a possibility, attempts were made to provide adequate central venous access as feeds and drug manipulations were undertaken. One of them received liver and SBTx nearly 3 years after presenting with end-stage central venous access, because attempts to achieve independence from parenteral nutrition had failed. The other child died immediately after a transhepatic venous catheter placement, possibly from a nutritional depletion syndrome as no physical cause of death was found. Direct intra-atrial catheters in transplanted children proved to be adequate for the management of uncomplicated transplantation, although the usual infusion protocol had to be modified considerably, and the lack of access would have been critical if massive blood transfusion had been required during the transplant procedure. It was possible to reestablish central venous access in all cases. However, this was time consuming and difficult to assemble a skilled team consisting of one of more: surgeon, cardiologist, interventional radiologist, and transplant anesthetist. Small bowel transplantation is easier and safer with adequate central venous access, and we advocate liaison with an SBTx center at an early stage.

The quest for a non-vector psyllid: Natural variation in acquisition and transmission of the huanglongbing pathogen ‘Candidatus Liberibacter asiaticus’ by Asian citrus psyllid isofemale lines

PubMed Central

Ammar, El-Desouky; Hall, David G.; Hosseinzadeh, Saeed

2018-01-01

Genetic variability in insect vectors is valuable to study vector competence determinants and to select non-vector populations that may help reduce the spread of vector-borne pathogens. We collected and tested vector competency of 15 isofemale lines of Asian citrus psyllid, Diaphorina citri, vector of ‘Candidatus Liberibacter asiaticus’ (CLas). CLas is associated with huanglongbing (citrus greening), the most serious citrus disease worldwide. D. citri adults were collected from orange jasmine (Murraya paniculata) hedges in Florida, and individual pairs (females and males) were caged on healthy Murraya plants for egg laying. The progeny from each pair that tested CLas-negative by qPCR were maintained on Murraya plants and considered an isofemale line. Six acquisition tests on D. citri adults that were reared as nymphs on CLas-infected citrus, from various generations of each line, were conducted to assess their acquisition rates (percentage of qPCR-positive adults). Three lines with mean acquisition rates of 28 to 32%, were classified as ‘good’ acquirers and three other lines were classified as ‘poor’ acquirers, with only 5 to 8% acquisition rates. All lines were further tested for their ability to inoculate CLas by confining CLas-exposed psyllids for one week onto healthy citrus leaves (6–10 adults/leaf/week), and testing the leaves for CLas by qPCR. Mean inoculation rates were 19 to 28% for the three good acquirer lines and 0 to 3% for the three poor acquirer lines. Statistical analyses indicated positive correlations between CLas acquisition and inoculation rates, as well as between CLas titer in the psyllids and CLas acquisition or inoculation rates. Phenotypic and molecular characterization of one of the good and one of the poor acquirer lines revealed differences between them in color morphs and hemocyanin expression, but not the composition of bacterial endosymbionts. Understanding the genetic architecture of CLas transmission will enable the development of new tools for combating this devastating citrus disease. PMID:29652934

Ambulatory activity classification with dendogram-based support vector machine: Application in lower-limb active exoskeleton.

PubMed

Mazumder, Oishee; Kundu, Ananda Sankar; Lenka, Prasanna Kumar; Bhaumik, Subhasis

2016-10-01

Ambulatory activity classification is an active area of research for controlling and monitoring state initiation, termination, and transition in mobility assistive devices such as lower-limb exoskeletons. State transition of lower-limb exoskeletons reported thus far are achieved mostly through the use of manual switches or state machine-based logic. In this paper, we propose a postural activity classifier using a 'dendogram-based support vector machine' (DSVM) which can be used to control a lower-limb exoskeleton. A pressure sensor-based wearable insole and two six-axis inertial measurement units (IMU) have been used for recognising two static and seven dynamic postural activities: sit, stand, and sit-to-stand, stand-to-sit, level walk, fast walk, slope walk, stair ascent and stair descent. Most of the ambulatory activities are periodic in nature and have unique patterns of response. The proposed classification algorithm involves the recognition of activity patterns on the basis of the periodic shape of trajectories. Polynomial coefficients extracted from the hip angle trajectory and the centre-of-pressure (CoP) trajectory during an activity cycle are used as features to classify dynamic activities. The novelty of this paper lies in finding suitable instrumentation, developing post-processing techniques, and selecting shape-based features for ambulatory activity classification. The proposed activity classifier is used to identify the activity states of a lower-limb exoskeleton. The DSVM classifier algorithm achieved an overall classification accuracy of 95.2%. Copyright © 2016 Elsevier B.V. All rights reserved.

Diagnosis of Tempromandibular Disorders Using Local Binary Patterns

PubMed Central

Haghnegahdar, A.A.; Kolahi, S.; Khojastepour, L.; Tajeripour, F.

2018-01-01

Background: Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment. Material and Methods: CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis. Results: K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity. Conclusion: We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages. PMID:29732343

T-ray relevant frequencies for osteosarcoma classification

NASA Astrophysics Data System (ADS)

Withayachumnankul, W.; Ferguson, B.; Rainsford, T.; Findlay, D.; Mickan, S. P.; Abbott, D.

2006-01-01

We investigate the classification of the T-ray response of normal human bone cells and human osteosarcoma cells, grown in culture. Given the magnitude and phase responses within a reliable spectral range as features for input vectors, a trained support vector machine can correctly classify the two cell types to some extent. Performance of the support vector machine is deteriorated by the curse of dimensionality, resulting from the comparatively large number of features in the input vectors. Feature subset selection methods are used to select only an optimal number of relevant features for inputs. As a result, an improvement in generalization performance is attainable, and the selected frequencies can be used for further describing different mechanisms of the cells, responding to T-rays. We demonstrate a consistent classification accuracy of 89.6%, while the only one fifth of the original features are retained in the data set.

Predicting healthcare associated infections using patients' experiences

NASA Astrophysics Data System (ADS)

Pratt, Michael A.; Chu, Henry

2016-05-01

Healthcare associated infections (HAI) are a major threat to patient safety and are costly to health systems. Our goal is to predict the HAI performance of a hospital using the patients' experience responses as input. We use four classifiers, viz. random forest, naive Bayes, artificial feedforward neural networks, and the support vector machine, to perform the prediction of six types of HAI. The six types include blood stream, urinary tract, surgical site, and intestinal infections. Experiments show that the random forest and support vector machine perform well across the six types of HAI.

Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study.

PubMed

Mourao-Miranda, J; Reinders, A A T S; Rocha-Rego, V; Lappin, J; Rondina, J; Morgan, C; Morgan, K D; Fearon, P; Jones, P B; Doody, G A; Murray, R M; Kapur, S; Dazzan, P

2012-05-01

To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode. One hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls. At baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035). We provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data.

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

PubMed

Hussain, Lal

2018-06-01

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

Ensemble based on static classifier selection for automated diagnosis of Mild Cognitive Impairment.

PubMed

Nanni, Loris; Lumini, Alessandra; Zaffonato, Nicolò

2018-05-15

Alzheimer's disease (AD) is the most common cause of neurodegenerative dementia in the elderly population. Scientific research is very active in the challenge of designing automated approaches to achieve an early and certain diagnosis. Recently an international competition among AD predictors has been organized: "A Machine learning neuroimaging challenge for automated diagnosis of Mild Cognitive Impairment" (MLNeCh). This competition is based on pre-processed sets of T1-weighted Magnetic Resonance Images (MRI) to be classified in four categories: stable AD, individuals with MCI who converted to AD, individuals with MCI who did not convert to AD and healthy controls. In this work, we propose a method to perform early diagnosis of AD, which is evaluated on MLNeCh dataset. Since the automatic classification of AD is based on the use of feature vectors of high dimensionality, different techniques of feature selection/reduction are compared in order to avoid the curse-of-dimensionality problem, then the classification method is obtained as the combination of Support Vector Machines trained using different clusters of data extracted from the whole training set. The multi-classifier approach proposed in this work outperforms all the stand-alone method tested in our experiments. The final ensemble is based on a set of classifiers, each trained on a different cluster of the training data. The proposed ensemble has the great advantage of performing well using a very reduced version of the data (the reduction factor is more than 90%). The MATLAB code for the ensemble of classifiers will be publicly available 1 to other researchers for future comparisons. Copyright © 2017 Elsevier B.V. All rights reserved.

Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study

PubMed Central

Mourao-Miranda, J.; Reinders, A. A. T. S.; Rocha-Rego, V.; Lappin, J.; Rondina, J.; Morgan, C.; Morgan, K. D.; Fearon, P.; Jones, P. B.; Doody, G. A.; Murray, R. M.; Kapur, S.; Dazzan, P.

2012-01-01

Background To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode. Method One hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls. Results At baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035). Conclusions We provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data. PMID:22059690

Support vector machine for automatic pain recognition

NASA Astrophysics Data System (ADS)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

Annual Research Progress Report. Fiscal Year 1989. Volume 2

DTIC Science & Technology

1989-10-01

Date : 4 --- ---- Date of Periodic Review - -- Results Objective(s): i) Develop a conscious, tethered or lightly sedated , nonhuman primate model...Approach: All animal studies will be conducted at Incarnate Word College Division of Nursing and the Sciences. All procedures will be done as outlined...Gary Zarr, LTC, AN Academy of Health Sciences Dept/Svc Associate Investigators: Dpartment of Nursing Jeff Serogrham, LTC, AN Key Words: Accumulative

CMMI for Services (SVC): The Strategic Landscape for Service

DTIC Science & Technology

2012-01-01

processes. • Many existing models are designed for specific services or industries . • Other existing models do not provide a clear improvement path...Production, such as engineering and manufacturing Disciplines and industries , such as education, health care, insurance, utilities, and hospitality...as a Service ―More and more major businesses and industries are being run on software and delivered as online services—from movies to agriculture

Clinical Investigation Program Report.

DTIC Science & Technology

1983-10-01

metastatic to liver. (0) H-83-63. Double-blind, randomized parallel comparison of two different dosage regimens of naproxen sodium in patients with...different dosage regimens of naproxen sodium in patients with bone pain due to m etastatic cancer. Start Date: May 83 Est Comp Date: Indefinite Principal...Investigator: Facility: LTC D Gandara, MD LAMC CPT R Mansour, MD D ept/Svc: Associate Investigators: Hematology-Oncology Key Words: naproxen sodium

Bilateral Breast Enlargement: An Unusual Presentation of Superior Vena Cava Obstruction in a Hemodialysis Patient with Fibrosing Mediastinitis

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

Goo, Dong Erk, E-mail: degoo@hosp.sch.ac.kr; Kim, Yong Jae; Choi, Deuk Lin

2011-02-15

A 67-year-old woman with end-stage renal disease presented with profound edema of both breasts. The presence of a patent hemodialysis basilic transposition fistula and superior vena cava obstruction (SVC), due to fibrosing mediastinitis, was demonstrated by the use of fistulography. Endovascular treatment with a balloon and stent caused immediate resolution of the breast edema.

Short-Term Effect of Autogenic Drainage on Ventilation Inhomogeneity in Adult Subjects With Stable Non-Cystic Fibrosis Bronchiectasis.

PubMed

Poncin, William; Reychler, Grégory; Leeuwerck, Noémie; Bauwens, Nathalie; Aubriot, Anne-Sophie; Nader, Candice; Liistro, Giuseppe; Gohy, Sophie

2017-05-01

Lung clearance index (LCI), a measure of ventilation inhomogeneity derived from a multiple-breath washout test, is a promising tool for assessing airway function in patients with non-cystic fibrosis bronchiectasis. However, it is unknown whether ventilation inhomogeneity could improve after successful elimination of excessive secretions within bronchiectasis. The objective of this work was to assess the short-term effects of lung secretion clearance using the autogenic drainage technique on standard lung function tests and LCI in subjects with non-cystic fibrosis bronchiectasis. Nitrogen-based multiple-breath washout, spirometry, and body plethysmography tests were performed 30 min before autogenic drainage in adults with stable non-cystic fibrosis bronchiectasis. The autogenic drainage session was followed by a 5-min break, after which the tests were repeated in the same order. Sputum expectorated during autogenic drainage was quantified as dry weight and correlated with change between post- and pre-measurements (Δ). Paired t test or Wilcoxon signed-rank tests were used to compare pre- and post-autogenic drainage measurement outcomes. A P value of ≤.05 was considered as statistically significant. Twenty-four subjects were studied (18 females, median age [range]: 65 [21-81] y). Mean ± SD LCI significantly improved after autogenic drainage (10.88 ± 2.62 vs 10.53 ± 2.35, P = .042). However, only 20% of subjects with mucus hyperproduction during autogenic drainage had a ΔLCI that exceeded measurement variability. The percent of predicted slow vital capacity (SVC%) also slightly improved (88.7 ± 19.3% vs 90 ± 19.1%, P = .02). ΔLCI was inversely related to dry sputum weight (r = -.48, P = .02) and ΔSVC% (r = -.64, P = .001). ΔSVC% also correlated with dry sputum weight (r = 0.46, P = .02). In adults with non-cystic fibrosis bronchiectasis and mucus hypersecretion, autogenic drainage improved ventilation inhomogeneity. LCI change may be the result of the maximum recruited lung volume and the amount of cleared mucus secretion. (ClinicalTrials.gov registration NCT02411981.). Copyright © 2017 by Daedalus Enterprises.

Chaotic Particle Swarm Optimization with Mutation for Classification

PubMed Central

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937

Follow up of a robust meta-signature to identify Zika virus infection in Aedes aegypti: another brick in the wall.

PubMed

Fukutani, Eduardo; Rodrigues, Moreno; Kasprzykowski, José Irahe; Araujo, Cintia Figueiredo de; Paschoal, Alexandre Rossi; Ramos, Pablo Ivan Pereira; Fukutani, Kiyoshi Ferreira; Queiroz, Artur Trancoso Lopo de

2018-01-01

The mosquito Aedes aegypti is the main vector of several arthropod-borne diseases that have global impacts. In a previous meta-analysis, our group identified a vector gene set containing 110 genes strongly associated with infections of dengue, West Nile and yellow fever viruses. Of these 110 genes, four genes allowed a highly accurate classification of infected status. More recently, a new study of Ae. aegypti infected with Zika virus (ZIKV) was published, providing new data to investigate whether this "infection" gene set is also altered during a ZIKV infection. Our hypothesis is that the infection-associated signature may also serve as a proxy to classify the ZIKV infection in the vector. Raw data associated with the NCBI/BioProject were downloaded and re-analysed. A total of 18 paired-end replicates corresponding to three ZIKV-infected samples and three controls were included in this study. The nMDS technique with a logistic regression was used to obtain the probabilities of belonging to a given class. Thus, to compare both gene sets, we used the area under the curve and performed a comparison using the bootstrap method. Our meta-signature was able to separate the infected mosquitoes from the controls with good predictive power to classify the Zika-infected mosquitoes.

Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions.

PubMed

Aksu, Yaman; Miller, David J; Kesidis, George; Yang, Qing X

2010-05-01

Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature "markers." For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE). We demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show that RFE assumes that the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom--the hyperplane's intercept and its squared 2-norm--with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results.

Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa

2018-07-01

Automatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction. For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall. From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier. Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.

PubMed

Li, Qiang; Gu, Yu; Jia, Jing

2017-01-30

Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

Development of a ten-signature classifier using a support vector machine integrated approach to subdivide the M1 stage into M1a and M1b stages of nasopharyngeal carcinoma with synchronous metastases to better predict patients' survival.

PubMed

Jiang, Rou; You, Rui; Pei, Xiao-Qing; Zou, Xiong; Zhang, Meng-Xia; Wang, Tong-Min; Sun, Rui; Luo, Dong-Hua; Huang, Pei-Yu; Chen, Qiu-Yan; Hua, Yi-Jun; Tang, Lin-Quan; Guo, Ling; Mo, Hao-Yuan; Qian, Chao-Nan; Mai, Hai-Qiang; Hong, Ming-Huang; Cai, Hong-Min; Chen, Ming-Yuan

2016-01-19

The aim of this study was to develop a prognostic classifier and subdivided the M1 stage for nasopharyngeal carcinoma patients with synchronous metastases (mNPC). A retrospective cohort of 347 mNPC patients was recruited between January 2000 and December 2010. Thirty hematological markers and 11 clinical characteristics were collected, and the association of these factors with overall survival (OS) was evaluated. Advanced machine learning schemes of a support vector machine (SVM) were used to select a subset of highly informative factors and to construct a prognostic model (mNPC-SVM). The mNPC-SVM classifier identified ten informative variables, including three clinical indexes and seven hematological markers. The median survival time for low-risk patients (M1a) as identified by the mNPC-SVM classifier was 38.0 months, and survival time was dramatically reduced to 13.8 months for high-risk patients (M1b) (P < 0.001). Multivariate adjustment using prognostic factors revealed that the mNPC-SVM classifier remained a powerful predictor of OS (M1a vs. M1b, hazard ratio, 3.45; 95% CI, 2.59 to 4.60, P < 0.001). Moreover, combination treatment of systemic chemotherapy and loco-regional radiotherapy was associated with significantly better survival outcomes than chemotherapy alone (the 5-year OS, 47.0% vs. 10.0%, P < 0.001) in the M1a subgroup but not in the M1b subgroup (12.0% vs. 3.0%, P = 0.101). These findings were validated by a separate cohort. In conclusion, the newly developed mNPC-SVM classifier led to more precise risk definitions that offer a promising subdivision of the M1 stage and individualized selection for future therapeutic regimens in mNPC patients.

A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier.

PubMed

Nanthagopal, A Padma; Rajamony, R Sukanesh

2012-07-01

The proposed system provides new textural information for segmenting tumours, efficiently and accurately and with less computational time, from benign and malignant tumour images, especially in smaller dimensions of tumour regions of computed tomography (CT) images. Region-based segmentation of tumour from brain CT image data is an important but time-consuming task performed manually by medical experts. The objective of this work is to segment brain tumour from CT images using combined grey and texture features with new edge features and nonlinear support vector machine (SVM) classifier. The selected optimal features are used to model and train the nonlinear SVM classifier to segment the tumour from computed tomography images and the segmentation accuracies are evaluated for each slice of the tumour image. The method is applied on real data of 80 benign, malignant tumour images. The results are compared with the radiologist labelled ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and the overlap similarity measure dice metric. From the analysis and performance measures such as segmentation accuracy and dice metric, it is inferred that better segmentation accuracy and higher dice metric are achieved with the normalized cut segmentation method than with the fuzzy c-means clustering method.

Acoustic surface perception from naturally occurring step sounds of a dexterous hexapod robot

NASA Astrophysics Data System (ADS)

Cuneyitoglu Ozkul, Mine; Saranli, Afsar; Yazicioglu, Yigit

2013-10-01

Legged robots that exhibit dynamic dexterity naturally interact with the surface to generate complex acoustic signals carrying rich information on the surface as well as the robot platform itself. However, the nature of a legged robot, which is a complex, hybrid dynamic system, renders the more common approach of model-based system identification impractical. The present paper focuses on acoustic surface identification and proposes a non-model-based analysis and classification approach adopted from the speech processing literature. A novel feature set composed of spectral band energies augmented by their vector time derivatives and time-domain averaged zero crossing rate is proposed. Using a multi-dimensional vector classifier, these features carry enough information to accurately classify a range of commonly occurring indoor and outdoor surfaces without using of any mechanical system model. A comparative experimental study is carried out and classification performance and computational complexity are characterized. Different feature combinations, classifiers and changes in critical design parameters are investigated. A realistic and representative acoustic data set is collected with the robot moving at different speeds on a number of surfaces. The study demonstrates promising performance of this non-model-based approach, even in an acoustically uncontrolled environment. The approach also has good chance of performing in real-time.

Classification of sodium MRI data of cartilage using machine learning.

PubMed

Madelin, Guillaume; Poidevin, Frederick; Makrymallis, Antonios; Regatte, Ravinder R

2015-11-01

To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: Support vector machine, k-nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested. Sodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification. Either Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes. Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data. © 2014 Wiley Periodicals, Inc.

Classification of CT examinations for COPD visual severity analysis

NASA Astrophysics Data System (ADS)

Tan, Jun; Zheng, Bin; Wang, Xingwei; Pu, Jiantao; Gur, David; Sciurba, Frank C.; Leader, J. Ken

2012-03-01

In this study we present a computational method of CT examination classification into visual assessed emphysema severity. The visual severity categories ranged from 0 to 5 and were rated by an experienced radiologist. The six categories were none, trace, mild, moderate, severe and very severe. Lung segmentation was performed for every input image and all image features are extracted from the segmented lung only. We adopted a two-level feature representation method for the classification. Five gray level distribution statistics, six gray level co-occurrence matrix (GLCM), and eleven gray level run-length (GLRL) features were computed for each CT image depicted segment lung. Then we used wavelets decomposition to obtain the low- and high-frequency components of the input image, and again extract from the lung region six GLCM features and eleven GLRL features. Therefore our feature vector length is 56. The CT examinations were classified using the support vector machine (SVM) and k-nearest neighbors (KNN) and the traditional threshold (density mask) approach. The SVM classifier had the highest classification performance of all the methods with an overall sensitivity of 54.4% and a 69.6% sensitivity to discriminate "no" and "trace visually assessed emphysema. We believe this work may lead to an automated, objective method to categorically classify emphysema severity on CT exam.

Morphological and wavelet features towards sonographic thyroid nodules evaluation.

PubMed

Tsantis, Stavros; Dimitropoulos, Nikos; Cavouras, Dionisis; Nikiforidis, George

2009-03-01

This paper presents a computer-based classification scheme that utilized various morphological and novel wavelet-based features towards malignancy risk evaluation of thyroid nodules in ultrasonography. The study comprised 85 ultrasound images-patients that were cytological confirmed (54 low-risk and 31 high-risk). A set of 20 features (12 based on nodules boundary shape and 8 based on wavelet local maxima located within each nodule) has been generated. Two powerful pattern recognition algorithms (support vector machines and probabilistic neural networks) have been designed and developed in order to quantify the power of differentiation of the introduced features. A comparative study has also been held, in order to estimate the impact speckle had onto the classification procedure. The diagnostic sensitivity and specificity of both classifiers was made by means of receiver operating characteristics (ROC) analysis. In the speckle-free feature set, the area under the ROC curve was 0.96 for the support vector machines classifier whereas for the probabilistic neural networks was 0.91. In the feature set with speckle, the corresponding areas under the ROC curves were 0.88 and 0.86 respectively for the two classifiers. The proposed features can increase the classification accuracy and decrease the rate of missing and misdiagnosis in thyroid cancer control.

Using support vector machines to improve elemental ion identification in macromolecular crystal structures

DOE PAGES

Morshed, Nader; Echols, Nathaniel; Adams, Paul D.

2015-04-25

In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here, the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalousmore » diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.« less

Automated Sentiment Analysis

DTIC Science & Technology

2009-06-01

questions. Our prototype text classifier uses a “vector similarity” approach. This is a well-known technique introduced by Salton , Wong, and Yang (1975...Loveman & T.M. Davies Jr. (Eds.), Guerrilla warfare. Lincoln, NE: University of Nebraska Press, 1985, 47-69. Salton , G., Wong, A., & Yang, C.S. “A

Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine.

PubMed

Lao, Zhiqiang; Shen, Dinggang; Liu, Dengfeng; Jawad, Abbas F; Melhem, Elias R; Launer, Lenore J; Bryan, R Nick; Davatzikos, Christos

2008-03-01

Brain lesions, especially white matter lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important. In this article, we present a computer-assisted WML segmentation method, based on local features extracted from multiparametric magnetic resonance imaging (MRI) sequences (ie, T1-weighted, T2-weighted, proton density-weighted, and fluid attenuation inversion recovery MRI scans). A support vector machine classifier is first trained on expert-defined WMLs, and is then used to classify new scans. Postprocessing analysis further reduces false positives by using anatomic knowledge and measures of distance from the training set. Cross-validation on a population of 35 patients from three different imaging sites with WMLs of varying sizes, shapes, and locations tests the robustness and accuracy of the proposed segmentation method, compared with the manual segmentation results from two experienced neuroradiologists.

Gender classification of running subjects using full-body kinematics

NASA Astrophysics Data System (ADS)

Williams, Christina M.; Flora, Jeffrey B.; Iftekharuddin, Khan M.

2016-05-01

This paper proposes novel automated gender classification of subjects while engaged in running activity. The machine learning techniques include preprocessing steps using principal component analysis followed by classification with linear discriminant analysis, and nonlinear support vector machines, and decision-stump with AdaBoost. The dataset consists of 49 subjects (25 males, 24 females, 2 trials each) all equipped with approximately 80 retroreflective markers. The trials are reflective of the subject's entire body moving unrestrained through a capture volume at a self-selected running speed, thus producing highly realistic data. The classification accuracy using leave-one-out cross validation for the 49 subjects is improved from 66.33% using linear discriminant analysis to 86.74% using the nonlinear support vector machine. Results are further improved to 87.76% by means of implementing a nonlinear decision stump with AdaBoost classifier. The experimental findings suggest that the linear classification approaches are inadequate in classifying gender for a large dataset with subjects running in a moderately uninhibited environment.

Tuning support vector machines for minimax and Neyman-Pearson classification.

PubMed

Davenport, Mark A; Baraniuk, Richard G; Scott, Clayton D

2010-10-01

This paper studies the training of support vector machine (SVM) classifiers with respect to the minimax and Neyman-Pearson criteria. In principle, these criteria can be optimized in a straightforward way using a cost-sensitive SVM. In practice, however, because these criteria require especially accurate error estimation, standard techniques for tuning SVM parameters, such as cross-validation, can lead to poor classifier performance. To address this issue, we first prove that the usual cost-sensitive SVM, here called the 2C-SVM, is equivalent to another formulation called the 2nu-SVM. We then exploit a characterization of the 2nu-SVM parameter space to develop a simple yet powerful approach to error estimation based on smoothing. In an extensive experimental study, we demonstrate that smoothing significantly improves the accuracy of cross-validation error estimates, leading to dramatic performance gains. Furthermore, we propose coordinate descent strategies that offer significant gains in computational efficiency, with little to no loss in performance.

Classification of inflammatory bowel diseases by means of Raman spectroscopic imaging of epithelium cells

NASA Astrophysics Data System (ADS)

Bielecki, Christiane; Bocklitz, Thomas W.; Schmitt, Michael; Krafft, Christoph; Marquardt, Claudio; Gharbi, Akram; Knösel, Thomas; Stallmach, Andreas; Popp, Juergen

2012-07-01

We report on a Raman microspectroscopic characterization of the inflammatory bowel diseases (IBD) Crohn's disease (CD) and ulcerative colitis (UC). Therefore, Raman maps of human colon tissue sections were analyzed by utilizing innovative chemometric approaches. First, support vector machines were applied to highlight the tissue morphology (=Raman spectroscopic histopathology). In a second step, the biochemical tissue composition has been studied by analyzing the epithelium Raman spectra of sections of healthy control subjects (n=11), subjects with CD (n=14), and subjects with UC (n=13). These three groups exhibit significantly different molecular specific Raman signatures, allowing establishment of a classifier (support-vector-machine). By utilizing this classifier it was possible to separate between healthy control patients, patients with CD, and patients with UC with an accuracy of 98.90%. The automatic design of both classification steps (visualization of the tissue morphology and molecular classification of IBD) paves the way for an objective clinical diagnosis of IBD by means of Raman spectroscopy in combination with chemometric approaches.

Learning vector quantization neural networks improve accuracy of transcranial color-coded duplex sonography in detection of middle cerebral artery spasm--preliminary report.

PubMed

Swiercz, Miroslaw; Kochanowicz, Jan; Weigele, John; Hurst, Robert; Liebeskind, David S; Mariak, Zenon; Melhem, Elias R; Krejza, Jaroslaw

2008-01-01

To determine the performance of an artificial neural network in transcranial color-coded duplex sonography (TCCS) diagnosis of middle cerebral artery (MCA) spasm. TCCS was prospectively acquired within 2 h prior to routine cerebral angiography in 100 consecutive patients (54M:46F, median age 50 years). Angiographic MCA vasospasm was classified as mild (<25% of vessel caliber reduction), moderate (25-50%), or severe (>50%). A Learning Vector Quantization neural network classified MCA spasm based on TCCS peak-systolic, mean, and end-diastolic velocity data. During a four-class discrimination task, accurate classification by the network ranged from 64.9% to 72.3%, depending on the number of neurons in the Kohonen layer. Accurate classification of vasospasm ranged from 79.6% to 87.6%, with an accuracy of 84.7% to 92.1% for the detection of moderate-to-severe vasospasm. An artificial neural network may increase the accuracy of TCCS in diagnosis of MCA spasm.

Breast Cancer Detection with Reduced Feature Set.

PubMed

Mert, Ahmet; Kılıç, Niyazi; Bilgili, Erdem; Akan, Aydin

2015-01-01

This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%-40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity.

Using Neural Networks to Classify Digitized Images of Galaxies

NASA Astrophysics Data System (ADS)

Goderya, S. N.; McGuire, P. C.

2000-12-01

Automated classification of Galaxies into Hubble types is of paramount importance to study the large scale structure of the Universe, particularly as survey projects like the Sloan Digital Sky Survey complete their data acquisition of one million galaxies. At present it is not possible to find robust and efficient artificial intelligence based galaxy classifiers. In this study we will summarize progress made in the development of automated galaxy classifiers using neural networks as machine learning tools. We explore the Bayesian linear algorithm, the higher order probabilistic network, the multilayer perceptron neural network and Support Vector Machine Classifier. The performance of any machine classifier is dependant on the quality of the parameters that characterize the different groups of galaxies. Our effort is to develop geometric and invariant moment based parameters as input to the machine classifiers instead of the raw pixel data. Such an approach reduces the dimensionality of the classifier considerably, and removes the effects of scaling and rotation, and makes it easier to solve for the unknown parameters in the galaxy classifier. To judge the quality of training and classification we develop the concept of Mathews coefficients for the galaxy classification community. Mathews coefficients are single numbers that quantify classifier performance even with unequal prior probabilities of the classes.

An Improved Ensemble Learning Method for Classifying High-Dimensional and Imbalanced Biomedicine Data.

PubMed

Yu, Hualong; Ni, Jun

2014-01-01

Training classifiers on skewed data can be technically challenging tasks, especially if the data is high-dimensional simultaneously, the tasks can become more difficult. In biomedicine field, skewed data type often appears. In this study, we try to deal with this problem by combining asymmetric bagging ensemble classifier (asBagging) that has been presented in previous work and an improved random subspace (RS) generation strategy that is called feature subspace (FSS). Specifically, FSS is a novel method to promote the balance level between accuracy and diversity of base classifiers in asBagging. In view of the strong generalization capability of support vector machine (SVM), we adopt it to be base classifier. Extensive experiments on four benchmark biomedicine data sets indicate that the proposed ensemble learning method outperforms many baseline approaches in terms of Accuracy, F-measure, G-mean and AUC evaluation criterions, thus it can be regarded as an effective and efficient tool to deal with high-dimensional and imbalanced biomedical data.

Effects of cultural characteristics on building an emotion classifier through facial expression analysis

NASA Astrophysics Data System (ADS)

da Silva, Flávio Altinier Maximiano; Pedrini, Helio

2015-03-01

Facial expressions are an important demonstration of humanity's humors and emotions. Algorithms capable of recognizing facial expressions and associating them with emotions were developed and employed to compare the expressions that different cultural groups use to show their emotions. Static pictures of predominantly occidental and oriental subjects from public datasets were used to train machine learning algorithms, whereas local binary patterns, histogram of oriented gradients (HOGs), and Gabor filters were employed to describe the facial expressions for six different basic emotions. The most consistent combination, formed by the association of HOG filter and support vector machines, was then used to classify the other cultural group: there was a strong drop in accuracy, meaning that the subtle differences of facial expressions of each culture affected the classifier performance. Finally, a classifier was trained with images from both occidental and oriental subjects and its accuracy was higher on multicultural data, evidencing the need of a multicultural training set to build an efficient classifier.

Classification of small lesions on dynamic breast MRI: Integrating dimension reduction and out-of-sample extension into CADx methodology

PubMed Central

Nagarajan, Mahesh B.; Huber, Markus B.; Schlossbauer, Thomas; Leinsinger, Gerda; Krol, Andrzej; Wismüller, Axel

2014-01-01

Objective While dimension reduction has been previously explored in computer aided diagnosis (CADx) as an alternative to feature selection, previous implementations of its integration into CADx do not ensure strict separation between training and test data required for the machine learning task. This compromises the integrity of the independent test set, which serves as the basis for evaluating classifier performance. Methods and Materials We propose, implement and evaluate an improved CADx methodology where strict separation is maintained. This is achieved by subjecting the training data alone to dimension reduction; the test data is subsequently processed with out-of-sample extension methods. Our approach is demonstrated in the research context of classifying small diagnostically challenging lesions annotated on dynamic breast magnetic resonance imaging (MRI) studies. The lesions were dynamically characterized through topological feature vectors derived from Minkowski functionals. These feature vectors were then subject to dimension reduction with different linear and non-linear algorithms applied in conjunction with out-of-sample extension techniques. This was followed by classification through supervised learning with support vector regression. Area under the receiver-operating characteristic curve (AUC) was evaluated as the metric of classifier performance. Results Of the feature vectors investigated, the best performance was observed with Minkowski functional ’perimeter’ while comparable performance was observed with ’area’. Of the dimension reduction algorithms tested with ’perimeter’, the best performance was observed with Sammon’s mapping (0.84 ± 0.10) while comparable performance was achieved with exploratory observation machine (0.82 ± 0.09) and principal component analysis (0.80 ± 0.10). Conclusions The results reported in this study with the proposed CADx methodology present a significant improvement over previous results reported with such small lesions on dynamic breast MRI. In particular, non-linear algorithms for dimension reduction exhibited better classification performance than linear approaches, when integrated into our CADx methodology. We also note that while dimension reduction techniques may not necessarily provide an improvement in classification performance over feature selection, they do allow for a higher degree of feature compaction. PMID:24355697

Marine Search, Rescue and Emergency Preparedness Study.

DTIC Science & Technology

1975-09-01

Stampede, Boca, and Prosser Creek Reservoirs:) U. S. Forest Svc - Troy Kurth, Recreation Officer ,, erville District, Clair Engle and Lewiston Reservoirs: U...COE - Olin M. Taylor, Jr., Resource Mgr. IDAHO Central Snake Projects Office: B/R - Robert J. Brown, Superintendent INDIAN Carlyle Lake: COE - Wayne L...department U/W: U. S. Coast Guard HOSPITAL Tahoe Forest Hosp., Truckee 587-3541 (18 mi.) WEAVERVILLE DISTRICT, CLAIR ENGLE AND LEWISTON RESERVOIRS U.S

2015 QuickCompass of Sexual Assault Prevention and Response-Related Responders (QSAPR): Tabulation of Responses

DTIC Science & Technology

2016-03-17

protective order (CPO)? ......................................... 66  j.  Making a Special Victims’ Counsel/Victims’ Legal Counsel (SVC/ VLC ) available...Related Responders DMDC vii i.  Special Victims’ Counsels/Victims’ Legal Counsels (SVCs/ VLCs ) ........ 81  j.  Healthcare providers...often have you faced the following barriers or challenges in implementing the elements of the DoD Sexual Assault Prevention Strategy

Distributed Common Ground System-Navy Increment 2 (DCGS-N Inc 2)

DTIC Science & Technology

2016-03-01

15 minutes Enter and be Managed in the Network: Reference SvcV-7, Consolidated Afloat Networks and Enterprise Services ( CANES ) CDD, DCGS-N Inc 2...Red, White , Gray Data and Tracks to Command and Control System. Continuous Stream from SCI Common Intelligence Picture to General Service (GENSER...AIS - Automatic Information System AOC - Air Operations Command CANES - Consolidated Afloat Networks and Enterprise Services CID - Center for

Ballistic Testing of Australian Bisalloy Steel for Armor Applications

DTIC Science & Technology

2007-06-01

CLASSIFICATION OF: 19a. NAME OF RESPONSIBLE PERSON Dwight D. Showalter a . REPORT UNCLASSIFIED b. ABSTRACT UNCLASSIFIED c . THIS PAGE UNCLASSIFIED 17...WASHINGTON PA 15301 A ND 2 SOUTHWEST RSCH INST C ANDERSON J WALKER 6220 CULEB SAN ANTONIO TX 7 DEPT OF APPL MECH & SVC R011 S NEMAT...Ballistic Testing of Australian Bisalloy Steel for Armor Applications by Dwight D. Showalter, William A . Gooch, Matt S. Burkins, Victoria

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

Shape Optimization of the Assisted Bi-directional Glenn surgery for stage-1 single ventricle palliation

NASA Astrophysics Data System (ADS)

Verma, Aekaansh; Shang, Jessica; Esmaily-Moghadam, Mahdi; Wong, Kwai; Marsden, Alison

2016-11-01

Babies born with a single functional ventricle typically undergo three open-heart surgeries starting as neonates. The first of these stages (BT shunt or Norwood) has the highest mortality rates of the three, approaching 30%. Proceeding directly to a stage-2 Glenn surgery has historically demonstrated inadequate pulmonary flow (PF) & high mortality. Recently, the Assisted Bi-directional Glenn (ABG) was proposed as a promising means to achieve a stable physiology by assisting the PF via an 'ejector pump' from the systemic circulation. We present preliminary parametrization and optimization results for the ABG geometry, with the goal of increasing PF. To limit excessive pressure increases in the Superior Vena Cava (SVC), the SVC pressure is included as a constraint. We use 3-D finite element flow simulations coupled with a single ventricle lumped parameter network to evaluate PF & the pressure constraint. We employ a derivative free optimization method- the Surrogate Management Framework, in conjunction with the OpenDIEL framework to simulate multiple simultaneous evaluations. Results show that nozzle diameter is the most important design parameter affecting ABG performance. The application of these results to patient specific situations will be discussed. This work was supported by an NSF CAREER award (OCI1150184) and by the XSEDE National Computing Resource.

Experimental measurements of energy augmentation for mechanical circulatory assistance in a patient-specific Fontan model.

PubMed

Chopski, Steven G; Rangus, Owen M; Moskowitz, William B; Throckmorton, Amy L

2014-09-01

A mechanical blood pump specifically designed to increase pressure in the great veins would improve hemodynamic stability in adolescent and adult Fontan patients having dysfunctional cavopulmonary circulation. This study investigates the impact of axial-flow blood pumps on pressure, flow rate, and energy augmentation in the total cavopulmonary circulation (TCPC) using a patient-specific Fontan model. The experiments were conducted for three mechanical support configurations, which included an axial-flow impeller alone in the inferior vena cava (IVC) and an impeller with one of two different protective stent designs. All of the pump configurations led to an increase in pressure generation and flow in the Fontan circuit. The increase in IVC flow was found to augment pulmonary arterial flow, having only a small impact on the pressure and flow in the superior vena cava (SVC). Retrograde flow was neither observed nor measured from the TCPC junction into the SVC. All of the pump configurations enhanced the rate of power gain of the cavopulmonary circulation by adding energy and rotational force to the fluid flow. We measured an enhancement of forward flow into the TCPC junction, reduction in IVC pressure, and only minimally increased pulmonary arterial pressure under conditions of pump support. Copyright © 2014 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

A Field Portable Hyperspectral Goniometer for Coastal Characterization

NASA Technical Reports Server (NTRS)

Bachmann, Charles M.; Gray, Deric; Abelev, Andrei; Philpot, William; Fusina, Robert A.; Musser, Joseph A.; Vermillion, Michael; Doctor, Katarina; White, Maurice; Georgiev, Georgi

2012-01-01

During an airborne multi-sensor remote sensing experiment at the Virginia Coast Reserve (VCR) Long Term Ecological Research (LTER) site in June 2011 (VCR '11), first measurements were taken with the new NRL Goniometer for Outdoor Portable Hyperspectral Earth Reflectance (GOPHER). GOPHER measures the angular distribution of hyperspectral reflectance. GOPHER was constructed for NRL by Spectra Vista Corporation (SVC) and the University of Lethbridge through a capital equipment purchase in 2010. The GOPHER spectrometer is an SVC HR -1024, which measures hyperspectral reflectance over the range from 350 -2500 nm, the visible, near infrared, and short-wave infrared. During measurements, the spectrometer travels along a zenith quarter -arc track that can rotate in azimuth, allowing for measurement of the bi-directional reflectance distribution function (BRDF) over the whole hemisphere. The zenith arc has a radius of approximately 2m, and the spectrometer scan pattern can be programmed on the fly during calibration and validation efforts. The spectrometer and zenith arc assembly can be raised and lowered along a mast to allow for measurement of uneven terrain or vegetation canopies of moderate height. Hydraulics on the chassis allow for leveling of the instrument in the field. At just over 400 lbs, GOPHER is a field portable instrument and can be transformed into a compact trailer assembly for movement over long distances in the field.

Apply network coding for H.264/SVC multicasting

NASA Astrophysics Data System (ADS)

Wang, Hui; Kuo, C.-C. Jay

2008-08-01

In a packet erasure network environment, video streaming benefits from error control in two ways to achieve graceful degradation. The first approach is application-level (or the link-level) forward error-correction (FEC) to provide erasure protection. The second error control approach is error concealment at the decoder end to compensate lost packets. A large amount of research work has been done in the above two areas. More recently, network coding (NC) techniques have been proposed for efficient data multicast over networks. It was shown in our previous work that multicast video streaming benefits from NC for its throughput improvement. An algebraic model is given to analyze the performance in this work. By exploiting the linear combination of video packets along nodes in a network and the SVC video format, the system achieves path diversity automatically and enables efficient video delivery to heterogeneous receivers in packet erasure channels. The application of network coding can protect video packets against the erasure network environment. However, the rank defficiency problem of random linear network coding makes the error concealment inefficiently. It is shown by computer simulation that the proposed NC video multicast scheme enables heterogenous receiving according to their capacity constraints. But it needs special designing to improve the video transmission performance when applying network coding.

MISR Level 2 TOA/Cloud Classifier parameters (MIL2TCCL_V2)

NASA Technical Reports Server (NTRS)

Diner, David J. (Principal Investigator)

The TOA/Cloud Classifiers contain the Angular Signature Cloud Mask (ASCM), a scene classifier calculated using support vector machine technology (SVM) both of which are on a 1.1 km grid, and cloud fractions at 17.6 km resolution that are available in different height bins (low, middle, high) and are also calculated on an angle-by-angle basis. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=17.6 km; Longitude_Resolution=17.6 km; Horizontal_Resolution_Range=10 km - < 50 km or approximately .09 degree - < .5 degree; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=Daily - < Weekly, Daily - < Weekly].

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

PubMed Central

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions. PMID:26089862

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

PubMed

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.

PubMed

Cho, Ming-Yuan; Hoang, Thi Thom

2017-01-01

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

Multiple hypotheses image segmentation and classification with application to dietary assessment.

PubMed

Zhu, Fengqing; Bosch, Marc; Khanna, Nitin; Boushey, Carol J; Delp, Edward J

2015-01-01

We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of an image to select stable segmentations based on the classifier's confidence score assigned to each segmented image region. Automatic segmented regions are classified using a multichannel feature classification system. For each segmented region, multiple feature spaces are formed. Feature vectors in each of the feature spaces are individually classified. The final decision is obtained by combining class decisions from individual feature spaces using decision rules. We show improved accuracy of segmenting food images with classifier feedback.

Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor.

PubMed

Xu, Chang; Wang, Yingguan; Bao, Xinghe; Li, Fengrong

2018-05-24

This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.

Automatic segmentation and classification of mycobacterium tuberculosis with conventional light microscopy

NASA Astrophysics Data System (ADS)

Xu, Chao; Zhou, Dongxiang; Zhai, Yongping; Liu, Yunhui

2015-12-01

This paper realizes the automatic segmentation and classification of Mycobacterium tuberculosis with conventional light microscopy. First, the candidate bacillus objects are segmented by the marker-based watershed transform. The markers are obtained by an adaptive threshold segmentation based on the adaptive scale Gaussian filter. The scale of the Gaussian filter is determined according to the color model of the bacillus objects. Then the candidate objects are extracted integrally after region merging and contaminations elimination. Second, the shape features of the bacillus objects are characterized by the Hu moments, compactness, eccentricity, and roughness, which are used to classify the single, touching and non-bacillus objects. We evaluated the logistic regression, random forest, and intersection kernel support vector machines classifiers in classifying the bacillus objects respectively. Experimental results demonstrate that the proposed method yields to high robustness and accuracy. The logistic regression classifier performs best with an accuracy of 91.68%.

Support Vector Machine Model for Automatic Detection and Classification of Seismic Events

NASA Astrophysics Data System (ADS)

Barros, Vesna; Barros, Lucas

2016-04-01

The automated processing of multiple seismic signals to detect, localize and classify seismic events is a central tool in both natural hazards monitoring and nuclear treaty verification. However, false detections and missed detections caused by station noise and incorrect classification of arrivals are still an issue and the events are often unclassified or poorly classified. Thus, machine learning techniques can be used in automatic processing for classifying the huge database of seismic recordings and provide more confidence in the final output. Applied in the context of the International Monitoring System (IMS) - a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) - we propose a fully automatic method for seismic event detection and classification based on a supervised pattern recognition technique called the Support Vector Machine (SVM). According to Kortström et al., 2015, the advantages of using SVM are handleability of large number of features and effectiveness in high dimensional spaces. Our objective is to detect seismic events from one IMS seismic station located in an area of high seismicity and mining activity and classify them as earthquakes or quarry blasts. It is expected to create a flexible and easily adjustable SVM method that can be applied in different regions and datasets. Taken a step further, accurate results for seismic stations could lead to a modification of the model and its parameters to make it applicable to other waveform technologies used to monitor nuclear explosions such as infrasound and hydroacoustic waveforms. As an authorized user, we have direct access to all IMS data and bulletins through a secure signatory account. A set of significant seismic waveforms containing different types of events (e.g. earthquake, quarry blasts) and noise is being analysed to train the model and learn the typical pattern of the signal from these events. Moreover, comparing the performance of the support-vector network to various classical learning algorithms used before in seismic detection and classification is an essential final step to analyze the advantages and disadvantages of the model.

An investigation for the development of an integrated optical data preprocessor. [preprocessing remote sensor outputs

NASA Technical Reports Server (NTRS)

Verber, C. M.; Kenan, R. P.; Hartman, N. F.; Chapman, C. M.

1980-01-01

A laboratory model of a 16 channel integrated optical data preprocessor was fabricated and tested in response to a need for a device to evaluate the outputs of a set of remote sensors. It does this by accepting the outputs of these sensors, in parallel, as the components of a multidimensional vector descriptive of the data and comparing this vector to one or more reference vectors which are used to classify the data set. The comparison is performed by taking the difference between the signal and reference vectors. The preprocessor is wholly integrated upon the surface of a LiNbO3 single crystal with the exceptions of the source and the detector. He-Ne laser light is coupled in and out of the waveguide by prism couplers. The integrated optical circuit consists of a titanium infused waveguide pattern, electrode structures and grating beam splitters. The waveguide and electrode patterns, by virtue of their complexity, make the vector subtraction device the most complex integrated optical structure fabricated to date.

Research on intrusion detection based on Kohonen network and support vector machine

NASA Astrophysics Data System (ADS)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning

PubMed Central

Roh, Jongryun; Park, Hyeong-jun; Lee, Kwang Jin; Hyeong, Joonho; Kim, Sayup

2018-01-01

Sitting posture monitoring systems (SPMSs) help assess the posture of a seated person in real-time and improve sitting posture. To date, SPMS studies reported have required many sensors mounted on the backrest plate and seat plate of a chair. The present study, therefore, developed a system that measures a total of six sitting postures including the posture that applied a load to the backrest plate, with four load cells mounted only on the seat plate. Various machine learning algorithms were applied to the body weight ratio measured by the developed SPMS to identify the method that most accurately classified the actual sitting posture of the seated person. After classifying the sitting postures using several classifiers, average and maximum classification rates of 97.20% and 97.94%, respectively, were obtained from nine subjects with a support vector machine using the radial basis function kernel; the results obtained by this classifier showed a statistically significant difference from the results of multiple classifications using other classifiers. The proposed SPMS was able to classify six sitting postures including the posture with loading on the backrest and showed the possibility of classifying the sitting posture even though the number of sensors is reduced. PMID:29329261

Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

PubMed

Kunimatsu, Akira; Kunimatsu, Natsuko; Yasaka, Koichiro; Akai, Hiroyuki; Kamiya, Kouhei; Watadani, Takeyuki; Mori, Harushi; Abe, Osamu

2018-05-16

Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classification by support vector machine, a method of traditional machine learning, using texture features computed from contrast-enhanced T 1 -weighted images. This retrospective study on preoperative brain tumor MRI included 76 consecutives, initially treated patients with glioblastoma (n = 55) or PCNSL (n = 21) from one institution, consisting of independent training group (n = 60: 44 glioblastomas and 16 PCNSLs) and test group (n = 16: 11 glioblastomas and 5 PCNSLs) sequentially separated by time periods. A total set of 67 texture features was computed on routine contrast-enhanced T 1 -weighted images of the training group, and the top four most discriminating features were selected as input variables to train support vector machine classifiers. These features were then evaluated on the test group with subsequent image classification. The area under the receiver operating characteristic curves on the training data was calculated at 0.99 (95% confidence interval [CI]: 0.96-1.00) for the classifier with a Gaussian kernel and 0.87 (95% CI: 0.77-0.95) for the classifier with a linear kernel. On the test data, both of the classifiers showed prediction accuracy of 75% (12/16) of the test images. Although further improvement is needed, our preliminary results suggest that machine learning-based image classification may provide complementary diagnostic information on routine brain MRI.

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

PubMed Central

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

2009-01-01

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

Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets.

PubMed

Sankari, E Siva; Manimegalai, D

2017-12-21

Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting residue-wise contact orders in proteins by support vector regression.

PubMed

Song, Jiangning; Burrage, Kevin

2006-10-03

The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

A new discriminative kernel from probabilistic models.

PubMed

Tsuda, Koji; Kawanabe, Motoaki; Rätsch, Gunnar; Sonnenburg, Sören; Müller, Klaus-Robert

2002-10-01

Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived; from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.

Implementation of support vector machine for classification of speech marked hijaiyah letters based on Mel frequency cepstrum coefficient feature extraction

NASA Astrophysics Data System (ADS)

Adhi Pradana, Wisnu; Adiwijaya; Novia Wisesty, Untari

2018-03-01

Support Vector Machine or commonly called SVM is one method that can be used to process the classification of a data. SVM classifies data from 2 different classes with hyperplane. In this study, the system was built using SVM to develop Arabic Speech Recognition. In the development of the system, there are 2 kinds of speakers that have been tested that is dependent speakers and independent speakers. The results from this system is an accuracy of 85.32% for speaker dependent and 61.16% for independent speakers.

SVM-based feature extraction and classification of aflatoxin contaminated corn using fluorescence hyperspectral data

USDA-ARS?s Scientific Manuscript database

Support Vector Machine (SVM) was used in the Genetic Algorithms (GA) process to select and classify a subset of hyperspectral image bands. The method was applied to fluorescence hyperspectral data for the detection of aflatoxin contamination in Aspergillus flavus infected single corn kernels. In the...

Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.

PubMed

Mwangi, Benson; Ebmeier, Klaus P; Matthews, Keith; Steele, J Douglas

2012-05-01

Quantitative abnormalities of brain structure in patients with major depressive disorder have been reported at a group level for decades. However, these structural differences appear subtle in comparison with conventional radiologically defined abnormalities, with considerable inter-subject variability. Consequently, it has not been possible to readily identify scans from patients with major depressive disorder at an individual level. Recently, machine learning techniques such as relevance vector machines and support vector machines have been applied to predictive classification of individual scans with variable success. Here we describe a novel hybrid method, which combines machine learning with feature selection and characterization, with the latter aimed at maximizing the accuracy of machine learning prediction. The method was tested using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 patients with major depressive disorder and matched controls were recruited from referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The generalization ability and predictive accuracy of the classifiers was tested using data left out of the training process. High prediction accuracy was achieved (~90%). While feature selection was important for maximizing high predictive accuracy with machine learning, feature characterization contributed only a modest improvement to relevance vector machine-based prediction (~5%). Notably, while the only information provided for training the classifiers was T(1)-weighted scans plus a categorical label (major depressive disorder versus controls), both relevance vector machine and support vector machine 'weighting factors' (used for making predictions) correlated strongly with subjective ratings of illness severity. These results indicate that machine learning techniques have the potential to inform clinical practice and research, as they can make accurate predictions about brain scan data from individual subjects. Furthermore, machine learning weighting factors may reflect an objective biomarker of major depressive disorder illness severity, based on abnormalities of brain structure.

LCD denoise and the vector mutual information method in the application of the gear fault diagnosis under different working conditions

NASA Astrophysics Data System (ADS)

Xiangfeng, Zhang; Hong, Jiang

2018-03-01

In this paper, the full vector LCD method is proposed to solve the misjudgment problem caused by the change of the working condition. First, the signal from different working condition is decomposed by LCD, to obtain the Intrinsic Scale Component (ISC)whose instantaneous frequency with physical significance. Then, calculate of the cross correlation coefficient between ISC and the original signal, signal denoising based on the principle of mutual information minimum. At last, calculate the sum of absolute Vector mutual information of the sample under different working condition and the denoised ISC as the characteristics to classify by use of Support vector machine (SVM). The wind turbines vibration platform gear box experiment proves that this method can identify fault characteristics under different working conditions. The advantage of this method is that it reduce dependence of man’s subjective experience, identify fault directly from the original data of vibration signal. It will has high engineering value.

Ub-ISAP: a streamlined UNIX pipeline for mining unique viral vector integration sites from next generation sequencing data.

PubMed

Kamboj, Atul; Hallwirth, Claus V; Alexander, Ian E; McCowage, Geoffrey B; Kramer, Belinda

2017-06-17

The analysis of viral vector genomic integration sites is an important component in assessing the safety and efficiency of patient treatment using gene therapy. Alongside this clinical application, integration site identification is a key step in the genetic mapping of viral elements in mutagenesis screens that aim to elucidate gene function. We have developed a UNIX-based vector integration site analysis pipeline (Ub-ISAP) that utilises a UNIX-based workflow for automated integration site identification and annotation of both single and paired-end sequencing reads. Reads that contain viral sequences of interest are selected and aligned to the host genome, and unique integration sites are then classified as transcription start site-proximal, intragenic or intergenic. Ub-ISAP provides a reliable and efficient pipeline to generate large datasets for assessing the safety and efficiency of integrating vectors in clinical settings, with broader applications in cancer research. Ub-ISAP is available as an open source software package at https://sourceforge.net/projects/ub-isap/ .

Protein Kinase Classification with 2866 Hidden Markov Models and One Support Vector Machine

NASA Technical Reports Server (NTRS)

Weber, Ryan; New, Michael H.; Fonda, Mark (Technical Monitor)

2002-01-01

The main application considered in this paper is predicting true kinases from randomly permuted kinases that share the same length and amino acid distributions as the true kinases. Numerous methods already exist for this classification task, such as HMMs, motif-matchers, and sequence comparison algorithms. We build on some of these efforts by creating a vector from the output of thousands of structurally based HMMs, created offline with Pfam-A seed alignments using SAM-T99, which then must be combined into an overall classification for the protein. Then we use a Support Vector Machine for classifying this large ensemble Pfam-Vector, with a polynomial and chisquared kernel. In particular, the chi-squared kernel SVM performs better than the HMMs and better than the BLAST pairwise comparisons, when predicting true from false kinases in some respects, but no one algorithm is best for all purposes or in all instances so we consider the particular strengths and weaknesses of each.

NGEE Arctic Canopy Spectral Reflectance, Barrow, Alaska, 2014-2016

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

Shawn Serbin; Wil Lieberman-Cribbin; Kim Ely

Measurements of full-spectrum (i.e. 350-2500nm) canopy spectral reflectance of Arctic plant species within the BEO, Barrow, Alaska. Spectra were collected using an Spectra Vista Corporation (SVC) HR-2014i and Spectral Evolution (SE) PSR+ instrument mounted on a tripod or monopod together with a Spectralon white plate to calibrate each measurement under variable illumination conditions. Data were collected in Barrow, Alaska during the 2014 to 2016 period.

The Copula and Subject Complement Construction in English and Its Equivalents in Finnish. Contrastive Papers: Jyvaskyla Contrastive Studies, 4. Reports from the Department of English, No. 4.

ERIC Educational Resources Information Center

Hamalainen, Eila

This paper discusses subject-verb-complement (SVC) clauses and their renderings in English and Finnish. Comparisons are made on the level of surface structure and with regard to equivalence in the sense of one construction being an optimum translation of the other. The definition of congruence, i.e., formal similarity and equal number of…

To Determine the Impact of OPMS on the Development of Commanders.

DTIC Science & Technology

1982-04-19

and training policies are designed to promote the development of leadership , managerial, and technical skills.5 In spite of the foregoing, there is...corps into commanders and staff, with the in- evitable consequence of each group convinced the other does not under- stand its problems . Further, the...developing leadership skills needed to effec- tively command troops* Caterory Label Combat Arms Combat Support Combat Svc Spt Strongly agree/agree 65% 58

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China

PubMed Central

Hao, Pengyu; Wang, Li; Niu, Zheng

2015-01-01

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern. PMID:26360597

Soft Computing Application in Fault Detection of Induction Motor

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

Konar, P.; Puhan, P. S.; Chattopadhyay, P. Dr.

2010-10-26

The paper investigates the effectiveness of different patter classifier like Feed Forward Back Propagation (FFBPN), Radial Basis Function (RBF) and Support Vector Machine (SVM) for detection of bearing faults in Induction Motor. The steady state motor current with Park's Transformation has been used for discrimination of inner race and outer race bearing defects. The RBF neural network shows very encouraging results for multi-class classification problems and is hoped to set up a base for incipient fault detection of induction motor. SVM is also found to be a very good fault classifier which is highly competitive with RBF.

The epidural needle guidance with an intelligent and automatic identification system for epidural anesthesia

NASA Astrophysics Data System (ADS)

Kao, Meng-Chun; Ting, Chien-Kun; Kuo, Wen-Chuan

2018-02-01

Incorrect placement of the needle causes medical complications in the epidural block, such as dural puncture or spinal cord injury. This study proposes a system which combines an optical coherence tomography (OCT) imaging probe with an automatic identification (AI) system to objectively identify the position of the epidural needle tip. The automatic identification system uses three features as image parameters to distinguish the different tissue by three classifiers. Finally, we found that the support vector machine (SVM) classifier has highest accuracy, specificity, and sensitivity, which reached to 95%, 98%, and 92%, respectively.

Color Image Classification Using Block Matching and Learning

NASA Astrophysics Data System (ADS)

Kondo, Kazuki; Hotta, Seiji

In this paper, we propose block matching and learning for color image classification. In our method, training images are partitioned into small blocks. Given a test image, it is also partitioned into small blocks, and mean-blocks corresponding to each test block are calculated with neighbor training blocks. Our method classifies a test image into the class that has the shortest total sum of distances between mean blocks and test ones. We also propose a learning method for reducing memory requirement. Experimental results show that our classification outperforms other classifiers such as support vector machine with bag of keypoints.

Machine learning approach to automatic exudate detection in retinal images from diabetic patients

NASA Astrophysics Data System (ADS)

Sopharak, Akara; Dailey, Matthew N.; Uyyanonvara, Bunyarit; Barman, Sarah; Williamson, Tom; Thet Nwe, Khine; Aye Moe, Yin

2010-01-01

Exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Early detection of exudates could improve patients' chances to avoid blindness. In this paper, we present a series of experiments on feature selection and exudates classification using naive Bayes and support vector machine (SVM) classifiers. We first fit the naive Bayes model to a training set consisting of 15 features extracted from each of 115,867 positive examples of exudate pixels and an equal number of negative examples. We then perform feature selection on the naive Bayes model, repeatedly removing features from the classifier, one by one, until classification performance stops improving. To find the best SVM, we begin with the best feature set from the naive Bayes classifier, and repeatedly add the previously-removed features to the classifier. For each combination of features, we perform a grid search to determine the best combination of hyperparameters ν (tolerance for training errors) and γ (radial basis function width). We compare the best naive Bayes and SVM classifiers to a baseline nearest neighbour (NN) classifier using the best feature sets from both classifiers. We find that the naive Bayes and SVM classifiers perform better than the NN classifier. The overall best sensitivity, specificity, precision, and accuracy are 92.28%, 98.52%, 53.05%, and 98.41%, respectively.

Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC.

PubMed

Zhai, Jing-Xuan; Cao, Tian-Jie; An, Ji-Yong; Bian, Yong-Tao

2017-11-07

It is a challenging task for fundamental research whether proteins can interact with their partners. Protein self-interaction (SIP) is a special case of PPIs, which plays a key role in the regulation of cellular functions. Due to the limitations of experimental self-interaction identification, it is very important to develop an effective biological tool for predicting SIPs based on protein sequences. In the study, we developed a novel computational method called RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) for detecting SIPs from protein sequences. Firstly, Average Blocks (AB) feature extraction method is employed to represent protein sequences on a Position Specific Scoring Matrix (PSSM). Secondly, Principal Component Analysis (PCA) method is used to reduce the dimension of AB vector for reducing the influence of noise. Then, by employing the Relevance Vector Machine (RVM) algorithm, the performance of RVM-AB is assessed and compared with the state-of-the-art support vector machine (SVM) classifier and other exiting methods on yeast and human datasets respectively. Using the fivefold test experiment, RVM-AB model achieved very high accuracies of 93.01% and 97.72% on yeast and human datasets respectively, which are significantly better than the method based on SVM classifier and other previous methods. The experimental results proved that the RVM-AB prediction model is efficient and robust. It can be an automatic decision support tool for detecting SIPs. For facilitating extensive studies for future proteomics research, the RVMAB server is freely available for academic use at http://219.219.62.123:8888/SIP_AB. Copyright © 2017 Elsevier Ltd. All rights reserved.

A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.

PubMed

Yan, Kang K; Zhao, Hongyu; Pang, Herbert

2017-12-06

High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking. In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.

Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

NASA Astrophysics Data System (ADS)

Yao, Y.; Liang, H.; Li, X.; Zhang, J.; He, J.

2017-09-01

With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

Characterization and classification of seven citrus herbs by liquid chromatography-quadrupole time-of-flight mass spectrometry and genetic algorithm optimized support vector machines.

PubMed

Duan, Li; Guo, Long; Liu, Ke; Liu, E-Hu; Li, Ping

2014-04-25

Citrus herbs have been widely used in traditional medicine and cuisine in China and other countries since the ancient time. However, the authentication and quality control of Citrus herbs has always been a challenging task due to their similar morphological characteristics and the diversity of the multi-components existed in the complicated matrix. In the present investigation, we developed a novel strategy to characterize and classify seven Citrus herbs based on chromatographic analysis and chemometric methods. Firstly, the chemical constituents in seven Citrus herbs were globally characterized by liquid chromatography combined with quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Based on their retention time, UV spectra and MS fragmentation behavior, a total of 75 compounds were identified or tentatively characterized in these herbal medicines. Secondly, a segmental monitoring method based on LC-variable wavelength detection was developed for simultaneous quantification of ten marker compounds in these Citrus herbs. Thirdly, based on the contents of the ten analytes, genetic algorithm optimized support vector machines (GA-SVM) was employed to differentiate and classify the 64 samples covering these seven herbs. The obtained classifier showed good prediction performance and the overall prediction accuracy reached 96.88%. The proposed strategy is expected to provide new insight for authentication and quality control of traditional herbs. Copyright © 2014 Elsevier B.V. All rights reserved.

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning.

PubMed

Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

2017-03-15

Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

PubMed Central

Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

2016-01-01

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides. PMID:27187430

Sleep versus wake classification from heart rate variability using computational intelligence: consideration of rejection in classification models.

PubMed

Lewicke, Aaron; Sazonov, Edward; Corwin, Michael J; Neuman, Michael; Schuckers, Stephanie

2008-01-01

Reliability of classification performance is important for many biomedical applications. A classification model which considers reliability in the development of the model such that unreliable segments are rejected would be useful, particularly, in large biomedical data sets. This approach is demonstrated in the development of a technique to reliably determine sleep and wake using only the electrocardiogram (ECG) of infants. Typically, sleep state scoring is a time consuming task in which sleep states are manually derived from many physiological signals. The method was tested with simultaneous 8-h ECG and polysomnogram (PSG) determined sleep scores from 190 infants enrolled in the collaborative home infant monitoring evaluation (CHIME) study. Learning vector quantization (LVQ) neural network, multilayer perceptron (MLP) neural network, and support vector machines (SVMs) are tested as the classifiers. After systematic rejection of difficult to classify segments, the models can achieve 85%-87% correct classification while rejecting only 30% of the data. This corresponds to a Kappa statistic of 0.65-0.68. With rejection, accuracy improves by about 8% over a model without rejection. Additionally, the impact of the PSG scored indeterminate state epochs is analyzed. The advantages of a reliable sleep/wake classifier based only on ECG include high accuracy, simplicity of use, and low intrusiveness. Reliability of the classification can be built directly in the model, such that unreliable segments are rejected.

Comparative evaluation of support vector machine classification for computer aided detection of breast masses in mammography

NASA Astrophysics Data System (ADS)

Lesniak, J. M.; Hupse, R.; Blanc, R.; Karssemeijer, N.; Székely, G.

2012-08-01

False positive (FP) marks represent an obstacle for effective use of computer-aided detection (CADe) of breast masses in mammography. Typically, the problem can be approached either by developing more discriminative features or by employing different classifier designs. In this paper, the usage of support vector machine (SVM) classification for FP reduction in CADe is investigated, presenting a systematic quantitative evaluation against neural networks, k-nearest neighbor classification, linear discriminant analysis and random forests. A large database of 2516 film mammography examinations and 73 input features was used to train the classifiers and evaluate for their performance on correctly diagnosed exams as well as false negatives. Further, classifier robustness was investigated using varying training data and feature sets as input. The evaluation was based on the mean exam sensitivity in 0.05-1 FPs on normals on the free-response receiver operating characteristic curve (FROC), incorporated into a tenfold cross validation framework. It was found that SVM classification using a Gaussian kernel offered significantly increased detection performance (P = 0.0002) compared to the reference methods. Varying training data and input features, SVMs showed improved exploitation of large feature sets. It is concluded that with the SVM-based CADe a significant reduction of FPs is possible outperforming other state-of-the-art approaches for breast mass CADe.

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China.

PubMed

Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

2016-05-11

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%-19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.

Webcam classification using simple features

NASA Astrophysics Data System (ADS)

Pramoun, Thitiporn; Choe, Jeehyun; Li, He; Chen, Qingshuang; Amornraksa, Thumrongrat; Lu, Yung-Hsiang; Delp, Edward J.

2015-03-01

Thousands of sensors are connected to the Internet and many of these sensors are cameras. The "Internet of Things" will contain many "things" that are image sensors. This vast network of distributed cameras (i.e. web cams) will continue to exponentially grow. In this paper we examine simple methods to classify an image from a web cam as "indoor/outdoor" and having "people/no people" based on simple features. We use four types of image features to classify an image as indoor/outdoor: color, edge, line, and text. To classify an image as having people/no people we use HOG and texture features. The features are weighted based on their significance and combined. A support vector machine is used for classification. Our system with feature weighting and feature combination yields 95.5% accuracy.

Identification of terrain cover using the optimum polarimetric classifier

NASA Technical Reports Server (NTRS)

Kong, J. A.; Swartz, A. A.; Yueh, H. A.; Novak, L. M.; Shin, R. T.

1988-01-01

A systematic approach for the identification of terrain media such as vegetation canopy, forest, and snow-covered fields is developed using the optimum polarimetric classifier. The covariance matrices for various terrain cover are computed from theoretical models of random medium by evaluating the scattering matrix elements. The optimal classification scheme makes use of a quadratic distance measure and is applied to classify a vegetation canopy consisting of both trees and grass. Experimentally measured data are used to validate the classification scheme. Analytical and Monte Carlo simulated classification errors using the fully polarimetric feature vector are compared with classification based on single features which include the phase difference between the VV and HH polarization returns. It is shown that the full polarimetric results are optimal and provide better classification performance than single feature measurements.

Support vector machine for the diagnosis of malignant mesothelioma

NASA Astrophysics Data System (ADS)

Ushasukhanya, S.; Nithyakalyani, A.; Sivakumar, V.

2018-04-01

Harmful mesothelioma is an illness in which threatening (malignancy) cells shape in the covering of the trunk or stomach area. Being presented to asbestos can influence the danger of threatening mesothelioma. Signs and side effects of threatening mesothelioma incorporate shortness of breath and agony under the rib confine. Tests that inspect within the trunk and belly are utilized to recognize (find) and analyse harmful mesothelioma. Certain elements influence forecast (shot of recuperation) and treatment choices. In this review, Support vector machine (SVM) classifiers were utilized for Mesothelioma sickness conclusion. SVM output is contrasted by concentrating on Mesothelioma’s sickness and findings by utilizing similar information set. The support vector machine algorithm gives 92.5% precision acquired by means of 3-overlap cross-approval. The Mesothelioma illness dataset were taken from an organization reports from Turkey.

Predicting the host of influenza viruses based on the word vector.

PubMed

Xu, Beibei; Tan, Zhiying; Li, Kenli; Jiang, Taijiao; Peng, Yousong

2017-01-01

Newly emerging influenza viruses continue to threaten public health. A rapid determination of the host range of newly discovered influenza viruses would assist in early assessment of their risk. Here, we attempted to predict the host of influenza viruses using the Support Vector Machine (SVM) classifier based on the word vector, a new representation and feature extraction method for biological sequences. The results show that the length of the word within the word vector, the sequence type (DNA or protein) and the species from which the sequences were derived for generating the word vector all influence the performance of models in predicting the host of influenza viruses. In nearly all cases, the models built on the surface proteins hemagglutinin (HA) and neuraminidase (NA) (or their genes) produced better results than internal influenza proteins (or their genes). The best performance was achieved when the model was built on the HA gene based on word vectors (words of three-letters long) generated from DNA sequences of the influenza virus. This results in accuracies of 99.7% for avian, 96.9% for human and 90.6% for swine influenza viruses. Compared to the method of sequence homology best-hit searches using the Basic Local Alignment Search Tool (BLAST), the word vector-based models still need further improvements in predicting the host of influenza A viruses.

Remote distinction of a noxious weed (musk thistle: Carduus nutans) using airborne hyperspectral imagery and the support vector machine classifier

USDA-ARS?s Scientific Manuscript database

Remote detection of invasive plant species using geospatial imagery may significantly improve monitoring, planning, and management practices by eliminating shortfalls such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion ex...