Sample records for automatic train location

  1. 49 CFR 236.557 - Receiver; location with respect to rail.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.557 Receiver... automatic cab signal, train stop, or train control device of locomotive equipped with onboard test equipment...

  2. 49 CFR 236.560 - Contact element, mechanical trip type; location with respect to rail.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and.... Contact element of automatic train stop device of the mechanical trip type shall be maintained at a height...

  3. Public knowledge of how to use an automatic external defibrillator in out-of-hospital cardiac arrest in Hong Kong.

    PubMed

    Fan, K L; Leung, L P; Poon, H T; Chiu, H Y; Liu, H L; Tang, W Y

    2016-12-01

    The survival rate of out-of-hospital cardiac arrest in Hong Kong is low. A long delay between collapse and defibrillation is a contributing factor. Public access to defibrillation may shorten this delay. It is unknown, however, whether Hong Kong's public is willing or able to use an automatic external defibrillator. This study aimed to evaluate public knowledge of how to use an automatic external defibrillator in out-of-hospital cardiac arrest. A face-to-face semi-structured questionnaire survey of the public was conducted in six locations with a high pedestrian flow in Hong Kong. In this study, 401 members of the public were interviewed. Most had no training in first aid (65.8%) or in use of an automatic external defibrillator (85.3%). Nearly all (96.5%) would call for help for a victim of out-of-hospital cardiac arrest but only 18.0% would use an automatic external defibrillator. Public knowledge of automatic external defibrillator use was low: 77.6% did not know the location of an automatic external defibrillator in the vicinity of their home or workplace. People who had ever been trained in both first aid and use of an automatic external defibrillator were more likely to respond to and help a victim of cardiac arrest, and to use an automatic external defibrillator. Public knowledge of automatic external defibrillator use is low in Hong Kong. A combination of training in first aid and in the use of an automatic external defibrillator is better than either one alone.

  4. Acoustic emission source location in complex structures using full automatic delta T mapping technique

    NASA Astrophysics Data System (ADS)

    Al-Jumaili, Safaa Kh.; Pearson, Matthew R.; Holford, Karen M.; Eaton, Mark J.; Pullin, Rhys

    2016-05-01

    An easy to use, fast to apply, cost-effective, and very accurate non-destructive testing (NDT) technique for damage localisation in complex structures is key for the uptake of structural health monitoring systems (SHM). Acoustic emission (AE) is a viable technique that can be used for SHM and one of the most attractive features is the ability to locate AE sources. The time of arrival (TOA) technique is traditionally used to locate AE sources, and relies on the assumption of constant wave speed within the material and uninterrupted propagation path between the source and the sensor. In complex structural geometries and complex materials such as composites, this assumption is no longer valid. Delta T mapping was developed in Cardiff in order to overcome these limitations; this technique uses artificial sources on an area of interest to create training maps. These are used to locate subsequent AE sources. However operator expertise is required to select the best data from the training maps and to choose the correct parameter to locate the sources, which can be a time consuming process. This paper presents a new and improved fully automatic delta T mapping technique where a clustering algorithm is used to automatically identify and select the highly correlated events at each grid point whilst the "Minimum Difference" approach is used to determine the source location. This removes the requirement for operator expertise, saving time and preventing human errors. A thorough assessment is conducted to evaluate the performance and the robustness of the new technique. In the initial test, the results showed excellent reduction in running time as well as improved accuracy of locating AE sources, as a result of the automatic selection of the training data. Furthermore, because the process is performed automatically, this is now a very simple and reliable technique due to the prevention of the potential source of error related to manual manipulation.

  5. Does training under consistent mapping conditions lead to automatic attention attraction to targets in search tasks?

    PubMed

    Lefebvre, Christine; Cousineau, Denis; Larochelle, Serge

    2008-11-01

    Schneider and Shiffrin (1977) proposed that training under consistent stimulus-response mapping (CM) leads to automatic target detection in search tasks. Other theories, such as Treisman and Gelade's (1980) feature integration theory, consider target-distractor discriminability as the main determinant of search performance. The first two experiments pit these two principles against each other. The results show that CM training is neither necessary nor sufficient to achieve optimal search performance. Two other experiments examine whether CM trained targets, presented as distractors in unattended display locations, attract attention away from current targets. The results are again found to vary with target-distractor similarity. Overall, the present study strongly suggests that CM training does not invariably lead to automatic attention attraction in search tasks.

  6. 49 CFR 236.560 - Contact element, mechanical trip type; location with respect to rail.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Contact element, mechanical trip type; location... Instructions; Locomotives § 236.560 Contact element, mechanical trip type; location with respect to rail. Contact element of automatic train stop device of the mechanical trip type shall be maintained at a height...

  7. Perceptual Units Training for Improving Word Analysis Skills. Technical Report No. 1.

    ERIC Educational Resources Information Center

    Weaver, Phyllis A.; And Others

    A training program was devised to develop automaticity of one subcomponent of reading--locating and disembedding multiletter units within words. The system involved the use of a training task that was implemented in a microcomputer-based game that required students to detect whether a target unit was presented within words that were shown in rapid…

  8. Automatic segmentation of right ventricle on ultrasound images using sparse matrix transform and level set

    NASA Astrophysics Data System (ADS)

    Qin, Xulei; Cong, Zhibin; Halig, Luma V.; Fei, Baowei

    2013-03-01

    An automatic framework is proposed to segment right ventricle on ultrasound images. This method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform (SMT), a training model, and a localized region based level set. First, the sparse matrix transform extracts main motion regions of myocardium as eigenimages by analyzing statistical information of these images. Second, a training model of right ventricle is registered to the extracted eigenimages in order to automatically detect the main location of the right ventricle and the corresponding transform relationship between the training model and the SMT-extracted results in the series. Third, the training model is then adjusted as an adapted initialization for the segmentation of each image in the series. Finally, based on the adapted initializations, a localized region based level set algorithm is applied to segment both epicardial and endocardial boundaries of the right ventricle from the whole series. Experimental results from real subject data validated the performance of the proposed framework in segmenting right ventricle from echocardiography. The mean Dice scores for both epicardial and endocardial boundaries are 89.1%+/-2.3% and 83.6+/-7.3%, respectively. The automatic segmentation method based on sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.

  9. Coordinated train control and energy management control strategies

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

    Gordon, S.P.; Lehrer, D.G.

    1998-05-01

    The Bay Area Rapid Transit (BART) system, in collaboration with Hughes Aircraft Company and Harmon Industries, as in the process of developing an Advanced Automatic Train Control (AATC) system to replace the current fixed-block automatic system. In the long run, the AATC system is expected to not only allow for safe short headway operation, but also to facilitate coordinated train control and energy management. This new system will employ spread spectrum radios, installed onboard trains, at wayside locations, and at control stations, to determine train locations and reliably transfer control information. Sandia National Laboratories has worked cooperatively with BART tomore » develop a simulator of the train control and the power consumption of the AATC system. The authors are now in the process of developing enhanced train control algorithms to supplement the safety critical controller in order to smooth out train trajectories through coordinated control of multiple trains, and to reduce energy consumption and power infrastructure requirements. The control algorithms so far considered include (1) reducing peak power consumption to avoid voltage sags, especially during an outage or while clearing a backup, (2) rapid and smooth recovery from a backup, (3) avoiding oscillations due to train interference, (4) limiting needle peaks in power demand at substations to some specified level, (5) coasting, and (6) coordinating train movement, e.g., starts/stops and hills.« less

  10. Reward modulates attention independently of action value in posterior parietal cortex

    PubMed Central

    Peck, Christopher J.; Jangraw, David C.; Suzuki, Mototaka; Efem, Richard; Gottlieb, Jacqueline

    2009-01-01

    While numerous studies explored the mechanisms of reward-based decisions (the choice of action based on expected gain), few asked how reward influences attention (the selection of information relevant for a decision). Here we show that a powerful determinant of attentional priority is the association between a stimulus and an appetitive reward. A peripheral cue heralded the delivery of reward (RC+) or no reward (RC−); to experience the predicted outcome monkeys made a saccade to a target that appeared unpredictably at the same or opposite location relative to the cue. Although the RC had no operant associations (did not specify the required saccade) they automatically biased attention, such that the RC+ attracted attention and RC− repelled attention from their location. Neurons in the lateral intraparietal area (LIP) encoded these attentional biases, maintaining sustained excitation at the location of an RC+ and inhibition at the location of an RC−. Contrary to the hypothesis that LIP encodes action value, neurons did not encode the expected reward of the saccade. Moreover, the cue-evoked biases were maladaptive, interfering with the required saccade, and they biases increased rather than abating with training, strikingly at odds with an adaptive decision process. After prolonged training valence selectivity appeared at shorter latencies and automatically transferred to a novel task context, suggesting that training produced visual plasticity. The results suggest that reward predictors gain automatic attentional priority regardless of their operant associations, and this valence-specific priority is encoded in LIP independently of the expected reward of an action. PMID:19741125

  11. Legal aspects of the application of the lay rescuer automatic external defibrillator (AED) program in South Korea.

    PubMed

    Bae, Hyuna

    2008-04-01

    The American Heart Association has stated that the automatic external defibrillator (AED) is a promising method for achieving rapid defibrillation, and emphasized that AED training and use should be available in every community. The demonstrated safety and effectiveness of the AED make it ideally suited for the delivery of early defibrillation by trained laypersons, and the placement of AEDs in selected locations for immediate use by trained laypersons may enable critical intervention that can significantly increase survival from out-of-hospital cardiac arrest. The American Heart Association recommends the installation of AEDs in public locations such as airports, thus allowing laypersons to conduct defibrillation and cardiopulmonary resuscitation on the occasion of adverse cardiopulmonary events. In Korea, the Ministry of Health and Welfare officially prohibits the installation of AEDs in public locations on the grounds that cardiopulmonary resuscitation and defibrillation are understood as medical practices that can be conducted only by licensed medical practitioners. The purpose of this article is to discuss the necessity for AEDs and the appropriate process for their implementation in Korea, by examining the current pre-AED status of Korea and the relevant legal aspects.

  12. Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

    NASA Astrophysics Data System (ADS)

    Althuwaynee, Omar F.; Pradhan, Biswajeet; Ahmad, Noordin

    2014-06-01

    This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies.

  13. If training data appears to be mislabeled, should we relabel it? Improving supervised learning algorithms for threat detection in ground penetrating radar data

    NASA Astrophysics Data System (ADS)

    Reichman, Daniël.; Collins, Leslie M.; Malof, Jordan M.

    2018-04-01

    This work focuses on the development of automatic buried threat detection (BTD) algorithms using ground penetrating radar (GPR) data. Buried threats tend to exhibit unique characteristics in GPR imagery, such as high energy hyperbolic shapes, which can be leveraged for detection. Many recent BTD algorithms are supervised, and therefore they require training with exemplars of GPR data collected over non-threat locations and threat locations, respectively. Frequently, data from non-threat GPR examples will exhibit high energy hyperbolic patterns, similar to those observed from a buried threat. Is it still useful therefore, to include such examples during algorithm training, and encourage an algorithm to label such data as a non-threat? Similarly, some true buried threat examples exhibit very little distinctive threat-like patterns. We investigate whether it is beneficial to treat such GPR data examples as mislabeled, and either (i) relabel them, or (ii) remove them from training. We study this problem using two algorithms to automatically identify mislabeled examples, if they are present, and examine the impact of removing or relabeling them for training. We conduct these experiments on a large collection of GPR data with several state-of-the-art GPR-based BTD algorithms.

  14. Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine

    NASA Technical Reports Server (NTRS)

    Schwabacher, Mark A.; Aguilar, Robert; Figueroa, Fernando F.

    2009-01-01

    The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically "learns" a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to "train" and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it "learned" a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location.

  15. High-speed railway signal trackside equipment patrol inspection system

    NASA Astrophysics Data System (ADS)

    Wu, Nan

    2018-03-01

    High-speed railway signal trackside equipment patrol inspection system comprehensively applies TDI (time delay integration), high-speed and highly responsive CMOS architecture, low illumination photosensitive technique, image data compression technique, machine vision technique and so on, installed on high-speed railway inspection train, and achieves the collection, management and analysis of the images of signal trackside equipment appearance while the train is running. The system will automatically filter out the signal trackside equipment images from a large number of the background image, and identify of the equipment changes by comparing the original image data. Combining with ledger data and train location information, the system accurately locate the trackside equipment, conscientiously guiding maintenance.

  16. Development of A Two-Stage Procedure for the Automatic Recognition of Dysfluencies in the Speech of Children Who Stutter: I. Psychometric Procedures Appropriate for Selection of Training Material for Lexical Dysfluency Classifiers

    PubMed Central

    Howell, Peter; Sackin, Stevie; Glenn, Kazan

    2007-01-01

    This program of work is intended to develop automatic recognition procedures to locate and assess stuttered dysfluencies. This and the following article together, develop and test recognizers for repetitions and prolongations. The automatic recognizers classify the speech in two stages: In the first, the speech is segmented and in the second the segments are categorized. The units that are segmented are words. Here assessments by human judges on the speech of 12 children who stutter are described using a corresponding procedure. The accuracy of word boundary placement across judges, categorization of the words as fluent, repetition or prolongation, and duration of the different fluency categories are reported. These measures allow reliable instances of repetitions and prolongations to be selected for training and assessing the recognizers in the subsequent paper. PMID:9328878

  17. Automatic behavior sensing for a bomb-detecting dog

    NASA Astrophysics Data System (ADS)

    Nguyen, Hoa G.; Nans, Adam; Talke, Kurt; Candela, Paul; Everett, H. R.

    2015-05-01

    Bomb-detecting dogs are trained to detect explosives through their sense of smell and often perform a specific behavior to indicate a possible bomb detection. This behavior is noticed by the dog handler, who confirms the probable explosives, determines the location, and forwards the information to an explosive ordnance disposal (EOD) team. To improve the speed and accuracy of this process and better integrate it with the EOD team's robotic explosive disposal operation, SPAWAR Systems Center Pacific has designed and prototyped an electronic dog collar that automatically tracks the dog's location and attitude, detects the indicative behavior, and records the data. To account for the differences between dogs, a 5-minute training routine can be executed before the mission to establish initial values for the k-mean clustering algorithm that classifies a specific dog's behavior. The recorded data include GPS location of the suspected bomb, the path the dog took to approach this location, and a video clip covering the detection event. The dog handler reviews and confirms the data before it is packaged up and forwarded on to the EOD team. The EOD team uses the video clip to better identify the type of bomb and for awareness of the surrounding environment before they arrive at the scene. Before the robotic neutralization operation commences at the site, the location and path data (which are supplied in a format understandable by the next-generation EOD robots—the Advanced EOD Robotic System) can be loaded into the robotic controller to automatically guide the robot to the bomb site. This paper describes the project with emphasis on the dog-collar hardware, behavior-classification software, and feasibility testing.

  18. Automatic classification of seismic events within a regional seismograph network

    NASA Astrophysics Data System (ADS)

    Tiira, Timo; Kortström, Jari; Uski, Marja

    2015-04-01

    A fully automatic method for seismic event classification within a sparse regional seismograph network is presented. The tool is based on a supervised pattern recognition technique, Support Vector Machine (SVM), trained here to distinguish weak local earthquakes from a bulk of human-made or spurious seismic events. The classification rules rely on differences in signal energy distribution between natural and artificial seismic sources. Seismic records are divided into four windows, P, P coda, S, and S coda. For each signal window STA is computed in 20 narrow frequency bands between 1 and 41 Hz. The 80 discrimination parameters are used as a training data for the SVM. The SVM models are calculated for 19 on-line seismic stations in Finland. The event data are compiled mainly from fully automatic event solutions that are manually classified after automatic location process. The station-specific SVM training events include 11-302 positive (earthquake) and 227-1048 negative (non-earthquake) examples. The best voting rules for combining results from different stations are determined during an independent testing period. Finally, the network processing rules are applied to an independent evaluation period comprising 4681 fully automatic event determinations, of which 98 % have been manually identified as explosions or noise and 2 % as earthquakes. The SVM method correctly identifies 94 % of the non-earthquakes and all the earthquakes. The results imply that the SVM tool can identify and filter out blasts and spurious events from fully automatic event solutions with a high level of confidence. The tool helps to reduce work-load in manual seismic analysis by leaving only ~5 % of the automatic event determinations, i.e. the probable earthquakes for more detailed seismological analysis. The approach presented is easy to adjust to requirements of a denser or wider high-frequency network, once enough training examples for building a station-specific data set are available.

  19. Automatic Detection of Welding Defects using Deep Neural Network

    NASA Astrophysics Data System (ADS)

    Hou, Wenhui; Wei, Ye; Guo, Jie; Jin, Yi; Zhu, Chang'an

    2018-01-01

    In this paper, we propose an automatic detection schema including three stages for weld defects in x-ray images. Firstly, the preprocessing procedure for the image is implemented to locate the weld region; Then a classification model which is trained and tested by the patches cropped from x-ray images is constructed based on deep neural network. And this model can learn the intrinsic feature of images without extra calculation; Finally, the sliding-window approach is utilized to detect the whole images based on the trained model. In order to evaluate the performance of the model, we carry out several experiments. The results demonstrate that the classification model we proposed is effective in the detection of welded joints quality.

  20. Mapping forest vegetation with ERTS-1 MSS data and automatic data processing techniques

    NASA Technical Reports Server (NTRS)

    Messmore, J.; Copeland, G. E.; Levy, G. F.

    1975-01-01

    This study was undertaken with the intent of elucidating the forest mapping capabilities of ERTS-1 MSS data when analyzed with the aid of LARS' automatic data processing techniques. The site for this investigation was the Great Dismal Swamp, a 210,000 acre wilderness area located on the Middle Atlantic coastal plain. Due to inadequate ground truth information on the distribution of vegetation within the swamp, an unsupervised classification scheme was utilized. Initially pictureprints, resembling low resolution photographs, were generated in each of the four ERTS-1 channels. Data found within rectangular training fields was then clustered into 13 spectral groups and defined statistically. Using a maximum likelihood classification scheme, the unknown data points were subsequently classified into one of the designated training classes. Training field data was classified with a high degree of accuracy (greater than 95%), and progress is being made towards identifying the mapped spectral classes.

  1. Mapping forest vegetation with ERTS-1 MSS data and automatic data processing techniques

    NASA Technical Reports Server (NTRS)

    Messmore, J.; Copeland, G. E.; Levy, G. F.

    1975-01-01

    This study was undertaken with the intent of elucidating the forest mapping capabilities of ERTS-1 MSS data when analyzed with the aid of LARS' automatic data processing techniques. The site for this investigation was the Great Dismal Swamp, a 210,000 acre wilderness area located on the Middle Atlantic coastal plain. Due to inadequate ground truth information on the distribution of vegetation within the swamp, an unsupervised classification scheme was utilized. Initially pictureprints, resembling low resolution photographs, were generated in each of the four ERTS-1 channels. Data found within rectangular training fields was then clustered into 13 spectral groups and defined statistically. Using a maximum likelihood classification scheme, the unknown data points were subsequently classified into one of the designated training classes. Training field data was classified with a high degree of accuracy (greater than 95 percent), and progress is being made towards identifying the mapped spectral classes.

  2. Clustering and classification of infrasonic events at Mount Etna using pattern recognition techniques

    NASA Astrophysics Data System (ADS)

    Cannata, A.; Montalto, P.; Aliotta, M.; Cassisi, C.; Pulvirenti, A.; Privitera, E.; Patanè, D.

    2011-04-01

    Active volcanoes generate sonic and infrasonic signals, whose investigation provides useful information for both monitoring purposes and the study of the dynamics of explosive phenomena. At Mt. Etna volcano (Italy), a pattern recognition system based on infrasonic waveform features has been developed. First, by a parametric power spectrum method, the features describing and characterizing the infrasound events were extracted: peak frequency and quality factor. Then, together with the peak-to-peak amplitude, these features constituted a 3-D ‘feature space’; by Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) three clusters were recognized inside it. After the clustering process, by using a common location method (semblance method) and additional volcanological information concerning the intensity of the explosive activity, we were able to associate each cluster to a particular source vent and/or a kind of volcanic activity. Finally, for automatic event location, clusters were used to train a model based on Support Vector Machine, calculating optimal hyperplanes able to maximize the margins of separation among the clusters. After the training phase this system automatically allows recognizing the active vent with no location algorithm and by using only a single station.

  3. 49 CFR 236.506 - Release of brakes after automatic application.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.506 Release of brakes after automatic application. The automatic train stop or train control apparatus shall prevent release of the...

  4. 49 CFR 236.506 - Release of brakes after automatic application.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.506 Release of brakes after automatic application. The automatic train stop or train control apparatus shall prevent release of the...

  5. 49 CFR 236.502 - Automatic brake application, initiation by restrictive block conditions stopping distance in...

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.502 Automatic brake application, initiation by restrictive block conditions stopping distance in advance. An automatic train-stop or train-control system shall operate to...

  6. 49 CFR 236.825 - System, automatic train control.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false System, automatic train control. 236.825 Section..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.825 System, automatic train control. A system so arranged that its operation will automatically...

  7. 49 CFR 236.825 - System, automatic train control.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 4 2011-10-01 2011-10-01 false System, automatic train control. 236.825 Section..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.825 System, automatic train control. A system so arranged that its operation will automatically...

  8. 49 CFR 236.552 - Insulation resistance; requirement.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic... control system, or automatic train stop system shall be not less than one megohm, and that of an... system, automatic train control system, or automatic train stop system, and 20,000 ohms for an...

  9. 49 CFR 236.826 - System, automatic train stop.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 4 2011-10-01 2011-10-01 false System, automatic train stop. 236.826 Section 236..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.826 System, automatic train stop. A system so arranged that its operation will automatically...

  10. 49 CFR 236.826 - System, automatic train stop.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false System, automatic train stop. 236.826 Section 236..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.826 System, automatic train stop. A system so arranged that its operation will automatically...

  11. Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set

    NASA Astrophysics Data System (ADS)

    Qin, Xulei; Cong, Zhibin; Fei, Baowei

    2013-11-01

    An automatic segmentation framework is proposed to segment the right ventricle (RV) in echocardiographic images. The method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform, a training model, and a localized region-based level set. First, the sparse matrix transform extracts main motion regions of the myocardium as eigen-images by analyzing the statistical information of the images. Second, an RV training model is registered to the eigen-images in order to locate the position of the RV. Third, the training model is adjusted and then serves as an optimized initialization for the segmentation of each image. Finally, based on the initializations, a localized, region-based level set algorithm is applied to segment both epicardial and endocardial boundaries in each echocardiograph. Three evaluation methods were used to validate the performance of the segmentation framework. The Dice coefficient measures the overall agreement between the manual and automatic segmentation. The absolute distance and the Hausdorff distance between the boundaries from manual and automatic segmentation were used to measure the accuracy of the segmentation. Ultrasound images of human subjects were used for validation. For the epicardial and endocardial boundaries, the Dice coefficients were 90.8 ± 1.7% and 87.3 ± 1.9%, the absolute distances were 2.0 ± 0.42 mm and 1.79 ± 0.45 mm, and the Hausdorff distances were 6.86 ± 1.71 mm and 7.02 ± 1.17 mm, respectively. The automatic segmentation method based on a sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.

  12. Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods.

    PubMed

    Kong, Xiangyi; Gong, Shun; Su, Lijuan; Howard, Newton; Kong, Yanguo

    2018-01-01

    Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  13. Practice-induced and sequential modulations of the Simon effect.

    PubMed

    Soetens, Eric; Maetens, Kathleen; Zeischka, Peter

    2010-05-01

    People react more quickly and more accurately to stimuli presented in locations corresponding to the response, as compared with noncorresponding locations, even when stimulus location is irrelevant (Simon effect [SE]). The explanation that SEs are caused by the automatic priming of a corresponding response has been questioned, because of the many exceptions to the effect. We replicated practice-induced and sequential modulations of the SE in two experiments--first, by training participants with blocks of location-relevant stimuli, and second, by mixing location-relevant and location-irrelevant trials. The decrease of the SE with incompatible training was relatively permanent in the blocked experiment, whereas the effect was temporary in the mixed experiment. The difference was caused by a more permanent reversal of the SE after incongruent trials, showing that sequential modulations depend on long-term practice effects. We suggest that there is a formation of a contralateral long-term memory stimulus-response link in blocked conditions and that short-term and long-term memory links are primed by preceding events.

  14. 49 CFR 235.5 - Changes requiring filing of application.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... system, automatic train stop, train control, or cab signal system or other similar appliance or device..., automatic train stop, train control, or cab signal system; or (3) The modification of a block signal system, interlocking, traffic control system, automatic train stop, train control, or cab signal system. (b) [Reserved...

  15. 49 CFR 235.5 - Changes requiring filing of application.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... system, automatic train stop, train control, or cab signal system or other similar appliance or device..., automatic train stop, train control, or cab signal system; or (3) The modification of a block signal system, interlocking, traffic control system, automatic train stop, train control, or cab signal system. (b) [Reserved...

  16. An Automated Motion Detection and Reward System for Animal Training.

    PubMed

    Miller, Brad; Lim, Audrey N; Heidbreder, Arnold F; Black, Kevin J

    2015-12-04

    A variety of approaches has been used to minimize head movement during functional brain imaging studies in awake laboratory animals. Many laboratories expend substantial effort and time training animals to remain essentially motionless during such studies. We could not locate an "off-the-shelf" automated training system that suited our needs.  We developed a time- and labor-saving automated system to train animals to hold still for extended periods of time. The system uses a personal computer and modest external hardware to provide stimulus cues, monitor movement using commercial video surveillance components, and dispense rewards. A custom computer program automatically increases the motionless duration required for rewards based on performance during the training session but allows changes during sessions. This system was used to train cynomolgus monkeys (Macaca fascicularis) for awake neuroimaging studies using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). The automated system saved the trainer substantial time, presented stimuli and rewards in a highly consistent manner, and automatically documented training sessions. We have limited data to prove the training system's success, drawn from the automated records during training sessions, but we believe others may find it useful. The system can be adapted to a range of behavioral training/recording activities for research or commercial applications, and the software is freely available for non-commercial use.

  17. Differential Arc expression in the hippocampus and striatum during the transition from attentive to automatic navigation on a plus maze

    PubMed Central

    Gardner, Robert S.; Suarez, Daniel F.; Robinson-Burton, Nadira K.; Rudnicky, Christopher J.; Gulati, Asish; Ascoli, Giorgio A.; Dumas, Theodore C.

    2016-01-01

    The strategies utilized to effectively perform a given task change with practice and experience. During a spatial navigation task, with relatively little training, performance is typically attentive enabling an individual to locate the position of a goal by relying on spatial landmarks. These (place) strategies require an intact hippocampus. With task repetition, performance becomes automatic; the same goal is reached using a fixed response or sequence of actions. These (response) strategies require an intact striatum. The current work aims to understand the activation patterns across these neural structures during this experience-dependent strategy transition. This was accomplished by region-specific measurement of activity-dependent immediate early gene expression among rats trained to different degrees on a dual-solution task (i.e., a task that can be solved using either place or response navigation). As expected, rats increased their reliance on response navigation with extended task experience. In addition, dorsal hippocampal expression of the immediate early gene Arc was considerably reduced in rats that used a response strategy late in training (as compared with hippocampal expression in rats that used a place strategy early in training). In line with these data, vicarious trial and error, a behavior linked to hippocampal function, also decreased with task repetition. Although Arc mRNA expression in dorsal medial or lateral striatum alone did not correlate with training stage, the ratio of expression in the medial striatum to that in the lateral striatum was relatively high among rats that used a place strategy early in training as compared with the ratio among over-trained response rats. Altogether, these results identify specific changes in the activation of dissociated neural systems that may underlie the experience-dependent emergence of response-based automatic navigation. PMID:26976088

  18. Morphological hippocampal markers for automated detection of Alzheimer's disease and mild cognitive impairment converters in magnetic resonance images.

    PubMed

    Ferrarini, Luca; Frisoni, Giovanni B; Pievani, Michela; Reiber, Johan H C; Ganzola, Rossana; Milles, Julien

    2009-01-01

    In this study, we investigated the use of hippocampal shape-based markers for automatic detection of Alzheimer's disease (AD) and mild cognitive impairment converters (MCI-c). Three-dimensional T1-weighted magnetic resonance images of 50 AD subjects, 50 age-matched controls, 15 MCI-c, and 15 MCI-non-converters (MCI-nc) were taken. Manual delineations of both hippocampi were obtained from normalized images. Fully automatic shape modeling was used to generate comparable meshes for both structures. Repeated permutation tests, run over a randomly sub-sampled training set (25 controls and 25 ADs), highlighted shape-based markers, mostly located in the CA1 sector, which consistently discriminated ADs and controls. Support vector machines (SVMs) were trained, using markers from either one or both hippocampi, to automatically classify control and AD subjects. Leave-1-out cross-validations over the remaining 25 ADs and 25 controls resulted in an optimal accuracy of 90% (sensitivity 92%), for markers in the left hippocampus. The same morphological markers were used to train SVMs for MCI-c versus MCI-nc classification: markers in the right hippocampus reached an accuracy (and sensitivity) of 80%. Due to the pattern recognition framework, our results statistically represent the expected performances of clinical set-ups, and compare favorably to analyses based on hippocampal volumes.

  19. Transfer of location-specific control to untrained locations.

    PubMed

    Weidler, Blaire J; Bugg, Julie M

    2016-11-01

    Recent research highlights a seemingly flexible and automatic form of cognitive control that is triggered by potent contextual cues, as exemplified by the location-specific proportion congruence effect--reduced compatibility effects in locations associated with a high as compared to low likelihood of conflict. We investigated just how flexible location-specific control is by examining whether novel locations effectively cue control for congruency-unbiased stimuli. In two experiments, biased (mostly compatible or mostly incompatible) training stimuli appeared in distinct locations. During a final block, unbiased (50% compatible) stimuli appeared in novel untrained locations spatially linked to biased locations. The flanker compatibly effect was reduced for unbiased stimuli in novel locations linked to a mostly incompatible compared to a mostly compatible location, indicating transfer. Transfer was observed when stimuli appeared along a linear function (Experiment 1) or in rings of a bullseye (Experiment 2). The novel transfer effects imply that location-specific control is more flexible than previously reported and further counter the complex stimulus-response learning account of location-specific proportion congruence effects. We propose that the representation and retrieval of control settings in untrained locations may depend on environmental support and the presentation of stimuli in novel locations that fall within the same categories of space as trained locations.

  20. 49 CFR 236.504 - Operation interconnected with automatic block-signal system.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.504... or train control system shall operate in connection with an automatic block signal system and shall...

  1. Simulator training to automaticity leads to improved skill transfer compared with traditional proficiency-based training: a randomized controlled trial.

    PubMed

    Stefanidis, Dimitrios; Scerbo, Mark W; Montero, Paul N; Acker, Christina E; Smith, Warren D

    2012-01-01

    We hypothesized that novices will perform better in the operating room after simulator training to automaticity compared with traditional proficiency based training (current standard training paradigm). Simulator-acquired skill translates to the operating room, but the skill transfer is incomplete. Secondary task metrics reflect the ability of trainees to multitask (automaticity) and may improve performance assessment on simulators and skill transfer by indicating when learning is complete. Novices (N = 30) were enrolled in an IRB-approved, blinded, randomized, controlled trial. Participants were randomized into an intervention (n = 20) and a control (n = 10) group. The intervention group practiced on the FLS suturing task until they achieved expert levels of time and errors (proficiency), were tested on a live porcine fundoplication model, continued simulator training until they achieved expert levels on a visual spatial secondary task (automaticity) and were retested on the operating room (OR) model. The control group participated only during testing sessions. Performance scores were compared within and between groups during testing sessions. : Intervention group participants achieved proficiency after 54 ± 14 and automaticity after additional 109 ± 57 repetitions. Participants achieved better scores in the OR after automaticity training [345 (range, 0-537)] compared with after proficiency-based training [220 (range, 0-452; P < 0.001]. Simulator training to automaticity takes more time but is superior to proficiency-based training, as it leads to improved skill acquisition and transfer. Secondary task metrics that reflect trainee automaticity should be implemented during simulator training to improve learning and skill transfer.

  2. Automated human skull landmarking with 2D Gabor wavelets

    NASA Astrophysics Data System (ADS)

    de Jong, Markus A.; Gül, Atilla; de Gijt, Jan Pieter; Koudstaal, Maarten J.; Kayser, Manfred; Wolvius, Eppo B.; Böhringer, Stefan

    2018-05-01

    Landmarking of CT scans is an important step in the alignment of skulls that is key in surgery planning, pre-/post-surgery comparisons, and morphometric studies. We present a novel method for automatically locating anatomical landmarks on the surface of cone beam CT-based image models of human skulls using 2D Gabor wavelets and ensemble learning. The algorithm is validated via human inter- and intra-rater comparisons on a set of 39 scans and a skull superimposition experiment with an established surgery planning software (Maxilim). Automatic landmarking results in an accuracy of 1–2 mm for a subset of landmarks around the nose area as compared to a gold standard derived from human raters. These landmarks are located in eye sockets and lower jaw, which is competitive with or surpasses inter-rater variability. The well-performing landmark subsets allow for the automation of skull superimposition in clinical applications. Our approach delivers accurate results, has modest training requirements (training set size of 30–40 items) and is generic, so that landmark sets can be easily expanded or modified to accommodate shifting landmark interests, which are important requirements for the landmarking of larger cohorts.

  3. 49 CFR 236.504 - Operation interconnected with automatic block-signal system.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Operation interconnected with automatic block... Operation interconnected with automatic block-signal system. (a) A continuous inductive automatic train stop or train control system shall operate in connection with an automatic block signal system and shall...

  4. F/FB-111 Avionics Test Station and Component Specialist/Technician. Automatic Test Stations Manual and Electronic Warfare Test Stations. Training Requirements Analysis (451X6). Volume 2

    DTIC Science & Technology

    1991-11-01

    F-111D RADAR SST TASK NOTES: SST IS LOCATED ONLY AT CANNON AFB, NM. IT CONSISTS OF AN MRU , EPU, LVPS, MFG, DDPU, ARS RACK, AND TRANSMITTER. THE SST...VOTES: SST IS LOCATED ONLY AT CANNON AFB, NM. IT CONSISTS OF AN MRU , EPU, LVPS, MFG, DDPU, ARS RACK, AND TRANSMITTER. THE SST WILL BE REPLACED BY DTS...NOTES: SST IS LOCATED ONLY AT CANNON AFB, NM. IT CONSISTS OF AN MRU , EPU, LVPS, MFG, DDPU, ARS RACK, AND TRANSMITTER. THE SST WILL BE REPLACED BY DTS

  5. Space Weather Effects on Mid-Latitude Railways: a Statistical Study of Anomalies observed in the Operation of Signaling and Train Control Equipment on the East-Siberian Railway

    NASA Astrophysics Data System (ADS)

    Kasinskii, V. V.; Ptitsyna, N. G.; Lyahov, N. N.; Dorman, L. I.; Villoresi, G.; Iucci, N.

    The end result of a long chain of space weather events beginning on the Sun is the induction of currents in ground-based long conductors as power lines pipelines and railways Intense geomagnetically induced currents GIC can hamper rail traffic by disturbing signaling and train control systems In few cases induced voltages were believed to have affected signaling equipment in Sweden Jansen et al 2000 and in the North of Russia Belov et al 2005 GIC threats have been a concern for technological systems at high-latitude locations due to disturbances driven by electrojet intensifications However other geomagnetic storm processes such as SSC and ring current enhancement can also cause GIC concerns for the technological systems Objective of this report is to continue our research Ptitsyna et al 2005 on possible influence of geomagnetic storms on mid-latitude railways and to perform a statistical research in addition to case studies This will help in providing a basis for railway companies to evaluate the risk of disruption to signaling and train control equipment and devise engineering solutions In the present report we analyzed anomalies in operation of automatic signaling and train control equipment occurred in 2004-2005 on the East-Siberian Railway located at mid-latitudes latitudes 51N-56N longitudes 96E-114E The anomalies consist mainly in unstable functioning and false operations in traffic automatic control systems rail chain switches locomotive control devices etc often resulting in false engagement of railway

  6. 49 CFR 236.739 - Device, acknowledging.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.739... locomotive equipped with an automatic train stop or train control device, an automatic brake application can be forestalled, or by means of which, on a locomotive equipped with an automatic cab signal device...

  7. Review of LOGEX. Main Report and Appendixes A-I

    DTIC Science & Technology

    1975-05-23

    been developed on an RCA Spectra 70 machine located at the Army Logistics Management Center, Fort Lee, Virginia. This was undoubtedly an outstanding...Control Number ADP - Automatic Data Processing ACT - Active Duty for Training ALMC - US Army Logistics Management Center AMO - Ammunition AR - Amy...Directorate CPT McClellan, LOGEX Directorate CPT Weaver, LOGEX Directorate United States Army Logistics Management Center Mr. Loper Mr. Ross United States

  8. 49 CFR 236.566 - Locomotive of each train operating in train stop, train control or cab signal territory; equipped.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and... controlled, of each train operating in automatic train stop, train control, or cab signal territory shall be..., train control or cab signal territory; equipped. 236.566 Section 236.566 Transportation Other...

  9. 49 CFR 236.566 - Locomotive of each train operating in train stop, train control or cab signal territory; equipped.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and... controlled, of each train operating in automatic train stop, train control, or cab signal territory shall be..., train control or cab signal territory; equipped. 236.566 Section 236.566 Transportation Other...

  10. Train Control and Operations

    DOT National Transportation Integrated Search

    1971-06-01

    ATO (automatic train operation) and ATC (automatic train control) systems are evaluated relative to available technology and cost-benefit. The technological evaluation shows that suitable mathematical models of the dynamics of long trains are require...

  11. Automated Management of Exercise Intervention at the Point of Care: Application of a Web-Based Leg Training System

    PubMed Central

    2015-01-01

    Background Recent advances in information and communication technology have prompted development of Web-based health tools to promote physical activity, the key component of cardiac rehabilitation and chronic disease management. Mobile apps can facilitate behavioral changes and help in exercise monitoring, although actual training usually takes place away from the point of care in specialized gyms or outdoors. Daily participation in conventional physical activities is expensive, time consuming, and mostly relies on self-management abilities of patients who are typically aged, overweight, and unfit. Facilitation of sustained exercise training at the point of care might improve patient engagement in cardiac rehabilitation. Objective In this study we aimed to test the feasibility of execution and automatic monitoring of several exercise regimens on-site using a Web-enabled leg training system. Methods The MedExercise leg rehabilitation machine was equipped with wireless temperature sensors in order to monitor its usage by the rise of temperature in the resistance unit (Δt°). Personal electronic devices such as laptop computers were fitted with wireless gateways and relevant software was installed to monitor the usage of training machines. Cloud-based software allowed monitoring of participant training over the Internet. Seven healthy participants applied the system at various locations with training protocols typically used in cardiac rehabilitation. The heart rates were measured by fingertip pulse oximeters. Results Exercising in home chairs, in bed, and under an office desk was made feasible and resulted in an intensity-dependent increase of participants’ heart rates and Δt° in training machine temperatures. Participants self-controlled their activities on smart devices, while a supervisor monitored them over the Internet. Individual Δt° reached during 30 minutes of moderate-intensity continuous training averaged 7.8°C (SD 1.6). These Δt° were used as personalized daily doses of exercise with automatic email alerts sent upon achieving them. During 1-week training at home, automatic notifications were received on 4.4 days (SD 1.8). Although the high intensity interval training regimen was feasible on-site, it was difficult for self- and remote management. Opportunistic leg exercise under the desk, while working with a computer, and training in bed while viewing television were less intensive than dosed exercise bouts, but allowed prolonged leg mobilization of 73.7 minutes/day (SD 29.7). Conclusions This study demonstrated the feasibility of self-control exercise training on-site, which was accompanied by online monitoring, electronic recording, personalization of exercise doses, and automatic reporting of adherence. The results suggest that this technology and its applications are useful for the delivery of Web-based exercise rehabilitation and cardiac training programs at the point of care. PMID:28582243

  12. 49 CFR 236.507 - Brake application; full service.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.507 Brake application; full service. The automatic train stop or train control apparatus shall, when operated, cause a full service...

  13. 49 CFR 236.507 - Brake application; full service.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.507 Brake application; full service. The automatic train stop or train control apparatus shall, when operated, cause a full service...

  14. 49 CFR 236.534 - Entrance to equipped territory; requirements.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.534... not exceed restricted speed, the automatic train stop, train control, or cab signal device shall be...

  15. 49 CFR 236.562 - Minimum rail current required.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.562 Minimum... continuous inductive automatic train stop or train control device to normal condition or to obtain a proceed...

  16. 49 CFR 236.534 - Entrance to equipped territory; requirements.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.534... not exceed restricted speed, the automatic train stop, train control, or cab signal device shall be...

  17. 49 CFR 236.587 - Departure test.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.587 Departure test. (a) The automatic train stop, train control, or cab signal apparatus on each locomotive, except a locomotive or a...

  18. 49 CFR 236.562 - Minimum rail current required.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.562 Minimum... continuous inductive automatic train stop or train control device to normal condition or to obtain a proceed...

  19. Training and subjective workload in a category search task

    NASA Technical Reports Server (NTRS)

    Vidulich, Michael A.; Pandit, Parimal

    1986-01-01

    This study examined automaticity as a means by which training influences mental workload. Two groups were trained in a category search task. One group received a training paradigm designed to promote the development of automaticity; the other group received a training paradigm designed to prohibit it. Resultant performance data showed the expected improvement as a result of the development of automaticity. Subjective workload assessments mirrored the performance results in most respects. The results supported the position that subjective mental workload assessments may be sensitive to the effect of training when it produces a lower level of cognitive load.

  20. Potential fault region detection in TFDS images based on convolutional neural network

    NASA Astrophysics Data System (ADS)

    Sun, Junhua; Xiao, Zhongwen

    2016-10-01

    In recent years, more than 300 sets of Trouble of Running Freight Train Detection System (TFDS) have been installed on railway to monitor the safety of running freight trains in China. However, TFDS is simply responsible for capturing, transmitting, and storing images, and fails to recognize faults automatically due to some difficulties such as such as the diversity and complexity of faults and some low quality images. To improve the performance of automatic fault recognition, it is of great importance to locate the potential fault areas. In this paper, we first introduce a convolutional neural network (CNN) model to TFDS and propose a potential fault region detection system (PFRDS) for simultaneously detecting four typical types of potential fault regions (PFRs). The experimental results show that this system has a higher performance of image detection to PFRs in TFDS. An average detection recall of 98.95% and precision of 100% are obtained, demonstrating the high detection ability and robustness against various poor imaging situations.

  1. Dynamic user data analysis and web composition technique using big data

    NASA Astrophysics Data System (ADS)

    Soundarya, P.; Vanitha, M.; Sumaiya Thaseen, I.

    2017-11-01

    In the existing system, a reliable service oriented system is built which is more important when compared with the traditional standalone system in the unpredictable internet service and it also a challenging task to build reliable web service. In the proposed system, the fault tolerance is determined by using the proposed heuristic algorithm. There are two kinds of strategies active and passive strategies. The user requirement is also formulated as local and global constraints. Different services are deployed in the modification process. Two bus reservation and two train reservation services are deployed along with hotel reservation service. User can choose any one of the bus reservation and specify their destination location. If corresponding destination is not available then automatic backup service to another bus reservation system is carried. If same, the service is not available then parallel service of train reservation is initiated. Automatic hotel reservation is also initiated based on the mode and type of travel of the user.

  2. 49 CFR 236.563 - Delay time.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.563 Delay time. Delay time of automatic train stop or train control system shall not exceed 8 seconds and the spacing of signals to meet the...

  3. 49 CFR 236.586 - Daily or after trip test.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.586 Daily or after trip test..., each locomotive equipped with an automatic cab signal or train stop or train control device operating...

  4. 49 CFR 236.588 - Periodic test.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.588 Periodic test. Except as provided in § 236.586, periodic test of the automatic train stop, train control, or cab signal apparatus...

  5. 49 CFR 236.509 - Two or more locomotives coupled.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.509 Two or more locomotives coupled. The automatic train stop, train control or cab signal apparatus shall be arranged so that when two or...

  6. 49 CFR 236.586 - Daily or after trip test.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.586 Daily or after trip test..., each locomotive equipped with an automatic cab signal or train stop or train control device operating...

  7. 49 CFR 236.509 - Two or more locomotives coupled.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.509 Two or more locomotives coupled. The automatic train stop, train control or cab signal apparatus shall be arranged so that when two or...

  8. 49 CFR 236.564 - Acknowledging time.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.564 Acknowledging time. Acknowledging time of intermittent automatic train-stop device shall be not more than 30 seconds. ...

  9. Training lay-people to use automatic external defibrillators: are all of their needs being met?

    PubMed

    Harrison-Paul, Russell; Timmons, Stephen; van Schalkwyk, Wilna Dirkse

    2006-10-01

    We explored the experiences of lay people who have been trained to use automatic external defibrillators. The research questions were: (1) How can training courses help prepare people for dealing with real life situations? (2) Who is ultimately responsible for providing critical incident debriefing and how should this be organised? (3) What is the best process for providing feedback to those who have used an AED? Fifty-three semi-structured, qualitative interviews were conducted, some with those who had been trained and others with trainers. Locations included airports, railway stations, private companies and first responder schemes. Geographically, we covered Nottinghamshire, Lincolnshire, Yorkshire, Staffordshire, Essex and the West Midlands in the UK. Our analysis of the data indicates that most people believe scenarios based within their place of work were most useful in preparing for 'real life'. Many people had not received critical incident debriefing after using an AED. There were a variety of systems in place to provide support after an incident, many of which were informal. Training scenarios should be conducted outside the classroom. There should be more focus on critical incident debriefing during training and a clear identification of who should provide support after an incident. Other issues which were of interest included: (1) people's views on do not attempt resuscitation (DNAR); (2) perceived boundaries of responsibility when using an AED; (3) when is someone no longer 'qualified' to use an AED?

  10. 49 CFR 236.747 - Forestall.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.747 Forestall. As applied to an automatic train stop or train control device, to prevent an automatic brake application by operation of an acknowledging device or by manual control of the speed of the train. ...

  11. 49 CFR 236.747 - Forestall.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.747 Forestall. As applied to an automatic train stop or train control device, to prevent an automatic brake application by operation of an acknowledging device or by manual control of the speed of the train. ...

  12. 49 CFR 236.508 - Interference with application of brakes by means of brake valve.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.508 Interference with application of brakes by means of brake valve. The automatic train stop, train control, or...

  13. 49 CFR 236.508 - Interference with application of brakes by means of brake valve.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.508 Interference with application of brakes by means of brake valve. The automatic train stop, train control, or...

  14. Supervision strategies for improved reliability of bus routes. Final report

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

    Not Available

    1991-09-01

    The synthesis will be of interest to transit agency managers and supervisors, as well as to operating and planning personnel who are concerned with the reliability and scheduling of buses. Information is provided on service monitoring, service supervision and control, and supervision strategies. Reliability of transit service is critical to bus transit ridership. The extent of service supervision has an important bearing on reliability. The report describes the various procedures that are used by transit agencies to monitor and maintain bus service reliability. Most transit systems conduct checks of the number of riders at maximum load points and monitor schedulemore » adherence at these locations. Other supervisory actions include service restoration techniques, and strategies such as schedule control, headway control, load control, extraboard management, and personnel selection and training. More sophisticated technologies, such as automatic passenger counting (APC) systems and automatic vehicle location and control (AVLC), have been employed by some transit agencies and are described in the synthesis.« less

  15. A new method of automatic landmark tagging for shape model construction via local curvature scale

    NASA Astrophysics Data System (ADS)

    Rueda, Sylvia; Udupa, Jayaram K.; Bai, Li

    2008-03-01

    Segmentation of organs in medical images is a difficult task requiring very often the use of model-based approaches. To build the model, we need an annotated training set of shape examples with correspondences indicated among shapes. Manual positioning of landmarks is a tedious, time-consuming, and error prone task, and almost impossible in the 3D space. To overcome some of these drawbacks, we devised an automatic method based on the notion of c-scale, a new local scale concept. For each boundary element b, the arc length of the largest homogeneous curvature region connected to b is estimated as well as the orientation of the tangent at b. With this shape description method, we can automatically locate mathematical landmarks selected at different levels of detail. The method avoids the use of landmarks for the generation of the mean shape. The selection of landmarks on the mean shape is done automatically using the c-scale method. Then, these landmarks are propagated to each shape in the training set, defining this way the correspondences among the shapes. Altogether 12 strategies are described along these lines. The methods are evaluated on 40 MRI foot data sets, the object of interest being the talus bone. The results show that, for the same number of landmarks, the proposed methods are more compact than manual and equally spaced annotations. The approach is applicable to spaces of any dimensionality, although we have focused in this paper on 2D shapes.

  16. Automatic computation of 2D cardiac measurements from B-mode echocardiography

    NASA Astrophysics Data System (ADS)

    Park, JinHyeong; Feng, Shaolei; Zhou, S. Kevin

    2012-03-01

    We propose a robust and fully automatic algorithm which computes the 2D echocardiography measurements recommended by America Society of Echocardiography. The algorithm employs knowledge-based imaging technologies which can learn the expert's knowledge from the training images and expert's annotation. Based on the models constructed from the learning stage, the algorithm searches initial location of the landmark points for the measurements by utilizing heart structure of left ventricle including mitral valve aortic valve. It employs the pseudo anatomic M-mode image generated by accumulating the line images in 2D parasternal long axis view along the time to refine the measurement landmark points. The experiment results with large volume of data show that the algorithm runs fast and is robust comparable to expert.

  17. 49 CFR 236.528 - Restrictive condition resulting from open hand-operated switch; requirement.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and... with circuit controller is used, the resultant restrictive condition of an automatic train stop or...

  18. 49 CFR 236.528 - Restrictive condition resulting from open hand-operated switch; requirement.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and... with circuit controller is used, the resultant restrictive condition of an automatic train stop or...

  19. 49 CFR Appendix B to Part 232 - Part 232 Prior to May 31, 2001 as Clarified Effective April 10, 2002

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... coupled to train, after which, an automatic brake application and release test of airbrakes on rear car... closing angle cocks for cutting off one or more cars from the rear end of train, automatic air brake must... automatic air brake must not be depended upon to hold a locomotive, cars or train, when standing on a grade...

  20. 49 CFR Appendix B to Part 232 - Part 232 Prior to May 31, 2001 as Clarified Effective April 10, 2002

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... coupled to train, after which, an automatic brake application and release test of airbrakes on rear car... closing angle cocks for cutting off one or more cars from the rear end of train, automatic air brake must... automatic air brake must not be depended upon to hold a locomotive, cars or train, when standing on a grade...

  1. Automated Management of Exercise Intervention at the Point of Care: Application of a Web-Based Leg Training System.

    PubMed

    Dedov, Vadim N; Dedova, Irina V

    2015-11-23

    Recent advances in information and communication technology have prompted development of Web-based health tools to promote physical activity, the key component of cardiac rehabilitation and chronic disease management. Mobile apps can facilitate behavioral changes and help in exercise monitoring, although actual training usually takes place away from the point of care in specialized gyms or outdoors. Daily participation in conventional physical activities is expensive, time consuming, and mostly relies on self-management abilities of patients who are typically aged, overweight, and unfit. Facilitation of sustained exercise training at the point of care might improve patient engagement in cardiac rehabilitation. In this study we aimed to test the feasibility of execution and automatic monitoring of several exercise regimens on-site using a Web-enabled leg training system. The MedExercise leg rehabilitation machine was equipped with wireless temperature sensors in order to monitor its usage by the rise of temperature in the resistance unit (Δt°). Personal electronic devices such as laptop computers were fitted with wireless gateways and relevant software was installed to monitor the usage of training machines. Cloud-based software allowed monitoring of participant training over the Internet. Seven healthy participants applied the system at various locations with training protocols typically used in cardiac rehabilitation. The heart rates were measured by fingertip pulse oximeters. Exercising in home chairs, in bed, and under an office desk was made feasible and resulted in an intensity-dependent increase of participants' heart rates and Δt° in training machine temperatures. Participants self-controlled their activities on smart devices, while a supervisor monitored them over the Internet. Individual Δt° reached during 30 minutes of moderate-intensity continuous training averaged 7.8°C (SD 1.6). These Δt° were used as personalized daily doses of exercise with automatic email alerts sent upon achieving them. During 1-week training at home, automatic notifications were received on 4.4 days (SD 1.8). Although the high intensity interval training regimen was feasible on-site, it was difficult for self- and remote management. Opportunistic leg exercise under the desk, while working with a computer, and training in bed while viewing television were less intensive than dosed exercise bouts, but allowed prolonged leg mobilization of 73.7 minutes/day (SD 29.7). This study demonstrated the feasibility of self-control exercise training on-site, which was accompanied by online monitoring, electronic recording, personalization of exercise doses, and automatic reporting of adherence. The results suggest that this technology and its applications are useful for the delivery of Web-based exercise rehabilitation and cardiac training programs at the point of care. ©Vadim N Dedov, Irina V Dedova. Originally published in JMIR Rehabilitation and Assistive Technology (http://rehab.jmir.org), 23.11.2015.

  2. Automatic Train Operation Using Autonomic Prediction of Train Runs

    NASA Astrophysics Data System (ADS)

    Asuka, Masashi; Kataoka, Kenji; Komaya, Kiyotoshi; Nishida, Syogo

    In this paper, we present an automatic train control method adaptable to disturbed train traffic conditions. The proposed method presumes transmission of detected time of a home track clearance to trains approaching to the station by employing equipment of Digital ATC (Automatic Train Control). Using the information, each train controls its acceleration by the method that consists of two approaches. First, by setting a designated restricted speed, the train controls its running time to arrive at the next station in accordance with predicted delay. Second, the train predicts the time at which it will reach the current braking pattern generated by Digital ATC, along with the time when the braking pattern transits ahead. By comparing them, the train correctly chooses the coasting drive mode in advance to avoid deceleration due to the current braking pattern. We evaluated the effectiveness of the proposed method regarding driving conditions, energy consumption and reduction of delays by simulation.

  3. Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification

    PubMed Central

    Hou, Le; Samaras, Dimitris; Kurc, Tahsin M.; Gao, Yi; Davis, James E.; Saltz, Joel H.

    2016-01-01

    Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN. PMID:27795661

  4. Computerized multiple image analysis on mammograms: performance improvement of nipple identification for registration of multiple views using texture convergence analyses

    NASA Astrophysics Data System (ADS)

    Zhou, Chuan; Chan, Heang-Ping; Sahiner, Berkman; Hadjiiski, Lubomir M.; Paramagul, Chintana

    2004-05-01

    Automated registration of multiple mammograms for CAD depends on accurate nipple identification. We developed two new image analysis techniques based on geometric and texture convergence analyses to improve the performance of our previously developed nipple identification method. A gradient-based algorithm is used to automatically track the breast boundary. The nipple search region along the boundary is then defined by geometric convergence analysis of the breast shape. Three nipple candidates are identified by detecting the changes along the gray level profiles inside and outside the boundary and the changes in the boundary direction. A texture orientation-field analysis method is developed to estimate the fourth nipple candidate based on the convergence of the tissue texture pattern towards the nipple. The final nipple location is determined from the four nipple candidates by a confidence analysis. Our training and test data sets consisted of 419 and 368 randomly selected mammograms, respectively. The nipple location identified on each image by an experienced radiologist was used as the ground truth. For 118 of the training and 70 of the test images, the radiologist could not positively identify the nipple, but provided an estimate of its location. These were referred to as invisible nipple images. In the training data set, 89.37% (269/301) of the visible nipples and 81.36% (96/118) of the invisible nipples could be detected within 1 cm of the truth. In the test data set, 92.28% (275/298) of the visible nipples and 67.14% (47/70) of the invisible nipples were identified within 1 cm of the truth. In comparison, our previous nipple identification method without using the two convergence analysis techniques detected 82.39% (248/301), 77.12% (91/118), 89.93% (268/298) and 54.29% (38/70) of the nipples within 1 cm of the truth for the visible and invisible nipples in the training and test sets, respectively. The results indicate that the nipple on mammograms can be detected accurately. This will be an important step towards automatic multiple image analysis for CAD techniques.

  5. Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

    NASA Astrophysics Data System (ADS)

    Osmanoglu, B.; Ozkan, C.; Sunar, F.

    2013-10-01

    After air strikes on July 14 and 15, 2006 the Jiyeh Power Station started leaking oil into the eastern Mediterranean Sea. The power station is located about 30 km south of Beirut and the slick covered about 170 km of coastline threatening the neighboring countries Turkey and Cyprus. Due to the ongoing conflict between Israel and Lebanon, cleaning efforts could not start immediately resulting in 12 000 to 15 000 tons of fuel oil leaking into the sea. In this paper we compare results from automatic and semi-automatic slick detection algorithms. The automatic detection method combines the probabilities calculated for each pixel from each image to obtain a joint probability, minimizing the adverse effects of atmosphere on oil spill detection. The method can readily utilize X-, C- and L-band data where available. Furthermore wind and wave speed observations can be used for a more accurate analysis. For this study, we utilize Envisat ASAR ScanSAR data. A probability map is generated based on the radar backscatter, effect of wind and dampening value. The semi-automatic algorithm is based on supervised classification. As a classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) classifier is used since it is more flexible and efficient than conventional maximum likelihood classifier for multisource and multi-temporal data. The learning algorithm for ANN MLP is chosen as the Levenberg-Marquardt (LM). Training and test data for supervised classification are composed from the textural information created from SAR images. This approach is semiautomatic because tuning the parameters of classifier and composing training data need a human interaction. We point out the similarities and differences between the two methods and their results as well as underlining their advantages and disadvantages. Due to the lack of ground truth data, we compare obtained results to each other, as well as other published oil slick area assessments.

  6. 77 FR 5294 - Petition for Waiver of Compliance

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-02-02

    ... automatic train supervision controls. This work initially includes certain tracks within PATH's Harrison... tracks, other yard tracks, and terminals as the Automatic Train Control (ATC, which is a type of PTC... the requirements of 49 CFR 235.5 to expedite successful installation of Positive Train Control (PTC...

  7. 49 CFR 236.513 - Audible indicator.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.513 Audible indicator. (a) The automatic cab signal... control system shall have a distinctive sound and be clearly audible throughout the cab under all...

  8. 49 CFR 236.513 - Audible indicator.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.513 Audible indicator. (a) The automatic cab signal... control system shall have a distinctive sound and be clearly audible throughout the cab under all...

  9. 49 CFR 236.514 - Interconnection of cab signal system with roadway signal system.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.514 Interconnection of cab signal system with roadway signal system. The automatic cab signal system shall be...

  10. 49 CFR 236.554 - Rate of pressure reduction; equalizing reservoir or brake pipe.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions... pressure or brake-pipe pressure reduction during an automatic brake application shall be at a rate not less...

  11. 49 CFR 236.511 - Cab signals controlled in accordance with block conditions stopping distance in advance.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards... automatic cab signal system shall be arranged so that cab signals will be continuously controlled in...

  12. 49 CFR 236.554 - Rate of pressure reduction; equalizing reservoir or brake pipe.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions... pressure or brake-pipe pressure reduction during an automatic brake application shall be at a rate not less...

  13. Real time coarse orientation detection in MR scans using multi-planar deep convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Bhatia, Parmeet S.; Reda, Fitsum; Harder, Martin; Zhan, Yiqiang; Zhou, Xiang Sean

    2017-02-01

    Automatically detecting anatomy orientation is an important task in medical image analysis. Specifically, the ability to automatically detect coarse orientation of structures is useful to minimize the effort of fine/accurate orientation detection algorithms, to initialize non-rigid deformable registration algorithms or to align models to target structures in model-based segmentation algorithms. In this work, we present a deep convolution neural network (DCNN)-based method for fast and robust detection of the coarse structure orientation, i.e., the hemi-sphere where the principal axis of a structure lies. That is, our algorithm predicts whether the principal orientation of a structure is in the northern hemisphere or southern hemisphere, which we will refer to as UP and DOWN, respectively, in the remainder of this manuscript. The only assumption of our method is that the entire structure is located within the scan's field-of-view (FOV). To efficiently solve the problem in 3D space, we formulated it as a multi-planar 2D deep learning problem. In the training stage, a large number coronal-sagittal slice pairs are constructed as 2-channel images to train a DCNN to classify whether a scan is UP or DOWN. During testing, we randomly sample a small number of coronal-sagittal 2-channel images and pass them through our trained network. Finally, coarse structure orientation is determined using majority voting. We tested our method on 114 Elbow MR Scans. Experimental results suggest that only five 2-channel images are sufficient to achieve a high success rate of 97.39%. Our method is also extremely fast and takes approximately 50 milliseconds per 3D MR scan. Our method is insensitive to the location of the structure in the FOV.

  14. Combined radiogrammetry and texture analysis for early diagnosis of osteoporosis using Indian and Swiss data.

    PubMed

    Areeckal, Anu Shaju; Kamath, Jagannath; Zawadynski, Sophie; Kocher, Michel; S, Sumam David

    2018-05-26

    Osteoporosis is a bone disorder characterized by bone loss and decreased bone strength. The most widely used technique for detection of osteoporosis is the measurement of bone mineral density (BMD) using dual energy X-ray absorptiometry (DXA). But DXA scans are expensive and not widely available in low-income economies. In this paper, we propose a low cost pre-screening tool for the detection of low bone mass, using cortical radiogrammetry of third metacarpal bone and trabecular texture analysis of distal radius from hand and wrist radiographs. An automatic segmentation algorithm to automatically locate and segment the third metacarpal bone and distal radius region of interest (ROI) is proposed. Cortical measurements such as combined cortical thickness (CCT), cortical area (CA), percent cortical area (PCA) and Barnett Nordin index (BNI) were taken from the shaft of third metacarpal bone. Texture analysis of trabecular network at the distal radius was performed using features obtained from histogram, gray level Co-occurrence matrix (GLCM) and morphological gradient method (MGM). The significant cortical and texture features were selected using independent sample t-test and used to train classifiers to classify healthy subjects and people with low bone mass. The proposed pre-screening tool was validated on two ethnic groups, Indian sample population and Swiss sample population. Data of 134 subjects from Indian sample population and 65 subjects from Swiss sample population were analysed. The proposed automatic segmentation approach shows a detection accuracy of 86% in detecting the third metacarpal bone shaft and 90% in accurately locating the distal radius ROI. Comparison of the automatic radiogrammetry to the ground truth provided by experts show a mean absolute error of 0.04 mm for cortical width of healthy group, 0.12 mm for cortical width of low bone mass group, 0.22 mm for medullary width of healthy group, and 0.26 mm for medullary width of low bone mass group. Independent sample t-test was used to select the most discriminant features, to be used as input for training the classifiers. Pearson correlation analysis of the extracted features with DXA-BMD of lumbar spine (DXA-LS) shows significantly high correlation values. Classifiers were trained with the most significant features in the Indian and Swiss sample data. Weighted KNN classifier shows the best test accuracy of 78% for Indian sample data and 100% for Swiss sample data. Hence, combined automatic radiogrammetry and texture analysis is shown to be an effective low cost pre-screening tool for early diagnosis of osteoporosis. Copyright © 2018 Elsevier Ltd. All rights reserved.

  15. Automated selection of computed tomography display parameters using neural networks

    NASA Astrophysics Data System (ADS)

    Zhang, Di; Neu, Scott; Valentino, Daniel J.

    2001-07-01

    A collection of artificial neural networks (ANN's) was trained to identify simple anatomical structures in a set of x-ray computed tomography (CT) images. These neural networks learned to associate a point in an image with the anatomical structure containing the point by using the image pixels located on the horizontal and vertical lines that ran through the point. The neural networks were integrated into a computer software tool whose function is to select an index into a list of CT window/level values from the location of the user's mouse cursor. Based upon the anatomical structure selected by the user, the software tool automatically adjusts the image display to optimally view the structure.

  16. Distributed solar photovoltaic array location and extent dataset for remote sensing object identification

    PubMed Central

    Bradbury, Kyle; Saboo, Raghav; L. Johnson, Timothy; Malof, Jordan M.; Devarajan, Arjun; Zhang, Wuming; M. Collins, Leslie; G. Newell, Richard

    2016-01-01

    Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment. PMID:27922592

  17. Distributed solar photovoltaic array location and extent dataset for remote sensing object identification

    NASA Astrophysics Data System (ADS)

    Bradbury, Kyle; Saboo, Raghav; L. Johnson, Timothy; Malof, Jordan M.; Devarajan, Arjun; Zhang, Wuming; M. Collins, Leslie; G. Newell, Richard

    2016-12-01

    Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

  18. Distributed solar photovoltaic array location and extent dataset for remote sensing object identification.

    PubMed

    Bradbury, Kyle; Saboo, Raghav; L Johnson, Timothy; Malof, Jordan M; Devarajan, Arjun; Zhang, Wuming; M Collins, Leslie; G Newell, Richard

    2016-12-06

    Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

  19. An efficient method for facial component detection in thermal images

    NASA Astrophysics Data System (ADS)

    Paul, Michael; Blanik, Nikolai; Blazek, Vladimir; Leonhardt, Steffen

    2015-04-01

    A method to detect certain regions in thermal images of human faces is presented. In this approach, the following steps are necessary to locate the periorbital and the nose regions: First, the face is segmented from the background by thresholding and morphological filtering. Subsequently, a search region within the face, around its center of mass, is evaluated. Automatically computed temperature thresholds are used per subject and image or image sequence to generate binary images, in which the periorbital regions are located by integral projections. Then, the located positions are used to approximate the nose position. It is possible to track features in the located regions. Therefore, these regions are interesting for different applications like human-machine interaction, biometrics and biomedical imaging. The method is easy to implement and does not rely on any training images or templates. Furthermore, the approach saves processing resources due to simple computations and restricted search regions.

  20. 49 CFR 236.567 - Restrictions imposed when device fails and/or is cut out en route.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions... restricted speed or if an automatic block signal system is in operation according to signal indication but...

  1. 49 CFR 236.512 - Cab signal indication when locomotive enters block where restrictive conditions obtain.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.512 Cab signal indication when locomotive enters block where restrictive conditions obtain. The automatic cab signal system shall be arranged so that when a locomotive enters or is...

  2. Chocolate equals stop. Chocolate-specific inhibition training reduces chocolate intake and go associations with chocolate.

    PubMed

    Houben, Katrijn; Jansen, Anita

    2015-04-01

    Earlier research has demonstrated that food-specific inhibition training wherein food cues are repeatedly and consistently mapped onto stop signals decreases food intake and bodyweight. The mechanisms underlying these training effects, however, remain unclear. It has been suggested that consistently pairing stimuli with stop signals induces automatic stop associations with those stimuli, thereby facilitating automatic, bottom-up inhibition. This study examined this hypothesis with respect to food-inhibition training. Participants performed a training that consistently paired chocolate with no go cues (chocolate/no-go) or with go cues (chocolate/go). Following training, we measured automatic associations between chocolate and stop versus go, as well as food intake and desire to eat. As expected, food that was consistently mapped onto stopping was indeed more associated with stopping versus going afterwards. In replication of previous results, participants in the no-go condition also showed less desire to eat and reduced food intake relative to the go condition. Together these findings support the idea that food-specific inhibition training prompts the development of automatic inhibition associations, which subsequently facilitate inhibitory control over unwanted food-related urges. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode.

    PubMed

    Ye, Tao; Wang, Baocheng; Song, Ping; Li, Juan

    2018-06-12

    Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN) is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net). It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.

  4. Snoring classified: The Munich-Passau Snore Sound Corpus.

    PubMed

    Janott, Christoph; Schmitt, Maximilian; Zhang, Yue; Qian, Kun; Pandit, Vedhas; Zhang, Zixing; Heiser, Clemens; Hohenhorst, Winfried; Herzog, Michael; Hemmert, Werner; Schuller, Björn

    2018-03-01

    Snoring can be excited in different locations within the upper airways during sleep. It was hypothesised that the excitation locations are correlated with distinct acoustic characteristics of the snoring noise. To verify this hypothesis, a database of snore sounds is developed, labelled with the location of sound excitation. Video and audio recordings taken during drug induced sleep endoscopy (DISE) examinations from three medical centres have been semi-automatically screened for snore events, which subsequently have been classified by ENT experts into four classes based on the VOTE classification. The resulting dataset containing 828 snore events from 219 subjects has been split into Train, Development, and Test sets. An SVM classifier has been trained using low level descriptors (LLDs) related to energy, spectral features, mel frequency cepstral coefficients (MFCC), formants, voicing, harmonic-to-noise ratio (HNR), spectral harmonicity, pitch, and microprosodic features. An unweighted average recall (UAR) of 55.8% could be achieved using the full set of LLDs including formants. Best performing subset is the MFCC-related set of LLDs. A strong difference in performance could be observed between the permutations of train, development, and test partition, which may be caused by the relatively low number of subjects included in the smaller classes of the strongly unbalanced data set. A database of snoring sounds is presented which are classified according to their sound excitation location based on objective criteria and verifiable video material. With the database, it could be demonstrated that machine classifiers can distinguish different excitation location of snoring sounds in the upper airway based on acoustic parameters. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Empirical study on neural network based predictive techniques for automatic number plate recognition

    NASA Astrophysics Data System (ADS)

    Shashidhara, M. S.; Indrakumar, S. S.

    2011-10-01

    The objective of this study is to provide an easy, accurate and effective technology for the Bangalore city traffic control. This is based on the techniques of image processing and laser beam technology. The core concept chosen here is an image processing technology by the method of automatic number plate recognition system. First number plate is recognized if any vehicle breaks the traffic rules in the signals. The number is fetched from the database of the RTO office by the process of automatic database fetching. Next this sends the notice and penalty related information to the vehicle owner email-id and an SMS sent to vehicle owner. In this paper, we use of cameras with zooming options & laser beams to get accurate pictures further applied image processing techniques such as Edge detection to understand the vehicle, Identifying the location of the number plate, Identifying the number plate for further use, Plain plate number, Number plate with additional information, Number plates in the different fonts. Accessing the database of the vehicle registration office to identify the name and address and other information of the vehicle number. The updates to be made to the database for the recording of the violation and penalty issues. A feed forward artificial neural network is used for OCR. This procedure is particularly important for glyphs that are visually similar such as '8' and '9' and results in training sets of between 25,000 and 40,000 training samples. Over training of the neural network is prevented by Bayesian regularization. The neural network output value is set to 0.05 when the input is not desired glyph, and 0.95 for correct input.

  6. A training approach to improve stepping automaticity while dual-tasking in Parkinson's disease: A prospective pilot study.

    PubMed

    Chomiak, Taylor; Watts, Alexander; Meyer, Nicole; Pereira, Fernando V; Hu, Bin

    2017-02-01

    Deficits in motor movement automaticity in Parkinson's disease (PD), especially during multitasking, are early and consistent hallmarks of cognitive function decline, which increases fall risk and reduces quality of life. This study aimed to test the feasibility and potential efficacy of a wearable sensor-enabled technological platform designed for an in-home music-contingent stepping-in-place (SIP) training program to improve step automaticity during dual-tasking (DT). This was a 4-week prospective intervention pilot study. The intervention uses a sensor system and algorithm that runs off the iPod Touch which calculates step height (SH) in real-time. These measurements were then used to trigger auditory (treatment group, music; control group, radio podcast) playback in real-time through wireless headphones upon maintenance of repeated large amplitude stepping. With small steps or shuffling, auditory playback stops, thus allowing participants to use anticipatory motor control to regain positive feedback. Eleven participants were recruited from an ongoing trial (Trial Number: ISRCTN06023392). Fear of falling (FES-I), general cognitive functioning (MoCA), self-reported freezing of gait (FOG-Q), and DT step automaticity were evaluated. While we found no significant effect of training on FES-I, MoCA, or FOG-Q, we did observe a significant group (music vs podcast) by training interaction in DT step automaticity (P<0.01). Wearable device technology can be used to enable musically-contingent SIP training to increase motor automaticity for people living with PD. The training approach described here can be implemented at home to meet the growing demand for self-management of symptoms by patients.

  7. Intelligent Image Analysis for Image-Guided Laser Hair Removal and Skin Therapy

    NASA Technical Reports Server (NTRS)

    Walker, Brian; Lu, Thomas; Chao, Tien-Hsin

    2012-01-01

    We present the development of advanced automatic target recognition (ATR) algorithms for the hair follicles identification in digital skin images to accurately direct the laser beam to remove the hair. The ATR system first performs a wavelet filtering to enhance the contrast of the hair features in the image. The system then extracts the unique features of the targets and sends the features to an Adaboost based classifier for training and recognition operations. The ATR system automatically classifies the hair, moles, or other skin lesion and provides the accurate coordinates of the intended hair follicle locations. The coordinates can be used to guide a scanning laser to focus energy only on the hair follicles. The intended benefit would be to protect the skin from unwanted laser exposure and to provide more effective skin therapy.

  8. A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm.

    PubMed

    Budak, Umit; Şengür, Abdulkadir; Guo, Yanhui; Akbulut, Yaman

    2017-12-01

    Microaneurysms (MAs) are known as early signs of diabetic-retinopathy which are called red lesions in color fundus images. Detection of MAs in fundus images needs highly skilled physicians or eye angiography. Eye angiography is an invasive and expensive procedure. Therefore, an automatic detection system to identify the MAs locations in fundus images is in demand. In this paper, we proposed a system to detect the MAs in colored fundus images. The proposed method composed of three stages. In the first stage, a series of pre-processing steps are used to make the input images more convenient for MAs detection. To this end, green channel decomposition, Gaussian filtering, median filtering, back ground determination, and subtraction operations are applied to input colored fundus images. After pre-processing, a candidate MAs extraction procedure is applied to detect potential regions. A five-stepped procedure is adopted to get the potential MA locations. Finally, deep convolutional neural network (DCNN) with reinforcement sample learning strategy is used to train the proposed system. The DCNN is trained with color image patches which are collected from ground-truth MA locations and non-MA locations. We conducted extensive experiments on ROC dataset to evaluate of our proposal. The results are encouraging.

  9. MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection

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

    Ghose, Soumya, E-mail: soumya.ghose@case.edu; Mitra, Jhimli; Rivest-Hénault, David

    Purpose: The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Methods: Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contoursmore » were selected in a spectral clustering manifold learning framework. This aids in clustering “similar” gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. Results: A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). Conclusions: An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold seeds are either correctly detected or a warning is raised for further manual intervention.« less

  10. MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection.

    PubMed

    Ghose, Soumya; Mitra, Jhimli; Rivest-Hénault, David; Fazlollahi, Amir; Stanwell, Peter; Pichler, Peter; Sun, Jidi; Fripp, Jurgen; Greer, Peter B; Dowling, Jason A

    2016-05-01

    The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contours were selected in a spectral clustering manifold learning framework. This aids in clustering "similar" gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold seeds are either correctly detected or a warning is raised for further manual intervention.

  11. Automatic road sign detecion and classification based on support vector machines and HOG descriptos

    NASA Astrophysics Data System (ADS)

    Adam, A.; Ioannidis, C.

    2014-05-01

    This paper examines the detection and classification of road signs in color-images acquired by a low cost camera mounted on a moving vehicle. A new method for the detection and classification of road signs is proposed based on color based detection, in order to locate regions of interest. Then, a circular Hough transform is applied to complete detection taking advantage of the shape properties of the road signs. The regions of interest are finally represented using HOG descriptors and are fed into trained Support Vector Machines (SVMs) in order to be recognized. For the training procedure, a database with several training examples depicting Greek road sings has been developed. Many experiments have been conducted and are presented, to measure the efficiency of the proposed methodology especially under adverse weather conditions and poor illumination. For the experiments training datasets consisting of different number of examples were used and the results are presented, along with some possible extensions of this work.

  12. Automated electroencephalography system and electroencephalographic coordinates of space motion sickness, part 1

    NASA Technical Reports Server (NTRS)

    Frost, J. D., Jr.

    1976-01-01

    A self-contained and portable device which permits clinical electroencephalography (EEG) to be conducted in remote locations by minimally trained, nontechnical personnel was developed and tested. The unit accomplishes semiautomatic acquisition of EEG data from the patient, simultaneous transmission of eight data channels to a central hospital facility over conventional telephone equipment, and automatic printing (at the remote site) of the EEG report generated at the central location. Consequently, this system enables the delivery of high-quality EEG diagnostic services in a geographically remote site with the accuracy and speed formerly possible only in certain large medical centers. Beside obvious potential clinical applications, this system serves as an initial prototype of a unit which could provide inflight EEG during future space missions.

  13. 49 CFR 236.830 - Time, acknowledging.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.830 Time, acknowledging. As applied to an intermittent automatic train stop system, a predetermined time within which an automatic brake application may be forestalled by means of the acknowledging device. ...

  14. A training approach to improve stepping automaticity while dual-tasking in Parkinson's disease

    PubMed Central

    Chomiak, Taylor; Watts, Alexander; Meyer, Nicole; Pereira, Fernando V.; Hu, Bin

    2017-01-01

    Abstract Background: Deficits in motor movement automaticity in Parkinson's disease (PD), especially during multitasking, are early and consistent hallmarks of cognitive function decline, which increases fall risk and reduces quality of life. This study aimed to test the feasibility and potential efficacy of a wearable sensor-enabled technological platform designed for an in-home music-contingent stepping-in-place (SIP) training program to improve step automaticity during dual-tasking (DT). Methods: This was a 4-week prospective intervention pilot study. The intervention uses a sensor system and algorithm that runs off the iPod Touch which calculates step height (SH) in real-time. These measurements were then used to trigger auditory (treatment group, music; control group, radio podcast) playback in real-time through wireless headphones upon maintenance of repeated large amplitude stepping. With small steps or shuffling, auditory playback stops, thus allowing participants to use anticipatory motor control to regain positive feedback. Eleven participants were recruited from an ongoing trial (Trial Number: ISRCTN06023392). Fear of falling (FES-I), general cognitive functioning (MoCA), self-reported freezing of gait (FOG-Q), and DT step automaticity were evaluated. Results: While we found no significant effect of training on FES-I, MoCA, or FOG-Q, we did observe a significant group (music vs podcast) by training interaction in DT step automaticity (P<0.01). Conclusion: Wearable device technology can be used to enable musically-contingent SIP training to increase motor automaticity for people living with PD. The training approach described here can be implemented at home to meet the growing demand for self-management of symptoms by patients. PMID:28151878

  15. [Using neural networks based template matching method to obtain redshifts of normal galaxies].

    PubMed

    Xu, Xin; Luo, A-li; Wu, Fu-chao; Zhao, Yong-heng

    2005-06-01

    Galaxies can be divided into two classes: normal galaxy (NG) and active galaxy (AG). In order to determine NG redshifts, an automatic effective method is proposed in this paper, which consists of the following three main steps: (1) From the template of normal galaxy, the two sets of samples are simulated, one with the redshift of 0.0-0.3, the other of 0.3-0.5, then the PCA is used to extract the main components, and train samples are projected to the main component subspace to obtain characteristic spectra. (2) The characteristic spectra are used to train a Probabilistic Neural Network to obtain a Bayes classifier. (3) An unknown real NG spectrum is first inputted to this Bayes classifier to determine the possible range of redshift, then the template matching is invoked to locate the redshift value within the estimated range. Compared with the traditional template matching technique with an unconstrained range, our proposed method not only halves the computational load, but also increases the estimation accuracy. As a result, the proposed method is particularly useful for automatic spectrum processing produced from a large-scale sky survey project.

  16. The research of automatic speed control algorithm based on Green CBTC

    NASA Astrophysics Data System (ADS)

    Lin, Ying; Xiong, Hui; Wang, Xiaoliang; Wu, Youyou; Zhang, Chuanqi

    2017-06-01

    Automatic speed control algorithm is one of the core technologies of train operation control system. It’s a typical multi-objective optimization control algorithm, which achieve the train speed control for timing, comfort, energy-saving and precise parking. At present, the train speed automatic control technology is widely used in metro and inter-city railways. It has been found that the automatic speed control technology can effectively reduce the driver’s intensity, and improve the operation quality. However, the current used algorithm is poor at energy-saving, even not as good as manual driving. In order to solve the problem of energy-saving, this paper proposes an automatic speed control algorithm based on Green CBTC system. Based on the Green CBTC system, the algorithm can adjust the operation status of the train to improve the efficient using rate of regenerative braking feedback energy while ensuring the timing, comfort and precise parking targets. Due to the reason, the energy-using of Green CBTC system is lower than traditional CBTC system. The simulation results show that the algorithm based on Green CBTC system can effectively reduce the energy-using due to the improvement of the using rate of regenerative braking feedback energy.

  17. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

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

    Park, Sang Hyun; Gao, Yaozong, E-mail: yzgao@cs.unc.edu; Shi, Yinghuan, E-mail: syh@nju.edu.cn

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correctmore » the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to evaluate both the efficiency and the robustness. The automatic segmentation results with the original average Dice similarity coefficient of 0.78 were improved to 0.865–0.872 after conducting 55–59 interactions by using the proposed method, where each editing procedure took less than 3 s. In addition, the proposed method obtained the most consistent editing results with respect to different user interactions, compared to other methods. Conclusions: The proposed method obtains robust editing results with few interactions for various wrong segmentation cases, by selecting the location-adaptive features and further imposing the manifold regularization. The authors expect the proposed method to largely reduce the laborious burdens of manual editing, as well as both the intra- and interobserver variability across clinicians.« less

  18. An automatic locating system for cloud-to-ground lightning. [which utilizes a microcomputer

    NASA Technical Reports Server (NTRS)

    Krider, E. P.; Pifer, A. E.; Uman, M. A.

    1980-01-01

    Automatic locating systems which respond to cloud to ground lightning and which discriminate against cloud discharges and background noise are described. Subsystems of the locating system, which include the direction finder and the position analyzer, are discussed. The direction finder senses the electromagnetic fields radiated by lightning on two orthogonal magnetic loop antennas and on a flat plate electric antenna. The position analyzer is a preprogrammed microcomputer system which automatically computes, maps, and records lightning locations in real time using data inputs from the direction finder. The use of the locating systems for wildfire management and fire weather forecasting is discussed.

  19. Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis

    PubMed Central

    Peng, Zhenyun; Zhang, Yaohui

    2014-01-01

    Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying. PMID:24592182

  20. Automated microaneurysm detection method based on double ring filter in retinal fundus images

    NASA Astrophysics Data System (ADS)

    Mizutani, Atsushi; Muramatsu, Chisako; Hatanaka, Yuji; Suemori, Shinsuke; Hara, Takeshi; Fujita, Hiroshi

    2009-02-01

    The presence of microaneurysms in the eye is one of the early signs of diabetic retinopathy, which is one of the leading causes of vision loss. We have been investigating a computerized method for the detection of microaneurysms on retinal fundus images, which were obtained from the Retinopathy Online Challenge (ROC) database. The ROC provides 50 training cases, in which "gold standard" locations of microaneurysms are provided, and 50 test cases without the gold standard locations. In this study, the computerized scheme was developed by using the training cases. Although the results for the test cases are also included, this paper mainly discusses the results for the training cases because the "gold standard" for the test cases is not known. After image preprocessing, candidate regions for microaneurysms were detected using a double-ring filter. Any potential false positives located in the regions corresponding to blood vessels were removed by automatic extraction of blood vessels from the images. Twelve image features were determined, and the candidate lesions were classified into microaneurysms or false positives using the rule-based method and an artificial neural network. The true positive fraction of the proposed method was 0.45 at 27 false positives per image. Forty-two percent of microaneurysms in the 50 training cases were considered invisible by the consensus of two co-investigators. When the method was evaluated for visible microaneurysms, the sensitivity for detecting microaneurysms was 65% at 27 false positives per image. Our computerized detection scheme could be improved for helping ophthalmologists in the early diagnosis of diabetic retinopathy.

  1. Automatic detection and notification of "wrong patient-wrong location'' errors in the operating room.

    PubMed

    Sandberg, Warren S; Häkkinen, Matti; Egan, Marie; Curran, Paige K; Fairbrother, Pamela; Choquette, Ken; Daily, Bethany; Sarkka, Jukka-Pekka; Rattner, David

    2005-09-01

    When procedures and processes to assure patient location based on human performance do not work as expected, patients are brought incrementally closer to a possible "wrong patient-wrong procedure'' error. We developed a system for automated patient location monitoring and management. Real-time data from an active infrared/radio frequency identification tracking system provides patient location data that are robust and can be compared with an "expected process'' model to automatically flag wrong-location events as soon as they occur. The system also generates messages that are automatically sent to process managers via the hospital paging system, thus creating an active alerting function to annunciate errors. We deployed the system to detect and annunciate "patient-in-wrong-OR'' events. The system detected all "wrong-operating room (OR)'' events, and all "wrong-OR'' locations were correctly assigned within 0.50+/-0.28 minutes (mean+/-SD). This corresponded to the measured latency of the tracking system. All wrong-OR events were correctly annunciated via the paging function. This experiment demonstrates that current technology can automatically collect sufficient data to remotely monitor patient flow through a hospital, provide decision support based on predefined rules, and automatically notify stakeholders of errors.

  2. Automatic detection of pelvic lymph nodes using multiple MR sequences

    NASA Astrophysics Data System (ADS)

    Yan, Michelle; Lu, Yue; Lu, Renzhi; Requardt, Martin; Moeller, Thomas; Takahashi, Satoru; Barentsz, Jelle

    2007-03-01

    A system for automatic detection of pelvic lymph nodes is developed by incorporating complementary information extracted from multiple MR sequences. A single MR sequence lacks sufficient diagnostic information for lymph node localization and staging. Correct diagnosis often requires input from multiple complementary sequences which makes manual detection of lymph nodes very labor intensive. Small lymph nodes are often missed even by highly-trained radiologists. The proposed system is aimed at assisting radiologists in finding lymph nodes faster and more accurately. To the best of our knowledge, this is the first such system reported in the literature. A 3-dimensional (3D) MR angiography (MRA) image is employed for extracting blood vessels that serve as a guide in searching for pelvic lymph nodes. Segmentation, shape and location analysis of potential lymph nodes are then performed using a high resolution 3D T1-weighted VIBE (T1-vibe) MR sequence acquired by Siemens 3T scanner. An optional contrast-agent enhanced MR image, such as post ferumoxtran-10 T2*-weighted MEDIC sequence, can also be incorporated to further improve detection accuracy of malignant nodes. The system outputs a list of potential lymph node locations that are overlaid onto the corresponding MR sequences and presents them to users with associated confidence levels as well as their sizes and lengths in each axis. Preliminary studies demonstrates the feasibility of automatic lymph node detection and scenarios in which this system may be used to assist radiologists in diagnosis and reporting.

  3. 77 FR 58170 - Proposed Renewal of Existing Information Collection; Fire Protection (Underground Coal Mines)

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-09-19

    ... the locations of automatic fire warning sensors and the intended air flow direction at these locations...) requires that a qualified person examine the automatic fire sensor and warning device systems on a weekly....1103-8(b) requires that a record of the weekly automatic fire sensor functional tests be maintained by...

  4. 49 CFR 236.503 - Automatic brake application; initiation when predetermined rate of speed exceeded.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... predetermined rate of speed exceeded. 236.503 Section 236.503 Transportation Other Regulations Relating to... § 236.503 Automatic brake application; initiation when predetermined rate of speed exceeded. An automatic train control system shall operate to initiate an automatic brake application when the speed of...

  5. 49 CFR 236.503 - Automatic brake application; initiation when predetermined rate of speed exceeded.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... predetermined rate of speed exceeded. 236.503 Section 236.503 Transportation Other Regulations Relating to... § 236.503 Automatic brake application; initiation when predetermined rate of speed exceeded. An automatic train control system shall operate to initiate an automatic brake application when the speed of...

  6. Speeding response, saving lives : automatic vehicle location capabilities for emergency services.

    DOT National Transportation Integrated Search

    1999-01-01

    Information from automatic vehicle location systems, when combined with computeraided dispatch software, can provide a rich source of data for analyzing emergency vehicle operations and evaluating agency performance.

  7. The Role of Working Memory in Spatial S-R Correspondence Effects

    ERIC Educational Resources Information Center

    Wuhr, Peter; Biebl, Rupert

    2011-01-01

    This study investigates the impact of working memory (WM) load on response conflicts arising from spatial (non) correspondence between irrelevant stimulus location and response location (Simon effect). The dominant view attributes the Simon effect to automatic processes of location-based response priming. The automaticity view predicts…

  8. Near-real time 3D probabilistic earthquakes locations at Mt. Etna volcano

    NASA Astrophysics Data System (ADS)

    Barberi, G.; D'Agostino, M.; Mostaccio, A.; Patane', D.; Tuve', T.

    2012-04-01

    Automatic procedure for locating earthquake in quasi-real time must provide a good estimation of earthquakes location within a few seconds after the event is first detected and is strongly needed for seismic warning system. The reliability of an automatic location algorithm is influenced by several factors such as errors in picking seismic phases, network geometry, and velocity model uncertainties. On Mt. Etna, the seismic network is managed by INGV and the quasi-real time earthquakes locations are performed by using an automatic-picking algorithm based on short-term-average to long-term-average ratios (STA/LTA) calculated from an approximate squared envelope function of the seismogram, which furnish a list of P-wave arrival times, and the location algorithm Hypoellipse, with a 1D velocity model. The main purpose of this work is to investigate the performances of a different automatic procedure to improve the quasi-real time earthquakes locations. In fact, as the automatic data processing may be affected by outliers (wrong picks), the use of a traditional earthquake location techniques based on a least-square misfit function (L2-norm) often yield unstable and unreliable solutions. Moreover, on Mt. Etna, the 1D model is often unable to represent the complex structure of the volcano (in particular the strong lateral heterogeneities), whereas the increasing accuracy in the 3D velocity models at Mt. Etna during recent years allows their use today in routine earthquake locations. Therefore, we selected, as reference locations, all the events occurred on Mt. Etna in the last year (2011) which was automatically detected and located by means of the Hypoellipse code. By using this dataset (more than 300 events), we applied a nonlinear probabilistic earthquake location algorithm using the Equal Differential Time (EDT) likelihood function, (Font et al., 2004; Lomax, 2005) which is much more robust in the presence of outliers in the data. Successively, by using a probabilistic non linear method (NonLinLoc, Lomax, 2001) and the 3D velocity model, derived from the one developed by Patanè et al. (2006) integrated with that obtained by Chiarabba et al. (2004), we obtained the best possible constraint on the location of the focii expressed as a probability density function (PDF) for the hypocenter location in 3D space. As expected, the obtained results, compared with the reference ones, show that the NonLinLoc software (applied to a 3D velocity model) is more reliable than the Hypoellipse code (applied to layered 1D velocity models), leading to more reliable automatic locations also when outliers are present.

  9. The Accuracy of GBM GRB Localizations

    NASA Astrophysics Data System (ADS)

    Briggs, Michael Stephen; Connaughton, V.; Meegan, C.; Hurley, K.

    2010-03-01

    We report an study of the accuracy of GBM GRB localizations, analyzing three types of localizations: those produced automatically by the GBM Flight Software on board GBM, those produced automatically with ground software in near real time, and localizations produced with human guidance. The two types of automatic locations are distributed in near real-time via GCN Notices; the human-guided locations are distributed on timescale of many minutes or hours using GCN Circulars. This work uses a Bayesian analysis that models the distribution of the GBM total location error by comparing GBM locations to more accurate locations obtained with other instruments. Reference locations are obtained from Swift, Super-AGILE, the LAT, and with the IPN. We model the GBM total location errors as having systematic errors in addition to the statistical errors and use the Bayesian analysis to constrain the systematic errors.

  10. Integration of Hand and Finger Location in External Spatial Coordinates for Tactile Localization

    ERIC Educational Resources Information Center

    Heed, Tobias; Backhaus, Jenny; Roder, Brigitte

    2012-01-01

    Tactile stimulus location is automatically transformed from somatotopic into external spatial coordinates, rendering information about the location of touch in three-dimensional space. This process is referred to as tactile remapping. Whereas remapping seems to occur automatically for the hands and feet, the fingers may constitute an exception in…

  11. Automatic Training of Rat Cyborgs for Navigation.

    PubMed

    Yu, Yipeng; Wu, Zhaohui; Xu, Kedi; Gong, Yongyue; Zheng, Nenggan; Zheng, Xiaoxiang; Pan, Gang

    2016-01-01

    A rat cyborg system refers to a biological rat implanted with microelectrodes in its brain, via which the outer electrical stimuli can be delivered into the brain in vivo to control its behaviors. Rat cyborgs have various applications in emergency, such as search and rescue in disasters. Prior to a rat cyborg becoming controllable, a lot of effort is required to train it to adapt to the electrical stimuli. In this paper, we build a vision-based automatic training system for rat cyborgs to replace the time-consuming manual training procedure. A hierarchical framework is proposed to facilitate the colearning between rats and machines. In the framework, the behavioral states of a rat cyborg are visually sensed by a camera, a parameterized state machine is employed to model the training action transitions triggered by rat's behavioral states, and an adaptive adjustment policy is developed to adaptively adjust the stimulation intensity. The experimental results of three rat cyborgs prove the effectiveness of our system. To the best of our knowledge, this study is the first to tackle automatic training of animal cyborgs.

  12. Automatic Training of Rat Cyborgs for Navigation

    PubMed Central

    Yu, Yipeng; Wu, Zhaohui; Xu, Kedi; Gong, Yongyue; Zheng, Nenggan; Zheng, Xiaoxiang; Pan, Gang

    2016-01-01

    A rat cyborg system refers to a biological rat implanted with microelectrodes in its brain, via which the outer electrical stimuli can be delivered into the brain in vivo to control its behaviors. Rat cyborgs have various applications in emergency, such as search and rescue in disasters. Prior to a rat cyborg becoming controllable, a lot of effort is required to train it to adapt to the electrical stimuli. In this paper, we build a vision-based automatic training system for rat cyborgs to replace the time-consuming manual training procedure. A hierarchical framework is proposed to facilitate the colearning between rats and machines. In the framework, the behavioral states of a rat cyborg are visually sensed by a camera, a parameterized state machine is employed to model the training action transitions triggered by rat's behavioral states, and an adaptive adjustment policy is developed to adaptively adjust the stimulation intensity. The experimental results of three rat cyborgs prove the effectiveness of our system. To the best of our knowledge, this study is the first to tackle automatic training of animal cyborgs. PMID:27436999

  13. Relationship of Automatic Data Processing Training Curriculum and Methodology in the Federal Government.

    ERIC Educational Resources Information Center

    Office of Education (DHEW), Washington, DC.

    A conference, held in Washington, D. C., in 1967 by the Association for Educational Data Systems and the U.S. Office of Education, attempted to lay the groundwork for an efficient automatic data processing training program for the Federal Government utilizing new instructional methodologies. The rapid growth of computer applications and computer…

  14. Assessment of the Denver Regional Transportation District's automatic vehicle location system

    DOT National Transportation Integrated Search

    2000-08-01

    The purpose of this evaluation was to determine how well the Denver Regional Transportation District's (RTD) automatic vehicle location (AVL) system achieved its major objectives of improving scheduling efficiency, improving the ability of dispatcher...

  15. Roadway weather information system and automatic vehicle location (AVL) coordination.

    DOT National Transportation Integrated Search

    2011-02-28

    Roadway Weather Information System and Automatic Vehicle Location Coordination involves the : development of an Inclement Weather Console that provides a new capability for the state of Oklahoma : to monitor weather-related roadway conditions. The go...

  16. Design and development of an automatic data acquisition system for a balance study using a smartcard system.

    PubMed

    Ambrozy, C; Kolar, N A; Rattay, F

    2010-01-01

    For measurement value logging of board angle values during balance training, it is necessary to develop a measurement system. This study will provide data for a balance study using the smartcard. The data acquisition comes automatically. An individually training plan for each proband is necessary. To store the proband identification a smartcard with an I2C data bus protocol and an E2PROM memory system is used. For reading the smartcard data a smartcard reader is connected via universal serial bus (USB) to a notebook. The data acquisition and smartcard read programme is designed with Microsoft® Visual C#. A training plan file contains the individual training plan for each proband. The data of the test persons are saved in a proband directory. Each event is automatically saved as a log-file for the exact documentation. This system makes study development easy and time-saving.

  17. Audio-visual imposture

    NASA Astrophysics Data System (ADS)

    Karam, Walid; Mokbel, Chafic; Greige, Hanna; Chollet, Gerard

    2006-05-01

    A GMM based audio visual speaker verification system is described and an Active Appearance Model with a linear speaker transformation system is used to evaluate the robustness of the verification. An Active Appearance Model (AAM) is used to automatically locate and track a speaker's face in a video recording. A Gaussian Mixture Model (GMM) based classifier (BECARS) is used for face verification. GMM training and testing is accomplished on DCT based extracted features of the detected faces. On the audio side, speech features are extracted and used for speaker verification with the GMM based classifier. Fusion of both audio and video modalities for audio visual speaker verification is compared with face verification and speaker verification systems. To improve the robustness of the multimodal biometric identity verification system, an audio visual imposture system is envisioned. It consists of an automatic voice transformation technique that an impostor may use to assume the identity of an authorized client. Features of the transformed voice are then combined with the corresponding appearance features and fed into the GMM based system BECARS for training. An attempt is made to increase the acceptance rate of the impostor and to analyzing the robustness of the verification system. Experiments are being conducted on the BANCA database, with a prospect of experimenting on the newly developed PDAtabase developed within the scope of the SecurePhone project.

  18. Segmentation of left atrial intracardiac ultrasound images for image guided cardiac ablation therapy

    NASA Astrophysics Data System (ADS)

    Rettmann, M. E.; Stephens, T.; Holmes, D. R.; Linte, C.; Packer, D. L.; Robb, R. A.

    2013-03-01

    Intracardiac echocardiography (ICE), a technique in which structures of the heart are imaged using a catheter navigated inside the cardiac chambers, is an important imaging technique for guidance in cardiac ablation therapy. Automatic segmentation of these images is valuable for guidance and targeting of treatment sites. In this paper, we describe an approach to segment ICE images by generating an empirical model of blood pool and tissue intensities. Normal, Weibull, Gamma, and Generalized Extreme Value (GEV) distributions are fit to histograms of tissue and blood pool pixels from a series of ICE scans. A total of 40 images from 4 separate studies were evaluated. The model was trained and tested using two approaches. In the first approach, the model was trained on all images from 3 studies and subsequently tested on the 40 images from the 4th study. This procedure was repeated 4 times using a leave-one-out strategy. This is termed the between-subjects approach. In the second approach, the model was trained on 10 randomly selected images from a single study and tested on the remaining 30 images in that study. This is termed the within-subjects approach. For both approaches, the model was used to automatically segment ICE images into blood and tissue regions. Each pixel is classified using the Generalized Liklihood Ratio Test across neighborhood sizes ranging from 1 to 49. Automatic segmentation results were compared against manual segmentations for all images. In the between-subjects approach, the GEV distribution using a neighborhood size of 17 was found to be the most accurate with a misclassification rate of approximately 17%. In the within-subjects approach, the GEV distribution using a neighborhood size of 19 was found to be the most accurate with a misclassification rate of approximately 15%. As expected, the majority of misclassified pixels were located near the boundaries between tissue and blood pool regions for both methods.

  19. Towards an Automatic Framework for Urban Settlement Mapping from Satellite Images: Applications of Geo-referenced Social Media and One Class Classification

    NASA Astrophysics Data System (ADS)

    Miao, Zelang

    2017-04-01

    Currently, urban dwellers comprise more than half of the world's population and this percentage is still dramatically increasing. The explosive urban growth over the next two decades poses long-term profound impact on people as well as the environment. Accurate and up-to-date delineation of urban settlements plays a fundamental role in defining planning strategies and in supporting sustainable development of urban settlements. In order to provide adequate data about urban extents and land covers, classifying satellite data has become a common practice, usually with accurate enough results. Indeed, a number of supervised learning methods have proven effective in urban area classification, but they usually depend on a large amount of training samples, whose collection is a time and labor expensive task. This issue becomes particularly serious when classifying large areas at the regional/global level. As an alternative to manual ground truth collection, in this work we use geo-referenced social media data. Cities and densely populated areas are an extremely fertile land for the production of individual geo-referenced data (such as GPS and social network data). Training samples derived from geo-referenced social media have several advantages: they are easy to collect, usually they are freely exploitable; and, finally, data from social media are spatially available in many locations, and with no doubt in most urban areas around the world. Despite these advantages, the selection of training samples from social media meets two challenges: 1) there are many duplicated points; 2) method is required to automatically label them as "urban/non-urban". The objective of this research is to validate automatic sample selection from geo-referenced social media and its applicability in one class classification for urban extent mapping from satellite images. The findings in this study shed new light on social media applications in the field of remote sensing.

  20. Evaluation of Automatic Vehicle Location accuracy

    DOT National Transportation Integrated Search

    1999-01-01

    This study assesses the accuracy of the Automatic Vehicle Location (AVL) data provided for the buses of the Ann Arbor Transportation Authority with Global Positioning System (GPS) technology. In a sample of eighty-nine bus trips two kinds of accuracy...

  1. Socioeconomic Impact Assessment of the Los Angeles Automatic Vehicle Monitoring (AVM) Demonstration

    DOT National Transportation Integrated Search

    1982-09-01

    This report presents a socioeconomic impact assessment of the Automatic Vehicle Monitoring (AVM) Demonstration in Los Angeles. An AVM system uses location, communication, and data processing subsystems to monitor the locations of appropriately equipp...

  2. Automated training site selection for large-area remote-sensing image analysis

    NASA Astrophysics Data System (ADS)

    McCaffrey, Thomas M.; Franklin, Steven E.

    1993-11-01

    A computer program is presented to select training sites automatically from remotely sensed digital imagery. The basic ideas are to guide the image analyst through the process of selecting typical and representative areas for large-area image classifications by minimizing bias, and to provide an initial list of potential classes for which training sites are required to develop a classification scheme or to verify classification accuracy. Reducing subjectivity in training site selection is achieved by using a purely statistical selection of homogeneous sites which then can be compared to field knowledge, aerial photography, or other remote-sensing imagery and ancillary data to arrive at a final selection of sites to be used to train the classification decision rules. The selection of the homogeneous sites uses simple tests based on the coefficient of variance, the F-statistic, and the Student's i-statistic. Comparisons of site means are conducted with a linear growing list of previously located homogeneous pixels. The program supports a common pixel-interleaved digital image format and has been tested on aerial and satellite optical imagery. The program is coded efficiently in the C programming language and was developed under AIX-Unix on an IBM RISC 6000 24-bit color workstation.

  3. Intelligent scanning: automated standard plane selection and biometric measurement of early gestational sac in routine ultrasound examination.

    PubMed

    Zhang, Ling; Chen, Siping; Chin, Chien Ting; Wang, Tianfu; Li, Shengli

    2012-08-01

    To assist radiologists and decrease interobserver variability when using 2D ultrasonography (US) to locate the standardized plane of early gestational sac (SPGS) and to perform gestational sac (GS) biometric measurements. In this paper, the authors report the design of the first automatic solution, called "intelligent scanning" (IS), for selecting SPGS and performing biometric measurements using real-time 2D US. First, the GS is efficiently and precisely located in each ultrasound frame by exploiting a coarse to fine detection scheme based on the training of two cascade AdaBoost classifiers. Next, the SPGS are automatically selected by eliminating false positives. This is accomplished using local context information based on the relative position of anatomies in the image sequence. Finally, a database-guided multiscale normalized cuts algorithm is proposed to generate the initial contour of the GS, based on which the GS is automatically segmented for measurement by a modified snake model. This system was validated on 31 ultrasound videos involving 31 pregnant volunteers. The differences between system performance and radiologist performance with respect to SPGS selection and length and depth (diameter) measurements are 7.5% ± 5.0%, 5.5% ± 5.2%, and 6.5% ± 4.6%, respectively. Additional validations prove that the IS precision is in the range of interobserver variability. Our system can display the SPGS along with biometric measurements in approximately three seconds after the video ends, when using a 1.9 GHz dual-core computer. IS of the GS from 2D real-time US is a practical, reproducible, and reliable approach.

  4. 49 CFR 236.552 - Insulation resistance; requirement.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION RULES, STANDARDS, AND INSTRUCTIONS GOVERNING THE INSTALLATION... resistance between wiring and ground of continuous inductive automatic cab signal system, automatic train...

  5. Identification and classification of transient pulses observed in magnetometer array data by time-domain principal component analysis filtering

    NASA Astrophysics Data System (ADS)

    Kappler, Karl N.; Schneider, Daniel D.; MacLean, Laura S.; Bleier, Thomas E.

    2017-08-01

    A method for identification of pulsations in time series of magnetic field data which are simultaneously present in multiple channels of data at one or more sensor locations is described. Candidate pulsations of interest are first identified in geomagnetic time series by inspection. Time series of these "training events" are represented in matrix form and transpose-multiplied to generate time-domain covariance matrices. The ranked eigenvectors of this matrix are stored as a feature of the pulsation. In the second stage of the algorithm, a sliding window (approximately the width of the training event) is moved across the vector-valued time-series comprising the channels on which the training event was observed. At each window position, the data covariance matrix and associated eigenvectors are calculated. We compare the orientation of the dominant eigenvectors of the training data to those from the windowed data and flag windows where the dominant eigenvectors directions are similar. This was successful in automatically identifying pulses which share polarization and appear to be from the same source process. We apply the method to a case study of continuously sampled (50 Hz) data from six observatories, each equipped with three-component induction coil magnetometers. We examine a 90-day interval of data associated with a cluster of four observatories located within 50 km of Napa, California, together with two remote reference stations-one 100 km to the north of the cluster and the other 350 km south. When the training data contains signals present in the remote reference observatories, we are reliably able to identify and extract global geomagnetic signals such as solar-generated noise. When training data contains pulsations only observed in the cluster of local observatories, we identify several types of non-plane wave signals having similar polarization.

  6. Speeding response -- saving lives : automatic vehicle location capabilities for emergency vehicles

    DOT National Transportation Integrated Search

    1999-01-01

    This brochure focuses on the application of automatic vehicle location systems to emergency services. It discusses how AVL works with emergency vehicles. how it accommodates a wide range of emergency situations, and the benefits of its use.

  7. Denver RTD's computer aided dispatch/automatic vehicle location system : the human factors consequences

    DOT National Transportation Integrated Search

    1999-09-01

    This report documents what happened to employees' work procedures when their employer when their employer installed Computer Aided Disptach/Automatic Vehicle Locator (CAD/AVL) technology to provide real-time surveillance of vehicles and to upgrade ra...

  8. Automatic segmentation of relevant structures in DCE MR mammograms

    NASA Astrophysics Data System (ADS)

    Koenig, Matthias; Laue, Hendrik; Boehler, Tobias; Peitgen, Heinz-Otto

    2007-03-01

    The automatic segmentation of relevant structures such as skin edge, chest wall, or nipple in dynamic contrast enhanced MR imaging (DCE MRI) of the breast provides additional information for computer aided diagnosis (CAD) systems. Automatic reporting using BI-RADS criteria benefits of information about location of those structures. Lesion positions can be automatically described relatively to such reference structures for reporting purposes. Furthermore, this information can assist data reduction for computation expensive preprocessing such as registration, or for visualization of only the segments of current interest. In this paper, a novel automatic method for determining the air-breast boundary resp. skin edge, for approximation of the chest wall, and locating of the nipples is presented. The method consists of several steps which are built on top of each other. Automatic threshold computation leads to the air-breast boundary which is then analyzed to determine the location of the nipple. Finally, results of both steps are starting point for approximation of the chest wall. The proposed process was evaluated on a large data set of DCE MRI recorded by T1 sequences and yielded reasonable results in all cases.

  9. Design description of the Tangaye Village photovoltaic power system

    NASA Astrophysics Data System (ADS)

    Martz, J. E.; Ratajczak, A. F.

    1982-06-01

    The engineering design of a stand alone photovoltaic (PV) powered grain mill and water pump for the village of Tangaye, Upper Volta is described. The socioeconomic effects of reducing the time required by women in rural areas for drawing water and grinding grain were studied. The suitability of photovoltaic technology for use in rural areas by people of limited technical training was demonstrated. The PV system consists of a 1.8-kW (peak) solar cell array, 540 ampere hours of battery storage, instrumentation, automatic controls, and a data collection and storage system. The PV system is situated near an improved village well and supplies d.c. power to a grain mill and a water pump. The array is located in a fenced area and the mill, battery, instruments, controls, and data system are in a mill building. A water storage tank is located near the well. The system employs automatic controls which provide battery charge regulation and system over and under voltage protection. This report includes descriptions of the engineering design of the system and of the load that it serves; a discussion of PV array and battery sizing methodology; descriptions of the mechanical and electrical designs including the array, battery, controls, and instrumentation; and a discussion of the safety features. The system became operational on March 1, 1979.

  10. Design description of the Tangaye Village photovoltaic power system

    NASA Technical Reports Server (NTRS)

    Martz, J. E.; Ratajczak, A. F.

    1982-01-01

    The engineering design of a stand alone photovoltaic (PV) powered grain mill and water pump for the village of Tangaye, Upper Volta is described. The socioeconomic effects of reducing the time required by women in rural areas for drawing water and grinding grain were studied. The suitability of photovoltaic technology for use in rural areas by people of limited technical training was demonstrated. The PV system consists of a 1.8-kW (peak) solar cell array, 540 ampere hours of battery storage, instrumentation, automatic controls, and a data collection and storage system. The PV system is situated near an improved village well and supplies d.c. power to a grain mill and a water pump. The array is located in a fenced area and the mill, battery, instruments, controls, and data system are in a mill building. A water storage tank is located near the well. The system employs automatic controls which provide battery charge regulation and system over and under voltage protection. This report includes descriptions of the engineering design of the system and of the load that it serves; a discussion of PV array and battery sizing methodology; descriptions of the mechanical and electrical designs including the array, battery, controls, and instrumentation; and a discussion of the safety features. The system became operational on March 1, 1979.

  11. Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification

    NASA Astrophysics Data System (ADS)

    Charfi, Imen; Miteran, Johel; Dubois, Julien; Atri, Mohamed; Tourki, Rached

    2013-10-01

    We propose a supervised approach to detect falls in a home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing evaluation of fall detection performance in a video stream, and an automatically optimized set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named STHF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user's trajectory with her/his orientation, projection histograms, and moments of orders 0, 1, and 2). We study the combinations of usual transformations of the features (Fourier transform, wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using support vector machine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained by combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol that enables performance to be improved by updating the training in the current location with normal activities records.

  12. Speeding response, saving lives : automatic vehicle location capabilities for emergency services

    DOT National Transportation Integrated Search

    1999-01-01

    This brochure focuses on the application of automatic vehicle location systems to emergency services. It discusses how AVL works with emergency vehicles and how it accommodates a wide range of emergency situations, and the benefits of ITS use. (3 p.)

  13. Encoding probabilistic brain atlases using Bayesian inference.

    PubMed

    Van Leemput, Koen

    2009-06-01

    This paper addresses the problem of creating probabilistic brain atlases from manually labeled training data. Probabilistic atlases are typically constructed by counting the relative frequency of occurrence of labels in corresponding locations across the training images. However, such an "averaging" approach generalizes poorly to unseen cases when the number of training images is limited, and provides no principled way of aligning the training datasets using deformable registration. In this paper, we generalize the generative image model implicitly underlying standard "average" atlases, using mesh-based representations endowed with an explicit deformation model. Bayesian inference is used to infer the optimal model parameters from the training data, leading to a simultaneous group-wise registration and atlas estimation scheme that encompasses standard averaging as a special case. We also use Bayesian inference to compare alternative atlas models in light of the training data, and show how this leads to a data compression problem that is intuitive to interpret and computationally feasible. Using this technique, we automatically determine the optimal amount of spatial blurring, the best deformation field flexibility, and the most compact mesh representation. We demonstrate, using 2-D training datasets, that the resulting models are better at capturing the structure in the training data than conventional probabilistic atlases. We also present experiments of the proposed atlas construction technique in 3-D, and show the resulting atlases' potential in fully-automated, pulse sequence-adaptive segmentation of 36 neuroanatomical structures in brain MRI scans.

  14. The effect of motor control training on abdominal muscle contraction during simulated weight bearing in elite cricketers.

    PubMed

    Hides, Julie A; Endicott, Timothy; Mendis, M Dilani; Stanton, Warren R

    2016-07-01

    To investigate whether motor control training alters automatic contraction of abdominal muscles in elite cricketers with low back pain (LBP) during performance of a simulated unilateral weight-bearing task. Clinical trial. 26 male elite-cricketers attended a 13-week cricket training camp. Prior to the camp, participants were allocated to a LBP or asymptomatic group. Real-time ultrasound imaging was used to assess automatic abdominal muscle response to axial loading. During the camp, the LBP group performed a staged motor control training program. Following the camp, the automatic response of the abdominal muscles was re-assessed. At pre-camp assessment, when participants were axially loaded with 25% of their own bodyweight, the LBP group showed a 15.5% thicker internal oblique (IO) muscle compared to the asymptomatic group (p = 0.009). The post-camp assessment showed that participants in the LBP group demonstrated less contraction of the IO muscle in response to axial loading compared with the asymptomatic group. A trend was found in the automatic recruitment pattern of the transversus abdominis (p = 0.08). Motor control training normalized excessive contraction of abdominal muscles in response to a low load task. This may be a useful strategy for rehabilitation of cricketers with LBP. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Multi-ball and one-ball geolocation and location verification

    NASA Astrophysics Data System (ADS)

    Nelson, D. J.; Townsend, J. L.

    2017-05-01

    We present analysis methods that may be used to geolocate emitters using one or more moving receivers. While some of the methods we present may apply to a broader class of signals, our primary interest is locating and tracking ships from short pulsed transmissions, such as the maritime Automatic Identification System (AIS.) The AIS signal is difficult to process and track since the pulse duration is only 25 milliseconds, and the pulses may only be transmitted every six to ten seconds. Several fundamental problems are addressed, including demodulation of AIS/GMSK signals, verification of the emitter location, accurate frequency and delay estimation and identification of pulse trains from the same emitter. In particular, we present several new correlation methods, including cross-cross correlation that greatly improves correlation accuracy over conventional methods and cross- TDOA and cross-FDOA functions that make it possible to estimate time and frequency delay without the need of computing a two dimensional cross-ambiguity surface. By isolating pulses from the same emitter and accurately tracking the received signal frequency, we are able to accurately estimate the emitter location from the received Doppler characteristics.

  16. Automatic anatomy partitioning of the torso region on CT images by using multiple organ localizations with a group-wise calibration technique

    NASA Astrophysics Data System (ADS)

    Zhou, Xiangrong; Morita, Syoichi; Zhou, Xinxin; Chen, Huayue; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Hoshi, Hiroaki; Fujita, Hiroshi

    2015-03-01

    This paper describes an automatic approach for anatomy partitioning on three-dimensional (3D) computedtomography (CT) images that divide the human torso into several volume-of-interesting (VOI) images based on anatomical definition. The proposed approach combines several individual detections of organ-location with a groupwise organ-location calibration and correction to achieve an automatic and robust multiple-organ localization task. The essence of the proposed method is to jointly detect the 3D minimum bounding box for each type of organ shown on CT images based on intra-organ-image-textures and inter-organ-spatial-relationship in the anatomy. Machine-learning-based template matching and generalized Hough transform-based point-distribution estimation are used in the detection and calibration processes. We apply this approach to the automatic partitioning of a torso region on CT images, which are divided into 35 VOIs presenting major organ regions and tissues required by routine diagnosis in clinical medicine. A database containing 4,300 patient cases of high-resolution 3D torso CT images is used for training and performance evaluations. We confirmed that the proposed method was successful in target organ localization on more than 95% of CT cases. Only two organs (gallbladder and pancreas) showed a lower success rate: 71 and 78% respectively. In addition, we applied this approach to another database that included 287 patient cases of whole-body CT images scanned for positron emission tomography (PET) studies and used for additional performance evaluation. The experimental results showed that no significant difference between the anatomy partitioning results from those two databases except regarding the spleen. All experimental results showed that the proposed approach was efficient and useful in accomplishing localization tasks for major organs and tissues on CT images scanned using different protocols.

  17. A discrete optimization approach for locating automatic vehicle identification readers for the provision of roadway travel times

    DOT National Transportation Integrated Search

    2002-11-01

    This paper develops an algorithm for optimally locating surveillance technologies with an emphasis on Automatic Vehicle Identification tag readers by maximizing the benefit that would accrue from measuring travel times on a transportation network. Th...

  18. 49 CFR 236.824 - System, automatic block signal.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 4 2011-10-01 2011-10-01 false System, automatic block signal. 236.824 Section..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.824 System, automatic block signal. A block signal system wherein the use of each block is...

  19. 49 CFR 236.824 - System, automatic block signal.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 49 Transportation 4 2014-10-01 2014-10-01 false System, automatic block signal. 236.824 Section..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.824 System, automatic block signal. A block signal system wherein the use of each block is...

  20. 49 CFR 236.824 - System, automatic block signal.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 49 Transportation 4 2013-10-01 2013-10-01 false System, automatic block signal. 236.824 Section..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.824 System, automatic block signal. A block signal system wherein the use of each block is...

  1. 49 CFR 236.576 - Roadway element.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Roadway § 236.576 Roadway element. Roadway...

  2. 49 CFR 236.576 - Roadway element.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Roadway § 236.576 Roadway element. Roadway...

  3. An automatic identification procedure to promote the use of FES-cycling training for hemiparetic patients.

    PubMed

    Ambrosini, Emilia; Ferrante, Simona; Schauer, Thomas; Ferrigno, Giancarlo; Molteni, Franco; Pedrocchi, Alessandra

    2014-01-01

    Cycling induced by Functional Electrical Stimulation (FES) training currently requires a manual setting of different parameters, which is a time-consuming and scarcely repeatable procedure. We proposed an automatic procedure for setting session-specific parameters optimized for hemiparetic patients. This procedure consisted of the identification of the stimulation strategy as the angular ranges during which FES drove the motion, the comparison between the identified strategy and the physiological muscular activation strategy, and the setting of the pulse amplitude and duration of each stimulated muscle. Preliminary trials on 10 healthy volunteers helped define the procedure. Feasibility tests on 8 hemiparetic patients (5 stroke, 3 traumatic brain injury) were performed. The procedure maximized the motor output within the tolerance constraint, identified a biomimetic strategy in 6 patients, and always lasted less than 5 minutes. Its reasonable duration and automatic nature make the procedure usable at the beginning of every training session, potentially enhancing the performance of FES-cycling training.

  4. Material classification and automatic content enrichment of images using supervised learning and knowledge bases

    NASA Astrophysics Data System (ADS)

    Mallepudi, Sri Abhishikth; Calix, Ricardo A.; Knapp, Gerald M.

    2011-02-01

    In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e.g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

  5. 49 CFR 236.556 - Adjustment of relay.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.556 Adjustment of relay...

  6. 49 CFR 236.527 - Roadway element insulation resistance.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.527 Roadway...

  7. 49 CFR 236.527 - Roadway element insulation resistance.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.527 Roadway...

  8. 49 CFR 236.556 - Adjustment of relay.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.556 Adjustment of relay...

  9. 49 CFR 236.555 - Repaired or rewound receiver coil.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.555 Repaired...

  10. 49 CFR 236.558-236.559 - [Reserved

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives §§ 236.558-236.559 [Reserved] ...

  11. 49 CFR 236.529 - Roadway element inductor; height and distance from rail.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions...

  12. 49 CFR 236.529 - Roadway element inductor; height and distance from rail.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions...

  13. 49 CFR 236.558-236.559 - [Reserved

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives §§ 236.558-236.559 [Reserved] ...

  14. 49 CFR 236.555 - Repaired or rewound receiver coil.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.555 Repaired...

  15. Implicit measures of beliefs about sport ability in swimming and basketball.

    PubMed

    Mascret, Nicolas; Falconetti, Jean-Louis; Cury, François

    2016-01-01

    Sport ability may be seen as relatively stable, genetically determined and not easily modified by practice, or as increasable with training, work and effort. Using the Implicit Association Test (IAT), the purpose of the present study is to examine whether the practice of a particular sport (swimming or basketball) can influence automatic beliefs about sport ability in these two sports. The IAT scores evidence that swimmers and basketball players automatically and implicitly associate their own sport with training rather than genetics, whereas non-sportspersons have no significant automatic association. This result is strengthened when perceived competence and intrinsic motivation in swimming or basketball are high.

  16. [The mediating role of anger in the relationship between automatic thoughts and physical aggression in adolescents].

    PubMed

    Yavuzer, Yasemin; Karataş, Zeynep

    2013-01-01

    This study aimed to examine the mediating role of anger in the relationship between automatic thoughts and physical aggression in adolescents. The study included 224 adolescents in the 9th grade of 3 different high schools in central Burdur during the 2011-2012 academic year. Participants completed the Aggression Questionnaire and Automatic Thoughts Scale in their classrooms during counseling sessions. Data were analyzed using simple and multiple linear regression analysis. There were positive correlations between the adolescents' automatic thoughts, and physical aggression, and anger. According to regression analysis, automatic thoughts effectively predicted the level of physical aggression (b= 0.233, P < 0.001)) and anger (b= 0.325, P < 0.001). Analysis of the mediating role of anger showed that anger fully mediated the relationship between automatic thoughts and physical aggression (Sobel z = 5.646, P < 0.001). Anger fully mediated the relationship between automatic thoughts and physical aggression. Providing adolescents with anger management skills training is very important for the prevention of physical aggression. Such training programs should include components related to the development of an awareness of dysfunctional and anger-triggering automatic thoughts, and how to change them. As the study group included adolescents from Burdur, the findings can only be generalized to groups with similar characteristics.

  17. Lessons learned from a pilot project of an automatic vehicle location system in an urban winter maintenance operations setting.

    DOT National Transportation Integrated Search

    2002-01-01

    This report documents the lessons learned during the evolution of the Virginia Department of Transportation's pilot project to use an automatic vehicle location (AVL) system during winter maintenance operations in an urban setting. AVL is a technolog...

  18. 49 CFR 236.401 - Automatic block signal system and interlocking standards applicable to traffic control systems.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Traffic Control Systems Standards § 236.401 Automatic... 49 Transportation 4 2011-10-01 2011-10-01 false Automatic block signal system and interlocking standards applicable to traffic control systems. 236.401 Section 236.401 Transportation Other Regulations...

  19. 49 CFR 236.532 - Strap iron inductor; use restricted.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.532 Strap iron...

  20. 49 CFR 236.532 - Strap iron inductor; use restricted.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.532 Strap iron...

  1. 49 CFR 236.577 - Test, acknowledgement, and cut-in circuits.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Roadway § 236.577 Test...

  2. Automatic detection of Martian dark slope streaks by machine learning using HiRISE images

    NASA Astrophysics Data System (ADS)

    Wang, Yexin; Di, Kaichang; Xin, Xin; Wan, Wenhui

    2017-07-01

    Dark slope streaks (DSSs) on the Martian surface are one of the active geologic features that can be observed on Mars nowadays. The detection of DSS is a prerequisite for studying its appearance, morphology, and distribution to reveal its underlying geological mechanisms. In addition, increasingly massive amounts of Mars high resolution data are now available. Hence, an automatic detection method for locating DSSs is highly desirable. In this research, we present an automatic DSS detection method by combining interest region extraction and machine learning techniques. The interest region extraction combines gradient and regional grayscale information. Moreover, a novel recognition strategy is proposed that takes the normalized minimum bounding rectangles (MBRs) of the extracted regions to calculate the Local Binary Pattern (LBP) feature and train a DSS classifier using the Adaboost machine learning algorithm. Comparative experiments using five different feature descriptors and three different machine learning algorithms show the superiority of the proposed method. Experimental results utilizing 888 extracted region samples from 28 HiRISE images show that the overall detection accuracy of our proposed method is 92.4%, with a true positive rate of 79.1% and false positive rate of 3.7%, which in particular indicates great performance of the method at eliminating non-DSS regions.

  3. Integrating visual learning within a model-based ATR system

    NASA Astrophysics Data System (ADS)

    Carlotto, Mark; Nebrich, Mark

    2017-05-01

    Automatic target recognition (ATR) systems, like human photo-interpreters, rely on a variety of visual information for detecting, classifying, and identifying manmade objects in aerial imagery. We describe the integration of a visual learning component into the Image Data Conditioner (IDC) for target/clutter and other visual classification tasks. The component is based on an implementation of a model of the visual cortex developed by Serre, Wolf, and Poggio. Visual learning in an ATR context requires the ability to recognize objects independent of location, scale, and rotation. Our method uses IDC to extract, rotate, and scale image chips at candidate target locations. A bootstrap learning method effectively extends the operation of the classifier beyond the training set and provides a measure of confidence. We show how the classifier can be used to learn other features that are difficult to compute from imagery such as target direction, and to assess the performance of the visual learning process itself.

  4. Locating faces in color photographs using neural networks

    NASA Astrophysics Data System (ADS)

    Brown, Joe R.; Talley, Jim

    1994-03-01

    This paper summarizes a research effort in finding the locations and sizes of faces in color images (photographs, video stills, etc.) if, in fact, faces are presented. Scenarios for using such a system include serving as the means of localizing skin for automatic color balancing during photo processing or it could be used as a front-end in a customs port of energy context for a system which identified persona non grata given a database of known faces. The approach presented here is a hybrid system including: a neural pre-processor, some conventional image processing steps, and a neural classifier as the final face/non-face discriminator. Neither the training (containing 17,655 faces) nor the test (containing 1829 faces) imagery databases were constrained in their content or quality. The results for the pilot system are reported along with a discussion for improving the current system.

  5. Automatic identification of bird targets with radar via patterns produced by wing flapping.

    PubMed

    Zaugg, Serge; Saporta, Gilbert; van Loon, Emiel; Schmaljohann, Heiko; Liechti, Felix

    2008-09-06

    Bird identification with radar is important for bird migration research, environmental impact assessments (e.g. wind farms), aircraft security and radar meteorology. In a study on bird migration, radar signals from birds, insects and ground clutter were recorded. Signals from birds show a typical pattern due to wing flapping. The data were labelled by experts into the four classes BIRD, INSECT, CLUTTER and UFO (unidentifiable signals). We present a classification algorithm aimed at automatic recognition of bird targets. Variables related to signal intensity and wing flapping pattern were extracted (via continuous wavelet transform). We used support vector classifiers to build predictive models. We estimated classification performance via cross validation on four datasets. When data from the same dataset were used for training and testing the classifier, the classification performance was extremely to moderately high. When data from one dataset were used for training and the three remaining datasets were used as test sets, the performance was lower but still extremely to moderately high. This shows that the method generalizes well across different locations or times. Our method provides a substantial gain of time when birds must be identified in large collections of radar signals and it represents the first substantial step in developing a real time bird identification radar system. We provide some guidelines and ideas for future research.

  6. Detection of Thermal Erosion Gullies from High-Resolution Images Using Deep Learning

    NASA Astrophysics Data System (ADS)

    Huang, L.; Liu, L.; Jiang, L.; Zhang, T.; Sun, Y.

    2017-12-01

    Thermal erosion gullies, one type of thermokarst landforms, develop due to thawing of ice-rich permafrost. Mapping the location and extent of thermal erosion gullies can help understand the spatial distribution of thermokarst landforms and their temporal evolution. Remote sensing images provide an effective way for mapping thermokarst landforms, especially thermokarst lakes. However, thermal erosion gullies are challenging to map from remote sensing images due to their small sizes and significant variations in geometric/radiometric properties. It is feasible to manually identify these features, as a few previous studies have carried out. However manual methods are labor-intensive, therefore, cannot be used for a large study area. In this work, we conduct automatic mapping of thermal erosion gullies from high-resolution images by using Deep Learning. Our study area is located in Eboling Mountain (Qinghai, China). Within a 6 km2 peatland area underlain by ice-rich permafrost, at least 20 thermal erosional gullies are well developed. The image used is a 15-cm-resolution Digital Orthophoto Map (DOM) generated in July 2016. First, we extracted 14 gully patches and ten non-gully patches as training data. And we performed image augmentation. Next, we fine-tuned the pre-trained model of DeepLab, a deep-learning algorithm for semantic image segmentation based on Deep Convolutional Neural Networks. Then, we performed inference on the whole DOM and obtained intermediate results in forms of polygons for all identified gullies. At last, we removed misidentified polygons based on a few pre-set criteria on the size and shape of each polygon. Our final results include 42 polygons. Validated against field measurements using GPS, most of the gullies are detected correctly. There are 20 false detections due to the small number and low quality of training images. We also found three new gullies that missed in the field observations. This study shows that (1) despite a challenging mapping task, DeepLab can detect small, irregular-shaped thermal erosion gullies with high accuracy. (2) Automatic detection is critical for mapping thermal erosion gully since manual mapping or field work may miss some targets even in a relatively small region. (3) The quantity and quality of training data are crucial for detection accuracy.

  7. Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching.

    PubMed

    Liao, Miao; Zhao, Yu-Qian; Liu, Xi-Yao; Zeng, Ye-Zhan; Zou, Bei-Ji; Wang, Xiao-Fang; Shih, Frank Y

    2017-05-01

    Identifying liver regions from abdominal computed tomography (CT) volumes is an important task for computer-aided liver disease diagnosis and surgical planning. This paper presents a fully automatic method for liver segmentation from CT volumes based on graph cuts and border marching. An initial slice is segmented by density peak clustering. Based on pixel- and patch-wise features, an intensity model and a PCA-based regional appearance model are developed to enhance the contrast between liver and background. Then, these models as well as the location constraint estimated iteratively are integrated into graph cuts in order to segment the liver in each slice automatically. Finally, a vessel compensation method based on the border marching is used to increase the segmentation accuracy. Experiments are conducted on a clinical data set we created and also on the MICCAI2007 Grand Challenge liver data. The results show that the proposed intensity, appearance models, and the location constraint are significantly effective for liver recognition, and the undersegmented vessels can be compensated by the border marching based method. The segmentation performances in terms of VOE, RVD, ASD, RMSD, and MSD as well as the average running time achieved by our method on the SLIVER07 public database are 5.8 ± 3.2%, -0.1 ± 4.1%, 1.0 ± 0.5mm, 2.0 ± 1.2mm, 21.2 ± 9.3mm, and 4.7 minutes, respectively, which are superior to those of existing methods. The proposed method does not require time-consuming training process and statistical model construction, and is capable of dealing with complicated shapes and intensity variations successfully. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Automatic detection of anatomical regions in frontal x-ray images: comparing convolutional neural networks to random forest

    NASA Astrophysics Data System (ADS)

    Olory Agomma, R.; Vázquez, C.; Cresson, T.; De Guise, J.

    2018-02-01

    Most algorithms to detect and identify anatomical structures in medical images require either to be initialized close to the target structure, or to know that the structure is present in the image, or to be trained on a homogeneous database (e.g. all full body or all lower limbs). Detecting these structures when there is no guarantee that the structure is present in the image, or when the image database is heterogeneous (mixed configurations), is a challenge for automatic algorithms. In this work we compared two state-of-the-art machine learning techniques in order to determine which one is the most appropriate for predicting targets locations based on image patches. By knowing the position of thirteen landmarks points, labelled by an expert in EOS frontal radiography, we learn the displacement between salient points detected in the image and these thirteen landmarks. The learning step is carried out with a machine learning approach by exploring two methods: Convolutional Neural Network (CNN) and Random Forest (RF). The automatic detection of the thirteen landmarks points in a new image is then obtained by averaging the positions of each one of these thirteen landmarks estimated from all the salient points in the new image. We respectively obtain for CNN and RF, an average prediction error (both mean and standard deviation in mm) of 29 +/-18 and 30 +/- 21 for the thirteen landmarks points, indicating the approximate location of anatomical regions. On the other hand, the learning time is 9 days for CNN versus 80 minutes for RF. We provide a comparison of the results between the two machine learning approaches.

  9. Brain activity across the development of automatic categorization: A comparison of categorization tasks using multi-voxel pattern analysis

    PubMed Central

    Soto, Fabian A.; Waldschmidt, Jennifer G.; Helie, Sebastien; Ashby, F. Gregory

    2013-01-01

    Previous evidence suggests that relatively separate neural networks underlie initial learning of rule-based and information-integration categorization tasks. With the development of automaticity, categorization behavior in both tasks becomes increasingly similar and exclusively related to activity in cortical regions. The present study uses multi-voxel pattern analysis to directly compare the development of automaticity in different categorization tasks. Each of three groups of participants received extensive training in a different categorization task: either an information-integration task, or one of two rule-based tasks. Four training sessions were performed inside an MRI scanner. Three different analyses were performed on the imaging data from a number of regions of interest (ROIs). The common patterns analysis had the goal of revealing ROIs with similar patterns of activation across tasks. The unique patterns analysis had the goal of revealing ROIs with dissimilar patterns of activation across tasks. The representational similarity analysis aimed at exploring (1) the similarity of category representations across ROIs and (2) how those patterns of similarities compared across tasks. The results showed that common patterns of activation were present in motor areas and basal ganglia early in training, but only in the former later on. Unique patterns were found in a variety of cortical and subcortical areas early in training, but they were dramatically reduced with training. Finally, patterns of representational similarity between brain regions became increasingly similar across tasks with the development of automaticity. PMID:23333700

  10. Combining MEDLINE and publisher data to create parallel corpora for the automatic translation of biomedical text

    PubMed Central

    2013-01-01

    Background Most of the institutional and research information in the biomedical domain is available in the form of English text. Even in countries where English is an official language, such as the United States, language can be a barrier for accessing biomedical information for non-native speakers. Recent progress in machine translation suggests that this technique could help make English texts accessible to speakers of other languages. However, the lack of adequate specialized corpora needed to train statistical models currently limits the quality of automatic translations in the biomedical domain. Results We show how a large-sized parallel corpus can automatically be obtained for the biomedical domain, using the MEDLINE database. The corpus generated in this work comprises article titles obtained from MEDLINE and abstract text automatically retrieved from journal websites, which substantially extends the corpora used in previous work. After assessing the quality of the corpus for two language pairs (English/French and English/Spanish) we use the Moses package to train a statistical machine translation model that outperforms previous models for automatic translation of biomedical text. Conclusions We have built translation data sets in the biomedical domain that can easily be extended to other languages available in MEDLINE. These sets can successfully be applied to train statistical machine translation models. While further progress should be made by incorporating out-of-domain corpora and domain-specific lexicons, we believe that this work improves the automatic translation of biomedical texts. PMID:23631733

  11. Early prediction of eruption site using lightning location data: An operational real-time system in Iceland

    NASA Astrophysics Data System (ADS)

    Arason, Þórður; Bjornsson, Halldór; Nína Petersen, Guðrún

    2013-04-01

    Eruption of subglacial volcanoes may lead to catastrophic floods and thus early determination of the exact eruption site may be critical to civil protection evacuation plans. A system is being developed that automatically monitors and analyses volcanic lightning in Iceland. The system predicts the eruption site location from mean lightning locations, taking into account upper level wind. In estimating mean lightning locations, outliers are automatically omitted. A simple wind correction is performed based on the vector wind at the 500 hPa pressure level in the latest radiosonde from Keflavík airport. The system automatically creates a web page with maps and tables showing individual lightning locations and mean locations with and without wind corrections along with estimates of uncetainty. A dormant automatic monitoring system, waiting for a rare event, potentially for several years, is quite susceptible to degeneration during the waiting period, e.g. due to computer or other IT-system upgrades. However, ordinary weather thunderstorms in Iceland should initiate special monitoring and automatic analysis of this system in the same fashion as during a volcanic eruption. Such ordinary weather thunderstorm events will be used to observe anomalies and malfunctions in the system. The essential elements of this system will be described. An example is presented of how the system would have worked during the first hours of the Grímsvötn 2011 eruption. In that case the exact eruption site, within the Grímsvötn caldera, was first known about 15 hours into the eruption.

  12. 49 CFR 236.589 - Relays.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.589 Relays. (a) Each relay shall be removed... train stop or train control system, at least once every two years; and (2) All other relays, at least...

  13. 49 CFR 236.589 - Relays.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.589 Relays. (a) Each relay shall be removed... train stop or train control system, at least once every two years; and (2) All other relays, at least...

  14. Providers issue brief: automated external defibrillators.

    PubMed

    Rothouse, M

    1999-06-25

    With expanded access to automatic external defibrillators, hundreds of lives could be saved on a daily basis. By training nonphysician providers, such as emergency medical service personnel or first responders, this life-saving medical equipment could help improve the survival rates for people suffering from cardiac arrest. During the last two years, state lawmakers have begun to enact legislation that develops training standards and provides immunity from civil liability for automatic external defibrillator users.

  15. 30 CFR 75.1103-5 - Automatic fire warning devices; actions and response.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... level reaches 10 parts per million above the established ambient level at any sensor location, automatic fire sensor and warning device systems shall provide an effective warning signal at the following... endangered and (ii) A map or schematic that shows the locations of sensors, and the intended air flow...

  16. 9 Is Always on Top: Assessing the Automaticity of Synaesthetic Number-Forms

    ERIC Educational Resources Information Center

    Jarick, Michelle; Dixon, Michael J.; Smilek, Daniel

    2011-01-01

    For number-form synaesthetes, digits occupy idiosyncratic spatial locations. Atypical to the mental number line that extends horizontally, the synaesthete (L) experiences the numbers 1-10 vertically. We used a spatial cueing task to demonstrate that L's attention could be automatically directed to locations within her number-space--being faster to…

  17. Automatic detection of cardiovascular risk in CT attenuation correction maps in Rb-82 PET/CTs

    NASA Astrophysics Data System (ADS)

    Išgum, Ivana; de Vos, Bob D.; Wolterink, Jelmer M.; Dey, Damini; Berman, Daniel S.; Rubeaux, Mathieu; Leiner, Tim; Slomka, Piotr J.

    2016-03-01

    CT attenuation correction (CTAC) images acquired with PET/CT visualize coronary artery calcium (CAC) and enable CAC quantification. CAC scores acquired with CTAC have been suggested as a marker of cardiovascular disease (CVD). In this work, an algorithm previously developed for automatic CAC scoring in dedicated cardiac CT was applied to automatic CAC detection in CTAC. The study included 134 consecutive patients undergoing 82-Rb PET/CT. Low-dose rest CTAC scans were acquired (100 kV, 11 mAs, 1.4mm×1.4mm×3mm voxel size). An experienced observer defined the reference standard with the clinically used intensity level threshold for calcium identification (130 HU). Five scans were removed from analysis due to artifacts. The algorithm extracted potential CAC by intensity-based thresholding and 3D connected component labeling. Each candidate was described by location, size, shape and intensity features. An ensemble of extremely randomized decision trees was used to identify CAC. The data set was randomly divided into training and test sets. Automatically identified CAC was quantified using volume and Agatston scores. In 33 test scans, the system detected on average 469mm3/730mm3 (64%) of CAC with 36mm3 false positive volume per scan. The intraclass correlation coefficient for volume scores was 0.84. Each patient was assigned to one of four CVD risk categories based on the Agatston score (0-10, 11-100, 101-400, <400). The correct CVD category was assigned to 85% of patients (Cohen's linearly weighted κ0.82). Automatic detection of CVD risk based on CAC scoring in rest CTAC images is feasible. This may enable large scale studies evaluating clinical value of CAC scoring in CTAC data.

  18. A PC-based computer package for automatic detection and location of earthquakes: Application to a seismic network in eastern sicity (Italy)

    NASA Astrophysics Data System (ADS)

    Patanè, Domenico; Ferrari, Ferruccio; Giampiccolo, Elisabetta; Gresta, Stefano

    Few automated data acquisition and processing systems operate on mainframes, some run on UNIX-based workstations and others on personal computers, equipped with either DOS/WINDOWS or UNIX-derived operating systems. Several large and complex software packages for automatic and interactive analysis of seismic data have been developed in recent years (mainly for UNIX-based systems). Some of these programs use a variety of artificial intelligence techniques. The first operational version of a new software package, named PC-Seism, for analyzing seismic data from a local network is presented in Patanè et al. (1999). This package, composed of three separate modules, provides an example of a new generation of visual object-oriented programs for interactive and automatic seismic data-processing running on a personal computer. In this work, we mainly discuss the automatic procedures implemented in the ASDP (Automatic Seismic Data-Processing) module and real time application to data acquired by a seismic network running in eastern Sicily. This software uses a multi-algorithm approach and a new procedure MSA (multi-station-analysis) for signal detection, phase grouping and event identification and location. It is designed for an efficient and accurate processing of local earthquake records provided by single-site and array stations. Results from ASDP processing of two different data sets recorded at Mt. Etna volcano by a regional network are analyzed to evaluate its performance. By comparing the ASDP pickings with those revised manually, the detection and subsequently the location capabilities of this software are assessed. The first data set is composed of 330 local earthquakes recorded in the Mt. Etna erea during 1997 by the telemetry analog seismic network. The second data set comprises about 970 automatic locations of more than 2600 local events recorded at Mt. Etna during the last eruption (July 2001) at the present network. For the former data set, a comparison of the automatic results with the manual picks indicates that the ASDP module can accurately pick 80% of the P-waves and 65% of S-waves. The on-line application on the latter data set shows that automatic locations are affected by larger errors, due to the preliminary setting of the configuration parameters in the program. However, both automatic ASDP and manual hypocenter locations are comparable within the estimated error bounds. New improvements of the PC-Seism software for on-line analysis are also discussed.

  19. An evaluation of automatic coronary artery calcium scoring methods with cardiac CT using the orCaScore framework.

    PubMed

    Wolterink, Jelmer M; Leiner, Tim; de Vos, Bob D; Coatrieux, Jean-Louis; Kelm, B Michael; Kondo, Satoshi; Salgado, Rodrigo A; Shahzad, Rahil; Shu, Huazhong; Snoeren, Miranda; Takx, Richard A P; van Vliet, Lucas J; van Walsum, Theo; Willems, Tineke P; Yang, Guanyu; Zheng, Yefeng; Viergever, Max A; Išgum, Ivana

    2016-05-01

    The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) events. In clinical practice, CAC is manually identified and automatically quantified in cardiac CT using commercially available software. This is a tedious and time-consuming process in large-scale studies. Therefore, a number of automatic methods that require no interaction and semiautomatic methods that require very limited interaction for the identification of CAC in cardiac CT have been proposed. Thus far, a comparison of their performance has been lacking. The objective of this study was to perform an independent evaluation of (semi)automatic methods for CAC scoring in cardiac CT using a publicly available standardized framework. Cardiac CT exams of 72 patients distributed over four CVD risk categories were provided for (semi)automatic CAC scoring. Each exam consisted of a noncontrast-enhanced calcium scoring CT (CSCT) and a corresponding coronary CT angiography (CCTA) scan. The exams were acquired in four different hospitals using state-of-the-art equipment from four major CT scanner vendors. The data were divided into 32 training exams and 40 test exams. A reference standard for CAC in CSCT was defined by consensus of two experts following a clinical protocol. The framework organizers evaluated the performance of (semi)automatic methods on test CSCT scans, per lesion, artery, and patient. Five (semi)automatic methods were evaluated. Four methods used both CSCT and CCTA to identify CAC, and one method used only CSCT. The evaluated methods correctly detected between 52% and 94% of CAC lesions with positive predictive values between 65% and 96%. Lesions in distal coronary arteries were most commonly missed and aortic calcifications close to the coronary ostia were the most common false positive errors. The majority (between 88% and 98%) of correctly identified CAC lesions were assigned to the correct artery. Linearly weighted Cohen's kappa for patient CVD risk categorization by the evaluated methods ranged from 0.80 to 1.00. A publicly available standardized framework for the evaluation of (semi)automatic methods for CAC identification in cardiac CT is described. An evaluation of five (semi)automatic methods within this framework shows that automatic per patient CVD risk categorization is feasible. CAC lesions at ambiguous locations such as the coronary ostia remain challenging, but their detection had limited impact on CVD risk determination.

  20. Automatic microscopy for mitotic cell location.

    NASA Technical Reports Server (NTRS)

    Herron, J.; Ranshaw, R.; Castle, J.; Wald, N.

    1972-01-01

    Advances are reported in the development of an automatic microscope with which to locate hematologic or other cells in mitosis for subsequent chromosome analysis. The system under development is designed to perform the functions of: slide scanning to locate metaphase cells; conversion of images of selected cells into binary form; and on-line computer analysis of the digitized image for significant cytogenetic data. Cell detection criteria are evaluated using a test sample of 100 mitotic cells and 100 artifacts.

  1. 76 FR 8699 - Reporting Requirements for Positive Train Control Expenses and Investments

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-02-15

    ... DEPARTMENT OF TRANSPORTATION Surface Transportation Board 49 CFR Part 1201 [Docket No. EP 706] Reporting Requirements for Positive Train Control Expenses and Investments AGENCY: Surface Transportation... Train Control, a federally mandated safety system that will automatically stop or slow a train before an...

  2. 49 CFR 236.526 - Roadway element not functioning properly.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.526 Roadway..., train control or cab signal system is not functioning as intended, the signal associated with such...

  3. 49 CFR 236.526 - Roadway element not functioning properly.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.526 Roadway..., train control or cab signal system is not functioning as intended, the signal associated with such...

  4. Changes in default mode network as automaticity develops in a categorization task.

    PubMed

    Shamloo, Farzin; Helie, Sebastien

    2016-10-15

    The default mode network (DMN) is a set of brain regions in which blood oxygen level dependent signal is suppressed during attentional focus on the external environment. Because automatic task processing requires less attention, development of automaticity in a rule-based categorization task may result in less deactivation and altered functional connectivity of the DMN when compared to the initial learning stage. We tested this hypothesis by re-analyzing functional magnetic resonance imaging data of participants trained in rule-based categorization for over 10,000 trials (Helie et al., 2010) [12,13]. The results show that some DMN regions are deactivated in initial training but not after automaticity has developed. There is also a significant decrease in DMN deactivation after extensive practice. Seed-based functional connectivity analyses with the precuneus, medial prefrontal cortex (two important DMN regions) and Brodmann area 6 (an important region in automatic categorization) were also performed. The results show increased functional connectivity with both DMN and non-DMN regions after the development of automaticity, and a decrease in functional connectivity between the medial prefrontal cortex and ventromedial orbitofrontal cortex. Together, these results further support the hypothesis of a strategy shift in automatic categorization and bridge the cognitive and neuroscientific conceptions of automaticity in showing that the reduced need for cognitive resources in automatic processing is accompanied by a disinhibition of the DMN and stronger functional connectivity between DMN and task-related brain regions. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. Automatic Earthquake Detection and Location by Waveform coherency in Alentejo (South Portugal) Using CatchPy

    NASA Astrophysics Data System (ADS)

    Custodio, S.; Matos, C.; Grigoli, F.; Cesca, S.; Heimann, S.; Rio, I.

    2015-12-01

    Seismic data processing is currently undergoing a step change, benefitting from high-volume datasets and advanced computer power. In the last decade, a permanent seismic network of 30 broadband stations, complemented by dense temporary deployments, covered mainland Portugal. This outstanding regional coverage currently enables the computation of a high-resolution image of the seismicity of Portugal, which contributes to fitting together the pieces of the regional seismo-tectonic puzzle. Although traditional manual inspections are valuable to refine automatic results they are impracticable with the big data volumes now available. When conducted alone they are also less objective since the criteria is defined by the analyst. In this work we present CatchPy, a scanning algorithm to detect earthquakes in continuous datasets. Our main goal is to implement an automatic earthquake detection and location routine in order to have a tool to quickly process large data sets, while at the same time detecting low magnitude earthquakes (i.e. lowering the detection threshold). CatchPY is designed to produce an event database that could be easily located using existing location codes (e.g.: Grigoli et al. 2013, 2014). We use CatchPy to perform automatic detection and location of earthquakes that occurred in Alentejo region (South Portugal), taking advantage of a dense seismic network deployed in the region for two years during the DOCTAR experiment. Results show that our automatic procedure is particularly suitable for small aperture networks. The event detection is performed by continuously computing the short-term-average/long-term-average of two different characteristic functions (CFs). For the P phases we used a CF based on the vertical energy trace while for S phases we used a CF based on the maximum eigenvalue of the instantaneous covariance matrix (Vidale 1991). Seismic event location is performed by waveform coherence analysis, scanning different hypocentral coordinates (Grigoli et al. 2013, 2014). The reliability of automatic detections, phase pickings and locations are tested trough the quantitative comparison with manual results. This work is supported by project QuakeLoc, reference: PTDC/GEO-FIQ/3522/2012

  6. 49 CFR 233.1 - Scope.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... TRANSPORTATION SIGNAL SYSTEMS REPORTING REQUIREMENTS § 233.1 Scope. This part prescribed reporting requirements with respect to methods of train operation, block signal systems, interlockings, traffic control systems, automatic train stop, train control, and cab signal systems, or other similar appliances, methods...

  7. 49 CFR 236.15 - Timetable instructions.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Rules and Instructions: All Systems General § 236.15 Timetable instructions. Automatic block, traffic control, train stop, train control and cab signal territory shall be designated in timetable instructions. ...

  8. 49 CFR 233.1 - Scope.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... TRANSPORTATION SIGNAL SYSTEMS REPORTING REQUIREMENTS § 233.1 Scope. This part prescribed reporting requirements with respect to methods of train operation, block signal systems, interlockings, traffic control systems, automatic train stop, train control, and cab signal systems, or other similar appliances, methods...

  9. 49 CFR 236.15 - Timetable instructions.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Rules and Instructions: All Systems General § 236.15 Timetable instructions. Automatic block, traffic control, train stop, train control and cab signal territory shall be designated in timetable instructions. ...

  10. Automatic and Controlled Response Inhibition: Associative Learning in the Go/No-Go and Stop-Signal Paradigms

    ERIC Educational Resources Information Center

    Verbruggen, Frederick; Logan, Gordon D.

    2008-01-01

    In 5 experiments, the authors examined the development of automatic response inhibition in the go/no-go paradigm and a modified version of the stop-signal paradigm. They hypothesized that automatic response inhibition may develop over practice when stimuli are consistently associated with stopping. All 5 experiments consisted of a training phase…

  11. Automatic and Controlled Response Inhibition: Associative Learning in the Go/No-Go and Stop-Signal Paradigms

    PubMed Central

    Verbruggen, Frederick; Logan, Gordon D.

    2008-01-01

    In five experiments, the authors examined the development of automatic response inhibition in the go/no-go paradigm and a modified version of the stop-signal paradigm. They hypothesized that automatic response inhibition may develop over practice when stimuli are consistently associated with stopping. All five experiments consisted of a training phase and a test phase in which the stimulus mapping was reversed for a subset of the stimuli. Consistent with the automatic-inhibition hypothesis, the authors found that responding in the test phase was slowed when the stimulus had been consistently associated with stopping in the training phase. In addition, they found that response inhibition benefited from consistent stimulus-stop associations. These findings suggest that response inhibition may rely on the retrieval of stimulus-stop associations after practice with consistent stimulus-stop mappings. Stimulus-stop mapping is typically consistent in the go/no-go paradigm, so automatic inhibition is likely to occur. However, stimulus-stop mapping is typically inconsistent in the stop-signal paradigm, so automatic inhibition is unlikely to occur. Thus, the results suggest that the two paradigms are not equivalent because they allow different kinds of response inhibition. PMID:18999358

  12. Applying a weighted random forests method to extract karst sinkholes from LiDAR data

    NASA Astrophysics Data System (ADS)

    Zhu, Junfeng; Pierskalla, William P.

    2016-02-01

    Detailed mapping of sinkholes provides critical information for mitigating sinkhole hazards and understanding groundwater and surface water interactions in karst terrains. LiDAR (Light Detection and Ranging) measures the earth's surface in high-resolution and high-density and has shown great potentials to drastically improve locating and delineating sinkholes. However, processing LiDAR data to extract sinkholes requires separating sinkholes from other depressions, which can be laborious because of the sheer number of the depressions commonly generated from LiDAR data. In this study, we applied the random forests, a machine learning method, to automatically separate sinkholes from other depressions in a karst region in central Kentucky. The sinkhole-extraction random forest was grown on a training dataset built from an area where LiDAR-derived depressions were manually classified through a visual inspection and field verification process. Based on the geometry of depressions, as well as natural and human factors related to sinkholes, 11 parameters were selected as predictive variables to form the dataset. Because the training dataset was imbalanced with the majority of depressions being non-sinkholes, a weighted random forests method was used to improve the accuracy of predicting sinkholes. The weighted random forest achieved an average accuracy of 89.95% for the training dataset, demonstrating that the random forest can be an effective sinkhole classifier. Testing of the random forest in another area, however, resulted in moderate success with an average accuracy rate of 73.96%. This study suggests that an automatic sinkhole extraction procedure like the random forest classifier can significantly reduce time and labor costs and makes its more tractable to map sinkholes using LiDAR data for large areas. However, the random forests method cannot totally replace manual procedures, such as visual inspection and field verification.

  13. Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score

    NASA Astrophysics Data System (ADS)

    Jiménez del Toro, Oscar; Atzori, Manfredo; Otálora, Sebastian; Andersson, Mats; Eurén, Kristian; Hedlund, Martin; Rönnquist, Peter; Müller, Henning

    2017-03-01

    The Gleason grading system was developed for assessing prostate histopathology slides. It is correlated to the outcome and incidence of relapse in prostate cancer. Although this grading is part of a standard protocol performed by pathologists, visual inspection of whole slide images (WSIs) has an inherent subjectivity when evaluated by different pathologists. Computer aided pathology has been proposed to generate an objective and reproducible assessment that can help pathologists in their evaluation of new tissue samples. Deep convolutional neural networks are a promising approach for the automatic classification of histopathology images and can hierarchically learn subtle visual features from the data. However, a large number of manual annotations from pathologists are commonly required to obtain sufficient statistical generalization when training new models that can evaluate the daily generated large amounts of pathology data. A fully automatic approach that detects prostatectomy WSIs with high-grade Gleason score is proposed. We evaluate the performance of various deep learning architectures training them with patches extracted from automatically generated regions-of-interest rather than from manually segmented ones. Relevant parameters for training the deep learning model such as size and number of patches as well as the inclusion or not of data augmentation are compared between the tested deep learning architectures. 235 prostate tissue WSIs with their pathology report from the publicly available TCGA data set were used. An accuracy of 78% was obtained in a balanced set of 46 unseen test images with different Gleason grades in a 2-class decision: high vs. low Gleason grade. Grades 7-8, which represent the boundary decision of the proposed task, were particularly well classified. The method is scalable to larger data sets with straightforward re-training of the model to include data from multiple sources, scanners and acquisition techniques. Automatically generated heatmaps for theWSIs could be useful for improving the selection of patches when training networks for big data sets and to guide the visual inspection of these images.

  14. Using deep learning to quantify the beauty of outdoor places.

    PubMed

    Seresinhe, Chanuki Illushka; Preis, Tobias; Moat, Helen Susannah

    2017-07-01

    Beautiful outdoor locations are protected by governments and have recently been shown to be associated with better health. But what makes an outdoor space beautiful? Does a beautiful outdoor location differ from an outdoor location that is simply natural? Here, we explore whether ratings of over 200 000 images of Great Britain from the online game Scenic-Or-Not , combined with hundreds of image features extracted using the Places Convolutional Neural Network, might help us understand what beautiful outdoor spaces are composed of. We discover that, as well as natural features such as 'Coast', 'Mountain' and 'Canal Natural', man-made structures such as 'Tower', 'Castle' and 'Viaduct' lead to places being considered more scenic. Importantly, while scenes containing 'Trees' tend to rate highly, places containing more bland natural green features such as 'Grass' and 'Athletic Fields' are considered less scenic. We also find that a neural network can be trained to automatically identify scenic places, and that this network highlights both natural and built locations. Our findings demonstrate how online data combined with neural networks can provide a deeper understanding of what environments we might find beautiful and offer quantitative insights for policymakers charged with design and protection of our built and natural environments.

  15. Detection, location, and quantification of structural damage by neural-net-processed moiré profilometry

    NASA Astrophysics Data System (ADS)

    Grossman, Barry G.; Gonzalez, Frank S.; Blatt, Joel H.; Hooker, Jeffery A.

    1992-03-01

    The development of efficient high speed techniques to recognize, locate, and quantify damage is vitally important for successful automated inspection systems such as ones used for the inspection of undersea pipelines. Two critical problems must be solved to achieve these goals: the reduction of nonuseful information present in the video image and automatic recognition and quantification of extent and location of damage. Artificial neural network processed moire profilometry appears to be a promising technique to accomplish this. Real time video moire techniques have been developed which clearly distinguish damaged and undamaged areas on structures, thus reducing the amount of extraneous information input into an inspection system. Artificial neural networks have demonstrated advantages for image processing, since they can learn the desired response to a given input and are inherently fast when implemented in hardware due to their parallel computing architecture. Video moire images of pipes with dents of different depths were used to train a neural network, with the desired output being the location and severity of the damage. The system was then successfully tested with a second series of moire images. The techniques employed and the results obtained are discussed.

  16. Fully automatic time-window selection using machine learning for global adjoint tomography

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Hill, J.; Lei, W.; Lefebvre, M. P.; Bozdag, E.; Komatitsch, D.; Tromp, J.

    2017-12-01

    Selecting time windows from seismograms such that the synthetic measurements (from simulations) and measured observations are sufficiently close is indispensable in a global adjoint tomography framework. The increasing amount of seismic data collected everyday around the world demands "intelligent" algorithms for seismic window selection. While the traditional FLEXWIN algorithm can be "automatic" to some extent, it still requires both human input and human knowledge or experience, and thus is not deemed to be fully automatic. The goal of intelligent window selection is to automatically select windows based on a learnt engine that is built upon a huge number of existing windows generated through the adjoint tomography project. We have formulated the automatic window selection problem as a classification problem. All possible misfit calculation windows are classified as either usable or unusable. Given a large number of windows with a known selection mode (select or not select), we train a neural network to predict the selection mode of an arbitrary input window. Currently, the five features we extract from the windows are its cross-correlation value, cross-correlation time lag, amplitude ratio between observed and synthetic data, window length, and minimum STA/LTA value. More features can be included in the future. We use these features to characterize each window for training a multilayer perceptron neural network (MPNN). Training the MPNN is equivalent to solve a non-linear optimization problem. We use backward propagation to derive the gradient of the loss function with respect to the weighting matrices and bias vectors and use the mini-batch stochastic gradient method to iteratively optimize the MPNN. Numerical tests show that with a careful selection of the training data and a sufficient amount of training data, we are able to train a robust neural network that is capable of detecting the waveforms in an arbitrary earthquake data with negligible detection error compared to existing selection methods (e.g. FLEXWIN). We will introduce in detail the mathematical formulation of the window-selection-oriented MPNN and show very encouraging results when applying the new algorithm to real earthquake data.

  17. Automatic image hanging protocol for chest radiographs in PACS.

    PubMed

    Luo, Hui; Hao, Wei; Foos, David H; Cornelius, Craig W

    2006-04-01

    Chest radiography is one of the most widely used techniques in diagnostic imaging. It comprises at least one-third of all diagnostic radiographic procedures in hospitals. However, in the picture archive and communication system, images are often stored with the projection and orientation unknown or mislabeled, which causes inefficiency for radiologists' interpretation. To address this problem, an automatic hanging protocol for chest radiographs is presented. The method targets the most effective region in a chest radiograph, and extracts a set of size-, rotation-, and translation-invariant features from it. Then, a well-trained classifier is used to recognize the projection. The orientation of the radiograph is later identified by locating the neck, heart, and abdomen positions in the radiographs. Initial experiments are performed on the radiographs collected from daily routine chest exams in hospitals and show promising results. Using the presented protocol, 98.2% of all cases could be hung correctly on projection view (without protocol, 62%), and 96.1% had correct orientation (without protocol, 75%). A workflow study on the protocol also demonstrates a significant improvement in efficiency for image display.

  18. Automatic delineation and 3D visualization of the human ventricular system using probabilistic neural networks

    NASA Astrophysics Data System (ADS)

    Hatfield, Fraser N.; Dehmeshki, Jamshid

    1998-09-01

    Neurosurgery is an extremely specialized area of medical practice, requiring many years of training. It has been suggested that virtual reality models of the complex structures within the brain may aid in the training of neurosurgeons as well as playing an important role in the preparation for surgery. This paper focuses on the application of a probabilistic neural network to the automatic segmentation of the ventricles from magnetic resonance images of the brain, and their three dimensional visualization.

  19. Adaptive Personalized Training Games for Individual and Collaborative Rehabilitation of People with Multiple Sclerosis

    PubMed Central

    2014-01-01

    Any rehabilitation involves people who are unique individuals with their own characteristics and rehabilitation needs, including patients suffering from Multiple Sclerosis (MS). The prominent variation of MS symptoms and the disease severity elevate a need to accommodate the patient diversity and support adaptive personalized training to meet every patient's rehabilitation needs. In this paper, we focus on integrating adaptivity and personalization in rehabilitation training for MS patients. We introduced the automatic adjustment of difficulty levels as an adaptation that can be provided in individual and collaborative rehabilitation training exercises for MS patients. Two user studies have been carried out with nine MS patients to investigate the outcome of this adaptation. The findings showed that adaptive personalized training trajectories have been successfully provided to MS patients according to their individual training progress, which was appreciated by the patients and the therapist. They considered the automatic adjustment of difficulty levels to provide more variety in the training and to minimize the therapists involvement in setting up the training. With regard to social interaction in the collaborative training exercise, we have observed some social behaviors between the patients and their training partner which indicated the development of social interaction during the training. PMID:24982862

  20. Automatic rule generation for high-level vision

    NASA Technical Reports Server (NTRS)

    Rhee, Frank Chung-Hoon; Krishnapuram, Raghu

    1992-01-01

    A new fuzzy set based technique that was developed for decision making is discussed. It is a method to generate fuzzy decision rules automatically for image analysis. This paper proposes a method to generate rule-based approaches to solve problems such as autonomous navigation and image understanding automatically from training data. The proposed method is also capable of filtering out irrelevant features and criteria from the rules.

  1. A Comprehensive and Cost-Effective Computer Infrastructure for K-12 Schools

    NASA Technical Reports Server (NTRS)

    Warren, G. P.; Seaton, J. M.

    1996-01-01

    Since 1993, NASA Langley Research Center has been developing and implementing a low-cost Internet connection model, including system architecture, training, and support, to provide Internet access for an entire network of computers. This infrastructure allows local area networks which exceed 50 machines per school to independently access the complete functionality of the Internet by connecting to a central site, using state-of-the-art commercial modem technology, through a single standard telephone line. By locating high-cost resources at this central site and sharing these resources and their costs among the school districts throughout a region, a practical, efficient, and affordable infrastructure for providing scale-able Internet connectivity has been developed. As the demand for faster Internet access grows, the model has a simple expansion path that eliminates the need to replace major system components and re-train personnel. Observations of optical Internet usage within an environment, particularly school classrooms, have shown that after an initial period of 'surfing,' the Internet traffic becomes repetitive. By automatically storing requested Internet information on a high-capacity networked disk drive at the local site (network based disk caching), then updating this information only when it changes, well over 80 percent of the Internet traffic that leaves a location can be eliminated by retrieving the information from the local disk cache.

  2. 49 CFR 236.730 - Coil, receiver.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.730 Coil, receiver. Concentric layers of insulated wire wound around the core of a receiver of an automatic train stop, train control or cab signal device on a locomotive. ...

  3. 49 CFR 236.505 - Proper operative relation between parts along roadway and parts on locomotive.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards... all conditions of speed, weather, wear, oscillation, and shock. ...

  4. 49 CFR 236.505 - Proper operative relation between parts along roadway and parts on locomotive.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards... all conditions of speed, weather, wear, oscillation, and shock. ...

  5. 49 CFR 236.505 - Proper operative relation between parts along roadway and parts on locomotive.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards... all conditions of speed, weather, wear, oscillation, and shock. ...

  6. 49 CFR 236.505 - Proper operative relation between parts along roadway and parts on locomotive.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards... all conditions of speed, weather, wear, oscillation, and shock. ...

  7. 49 CFR 236.505 - Proper operative relation between parts along roadway and parts on locomotive.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards... all conditions of speed, weather, wear, oscillation, and shock. ...

  8. 78 FR 75442 - Emergency Order Under 49 U.S.C. 20104 Establishing Requirements for Controlling Passenger Train...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-12-11

    ...- North Commuter Railroad Company (Metro-North) take certain actions to control passenger train speed at...-approved action plan that institutes modifications to its existing Automatic Train Control System or other... qualified railroad employees be present in the control compartment of Metro-North's passenger trains when...

  9. Fast rail corrugation detection based on texture filtering

    NASA Astrophysics Data System (ADS)

    Xiao, Jie; Lu, Kaixia

    2018-02-01

    The condition detection of rails in high-speed railway is one of the important means to ensure the safety of railway transportation. In order to replace the traditional manual inspection, save manpower and material resources, and improve the detection speed and accuracy, it is of great significance to develop a machine vision system for locating and identifying defects on rails automatically. Rail defects exhibit different properties and are divided into various categories related to the type and position of flaws on the rail. Several kinds of interrelated factors cause rail defects such as type of rail, construction conditions, and speed and/or frequency of trains using the rail. Rail corrugation is a particular kind of defects that produce an undulatory deformation on the rail heads. In high speed train, the corrugation induces harmful vibrations on wheels and its components and reduces the lifetime of rails. This type of defects should be detected to avoid rail fractures. In this paper, a novel method for fast rail corrugation detection based on texture filtering was proposed.

  10. AFETR Instrumentation Handbook

    DTIC Science & Technology

    1971-09-01

    of time. From this, vehicle velocity and acceleration can be computed. LOCATION Three Askanias are mobile and may be located at selected universal...Being mobile , these cinetheodolites may be placed for optimum launch coverage. Preprogrammed focusing is provided for automatic focus from 2000 and 8000...console trailer. IR (lead sulfide sensor ) Automatic Tracking System with 1 to 20 miles range. Elevation range: -10 deg to +90 deg Azimuth range: 350

  11. 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.

  12. Automatic attentional orienting to other people's gaze in schizophrenia.

    PubMed

    Langdon, Robyn; Seymour, Kiley; Williams, Tracey; Ward, Philip B

    2017-08-01

    Explicit tests of social cognition have revealed pervasive deficits in schizophrenia. Less is known of automatic social cognition in schizophrenia. We used a spatial orienting task to investigate automatic shifts of attention cued by another person's eye gaze in 29 patients and 28 controls. Central photographic images of a face with eyes shifted left or right, or looking straight ahead, preceded targets that appeared left or right of the cue. To examine automatic effects, cue direction was non-predictive of target location. Cue-target intervals were 100, 300, and 800 ms. In non-social control trials, arrows replaced eye-gaze cues. Both groups showed automatic attentional orienting indexed by faster reaction times (RTs) when arrows were congruent with target location across all cue-target intervals. Similar congruency effects were seen for eye-shift cues at 300 and 800 ms intervals, but patients showed significantly larger congruency effects at 800 ms, which were driven by delayed responses to incongruent target locations. At short 100-ms cue-target intervals, neither group showed faster RTs for congruent than for incongruent eye-shift cues, but patients were significantly slower to detect targets after direct-gaze cues. These findings conflict with previous studies using schematic line drawings of eye-shifts that have found automatic attentional orienting to be reduced in schizophrenia. Instead, our data indicate that patients display abnormalities in responding to gaze direction at various stages of gaze processing-reflected by a stronger preferential capture of attention by another person's direct eye contact at initial stages of gaze processing and difficulties disengaging from a gazed-at location once shared attention is established.

  13. Translations on USSR Military Affairs, Number 1280

    DTIC Science & Technology

    1977-06-17

    engineer, the conclusion was automatic : he is an undisciplined person. However, this idea was totally inconsistent with the image I had developed of V...pro- jectors, trainers, all sorts of simulators, automatic devices, and so forth. As is known, the technical devices for the mass training and...in the equipment and assemblies. In possessing "feedback," within a few seconds they can record and automatically analyze the actions of the

  14. On the automaticity of response inhibition in individuals with alcoholism.

    PubMed

    Noël, Xavier; Brevers, Damien; Hanak, Catherine; Kornreich, Charles; Verbanck, Paul; Verbruggen, Frederick

    2016-06-01

    Response inhibition is usually considered a hallmark of executive control. However, recent work indicates that stop performance can become associatively mediated ('automatic') over practice. This study investigated automatic response inhibition in sober and recently detoxified individuals with alcoholism.. We administered to forty recently detoxified alcoholics and forty healthy participants a modified stop-signal task that consisted of a training phase in which a subset of the stimuli was consistently associated with stopping or going, and a test phase in which this mapping was reversed. In the training phase, stop performance improved for the consistent stop stimuli, compared with control stimuli that were not associated with going or stopping. In the test phase, go performance tended to be impaired for old stop stimuli. Combined, these findings support the automatic inhibition hypothesis. Importantly, performance was similar in both groups, which indicates that automatic inhibitory control develops normally in individuals with alcoholism.. This finding is specific to individuals with alcoholism without other psychiatric disorders, which is rather atypical and prevents generalization. Personalized stimuli with a stronger affective content should be used in future studies. These results advance our understanding of behavioral inhibition in individuals with alcoholism. Furthermore, intact automatic inhibitory control may be an important element of successful cognitive remediation of addictive behaviors.. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Automatic data processing and analysis system for monitoring region around a planned nuclear power plant

    NASA Astrophysics Data System (ADS)

    Kortström, Jari; Tiira, Timo; Kaisko, Outi

    2016-03-01

    The Institute of Seismology of University of Helsinki is building a new local seismic network, called OBF network, around planned nuclear power plant in Northern Ostrobothnia, Finland. The network will consist of nine new stations and one existing station. The network should be dense enough to provide azimuthal coverage better than 180° and automatic detection capability down to ML -0.1 within a radius of 25 km from the site.The network construction work began in 2012 and the first four stations started operation at the end of May 2013. We applied an automatic seismic signal detection and event location system to a network of 13 stations consisting of the four new stations and the nearest stations of Finnish and Swedish national seismic networks. Between the end of May and December 2013 the network detected 214 events inside the predefined area of 50 km radius surrounding the planned nuclear power plant site. Of those detections, 120 were identified as spurious events. A total of 74 events were associated with known quarries and mining areas. The average location error, calculated as a difference between the announced location from environment authorities and companies and the automatic location, was 2.9 km. During the same time period eight earthquakes between magnitude range 0.1-1.0 occurred within the area. Of these seven could be automatically detected. The results from the phase 1 stations of the OBF network indicates that the planned network can achieve its goals.

  16. Automatic nipple detection on 3D images of an automated breast ultrasound system (ABUS)

    NASA Astrophysics Data System (ADS)

    Javanshir Moghaddam, Mandana; Tan, Tao; Karssemeijer, Nico; Platel, Bram

    2014-03-01

    Recent studies have demonstrated that applying Automated Breast Ultrasound in addition to mammography in women with dense breasts can lead to additional detection of small, early stage breast cancers which are occult in corresponding mammograms. In this paper, we proposed a fully automatic method for detecting the nipple location in 3D ultrasound breast images acquired from Automated Breast Ultrasound Systems. The nipple location is a valuable landmark to report the position of possible abnormalities in a breast or to guide image registration. To detect the nipple location, all images were normalized. Subsequently, features have been extracted in a multi scale approach and classification experiments were performed using a gentle boost classifier to identify the nipple location. The method was applied on a dataset of 100 patients with 294 different 3D ultrasound views from Siemens and U-systems acquisition systems. Our database is a representative sample of cases obtained in clinical practice by four medical centers. The automatic method could accurately locate the nipple in 90% of AP (Anterior-Posterior) views and in 79% of the other views.

  17. Basic forest cover mapping using digitized remote sensor data and automated data processing techniques

    NASA Technical Reports Server (NTRS)

    Coggeshall, M. E.; Hoffer, R. M.

    1973-01-01

    Remote sensing equipment and automatic data processing techniques were employed as aids in the institution of improved forest resource management methods. On the basis of automatically calculated statistics derived from manually selected training samples, the feature selection processor of LARSYS selected, upon consideration of various groups of the four available spectral regions, a series of channel combinations whose automatic classification performances (for six cover types, including both deciduous and coniferous forest) were tested, analyzed, and further compared with automatic classification results obtained from digitized color infrared photography.

  18. Automatic crown cover mapping to improve forest inventory

    Treesearch

    Claude Vidal; Jean-Guy Boureau; Nicolas Robert; Nicolas Py; Josiane Zerubia; Xavier Descombes; Guillaume Perrin

    2009-01-01

    To automatically analyze near infrared aerial photographs, the French National Institute for Research in Computer Science and Control developed together with the French National Forest Inventory (NFI) a method for automatic crown cover mapping. This method uses a Reverse Jump Monte Carlo Markov Chain algorithm to locate the crowns and describe those using ellipses or...

  19. An automatic multi-atlas prostate segmentation in MRI using a multiscale representation and a label fusion strategy

    NASA Astrophysics Data System (ADS)

    Álvarez, Charlens; Martínez, Fabio; Romero, Eduardo

    2015-01-01

    The pelvic magnetic Resonance images (MRI) are used in Prostate cancer radiotherapy (RT), a process which is part of the radiation planning. Modern protocols require a manual delineation, a tedious and variable activity that may take about 20 minutes per patient, even for trained experts. That considerable time is an important work ow burden in most radiological services. Automatic or semi-automatic methods might improve the efficiency by decreasing the measure times while conserving the required accuracy. This work presents a fully automatic atlas- based segmentation strategy that selects the more similar templates for a new MRI using a robust multi-scale SURF analysis. Then a new segmentation is achieved by a linear combination of the selected templates, which are previously non-rigidly registered towards the new image. The proposed method shows reliable segmentations, obtaining an average DICE Coefficient of 79%, when comparing with the expert manual segmentation, under a leave-one-out scheme with the training database.

  20. 49 CFR 236.516 - Power supply.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 4 2011-10-01 2011-10-01 false Power supply. 236.516 Section 236.516..., Train Control and Cab Signal Systems Standards § 236.516 Power supply. Automatic cab signal, train stop, or train control device hereafter installed shall operate from a separate or isolated power supply...

  1. 49 CFR 236.516 - Power supply.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Power supply. 236.516 Section 236.516..., Train Control and Cab Signal Systems Standards § 236.516 Power supply. Automatic cab signal, train stop, or train control device hereafter installed shall operate from a separate or isolated power supply...

  2. 49 CFR 236.590 - Pneumatic apparatus.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Pneumatic apparatus. 236.590 Section 236.590..., Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.590 Pneumatic apparatus. Automatic train stop, train control, or cab signal pneumatic apparatus shall be inspected, cleaned, and the...

  3. 49 CFR 236.590 - Pneumatic apparatus.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 4 2011-10-01 2011-10-01 false Pneumatic apparatus. 236.590 Section 236.590..., Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.590 Pneumatic apparatus. Automatic train stop, train control, or cab signal pneumatic apparatus shall be inspected, cleaned, and the...

  4. Development of ATC for High Speed and High Density Commuter Line

    NASA Astrophysics Data System (ADS)

    Okutani, Tamio; Nakamura, Nobuyuki; Araki, Hisato; Irie, Shouji; Osa, Hiroki; Sano, Minoru; Ikeda, Keigo; Ozawa, Hiroyuki

    A new ATC (Automatic Train Control) system has been developed with solutions to realize short train headway by assured braking utilizing digital data transmission via rails; the digital data for the ATP (Automatic Train Protection) function; and to achieve EMC features for both AC and DC sections. The DC section is of the unprecedented DC traction power supply system utilizing IGBT PWM converter at all DC substations. Within the AC section, train traction force is controlled by PWM converter/inverters. The carrier frequencies of the digital data signals and chopping frequency of PWM traction power converters on-board are decided via spectral analysis of noise up to degraded mode cases of equipment. Developed system was equipped to the Tukuba Express Line, new commuter line of Tokyo metropolitan area, and opened since Aug. 2005.

  5. Automatic speech recognition using a predictive echo state network classifier.

    PubMed

    Skowronski, Mark D; Harris, John G

    2007-04-01

    We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

  6. Aviation Careers Series: Airline Non-Flying Careers

    DOT National Transportation Integrated Search

    1996-01-01

    TRAVLINK demonstrated the use of Automatic Vehicle Location (AVL), ComputerAided dispatch (CAD), and Automatic Vehicle Identification (AVI) systems on Metropolitan Council Transit Operations (MCTO) buses in Minneapolis, Minnesota and western suburbs,...

  7. Validating automatic semantic annotation of anatomy in DICOM CT images

    NASA Astrophysics Data System (ADS)

    Pathak, Sayan D.; Criminisi, Antonio; Shotton, Jamie; White, Steve; Robertson, Duncan; Sparks, Bobbi; Munasinghe, Indeera; Siddiqui, Khan

    2011-03-01

    In the current health-care environment, the time available for physicians to browse patients' scans is shrinking due to the rapid increase in the sheer number of images. This is further aggravated by mounting pressure to become more productive in the face of decreasing reimbursement. Hence, there is an urgent need to deliver technology which enables faster and effortless navigation through sub-volume image visualizations. Annotating image regions with semantic labels such as those derived from the RADLEX ontology can vastly enhance image navigation and sub-volume visualization. This paper uses random regression forests for efficient, automatic detection and localization of anatomical structures within DICOM 3D CT scans. A regression forest is a collection of decision trees which are trained to achieve direct mapping from voxels to organ location and size in a single pass. This paper focuses on comparing automated labeling with expert-annotated ground-truth results on a database of 50 highly variable CT scans. Initial investigations show that regression forest derived localization errors are smaller and more robust than those achieved by state-of-the-art global registration approaches. The simplicity of the algorithm's context-rich visual features yield typical runtimes of less than 10 seconds for a 5123 voxel DICOM CT series on a single-threaded, single-core machine running multiple trees; each tree taking less than a second. Furthermore, qualitative evaluation demonstrates that using the detected organs' locations as index into the image volume improves the efficiency of the navigational workflow in all the CT studies.

  8. Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain.

    PubMed

    Ali, Anjum A; Dale, Anders M; Badea, Alexandra; Johnson, G Allan

    2005-08-15

    We present the automated segmentation of magnetic resonance microscopy (MRM) images of the C57BL/6J mouse brain into 21 neuroanatomical structures, including the ventricular system, corpus callosum, hippocampus, caudate putamen, inferior colliculus, internal capsule, globus pallidus, and substantia nigra. The segmentation algorithm operates on multispectral, three-dimensional (3D) MR data acquired at 90-microm isotropic resolution. Probabilistic information used in the segmentation is extracted from training datasets of T2-weighted, proton density-weighted, and diffusion-weighted acquisitions. Spatial information is employed in the form of prior probabilities of occurrence of a structure at a location (location priors) and the pairwise probabilities between structures (contextual priors). Validation using standard morphometry indices shows good consistency between automatically segmented and manually traced data. Results achieved in the mouse brain are comparable with those achieved in human brain studies using similar techniques. The segmentation algorithm shows excellent potential for routine morphological phenotyping of mouse models.

  9. An automatic tsunami warning system: TREMORS application in Europe

    NASA Astrophysics Data System (ADS)

    Reymond, D.; Robert, S.; Thomas, Y.; Schindelé, F.

    1996-03-01

    An integrated system named TREMORS (Tsunami Risk Evaluation through seismic Moment of a Real-time System) has been installed in EVORA station, in Portugal which has been affected by historical tsunamis. The system is based on a three component long period seismic station linked to a compatible IBM_PC with a specific software. The goals of this system are the followings: detect earthquake, locate them, compute their seismic moment, give a seismic warning. The warnings are based on the seismic moment estimation and all the processing are made automatically. The finality of this study is to check the quality of estimation of the main parameters of interest in a goal of tsunami warning: the location which depends of azimuth and distance, and at last the seismic moment, M 0, which controls the earthquake size. The sine qua non condition for obtaining an automatic location is that the 3 main seismic phases P, S, R must be visible. This study gives satisfying results (automatic analysis): ± 5° errors in azimuth and epicentral distance, and a standard deviation of less than a factor 2 for the seismic moment M 0.

  10. Automatic Geo-location Correction of Satellite Imagery

    DTIC Science & Technology

    2014-09-25

    orientation of large stereo satellite image blocks.," Int. Arch. Photogrammetry and Remote Sensing Spatial Inf. Sci, vol. 39, pp. 209-214, 2012. [6...Coefficient (RPC) model to represent both the internal and external orientation of a satellite image in one Automatic Geo-location Correction of Satellite...Applications of Digital Image Processing VI, vol. 432, 1983. [9] Edward M Mikhail, James S Bethel, and J C McGlone, Introduction to Modern Photogrammetry

  11. 49 CFR 236.553 - Seal, where required.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.553 Seal, where required. Seal shall be maintained on any device other than brake-pipe cut-out cock (double-heading cock), by...

  12. 49 CFR 236.553 - Seal, where required.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.553 Seal, where required. Seal shall be maintained on any device other than brake-pipe cut-out cock (double-heading cock), by...

  13. 49 CFR 236.567 - Restrictions imposed when device fails and/or is cut out en route.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... cut out en route. 236.567 Section 236.567 Transportation Other Regulations Relating to Transportation... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions...

  14. 49 CFR 236.515 - Visibility of cab signals.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Visibility of cab signals. 236.515 Section 236.515..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.515 Visibility of cab signals. The cab signals...

  15. 49 CFR 236.515 - Visibility of cab signals.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 4 2011-10-01 2011-10-01 false Visibility of cab signals. 236.515 Section 236.515..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.515 Visibility of cab signals. The cab signals...

  16. 49 CFR 236.588 - Periodic test.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 49 Transportation 4 2014-10-01 2014-10-01 false Periodic test. 236.588 Section 236.588..., Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.588 Periodic test. Except as provided in § 236.586, periodic test of the automatic train stop, train control, or cab signal apparatus...

  17. 49 CFR 236.588 - Periodic test.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Periodic test. 236.588 Section 236.588..., Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.588 Periodic test. Except as provided in § 236.586, periodic test of the automatic train stop, train control, or cab signal apparatus...

  18. 49 CFR 236.515 - Visibility of cab signals.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 49 Transportation 4 2014-10-01 2014-10-01 false Visibility of cab signals. 236.515 Section 236.515..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.515 Visibility of cab signals. The cab signals...

  19. 49 CFR 236.515 - Visibility of cab signals.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 49 Transportation 4 2012-10-01 2012-10-01 false Visibility of cab signals. 236.515 Section 236.515..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.515 Visibility of cab signals. The cab signals...

  20. 49 CFR 236.515 - Visibility of cab signals.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 49 Transportation 4 2013-10-01 2013-10-01 false Visibility of cab signals. 236.515 Section 236.515..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.515 Visibility of cab signals. The cab signals...

  1. Automatic segmentation of the puborectalis muscle in 3D transperineal ultrasound.

    PubMed

    van den Noort, Frieda; Grob, Anique T M; Slump, Cornelis H; van der Vaart, Carl H; van Stralen, Marijn

    2017-10-11

    The introduction of 3D analysis of the puborectalis muscle, for diagnostic purposes, into daily practice is hindered by the need for appropriate training of the observers. Automatic 3D segmentation of the puborectalis muscle in 3D transperineal ultrasound may aid to its adaption in clinical practice. A manual 3D segmentation protocol was developed to segment the puborectalis muscle. The data of 20 women, in their first trimester of pregnancy, was used to validate the reproducibility of this protocol. For automatic segmentation, active appearance models of the puborectalis muscle were developed. Those models were trained using manual segmentation data of 50 women. The performance of both manual and automatic segmentation was analyzed by measuring the overlap and distance between the segmentations. Also, the interclass correlation coefficients and their 95% confidence intervals were determined for mean echogenicity and volume of the puborectalis muscle. The ICC values of mean echogenicity (0.968-0.991) and volume (0.626-0.910) are good to very good for both automatic and manual segmentation. The results of overlap and distance for manual segmentation are as expected, showing only few pixels (2-3) mismatch on average and a reasonable overlap. Based on overlap and distance 5 mismatches in automatic segmentation were detected, resulting in an automatic segmentation a success rate of 90%. In conclusion, this study presents a reliable manual and automatic 3D segmentation of the puborectalis muscle. This will facilitate future investigation of the puborectalis muscle. It also allows for reliable measurements of clinically potentially valuable parameters like mean echogenicity. This article is protected by copyright. All rights reserved.

  2. Realistic radio communications in pilot simulator training

    DOT National Transportation Integrated Search

    2000-12-01

    This report summarizes the first-year efforts of assessing the requirement and feasibility of simulating radio communication automatically. A review of the training and crew resource/task management literature showed both practical and theoretical su...

  3. An automatically-shifted two-speed transaxle system for an electric vehicle

    NASA Technical Reports Server (NTRS)

    Gordon, H. S.; Hassman, G. V.

    1980-01-01

    An automatic shifting scheme for a two speed transaxle for use with an electric vehicle propulsion system is described. The transaxle system was to be installed in an instrumented laboratory propulsion system of an ac electric vehicle drive train. The transaxle which had been fabricated is also described.

  4. 14 CFR 60.27 - Automatic loss of qualification and procedures for restoration of qualification.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... 14 Aeronautics and Space 2 2014-01-01 2014-01-01 false Automatic loss of qualification and procedures for restoration of qualification. 60.27 Section 60.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) AIRMEN FLIGHT SIMULATION TRAINING DEVICE INITIAL AND...

  5. 14 CFR 60.27 - Automatic loss of qualification and procedures for restoration of qualification.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... 14 Aeronautics and Space 2 2013-01-01 2013-01-01 false Automatic loss of qualification and procedures for restoration of qualification. 60.27 Section 60.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) AIRMEN FLIGHT SIMULATION TRAINING DEVICE INITIAL AND...

  6. 14 CFR 60.27 - Automatic loss of qualification and procedures for restoration of qualification.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... 14 Aeronautics and Space 2 2011-01-01 2011-01-01 false Automatic loss of qualification and procedures for restoration of qualification. 60.27 Section 60.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) AIRMEN FLIGHT SIMULATION TRAINING DEVICE INITIAL AND...

  7. 14 CFR 60.27 - Automatic loss of qualification and procedures for restoration of qualification.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 14 Aeronautics and Space 2 2010-01-01 2010-01-01 false Automatic loss of qualification and procedures for restoration of qualification. 60.27 Section 60.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) AIRMEN FLIGHT SIMULATION TRAINING DEVICE INITIAL AND...

  8. 14 CFR 60.27 - Automatic loss of qualification and procedures for restoration of qualification.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... 14 Aeronautics and Space 2 2012-01-01 2012-01-01 false Automatic loss of qualification and procedures for restoration of qualification. 60.27 Section 60.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) AIRMEN FLIGHT SIMULATION TRAINING DEVICE INITIAL AND...

  9. A System Approach to Navy Medical Education and Training. Appendix 40. Competency Curricula for Pharmacy Assistant and Pharmacy Technician.

    DTIC Science & Technology

    1974-08-31

    Procedures and techniques for compounding syrups, collodion, waters, spirits, liniments Use and maintenance of automatic liquid prepacker IIi [ o [ I... liniments , glycerites, elixirs Use and maintenance of automatic liquid prepacker 31 J ] Competency: PHARMACY TECHNICIAN (PHT) Unit II: Compounding

  10. Contextual Modulation of Mirror and Countermirror Sensorimotor Associations

    ERIC Educational Resources Information Center

    Cook, Richard; Dickinson, Anthony; Heyes, Cecilia

    2012-01-01

    Automatic imitation--the unintended copying of observed actions--is thought to be a behavioral product of the mirror neuron system (MNS). Evidence that the MNS develops through associative learning comes from previous research showing that automatic imitation is attenuated by countermirror training, in which the observation of one action is paired…

  11. Acquisition of Automatic Imitation Is Sensitive to Sensorimotor Contingency

    ERIC Educational Resources Information Center

    Cook, Richard; Press, Clare; Dickinson, Anthony; Heyes, Cecilia

    2010-01-01

    The associative sequence learning model proposes that the development of the mirror system depends on the same mechanisms of associative learning that mediate Pavlovian and instrumental conditioning. To test this model, two experiments used the reduction of automatic imitation through incompatible sensorimotor training to assess whether mirror…

  12. Manually locating physical and virtual reality objects.

    PubMed

    Chen, Karen B; Kimmel, Ryan A; Bartholomew, Aaron; Ponto, Kevin; Gleicher, Michael L; Radwin, Robert G

    2014-09-01

    In this study, we compared how users locate physical and equivalent three-dimensional images of virtual objects in a cave automatic virtual environment (CAVE) using the hand to examine how human performance (accuracy, time, and approach) is affected by object size, location, and distance. Virtual reality (VR) offers the promise to flexibly simulate arbitrary environments for studying human performance. Previously, VR researchers primarily considered differences between virtual and physical distance estimation rather than reaching for close-up objects. Fourteen participants completed manual targeting tasks that involved reaching for corners on equivalent physical and virtual boxes of three different sizes. Predicted errors were calculated from a geometric model based on user interpupillary distance, eye location, distance from the eyes to the projector screen, and object. Users were 1.64 times less accurate (p < .001) and spent 1.49 times more time (p = .01) targeting virtual versus physical box corners using the hands. Predicted virtual targeting errors were on average 1.53 times (p < .05) greater than the observed errors for farther virtual targets but not significantly different for close-up virtual targets. Target size, location, and distance, in addition to binocular disparity, affected virtual object targeting inaccuracy. Observed virtual box inaccuracy was less than predicted for farther locations, suggesting possible influence of cues other than binocular vision. Human physical interaction with objects in VR for simulation, training, and prototyping involving reaching and manually handling virtual objects in a CAVE are more accurate than predicted when locating farther objects.

  13. Experience of automation failures in training: effects on trust, automation bias, complacency and performance.

    PubMed

    Sauer, Juergen; Chavaillaz, Alain; Wastell, David

    2016-06-01

    This work examined the effects of operators' exposure to various types of automation failures in training. Forty-five participants were trained for 3.5 h on a simulated process control environment. During training, participants either experienced a fully reliable, automatic fault repair facility (i.e. faults detected and correctly diagnosed), a misdiagnosis-prone one (i.e. faults detected but not correctly diagnosed) or a miss-prone one (i.e. faults not detected). One week after training, participants were tested for 3 h, experiencing two types of automation failures (misdiagnosis, miss). The results showed that automation bias was very high when operators trained on miss-prone automation encountered a failure of the diagnostic system. Operator errors resulting from automation bias were much higher when automation misdiagnosed a fault than when it missed one. Differences in trust levels that were instilled by the different training experiences disappeared during the testing session. Practitioner Summary: The experience of automation failures during training has some consequences. A greater potential for operator errors may be expected when an automatic system failed to diagnose a fault than when it failed to detect one.

  14. Automatic methods of the processing of data from track detectors on the basis of the PAVICOM facility

    NASA Astrophysics Data System (ADS)

    Aleksandrov, A. B.; Goncharova, L. A.; Davydov, D. A.; Publichenko, P. A.; Roganova, T. M.; Polukhina, N. G.; Feinberg, E. L.

    2007-02-01

    New automatic methods essentially simplify and increase the rate of the processing of data from track detectors. This provides a possibility of processing large data arrays and considerably improves their statistical significance. This fact predetermines the development of new experiments which plan to use large-volume targets, large-area emulsion, and solid-state track detectors [1]. In this regard, the problem of training qualified physicists who are capable of operating modern automatic equipment is very important. Annually, about ten Moscow students master the new methods, working at the Lebedev Physical Institute at the PAVICOM facility [2 4]. Most students specializing in high-energy physics are only given an idea of archaic manual methods of the processing of data from track detectors. In 2005, on the basis of the PAVICOM facility and the physicstraining course of Moscow State University, a new training work was prepared. This work is devoted to the determination of the energy of neutrons passing through a nuclear emulsion. It provides the possibility of acquiring basic practical skills of the processing of data from track detectors using automatic equipment and can be included in the educational process of students of any physical faculty. Those who have mastered the methods of automatic data processing in a simple and pictorial example of track detectors will be able to apply their knowledge in various fields of science and technique. Formulation of training works for pregraduate and graduate students is a new additional aspect of application of the PAVICOM facility described earlier in [4].

  15. Patient-Specific Seizure Detection in Long-Term EEG Using Signal-Derived Empirical Mode Decomposition (EMD)-based Dictionary Approach.

    PubMed

    Kaleem, Muhammad; Gurve, Dharmendra; Guergachi, Aziz; Krishnan, Sridhar

    2018-06-25

    The objective of the work described in this paper is development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings. Approach: A novel patient-specific seizure detection approach based on signal-derived Empirical Mode Decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches. Main results: The performance of the proposed approach is validated using the CHB-MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability of the approach for seizure detection in long-term multi-channel EEG recordings is discussed. Significance: The proposed approach describes a computationally efficient method for automatic seizure detection in long-term multi-channel EEG recordings. The method does not rely on hand-engineered features, as are required in traditional approaches. Furthermore, the approach is suitable for scenarios where the dictionary once formed and trained can be used for automatic seizure detection of newly recorded data, making the approach suitable for long-term multi-channel EEG recordings. © 2018 IOP Publishing Ltd.

  16. Automatic detection and visualisation of MEG ripple oscillations in epilepsy.

    PubMed

    van Klink, Nicole; van Rosmalen, Frank; Nenonen, Jukka; Burnos, Sergey; Helle, Liisa; Taulu, Samu; Furlong, Paul Lawrence; Zijlmans, Maeike; Hillebrand, Arjan

    2017-01-01

    High frequency oscillations (HFOs, 80-500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80-250 Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~ 2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting.

  17. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning.

    PubMed

    Norouzzadeh, Mohammad Sadegh; Nguyen, Anh; Kosmala, Margaret; Swanson, Alexandra; Palmer, Meredith S; Packer, Craig; Clune, Jeff

    2018-06-19

    Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into "big data" sciences. Motion-sensor "camera traps" enable collecting wildlife pictures inexpensively, unobtrusively, and frequently. However, extracting information from these pictures remains an expensive, time-consuming, manual task. We demonstrate that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence. We train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2 million-image Snapshot Serengeti dataset. Our deep neural networks automatically identify animals with >93.8% accuracy, and we expect that number to improve rapidly in years to come. More importantly, if our system classifies only images it is confident about, our system can automate animal identification for 99.3% of the data while still performing at the same 96.6% accuracy as that of crowdsourced teams of human volunteers, saving >8.4 y (i.e., >17,000 h at 40 h/wk) of human labeling effort on this 3.2 million-image dataset. Those efficiency gains highlight the importance of using deep neural networks to automate data extraction from camera-trap images, reducing a roadblock for this widely used technology. Our results suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild. Copyright © 2018 the Author(s). Published by PNAS.

  18. An Algorithm for Automatically Modifying Train Crew Schedule

    NASA Astrophysics Data System (ADS)

    Takahashi, Satoru; Kataoka, Kenji; Kojima, Teruhito; Asami, Masayuki

    Once the break-down of the train schedule occurs, the crew schedule as well as the train schedule has to be modified as quickly as possible to restore them. In this paper, we propose an algorithm for automatically modifying a crew schedule that takes all constraints into consideration, presenting a model of the combined problem of crews and trains. The proposed algorithm builds an initial solution by relaxing some of the constraint conditions, and then uses a Taboo-search method to revise this solution in order to minimize the degree of constraint violation resulting from these relaxed conditions. Then we show not only that the algorithm can generate a constraint satisfaction solution, but also that the solution will satisfy the experts. That is, we show the proposed algorithm is capable of producing a usable solution in a short time by applying to actual cases of train-schedule break-down, and that the solution is at least as good as those produced manually, by comparing the both solutions with several point of view.

  19. Dynamic simulation of train derailments

    DOT National Transportation Integrated Search

    2006-11-05

    This paper describes a planar rigid-body model to examine the gross motions of rail cars in a train derailment. The model is implemented using a commercial software package called ADAMS (Automatic Dynamic Analysis of Mechanical Systems). The results ...

  20. Automatic control of negative emotions: evidence that structured practice increases the efficiency of emotion regulation.

    PubMed

    Christou-Champi, Spyros; Farrow, Tom F D; Webb, Thomas L

    2015-01-01

    Emotion regulation (ER) is vital to everyday functioning. However, the effortful nature of many forms of ER may lead to regulation being inefficient and potentially ineffective. The present research examined whether structured practice could increase the efficiency of ER. During three training sessions, comprising a total of 150 training trials, participants were presented with negatively valenced images and asked either to "attend" (control condition) or "reappraise" (ER condition). A further group of participants did not participate in training but only completed follow-up measures. Practice increased the efficiency of ER as indexed by decreased time required to regulate emotions and increased heart rate variability (HRV). Furthermore, participants in the ER condition spontaneously regulated their negative emotions two weeks later and reported being more habitual in their use of ER. These findings indicate that structured practice can facilitate the automatic control of negative emotions and that these effects persist beyond training.

  1. Using deep learning to quantify the beauty of outdoor places

    PubMed Central

    2017-01-01

    Beautiful outdoor locations are protected by governments and have recently been shown to be associated with better health. But what makes an outdoor space beautiful? Does a beautiful outdoor location differ from an outdoor location that is simply natural? Here, we explore whether ratings of over 200 000 images of Great Britain from the online game Scenic-Or-Not, combined with hundreds of image features extracted using the Places Convolutional Neural Network, might help us understand what beautiful outdoor spaces are composed of. We discover that, as well as natural features such as ‘Coast’, ‘Mountain’ and ‘Canal Natural’, man-made structures such as ‘Tower’, ‘Castle’ and ‘Viaduct’ lead to places being considered more scenic. Importantly, while scenes containing ‘Trees’ tend to rate highly, places containing more bland natural green features such as ‘Grass’ and ‘Athletic Fields’ are considered less scenic. We also find that a neural network can be trained to automatically identify scenic places, and that this network highlights both natural and built locations. Our findings demonstrate how online data combined with neural networks can provide a deeper understanding of what environments we might find beautiful and offer quantitative insights for policymakers charged with design and protection of our built and natural environments. PMID:28791142

  2. Digital Map Requirements For Automatic Vehicle Location

    DOT National Transportation Integrated Search

    1998-12-01

    New Jersey Transit (NJT) is currently investigating acquisition of an automated vehicle locator (AVL) system. The purpose of the AVL system is to monitor the location of buses. Knowing the location of a bus enables the agency to manage the bus fleet ...

  3. Artificial intelligence in sports on the example of weight training.

    PubMed

    Novatchkov, Hristo; Baca, Arnold

    2013-01-01

    The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key pointsArtificial intelligence is a promising field for sport-related analysis.Implementations integrating pattern recognition techniques enable the automatic evaluation of data measurements.Artificial neural networks applied for the analysis of weight training data show good performance and high classification rates.

  4. Artificial Intelligence in Sports on the Example of Weight Training

    PubMed Central

    Novatchkov, Hristo; Baca, Arnold

    2013-01-01

    The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key points Artificial intelligence is a promising field for sport-related analysis. Implementations integrating pattern recognition techniques enable the automatic evaluation of data measurements. Artificial neural networks applied for the analysis of weight training data show good performance and high classification rates. PMID:24149722

  5. A Machine Vision System for Automatically Grading Hardwood Lumber - (Proceedings)

    Treesearch

    Richard W. Conners; Tai-Hoon Cho; Chong T. Ng; Thomas H. Drayer; Joe G. Tront; Philip A. Araman; Robert L. Brisbon

    1990-01-01

    Any automatic system for grading hardwood lumber can conceptually be divided into two components. One of these is a machine vision system for locating and identifying grading defects. The other is an automatic grading program that accepts as input the output of the machine vision system and, based on these data, determines the grade of a board. The progress that has...

  6. Improved nutrient removal using in situ continuous on-line sensors with short response time.

    PubMed

    Ingildsen, P; Wendelboe, H

    2003-01-01

    Nutrient sensors that can be located directly in the activated sludge processes are gaining in number at wastewater treatment plants. The in situ location of the sensors means that they can be located close to the processes that they aim to control and hence are perfectly suited for automatic process control. Compared to the location of automatic analysers in the effluent from the sedimentation reactors the in situ location means a large reduction in the response time. The settlers typically work as a first-order delay on the signal with a retention time in the range of 4-12 hours depending on the size of the settlers. Automatic process control of the nitrogen and phosphorus removal processes means that considerable improvements in the performance of aeration, internal recirculation, carbon dosage and phosphate precipitation dosage can be reached by using a simple control structure as well as simple PID controllers. The performance improvements can be seen in decreased energy and chemicals consumption and less variation in effluent concentrations of ammonium, total nitrogen and phosphate. Simple control schemes are demonstrated for the pre-denitrification and the post precipitation system by means of full-scale plant experiments and model simulations.

  7. Introduction To ITS/CVO Participant Manual, Course 1

    DOT National Transportation Integrated Search

    1999-08-01

    WEIGH-IN-MOTION OR WIM, COMMERCIAL VEHICLE INFORMATION SYSTEMS AND NETWORK OR CVISN, AUTOMATIC VEHICLE INDENTIFICATION OR AVI, AUTOMATIC VEHICLE LOCATION OR AVL, ELECTRONIC DATA INTERCHANGE OR EDI, GLOCAL POSITIONING SYSTEM OR GPS, INTERNET OR WORD W...

  8. Automatic ground control point recognition with parallel associative memory

    NASA Technical Reports Server (NTRS)

    Al-Tahir, Raid; Toth, Charles K.; Schenck, Anton F.

    1990-01-01

    The basic principle of the associative memory is to match the unknown input pattern against a stored training set, and responding with the 'closest match' and the corresponding label. Generally, an associative memory system requires two preparatory steps: selecting attributes of the pattern class, and training the system by associating patterns with labels. Experimental results gained from using Parallel Associative Memory are presented. The primary concern is an automatic search for ground control points in aerial photographs. Synthetic patterns are tested followed by real data. The results are encouraging as a relatively high level of correct matches is reached.

  9. Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations

    PubMed Central

    Ribeiro, Sidarta; Pereira, Danillo R.; Papa, João P.; de Albuquerque, Victor Hugo C.

    2016-01-01

    Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available. PMID:27654941

  10. Image segmentation using local shape and gray-level appearance models

    NASA Astrophysics Data System (ADS)

    Seghers, Dieter; Loeckx, Dirk; Maes, Frederik; Suetens, Paul

    2006-03-01

    A new generic model-based segmentation scheme is presented, which can be trained from examples akin to the Active Shape Model (ASM) approach in order to acquire knowledge about the shape to be segmented and about the gray-level appearance of the object in the image. Because in the ASM approach the intensity and shape models are typically applied alternately during optimizing as first an optimal target location is selected for each landmark separately based on local gray-level appearance information only to which the shape model is fitted subsequently, the ASM may be misled in case of wrongly selected landmark locations. Instead, the proposed approach optimizes for shape and intensity characteristics simultaneously. Local gray-level appearance information at the landmark points extracted from feature images is used to automatically detect a number of plausible candidate locations for each landmark. The shape information is described by multiple landmark-specific statistical models that capture local dependencies between adjacent landmarks on the shape. The shape and intensity models are combined in a single cost function that is optimized non-iteratively using dynamic programming which allows to find the optimal landmark positions using combined shape and intensity information, without the need for initialization.

  11. Development of a novel constellation based landmark detection algorithm

    NASA Astrophysics Data System (ADS)

    Ghayoor, Ali; Vaidya, Jatin G.; Johnson, Hans J.

    2013-03-01

    Anatomical landmarks such as the anterior commissure (AC) and posterior commissure (PC) are commonly used by researchers for co-registration of images. In this paper, we present a novel, automated approach for landmark detection that combines morphometric constraining and statistical shape models to provide accurate estimation of landmark points. This method is made robust to large rotations in initial head orientation by extracting extra information of the eye centers using a radial Hough transform and exploiting the centroid of head mass (CM) using a novel estimation approach. To evaluate the effectiveness of this method, the algorithm is trained on a set of 20 images with manually selected landmarks, and a test dataset is used to compare the automatically detected against the manually detected landmark locations of the AC, PC, midbrain-pons junction (MPJ), and fourth ventricle notch (VN4). The results show that the proposed method is accurate as the average error between the automatically and manually labeled landmark points is less than 1 mm. Also, the algorithm is highly robust as it was successfully run on a large dataset that included different kinds of images with various orientation, spacing, and origin.

  12. A convolutional neural network for intracranial hemorrhage detection in non-contrast CT

    NASA Astrophysics Data System (ADS)

    Patel, Ajay; Manniesing, Rashindra

    2018-02-01

    The assessment of the presence of intracranial hemorrhage is a crucial step in the work-up of patients requiring emergency care. Fast and accurate detection of intracranial hemorrhage can aid treating physicians by not only expediting and guiding diagnosis, but also supporting choices for secondary imaging, treatment and intervention. However, the automatic detection of intracranial hemorrhage is complicated by the variation in appearance on non-contrast CT images as a result of differences in etiology and location. We propose a method using a convolutional neural network (CNN) for the automatic detection of intracranial hemorrhage. The method is trained on a dataset comprised of cerebral CT studies for which the presence of hemorrhage has been labeled for each axial slice. A separate test dataset of 20 images is used for quantitative evaluation and shows a sensitivity of 0.87, specificity of 0.97 and accuracy of 0.95. The average processing time for a single three-dimensional (3D) CT volume was 2.7 seconds. The proposed method is capable of fast and automated detection of intracranial hemorrhages in non-contrast CT without being limited to a specific subtype of pathology.

  13. Application of automatic vehicle location in law enforcement: An introductory planning guide

    NASA Technical Reports Server (NTRS)

    Hansen, G. R.; Leflang, W. G.

    1976-01-01

    A set of planning guidelines for the application of automatic vehicle location (AVL) to law enforcement is presented. Some essential characteristics and applications of AVL are outlined; systems in the operational or planning phases are discussed. Requirements analysis, system concept design, implementation planning, and performance and cost modeling are described and demonstrated with numerous examples. A detailed description of a typical law enforcement AVL system, and a list of vendor sources are given in appendixes.

  14. Method for stitching microbial images using a neural network

    NASA Astrophysics Data System (ADS)

    Semenishchev, E. A.; Voronin, V. V.; Marchuk, V. I.; Tolstova, I. V.

    2017-05-01

    Currently an analog microscope has a wide distribution in the following fields: medicine, animal husbandry, monitoring technological objects, oceanography, agriculture and others. Automatic method is preferred because it will greatly reduce the work involved. Stepper motors are used to move the microscope slide and allow to adjust the focus in semi-automatic or automatic mode view with transfer images of microbiological objects from the eyepiece of the microscope to the computer screen. Scene analysis allows to locate regions with pronounced abnormalities for focusing specialist attention. This paper considers the method for stitching microbial images, obtained of semi-automatic microscope. The method allows to keep the boundaries of objects located in the area of capturing optical systems. Objects searching are based on the analysis of the data located in the area of the camera view. We propose to use a neural network for the boundaries searching. The stitching image boundary is held of the analysis borders of the objects. To auto focus, we use the criterion of the minimum thickness of the line boundaries of object. Analysis produced the object located in the focal axis of the camera. We use method of recovery of objects borders and projective transform for the boundary of objects which are based on shifted relative to the focal axis. Several examples considered in this paper show the effectiveness of the proposed approach on several test images.

  15. Analysis of biases from parallel observations of co-located manual and automatic weather stations in Indonesia

    NASA Astrophysics Data System (ADS)

    Sopaheluwakan, Ardhasena; Fajariana, Yuaning; Satyaningsih, Ratna; Aprilina, Kharisma; Astuti Nuraini, Tri; Ummiyatul Badriyah, Imelda; Lukita Sari, Dyah; Haryoko, Urip

    2017-04-01

    Inhomogeneities are often found in long records of climate data. These can occur because of various reasons, among others such as relocation of observation site, changes in observation method, and the transition to automated instruments. Changes to these automated systems are inevitable, and it is taking place worldwide in many of the National Meteorological Services. However this shift of observational practice must be done cautiously and a sufficient period of parallel observation of co-located manual and automated systems should take place as suggested by the World Meteorological Organization. With a sufficient parallel observation period, biases between the two systems can be analyzed. In this study we analyze the biases of a yearlong parallel observation of manual and automatic weather stations in 30 locations in Indonesia. The location of the sites spans from east to west of approximately 45 longitudinal degrees covering different climate characteristics and geographical settings. We study measurements taken by both sensors for temperature and rainfall parameters. We found that the biases from both systems vary from place to place and are more dependent to the setting of the instrument rather than to the climatic and geographical factors. For instance, daytime observations of the automatic weather stations are found to be consistently higher than the manual observation, and vice versa night time observations of the automatic weather stations are lower than the manual observation.

  16. Developing and Evaluating an Oral Skills Training Website Supported by Automatic Speech Recognition Technology

    ERIC Educational Resources Information Center

    Chen, Howard Hao-Jan

    2011-01-01

    Oral communication ability has become increasingly important to many EFL students. Several commercial software programs based on automatic speech recognition (ASR) technologies are available but their prices are not affordable for many students. This paper will demonstrate how the Microsoft Speech Application Software Development Kit (SASDK), a…

  17. Model-Based Reasoning in Humans Becomes Automatic with Training.

    PubMed

    Economides, Marcos; Kurth-Nelson, Zeb; Lübbert, Annika; Guitart-Masip, Marc; Dolan, Raymond J

    2015-09-01

    Model-based and model-free reinforcement learning (RL) have been suggested as algorithmic realizations of goal-directed and habitual action strategies. Model-based RL is more flexible than model-free but requires sophisticated calculations using a learnt model of the world. This has led model-based RL to be identified with slow, deliberative processing, and model-free RL with fast, automatic processing. In support of this distinction, it has recently been shown that model-based reasoning is impaired by placing subjects under cognitive load--a hallmark of non-automaticity. Here, using the same task, we show that cognitive load does not impair model-based reasoning if subjects receive prior training on the task. This finding is replicated across two studies and a variety of analysis methods. Thus, task familiarity permits use of model-based reasoning in parallel with other cognitive demands. The ability to deploy model-based reasoning in an automatic, parallelizable fashion has widespread theoretical implications, particularly for the learning and execution of complex behaviors. It also suggests a range of important failure modes in psychiatric disorders.

  18. Lead Exposure in Military Outdoor Firing Ranges.

    PubMed

    Greenberg, Nili; Frimer, Ron; Meyer, Robert; Derazne, Estella; Chodick, Gabrial

    2016-09-01

    Several studies have reported significant airborne lead exposures during training at indoor firing ranges. Scarce attention has been given to airborne lead exposures in outdoor shooting ranges with automatic weapons. To assess the prevalence and magnitude of airborne and blood lead levels (BLL) among firing instructors and shooters in military outdoor ranges. Exposure assessment, for both trainees and instructors, included airborne and BLL during basic and advanced training at outdoor firing ranges. Personal airborne samples were collected in both day and night shooting during both training periods. During basic training, there is 95% likelihood that up to 25% of instructors and 99% likelihood that up to 5% of trainees might be exposed above the action level (AL) (25 μg/m(3)). During advanced training, there is 90% likelihood that 10% of instructors and 99% likelihood that up to 10% of trainees might be exposed above the AL. Military personnel participating in automatic weapon marksmanship training can be exposed to considerable levels of airborne lead during outdoor firing range training. As a result, the Israel Defense Force Medical Corp has classified firing range instructors as workers that require periodic medical examinations. Reprint & Copyright © 2016 Association of Military Surgeons of the U.S.

  19. Towards Autonomous Agriculture: Automatic Ground Detection Using Trinocular Stereovision

    PubMed Central

    Reina, Giulio; Milella, Annalisa

    2012-01-01

    Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this paper, a self-learning framework is proposed to automatically train a ground classifier for scene interpretation and autonomous navigation based on multi-baseline stereovision. The use of rich 3D data is emphasized where the sensor output includes range and color information of the surrounding environment. Two distinct classifiers are presented, one based on geometric data that can detect the broad class of ground and one based on color data that can further segment ground into subclasses. The geometry-based classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate geometric appearance of 3D stereo-generated data with class labels. Then, it makes predictions based on past observations. It serves as well to provide training labels to the color-based classifier. Once trained, the color-based classifier is able to recognize similar terrain classes in stereo imagery. The system is continuously updated online using the latest stereo readings, thus making it feasible for long range and long duration navigation, over changing environments. Experimental results, obtained with a tractor test platform operating in a rural environment, are presented to validate this approach, showing an average classification precision and recall of 91.0% and 77.3%, respectively.

  20. Understanding ITS/CVO Technology Applications, Student Manual, Course 3

    DOT National Transportation Integrated Search

    1999-01-01

    WEIGHT-IN-MOTION OR WIM, COMMERCIAL VEHICLE INFORMATION SYSTEMS AND NETWORK OR CVISN, AUTOMATIC VEHICLE IDENTIFICATION OR AVI, AUTOMATIC LOCATION OR AVL, ELECTRONIC DATA INTERCHANGE OR EDI, GLOBAL POSITIONING SYSTEM OR GPS, INTERNET OR WORLD WIDE WEB...

  1. Automatic interpretation of ERTS data for forest management

    NASA Technical Reports Server (NTRS)

    Kirvida, L.; Johnson, G. R.

    1973-01-01

    Automatic stratification of forested land from ERTS-1 data provides a valuable tool for resource management. The results are useful for wood product yield estimates, recreation and wild life management, forest inventory and forest condition monitoring. Automatic procedures based on both multi-spectral and spatial features are evaluated. With five classes, training and testing on the same samples, classification accuracy of 74% was achieved using the MSS multispectral features. When adding texture computed from 8 x 8 arrays, classification accuracy of 99% was obtained.

  2. A Machine Vision System for Automatically Grading Hardwood Lumber - (Industrial Metrology)

    Treesearch

    Richard W. Conners; Tai-Hoon Cho; Chong T. Ng; Thomas T. Drayer; Philip A. Araman; Robert L. Brisbon

    1992-01-01

    Any automatic system for grading hardwood lumber can conceptually be divided into two components. One of these is a machine vision system for locating and identifying grading defects. The other is an automatic grading program that accepts as input the output of the machine vision system and, based on these data, determines the grade of a board. The progress that has...

  3. Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

    PubMed

    Wang, Jun Yi; Ngo, Michael M; Hessl, David; Hagerman, Randi J; Rivera, Susan M

    2016-01-01

    Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well.

  4. Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem

    PubMed Central

    Wang, Jun Yi; Ngo, Michael M.; Hessl, David; Hagerman, Randi J.; Rivera, Susan M.

    2016-01-01

    Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer’s segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well. PMID:27213683

  5. 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.

  6. Automatic vehicle location system

    NASA Technical Reports Server (NTRS)

    Hansen, G. R., Jr. (Inventor)

    1973-01-01

    An automatic vehicle detection system is disclosed, in which each vehicle whose location is to be detected carries active means which interact with passive elements at each location to be identified. The passive elements comprise a plurality of passive loops arranged in a sequence along the travel direction. Each of the loops is tuned to a chosen frequency so that the sequence of the frequencies defines the location code. As the vehicle traverses the sequence of the loops as it passes over each loop, signals only at the frequency of the loop being passed over are coupled from a vehicle transmitter to a vehicle receiver. The frequencies of the received signals in the receiver produce outputs which together represent a code of the traversed location. The code location is defined by a painted pattern which reflects light to a vehicle carried detector whose output is used to derive the code defined by the pattern.

  7. 49 CFR 236.744 - Element, roadway.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 49 Transportation 4 2013-10-01 2013-10-01 false Element, roadway. 236.744 Section 236.744 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Element, roadway. That portion of the roadway apparatus of automatic train stop, train control, or cab...

  8. 49 CFR 236.744 - Element, roadway.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 4 2011-10-01 2011-10-01 false Element, roadway. 236.744 Section 236.744 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Element, roadway. That portion of the roadway apparatus of automatic train stop, train control, or cab...

  9. 49 CFR 236.744 - Element, roadway.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 49 Transportation 4 2014-10-01 2014-10-01 false Element, roadway. 236.744 Section 236.744 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Element, roadway. That portion of the roadway apparatus of automatic train stop, train control, or cab...

  10. 49 CFR 236.744 - Element, roadway.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 49 Transportation 4 2012-10-01 2012-10-01 false Element, roadway. 236.744 Section 236.744 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Element, roadway. That portion of the roadway apparatus of automatic train stop, train control, or cab...

  11. Quality of Life Effects of Automatic External Defibrillators in the Home: Results from the Home Automatic External Defibrillator Trial (HAT)

    PubMed Central

    Mark, Daniel B.; Anstrom, Kevin J.; McNulty, Steven E.; Flaker, Greg C.; Tonkin, Andrew M.; Smith, Warren M.; Toff, William D.; Dorian, Paul; Clapp-Channing, Nancy E.; Anderson, Jill; Johnson, George; Schron, Eleanor B.; Poole, Jeanne E.; Lee, Kerry L.; Bardy, Gust H.

    2010-01-01

    Background Public access automatic external defibrillators (AEDs) can save lives, but most deaths from out-of-hospital sudden cardiac arrest occur at home. The Home Automatic External Defibrillator Trial (HAT) found no survival advantage for adding a home AED to cardiopulmonary resuscitation (CPR) training for 7001 patients with a prior anterior wall myocardial infarction. Quality of life (QOL) outcomes for both the patient and spouse/companion were secondary endpoints. Methods A subset of 1007 study patients and their spouse/companions was randomly selected for ascertainment of QOL by structured interview at baseline and 12 and 24 months following enrollment. The primary QOL measures were the Medical Outcomes Study 36-Item Short-Form (SF-36) psychological well-being (reflecting anxiety and depression) and vitality (reflecting energy and fatigue) subscales. Results For patients and spouse/companions, the psychological well-being and vitality scales did not differ significantly between those randomly assigned an AED plus CPR training and controls who received CPR training only. None of the other QOL measures collected showed a clinically and statistically significant difference between treatment groups. Patients in the AED group were more likely to report being extremely or quite a bit reassured by their treatment assignment. Spouse/companions in the AED group reported being less often nervous about the possibility of using AED/CPR treatment than those in the CPR group. Conclusions Adding access to a home AED to CPR training did not affect quality of life either for patients with a prior anterior myocardial infarction or their spouse/companion but did provide more reassurance to the patients without increasing anxiety for spouse/companions. PMID:20362722

  12. 49 CFR 236.531 - Trip arm; height and distance from rail.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 49 Transportation 4 2014-10-01 2014-10-01 false Trip arm; height and distance from rail. 236.531... Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.531 Trip arm; height and distance from rail. Trip arm of automatic train stop device when in the stop position shall be...

  13. 49 CFR 236.531 - Trip arm; height and distance from rail.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 4 2011-10-01 2011-10-01 false Trip arm; height and distance from rail. 236.531... Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.531 Trip arm; height and distance from rail. Trip arm of automatic train stop device when in the stop position shall be...

  14. 49 CFR 236.531 - Trip arm; height and distance from rail.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 49 Transportation 4 2013-10-01 2013-10-01 false Trip arm; height and distance from rail. 236.531... Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.531 Trip arm; height and distance from rail. Trip arm of automatic train stop device when in the stop position shall be...

  15. 49 CFR 236.531 - Trip arm; height and distance from rail.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Trip arm; height and distance from rail. 236.531... Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.531 Trip arm; height and distance from rail. Trip arm of automatic train stop device when in the stop position shall be...

  16. 49 CFR 236.531 - Trip arm; height and distance from rail.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 49 Transportation 4 2012-10-01 2012-10-01 false Trip arm; height and distance from rail. 236.531... Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.531 Trip arm; height and distance from rail. Trip arm of automatic train stop device when in the stop position shall be...

  17. ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

    PubMed

    Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

    2016-07-07

    Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

  18. ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography

    NASA Astrophysics Data System (ADS)

    Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

    2016-07-01

    Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

  19. Headway Deviation Effects on Bus Passenger Loads : Analysis of Tri-Met's Archived AVL-APC Data

    DOT National Transportation Integrated Search

    2003-01-01

    In this paper we empirically analyze the relationship between transit service headway deviations and passenger loads, using archived data from Tri-Met's automatic vehicle location and automatic passenger counter systems. The analysis employs twostage...

  20. Data visualization as a tool for improved decision making within transit agencies

    DOT National Transportation Integrated Search

    2007-02-01

    TriMet, the regional transit provider in the Portland, OR, area has been a leader in bus transit performance monitoring using data collected via automatic vehicle location and automatic passenger counter technologies. This information is collected an...

  1. Experiments on Four Different Techniques for Automatically Locating Land Vehicles - A Summary of Results

    DOT National Transportation Integrated Search

    1977-06-01

    In 1975, to further the development and to refine and dmonstrate multiuser Automatic Vehicle Monitoring (AVM) application, the Urban Mass Transportation Administration and the Transportation Systems Center (TSC) initiated a two-phase program. Phase I...

  2. Automatic Vehicle Location: Successful Transit Applications

    DOT National Transportation Integrated Search

    2002-11-01

    Belief in the value of AVL is substantiated by statements of benefits contained earlier in this study. Even so, none of the study agencies are making full use of the voluminous amount of AVL data automatically recorded by the system. Efforts to make ...

  3. Differential effects of spaced vs. massed training in long-term object-identity and object-location recognition memory.

    PubMed

    Bello-Medina, Paola C; Sánchez-Carrasco, Livia; González-Ornelas, Nadia R; Jeffery, Kathryn J; Ramírez-Amaya, Víctor

    2013-08-01

    Here we tested whether the well-known superiority of spaced training over massed training is equally evident in both object identity and object location recognition memory. We trained animals with objects placed in a variable or in a fixed location to produce a location-independent object identity memory or a location-dependent object representation. The training consisted of 5 trials that occurred either on one day (Massed) or over the course of 5 consecutive days (Spaced). The memory test was done in independent groups of animals either 24h or 7 days after the last training trial. In each test the animals were exposed to either a novel object, when trained with the objects in variable locations, or to a familiar object in a novel location, when trained with objects in fixed locations. The difference in time spent exploring the changed versus the familiar objects was used as a measure of recognition memory. For the object-identity-trained animals, spaced training produced clear evidence of recognition memory after both 24h and 7 days, but massed-training animals showed it only after 24h. In contrast, for the object-location-trained animals, recognition memory was evident after both retention intervals and with both training procedures. When objects were placed in variable locations for the two types of training and the test was done with a brand-new location, only the spaced-training animals showed recognition at 24h, but surprisingly, after 7 days, animals trained using both procedures were able to recognize the change, suggesting a post-training consolidation process. We suggest that the two training procedures trigger different neural mechanisms that may differ in the two segregated streams that process object information and that may consolidate differently. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. 49 CFR 214.331 - Definite train location.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Definite train location. 214.331 Section 214.331... location. A roadway worker may establish on-track safety by using definite train location only where... may only use definite train location to establish on-track safety at points where such procedures were...

  5. Accurate Reading with Sequential Presentation of Single Letters

    PubMed Central

    Price, Nicholas S. C.; Edwards, Gemma L.

    2012-01-01

    Rapid, accurate reading is possible when isolated, single words from a sentence are sequentially presented at a fixed spatial location. We investigated if reading of words and sentences is possible when single letters are rapidly presented at the fovea under user-controlled or automatically controlled rates. When tested with complete sentences, trained participants achieved reading rates of over 60 wpm and accuracies of over 90% with the single letter reading (SLR) method and naive participants achieved average reading rates over 30 wpm with greater than 90% accuracy. Accuracy declined as individual letters were presented for shorter periods of time, even when the overall reading rate was maintained by increasing the duration of spaces between words. Words in the lexicon that occur more frequently were identified with higher accuracy and more quickly, demonstrating that trained participants have lexical access. In combination, our data strongly suggest that comprehension is possible and that SLR is a practicable form of reading under conditions in which normal scanning of text is not possible, or for scenarios with limited spatial and temporal resolution such as patients with low vision or prostheses. PMID:23115548

  6. Automatic cross-sectioning and monitoring system locates defects in electronic devices

    NASA Technical Reports Server (NTRS)

    Jacobs, G.; Slaughter, B.

    1971-01-01

    System consists of motorized grinding and lapping apparatus, sample holder, and electronic control circuit. Low power microscope examines device to pinpoint location of circuit defect, and monitor displays output signal when defect is located exactly.

  7. Automatic sleep stage classification using two-channel electro-oculography.

    PubMed

    Virkkala, Jussi; Hasan, Joel; Värri, Alpo; Himanen, Sari-Leena; Müller, Kiti

    2007-10-15

    An automatic method for the classification of wakefulness and sleep stages SREM, S1, S2 and SWS was developed based on our two previous studies. The method is based on a two-channel electro-oculography (EOG) referenced to the left mastoid (M1). Synchronous electroencephalographic (EEG) activity in S2 and SWS was detected by calculating cross-correlation and peak-to-peak amplitude difference in the 0.5-6 Hz band between the two EOG channels. An automatic slow eye-movement (SEM) estimation was used to indicate wakefulness, SREM and S1. Beta power 18-30 Hz and alpha power 8-12 Hz was also used for wakefulness detection. Synchronous 1.5-6 Hz EEG activity and absence of large eye movements was used for S1 separation from SREM. Simple smoothing rules were also applied. Sleep EEG, EOG and EMG were recorded from 265 subjects. The system was tuned using data from 132 training subjects and then applied to data from 131 validation subjects that were different to the training subjects. Cohen's Kappa between the visual and the developed new automatic scoring in separating 30s wakefulness, SREM, S1, S2 and SWS epochs was substantial 0.62 with epoch by epoch agreement of 72%. With automatic subject specific alpha thresholds for offline applications results improved to 0.63 and 73%. The automatic method can be further developed and applied for ambulatory sleep recordings by using only four disposable, self-adhesive and self-applicable electrodes.

  8. 49 CFR 235.1 - Scope.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... SIGNAL SYSTEM OR RELIEF FROM THE REQUIREMENTS OF PART 236 § 235.1 Scope. This part prescribes application for approval to discontinue or materially modify block signal systems, interlockings, traffic control systems, automatic train stop, train control, or cab signal systems, or other similar appliances, devices...

  9. 49 CFR 236.831 - Time, delay.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Time, delay. 236.831 Section 236.831 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Time, delay. As applied to an automatic train stop or train control system, the time which elapses...

  10. Effectiveness of automated external defibrillators in high schools in greater Boston.

    PubMed

    England, Hannah; Hoffman, Caitlin; Hodgman, Thomas; Singh, Sushil; Homoud, Munther; Weinstock, Jonathan; Link, Mark; Estes, N A Mark

    2005-06-15

    A program using a strategy of donating a single automatic external defibrillator to 35 schools in the Boston area resulted in compliance with American Heart Association guidelines on automatic external defibrillator placement and training and 2 successful resuscitations from sudden cardiac arrest. Participating schools indicated a high degree of satisfaction with the program.

  11. Mindfulness-Based Parent Training: Strategies to Lessen the Grip of Automaticity in Families with Disruptive Children

    ERIC Educational Resources Information Center

    Dumas, Jean E.

    2005-01-01

    Disagreements and conflicts in families with disruptive children often reflect rigid patterns of behavior that have become overlearned and automatized with repeated practice. These patterns are mindless: They are performed with little or no awareness and are highly resistant to change. This article introduces a new, mindfulness-based model of…

  12. Automatic/Control Processing Concepts and Their Implications for the Training of Skills.

    DTIC Science & Technology

    1982-04-01

    driving a car are examples of automatic processes. Controll p s is comparatively slow, serial, limited by short-term memory, and requires subject effort...development has convinced us that moivation a oftn more Jmportn nti mAn =other iJli velLJoa jjthpgy gI. njj Lautomatic U_2,LLjjk. Motivation Is much more

  13. Automatic detection of apical roots in oral radiographs

    NASA Astrophysics Data System (ADS)

    Wu, Yi; Xie, Fangfang; Yang, Jie; Cheng, Erkang; Megalooikonomou, Vasileios; Ling, Haibin

    2012-03-01

    The apical root regions play an important role in analysis and diagnosis of many oral diseases. Automatic detection of such regions is consequently the first step toward computer-aided diagnosis of these diseases. In this paper we propose an automatic method for periapical root region detection by using the state-of-theart machine learning approaches. Specifically, we have adapted the AdaBoost classifier for apical root detection. One challenge in the task is the lack of training cases especially for diseased ones. To handle this problem, we boost the training set by including more root regions that are close to the annotated ones and decompose the original images to randomly generate negative samples. Based on these training samples, the Adaboost algorithm in combination with Haar wavelets is utilized in this task to train an apical root detector. The learned detector usually generates a large amount of true and false positives. In order to reduce the number of false positives, a confidence score for each candidate detection result is calculated for further purification. We first merge the detected regions by combining tightly overlapped detected candidate regions and then we use the confidence scores from the Adaboost detector to eliminate the false positives. The proposed method is evaluated on a dataset containing 39 annotated digitized oral X-Ray images from 21 patients. The experimental results show that our approach can achieve promising detection accuracy.

  14. Possible End to an Endless Quest? Cognitive Bias Modification for Excessive Multiplayer Online Gamers.

    PubMed

    Rabinovitz, Sharon; Nagar, Maayan

    2015-10-01

    Cognitive biases have previously been recognized as key mechanisms that contribute to the development, maintenance, and relapse of addictive behaviors. The same mechanisms have been recently found in problematic computer gaming. The present study aims to investigate whether excessive massively multiplayer online role-playing gamers (EG) demonstrate an approach bias toward game-related cues compared to neutral stimuli; to test whether these automatic action tendencies can be implicitly modified in a single session training; and to test whether this training affects game urges and game-seeking behavior. EG (n=38) were randomly assigned to a condition in which they were implicitly trained to avoid or to approach gaming cues by pushing or pulling a joystick, using a computerized intervention (cognitive bias modification via the Approach Avoidance Task). EG demonstrated an approach bias for gaming cues compared with neutral, movie cues. Single session training significantly decreased automatic action tendencies to approach gaming cues. These effects occurred outside subjective awareness. Furthermore, approach bias retraining reduced subjective urges and intentions to play, as well as decreased game-seeking behavior. Retraining automatic processes may be beneficial in changing addictive impulses in EG. Yet, large-scale trials and long-term follow-up are warranted. The results extend the application of cognitive bias modification from substance use disorders to behavioral addictions, and specifically to Internet gaming disorder. Theoretical implications are discussed.

  15. Automatic classification of blank substrate defects

    NASA Astrophysics Data System (ADS)

    Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati

    2014-10-01

    Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask Technology Center (MPMask). The Calibre ADC tool was qualified on production mask blanks against the manual classification. The classification accuracy of ADC is greater than 95% for critical defects with an overall accuracy of 90%. The sensitivity to weak defect signals and locating the defect in the images is a challenge we are resolving. The performance of the tool has been demonstrated on multiple mask types and is ready for deployment in full volume mask manufacturing production flow. Implementation of Calibre ADC is estimated to reduce the misclassification of critical defects by 60-80%.

  16. Report on Phase 1 Tests of Fairchild Automatic Vehicle Monitoring (AVM) System

    DOT National Transportation Integrated Search

    1977-08-01

    During the winter of 1976-77 four different techniques for automatically locating land vehicles were tested in both the low and high-rise regions in Philadelphia, Pennsylvania. The tests were carried out by four different companies under separate con...

  17. Relearning of Writing Skills in Parkinson's Disease After Intensive Amplitude Training.

    PubMed

    Nackaerts, Evelien; Heremans, Elke; Vervoort, Griet; Smits-Engelsman, Bouwien C M; Swinnen, Stephan P; Vandenberghe, Wim; Bergmans, Bruno; Nieuwboer, Alice

    2016-08-01

    Micrographia occurs in approximately 60% of people with Parkinson's disease (PD). Although handwriting is an important task in daily life, it is not clear whether relearning and consolidation (ie the solid storage in motor memory) of this skill is possible in PD. The objective was to conduct for the first time a controlled study into the effects of intensive motor learning to improve micrographia in PD. In this placebo-controlled study, 38 right-handed people with PD were randomized into 2 groups, receiving 1 of 2 equally time-intensive training programs (30 min/day, 5 days/week for 6 weeks). The experimental group (n = 18) performed amplitude training focused at improving writing size. The placebo group (n = 20) received stretch and relaxation exercises. Participants' writing skills were assessed using a touch-sensitive writing tablet and a pen-and-paper test, pre- and posttraining, and after a 6-week retention period. The primary outcome was change in amplitude during several tests of consolidation: (1) transfer, using trained and untrained sequences performed with and without target zones; and (2) automatization, using single- and dual-task sequences. The group receiving amplitude training significantly improved in amplitude and variability of amplitude on the transfer and automatization task. Effect sizes varied between 7% and 17%, and these benefits were maintained after the 6-week retention period. Moreover, there was transfer to daily life writing. These results show automatization, transfer, and retention of increased writing size (diminished micrographia) after intensive amplitude training, indicating that consolidation of motor learning is possible in PD. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

  18. Tidal analysis and Arrival Process Mining Using Automatic Identification System (AIS) Data

    DTIC Science & Technology

    2017-01-01

    files, organized by location. The data were processed using the Python programming language (van Rossum and Drake 2001), the Pandas data analysis...ER D C/ CH L TR -1 7- 2 Coastal Inlets Research Program Tidal Analysis and Arrival Process Mining Using Automatic Identification System...17-2 January 2017 Tidal Analysis and Arrival Process Mining Using Automatic Identification System (AIS) Data Brandan M. Scully Coastal and

  19. An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images.

    PubMed

    Hashimoto, Shinichi; Ogihara, Hiroyuki; Suenaga, Masato; Fujita, Yusuke; Terai, Shuji; Hamamoto, Yoshihiko; Sakaida, Isao

    2017-08-01

    Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

  20. A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface.

    PubMed

    Zhou, Bangyan; Wu, Xiaopei; Lv, Zhao; Zhang, Lei; Guo, Xiaojin

    2016-01-01

    Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The "high quality" training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system.

  1. Automatic Retrieval of Newly Instructed Cue-Task Associations Seen in Task-Conflict Effects in the First Trial after Cue-Task Instructions.

    PubMed

    Meiran, Nachshon; Pereg, Maayan

    2017-01-01

    Novel stimulus-response associations are retrieved automatically even without prior practice. Is this true for novel cue-task associations? The experiment involved miniblocks comprising three phases and task switching. In the INSTRUCTION phase, two new stimuli (or familiar cues) were arbitrarily assigned as cues for up-down/right-left tasks performed on placeholder locations. In the UNIVALENT phase, there was no task cue since placeholder's location afforded one task but the placeholders were the stimuli that we assigned as task cues for the following BIVALENT phase (involving target locations affording both tasks). Thus, participants held the novel cue-task associations in memory while executing the UNIVALENT phase. Results show poorer performance in the first univalent trial when the placeholder was associated with the opposite task (incompatible) than when it was compatible, an effect that was numerically larger with newly instructed cues than with familiar cues. These results indicate automatic retrieval of newly instructed cue-task associations.

  2. DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG.

    PubMed

    Supratak, Akara; Dong, Hao; Wu, Chao; Guo, Yike

    2017-11-01

    This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features, which require prior knowledge of sleep analysis. Only a few of them encode the temporal information, such as transition rules, which is important for identifying the next sleep stages, into the extracted features. In the proposed model, we utilize convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs. We implement a two-step training algorithm to train our model efficiently. We evaluated our model using different single-channel EEGs (F4-EOG (left), Fpz-Cz, and Pz-Oz) from two public sleep data sets, that have different properties (e.g., sampling rate) and scoring standards (AASM and R&K). The results showed that our model achieved similar overall accuracy and macro F1-score (MASS: 86.2%-81.7, Sleep-EDF: 82.0%-76.9) compared with the state-of-the-art methods (MASS: 85.9%-80.5, Sleep-EDF: 78.9%-73.7) on both data sets. This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different data sets without utilizing any hand-engineered features.

  3. Effects of Word Recognition Training in a Picture-Word Interference Task: Automaticity vs. Speed.

    ERIC Educational Resources Information Center

    Ehri, Linnea C.

    First and second graders were taught to recognize a set of written words either more accurately or more rapidly. Both before and after word training, they named pictures printed with and without these words as distractors. Of interest was whether training would enhance or diminish the interference created by these words in the picture naming task.…

  4. In-Flight Simulator for IFR Training

    NASA Technical Reports Server (NTRS)

    Parker, L. C.

    1986-01-01

    Computer-controlled unit feeds navigation signals to airplane instruments. Electronic training system allows students to learn to fly according to instrument flight rules (IFR) in uncrowded airspace. New system self-contained IFR simulator carried aboard training plane. Generates signals and commands for standard instruments on airplane, including navigational receiver, distance-measuring equipment, automatic direction finder, a marker-beacon receiver, altimeter, airspeed indicator, and heading indicator.

  5. Advanced Simulation in Undergraduate Pilot Training: Automatic Instructional System. Final Report for the Period March 1971-January 1975.

    ERIC Educational Resources Information Center

    Faconti, Victor; Epps, Robert

    The Advanced Simulator for Undergraduate Pilot Training (ASUPT) was designed to investigate the role of simulation in the future Undergraduate Pilot Training (UPT) program. The Automated Instructional System designed for the ASUPT simulator was described in this report. The development of the Automated Instructional System for ASUPT was based upon…

  6. 49 CFR 236.567 - Restrictions imposed when device fails and/or is cut out en route.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... cut out en route. 236.567 Section 236.567 Transportation Other Regulations Relating to Transportation...; Locomotives § 236.567 Restrictions imposed when device fails and/or is cut out en route. Where an automatic train stop, train control, or cab signal device fails and/or is cut out enroute, train may proceed at...

  7. Towards the Development of a Comprehensive Pedagogical Framework for Pronunciation Training Based on Adapted Automatic Speech Recognition Systems

    ERIC Educational Resources Information Center

    Ali, Saandia

    2016-01-01

    This paper reports on the early stages of a locally funded research and development project taking place at Rennes 2 university. It aims at developing a comprehensive pedagogical framework for pronunciation training for adult learners of English. This framework will combine a direct approach to pronunciation training (face-to-face teaching) with…

  8. Defense Management Education and Training Catalog.

    ERIC Educational Resources Information Center

    Office of the Assistant Secretary of Defense for Manpower and Reserve Affairs (DOD), Washington, DC.

    This catalog provides information on a wide variety of courses, programs, and school made available by Department of Defense organizations. The program consists of eighteen primarily service-operated schools offering joint training in management covering a wide variety of subjects including automatic data processing, production management,…

  9. 49 CFR 236.722 - Circuit, cut-in.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 4 2011-10-01 2011-10-01 false Circuit, cut-in. 236.722 Section 236.722 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Circuit, cut-in. A roadway circuit at the entrance to automatic train stop, train control or cab signal...

  10. Techniques and Training.

    ERIC Educational Resources Information Center

    Davies, Maire Messenger

    1992-01-01

    Provides an overview of articles included in this issue that address automatic evaluation of public service announcements about AIDS that are aimed at high-risk, low-literacy individuals; how children construe television programs; the concept of quality in broadcasting; and the use of video for inservice teacher training. (LRW)

  11. 49 CFR 236.722 - Circuit, cut-in.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Circuit, cut-in. 236.722 Section 236.722 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Circuit, cut-in. A roadway circuit at the entrance to automatic train stop, train control or cab signal...

  12. 49 CFR 236.564 - Acknowledging time.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Acknowledging time. 236.564 Section 236.564..., Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.564 Acknowledging time. Acknowledging time of intermittent automatic train-stop device shall be not more than 30 seconds. ...

  13. 49 CFR 236.564 - Acknowledging time.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 49 Transportation 4 2014-10-01 2014-10-01 false Acknowledging time. 236.564 Section 236.564..., Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.564 Acknowledging time. Acknowledging time of intermittent automatic train-stop device shall be not more than 30 seconds. ...

  14. Automatic measurement of voice onset time using discriminative structured prediction.

    PubMed

    Sonderegger, Morgan; Keshet, Joseph

    2012-12-01

    A discriminative large-margin algorithm for automatic measurement of voice onset time (VOT) is described, considered as a case of predicting structured output from speech. Manually labeled data are used to train a function that takes as input a speech segment of an arbitrary length containing a voiceless stop, and outputs its VOT. The function is explicitly trained to minimize the difference between predicted and manually measured VOT; it operates on a set of acoustic feature functions designed based on spectral and temporal cues used by human VOT annotators. The algorithm is applied to initial voiceless stops from four corpora, representing different types of speech. Using several evaluation methods, the algorithm's performance is near human intertranscriber reliability, and compares favorably with previous work. Furthermore, the algorithm's performance is minimally affected by training and testing on different corpora, and remains essentially constant as the amount of training data is reduced to 50-250 manually labeled examples, demonstrating the method's practical applicability to new datasets.

  15. Automatic categorization of anatomical landmark-local appearances based on diffeomorphic demons and spectral clustering for constructing detector ensembles.

    PubMed

    Hanaoka, Shouhei; Masutani, Yoshitaka; Nemoto, Mitsutaka; Nomura, Yukihiro; Yoshikawa, Takeharu; Hayashi, Naoto; Ohtomo, Kuni

    2012-01-01

    A method for categorizing landmark-local appearances extracted from computed tomography (CT) datasets is presented. Anatomical landmarks in the human body inevitably have inter-individual variations that cause difficulty in automatic landmark detection processes. The goal of this study is to categorize subjects (i.e., training datasets) according to local shape variations of such a landmark so that each subgroup has less shape variation and thus the machine learning of each landmark detector is much easier. The similarity between each subject pair is measured based on the non-rigid registration result between them. These similarities are used by the spectral clustering process. After the clustering, all training datasets in each cluster, as well as synthesized intermediate images calculated from all subject-pairs in the cluster, are used to train the corresponding subgroup detector. All of these trained detectors compose a detector ensemble to detect the target landmark. Evaluation with clinical CT datasets showed great improvement in the detection performance.

  16. Scanning Seismic Intrusion Detector

    NASA Technical Reports Server (NTRS)

    Lee, R. D.

    1982-01-01

    Scanning seismic intrusion detector employs array of automatically or manually scanned sensors to determine approximate location of intruder. Automatic-scanning feature enables one operator to tend system of many sensors. Typical sensors used with new system are moving-coil seismic pickups. Detector finds uses in industrial security systems.

  17. Automatic photointerpretation for plant species and stress identification (ERTS-A1)

    NASA Technical Reports Server (NTRS)

    Swanlund, G. D. (Principal Investigator); Kirvida, L.; Johnson, G. R.

    1973-01-01

    The author has identified the following significant results. Automatic stratification of forested land from ERTS-1 data provides a valuable tool for resource management. The results are useful for wood product yield estimates, recreation and wildlife management, forest inventory, and forest condition monitoring. Automatic procedures based on both multispectral and spatial features are evaluated. With five classes, training and testing on the same samples, classification accuracy of 74 percent was achieved using the MSS multispectral features. When adding texture computed from 8 x 8 arrays, classification accuracy of 90 percent was obtained.

  18. AUTOMATIC COUNTING APPARATUS

    DOEpatents

    Howell, W.D.

    1957-08-20

    An apparatus for automatically recording the results of counting operations on trains of electrical pulses is described. The disadvantages of prior devices utilizing the two common methods of obtaining the count rate are overcome by this apparatus; in the case of time controlled operation, the disclosed system automatically records amy information stored by the scaler but not transferred to the printer at the end of the predetermined time controlled operations and, in the case of count controlled operation, provision is made to prevent a weak sample from occupying the apparatus for an excessively long period of time.

  19. Automatic picker of P & S first arrivals and robust event locator

    NASA Astrophysics Data System (ADS)

    Pinsky, V.; Polozov, A.; Hofstetter, A.

    2003-12-01

    We report on further development of automatic all distances location procedure designed for a regional network. The procedure generalizes the previous "loca l" (R < 500 km) and "regional" (500 < R < 2000 km) routines and comprises: a) preliminary data processing (filtering and de-spiking), b) phase identificatio n, c) P, S first arrival picking, d) preliminary location and e) robust grid-search optimization procedure. Innovations concern phase identification, automa tic picking and teleseismic location. A platform free flexible Java interface was recently created, allowing easy parameter tuning and on/off switching to t he full-scale manual picking mode. Identification of the regional P and S phase is provided by choosing between the two largest peaks in the envelope curve. For automatic on-time estimation we utilize now ratio of two STAs, calculated in two consecutive and equal time windows (instead of previously used Akike Information Criterion). "Teleseismic " location is split in two stages: preliminary and final one. The preliminary part estimates azimuth and apparent velocity by fitting a plane wave to the P automatic pickings. The apparent velocity criterion is used to decide about strategy of the following computations: teleseismic or regional. The preliminary estimates of azimuth and apparent velocity provide starting value for the final teleseismic and regional location. Apparent velocity is used to get first a pproximation distance to the source on the basis of the P, Pn, Pg travel-timetables. The distance estimate together with the preliminary azimuth estimate provides first approximations of the source latitude and longitude via sine and cosine theorems formulated for the spherical triangle. Final location is based on robust grid-search optimization procedure, weighting the number of pickings that simultaneously fit the model travel times. The grid covers initial location and becomes finer while approaching true hypocenter. The target function is a sum of the bell-shaped characteristic functions, used to emphasize true pickings and eliminate outliers. The final solution is a grid point that provides maximum to the target function. The procedure was applied to a list of ML > 4 earthquakes recorded by the Israel Seismic Network (ISN) in the 1999-2002 time period. Most of them are badly constrained relative the network. However, the results of location with average normalized error relative bulletin solutions e=dr/R of 5% were obtained, in each of the distance ranges. The first version of the procedure was incorporated in the national Early Warning System in 2001. Recently, we started to send automatic Early Warn ing reports, to the EMSC Real Time Bulletin. Initially reported some teleseismic location discrepancies have been eliminated by introduction of station corrections.

  20. Creating a medical dictionary using word alignment: the influence of sources and resources.

    PubMed

    Nyström, Mikael; Merkel, Magnus; Petersson, Håkan; Ahlfeldt, Hans

    2007-11-23

    Automatic word alignment of parallel texts with the same content in different languages is among other things used to generate dictionaries for new translations. The quality of the generated word alignment depends on the quality of the input resources. In this paper we report on automatic word alignment of the English and Swedish versions of the medical terminology systems ICD-10, ICF, NCSP, KSH97-P and parts of MeSH and how the terminology systems and type of resources influence the quality. We automatically word aligned the terminology systems using static resources, like dictionaries, statistical resources, like statistically derived dictionaries, and training resources, which were generated from manual word alignment. We varied which part of the terminology systems that we used to generate the resources, which parts that we word aligned and which types of resources we used in the alignment process to explore the influence the different terminology systems and resources have on the recall and precision. After the analysis, we used the best configuration of the automatic word alignment for generation of candidate term pairs. We then manually verified the candidate term pairs and included the correct pairs in an English-Swedish dictionary. The results indicate that more resources and resource types give better results but the size of the parts used to generate the resources only partly affects the quality. The most generally useful resources were generated from ICD-10 and resources generated from MeSH were not as general as other resources. Systematic inter-language differences in the structure of the terminology system rubrics make the rubrics harder to align. Manually created training resources give nearly as good results as a union of static resources, statistical resources and training resources and noticeably better results than a union of static resources and statistical resources. The verified English-Swedish dictionary contains 24,000 term pairs in base forms. More resources give better results in the automatic word alignment, but some resources only give small improvements. The most important type of resource is training and the most general resources were generated from ICD-10.

  1. Development of automatic body condition scoring using a low-cost 3-dimensional Kinect camera.

    PubMed

    Spoliansky, Roii; Edan, Yael; Parmet, Yisrael; Halachmi, Ilan

    2016-09-01

    Body condition scoring (BCS) is a farm-management tool for estimating dairy cows' energy reserves. Today, BCS is performed manually by experts. This paper presents a 3-dimensional algorithm that provides a topographical understanding of the cow's body to estimate BCS. An automatic BCS system consisting of a Kinect camera (Microsoft Corp., Redmond, WA) triggered by a passive infrared motion detector was designed and implemented. Image processing and regression algorithms were developed and included the following steps: (1) image restoration, the removal of noise; (2) object recognition and separation, identification and separation of the cows; (3) movie and image selection, selection of movies and frames that include the relevant data; (4) image rotation, alignment of the cow parallel to the x-axis; and (5) image cropping and normalization, removal of irrelevant data, setting the image size to 150×200 pixels, and normalizing image values. All steps were performed automatically, including image selection and classification. Fourteen individual features per cow, derived from the cows' topography, were automatically extracted from the movies and from the farm's herd-management records. These features appear to be measurable in a commercial farm. Manual BCS was performed by a trained expert and compared with the output of the training set. A regression model was developed, correlating the features with the manual BCS references. Data were acquired for 4 d, resulting in a database of 422 movies of 101 cows. Movies containing cows' back ends were automatically selected (389 movies). The data were divided into a training set of 81 cows and a test set of 20 cows; both sets included the identical full range of BCS classes. Accuracy tests gave a mean absolute error of 0.26, median absolute error of 0.19, and coefficient of determination of 0.75, with 100% correct classification within 1 step and 91% correct classification within a half step for BCS classes. Results indicated good repeatability, with all standard deviations under 0.33. The algorithm is independent of the background and requires 10 cows for training with approximately 30 movies of 4 s each. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  2. Creating a medical dictionary using word alignment: The influence of sources and resources

    PubMed Central

    Nyström, Mikael; Merkel, Magnus; Petersson, Håkan; Åhlfeldt, Hans

    2007-01-01

    Background Automatic word alignment of parallel texts with the same content in different languages is among other things used to generate dictionaries for new translations. The quality of the generated word alignment depends on the quality of the input resources. In this paper we report on automatic word alignment of the English and Swedish versions of the medical terminology systems ICD-10, ICF, NCSP, KSH97-P and parts of MeSH and how the terminology systems and type of resources influence the quality. Methods We automatically word aligned the terminology systems using static resources, like dictionaries, statistical resources, like statistically derived dictionaries, and training resources, which were generated from manual word alignment. We varied which part of the terminology systems that we used to generate the resources, which parts that we word aligned and which types of resources we used in the alignment process to explore the influence the different terminology systems and resources have on the recall and precision. After the analysis, we used the best configuration of the automatic word alignment for generation of candidate term pairs. We then manually verified the candidate term pairs and included the correct pairs in an English-Swedish dictionary. Results The results indicate that more resources and resource types give better results but the size of the parts used to generate the resources only partly affects the quality. The most generally useful resources were generated from ICD-10 and resources generated from MeSH were not as general as other resources. Systematic inter-language differences in the structure of the terminology system rubrics make the rubrics harder to align. Manually created training resources give nearly as good results as a union of static resources, statistical resources and training resources and noticeably better results than a union of static resources and statistical resources. The verified English-Swedish dictionary contains 24,000 term pairs in base forms. Conclusion More resources give better results in the automatic word alignment, but some resources only give small improvements. The most important type of resource is training and the most general resources were generated from ICD-10. PMID:18036221

  3. Vehicle classification in WAMI imagery using deep network

    NASA Astrophysics Data System (ADS)

    Yi, Meng; Yang, Fan; Blasch, Erik; Sheaff, Carolyn; Liu, Kui; Chen, Genshe; Ling, Haibin

    2016-05-01

    Humans have always had a keen interest in understanding activities and the surrounding environment for mobility, communication, and survival. Thanks to recent progress in photography and breakthroughs in aviation, we are now able to capture tens of megapixels of ground imagery, namely Wide Area Motion Imagery (WAMI), at multiple frames per second from unmanned aerial vehicles (UAVs). WAMI serves as a great source for many applications, including security, urban planning and route planning. These applications require fast and accurate image understanding which is time consuming for humans, due to the large data volume and city-scale area coverage. Therefore, automatic processing and understanding of WAMI imagery has been gaining attention in both industry and the research community. This paper focuses on an essential step in WAMI imagery analysis, namely vehicle classification. That is, deciding whether a certain image patch contains a vehicle or not. We collect a set of positive and negative sample image patches, for training and testing the detector. Positive samples are 64 × 64 image patches centered on annotated vehicles. We generate two sets of negative images. The first set is generated from positive images with some location shift. The second set of negative patches is generated from randomly sampled patches. We also discard those patches if a vehicle accidentally locates at the center. Both positive and negative samples are randomly divided into 9000 training images and 3000 testing images. We propose to train a deep convolution network for classifying these patches. The classifier is based on a pre-trained AlexNet Model in the Caffe library, with an adapted loss function for vehicle classification. The performance of our classifier is compared to several traditional image classifier methods using Support Vector Machine (SVM) and Histogram of Oriented Gradient (HOG) features. While the SVM+HOG method achieves an accuracy of 91.2%, the accuracy of our deep network-based classifier reaches 97.9%.

  4. Novel Symbol Learning-Induced Stroop Effect: Evidence for a Strategy-Based, Utility Learning Model

    ERIC Educational Resources Information Center

    Wang, Jin; Tang, Huijun; Deng, Yuan

    2016-01-01

    The automaticity level and attention priority/strategy are two major theories that have attempted to explain the mechanism underlying the Stroop effect. Training is an effective way to manipulate the experience with the two dimensions (ink color and color word) in the Stroop task. In order to distinguish the above two factors (the automaticity or…

  5. Automatic rule generation for high-level vision

    NASA Technical Reports Server (NTRS)

    Rhee, Frank Chung-Hoon; Krishnapuram, Raghu

    1992-01-01

    Many high-level vision systems use rule-based approaches to solving problems such as autonomous navigation and image understanding. The rules are usually elaborated by experts. However, this procedure may be rather tedious. In this paper, we propose a method to generate such rules automatically from training data. The proposed method is also capable of filtering out irrelevant features and criteria from the rules.

  6. A dual-systems perspective on addiction: contributions from neuroimaging and cognitive training.

    PubMed

    McClure, Samuel M; Bickel, Warren K

    2014-10-01

    Dual-systems theories explain lapses in self-control in terms of a conflict between automatic and deliberative modes of behavioral control. Numerous studies have now tested whether the brain areas that control behavior are organized in a manner consistent with dual-systems models. Brain regions directly associated with the mesolimbic dopamine system, the nucleus accumbens and ventromedial prefrontal cortex in particular, capture some of the features assumed by automatic processing. Regions in the lateral prefrontal cortex are more closely linked to deliberative processing and the exertion of self-control in the suppression of impulses. While identifying these regions crudely supports dual-systems theories, important modifications to what constitutes automatic and deliberative behavioral control are also suggested. Experiments have identified various means by which automatic processes may be sculpted. Additional work decomposes deliberative processes into component functions such as generalized working memory, reappraisal of emotional stimuli, and prospection. The importance of deconstructing dual-systems models into specific cognitive processes is clear for understanding and treating addiction. We discuss intervention possibilities suggested by recent research, and focus in particular on cognitive training approaches to bolster deliberative control processes that may aid quit attempts. © 2014 New York Academy of Sciences.

  7. A dual-systems perspective on addiction: contributions from neuroimaging and cognitive training

    PubMed Central

    McClure, Samuel M.; Bickel, Warren K.

    2014-01-01

    Dual-systems theories explain lapses in self-control in terms of a conflict between automatic and deliberative modes of behavioral control. Numerous studies have now tested whether the brain areas that control behavior are organized in a manner consistent with dual-systems models. Brain regions directly associated with the mesolimbic dopamine system, the nucleus accumbens (NAcc) and ventromedial prefrontal cortex (vmPFC) in particular, capture some of the features assumed by automatic processing. Regions in the lateral prefrontal cortex (lPFC) are more closely linked to deliberative processing and the exertion of self-control in the suppression of impulses. While identifying these regions crudely supports dual-system theories, important modifications to what constitutes automatic and deliberative behavioral control are also suggested. Experiments have identified various means by which automatic processes may be sculpted. Additional work decomposes deliberative processes into component functions such as generalized working memory, reappraisal of emotional stimuli, and prospection. The importance of deconstructing dual-systems models into specific cognitive processes is clear for understanding and treating addiction. We discuss intervention possibilities suggested by recent research, and focus in particular on cognitive training approaches to bolster deliberative control processes that may aid quit attempts. PMID:25336389

  8. Acquisition of automatic imitation is sensitive to sensorimotor contingency.

    PubMed

    Cook, Richard; Press, Clare; Dickinson, Anthony; Heyes, Cecilia

    2010-08-01

    The associative sequence learning model proposes that the development of the mirror system depends on the same mechanisms of associative learning that mediate Pavlovian and instrumental conditioning. To test this model, two experiments used the reduction of automatic imitation through incompatible sensorimotor training to assess whether mirror system plasticity is sensitive to contingency (i.e., the extent to which activation of one representation predicts activation of another). In Experiment 1, residual automatic imitation was measured following incompatible training in which the action stimulus was a perfect predictor of the response (contingent) or not at all predictive of the response (noncontingent). A contingency effect was observed: There was less automatic imitation indicative of more learning in the contingent group. Experiment 2 replicated this contingency effect and showed that, as predicted by associative learning theory, it can be abolished by signaling trials in which the response occurs in the absence of an action stimulus. These findings support the view that mirror system development depends on associative learning and indicate that this learning is not purely Hebbian. If this is correct, associative learning theory could be used to explain, predict, and intervene in mirror system development.

  9. 29 CFR 4050.8 - Automatic lump sum.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... Relating to Labor (Continued) PENSION BENEFIT GUARANTY CORPORATION PLAN TERMINATIONS MISSING PARTICIPANTS § 4050.8 Automatic lump sum. This section applies to a missing participant whose designated benefit was... PBGC pays the benefit. (2) Payee. Payment will be made— (i) To the missing participant, if located; (ii...

  10. 49 CFR 236.567 - Restrictions imposed when device fails and/or is cut out en route.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 49 Transportation 4 2014-10-01 2014-10-01 false Restrictions imposed when device fails and/or is...; Locomotives § 236.567 Restrictions imposed when device fails and/or is cut out en route. Where an automatic train stop, train control, or cab signal device fails and/or is cut out enroute, train may proceed at...

  11. Utility of Automatic Lighting Design in 3-D Virtual Training Environment

    DTIC Science & Technology

    2004-01-01

    Many training environments have emphasized realism . Although realism may be important for many training applications, it is not essential for...achieving presence, attention, and emotional engagement (Zimmons, 2004). Also, realism is not always in conflict with providing atmosphere or mood, as...applied to the scene to heighten the audience’s emotional experience, while maintaining the perceived realism of the environment portrayed (Block, 2001

  12. Machine Tool Technology. Automatic Screw Machine Troubleshooting & Set-Up Training Outlines [and] Basic Operator's Skills Set List.

    ERIC Educational Resources Information Center

    Anoka-Hennepin Technical Coll., Minneapolis, MN.

    This set of two training outlines and one basic skills set list are designed for a machine tool technology program developed during a project to retrain defense industry workers at risk of job loss or dislocation because of conversion of the defense industry. The first troubleshooting training outline lists the categories of problems that develop…

  13. Implicit Shape Models for Object Detection in 3d Point Clouds

    NASA Astrophysics Data System (ADS)

    Velizhev, A.; Shapovalov, R.; Schindler, K.

    2012-07-01

    We present a method for automatic object localization and recognition in 3D point clouds representing outdoor urban scenes. The method is based on the implicit shape models (ISM) framework, which recognizes objects by voting for their center locations. It requires only few training examples per class, which is an important property for practical use. We also introduce and evaluate an improved version of the spin image descriptor, more robust to point density variation and uncertainty in normal direction estimation. Our experiments reveal a significant impact of these modifications on the recognition performance. We compare our results against the state-of-the-art method and get significant improvement in both precision and recall on the Ohio dataset, consisting of combined aerial and terrestrial LiDAR scans of 150,000 m2 of urban area in total.

  14. Pervasive community care platform: Ambient Intelligence leveraging sensor networks and mobile agents

    NASA Astrophysics Data System (ADS)

    Su, Chuan-Jun; Chiang, Chang-Yu

    2014-04-01

    Several powerful trends are contributing to an aging of much of the world's population, especially in economically developed countries. To mitigate the negative effects of rapidly ageing populations, societies must act early to plan for the welfare, medical care and residential arrangements of their senior citizens, and for the manpower and associated training needed to execute these plans. This paper describes the development of an Ambient Intelligent Community Care Platform (AICCP), which creates an environment of Ambient Intelligence through the use of sensor network and mobile agent (MA) technologies. The AICCP allows caregivers to quickly and accurately locate their charges; access, update and share critical treatment and wellness data; and automatically archive all records. The AICCP presented in this paper is expected to enable caregivers and communities to offer pervasive, accurate and context-aware care services.

  15. Automatic target recognition and detection in infrared imagery under cluttered background

    NASA Astrophysics Data System (ADS)

    Gundogdu, Erhan; Koç, Aykut; Alatan, A. Aydın.

    2017-10-01

    Visual object classification has long been studied in visible spectrum by utilizing conventional cameras. Since the labeled images has recently increased in number, it is possible to train deep Convolutional Neural Networks (CNN) with significant amount of parameters. As the infrared (IR) sensor technology has been improved during the last two decades, labeled images extracted from IR sensors have been started to be used for object detection and recognition tasks. We address the problem of infrared object recognition and detection by exploiting 15K images from the real-field with long-wave and mid-wave IR sensors. For feature learning, a stacked denoising autoencoder is trained in this IR dataset. To recognize the objects, the trained stacked denoising autoencoder is fine-tuned according to the binary classification loss of the target object. Once the training is completed, the test samples are propagated over the network, and the probability of the test sample belonging to a class is computed. Moreover, the trained classifier is utilized in a detect-by-classification method, where the classification is performed in a set of candidate object boxes and the maximum confidence score in a particular location is accepted as the score of the detected object. To decrease the computational complexity, the detection step at every frame is avoided by running an efficient correlation filter based tracker. The detection part is performed when the tracker confidence is below a pre-defined threshold. The experiments conducted on the real field images demonstrate that the proposed detection and tracking framework presents satisfactory results for detecting tanks under cluttered background.

  16. Development of a Deep Learning Algorithm for Automatic Diagnosis of Diabetic Retinopathy.

    PubMed

    Raju, Manoj; Pagidimarri, Venkatesh; Barreto, Ryan; Kadam, Amrit; Kasivajjala, Vamsichandra; Aswath, Arun

    2017-01-01

    This paper mainly focuses on the deep learning application in classifying the stage of diabetic retinopathy and detecting the laterality of the eye using funduscopic images. Diabetic retinopathy is a chronic, progressive, sight-threatening disease of the retinal blood vessels. Ophthalmologists diagnose diabetic retinopathy through early funduscopic screening. Normally, there is a time delay in reporting and intervention, apart from the financial cost and risk of blindness associated with it. Using a convolutional neural network based approach for automatic diagnosis of diabetic retinopathy, we trained the prediction network on the publicly available Kaggle dataset. Approximately 35,000 images were used to train the network, which observed a sensitivity of 80.28% and a specificity of 92.29% on the validation dataset of ~53,000 images. Using 8,810 images, the network was trained for detecting the laterality of the eye and observed an accuracy of 93.28% on the validation set of 8,816 images.

  17. Evidence of automatic processing in sequence learning using process-dissociation

    PubMed Central

    Mong, Heather M.; McCabe, David P.; Clegg, Benjamin A.

    2012-01-01

    This paper proposes a way to apply process-dissociation to sequence learning in addition and extension to the approach used by Destrebecqz and Cleeremans (2001). Participants were trained on two sequences separated from each other by a short break. Following training, participants self-reported their knowledge of the sequences. A recognition test was then performed which required discrimination of two trained sequences, either under the instructions to call any sequence encountered in the experiment “old” (the inclusion condition), or only sequence fragments from one half of the experiment “old” (the exclusion condition). The recognition test elicited automatic and controlled process estimates using the process dissociation procedure, and suggested both processes were involved. Examining the underlying processes supporting performance may provide more information on the fundamental aspects of the implicit and explicit constructs than has been attainable through awareness testing. PMID:22679465

  18. The Future of Biofeedback Training in the Field of Special Education.

    ERIC Educational Resources Information Center

    Walton, Wilbur T.

    Biofeedback training (a process of feeding back to an organism information pertaining to its physiological functions using signals transmitted through sensory receptors) stands as one method to better educate and treat handicapped children. Recent research establishes that it is possible to influence automatic processes (such as breathing)…

  19. 76 FR 51463 - Petition for Waiver of Compliance

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-08-18

    ... territory. Specifically, this petition is made in connection with the implementation of PATH's Automatic Train Control (ATC) project and the plan to use unequipped PA-4 cars as maintenance-of-way (MOW) work... control (CBTC) technology throughout the PATH rail network, as described in the Positive Train Control...

  20. 46 CFR 76.33-20 - Operation and installation.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... EQUIPMENT Smoke Detecting System, Details § 76.33-20 Operation and installation. (a) The system shall be so arranged and installed that the presence of smoke in any of the protected spaces will automatically be... automatically indicate the zone in which the smoke originated. The detecting cabinet shall normally be located...

  1. 46 CFR 76.33-20 - Operation and installation.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... EQUIPMENT Smoke Detecting System, Details § 76.33-20 Operation and installation. (a) The system shall be so arranged and installed that the presence of smoke in any of the protected spaces will automatically be... automatically indicate the zone in which the smoke originated. The detecting cabinet shall normally be located...

  2. 46 CFR 76.33-20 - Operation and installation.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... EQUIPMENT Smoke Detecting System, Details § 76.33-20 Operation and installation. (a) The system shall be so arranged and installed that the presence of smoke in any of the protected spaces will automatically be... automatically indicate the zone in which the smoke originated. The detecting cabinet shall normally be located...

  3. 46 CFR 76.33-20 - Operation and installation.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... EQUIPMENT Smoke Detecting System, Details § 76.33-20 Operation and installation. (a) The system shall be so arranged and installed that the presence of smoke in any of the protected spaces will automatically be... automatically indicate the zone in which the smoke originated. The detecting cabinet shall normally be located...

  4. Automatic patient respiration failure detection system with wireless transmission

    NASA Technical Reports Server (NTRS)

    Dimeff, J.; Pope, J. M.

    1968-01-01

    Automatic respiration failure detection system detects respiration failure in patients with a surgically implanted tracheostomy tube, and actuates an audible and/or visual alarm. The system incorporates a miniature radio transmitter so that the patient is unencumbered by wires yet can be monitored from a remote location.

  5. Automatic Vehicle Location successful transit applications : a cross-cutting study : improving service and safety

    DOT National Transportation Integrated Search

    2000-08-01

    Belief in the value of AVL is substantiated by statements of benefits contained earlier in this study. Even so, none of the study agencies are making full use of the voluminous amount of AVL data automatically recorded by the system. Efforts to make ...

  6. Cheating experience: Guiding novices to adopt the gaze strategies of experts expedites the learning of technical laparoscopic skills.

    PubMed

    Vine, Samuel J; Masters, Rich S W; McGrath, John S; Bright, Elizabeth; Wilson, Mark R

    2012-07-01

    Previous research has demonstrated that trainees can be taught (via explicit verbal instruction) to adopt the gaze strategies of expert laparoscopic surgeons. The current study examined a software template designed to guide trainees to adopt expert gaze control strategies passively, without being provided with explicit instructions. We examined 27 novices (who had no laparoscopic training) performing 50 learning trials of a laparoscopic training task in either a discovery-learning (DL) group or a gaze-training (GT) group while wearing an eye tracker to assess gaze control. The GT group performed trials using a surgery-training template (STT); software that is designed to guide expert-like gaze strategies by highlighting the key locations on the monitor screen. The DL group had a normal, unrestricted view of the scene on the monitor screen. Both groups then took part in a nondelayed retention test (to assess learning) and a stress test (under social evaluative threat) with a normal view of the scene. The STT was successful in guiding the GT group to adopt an expert-like gaze strategy (displaying more target-locking fixations). Adopting expert gaze strategies led to an improvement in performance for the GT group, which outperformed the DL group in both retention and stress tests (faster completion time and fewer errors). The STT is a practical and cost-effective training interface that automatically promotes an optimal gaze strategy. Trainees who are trained to adopt the efficient target-locking gaze strategy of experts gain a performance advantage over trainees left to discover their own strategies for task completion. Copyright © 2012 Mosby, Inc. All rights reserved.

  7. Computer automation of ultrasonic testing. [inspection of ultrasonic welding

    NASA Technical Reports Server (NTRS)

    Yee, B. G. W.; Kerlin, E. E.; Gardner, A. H.; Dunmyer, D.; Wells, T. G.; Robinson, A. R.; Kunselman, J. S.; Walker, T. C.

    1974-01-01

    Report describes a prototype computer-automated ultrasonic system developed for the inspection of weldments. This system can be operated in three modes: manual, automatic, and computer-controlled. In the computer-controlled mode, the system will automatically acquire, process, analyze, store, and display ultrasonic inspection data in real-time. Flaw size (in cross-section), location (depth), and type (porosity-like or crack-like) can be automatically discerned and displayed. The results and pertinent parameters are recorded.

  8. Ubiquitous Stereo Vision for Controlling Safety on Platforms in Railroad Station

    NASA Astrophysics Data System (ADS)

    Yoda, Ikushi; Hosotani, Daisuke; Sakaue, Katushiko

    Dozens of people are killed every year when they fall off of train platforms, making this an urgent issue to be addressed by the railroads, especially in the major cities. This concern prompted the present work that is now in progress to develop a Ubiquitous Stereo Vision based system for safety management at the edge of rail station platforms. In this approach, a series of stereo cameras are installed in a row on the ceiling that are pointed downward at the edge of the platform to monitor the disposition of people waiting for the train. The purpose of the system is to determine automatically and in real-time whether anyone or anything is in the danger zone at the very edge of the platform, whether anyone has actually fallen off the platform, or whether there is any sign of these things happening. The system could be configured to automatically switch over to a surveillance monitor or automatically connect to an emergency brake system in the event of trouble.

  9. Objective assessment of the aesthetic outcomes of breast cancer treatment: toward automatic localization of fiducial points on digital photographs

    NASA Astrophysics Data System (ADS)

    Udpa, Nitin; Sampat, Mehul P.; Kim, Min Soon; Reece, Gregory P.; Markey, Mia K.

    2007-03-01

    The contemporary goals of breast cancer treatment are not limited to cure but include maximizing quality of life. All breast cancer treatment can adversely affect breast appearance. Developing objective, quantifiable methods to assess breast appearance is important to understand the impact of deformity on patient quality of life, guide selection of current treatments, and make rational treatment advances. A few measures of aesthetic properties such as symmetry have been developed. They are computed from the distances between manually identified fiducial points on digital photographs. However, this is time-consuming and subject to intra- and inter-observer variability. The purpose of this study is to investigate methods for automatic localization of fiducial points on anterior-posterior digital photographs taken to document the outcomes of breast reconstruction. Particular emphasis is placed on automatic localization of the nipple complex since the most widely used aesthetic measure, the Breast Retraction Assessment, quantifies the symmetry of nipple locations. The nipple complexes are automatically localized using normalized cross-correlation with a template bank of variants of Gaussian and Laplacian of Gaussian filters. A probability map of likely nipple locations determined from the image database is used to reduce the number of false positive detections from the matched filter operation. The accuracy of the nipple detection was evaluated relative to markings made by three human observers. The impact of using the fiducial point locations as identified by the automatic method, as opposed to the manual method, on the calculation of the Breast Retraction Assessment was also evaluated.

  10. Automated Wildfire Detection Through Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

    2005-01-01

    We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

  11. Karst show caves - how DTN technology as used in space assists automatic environmental monitoring and tourist protection - experiment in Postojna cave

    NASA Astrophysics Data System (ADS)

    Gabrovšek, F.; Grašič, B.; Božnar, M. Z.; Mlakar, P.; Udén, M.; Davies, E.

    2013-10-01

    The paper presents an experiment demonstrating a novel and successful application of Delay- and Disruption-Tolerant Networking (DTN) technology for automatic data transfer in a karst cave Early Warning and Measuring System. The experiment took place inside the Postojna Cave in Slovenia, which is open to tourists. Several automatic meteorological measuring stations are set up inside the cave, as an adjunct to the surveillance infrastructure; the regular data transfer provided by the DTN technology allows the surveillance system to take on the role of an Early Warning System (EWS). One of the stations is set up alongside the railway tracks, which allows the tourist to travel inside the cave by train. The experiment was carried out by placing a DTN "data mule" (a DTN-enabled computer with WiFi connection) on the train and by upgrading the meteorological station with a DTN-enabled WiFi transmission system. When the data mule is in the wireless drive-by mode, it collects measurement data from the station over a period of several seconds as the train passes the stationary equipment, and delivers data at the final train station by the cave entrance. This paper describes an overview of the experimental equipment and organisation allowing the use of a DTN system for data collection and an EWS inside karst caves where there is a regular traffic of tourists and researchers.

  12. Karst show caves - how DTN technology as used in space assists automatic environmental monitoring and tourist protection - experiment in Postojna Cave

    NASA Astrophysics Data System (ADS)

    Gabrovšek, F.; Grašič, B.; Božnar, M. Z.; Mlakar, P.; Udén, M.; Davies, E.

    2014-02-01

    The paper presents an experiment demonstrating a novel and successful application of delay- and disruption-tolerant networking (DTN) technology for automatic data transfer in a karst cave early warning and measuring system. The experiment took place inside the Postojna Cave in Slovenia, which is open to tourists. Several automatic meteorological measuring stations are set up inside the cave, as an adjunct to the surveillance infrastructure; the regular data transfer provided by the DTN technology allows the surveillance system to take on the role of an early warning system (EWS). One of the stations is set up alongside the railway tracks, which allows the tourist to travel inside the cave by train. The experiment was carried out by placing a DTN "data mule" (a DTN-enabled computer with WiFi connection) on the train and by upgrading the meteorological station with a DTN-enabled WiFi transmission system. When the data mule is in the wireless drive-by mode, it collects measurement data from the station over a period of several seconds as the train without stopping passes the stationary equipment, and delivers data at the final train station by the cave entrance. This paper describes an overview of the experimental equipment and organization allowing the use of a DTN system for data collection and an EWS inside karst caves where there is regular traffic of tourists and researchers.

  13. Cardiopulmonary resuscitation and automatic external defibrillator training in schools: "is anyone learning how to save a life?".

    PubMed

    Hart, Devin; Flores-Medrano, Oscar; Brooks, Steve; Buick, Jason E; Morrison, Laurie J

    2013-09-01

    Bystander resuscitation efforts, such as cardiopulmonary resuscitation (CPR) and use of an automatic external defibrillator (AED), save lives in cardiac arrest cases. School training in CPR and AED use may increase the currently low community rates of bystander resuscitation. The study objective was to determine the rates of CPR and AED training in Toronto secondary schools and to identify barriers to training and training techniques. This prospective study consisted of telephone interviews conducted with key school staff knowledgeable about CPR and AED teaching. An encrypted Web-based tool with prespecified variables and built-in logic was employed to standardize data collection. Of 268 schools contacted, 93% were available for interview and 83% consented to participate. Students and staff were trained in CPR in 51% and 80% of schools, respectively. Private schools had the lowest training rate (39%). Six percent of schools provided AED training to students and 47% provided AED training to staff. Forty-eight percent of schools had at least one AED installed, but 25% were unaware if their AED was registered with emergency services dispatch. Cost (17%), perceived need (11%), and school population size (10%) were common barriers to student training. Frequently employed training techniques were interactive (32%), didactic instruction (30%) and printed material (16%). CPR training rates for staff and students were moderate overall and lowest in private schools, whereas training rates in AED use were poor in all schools. Identified barriers to training include cost and student population size (perceived to be too small to be cost-effective or too large to be implemented). Future studies should assess the application of convenient and cost-effective teaching alternatives not presently in use.

  14. Automatic Insall-Salvati ratio measurement on lateral knee x-ray images using model-guided landmark localization

    NASA Astrophysics Data System (ADS)

    Chen, Hsin-Chen; Lin, Chii-Jeng; Wu, Chia-Hsing; Wang, Chien-Kuo; Sun, Yung-Nien

    2010-11-01

    The Insall-Salvati ratio (ISR) is important for detecting two common clinical signs of knee disease: patella alta and patella baja. Furthermore, large inter-operator differences in ISR measurement make an objective measurement system necessary for better clinical evaluation. In this paper, we define three specific bony landmarks for determining the ISR and then propose an x-ray image analysis system to localize these landmarks and measure the ISR. Due to inherent artifacts in x-ray images, such as unevenly distributed intensities, which make landmark localization difficult, we hence propose a registration-assisted active-shape model (RAASM) to localize these landmarks. We first construct a statistical model from a set of training images based on x-ray image intensity and patella shape. Since a knee x-ray image contains specific anatomical structures, we then design an algorithm, based on edge tracing, for patella feature extraction in order to automatically align the model to the patella image. We can estimate the landmark locations as well as the ISR after registration-assisted model fitting. Our proposed method successfully overcomes drawbacks caused by x-ray image artifacts. Experimental results show great agreement between the ISRs measured by the proposed method and by orthopedic clinicians.

  15. Automatic estimation of voice onset time for word-initial stops by applying random forest to onset detection.

    PubMed

    Lin, Chi-Yueh; Wang, Hsiao-Chuan

    2011-07-01

    The voice onset time (VOT) of a stop consonant is the interval between its burst onset and voicing onset. Among a variety of research topics on VOT, one that has been studied for years is how VOTs are efficiently measured. Manual annotation is a feasible way, but it becomes a time-consuming task when the corpus size is large. This paper proposes an automatic VOT estimation method based on an onset detection algorithm. At first, a forced alignment is applied to identify the locations of stop consonants. Then a random forest based onset detector searches each stop segment for its burst and voicing onsets to estimate a VOT. The proposed onset detection can detect the onsets in an efficient and accurate manner with only a small amount of training data. The evaluation data extracted from the TIMIT corpus were 2344 words with a word-initial stop. The experimental results showed that 83.4% of the estimations deviate less than 10 ms from their manually labeled values, and 96.5% of the estimations deviate by less than 20 ms. Some factors that influence the proposed estimation method, such as place of articulation, voicing of a stop consonant, and quality of succeeding vowel, were also investigated. © 2011 Acoustical Society of America

  16. Automatic Classification of Time-variable X-Ray Sources

    NASA Astrophysics Data System (ADS)

    Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M.

    2014-05-01

    To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ~97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7-500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

  17. System and method for charging a plug-in electric vehicle

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

    Bassham, Marjorie A.; Spigno, Jr., Ciro A.; Muller, Brett T.

    2017-05-02

    A charging system and method that may be used to automatically apply customized charging settings to a plug-in electric vehicle, where application of the settings is based on the vehicle's location. According to an exemplary embodiment, a user may establish and save a separate charging profile with certain customized charging settings for each geographic location where they plan to charge their plug-in electric vehicle. Whenever the plug-in electric vehicle enters a new geographic area, the charging method may automatically apply the charging profile that corresponds to that area. Thus, the user does not have to manually change or manipulate themore » charging settings every time they charge the plug-in electric vehicle in a new location.« less

  18. Automatic construction of a recurrent neural network based classifier for vehicle passage detection

    NASA Astrophysics Data System (ADS)

    Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

    2017-03-01

    Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

  19. Target recognition based on convolutional neural network

    NASA Astrophysics Data System (ADS)

    Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

    2017-11-01

    One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

  20. Training Strategies for the M1 Abrams Tank Driver Trainer

    DTIC Science & Technology

    1984-10-01

    positive reinforcement. The automatic freeze after incorrect performance, for example, may even be aversive to the trainee. The TECEP learning algorithms ...Aagard, J.A. and Braby, R. Learning Guidelines and Algorithms for Types of Training Objectives. (TAEG Report No. 23). Orlando, FL: Training Analysis and...checklist ite. flake it identical to operational setting. () Cresponde to the g;uideli ne number Tor thiss oast. Figure B-I. Learning Algorithm for

  1. A dual-user teleoperation system with Online Authority Adjustment for haptic training.

    PubMed

    Fei Liu; Leleve, Arnaud; Eberard, Damien; Redarce, Tanneguy

    2015-08-01

    This paper introduces a dual-user teleoperation system for hands-on medical training. A shared control based architecture is presented for authority management. In this structure, the combination of control signals is obtained using a dominance factor. Its main improvement is Online Authority Adjustment (OAA): the authority can be adjusted manually/automatically during the training progress. Experimental results are provided to validate the performances of the system.

  2. Automatic location of disruption times in JET

    NASA Astrophysics Data System (ADS)

    Moreno, R.; Vega, J.; Murari, A.

    2014-11-01

    The loss of stability and confinement in tokamak plasmas can induce critical events known as disruptions. Disruptions produce strong electromagnetic forces and thermal loads which can damage fundamental components of the devices. Determining the disruption time is extremely important for various disruption studies: theoretical models, physics-driven models, or disruption predictors. In JET, during the experimental campaigns with the JET-C (Carbon Fiber Composite) wall, a common criterion to determine the disruption time consisted of locating the time of the thermal quench. However, with the metallic ITER-like wall (JET-ILW), this criterion is usually not valid. Several thermal quenches may occur previous to the current quench but the temperature recovers. Therefore, a new criterion has to be defined. A possibility is to use the start of the current quench as disruption time. This work describes the implementation of an automatic data processing method to estimate the disruption time according to this new definition. This automatic determination allows both reducing human efforts to locate the disruption times and standardizing the estimates (with the benefit of being less vulnerable to human errors).

  3. Approach bias modification in inpatient psychiatric smokers.

    PubMed

    Machulska, Alla; Zlomuzica, Armin; Rinck, Mike; Assion, Hans-Jörg; Margraf, Jürgen

    2016-05-01

    Drug-related automatic approach tendencies contribute to the development and maintenance of addictive behavior. The present study investigated whether a nicotine-related approach bias can be modified in smokers undergoing inpatient psychiatric treatment by using a novel training variant of the nicotine Approach-Avoidance-Task (AAT). Additionally, we assessed whether the AAT-training would affect smoking behavior. Inpatient smokers were randomly assigned to either an AAT-training or a sham-training condition. In the AAT-training condition, smokers were indirectly instructed to make avoidance movements in response to nicotine-related pictures and to make approach movements in response to tooth-cleaning pictures. In the sham-training condition, no contingency between picture content und arm movements existed. Trainings were administered in four sessions, accompanied by a brief smoking-cessation intervention. Smoking-related self-report measures and automatic approach biases toward smoking cues were measured before and after training. Three months after training, daily nicotine consumption was obtained. A total of 205 participants were recruited, and data from 139 participants were considered in the final analysis. Prior to the trainings, smokers in both conditions exhibited a stronger approach bias for nicotine-related pictures than for tooth-cleaning pictures. After both trainings, this difference was no longer evident. Although reduced smoking behavior at posttest was observed after both trainings, only the AAT-training led to a larger reduction of nicotine consumption at a three-month follow-up. Our preliminary data partially support the conclusion that the AAT might be a feasible tool to reduce smoking in the long-term in psychiatric patients, albeit its effect on other smoking-related measures remains to be explored. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. NREL Transportation Project to Reduce Fuel Usage

    Science.gov Websites

    and communication software was developed by NREL researchers to display a vehicle's location automatically and transmit a map of the its location over the Internet. After developing the communication vehicle location and communication technology to track and direct vehicle fleet movements," said the

  5. Automatic determination of the artery vein ratio in retinal images

    NASA Astrophysics Data System (ADS)

    Niemeijer, Meindert; van Ginneken, Bram; Abràmoff, Michael D.

    2010-03-01

    A lower ratio between the width of the arteries and veins (Arteriolar-to-Venular diameter Ratio, AVR) on the retina, is well established to be predictive of stroke and other cardiovascular events in adults, as well as an increased risk of retinopathy of prematurity in premature infants. This work presents an automatic method that detects the location of the optic disc, determines the appropriate region of interest (ROI), classifies the vessels in the ROI into arteries and veins, measures their widths and calculates the AVR. After vessel segmentation and vessel width determination the optic disc is located and the system eliminates all vessels outside the AVR measurement ROI. The remaining vessels are thinned, vessel crossing and bifurcation points are removed leaving a set of vessel segments containing centerline pixels. Features are extracted from each centerline pixel that are used to assign them a soft label indicating the likelihood the pixel is part of a vein. As all centerline pixels in a connected segment should be the same type, the median soft label is assigned to each centerline pixel in the segment. Next artery vein pairs are matched using an iterative algorithm and the widths of the vessels is used to calculate the AVR. We train and test the algorithm using a set of 25 high resolution digital color fundus photographs a reference standard that indicates for the major vessels in the images whether they are an artery or a vein. We compared the AVR values produced by our system with those determined using a computer assisted method in 15 high resolution digital color fundus photographs and obtained a correlation coefficient of 0.881.

  6. Exploiting the systematic review protocol for classification of medical abstracts.

    PubMed

    Frunza, Oana; Inkpen, Diana; Matwin, Stan; Klement, William; O'Blenis, Peter

    2011-01-01

    To determine whether the automatic classification of documents can be useful in systematic reviews on medical topics, and specifically if the performance of the automatic classification can be enhanced by using the particular protocol of questions employed by the human reviewers to create multiple classifiers. The test collection is the data used in large-scale systematic review on the topic of the dissemination strategy of health care services for elderly people. From a group of 47,274 abstracts marked by human reviewers to be included in or excluded from further screening, we randomly selected 20,000 as a training set, with the remaining 27,274 becoming a separate test set. As a machine learning algorithm we used complement naïve Bayes. We tested both a global classification method, where a single classifier is trained on instances of abstracts and their classification (i.e., included or excluded), and a novel per-question classification method that trains multiple classifiers for each abstract, exploiting the specific protocol (questions) of the systematic review. For the per-question method we tested four ways of combining the results of the classifiers trained for the individual questions. As evaluation measures, we calculated precision and recall for several settings of the two methods. It is most important not to exclude any relevant documents (i.e., to attain high recall for the class of interest) but also desirable to exclude most of the non-relevant documents (i.e., to attain high precision on the class of interest) in order to reduce human workload. For the global method, the highest recall was 67.8% and the highest precision was 37.9%. For the per-question method, the highest recall was 99.2%, and the highest precision was 63%. The human-machine workflow proposed in this paper achieved a recall value of 99.6%, and a precision value of 17.8%. The per-question method that combines classifiers following the specific protocol of the review leads to better results than the global method in terms of recall. Because neither method is efficient enough to classify abstracts reliably by itself, the technology should be applied in a semi-automatic way, with a human expert still involved. When the workflow includes one human expert and the trained automatic classifier, recall improves to an acceptable level, showing that automatic classification techniques can reduce the human workload in the process of building a systematic review. Copyright © 2010 Elsevier B.V. All rights reserved.

  7. Pervasive Sound Sensing: A Weakly Supervised Training Approach.

    PubMed

    Kelly, Daniel; Caulfield, Brian

    2016-01-01

    Modern smartphones present an ideal device for pervasive sensing of human behavior. Microphones have the potential to reveal key information about a person's behavior. However, they have been utilized to a significantly lesser extent than other smartphone sensors in the context of human behavior sensing. We postulate that, in order for microphones to be useful in behavior sensing applications, the analysis techniques must be flexible and allow easy modification of the types of sounds to be sensed. A simplification of the training data collection process could allow a more flexible sound classification framework. We hypothesize that detailed training, a prerequisite for the majority of sound sensing techniques, is not necessary and that a significantly less detailed and time consuming data collection process can be carried out, allowing even a nonexpert to conduct the collection, labeling, and training process. To test this hypothesis, we implement a diverse density-based multiple instance learning framework, to identify a target sound, and a bag trimming algorithm, which, using the target sound, automatically segments weakly labeled sound clips to construct an accurate training set. Experiments reveal that our hypothesis is a valid one and results show that classifiers, trained using the automatically segmented training sets, were able to accurately classify unseen sound samples with accuracies comparable to supervised classifiers, achieving an average F -measure of 0.969 and 0.87 for two weakly supervised datasets.

  8. The Nagoya cosmic-ray muon spectrometer 3, part 3: Automatic film scanning equipment

    NASA Technical Reports Server (NTRS)

    Shibata, S.; Kamiya, Y.; Iijima, K.; Iida, S.

    1985-01-01

    In the regular operation of the Nagoya cosmic-ray muon spectrometer, about 2000 events per day will be recorded on the photographic film. To derive the track locations from such a huge number of photographs with high accuracy in a short time, an automatic film scanning device has been developed.

  9. 76 FR 4443 - Privacy Act of 1974; Report of Modified or Altered System of Records

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-01-25

    ... located nearby. The computer room is protected by an automatic sprinkler system, automatic sensors (e.g... 1974; Report of Modified or Altered System of Records AGENCY: National Center for HIV, STD and TB... Services (DHHS). ACTION: Notification of Proposed Altered System of Records. SUMMARY: The Department of...

  10. 49 CFR 571.123 - Standard No. 123; Motorcycle controls and displays.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... handgrip. If a motorcycle with an automatic clutch other than a scooter is equipped with a supplemental rear brake control, the control shall be located on the left handlebar. If a scooter with an automatic... equipped with self-proportioning or antilock braking devices utilizing a single control for front and rear...

  11. Automatic identification of IASLC-defined mediastinal lymph node stations on CT scans using multi-atlas organ segmentation

    NASA Astrophysics Data System (ADS)

    Hoffman, Joanne; Liu, Jiamin; Turkbey, Evrim; Kim, Lauren; Summers, Ronald M.

    2015-03-01

    Station-labeling of mediastinal lymph nodes is typically performed to identify the location of enlarged nodes for cancer staging. Stations are usually assigned in clinical radiology practice manually by qualitative visual assessment on CT scans, which is time consuming and highly variable. In this paper, we developed a method that automatically recognizes the lymph node stations in thoracic CT scans based on the anatomical organs in the mediastinum. First, the trachea, lungs, and spines are automatically segmented to locate the mediastinum region. Then, eight more anatomical organs are simultaneously identified by multi-atlas segmentation. Finally, with the segmentation of those anatomical organs, we convert the text definitions of the International Association for the Study of Lung Cancer (IASLC) lymph node map into patient-specific color-coded CT image maps. Thus, a lymph node station is automatically assigned to each lymph node. We applied this system to CT scans of 86 patients with 336 mediastinal lymph nodes measuring equal or greater than 10 mm. 84.8% of mediastinal lymph nodes were correctly mapped to their stations.

  12. Strategies for automatic processing of large aftershock sequences

    NASA Astrophysics Data System (ADS)

    Kvaerna, T.; Gibbons, S. J.

    2017-12-01

    Aftershock sequences following major earthquakes present great challenges to seismic bulletin generation. The analyst resources needed to locate events increase with increased event numbers as the quality of underlying, fully automatic, event lists deteriorates. While current pipelines, designed a generation ago, are usually limited to single passes over the raw data, modern systems also allow multiple passes. Processing the raw data from each station currently generates parametric data streams that are later subject to phase-association algorithms which form event hypotheses. We consider a major earthquake scenario and propose to define a region of likely aftershock activity in which we will detect and accurately locate events using a separate, specially targeted, semi-automatic process. This effort may use either pattern detectors or more general algorithms that cover wider source regions without requiring waveform similarity. An iterative procedure to generate automatic bulletins would incorporate all the aftershock event hypotheses generated by the auxiliary process, and filter all phases from these events from the original detection lists prior to a new iteration of the global phase-association algorithm.

  13. The automaticity of vantage point shifts within a synaesthetes' spatial calendar.

    PubMed

    Jarick, Michelle; Jensen, Candice; Dixon, Michael J; Smilek, Daniel

    2011-09-01

    Time-space synaesthetes report that time units (e.g., months, days, hours) occupy idiosyncratic spatial locations. For the synaesthete (L), the months of the year are projected out in external space in the shape of a 'scoreboard 7', where January to July extend across the top from left to right and August to December make up the vertical segment from top to bottom. Interestingly, L can change the mental vantage point (MVP) from where she views her month-space depending on whether she sees or hears the month name. We used a spatial cueing task to demonstrate that L's attention could be directed to locations within her time-space and change vantage points automatically - from trial to trial. We also sought to eliminate any influence of strategy on L's performance by shortening the interval between the cue and target onset to only 150 ms, and have the targets fall in synaesthetically cued locations on only 15% of trials. If L's performance was attributable to intentionally using the cue to predict target location, these manipulations should eliminate any cueing effects. In two separate experiments, we found that L still showed an attentional bias consistent with her synaesthesia. Thus, we attribute L's rapid and resilient cueing effects to the automaticity of her spatial forms. ©2011 The British Psychological Society.

  14. AN EIGHT WEEK SUMMER INSTITUTE TRAINING PROGRAM TO TRAIN INSTRUCTORS OF INSTRUMENTATION TECHNOLOGY.

    ERIC Educational Resources Information Center

    MCKEE, DELBERT A.

    A SUMMER INSTITUTE IN INSTRUMENTATION TECHNOLOGY WAS HELD TO PROVIDE TEACHERS WITH CURRENT KNOWLEDGE ON AUTOMATIC, PROCESS-CONTROL INSTRUMENTATION. A PREVIOUSLY DEVELOPED GUIDE FOR A 2-YEAR, POST-HIGH SCHOOL CURRICULUM PROVIDED THE BASIS FOR INSTRUCTION AND DISCUSSION DURING THE INSTITUTE. THREE COURSES IN MEASUREMENT AND INSTRUMENT SHOP…

  15. Cortical Reorganization in Dyslexic Children after Phonological Training: Evidence from Early Evoked Potentials

    ERIC Educational Resources Information Center

    Spironelli, Chiara; Penolazzi, Barbara; Vio, Claudio; Angrilli, Alessandro

    2010-01-01

    Brain plasticity was investigated in 14 Italian children affected by developmental dyslexia after 6 months of phonological training. The means used to measure language reorganization was the recognition potential, an early wave, also called N150, elicited by automatic word recognition. This component peaks over the left temporo-occipital cortex…

  16. Representing System Behaviors and Expert Behaviors for Intelligent Tutoring. Technical Report No. 108.

    ERIC Educational Resources Information Center

    Towne, Douglas M.; And Others

    Simulation-based software tools that can infer system behaviors from a deep model of the system have the potential for automatically building the semantic representations required to support intelligent tutoring in fault diagnosis. The Intelligent Maintenance Training System (IMTS) is such a resource, designed for use in training troubleshooting…

  17. Second-order sliding mode controller with model reference adaptation for automatic train operation

    NASA Astrophysics Data System (ADS)

    Ganesan, M.; Ezhilarasi, D.; Benni, Jijo

    2017-11-01

    In this paper, a new approach to model reference based adaptive second-order sliding mode control together with adaptive state feedback is presented to control the longitudinal dynamic motion of a high speed train for automatic train operation with the objective of minimal jerk travel by the passengers. The nonlinear dynamic model for the longitudinal motion of the train comprises of a locomotive and coach subsystems is constructed using multiple point-mass model by considering the forces acting on the vehicle. An adaptation scheme using Lyapunov criterion is derived to tune the controller gains by considering a linear, stable reference model that ensures the stability of the system in closed loop. The effectiveness of the controller tracking performance is tested under uncertain passenger load, coupler-draft gear parameters, propulsion resistance coefficients variations and environmental disturbances due to side wind and wet rail conditions. The results demonstrate improved tracking performance of the proposed control scheme with a least jerk under maximum parameter uncertainties when compared to constant gain second-order sliding mode control.

  18. Gear Tooth Wear Detection Algorithm

    NASA Technical Reports Server (NTRS)

    Delgado, Irebert R.

    2015-01-01

    Vibration-based condition indicators continue to be developed for Health Usage Monitoring of rotorcraft gearboxes. Testing performed at NASA Glenn Research Center have shown correlations between specific condition indicators and specific types of gear wear. To speed up the detection and analysis of gear teeth, an image detection program based on the Viola-Jones algorithm was trained to automatically detect spiral bevel gear wear pitting. The detector was tested using a training set of gear wear pictures and a blind set of gear wear pictures. The detector accuracy for the training set was 75 percent while the accuracy for the blind set was 15 percent. Further improvements on the accuracy of the detector are required but preliminary results have shown its ability to automatically detect gear tooth wear. The trained detector would be used to quickly evaluate a set of gear or pinion pictures for pits, spalls, or abrasive wear. The results could then be used to correlate with vibration or oil debris data. In general, the program could be retrained to detect features of interest from pictures of a component taken over a period of time.

  19. Image simulation for automatic license plate recognition

    NASA Astrophysics Data System (ADS)

    Bala, Raja; Zhao, Yonghui; Burry, Aaron; Kozitsky, Vladimir; Fillion, Claude; Saunders, Craig; Rodríguez-Serrano, José

    2012-01-01

    Automatic license plate recognition (ALPR) is an important capability for traffic surveillance applications, including toll monitoring and detection of different types of traffic violations. ALPR is a multi-stage process comprising plate localization, character segmentation, optical character recognition (OCR), and identification of originating jurisdiction (i.e. state or province). Training of an ALPR system for a new jurisdiction typically involves gathering vast amounts of license plate images and associated ground truth data, followed by iterative tuning and optimization of the ALPR algorithms. The substantial time and effort required to train and optimize the ALPR system can result in excessive operational cost and overhead. In this paper we propose a framework to create an artificial set of license plate images for accelerated training and optimization of ALPR algorithms. The framework comprises two steps: the synthesis of license plate images according to the design and layout for a jurisdiction of interest; and the modeling of imaging transformations and distortions typically encountered in the image capture process. Distortion parameters are estimated by measurements of real plate images. The simulation methodology is successfully demonstrated for training of OCR.

  20. The Design and Operation of Suborbital Low Cost and Low Risk Vehicle to the Edge of Space (SOLVES)

    NASA Astrophysics Data System (ADS)

    Ridzuan Zakaria, Norul; Nasrun, Nasri; Rashidy Zulkifi, Mohd; Izmir Yamin, Mohd; Othman, Jamaludin; Rafidi Zakaria, Norul

    2013-09-01

    Inclusive in the planning of Spaceport Malaysia are 2 local suborbital vehicles development. One of the vehicles is called SOLVES or Suborbital Low Cost and Low Risk Vehicle to the Edge of Space. The emphasis on the design and operation of SOLVES is green and robotic technology, where both green technology and robotic technology are used to protect the environment and enhance safety. As SOLVES climbs, its center of gravity stabilizes and remains at the bottom as its propellant being used until it depletes, due to the position of the vehicle's passenger cabin and its engines at its lower end. It will reach 80km from sea level generally known as "the edge of space" due to its momentum although its propellant will be depleted at a lower altitude. As the suborbital vehicle descends tail first, its wings automatically extend and rotate at horizontal axes perpendicular to the fuselage. These naturally and passively rotating wings ensure controlled low velocity and stable descend of the vehicle. The passenger cabin also rotates automatically at a steady low speed at the centerline of its fuselage as it descends, caused naturally by the lift force, enabling its passengers a surrounding 360 degrees view. SOLVES is steered automatically to its landing point by an electrical propulsion system with a vectoring nozzle. The electrical propulsion minimizes space and weight and is free of pollution and noise. Its electrical power comes from a battery aided by power generated by the naturally rotating wings. When the vehicle lands, it is in the safest mode as its propellant is depleted and its center of gravity remains at the bottom of its cabin. The cabin, being located at the bottom of the fuselage, enables very convenient, rapid and safe entry and exit of its passengers. SOLVES will be a robotic suborbital vehicle with green technology. The vehicle will carry 4 passengers and each passenger will be trained to land the vehicle manually if the fully automated landing system fails and therefore it will be engineered for simple operation by trained passengers. However, for certification by aviation authorities the vehicle may be operational with 3 passengers and a pilot. A specific operation considered for SOLVES is navaloperation where the suborbital vehicle will be operating from a seaborne spaceport, probably a superyacht with spacepad for the vertical launching and landing of the vehicle. Such naval operation enables the vehicle to fly above exotic locations reachable by sea. SOLVES is also planned for further development into reusable rocket booster to carry small suborbiter to 160km from sea level, enables the passengers aboard the suborbiter to experience longer zero gravity time and more effective suborbital flight.

  1. Development of an automated ultrasonic testing system

    NASA Astrophysics Data System (ADS)

    Shuxiang, Jiao; Wong, Brian Stephen

    2005-04-01

    Non-Destructive Testing is necessary in areas where defects in structures emerge over time due to wear and tear and structural integrity is necessary to maintain its usability. However, manual testing results in many limitations: high training cost, long training procedure, and worse, the inconsistent test results. A prime objective of this project is to develop an automatic Non-Destructive testing system for a shaft of the wheel axle of a railway carriage. Various methods, such as the neural network, pattern recognition methods and knowledge-based system are used for the artificial intelligence problem. In this paper, a statistical pattern recognition approach, Classification Tree is applied. Before feature selection, a thorough study on the ultrasonic signals produced was carried out. Based on the analysis of the ultrasonic signals, three signal processing methods were developed to enhance the ultrasonic signals: Cross-Correlation, Zero-Phase filter and Averaging. The target of this step is to reduce the noise and make the signal character more distinguishable. Four features: 1. The Auto Regressive Model Coefficients. 2. Standard Deviation. 3. Pearson Correlation 4. Dispersion Uniformity Degree are selected. And then a Classification Tree is created and applied to recognize the peak positions and amplitudes. Searching local maximum is carried out before feature computing. This procedure reduces much computation time in the real-time testing. Based on this algorithm, a software package called SOFRA was developed to recognize the peaks, calibrate automatically and test a simulated shaft automatically. The automatic calibration procedure and the automatic shaft testing procedure are developed.

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

    Ding, Fei; Jiang, Huaiguang; Tan, Jin

    This paper proposes an event-driven approach for reconfiguring distribution systems automatically. Specifically, an optimal synchrophasor sensor placement (OSSP) is used to reduce the number of synchrophasor sensors while keeping the whole system observable. Then, a wavelet-based event detection and location approach is used to detect and locate the event, which performs as a trigger for network reconfiguration. With the detected information, the system is then reconfigured using the hierarchical decentralized approach to seek for the new optimal topology. In this manner, whenever an event happens the distribution network can be reconfigured automatically based on the real-time information that is observablemore » and detectable.« less

  3. Automation of the Image Analysis for Thermographic Inspection

    NASA Technical Reports Server (NTRS)

    Plotnikov, Yuri A.; Winfree, William P.

    1998-01-01

    Several data processing procedures for the pulse thermal inspection require preliminary determination of an unflawed region. Typically, an initial analysis of the thermal images is performed by an operator to determine the locations of unflawed and the defective areas. In the present work an algorithm is developed for automatically determining a reference point corresponding to an unflawed region. Results are obtained for defects which are arbitrarily located in the inspection region. A comparison is presented of the distributions of derived values with right and wrong localization of the reference point. Different algorithms of automatic determination of the reference point are compared.

  4. Automatic building identification under bomb damage conditions

    NASA Astrophysics Data System (ADS)

    Woodley, Robert; Noll, Warren; Barker, Joseph; Wunsch, Donald C., II

    2009-05-01

    Given the vast amount of image intelligence utilized in support of planning and executing military operations, a passive automated image processing capability for target identification is urgently required. Furthermore, transmitting large image streams from remote locations would quickly use available band width (BW) precipitating the need for processing to occur at the sensor location. This paper addresses the problem of automatic target recognition for battle damage assessment (BDA). We utilize an Adaptive Resonance Theory approach to cluster templates of target buildings. The results show that the network successfully classifies targets from non-targets in a virtual test bed environment.

  5. Innovative telecommunications for law enforcement

    NASA Technical Reports Server (NTRS)

    Sohn, R. L.

    1976-01-01

    The operation of computer-aided dispatch, mobile digital communications, and automatic vehicle location systems used in law enforcement is discussed, and characteristics of systems used by different agencies are compared. With reference to computer-aided dispatch systems, the data base components, dispatcher work load, extent of usage, and design trends are surveyed. The capabilities, levels of communication, and traffic load of mobile digital communications systems are examined. Different automatic vehicle location systems are distinguished, and two systems are evaluated. Other aspects of the application of innovative technology to operational command, control, and communications systems for law enforcement agencies are described.

  6. AED training and its impact on skill acquisition, retention and performance--a systematic review of alternative training methods.

    PubMed

    Yeung, Joyce; Okamoto, Deems; Soar, Jasmeet; Perkins, Gavin D

    2011-06-01

    The most popular method of training in basic life support and AED use remains instructor-led training courses. This systematic review examines the evidence for different training methods of basic life support providers (laypersons and healthcare providers) using standard instructor-led courses as comparators, to assess whether alternative method of training can lead to effective skill acquisition, skill retention and actual performance whilst using the AED. OVID Medline (including Medline 1950-November 2010; EMBASE 1988-November 2010) was searched using "training" OR "teaching" OR "education" as text words. Search was then combined by using AND "AED" OR "automatic external defibrillator" as MESH words. Additionally, the American Heart Association Endnote library was searched with the terms "AED" and "automatic external defibrillator". Resuscitation journal was hand searched for relevant articles. 285 articles were identified. After duplicates were removed, 172 references were reviewed for relevance. From this 22 papers were scrutinized and 18 were included. All were manikin studies. Four LOE 1 studies, seven LOE 2 studies and three LOE 4 studies were supportive of alternative AED training methods. One LOE 2 study was neutral. Three LOE 1 studies provided opposing evidence. There is good evidence to support alternative methods of AED training including lay instructors, self directed learning and brief training. There is also evidence to support that no training is needed but even brief training can improve speed of shock delivery and electrode pad placement. Features of AED can have an impact on its use and further research should be directed to making devices user-friendly and robust to untrained layperson. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  7. An Interactive Decision Support System for Scheduling Fighter Pilot Training

    DTIC Science & Technology

    2002-03-26

    Deitel , H.M. and Deitel , P.J. C: How to Program , 2nd ed., Prentice Hall, 1994. 8. Deitel , H.M. and Deitel , P.J. How to Program Java...Visual Basic Programming language, the Excel tool is modified in several ways. Scheduling Dispatch rules are implemented to automatically generate... programming language, the Excel tool was modified in several ways. Scheduling dispatch rules are implemented to automatically generate

  8. Use of LANDSAT data for automatic classification and area estimation of sugarcane plantation in Sao Paulo state, Brazil

    NASA Technical Reports Server (NTRS)

    Dejesusparada, N. (Principal Investigator); Mendonca, F. J.

    1980-01-01

    Ten segments of the size 20 x 10 km were aerially photographed and used as training areas for automatic classifications. The study areas was covered by four LANDSAT paths: 235, 236, 237, and 238. The percentages of overall correct classification for these paths range from 79.56 percent for path 238 to 95.59 percent for path 237.

  9. Remembering spatial locations: effects of material and intelligence.

    PubMed

    Zucco, G M; Tessari, A; Soresi, S

    1995-04-01

    The aim of the present work was to test some of the criteria for automaticity of spatial-location coding claimed by Hasher and Zacks, particularly individual differences (as intelligence invariance) and effortful encoding strategies. Two groups of subjects, 15 with mental retardation (Down Syndrome, mean chronological age, 20.9 yr.; mean mental age, 11.6 yr.) and 15 normal children (mean age, 11.5 yr.), were administered four kinds of stimuli (pictures, concrete words, nonsense pictures, and abstract words) at one location on a card. Subsequently, subjects were presented the items on the card's centre and were required to place the items in their original locations. Analysis indicated that those with Down Syndrome scored lower than normal children on the four tasks and that stimuli were better or worse remembered according to their characteristics, e.g., their imaginability. Results do not support some of the conditions claimed to be necessary criteria for automaticity in the recall of spatial locations as stated by Hasher and Zacks.

  10. Automatic welding detection by an intelligent tool pipe inspection

    NASA Astrophysics Data System (ADS)

    Arizmendi, C. J.; Garcia, W. L.; Quintero, M. A.

    2015-07-01

    This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called “smart pig” in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

  11. Automatic feature design for optical character recognition using an evolutionary search procedure.

    PubMed

    Stentiford, F W

    1985-03-01

    An automatic evolutionary search is applied to the problem of feature extraction in an OCR application. A performance measure based on feature independence is used to generate features which do not appear to suffer from peaking effects [17]. Features are extracted from a training set of 30 600 machine printed 34 class alphanumeric characters derived from British mail. Classification results on the training set and a test set of 10 200 characters are reported for an increasing number of features. A 1.01 percent forced decision error rate is obtained on the test data using 316 features. The hardware implementation should be cheap and fast to operate. The performance compares favorably with current low cost OCR page readers.

  12. Cable-fault locator

    NASA Technical Reports Server (NTRS)

    Cason, R. L.; Mcstay, J. J.; Heymann, A. P., Sr.

    1979-01-01

    Inexpensive system automatically indicates location of short-circuited section of power cable. Monitor does not require that cable be disconnected from its power source or that test signals be applied. Instead, ground-current sensors are installed in manholes or at other selected locations along cable run. When fault occurs, sensors transmit information about fault location to control center. Repair crew can be sent to location and cable can be returned to service with minimum of downtime.

  13. Variably Transmittive, Electronically-Controlled Eyewear

    NASA Technical Reports Server (NTRS)

    Chapman, John J. (Inventor); Glaab, Louis J. (Inventor); Schott, Timothy D. (Inventor); Howell, Charles T. (Inventor); Fleck, Vincent J. (Inventor)

    2013-01-01

    A system and method for flight training and evaluation of pilots comprises electronically activated vision restriction glasses that detect the pilot's head position and automatically darken and restrict the pilot's ability to see through the front and side windscreens when the pilot-in-training attempts to see out the windscreen. Thus, the pilot-in-training sees only within the aircraft cockpit, forcing him or her to fly by instruments in the most restricted operational mode.

  14. Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks

    PubMed Central

    Richter, Philipp; Toledano-Ayala, Manuel

    2015-01-01

    Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS) signals propagate poorly. To enable wireless local area network (WLAN) location fingerprinting in larger areas while maintaining accuracy, methods to reduce the effort of radio map creation must be consolidated and automatized. Gaussian process regression has been applied to overcome this issue, also with auspicious results, but the fit of the model was never thoroughly assessed. Instead, most studies trained a readily available model, relying on the zero mean and squared exponential covariance function, without further scrutinization. This paper studies the Gaussian process regression model selection for WLAN fingerprinting in indoor and outdoor environments. We train several models for indoor/outdoor- and combined areas; we evaluate them quantitatively and compare them by means of adequate model measures, hence assessing the fit of these models directly. To illuminate the quality of the model fit, the residuals of the proposed model are investigated, as well. Comparative experiments on the positioning performance verify and conclude the model selection. In this way, we show that the standard model is not the most appropriate, discuss alternatives and present our best candidate. PMID:26370996

  15. Detection of oranges from a color image of an orange tree

    NASA Astrophysics Data System (ADS)

    Weeks, Arthur R.; Gallagher, A.; Eriksson, J.

    1999-10-01

    The progress of robotic and machine vision technology has increased the demand for sophisticated methods for performing automatic harvesting of fruit. The harvesting of fruit, until recently, has been performed manually and is quite labor intensive. An automatic robot harvesting system that uses machine vision to locate and extract the fruit would free the agricultural industry from the ups and downs of the labor market. The environment in which robotic fruit harvesters must work presents many challenges due to the inherent variability from one location to the next. This paper takes a step towards this goal by outlining a machine vision algorithm that detects and accurately locates oranges from a color image of an orange tree. Previous work in this area has focused on differentiating the orange regions from the rest of the picture and not locating the actual oranges themselves. Failure to locate the oranges, however, leads to a reduced number of successful pick attempts. This paper presents a new approach for orange region segmentation in which the circumference of the individual oranges as well as partially occluded oranges are located. Accurately defining the circumference of each orange allows a robotic harvester to cut the stem of the orange by either scanning the top of the orange with a laser or by directing a robotic arm towards the stem to automatically cut it. A modified version of the K- means algorithm is used to initially segment the oranges from the canopy of the orange tree. Morphological processing is then used to locate occluded oranges and an iterative circle finding algorithm is used to define the circumference of the segmented oranges.

  16. Prescription and over-the-counter medications tool kit.

    DOT National Transportation Integrated Search

    2011-04-01

    Automatic vehicle location (AVL) is a computer-based vehicle tracking system. For transit, the actual real-time position of each vehicle is measured and its location is relayed to a control center. Actual position determination and relay techniques v...

  17. Mitosis Counting in Breast Cancer: Object-Level Interobserver Agreement and Comparison to an Automatic Method

    PubMed Central

    Veta, Mitko; van Diest, Paul J.; Jiwa, Mehdi; Al-Janabi, Shaimaa; Pluim, Josien P. W.

    2016-01-01

    Background Tumor proliferation speed, most commonly assessed by counting of mitotic figures in histological slide preparations, is an important biomarker for breast cancer. Although mitosis counting is routinely performed by pathologists, it is a tedious and subjective task with poor reproducibility, particularly among non-experts. Inter- and intraobserver reproducibility of mitosis counting can be improved when a strict protocol is defined and followed. Previous studies have examined only the agreement in terms of the mitotic count or the mitotic activity score. Studies of the observer agreement at the level of individual objects, which can provide more insight into the procedure, have not been performed thus far. Methods The development of automatic mitosis detection methods has received large interest in recent years. Automatic image analysis is viewed as a solution for the problem of subjectivity of mitosis counting by pathologists. In this paper we describe the results from an interobserver agreement study between three human observers and an automatic method, and make two unique contributions. For the first time, we present an analysis of the object-level interobserver agreement on mitosis counting. Furthermore, we train an automatic mitosis detection method that is robust with respect to staining appearance variability and compare it with the performance of expert observers on an “external” dataset, i.e. on histopathology images that originate from pathology labs other than the pathology lab that provided the training data for the automatic method. Results The object-level interobserver study revealed that pathologists often do not agree on individual objects, even if this is not reflected in the mitotic count. The disagreement is larger for objects from smaller size, which suggests that adding a size constraint in the mitosis counting protocol can improve reproducibility. The automatic mitosis detection method can perform mitosis counting in an unbiased way, with substantial agreement with human experts. PMID:27529701

  18. Mitosis Counting in Breast Cancer: Object-Level Interobserver Agreement and Comparison to an Automatic Method.

    PubMed

    Veta, Mitko; van Diest, Paul J; Jiwa, Mehdi; Al-Janabi, Shaimaa; Pluim, Josien P W

    2016-01-01

    Tumor proliferation speed, most commonly assessed by counting of mitotic figures in histological slide preparations, is an important biomarker for breast cancer. Although mitosis counting is routinely performed by pathologists, it is a tedious and subjective task with poor reproducibility, particularly among non-experts. Inter- and intraobserver reproducibility of mitosis counting can be improved when a strict protocol is defined and followed. Previous studies have examined only the agreement in terms of the mitotic count or the mitotic activity score. Studies of the observer agreement at the level of individual objects, which can provide more insight into the procedure, have not been performed thus far. The development of automatic mitosis detection methods has received large interest in recent years. Automatic image analysis is viewed as a solution for the problem of subjectivity of mitosis counting by pathologists. In this paper we describe the results from an interobserver agreement study between three human observers and an automatic method, and make two unique contributions. For the first time, we present an analysis of the object-level interobserver agreement on mitosis counting. Furthermore, we train an automatic mitosis detection method that is robust with respect to staining appearance variability and compare it with the performance of expert observers on an "external" dataset, i.e. on histopathology images that originate from pathology labs other than the pathology lab that provided the training data for the automatic method. The object-level interobserver study revealed that pathologists often do not agree on individual objects, even if this is not reflected in the mitotic count. The disagreement is larger for objects from smaller size, which suggests that adding a size constraint in the mitosis counting protocol can improve reproducibility. The automatic mitosis detection method can perform mitosis counting in an unbiased way, with substantial agreement with human experts.

  19. Sources of Infrasound events listed in IDC Reviewed Event Bulletin

    NASA Astrophysics Data System (ADS)

    Bittner, Paulina; Polich, Paul; Gore, Jane; Ali, Sherif; Medinskaya, Tatiana; Mialle, Pierrick

    2017-04-01

    Until 2003 two waveform technologies, i.e. seismic and hydroacoustic were used to detect and locate events included in the International Data Centre (IDC) Reviewed Event Bulletin (REB). The first atmospheric event was published in the REB in 2003, however automatic processing required significant improvements to reduce the number of false events. In the beginning of 2010 the infrasound technology was reintroduced to the IDC operations and has contributed to both automatic and reviewed IDC bulletins. The primary contribution of infrasound technology is to detect atmospheric events. These events may also be observed at seismic stations, which will significantly improve event location. Examples sources of REB events, which were detected by the International Monitoring System (IMS) infrasound network were fireballs (e.g. Bangkok fireball, 2015), volcanic eruptions (e.g. Calbuco, Chile 2015) and large surface explosions (e.g. Tjanjin, China 2015). Query blasts (e.g. Zheleznogorsk) and large earthquakes (e.g. Italy 2016) belong to events primarily recorded at seismic stations of the IMS network but often detected at the infrasound stations. In case of earthquakes analysis of infrasound signals may help to estimate the area affected by ground vibration. Infrasound associations to query blast events may help to obtain better source location. The role of IDC analysts is to verify and improve location of events detected by the automatic system and to add events which were missed in the automatic process. Open source materials may help to identify nature of some events. Well recorded examples may be added to the Reference Infrasound Event Database to help in analysis process. This presentation will provide examples of events generated by different sources which were included in the IDC bulletins.

  20. Automatic Cell Segmentation Using a Shape-Classification Model in Immunohistochemically Stained Cytological Images

    NASA Astrophysics Data System (ADS)

    Shah, Shishir

    This paper presents a segmentation method for detecting cells in immunohistochemically stained cytological images. A two-phase approach to segmentation is used where an unsupervised clustering approach coupled with cluster merging based on a fitness function is used as the first phase to obtain a first approximation of the cell locations. A joint segmentation-classification approach incorporating ellipse as a shape model is used as the second phase to detect the final cell contour. The segmentation model estimates a multivariate density function of low-level image features from training samples and uses it as a measure of how likely each image pixel is to be a cell. This estimate is constrained by the zero level set, which is obtained as a solution to an implicit representation of an ellipse. Results of segmentation are presented and compared to ground truth measurements.

  1. Automatic, time-interval traffic counts for recreation area management planning

    Treesearch

    D. L. Erickson; C. J. Liu; H. K. Cordell

    1980-01-01

    Automatic, time-interval recorders were used to count directional vehicular traffic on a multiple entry/exit road network in the Red River Gorge Geological Area, Daniel Boone National Forest. Hourly counts of entering and exiting traffic differed according to recorder location, but an aggregated distribution showed a delayed peak in exiting traffic thought to be...

  2. An automatic camera device for measuring waterfowl use

    USGS Publications Warehouse

    Cowardin, L.M.; Ashe, J.E.

    1965-01-01

    A Yashica Sequelle camera was modified and equipped with a timing device so that it would take pictures automatically at 15-minute intervals. Several of these cameras were used to photograph randomly selected quadrats located in different marsh habitats. The number of birds photographed in the different areas was used as an index of waterfowl use.

  3. Automated Grading of Rough Hardwood Lumber

    Treesearch

    Richard W. Conners; Tai-Hoon Cho; Philip A. Araman

    1989-01-01

    Any automatic hardwood grading system must have two components. The first of these is a computer vision system for locating and identifying defects on rough lumber. The second is a system for automatically grading boards based on the output of the computer vision system. This paper presents research results aimed at developing the first of these components. The...

  4. Localizing tuberculosis in chest radiographs with deep learning

    NASA Astrophysics Data System (ADS)

    Xue, Zhiyun; Jaeger, Stefan; Antani, Sameer; Long, L. Rodney; Karargyris, Alexandros; Siegelman, Jenifer; Folio, Les R.; Thoma, George R.

    2018-03-01

    Chest radiography (CXR) has been used as an effective tool for screening tuberculosis (TB). Because of the lack of radiological expertise in resource-constrained regions, automatic analysis of CXR is appealing as a "first reader". In addition to screening the CXR for disease, it is critical to highlight locations of the disease in abnormal CXRs. In this paper, we focus on the task of locating TB in CXRs which is more challenging due to the intrinsic difficulty of locating the abnormality. The method is based on applying a convolutional neural network (CNN) to classify the superpixels generated from the lung area. Specifically, it consists of four major components: lung ROI extraction, superpixel segmentation, multi-scale patch generation/labeling, and patch classification. The TB regions are located by identifying those superpixels whose corresponding patches are classified as abnormal by the CNN. The method is tested on a publicly available TB CXR dataset which contains 336 TB images showing various manifestations of TB. The TB regions in the images were marked by radiologists. To evaluate the method, the images are split into training, validation, and test sets with all the manifestations being represented in each set. The performance is evaluated at both the patch level and image level. The classification accuracy on the patch test set is 72.8% and the average Dice index for the test images is 0.67. The factors that may contribute to misclassification are discussed and directions for future work are addressed.

  5. Transcription and Annotation of a Japanese Accented Spoken Corpus of L2 Spanish for the Development of CAPT Applications

    ERIC Educational Resources Information Center

    Carranza, Mario

    2016-01-01

    This paper addresses the process of transcribing and annotating spontaneous non-native speech with the aim of compiling a training corpus for the development of Computer Assisted Pronunciation Training (CAPT) applications, enhanced with Automatic Speech Recognition (ASR) technology. To better adapt ASR technology to CAPT tools, the recognition…

  6. A Training Manual in Conducting a Workshop in the Design, Construction, Operation, Maintenance and Repair of Hydrams.

    ERIC Educational Resources Information Center

    Peace Corps, Washington, DC. Office of Programming and Training Coordination.

    This manual presents a comprehensive training design, suggested procedures, and materials for conducting a workshop in the design, construction, operation, maintenance, and repair of hydrams, and in the planning and implementation of hydram projects. Hydrams (hydraulic rams, hydraulic ram pumps, automatic hydraulic ram pumps, rams) are devices…

  7. Teaching Health Center Graduate Medical Education Locations Predominantly Located in Federally Designated Underserved Areas.

    PubMed

    Barclift, Songhai C; Brown, Elizabeth J; Finnegan, Sean C; Cohen, Elena R; Klink, Kathleen

    2016-05-01

    Background The Teaching Health Center Graduate Medical Education (THCGME) program is an Affordable Care Act funding initiative designed to expand primary care residency training in community-based ambulatory settings. Statute suggests, but does not require, training in underserved settings. Residents who train in underserved settings are more likely to go on to practice in similar settings, and graduates more often than not practice near where they have trained. Objective The objective of this study was to describe and quantify federally designated clinical continuity training sites of the THCGME program. Methods Geographic locations of the training sites were collected and characterized as Health Professional Shortage Area, Medically Underserved Area, Population, or rural areas, and were compared with the distribution of Centers for Medicare and Medicaid Services (CMS)-funded training positions. Results More than half of the teaching health centers (57%) are located in states that are in the 4 quintiles with the lowest CMS-funded resident-to-population ratio. Of the 109 training sites identified, more than 70% are located in federally designated high-need areas. Conclusions The THCGME program is a model that funds residency training in community-based ambulatory settings. Statute suggests, but does not explicitly require, that training take place in underserved settings. Because the majority of the 109 clinical training sites of the 60 funded programs in 2014-2015 are located in federally designated underserved locations, the THCGME program deserves further study as a model to improve primary care distribution into high-need communities.

  8. Assessment of an electronic learning system for colon capsule endoscopy: a pilot study.

    PubMed

    Watabe, Hirotsugu; Nakamura, Tetsuya; Yamada, Atsuo; Kakugawa, Yasuo; Nouda, Sadaharu; Terano, Akira

    2016-06-01

    Training for colon capsule endoscopy (CCE) procedures is currently performed as a lecture and hands-on seminar. The aims of this pilot study were to assess the utility of an electronic learning system for CCE (ELCCE) designed for the Japanese Association for Capsule Endoscopy using an objective scoring engine, and to evaluate the efficacy of ELCCE on the acquisition of CCE reading competence. ELCCE is an Internet-based learning system with the following steps: step 1, introduction; step 2, CCE reading competence assessment test (CCAT), which evaluates the competence of CCE reading prior to training; step 3, learning reading theory; step 4, training with guidance; step 5, training without guidance; step 6, final assessment; and step 7, the same as step 2. The CCAT, step 5 and step 6 were scored automatically according to: lesion detection, diagnosis (location, size, shape of lesion), management recommendations, and quality of view. Ten trainee endoscopists were initially recruited (cohort 1), followed by a validating cohort of 11 trainee endoscopists (cohort 2). All but one participant finished ELCCE training within 7 weeks. In step 6, accuracy ranged from 53 to 98 % and was not impacted by step 2 pretest scores. The average CCAT scores significantly increased between step 2 pretest and step 7 in both cohorts, from 42 ± 18 % to 79 ± 15 % in cohort 1 (p = 0.0004), and from 52 ± 15 % to 79 ± 14 % in cohort 2 (p = 0.0003). ELCCE is useful and effective for improving CCE reading competence.

  9. Selection of a location method for highway data systems.

    DOT National Transportation Integrated Search

    1973-01-01

    The Virginia Department of Highways has many data systems, and among them highway locations are referenced in various ways. While each of the several present systems is useful for its primary intended purpose, the ability to automatically relate the ...

  10. Retraining automatic action tendencies changes alcoholic patients' approach bias for alcohol and improves treatment outcome.

    PubMed

    Wiers, Reinout W; Eberl, Carolin; Rinck, Mike; Becker, Eni S; Lindenmeyer, Johannes

    2011-04-01

    This study tested the effects of a new cognitive-bias modification (CBM) intervention that targeted an approach bias for alcohol in 214 alcoholic inpatients. Patients were assigned to one of two experimental conditions, in which they were explicitly or implicitly trained to make avoidance movements (pushing a joystick) in response to alcohol pictures, or to one of two control conditions, in which they received no training or sham training. Four brief sessions of experimental CBM preceded regular inpatient treatment. In the experimental conditions only, patients' approach bias changed into an avoidance bias for alcohol. This effect generalized to untrained pictures in the task used in the CBM and to an Implicit Association Test, in which alcohol and soft-drink words were categorized with approach and avoidance words. Patients in the experimental conditions showed better treatment outcomes a year later. These findings indicate that a short intervention can change alcoholics' automatic approach bias for alcohol and may improve treatment outcome.

  11. SVM-based automatic diagnosis method for keratoconus

    NASA Astrophysics Data System (ADS)

    Gao, Yuhong; Wu, Qiang; Li, Jing; Sun, Jiande; Wan, Wenbo

    2017-06-01

    Keratoconus is a progressive cornea disease that can lead to serious myopia and astigmatism, or even to corneal transplantation, if it becomes worse. The early detection of keratoconus is extremely important to know and control its condition. In this paper, we propose an automatic diagnosis algorithm for keratoconus to discriminate the normal eyes and keratoconus ones. We select the parameters obtained by Oculyzer as the feature of cornea, which characterize the cornea both directly and indirectly. In our experiment, 289 normal cases and 128 keratoconus cases are divided into training and test sets respectively. Far better than other kernels, the linear kernel of SVM has sensitivity of 94.94% and specificity of 97.87% with all the parameters training in the model. In single parameter experiment of linear kernel, elevation with 92.03% sensitivity and 98.61% specificity and thickness with 97.28% sensitivity and 97.82% specificity showed their good classification abilities. Combining elevation and thickness of the cornea, the proposed method can reach 97.43% sensitivity and 99.19% specificity. The experiments demonstrate that the proposed automatic diagnosis method is feasible and reliable.

  12. Associative (not Hebbian) learning and the mirror neuron system.

    PubMed

    Cooper, Richard P; Cook, Richard; Dickinson, Anthony; Heyes, Cecilia M

    2013-04-12

    The associative sequence learning (ASL) hypothesis suggests that sensorimotor experience plays an inductive role in the development of the mirror neuron system, and that it can play this crucial role because its effects are mediated by learning that is sensitive to both contingency and contiguity. The Hebbian hypothesis proposes that sensorimotor experience plays a facilitative role, and that its effects are mediated by learning that is sensitive only to contiguity. We tested the associative and Hebbian accounts by computational modelling of automatic imitation data indicating that MNS responsivity is reduced more by contingent and signalled than by non-contingent sensorimotor training (Cook et al. [7]). Supporting the associative account, we found that the reduction in automatic imitation could be reproduced by an existing interactive activation model of imitative compatibility when augmented with Rescorla-Wagner learning, but not with Hebbian or quasi-Hebbian learning. The work argues for an associative, but against a Hebbian, account of the effect of sensorimotor training on automatic imitation. We argue, by extension, that associative learning is potentially sufficient for MNS development. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  13. Processing of Crawled Urban Imagery for Building Use Classification

    NASA Astrophysics Data System (ADS)

    Tutzauer, P.; Haala, N.

    2017-05-01

    Recent years have shown a shift from pure geometric 3D city models to data with semantics. This is induced by new applications (e.g. Virtual/Augmented Reality) and also a requirement for concepts like Smart Cities. However, essential urban semantic data like building use categories is often not available. We present a first step in bridging this gap by proposing a pipeline to use crawled urban imagery and link it with ground truth cadastral data as an input for automatic building use classification. We aim to extract this city-relevant semantic information automatically from Street View (SV) imagery. Convolutional Neural Networks (CNNs) proved to be extremely successful for image interpretation, however, require a huge amount of training data. Main contribution of the paper is the automatic provision of such training datasets by linking semantic information as already available from databases provided from national mapping agencies or city administrations to the corresponding façade images extracted from SV. Finally, we present first investigations with a CNN and an alternative classifier as a proof of concept.

  14. Automatic classification of time-variable X-ray sources

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

    Lo, Kitty K.; Farrell, Sean; Murphy, Tara

    2014-05-01

    To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, andmore » other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.« less

  15. Improving labeling efficiency in automatic quality control of MRSI data.

    PubMed

    Pedrosa de Barros, Nuno; McKinley, Richard; Wiest, Roland; Slotboom, Johannes

    2017-12-01

    To improve the efficiency of the labeling task in automatic quality control of MR spectroscopy imaging data. 28'432 short and long echo time (TE) spectra (1.5 tesla; point resolved spectroscopy (PRESS); repetition time (TR)= 1,500 ms) from 18 different brain tumor patients were labeled by two experts as either accept or reject, depending on their quality. For each spectrum, 47 signal features were extracted. The data was then used to run several simulations and test an active learning approach using uncertainty sampling. The performance of the classifiers was evaluated as a function of the number of patients in the training set, number of spectra in the training set, and a parameter α used to control the level of classification uncertainty required for a new spectrum to be selected for labeling. The results showed that the proposed strategy allows reductions of up to 72.97% for short TE and 62.09% for long TE in the amount of data that needs to be labeled, without significant impact in classification accuracy. Further reductions are possible with significant but minimal impact in performance. Active learning using uncertainty sampling is an effective way to increase the labeling efficiency for training automatic quality control classifiers. Magn Reson Med 78:2399-2405, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  16. Water Mapping Using Multispectral Airborne LIDAR Data

    NASA Astrophysics Data System (ADS)

    Yan, W. Y.; Shaker, A.; LaRocque, P. E.

    2018-04-01

    This study investigates the use of the world's first multispectral airborne LiDAR sensor, Optech Titan, manufactured by Teledyne Optech to serve the purpose of automatic land-water classification with a particular focus on near shore region and river environment. Although there exist recent studies utilizing airborne LiDAR data for shoreline detection and water surface mapping, the majority of them only perform experimental testing on clipped data subset or rely on data fusion with aerial/satellite image. In addition, most of the existing approaches require manual intervention or existing tidal/datum data for sample collection of training data. To tackle the drawbacks of previous approaches, we propose and develop an automatic data processing workflow for land-water classification using multispectral airborne LiDAR data. Depending on the nature of the study scene, two methods are proposed for automatic training data selection. The first method utilizes the elevation/intensity histogram fitted with Gaussian mixture model (GMM) to preliminarily split the land and water bodies. The second method mainly relies on the use of a newly developed scan line elevation intensity ratio (SLIER) to estimate the water surface data points. Regardless of the training methods being used, feature spaces can be constructed using the multispectral LiDAR intensity, elevation and other features derived from these parameters. The comprehensive workflow was tested with two datasets collected for different near shore region and river environment, where the overall accuracy yielded better than 96 %.

  17. Automatically processed alpha-track radon monitor

    DOEpatents

    Langner, Jr., G. Harold

    1993-01-01

    An automatically processed alpha-track radon monitor is provided which includes a housing having an aperture allowing radon entry, and a filter that excludes the entry of radon daughters into the housing. A flexible track registration material is located within the housing that records alpha-particle emissions from the decay of radon and radon daughters inside the housing. The flexible track registration material is capable of being spliced such that the registration material from a plurality of monitors can be spliced into a single strip to facilitate automatic processing of the registration material from the plurality of monitors. A process for the automatic counting of radon registered by a radon monitor is also provided.

  18. Automatically processed alpha-track radon monitor

    DOEpatents

    Langner, G.H. Jr.

    1993-01-12

    An automatically processed alpha-track radon monitor is provided which includes a housing having an aperture allowing radon entry, and a filter that excludes the entry of radon daughters into the housing. A flexible track registration material is located within the housing that records alpha-particle emissions from the decay of radon and radon daughters inside the housing. The flexible track registration material is capable of being spliced such that the registration material from a plurality of monitors can be spliced into a single strip to facilitate automatic processing of the registration material from the plurality of monitors. A process for the automatic counting of radon registered by a radon monitor is also provided.

  19. Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches

    NASA Astrophysics Data System (ADS)

    Amit, Guy; Ben-Ari, Rami; Hadad, Omer; Monovich, Einat; Granot, Noa; Hashoul, Sharbell

    2017-03-01

    Diagnostic interpretation of breast MRI studies requires meticulous work and a high level of expertise. Computerized algorithms can assist radiologists by automatically characterizing the detected lesions. Deep learning approaches have shown promising results in natural image classification, but their applicability to medical imaging is limited by the shortage of large annotated training sets. In this work, we address automatic classification of breast MRI lesions using two different deep learning approaches. We propose a novel image representation for dynamic contrast enhanced (DCE) breast MRI lesions, which combines the morphological and kinetics information in a single multi-channel image. We compare two classification approaches for discriminating between benign and malignant lesions: training a designated convolutional neural network and using a pre-trained deep network to extract features for a shallow classifier. The domain-specific trained network provided higher classification accuracy, compared to the pre-trained model, with an area under the ROC curve of 0.91 versus 0.81, and an accuracy of 0.83 versus 0.71. Similar accuracy was achieved in classifying benign lesions, malignant lesions, and normal tissue images. The trained network was able to improve accuracy by using the multi-channel image representation, and was more robust to reductions in the size of the training set. A small-size convolutional neural network can learn to accurately classify findings in medical images using only a few hundred images from a few dozen patients. With sufficient data augmentation, such a network can be trained to outperform a pre-trained out-of-domain classifier. Developing domain-specific deep-learning models for medical imaging can facilitate technological advancements in computer-aided diagnosis.

  20. Finding geospatial pattern of unstructured data by clustering routes

    NASA Astrophysics Data System (ADS)

    Boustani, M.; Mattmann, C. A.; Ramirez, P.; Burke, W.

    2016-12-01

    Today the majority of data generated has a geospatial context to it. Either in attribute form as a latitude or longitude, or name of location or cross referenceable using other means such as an external gazetteer or location service. Our research is interested in exploiting geospatial location and context in unstructured data such as that found on the web in HTML pages, images, videos, documents, and other areas, and in structured information repositories found on intranets, in scientific environments, and otherwise. We are working together on the DARPA MEMEX project to exploit open source software tools such as the Lucene Geo Gazetteer, Apache Tika, Apache Lucene, and Apache OpenNLP, to automatically extract, and make meaning out of geospatial information. In particular, we are interested in unstructured descriptors e.g., a phone number, or a named entity, and the ability to automatically learn geospatial paths related to these descriptors. For example, a particular phone number may represent an entity that travels on a monthly basis, according to easily identifiable and somes more difficult to track patterns. We will present a set of automatic techniques to extract descriptors, and then to geospatially infer their paths across unstructured data.

  1. Distributed neural signatures of natural audiovisual speech and music in the human auditory cortex.

    PubMed

    Salmi, Juha; Koistinen, Olli-Pekka; Glerean, Enrico; Jylänki, Pasi; Vehtari, Aki; Jääskeläinen, Iiro P; Mäkelä, Sasu; Nummenmaa, Lauri; Nummi-Kuisma, Katarina; Nummi, Ilari; Sams, Mikko

    2017-08-15

    During a conversation or when listening to music, auditory and visual information are combined automatically into audiovisual objects. However, it is still poorly understood how specific type of visual information shapes neural processing of sounds in lifelike stimulus environments. Here we applied multi-voxel pattern analysis to investigate how naturally matching visual input modulates supratemporal cortex activity during processing of naturalistic acoustic speech, singing and instrumental music. Bayesian logistic regression classifiers with sparsity-promoting priors were trained to predict whether the stimulus was audiovisual or auditory, and whether it contained piano playing, speech, or singing. The predictive performances of the classifiers were tested by leaving one participant at a time for testing and training the model using the remaining 15 participants. The signature patterns associated with unimodal auditory stimuli encompassed distributed locations mostly in the middle and superior temporal gyrus (STG/MTG). A pattern regression analysis, based on a continuous acoustic model, revealed that activity in some of these MTG and STG areas were associated with acoustic features present in speech and music stimuli. Concurrent visual stimulus modulated activity in bilateral MTG (speech), lateral aspect of right anterior STG (singing), and bilateral parietal opercular cortex (piano). Our results suggest that specific supratemporal brain areas are involved in processing complex natural speech, singing, and piano playing, and other brain areas located in anterior (facial speech) and posterior (music-related hand actions) supratemporal cortex are influenced by related visual information. Those anterior and posterior supratemporal areas have been linked to stimulus identification and sensory-motor integration, respectively. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. An algebraic multigrid method for Q2-Q1 mixed discretizations of the Navier-Stokes equations

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

    Prokopenko, Andrey; Tuminaro, Raymond S.

    Algebraic multigrid (AMG) preconditioners are considered for discretized systems of partial differential equations (PDEs) where unknowns associated with different physical quantities are not necessarily co-located at mesh points. Speci cally, we investigate a Q 2-Q 1 mixed finite element discretization of the incompressible Navier-Stokes equations where the number of velocity nodes is much greater than the number of pressure nodes. Consequently, some velocity degrees-of-freedom (dofs) are defined at spatial locations where there are no corresponding pressure dofs. Thus, AMG approaches lever- aging this co-located structure are not applicable. This paper instead proposes an automatic AMG coarsening that mimics certain pressure/velocitymore » dof relationships of the Q 2-Q 1 discretization. The main idea is to first automatically define coarse pressures in a somewhat standard AMG fashion and then to carefully (but automatically) choose coarse velocity unknowns so that the spatial location relationship between pressure and velocity dofs resembles that on the nest grid. To define coefficients within the inter-grid transfers, an energy minimization AMG (EMIN-AMG) is utilized. EMIN-AMG is not tied to specific coarsening schemes and grid transfer sparsity patterns, and so it is applicable to the proposed coarsening. Numerical results highlighting solver performance are given on Stokes and incompressible Navier-Stokes problems.« less

  3. Automatic frame-centered object representation and integration revealed by iconic memory, visual priming, and backward masking.

    PubMed

    Lin, Zhicheng; He, Sheng

    2012-10-25

    Object identities ("what") and their spatial locations ("where") are processed in distinct pathways in the visual system, raising the question of how the what and where information is integrated. Because of object motions and eye movements, the retina-based representations are unstable, necessitating nonretinotopic representation and integration. A potential mechanism is to code and update objects according to their reference frames (i.e., frame-centered representation and integration). To isolate frame-centered processes, in a frame-to-frame apparent motion configuration, we (a) presented two preceding or trailing objects on the same frame, equidistant from the target on the other frame, to control for object-based (frame-based) effect and space-based effect, and (b) manipulated the target's relative location within its frame to probe frame-centered effect. We show that iconic memory, visual priming, and backward masking depend on objects' relative frame locations, orthogonal of the retinotopic coordinate. These findings not only reveal that iconic memory, visual priming, and backward masking can be nonretinotopic but also demonstrate that these processes are automatically constrained by contextual frames through a frame-centered mechanism. Thus, object representation is robustly and automatically coupled to its reference frame and continuously being updated through a frame-centered, location-specific mechanism. These findings lead to an object cabinet framework, in which objects ("files") within the reference frame ("cabinet") are orderly coded relative to the frame.

  4. An algebraic multigrid method for Q2-Q1 mixed discretizations of the Navier-Stokes equations

    DOE PAGES

    Prokopenko, Andrey; Tuminaro, Raymond S.

    2016-07-01

    Algebraic multigrid (AMG) preconditioners are considered for discretized systems of partial differential equations (PDEs) where unknowns associated with different physical quantities are not necessarily co-located at mesh points. Speci cally, we investigate a Q 2-Q 1 mixed finite element discretization of the incompressible Navier-Stokes equations where the number of velocity nodes is much greater than the number of pressure nodes. Consequently, some velocity degrees-of-freedom (dofs) are defined at spatial locations where there are no corresponding pressure dofs. Thus, AMG approaches lever- aging this co-located structure are not applicable. This paper instead proposes an automatic AMG coarsening that mimics certain pressure/velocitymore » dof relationships of the Q 2-Q 1 discretization. The main idea is to first automatically define coarse pressures in a somewhat standard AMG fashion and then to carefully (but automatically) choose coarse velocity unknowns so that the spatial location relationship between pressure and velocity dofs resembles that on the nest grid. To define coefficients within the inter-grid transfers, an energy minimization AMG (EMIN-AMG) is utilized. EMIN-AMG is not tied to specific coarsening schemes and grid transfer sparsity patterns, and so it is applicable to the proposed coarsening. Numerical results highlighting solver performance are given on Stokes and incompressible Navier-Stokes problems.« less

  5. Smartphone-Based System for Learning and Inferring Hearing Aid Settings.

    PubMed

    Aldaz, Gabriel; Puria, Sunil; Leifer, Larry J

    2016-10-01

    Previous research has shown that hearing aid wearers can successfully self-train their instruments' gain-frequency response and compression parameters in everyday situations. Combining hearing aids with a smartphone introduces additional computing power, memory, and a graphical user interface that may enable greater setting personalization. To explore the benefits of self-training with a smartphone-based hearing system, a parameter space was chosen with four possible combinations of microphone mode (omnidirectional and directional) and noise reduction state (active and off). The baseline for comparison was the "untrained system," that is, the manufacturer's algorithm for automatically selecting microphone mode and noise reduction state based on acoustic environment. The "trained system" first learned each individual's preferences, self-entered via a smartphone in real-world situations, to build a trained model. The system then predicted the optimal setting (among available choices) using an inference engine, which considered the trained model and current context (e.g., sound environment, location, and time). To develop a smartphone-based prototype hearing system that can be trained to learn preferred user settings. Determine whether user study participants showed a preference for trained over untrained system settings. An experimental within-participants study. Participants used a prototype hearing system-comprising two hearing aids, Android smartphone, and body-worn gateway device-for ∼6 weeks. Sixteen adults with mild-to-moderate sensorineural hearing loss (HL) (ten males, six females; mean age = 55.5 yr). Fifteen had ≥6 mo of experience wearing hearing aids, and 14 had previous experience using smartphones. Participants were fitted and instructed to perform daily comparisons of settings ("listening evaluations") through a smartphone-based software application called Hearing Aid Learning and Inference Controller (HALIC). In the four-week-long training phase, HALIC recorded individual listening preferences along with sensor data from the smartphone-including environmental sound classification, sound level, and location-to build trained models. In the subsequent two-week-long validation phase, participants performed blinded listening evaluations comparing settings predicted by the trained system ("trained settings") to those suggested by the hearing aids' untrained system ("untrained settings"). We analyzed data collected on the smartphone and hearing aids during the study. We also obtained audiometric and demographic information. Overall, the 15 participants with valid data significantly preferred trained settings to untrained settings (paired-samples t test). Seven participants had a significant preference for trained settings, while one had a significant preference for untrained settings (binomial test). The remaining seven participants had nonsignificant preferences. Pooling data across participants, the proportion of times that each setting was chosen in a given environmental sound class was on average very similar. However, breaking down the data by participant revealed strong and idiosyncratic individual preferences. Fourteen participants reported positive feelings of clarity, competence, and mastery when training via HALIC. The obtained data, as well as subjective participant feedback, indicate that smartphones could become viable tools to train hearing aids. Individuals who are tech savvy and have milder HL seem well suited to take advantages of the benefits offered by training with a smartphone. American Academy of Audiology

  6. Is gaze following purely reflexive or goal-directed instead? Revisiting the automaticity of orienting attention by gaze cues.

    PubMed

    Ricciardelli, Paola; Carcagno, Samuele; Vallar, Giuseppe; Bricolo, Emanuela

    2013-01-01

    Distracting gaze has been shown to elicit automatic gaze following. However, it is still debated whether the effects of perceived gaze are a simple automatic spatial orienting response or are instead sensitive to the context (i.e. goals and task demands). In three experiments, we investigated the conditions under which gaze following occurs. Participants were instructed to saccade towards one of two lateral targets. A face distracter, always present in the background, could gaze towards: (a) a task-relevant target--("matching" goal-directed gaze shift)--congruent or incongruent with the instructed direction, (b) a task-irrelevant target, orthogonal to the one instructed ("non-matching" goal-directed gaze shift), or (c) an empty spatial location (no-goal-directed gaze shift). Eye movement recordings showed faster saccadic latencies in correct trials in congruent conditions especially when the distracting gaze shift occurred before the instruction to make a saccade. Interestingly, while participants made a higher proportion of gaze-following errors (i.e. errors in the direction of the distracting gaze) in the incongruent conditions when the distracter's gaze shift preceded the instruction onset indicating an automatic gaze following, they never followed the distracting gaze when it was directed towards an empty location or a stimulus that was never the target. Taken together, these findings suggest that gaze following is likely to be a product of both automatic and goal-driven orienting mechanisms.

  7. Acute Alcohol Consumption Impairs Controlled but Not Automatic Processes in a Psychophysical Pointing Paradigm

    PubMed Central

    Johnston, Kevin; Timney, Brian; Goodale, Melvyn A.

    2013-01-01

    Numerous studies have investigated the effects of alcohol consumption on controlled and automatic cognitive processes. Such studies have shown that alcohol impairs performance on tasks requiring conscious, intentional control, while leaving automatic performance relatively intact. Here, we sought to extend these findings to aspects of visuomotor control by investigating the effects of alcohol in a visuomotor pointing paradigm that allowed us to separate the influence of controlled and automatic processes. Six male participants were assigned to an experimental “correction” condition in which they were instructed to point at a visual target as quickly and accurately as possible. On a small percentage of trials, the target “jumped” to a new location. On these trials, the participants’ task was to amend their movement such that they pointed to the new target location. A second group of 6 participants were assigned to a “countermanding” condition, in which they were instructed to terminate their movements upon detection of target “jumps”. In both the correction and countermanding conditions, participants served as their own controls, taking part in alcohol and no-alcohol conditions on separate days. Alcohol had no effect on participants’ ability to correct movements “in flight”, but impaired the ability to withhold such automatic corrections. Our data support the notion that alcohol selectively impairs controlled processes in the visuomotor domain. PMID:23861934

  8. Automatic query formulations in information retrieval.

    PubMed

    Salton, G; Buckley, C; Fox, E A

    1983-07-01

    Modern information retrieval systems are designed to supply relevant information in response to requests received from the user population. In most retrieval environments the search requests consist of keywords, or index terms, interrelated by appropriate Boolean operators. Since it is difficult for untrained users to generate effective Boolean search requests, trained search intermediaries are normally used to translate original statements of user need into useful Boolean search formulations. Methods are introduced in this study which reduce the role of the search intermediaries by making it possible to generate Boolean search formulations completely automatically from natural language statements provided by the system patrons. Frequency considerations are used automatically to generate appropriate term combinations as well as Boolean connectives relating the terms. Methods are covered to produce automatic query formulations both in a standard Boolean logic system, as well as in an extended Boolean system in which the strict interpretation of the connectives is relaxed. Experimental results are supplied to evaluate the effectiveness of the automatic query formulation process, and methods are described for applying the automatic query formulation process in practice.

  9. Capacitive system detects and locates fluid leaks

    NASA Technical Reports Server (NTRS)

    1966-01-01

    Electronic monitoring system automatically detects and locates minute leaks in seams of large fluid storage tanks and pipelines covered with thermal insulation. The system uses a capacitive tape-sensing element that is adhesively bonded over seams where fluid leaks are likely to occur.

  10. Vertical-Control Subsystem for Automatic Coal Mining

    NASA Technical Reports Server (NTRS)

    Griffiths, W. R.; Smirlock, M.; Aplin, J.; Fish, R. B.; Fish, D.

    1984-01-01

    Guidance and control system automatically positions cutting drums of double-ended longwall shearer so they follow coal seam. System determines location of upper interface between coal and shale and continuously adjusts cutting-drum positions, upward or downward, to track undulating interface. Objective to keep cutting edges as close as practicable to interface and thus extract as much coal as possible from seam.

  11. Automatic targeting of plasma spray gun

    DOEpatents

    Abbatiello, Leonard A.; Neal, Richard E.

    1978-01-01

    A means for monitoring the material portion in the flame of a plasma spray gun during spraying operations is provided. A collimated detector, sensitive to certain wavelengths of light emission, is used to locate the centroid of the material with each pass of the gun. The response from the detector is then relayed to the gun controller to be used to automatically realign the gun.

  12. Method of center localization for objects containing concentric arcs

    NASA Astrophysics Data System (ADS)

    Kuznetsova, Elena G.; Shvets, Evgeny A.; Nikolaev, Dmitry P.

    2015-02-01

    This paper proposes a method for automatic center location of objects containing concentric arcs. The method utilizes structure tensor analysis and voting scheme optimized with Fast Hough Transform. Two applications of the proposed method are considered: (i) wheel tracking in video-based system for automatic vehicle classification and (ii) tree growth rings analysis on a tree cross cut image.

  13. Automatic approach to deriving fuzzy slope positions

    NASA Astrophysics Data System (ADS)

    Zhu, Liang-Jun; Zhu, A.-Xing; Qin, Cheng-Zhi; Liu, Jun-Zhi

    2018-03-01

    Fuzzy characterization of slope positions is important for geographic modeling. Most of the existing fuzzy classification-based methods for fuzzy characterization require extensive user intervention in data preparation and parameter setting, which is tedious and time-consuming. This paper presents an automatic approach to overcoming these limitations in the prototype-based inference method for deriving fuzzy membership value (or similarity) to slope positions. The key contribution is a procedure for finding the typical locations and setting the fuzzy inference parameters for each slope position type. Instead of being determined totally by users in the prototype-based inference method, in the proposed approach the typical locations and fuzzy inference parameters for each slope position type are automatically determined by a rule set based on prior domain knowledge and the frequency distributions of topographic attributes. Furthermore, the preparation of topographic attributes (e.g., slope gradient, curvature, and relative position index) is automated, so the proposed automatic approach has only one necessary input, i.e., the gridded digital elevation model of the study area. All compute-intensive algorithms in the proposed approach were speeded up by parallel computing. Two study cases were provided to demonstrate that this approach can properly, conveniently and quickly derive the fuzzy slope positions.

  14. Automatic Determination of the Conic Coronal Mass Ejection Model Parameters

    NASA Technical Reports Server (NTRS)

    Pulkkinen, A.; Oates, T.; Taktakishvili, A.

    2009-01-01

    Characterization of the three-dimensional structure of solar transients using incomplete plane of sky data is a difficult problem whose solutions have potential for societal benefit in terms of space weather applications. In this paper transients are characterized in three dimensions by means of conic coronal mass ejection (CME) approximation. A novel method for the automatic determination of cone model parameters from observed halo CMEs is introduced. The method uses both standard image processing techniques to extract the CME mass from white-light coronagraph images and a novel inversion routine providing the final cone parameters. A bootstrap technique is used to provide model parameter distributions. When combined with heliospheric modeling, the cone model parameter distributions will provide direct means for ensemble predictions of transient propagation in the heliosphere. An initial validation of the automatic method is carried by comparison to manually determined cone model parameters. It is shown using 14 halo CME events that there is reasonable agreement, especially between the heliocentric locations of the cones derived with the two methods. It is argued that both the heliocentric locations and the opening half-angles of the automatically determined cones may be more realistic than those obtained from the manual analysis

  15. Portable automatic text classification for adverse drug reaction detection via multi-corpus training.

    PubMed

    Sarker, Abeed; Gonzalez, Graciela

    2015-02-01

    Automatic detection of adverse drug reaction (ADR) mentions from text has recently received significant interest in pharmacovigilance research. Current research focuses on various sources of text-based information, including social media-where enormous amounts of user posted data is available, which have the potential for use in pharmacovigilance if collected and filtered accurately. The aims of this study are: (i) to explore natural language processing (NLP) approaches for generating useful features from text, and utilizing them in optimized machine learning algorithms for automatic classification of ADR assertive text segments; (ii) to present two data sets that we prepared for the task of ADR detection from user posted internet data; and (iii) to investigate if combining training data from distinct corpora can improve automatic classification accuracies. One of our three data sets contains annotated sentences from clinical reports, and the two other data sets, built in-house, consist of annotated posts from social media. Our text classification approach relies on generating a large set of features, representing semantic properties (e.g., sentiment, polarity, and topic), from short text nuggets. Importantly, using our expanded feature sets, we combine training data from different corpora in attempts to boost classification accuracies. Our feature-rich classification approach performs significantly better than previously published approaches with ADR class F-scores of 0.812 (previously reported best: 0.770), 0.538 and 0.678 for the three data sets. Combining training data from multiple compatible corpora further improves the ADR F-scores for the in-house data sets to 0.597 (improvement of 5.9 units) and 0.704 (improvement of 2.6 units) respectively. Our research results indicate that using advanced NLP techniques for generating information rich features from text can significantly improve classification accuracies over existing benchmarks. Our experiments illustrate the benefits of incorporating various semantic features such as topics, concepts, sentiments, and polarities. Finally, we show that integration of information from compatible corpora can significantly improve classification performance. This form of multi-corpus training may be particularly useful in cases where data sets are heavily imbalanced (e.g., social media data), and may reduce the time and costs associated with the annotation of data in the future. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  16. Portable Automatic Text Classification for Adverse Drug Reaction Detection via Multi-corpus Training

    PubMed Central

    Gonzalez, Graciela

    2014-01-01

    Objective Automatic detection of Adverse Drug Reaction (ADR) mentions from text has recently received significant interest in pharmacovigilance research. Current research focuses on various sources of text-based information, including social media — where enormous amounts of user posted data is available, which have the potential for use in pharmacovigilance if collected and filtered accurately. The aims of this study are: (i) to explore natural language processing approaches for generating useful features from text, and utilizing them in optimized machine learning algorithms for automatic classification of ADR assertive text segments; (ii) to present two data sets that we prepared for the task of ADR detection from user posted internet data; and (iii) to investigate if combining training data from distinct corpora can improve automatic classification accuracies. Methods One of our three data sets contains annotated sentences from clinical reports, and the two other data sets, built in-house, consist of annotated posts from social media. Our text classification approach relies on generating a large set of features, representing semantic properties (e.g., sentiment, polarity, and topic), from short text nuggets. Importantly, using our expanded feature sets, we combine training data from different corpora in attempts to boost classification accuracies. Results Our feature-rich classification approach performs significantly better than previously published approaches with ADR class F-scores of 0.812 (previously reported best: 0.770), 0.538 and 0.678 for the three data sets. Combining training data from multiple compatible corpora further improves the ADR F-scores for the in-house data sets to 0.597 (improvement of 5.9 units) and 0.704 (improvement of 2.6 units) respectively. Conclusions Our research results indicate that using advanced NLP techniques for generating information rich features from text can significantly improve classification accuracies over existing benchmarks. Our experiments illustrate the benefits of incorporating various semantic features such as topics, concepts, sentiments, and polarities. Finally, we show that integration of information from compatible corpora can significantly improve classification performance. This form of multi-corpus training may be particularly useful in cases where data sets are heavily imbalanced (e.g., social media data), and may reduce the time and costs associated with the annotation of data in the future. PMID:25451103

  17. Constructing distributed Hippocratic video databases for privacy-preserving online patient training and counseling.

    PubMed

    Peng, Jinye; Babaguchi, Noboru; Luo, Hangzai; Gao, Yuli; Fan, Jianping

    2010-07-01

    Digital video now plays an important role in supporting more profitable online patient training and counseling, and integration of patient training videos from multiple competitive organizations in the health care network will result in better offerings for patients. However, privacy concerns often prevent multiple competitive organizations from sharing and integrating their patient training videos. In addition, patients with infectious or chronic diseases may not want the online patient training organizations to identify who they are or even which video clips they are interested in. Thus, there is an urgent need to develop more effective techniques to protect both video content privacy and access privacy . In this paper, we have developed a new approach to construct a distributed Hippocratic video database system for supporting more profitable online patient training and counseling. First, a new database modeling approach is developed to support concept-oriented video database organization and assign a degree of privacy of the video content for each database level automatically. Second, a new algorithm is developed to protect the video content privacy at the level of individual video clip by filtering out the privacy-sensitive human objects automatically. In order to integrate the patient training videos from multiple competitive organizations for constructing a centralized video database indexing structure, a privacy-preserving video sharing scheme is developed to support privacy-preserving distributed classifier training and prevent the statistical inferences from the videos that are shared for cross-validation of video classifiers. Our experiments on large-scale video databases have also provided very convincing results.

  18. Advanced correlation grid: Analysis and visualisation of functional connectivity among multiple spike trains.

    PubMed

    Masud, Mohammad Shahed; Borisyuk, Roman; Stuart, Liz

    2017-07-15

    This study analyses multiple spike trains (MST) data, defines its functional connectivity and subsequently visualises an accurate diagram of connections. This is a challenging problem. For example, it is difficult to distinguish the common input and the direct functional connection of two spike trains. The new method presented in this paper is based on the traditional pairwise cross-correlation function (CCF) and a new combination of statistical techniques. First, the CCF is used to create the Advanced Correlation Grid (ACG) correlation where both the significant peak of the CCF and the corresponding time delay are used for detailed analysis of connectivity. Second, these two features of functional connectivity are used to classify connections. Finally, the visualization technique is used to represent the topology of functional connections. Examples are presented in the paper to demonstrate the new Advanced Correlation Grid method and to show how it enables discrimination between (i) influence from one spike train to another through an intermediate spike train and (ii) influence from one common spike train to another pair of analysed spike trains. The ACG method enables scientists to automatically distinguish between direct connections from spurious connections such as common source connection and indirect connection whereas existing methods require in-depth analysis to identify such connections. The ACG is a new and effective method for studying functional connectivity of multiple spike trains. This method can identify accurately all the direct connections and can distinguish common source and indirect connections automatically. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Reward processing in the value-driven attention network: reward signals tracking cue identity and location.

    PubMed

    Anderson, Brian A

    2017-03-01

    Through associative reward learning, arbitrary cues acquire the ability to automatically capture visual attention. Previous studies have examined the neural correlates of value-driven attentional orienting, revealing elevated activity within a network of brain regions encompassing the visual corticostriatal loop [caudate tail, lateral occipital complex (LOC) and early visual cortex] and intraparietal sulcus (IPS). Such attentional priority signals raise a broader question concerning how visual signals are combined with reward signals during learning to create a representation that is sensitive to the confluence of the two. This study examines reward signals during the cued reward training phase commonly used to generate value-driven attentional biases. High, compared with low, reward feedback preferentially activated the value-driven attention network, in addition to regions typically implicated in reward processing. Further examination of these reward signals within the visual system revealed information about the identity of the preceding cue in the caudate tail and LOC, and information about the location of the preceding cue in IPS, while early visual cortex represented both location and identity. The results reveal teaching signals within the value-driven attention network during associative reward learning, and further suggest functional specialization within different regions of this network during the acquisition of an integrated representation of stimulus value. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  20. Automatic morphological classification of galaxy images

    PubMed Central

    Shamir, Lior

    2009-01-01

    We describe an image analysis supervised learning algorithm that can automatically classify galaxy images. The algorithm is first trained using a manually classified images of elliptical, spiral, and edge-on galaxies. A large set of image features is extracted from each image, and the most informative features are selected using Fisher scores. Test images can then be classified using a simple Weighted Nearest Neighbor rule such that the Fisher scores are used as the feature weights. Experimental results show that galaxy images from Galaxy Zoo can be classified automatically to spiral, elliptical and edge-on galaxies with accuracy of ~90% compared to classifications carried out by the author. Full compilable source code of the algorithm is available for free download, and its general-purpose nature makes it suitable for other uses that involve automatic image analysis of celestial objects. PMID:20161594

  1. Automatic segmentation of the lateral geniculate nucleus: Application to control and glaucoma patients.

    PubMed

    Wang, Jieqiong; Miao, Wen; Li, Jing; Li, Meng; Zhen, Zonglei; Sabel, Bernhard; Xian, Junfang; He, Huiguang

    2015-11-30

    The lateral geniculate nucleus (LGN) is a key relay center of the visual system. Because the LGN morphology is affected by different diseases, it is of interest to analyze its morphology by segmentation. However, existing LGN segmentation methods are non-automatic, inefficient and prone to experimenters' bias. To address these problems, we proposed an automatic LGN segmentation algorithm based on T1-weighted imaging. First, the prior information of LGN was used to create a prior mask. Then region growing was applied to delineate LGN. We evaluated this automatic LGN segmentation method by (1) comparison with manually segmented LGN, (2) anatomically locating LGN in the visual system via LGN-based tractography, (3) application to control and glaucoma patients. The similarity coefficients of automatic segmented LGN and manually segmented one are 0.72 (0.06) for the left LGN and 0.77 (0.07) for the right LGN. LGN-based tractography shows the subcortical pathway seeding from LGN passes the optic tract and also reaches V1 through the optic radiation, which is consistent with the LGN location in the visual system. In addition, LGN asymmetry as well as LGN atrophy along with age is observed in normal controls. The investigation of glaucoma effects on LGN volumes demonstrates that the bilateral LGN volumes shrink in patients. The automatic LGN segmentation is objective, efficient, valid and applicable. Experiment results proved the validity and applicability of the algorithm. Our method will speed up the research on visual system and greatly enhance studies of different vision-related diseases. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. An Empirical Examination of EFL Learners' Perceptual Learning Styles and Acceptance of ASR-Based Computer-Assisted Pronunciation Training

    ERIC Educational Resources Information Center

    Hsu, Liwei

    2016-01-01

    This study aims to explore the structural relationships among the variables of EFL (English as a foreign language) learners' perceptual learning styles and Technology Acceptance Model (TAM). Three hundred and forty-one (n = 341) EFL learners were invited to join a self-regulated English pronunciation training program through automatic speech…

  3. 40 CFR 265.16 - Personnel training.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... automatic waste feed cut-off systems; (iii) Communications or alarm -systems; (iv) Response to fires or... bargaining unit, but must include the requisite skill, education, or other qualifications, and duties of...

  4. 40 CFR 265.16 - Personnel training.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... automatic waste feed cut-off systems; (iii) Communications or alarm -systems; (iv) Response to fires or... bargaining unit, but must include the requisite skill, education, or other qualifications, and duties of...

  5. 40 CFR 265.16 - Personnel training.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... automatic waste feed cut-off systems; (iii) Communications or alarm -systems; (iv) Response to fires or... bargaining unit, but must include the requisite skill, education, or other qualifications, and duties of...

  6. Chemical name extraction based on automatic training data generation and rich feature set.

    PubMed

    Yan, Su; Spangler, W Scott; Chen, Ying

    2013-01-01

    The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.

  7. SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound.

    PubMed

    Baumgartner, Christian F; Kamnitsas, Konstantinos; Matthew, Jacqueline; Fletcher, Tara P; Smith, Sandra; Koch, Lisa M; Kainz, Bernhard; Rueckert, Daniel

    2017-11-01

    Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks, which can automatically detect 13 fetal standard views in freehand 2-D ultrasound data as well as provide a localization of the fetal structures via a bounding box. An important contribution is that the network learns to localize the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localization task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localization on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modeling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localization task.

  8. Automatic segmentation of thoracic aorta segments in low-dose chest CT

    NASA Astrophysics Data System (ADS)

    Noothout, Julia M. H.; de Vos, Bob D.; Wolterink, Jelmer M.; Išgum, Ivana

    2018-03-01

    Morphological analysis and identification of pathologies in the aorta are important for cardiovascular diagnosis and risk assessment in patients. Manual annotation is time-consuming and cumbersome in CT scans acquired without contrast enhancement and with low radiation dose. Hence, we propose an automatic method to segment the ascending aorta, the aortic arch and the thoracic descending aorta in low-dose chest CT without contrast enhancement. Segmentation was performed using a dilated convolutional neural network (CNN), with a receptive field of 131 × 131 voxels, that classified voxels in axial, coronal and sagittal image slices. To obtain a final segmentation, the obtained probabilities of the three planes were averaged per class, and voxels were subsequently assigned to the class with the highest class probability. Two-fold cross-validation experiments were performed where ten scans were used to train the network and another ten to evaluate the performance. Dice coefficients of 0.83 +/- 0.07, 0.86 +/- 0.06 and 0.88 +/- 0.05, and Average Symmetrical Surface Distances (ASSDs) of 2.44 +/- 1.28, 1.56 +/- 0.68 and 1.87 +/- 1.30 mm were obtained for the ascending aorta, the aortic arch and the descending aorta, respectively. The results indicate that the proposed method could be used in large-scale studies analyzing the anatomical location of pathology and morphology of the thoracic aorta.

  9. Arterial tree tracking from anatomical landmarks in magnetic resonance angiography scans

    NASA Astrophysics Data System (ADS)

    O'Neil, Alison; Beveridge, Erin; Houston, Graeme; McCormick, Lynne; Poole, Ian

    2014-03-01

    This paper reports on arterial tree tracking in fourteen Contrast Enhanced MRA volumetric scans, given the positions of a predefined set of vascular landmarks, by using the A* algorithm to find the optimal path for each vessel based on voxel intensity and a learnt vascular probability atlas. The algorithm is intended for use in conjunction with an automatic landmark detection step, to enable fully automatic arterial tree tracking. The scan is filtered to give two further images using the top-hat transform with 4mm and 8mm cubic structuring elements. Vessels are then tracked independently on the scan in which the vessel of interest is best enhanced, as determined from knowledge of typical vessel diameter and surrounding structures. A vascular probability atlas modelling expected vessel location and orientation is constructed by non-rigidly registering the training scans to the test scan using a 3D thin plate spline to match landmark correspondences, and employing kernel density estimation with the ground truth center line points to form a probability density distribution. Threshold estimation by histogram analysis is used to segment background from vessel intensities. The A* algorithm is run using a linear cost function constructed from the threshold and the vascular atlas prior. Tracking results are presented for all major arteries excluding those in the upper limbs. An improvement was observed when tracking was informed by contextual information, with particular benefit for peripheral vessels.

  10. Automatic detection of multiple UXO-like targets using magnetic anomaly inversion and self-adaptive fuzzy c-means clustering

    NASA Astrophysics Data System (ADS)

    Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoquan; Li, Zhining

    2017-12-01

    We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.

  11. A Radio-Map Automatic Construction Algorithm Based on Crowdsourcing

    PubMed Central

    Yu, Ning; Xiao, Chenxian; Wu, Yinfeng; Feng, Renjian

    2016-01-01

    Traditional radio-map-based localization methods need to sample a large number of location fingerprints offline, which requires huge amount of human and material resources. To solve the high sampling cost problem, an automatic radio-map construction algorithm based on crowdsourcing is proposed. The algorithm employs the crowd-sourced information provided by a large number of users when they are walking in the buildings as the source of location fingerprint data. Through the variation characteristics of users’ smartphone sensors, the indoor anchors (doors) are identified and their locations are regarded as reference positions of the whole radio-map. The AP-Cluster method is used to cluster the crowdsourced fingerprints to acquire the representative fingerprints. According to the reference positions and the similarity between fingerprints, the representative fingerprints are linked to their corresponding physical locations and the radio-map is generated. Experimental results demonstrate that the proposed algorithm reduces the cost of fingerprint sampling and radio-map construction and guarantees the localization accuracy. The proposed method does not require users’ explicit participation, which effectively solves the resource-consumption problem when a location fingerprint database is established. PMID:27070623

  12. Operational EEW Networks in Turkey

    NASA Astrophysics Data System (ADS)

    Zulfikar, Can; Pinar, Ali

    2016-04-01

    There are several EEW networks and algorithms under operation in Turkey. The first EEW system was deployed in Istanbul in 2002 after the 1999 Mw7.4 Kocaeli and Mw7.1 Duzce earthquake events. The system consisted of 10 strong motion stations located as close as possible to the main Marmara Fault line. The system was upgraded by 5 OBS (Ocean Bottom Seismometer) in 2012 located in Marmara Sea. The system works in threshold based algorithm. The alert is given according to exceedance of certain threshold levels of amplitude of ground motion acceleration in certain time interval at least in 3 stations. Currently, there are two end-users of EEW system in Istanbul. The critical facilities of Istanbul Gas Distribution Company (IGDAS) and Marmaray Tube tunnel receives the EEW information in order to activate their automatic shut-off mechanisms. The IGDAS has their own strong motion network located at their district regulators. After receiving the EEW signal if the threshold values of ground motion parameters are exceeded the gas-flow is cut automatically at the district regulators. The IGDAS has 750 district regulators distributed in Istanbul. At the moment, the 110 of them are instrumented with strong motion accelerometers. As a 2nd stage of the on-going project, the IGDAS company proposes to install strong motion accelerometers to all remaining district regulators. The Marmaray railway tube tunnel is the world's deepest immersed tube tunnel with 60m undersea depth. The tunnel has 1.4km length with 13 segments. The tunnel is monitored with 2 strong motion accelerometers in each segment, 26 in total. Once the EEW signal is received, the monitoring system is activated and the recording ground motion parameters are calculated in real-time. Depending on the exceedance of threshold levels, further actions are taken such as reducing the train speed, stopping the train before entering the tunnel etc. In Istanbul, there are also on-site EEW system applied in several high-rise buildings. As similar to threshold based algorithm, once the threshold level is exceeded in several strong motion accelerometers installed in the high-rise building, the automated shut-off mechanism is activated in order to prevent secondary damage effects of the earthquakes. In addition to the threshold based EEW system, the regional EEW algorithms Virtual Seismologist (VS) as implemented in SeisComP3 VS(SC3) and PRESTo have been also implemented in Marmara region of Turkey. These applications use the regional seismic networks. The purpose of the regional EEW systems is to determine the magnitude and location of the event from the P-wave information of the closest 3-4 stations and forward this information to interested sites. The regional EEW systems are also important for Istanbul in order to detect far distance earthquake events and provide alert especially for the high-rise buildings for their long duration shaking.

  13. Automatic forest-fire measuring using ground stations and Unmanned Aerial Systems.

    PubMed

    Martínez-de Dios, José Ramiro; Merino, Luis; Caballero, Fernando; Ollero, Anibal

    2011-01-01

    This paper presents a novel system for automatic forest-fire measurement using cameras distributed at ground stations and mounted on Unmanned Aerial Systems (UAS). It can obtain geometrical measurements of forest fires in real-time such as the location and shape of the fire front, flame height and rate of spread, among others. Measurement of forest fires is a challenging problem that is affected by numerous potential sources of error. The proposed system addresses them by exploiting the complementarities between infrared and visual cameras located at different ground locations together with others onboard Unmanned Aerial Systems (UAS). The system applies image processing and geo-location techniques to obtain forest-fire measurements individually from each camera and then integrates the results from all the cameras using statistical data fusion techniques. The proposed system has been extensively tested and validated in close-to-operational conditions in field fire experiments with controlled safety conditions carried out in Portugal and Spain from 2001 to 2006.

  14. Automatic Forest-Fire Measuring Using Ground Stations and Unmanned Aerial Systems

    PubMed Central

    Martínez-de Dios, José Ramiro; Merino, Luis; Caballero, Fernando; Ollero, Anibal

    2011-01-01

    This paper presents a novel system for automatic forest-fire measurement using cameras distributed at ground stations and mounted on Unmanned Aerial Systems (UAS). It can obtain geometrical measurements of forest fires in real-time such as the location and shape of the fire front, flame height and rate of spread, among others. Measurement of forest fires is a challenging problem that is affected by numerous potential sources of error. The proposed system addresses them by exploiting the complementarities between infrared and visual cameras located at different ground locations together with others onboard Unmanned Aerial Systems (UAS). The system applies image processing and geo-location techniques to obtain forest-fire measurements individually from each camera and then integrates the results from all the cameras using statistical data fusion techniques. The proposed system has been extensively tested and validated in close-to-operational conditions in field fire experiments with controlled safety conditions carried out in Portugal and Spain from 2001 to 2006. PMID:22163958

  15. Design and implementation of online automatic judging system

    NASA Astrophysics Data System (ADS)

    Liang, Haohui; Chen, Chaojie; Zhong, Xiuyu; Chen, Yuefeng

    2017-06-01

    For lower efficiency and poorer reliability in programming training and competition by currently artificial judgment, design an Online Automatic Judging (referred to as OAJ) System. The OAJ system including the sandbox judging side and Web side, realizes functions of automatically compiling and running the tested codes, and generating evaluation scores and corresponding reports. To prevent malicious codes from damaging system, the OAJ system utilizes sandbox, ensuring the safety of the system. The OAJ system uses thread pools to achieve parallel test, and adopt database optimization mechanism, such as horizontal split table, to improve the system performance and resources utilization rate. The test results show that the system has high performance, high reliability, high stability and excellent extensibility.

  16. Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

    PubMed Central

    2017-01-01

    In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. PMID:29236718

  17. Automatic recognition of falls in gait-slip training: Harness load cell based criteria.

    PubMed

    Yang, Feng; Pai, Yi-Chung

    2011-08-11

    Over-head-harness systems, equipped with load cell sensors, are essential to the participants' safety and to the outcome assessment in perturbation training. The purpose of this study was to first develop an automatic outcome recognition criterion among young adults for gait-slip training and then verify such criterion among older adults. Each of 39 young and 71 older subjects, all protected by safety harness, experienced 8 unannounced, repeated slips, while walking on a 7m walkway. Each trial was monitored with a motion capture system, bilateral ground reaction force (GRF), harness force, and video recording. The fall trials were first unambiguously indentified with careful visual inspection of all video records. The recoveries without balance loss (in which subjects' trailing foot landed anteriorly to the slipping foot) were also first fully recognized from motion and GRF analyses. These analyses then set the gold standard for the outcome recognition with load cell measurements. Logistic regression analyses based on young subjects' data revealed that the peak load cell force was the best predictor of falls (with 100% accuracy) at the threshold of 30% body weight. On the other hand, the peak moving average force of load cell across 1s period, was the best predictor (with 100% accuracy) separating recoveries with backward balance loss (in which the recovery step landed posterior to slipping foot) from harness assistance at the threshold of 4.5% body weight. These threshold values were fully verified using the data from older adults (100% accuracy in recognizing falls). Because of the increasing popularity in the perturbation training coupling with the protective over-head-harness system, this new criterion could have far reaching implications in automatic outcome recognition during the movement therapy. Copyright © 2011 Elsevier Ltd. All rights reserved.

  18. 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.

  19. Auspice: Automatic Service Planning in Cloud/Grid Environments

    NASA Astrophysics Data System (ADS)

    Chiu, David; Agrawal, Gagan

    Recent scientific advances have fostered a mounting number of services and data sets available for utilization. These resources, though scattered across disparate locations, are often loosely coupled both semantically and operationally. This loosely coupled relationship implies the possibility of linking together operations and data sets to answer queries. This task, generally known as automatic service composition, therefore abstracts the process of complex scientific workflow planning from the user. We have been exploring a metadata-driven approach toward automatic service workflow composition, among other enabling mechanisms, in our system, Auspice: Automatic Service Planning in Cloud/Grid Environments. In this paper, we present a complete overview of our system's unique features and outlooks for future deployment as the Cloud computing paradigm becomes increasingly eminent in enabling scientific computing.

  20. Feature-based attention potentiates recovery of fine direction discrimination in cortically blind patients.

    PubMed

    Cavanaugh, Matthew R; Barbot, Antoine; Carrasco, Marisa; Huxlin, Krystel R

    2017-12-10

    Training chronic, cortically-blind (CB) patients on a coarse [left-right] direction discrimination and integration (CDDI) task recovers performance on this task at trained, blind field locations. However, fine direction difference (FDD) thresholds remain elevated at these locations, limiting the usefulness of recovered vision in daily life. Here, we asked if this FDD impairment can be overcome by training CB subjects with endogenous, feature-based attention (FBA) cues. Ten CB subjects were recruited and trained on CDDI and FDD with an FBA cue or FDD with a neutral cue. After completion of each training protocol, FDD thresholds were re-measured with both neutral and FBA cues at trained, blind-field locations and at corresponding, intact-field locations. In intact portions of the visual field, FDD thresholds were lower when tested with FBA than neutral cues. Training subjects in the blind field on the CDDI task improved FDD performance to the point that a threshold could be measured, but these locations remained impaired relative to the intact field. FDD training with neutral cues resulted in better blind field FDD thresholds than CDDI training, but thresholds remained impaired relative to intact field levels, regardless of testing cue condition. Importantly, training FDD in the blind field with FBA lowered FDD thresholds relative to CDDI training, and allowed the blind field to reach thresholds similar to the intact field, even when FBA trained subjects were tested with a neutral rather than FBA cue. Finally, FDD training appeared to also recover normal integration thresholds at trained, blind-field locations, providing an interesting double dissociation with respect to CDDI training. In summary, mechanisms governing FBA appear to function normally in both intact and impaired regions of the visual field following V1 damage. Our results mark the first time that FDD thresholds in CB fields have been seen to reach intact field levels of performance. Moreover, FBA can be leveraged during visual training to recover normal, fine direction discrimination and integration performance at trained, blind-field locations, potentiating visual recovery of more complex and precise aspects of motion perception in cortically-blinded fields. Copyright © 2017 Elsevier Ltd. All rights reserved.

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