Safety features of subcritical fluid fueled systems

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

Bell, C.R.

1995-10-01

Accelerator-driven transmutation technology has been under study at Los Alamos for several years for application to nuclear waste treatment, tritium production, energy generation, and recently, to the disposition of excess weapons plutonium. Studies and evaluations performed to date at Los Alamos have led to a current focus on a fluid-fuel, fission system operating in a neutron source-supported subcritical mode, using molten salt reactor technology and accelerator-driven proton-neutron spallation. In this paper, the safety features and characteristics of such systems are explored from the perspective of the fundamental nuclear safety objectives that any reactor-type system should address. This exploration is qualitativemore » in nature and uses current vintage solid-fueled reactors as a baseline for comparison. Based on the safety perspectives presented, such systems should be capable of meeting the fundamental nuclear safety objectives. In addition, they should be able to provide the safety robustness desired for advanced reactors. However, the manner in which safety objectives and robustness are achieved is very different from that associated with conventional reactors. Also, there are a number of safety design and operational challenges that will have to be addressed for the safety potential of such systems to be credible.« less

A novel feature ranking algorithm for biometric recognition with PPG signals.

PubMed

Reşit Kavsaoğlu, A; Polat, Kemal; Recep Bozkurt, M

2014-06-01

This study is intended for describing the application of the Photoplethysmography (PPG) signal and the time domain features acquired from its first and second derivatives for biometric identification. For this purpose, a sum of 40 features has been extracted and a feature-ranking algorithm is proposed. This proposed algorithm calculates the contribution of each feature to biometric recognition and collocates the features, the contribution of which is from great to small. While identifying the contribution of the features, the Euclidean distance and absolute distance formulas are used. The efficiency of the proposed algorithms is demonstrated by the results of the k-NN (k-nearest neighbor) classifier applications of the features. During application, each 15-period-PPG signal belonging to two different durations from each of the thirty healthy subjects were used with a PPG data acquisition card. The first PPG signals recorded from the subjects were evaluated as the 1st configuration; the PPG signals recorded later at a different time as the 2nd configuration and the combination of both were evaluated as the 3rd configuration. When the results were evaluated for the k-NN classifier model created along with the proposed algorithm, an identification of 90.44% for the 1st configuration, 94.44% for the 2nd configuration, and 87.22% for the 3rd configuration has successfully been attained. The obtained results showed that both the proposed algorithm and the biometric identification model based on this developed PPG signal are very promising for contactless recognizing the people with the proposed method. Copyright © 2014 Elsevier Ltd. All rights reserved.

Robust Features Of Surface Electromyography Signal

NASA Astrophysics Data System (ADS)

Sabri, M. I.; Miskon, M. F.; Yaacob, M. R.

2013-12-01

Nowadays, application of robotics in human life has been explored widely. Robotics exoskeleton system are one of drastically areas in recent robotic research that shows mimic impact in human life. These system have been developed significantly to be used for human power augmentation, robotics rehabilitation, human power assist, and haptic interaction in virtual reality. This paper focus on solving challenges in problem using neural signals and extracting human intent. Commonly, surface electromyography signal (sEMG) are used in order to control human intent for application exoskeleton robot. But the problem lies on difficulty of pattern recognition of the sEMG features due to high noises which are electrode and cable motion artifact, electrode noise, dermic noise, alternating current power line interface, and other noise came from electronic instrument. The main objective in this paper is to study the best features of electromyography in term of time domain (statistical analysis) and frequency domain (Fast Fourier Transform).The secondary objectives is to map the relationship between torque and best features of muscle unit activation potential (MaxPS and RMS) of biceps brachii. This project scope use primary data of 2 male sample subject which using same dominant hand (right handed), age between 20-27 years old, muscle diameter 32cm to 35cm and using single channel muscle (biceps brachii muscle). The experiment conduct 2 times repeated task of contraction and relaxation of biceps brachii when lifting different load from no load to 3kg with ascending 1kg The result shows that Fast Fourier Transform maximum power spectrum (MaxPS) has less error than mean value of reading compare to root mean square (RMS) value. Thus, Fast Fourier Transform maximum power spectrum (MaxPS) show the linear relationship against torque experience by elbow joint to lift different load. As the conclusion, the best features is MaxPS because it has the lowest error than other features and show

Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals

PubMed Central

Xia, Zhanguo; Xia, Shixiong; Wan, Ling; Cai, Shiyu

2012-01-01

Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. PMID:23202017

Time-frequency Features for Impedance Cardiography Signals During Anesthesia Using Different Distribution Kernels.

PubMed

Muñoz, Jesús Escrivá; Gambús, Pedro; Jensen, Erik W; Vallverdú, Montserrat

2018-01-01

This works investigates the time-frequency content of impedance cardiography signals during a propofol-remifentanil anesthesia. In the last years, impedance cardiography (ICG) is a technique which has gained much attention. However, ICG signals need further investigation. Time-Frequency Distributions (TFDs) with 5 different kernels are used in order to analyze impedance cardiography signals (ICG) before the start of the anesthesia and after the loss of consciousness. In total, ICG signals from one hundred and thirty-one consecutive patients undergoing major surgery under general anesthesia were analyzed. Several features were extracted from the calculated TFDs in order to characterize the time-frequency content of the ICG signals. Differences between those features before and after the loss of consciousness were studied. The Extended Modified Beta Distribution (EMBD) was the kernel for which most features shows statistically significant changes between before and after the loss of consciousness. Among all analyzed features, those based on entropy showed a sensibility, specificity and area under the curve of the receiver operating characteristic above 60%. The anesthetic state of the patient is reflected on linear and non-linear features extracted from the TFDs of the ICG signals. Especially, the EMBD is a suitable kernel for the analysis of ICG signals and offers a great range of features which change according to the patient's anesthesia state in a statistically significant way. Schattauer GmbH.

Evaluating the safety effects of signal improvements.

DOT National Transportation Integrated Search

2013-08-01

There is a need to evaluate the effectiveness of the traffic signal improvements through the development of Crash Modification Factors (CMFs). Recent research has shown that traditional safety evaluation methods have been inadequate in developing CMF...

Fractal Complexity-Based Feature Extraction Algorithm of Communication Signals

NASA Astrophysics Data System (ADS)

Wang, Hui; Li, Jingchao; Guo, Lili; Dou, Zheng; Lin, Yun; Zhou, Ruolin

How to analyze and identify the characteristics of radiation sources and estimate the threat level by means of detecting, intercepting and locating has been the central issue of electronic support in the electronic warfare, and communication signal recognition is one of the key points to solve this issue. Aiming at accurately extracting the individual characteristics of the radiation source for the increasingly complex communication electromagnetic environment, a novel feature extraction algorithm for individual characteristics of the communication radiation source based on the fractal complexity of the signal is proposed. According to the complexity of the received signal and the situation of environmental noise, use the fractal dimension characteristics of different complexity to depict the subtle characteristics of the signal to establish the characteristic database, and then identify different broadcasting station by gray relation theory system. The simulation results demonstrate that the algorithm can achieve recognition rate of 94% even in the environment with SNR of -10dB, and this provides an important theoretical basis for the accurate identification of the subtle features of the signal at low SNR in the field of information confrontation.

High-speed digital signal normalization for feature identification

NASA Technical Reports Server (NTRS)

Ortiz, J. A.; Meredith, B. D.

1983-01-01

A design approach for high speed normalization of digital signals was developed. A reciprocal look up table technique is employed, where a digital value is mapped to its reciprocal via a high speed memory. This reciprocal is then multiplied with an input signal to obtain the normalized result. Normalization improves considerably the accuracy of certain feature identification algorithms. By using the concept of pipelining the multispectral sensor data processing rate is limited only by the speed of the multiplier. The breadboard system was found to operate at an execution rate of five million normalizations per second. This design features high precision, a reduced hardware complexity, high flexibility, and expandability which are very important considerations for spaceborne applications. It also accomplishes a high speed normalization rate essential for real time data processing.

Classification of epileptic EEG signals based on simple random sampling and sequential feature selection.

PubMed

Ghayab, Hadi Ratham Al; Li, Yan; Abdulla, Shahab; Diykh, Mohammed; Wan, Xiangkui

2016-06-01

Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. The experimental results show that the method achieves 99.90, 99.80 and 100 % for classification accuracy, sensitivity and specificity, respectively.

An energy ratio feature extraction method for optical fiber vibration signal

NASA Astrophysics Data System (ADS)

Sheng, Zhiyong; Zhang, Xinyan; Wang, Yanping; Hou, Weiming; Yang, Dan

2018-03-01

The intrusion events in the optical fiber pre-warning system (OFPS) are divided into two types which are harmful intrusion event and harmless interference event. At present, the signal feature extraction methods of these two types of events are usually designed from the view of the time domain. However, the differences of time-domain characteristics for different harmful intrusion events are not obvious, which cannot reflect the diversity of them in detail. We find that the spectrum distribution of different intrusion signals has obvious differences. For this reason, the intrusion signal is transformed into the frequency domain. In this paper, an energy ratio feature extraction method of harmful intrusion event is drawn on. Firstly, the intrusion signals are pre-processed and the power spectral density (PSD) is calculated. Then, the energy ratio of different frequency bands is calculated, and the corresponding feature vector of each type of intrusion event is further formed. The linear discriminant analysis (LDA) classifier is used to identify the harmful intrusion events in the paper. Experimental results show that the algorithm improves the recognition rate of the intrusion signal, and further verifies the feasibility and validity of the algorithm.

Low-cost safety measures at signalized intersections.

DOT National Transportation Integrated Search

2008-05-01

The objectives of this study were to: a) identify intersections with a high number of crashes involving a driver disregarding the traffic signal, b) identify types of low-cost safety measures which may be used as a countermeasure for red light runnin...

Adaptive fault feature extraction from wayside acoustic signals from train bearings

NASA Astrophysics Data System (ADS)

Zhang, Dingcheng; Entezami, Mani; Stewart, Edward; Roberts, Clive; Yu, Dejie

2018-07-01

Wayside acoustic detection of train bearing faults plays a significant role in maintaining safety in the railway transport system. However, the bearing fault information is normally masked by strong background noises and harmonic interferences generated by other components (e.g. axles and gears). In order to extract the bearing fault feature information effectively, a novel method called improved singular value decomposition (ISVD) with resonance-based signal sparse decomposition (RSSD), namely the ISVD-RSSD method, is proposed in this paper. A Savitzky-Golay (S-G) smoothing filter is used to filter singular vectors (SVs) in the ISVD method as an extension of the singular value decomposition (SVD) theorem. Hilbert spectrum entropy and a stepwise optimisation strategy are used to optimize the S-G filter's parameters. The RSSD method is able to nonlinearly decompose the wayside acoustic signal of a faulty train bearing into high and low resonance components, the latter of which contains bearing fault information. However, the high level of noise usually results in poor decomposition results from the RSSD method. Hence, the collected wayside acoustic signal must first be de-noised using the ISVD component of the ISVD-RSSD method. Next, the de-noised signal is decomposed by using the RSSD method. The obtained low resonance component is then demodulated with a Hilbert transform such that the bearing fault can be detected by observing Hilbert envelope spectra. The effectiveness of the ISVD-RSSD method is verified through both laboratory field-based experiments as described in the paper. The results indicate that the proposed method is superior to conventional spectrum analysis and ensemble empirical mode decomposition methods.

Impact of design features upon perceived tool usability and safety

NASA Astrophysics Data System (ADS)

Wiker, Steven F.; Seol, Mun-Su

2005-11-01

While injuries from powered hand tools are caused by a number of factors, this study looks specifically at the impact of the tools design features on perceived tool usability and safety. The tools used in this study are circular saws, power drills and power nailers. Sixty-nine males and thirty-two females completed an anonymous web-based questionnaire that provided orthogonal view photographs of the various tools. Subjects or raters provided: 1) description of the respondents or raters, 2) description of the responses from the raters, and 3) analysis of the interrelationships among respondent ratings of tool safety and usability, physical metrics of the tool, and rater demographic information. The results of the study found that safety and usability were dependent materially upon rater history of use and experience, but not upon training in safety and usability, or quality of design features of the tools (e.g., grip diameters, trigger design, guards, etc.). Thus, positive and negative transfer of prior experience with use of powered hand tools is far more important than any expectancy that may be driven by prior safety and usability training, or from the visual cues that are provided by the engineering design of the tool.

Coordinated pre-preemption of traffic signals to enhance railroad grade crossing safety in urban areas and estimation of train impacts to arterial travel time delay.

DOT National Transportation Integrated Search

2014-01-01

This research project investigated the potential for using advanced features of traffic signal system software platforms : (ATMS.now), prevalent in Florida, to alleviate safety and mobility problems at highway-railroad at-grade crossings and : adjace...

Safety Signals as Instrumental Reinforcers during Free-Operant Avoidance

ERIC Educational Resources Information Center

Fernando, Anushka B. P.; Urcelay, Gonzalo P.; Mar, Adam C.; Dickinson, Anthony; Robbins, Trevor W.

2014-01-01

Safety signals provide "relief" through predicting the absence of an aversive event. At issue is whether these signals also act as instrumental reinforcers. Four experiments were conducted using a free-operant lever-press avoidance paradigm in which each press avoided shock and was followed by the presentation of a 5-sec auditory safety…

Genetic algorithm for the optimization of features and neural networks in ECG signals classification

NASA Astrophysics Data System (ADS)

Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

2017-01-01

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

Continuous Evaluation Of In-Service Highway Safety Feature Performance

DOT National Transportation Integrated Search

2002-09-01

This report documents the research effort, findings, conclusions, and recommendations of a study to develop a program for the continuous in-service evaluation of highway safety features. The study consisted of two phases and eight tasks. An in-servic...

A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.

PubMed

Zarei, Roozbeh; He, Jing; Siuly, Siuly; Zhang, Yanchun

2017-07-01

Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time

The impact of signal normalization on seizure detection using line length features.

PubMed

Logesparan, Lojini; Rodriguez-Villegas, Esther; Casson, Alexander J

2015-10-01

Accurate automated seizure detection remains a desirable but elusive target for many neural monitoring systems. While much attention has been given to the different feature extractions that can be used to highlight seizure activity in the EEG, very little formal attention has been given to the normalization that these features are routinely paired with. This normalization is essential in patient-independent algorithms to correct for broad-level differences in the EEG amplitude between people, and in patient-dependent algorithms to correct for amplitude variations over time. It is crucial, however, that the normalization used does not have a detrimental effect on the seizure detection process. This paper presents the first formal investigation into the impact of signal normalization techniques on seizure discrimination performance when using the line length feature to emphasize seizure activity. Comparing five normalization methods, based upon the mean, median, standard deviation, signal peak and signal range, we demonstrate differences in seizure detection accuracy (assessed as the area under a sensitivity-specificity ROC curve) of up to 52 %. This is despite the same analysis feature being used in all cases. Further, changes in performance of up to 22 % are present depending on whether the normalization is applied to the raw EEG itself or directly to the line length feature. Our results highlight the median decaying memory as the best current approach for providing normalization when using line length features, and they quantify the under-appreciated challenge of providing signal normalization that does not impair seizure detection algorithm performance.

Intelligent feature selection techniques for pattern classification of Lamb wave signals

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

Hinders, Mark K.; Miller, Corey A.

2014-02-18

Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crossholemore » tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.« less

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals

PubMed Central

Zhao, Ming; Lin, Jing; Miao, Yonghao; Xu, Xiaoqiang

2016-01-01

Vibration signals measured in the run-up/coast-down (R/C) processes usually carry rich information about the health status of machinery. However, a major challenge in R/C signals analysis lies in how to exploit more diagnostic information, and how this information could be properly integrated to achieve a more reliable maintenance decision. Aiming at this problem, a framework of R/C signals analysis is presented for the health assessment of gearbox. In the proposed methodology, we first investigate the data preprocessing and feature selection issues for R/C signals. Based on that, a sparsity-guided feature enhancement scheme is then proposed to extract the weak phase jitter associated with gear defect. In order for an effective feature mining and integration under R/C, a generalized phase demodulation technique is further established to reveal the evolution of modulation feature with operating speed and rotation angle. The experimental results indicate that the proposed methodology could not only detect the presence of gear damage, but also offer a novel insight into the dynamic behavior of gearbox. PMID:27827831

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals.

PubMed

Zhao, Ming; Lin, Jing; Miao, Yonghao; Xu, Xiaoqiang

2016-11-02

Vibration signals measured in the run-up/coast-down (R/C) processes usually carry rich information about the health status of machinery. However, a major challenge in R/C signals analysis lies in how to exploit more diagnostic information, and how this information could be properly integrated to achieve a more reliable maintenance decision. Aiming at this problem, a framework of R/C signals analysis is presented for the health assessment of gearbox. In the proposed methodology, we first investigate the data preprocessing and feature selection issues for R/C signals. Based on that, a sparsity-guided feature enhancement scheme is then proposed to extract the weak phase jitter associated with gear defect. In order for an effective feature mining and integration under R/C, a generalized phase demodulation technique is further established to reveal the evolution of modulation feature with operating speed and rotation angle. The experimental results indicate that the proposed methodology could not only detect the presence of gear damage, but also offer a novel insight into the dynamic behavior of gearbox.

Research on the feature extraction and pattern recognition of the distributed optical fiber sensing signal

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Sun, Qi; Pi, Shaohua; Wu, Hongyan

2014-09-01

In this paper, feature extraction and pattern recognition of the distributed optical fiber sensing signal have been studied. We adopt Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, wavelet packet energy feature extraction and wavelet packet Shannon entropy feature extraction methods to obtain sensing signals (such as speak, wind, thunder and rain signals, etc.) characteristic vectors respectively, and then perform pattern recognition via RBF neural network. Performances of these three feature extraction methods are compared according to the results. We choose MFCC characteristic vector to be 12-dimensional. For wavelet packet feature extraction, signals are decomposed into six layers by Daubechies wavelet packet transform, in which 64 frequency constituents as characteristic vector are respectively extracted. In the process of pattern recognition, the value of diffusion coefficient is introduced to increase the recognition accuracy, while keeping the samples for testing algorithm the same. Recognition results show that wavelet packet Shannon entropy feature extraction method yields the best recognition accuracy which is up to 97%; the performance of 12-dimensional MFCC feature extraction method is less satisfactory; the performance of wavelet packet energy feature extraction method is the worst.

Augmenting the decomposition of EMG signals using supervised feature extraction techniques.

PubMed

Parsaei, Hossein; Gangeh, Mehrdad J; Stashuk, Daniel W; Kamel, Mohamed S

2012-01-01

Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its constituent motor unit potential trains (MUPTs). In this work, the possibility of improving the decomposing results using two supervised feature extraction methods, i.e., Fisher discriminant analysis (FDA) and supervised principal component analysis (SPCA), is explored. Using the MUP labels provided by a decomposition-based quantitative EMG system as a training data for FDA and SPCA, the MUPs are transformed into a new feature space such that the MUPs of a single MU become as close as possible to each other while those created by different MUs become as far as possible. The MUPs are then reclassified using a certainty-based classification algorithm. Evaluation results using 10 simulated EMG signals comprised of 3-11 MUPTs demonstrate that FDA and SPCA on average improve the decomposition accuracy by 6%. The improvement for the most difficult-to-decompose signal is about 12%, which shows the proposed approach is most beneficial in the decomposition of more complex signals.

Operational and safety efficiency of traffic signal installations.

DOT National Transportation Integrated Search

2008-05-01

The basic principle of signalization is the provision of a safe and effective means for time and space allocation at an intersection for both vehicular and pedestrian needs. The safety community concurs on the fact that there is the potential for saf...

Features extraction of EMG signal using time domain analysis for arm rehabilitation device

NASA Astrophysics Data System (ADS)

Jali, Mohd Hafiz; Ibrahim, Iffah Masturah; Sulaima, Mohamad Fani; Bukhari, W. M.; Izzuddin, Tarmizi Ahmad; Nasir, Mohamad Na'im

2015-05-01

Rehabilitation device is used as an exoskeleton for people who had failure of their limb. Arm rehabilitation device may help the rehab program whom suffers from arm disability. The device that is used to facilitate the tasks of the program should improve the electrical activity in the motor unit and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. In order to prevent the muscles from paralysis becomes spasticity, the force of movements should minimize the mental efforts. Therefore, the rehabilitation device should analyze the surface EMG signal of normal people that can be implemented to the device. The signal is collected according to procedure of surface electromyography for non-invasive assessment of muscles (SENIAM). The EMG signal is implemented to set the movements' pattern of the arm rehabilitation device. The filtered EMG signal was extracted for features of Standard Deviation (STD), Mean Absolute Value (MAV) and Root Mean Square (RMS) in time-domain. The extraction of EMG data is important to have the reduced vector in the signal features with less of error. In order to determine the best features for any movements, several trials of extraction methods are used by determining the features with less of errors. The accurate features can be use for future works of rehabilitation control in real-time.

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Feature reconstruction of LFP signals based on PLSR in the neural information decoding study.

PubMed

Yonghui Dong; Zhigang Shang; Mengmeng Li; Xinyu Liu; Hong Wan

2017-07-01

To solve the problems of Signal-to-Noise Ratio (SNR) and multicollinearity when the Local Field Potential (LFP) signals is used for the decoding of animal motion intention, a feature reconstruction of LFP signals based on partial least squares regression (PLSR) in the neural information decoding study is proposed in this paper. Firstly, the feature information of LFP coding band is extracted based on wavelet transform. Then the PLSR model is constructed by the extracted LFP coding features. According to the multicollinearity characteristics among the coding features, several latent variables which contribute greatly to the steering behavior are obtained, and the new LFP coding features are reconstructed. Finally, the K-Nearest Neighbor (KNN) method is used to classify the reconstructed coding features to verify the decoding performance. The results show that the proposed method can achieve the highest accuracy compared to the other three methods and the decoding effect of the proposed method is robust.

Responding to Vaccine Safety Signals during Pandemic Influenza: A Modeling Study

PubMed Central

Maro, Judith C.; Fryback, Dennis G.; Lieu, Tracy A.; Lee, Grace M.; Martin, David B.

2014-01-01

Background Managing emerging vaccine safety signals during an influenza pandemic is challenging. Federal regulators must balance vaccine risks against benefits while maintaining public confidence in the public health system. Methods We developed a multi-criteria decision analysis model to explore regulatory decision-making in the context of emerging vaccine safety signals during a pandemic. We simulated vaccine safety surveillance system capabilities and used an age-structured compartmental model to develop potential pandemic scenarios. We used an expert-derived multi-attribute utility function to evaluate potential regulatory responses by combining four outcome measures into a single measure of interest: 1) expected vaccination benefit from averted influenza; 2) expected vaccination risk from vaccine-associated febrile seizures; 3) expected vaccination risk from vaccine-associated Guillain-Barre Syndrome; and 4) expected change in vaccine-seeking behavior in future influenza seasons. Results Over multiple scenarios, risk communication, with or without suspension of vaccination of high-risk persons, were the consistently preferred regulatory responses over no action or general suspension when safety signals were detected during a pandemic influenza. On average, the expert panel valued near-term vaccine-related outcomes relative to long-term projected outcomes by 3∶1. However, when decision-makers had minimal ability to influence near-term outcomes, the response was selected primarily by projected impacts on future vaccine-seeking behavior. Conclusions The selected regulatory response depends on how quickly a vaccine safety signal is identified relative to the peak of the pandemic and the initiation of vaccination. Our analysis suggested two areas for future investment: efforts to improve the size and timeliness of the surveillance system and behavioral research to understand changes in vaccine-seeking behavior. PMID:25536228

Differential use of danger and safety signals in an animal model of anxiety vulnerability: The behavioral economics of avoidance.

PubMed

Spiegler, Kevin M; Fortress, Ashley M; Pang, Kevin C H

2018-03-02

Differential processing of danger and safety signals may underlie symptoms of anxiety disorders and posttraumatic stress disorder. One symptom common to these disorders is pathological avoidance. The present study examined whether danger and safety signals influence avoidance differently in anxiety-vulnerable Wistar-Kyoto (WKY) rats and Sprague Dawley (SD) rats. SD and WKY rats were tested in a novel progressive ratio avoidance task with and without danger or safety signals. Two components of reinforcement, hedonic value and motivation, were determined by fitting an exponentiated demand equation to the data. Hedonic value of avoidance did not differ between SD and WKY rats, but WKY rats had greater motivation to avoid than SD rats. Removal of the safety signal reduced motivation to avoid in SD, but not WKY, rats. Removal of the danger signal did not alter avoidance in either strain. When danger and safety signals were presented simultaneously, WKY rats responded to the danger signals, whereas SD rats responded to the safety signal. The results provide evidence that 1) safety signals enhance motivation to avoid in SD rats, 2) both danger and safety signals influence motivation in WKY rats, and 3) danger signals take precedence over safety signals when presented simultaneously in WKY rats. Thus, anxiety vulnerability is associated with preferential use of danger signals to motivate avoidance. The differential use of danger and safety signals has important implications for the etiology and treatment of pathological avoidance in anxiety disorders and posttraumatic stress disorder. Copyright © 2017. Published by Elsevier Inc.

Using signals associated with safety in avoidance learning: computational model of sex differences

PubMed Central

Beck, Kevin D.; Pang, Kevin C.H.; Myers, Catherine E.

2015-01-01

Avoidance behavior involves learning responses that prevent upcoming aversive events; these responses typically extinguish when the aversive events stop materializing. Stimuli that signal safety from aversive events can paradoxically inhibit extinction of avoidance behavior. In animals, males and females process safety signals differently. These differences help explain why women are more likely to be diagnosed with an anxiety disorder and exhibit differences in symptom presentation and course compared to men. In the current study, we extend an existing model of strain differences in avoidance behavior to simulate sex differences in rats. The model successfully replicates data showing that the omission of a signal associated with a period of safety can facilitate extinction in females, but not males, and makes novel predictions that this effect should depend on the duration of the period, the duration of the signal itself, and its occurrence within that period. Non-reinforced responses during the safe period were also found to be important in the expression of these patterns. The model also allowed us to explore underlying mechanisms for the observed sex effects, such as whether safety signals serve as occasion setters for aversive events, to determine why removing them can facilitate extinction of avoidance. The simulation results argue against this account, and instead suggest the signal may serve as a conditioned reinforcer of avoidance behavior. PMID:26213650

Seismic signal auto-detecing from different features by using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Huang, Y.; Zhou, Y.; Yue, H.; Zhou, S.

2017-12-01

We try Convolutional Neural Network to detect some features of seismic data and compare their efficience. The features include whether a signal is seismic signal or noise and the arrival time of P and S phase and each feature correspond to a Convolutional Neural Network. We first use traditional STA/LTA to recongnize some events and then use templete matching to find more events as training set for the Neural Network. To make the training set more various, we add some noise to the seismic data and make some synthetic seismic data and noise. The 3-component raw signal and time-frequancy ananlyze are used as the input data for our neural network. Our Training is performed on GPUs to achieve efficient convergence. Our method improved the precision in comparison with STA/LTA and template matching. We will move to recurrent neural network to see if this kind network is better in detect P and S phase.

Safety surrogate histograms (SSH): A novel real-time safety assessment of dilemma zone related conflicts at signalized intersections.

PubMed

Ghanipoor Machiani, Sahar; Abbas, Montasir

2016-11-01

Drivers' indecisiveness in dilemma zones (DZ) could result in crash-prone situations at signalized intersections. DZ is to the area ahead of an intersection in which drivers encounter a dilemma regarding whether to stop or proceed through the intersection when the signal turns yellow. An improper decision to stop by the leading driver, combined with the following driver deciding to go, can result in a rear-end collision, unless the following driver recognizes a collision is imminent and adjusts his or her behavior at or shortly after the onset of yellow. Considering the significance of DZ-related crashes, a comprehensive safety measure is needed to characterize the level of safety at signalized intersections. In this study, a novel safety surrogate measure was developed utilizing real-time radar field data. This new measure, called safety surrogate histogram (SSH), captures the degree and frequency of DZ-related conflicts at each intersection approach. SSH includes detailed information regarding the possibility of crashes, because it is calculated based on the vehicles conflicts. An example illustrating the application of the new methodology at two study sites in Virginia is presented and discussed, and a comparison is provided between SSH and other DZ-related safety surrogate measures mentioned in the literature. The results of the study reveal the efficacy of the SSH as complementary to existing surrogate measures. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evaluation of features to support safety and quality in general practice clinical software

PubMed Central

2011-01-01

Background Electronic prescribing is now the norm in many countries. We wished to find out if clinical software systems used by general practitioners in Australia include features (functional capabilities and other characteristics) that facilitate improved patient safety and care, with a focus on quality use of medicines. Methods Seven clinical software systems used in general practice were evaluated. Fifty software features that were previously rated as likely to have a high impact on safety and/or quality of care in general practice were tested and are reported here. Results The range of results for the implementation of 50 features across the 7 clinical software systems was as follows: 17-31 features (34-62%) were fully implemented, 9-13 (18-26%) partially implemented, and 9-20 (18-40%) not implemented. Key findings included: Access to evidence based drug and therapeutic information was limited. Decision support for prescribing was available but varied markedly between systems. During prescribing there was potential for medicine mis-selection in some systems, and linking a medicine with its indication was optional. The definition of 'current medicines' versus 'past medicines' was not always clear. There were limited resources for patients, and some medicines lists for patients were suboptimal. Results were provided to the software vendors, who were keen to improve their systems. Conclusions The clinical systems tested lack some of the features expected to support patient safety and quality of care. Standards and certification for clinical software would ensure that safety features are present and that there is a minimum level of clinical functionality that clinicians could expect to find in any system.

An approach to predict Sudden Cardiac Death (SCD) using time domain and bispectrum features from HRV signal.

PubMed

Houshyarifar, Vahid; Chehel Amirani, Mehdi

2016-08-12

In this paper we present a method to predict Sudden Cardiac Arrest (SCA) with higher order spectral (HOS) and linear (Time) features extracted from heart rate variability (HRV) signal. Predicting the occurrence of SCA is important in order to avoid the probability of Sudden Cardiac Death (SCD). This work is a challenge to predict five minutes before SCA onset. The method consists of four steps: pre-processing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In second step, bispectrum features of HRV signal and time-domain features are obtained. Six features are extracted from bispectrum and two features from time-domain. In the next step, these features are reduced to one feature by the linear discriminant analysis (LDA) technique. Finally, KNN and support vector machine-based classifiers are used to classify the HRV signals. We used two database named, MIT/BIH Sudden Cardiac Death (SCD) Database and Physiobank Normal Sinus Rhythm (NSR). In this work we achieved prediction of SCD occurrence for six minutes before the SCA with the accuracy over 91%.

Evaluating safety and operations of high-speed signalized intersections.

DOT National Transportation Integrated Search

2010-03-01

This Final Report reviews a research effort to evaluate the safety and operations of high-speed intersections in the State of : Oregon. In particular, this research effort focuses on four-leg, signalized intersections with speed limits of 45 mph or :...

Evaluating the impact of grade crossing safety factors through signal detection theory

DOT National Transportation Integrated Search

2012-10-22

The purpose of this effort was to apply signal detection theory to descriptively model the impact : of five grade crossing safety factors to understand their effect on driver decision making. The : safety factors consisted of: improving commercial mo...

Feature Extraction from Subband Brain Signals and Its Classification

NASA Astrophysics Data System (ADS)

Mukul, Manoj Kumar; Matsuno, Fumitoshi

This paper considers both the non-stationarity as well as independence/uncorrelated criteria along with the asymmetry ratio over the electroencephalogram (EEG) signals and proposes a hybrid approach of the signal preprocessing methods before the feature extraction. A filter bank approach of the discrete wavelet transform (DWT) is used to exploit the non-stationary characteristics of the EEG signals and it decomposes the raw EEG signals into the subbands of different center frequencies called as rhythm. A post processing of the selected subband by the AMUSE algorithm (a second order statistics based ICA/BSS algorithm) provides the separating matrix for each class of the movement imagery. In the subband domain the orthogonality as well as orthonormality criteria over the whitening matrix and separating matrix do not come respectively. The human brain has an asymmetrical structure. It has been observed that the ratio between the norms of the left and right class separating matrices should be different for better discrimination between these two classes. The alpha/beta band asymmetry ratio between the separating matrices of the left and right classes will provide the condition to select an appropriate multiplier. So we modify the estimated separating matrix by an appropriate multiplier in order to get the required asymmetry and extend the AMUSE algorithm in the subband domain. The desired subband is further subjected to the updated separating matrix to extract subband sub-components from each class. The extracted subband sub-components sources are further subjected to the feature extraction (power spectral density) step followed by the linear discriminant analysis (LDA).

The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals

PubMed Central

Hatamikia, Sepideh; Maghooli, Keivan; Nasrabadi, Ali Motie

2014-01-01

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively. PMID:25298928

Singular value decomposition based feature extraction technique for physiological signal analysis.

PubMed

Chang, Cheng-Ding; Wang, Chien-Chih; Jiang, Bernard C

2012-06-01

Multiscale entropy (MSE) is one of the popular techniques to calculate and describe the complexity of the physiological signal. Many studies use this approach to detect changes in the physiological conditions in the human body. However, MSE results are easily affected by noise and trends, leading to incorrect estimation of MSE values. In this paper, singular value decomposition (SVD) is adopted to replace MSE to extract the features of physiological signals, and adopt the support vector machine (SVM) to classify the different physiological states. A test data set based on the PhysioNet website was used, and the classification results showed that using SVD to extract features of the physiological signal could attain a classification accuracy rate of 89.157%, which is higher than that using the MSE value (71.084%). The results show the proposed analysis procedure is effective and appropriate for distinguishing different physiological states. This promising result could be used as a reference for doctors in diagnosis of congestive heart failure (CHF) disease.

Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

PubMed

Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Mubin, Marizan; Saad, Ismail

2016-01-01

In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

An exploratory study of the relationship between socioeconomic status and motor vehicle safety features.

PubMed

Girasek, Deborah C; Taylor, Brett

2010-04-01

The purpose of this study was to assess the association between motor vehicle owners' socioeconomic status (SES) and the safety of their motor vehicles. Truncated vehicle identification numbers (VINs) were obtained from the Maryland Motor Vehicle Administration office. ZIP code-level income and educational data were assigned to each VIN. Software was used to identify safety-related vehicle characteristics including crash test rating, availability of electronic stability control and side impact air bags, age, and weight. Correlations and analyses of variance were performed to assess whether a ZIP code's median household income and educational level were associated with its proportion of registered vehicles with safety features. For 13 of the 16 correlations performed, SES was significantly associated with the availability of vehicle safety features in a direction that favored upper-income individuals. Vehicle weight was not associated with income or education. When ZIP codes were divided into median household income quintiles, their mean proportions of safety features also differed significantly, in the same direction, for availability of electronic stability control, side impact air bags, vehicle age, and crash test ratings. Safer motor vehicles appear to be distributed along socioeconomic lines, with lower income groups experiencing more risk. This previously unidentified mechanism of disparity merits further study and the attention of policy makers.

[Drug safety warnings in psychiatry: adverse drug reactions' signaling from 2002 to 2014].

PubMed

Prisco, Vincenzo; Iannaccone, Teresa; Tusciano, Adriano; Boccardi, Mariangela; Perris, Francesco; Capuano, Annalisa; Catapano, Francesco; Fabrazzo, Michele

2016-01-01

Monitoring drug-related side effects in psychiatric patients is highly recommended. In fact, frequent exposure to long-term polipharmacotherapy, poor compliance to pharmachological treatment and comorbidity with organic illnesses requiring the prescription of other drugs are causes of pharmacokinetic/pharmacodynamic interactions. These vulnerability factors result in a certain increase in adverse drug reactions (ADRs). This study performes an analysis of Italian Medicine Agency data, in the section "signal analysis", to attempt an assessment of the safety warnings among the different psychotropic drug classes, belonging to the ATC class: N03 (antiepileptics), N05 (antipsychotics), N06 (psycho-analectic drugs). Then we analysed, in a descriptive way, the different association between the drug and the related ADR, evaluating the different safety profiles, in relation to experimental studies, supporting the importance of the signal. In the last years, among the new 25 ADRs, 10 were related to antidepressant drugs (8 SSRI, 1 mirtazapine, 1 agomelatine). In relation to antipsychotic drugs, 6 new correlations were found between drug and ADR onset, mainly among atypical antispychotics. Other correlations (6 above all) were found among antiepileptic drugs. Among benzodiazepines, a signal linked to rabdomylysis onset was found. It is also recommended an evaluation of safety profile in relation to zolpidem prescription. The results of our systematic review are a motivational input, considering the continuous increase of safety warnings, to attentively monitor drug's prescription. Spontaneous ADRs' signaling is a classical system to provide the required attention in relation to a potential risk. The clinician in charge must report this because he is the key figure in the drugs' safety process. In psychiatry, in which a long-term pharmachological therapy is frequent, clinicians are requested to find and signal ADRs to the competent authority.

The Safety of Older Pedestrians at Signal-Controlled Crossings.

ERIC Educational Resources Information Center

Harrell, W. Andrew

1996-01-01

Observes the extent to which pedestrians checked for oncoming traffic before crossing signal-controlled intersections on busy city streets. Pedestrians over the age of 50 were the most cautious, especially under dangerous traffic conditions. Older pedestrians were least likely to use other pedestrians as "guides" to safety, instead…

Safety benefits of implementing adaptive signal control technology : survey results.

DOT National Transportation Integrated Search

2013-01-01

The safety benefits and costs associated with implementing adaptive signal control technology (ASCT) were evaluated in : this study. A user-friendly online survey was distributed to 62 agencies that had implemented ASCT in the United States. : Twenty...

Bayesian hierarchical modeling for detecting safety signals in clinical trials.

PubMed

Xia, H Amy; Ma, Haijun; Carlin, Bradley P

2011-09-01

Detection of safety signals from clinical trial adverse event data is critical in drug development, but carries a challenging statistical multiplicity problem. Bayesian hierarchical mixture modeling is appealing for its ability to borrow strength across subgroups in the data, as well as moderate extreme findings most likely due merely to chance. We implement such a model for subject incidence (Berry and Berry, 2004 ) using a binomial likelihood, and extend it to subject-year adjusted incidence rate estimation under a Poisson likelihood. We use simulation to choose a signal detection threshold, and illustrate some effective graphics for displaying the flagged signals.

Characterization of electroencephalography signals for estimating saliency features in videos.

PubMed

Liang, Zhen; Hamada, Yasuyuki; Oba, Shigeyuki; Ishii, Shin

2018-05-12

Understanding the functions of the visual system has been one of the major targets in neuroscience formany years. However, the relation between spontaneous brain activities and visual saliency in natural stimuli has yet to be elucidated. In this study, we developed an optimized machine learning-based decoding model to explore the possible relationships between the electroencephalography (EEG) characteristics and visual saliency. The optimal features were extracted from the EEG signals and saliency map which was computed according to an unsupervised saliency model ( Tavakoli and Laaksonen, 2017). Subsequently, various unsupervised feature selection/extraction techniques were examined using different supervised regression models. The robustness of the presented model was fully verified by means of ten-fold or nested cross validation procedure, and promising results were achieved in the reconstruction of saliency features based on the selected EEG characteristics. Through the successful demonstration of using EEG characteristics to predict the real-time saliency distribution in natural videos, we suggest the feasibility of quantifying visual content through measuring brain activities (EEG signals) in real environments, which would facilitate the understanding of cortical involvement in the processing of natural visual stimuli and application developments motivated by human visual processing. Copyright © 2018 Elsevier Ltd. All rights reserved.

SigFlux: a novel network feature to evaluate the importance of proteins in signal transduction networks.

PubMed

Liu, Wei; Li, Dong; Zhang, Jiyang; Zhu, Yunping; He, Fuchu

2006-11-27

Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. We developed a novel network feature to evaluate the importance of proteins in signal transduction networks, that we call SigFlux, based on the concept of minimal path sets (MPSs). An MPS is a minimal set of nodes that can perform the signal propagation from ligands to target genes or feedback loops. We define SigFlux as the number of MPSs in which each protein is involved. We applied this network feature to the large signal transduction network in the hippocampal CA1 neuron of mice. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Compared with another commonly used network feature, connectivity, SigFlux has similar or better ability as connectivity to reflect a protein's essentiality. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are abundant in receptors and transcriptional factors, indicating that SigFlux candescribe the importance of proteins within the context of the entire network. SigFlux is a useful network feature in signal transduction networks that allows the prediction of the essentiality and conservation of proteins. With this novel network feature, proteins that participate in more pathways or feedback loops within a signaling network are proved far more likely to be essential and conserved during evolution than their counterparts.

Real-Time Sensor Validation, Signal Reconstruction, and Feature Detection for an RLV Propulsion Testbed

NASA Technical Reports Server (NTRS)

Jankovsky, Amy L.; Fulton, Christopher E.; Binder, Michael P.; Maul, William A., III; Meyer, Claudia M.

1998-01-01

A real-time system for validating sensor health has been developed in support of the reusable launch vehicle program. This system was designed for use in a propulsion testbed as part of an overall effort to improve the safety, diagnostic capability, and cost of operation of the testbed. The sensor validation system was designed and developed at the NASA Lewis Research Center and integrated into a propulsion checkout and control system as part of an industry-NASA partnership, led by Rockwell International for the Marshall Space Flight Center. The system includes modules for sensor validation, signal reconstruction, and feature detection and was designed to maximize portability to other applications. Review of test data from initial integration testing verified real-time operation and showed the system to perform correctly on both hard and soft sensor failure test cases. This paper discusses the design of the sensor validation and supporting modules developed at LeRC and reviews results obtained from initial test cases.

Safety effects of traffic signing for left turn flashing yellow arrow signals.

PubMed

Schattler, Kerrie L; Gulla, Cody J; Wallenfang, Travis J; Burdett, Beau A; Lund, Jessica A

2015-02-01

In 2010, the left turn flashing yellow arrow (FYA) signal displays were installed at signalized intersections on state routes in the Peoria, Illinois, area. Supplemental traffic signs with text "Left Turn Yield on Flashing Yellow Arrow" were mounted on the mast arm adjacent to the left turn signal at over half of the FYA installations. The purpose of this paper is to present the results of the effectiveness evaluation of the FYA supplemental sign on safety. Analyses are presented on the effects of the FYA supplemental sign for all drivers and a subset of drivers age 65 and older. A crash-based comparison of 164 FYA approaches including 90 approaches with the sign and 74 approaches without the sign showed greater crash reductions when the supplemental FYA sign was present. The results also showed that crashes involving drivers age 65 and older did not experience the same magnitudes of crash reductions as compared to all drivers. The findings of this research indicate that supplemental FYA signs may help in improving safety for left-turning vehicles during the permissive interval. Thus, it is recommended that supplemental signs be used when initially implementing the FYA, and that effort to educate the driving public on new traffic control be made to further improve safety at signalized intersections. Copyright © 2014. Published by Elsevier Ltd.

Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals.

PubMed

Elhaj, Fatin A; Salim, Naomie; Harris, Arief R; Swee, Tan Tian; Ahmed, Taqwa

2016-04-01

Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart abnormalities. Due to the presence of noise, the non-stationary nature of the ECG signal (i.e. the changing morphology of the ECG signal with respect to time) and the irregularity of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. The computer-aided analysis of ECG results assists physicians to detect cardiovascular diseases. The development of many existing arrhythmia systems has depended on the findings from linear experiments on ECG data which achieve high performance on noise-free data. However, nonlinear experiments characterize the ECG signal more effectively sense, extract hidden information in the ECG signal, and achieve good performance under noisy conditions. This paper investigates the representation ability of linear and nonlinear features and proposes a combination of such features in order to improve the classification of ECG data. In this study, five types of beat classes of arrhythmia as recommended by the Association for Advancement of Medical Instrumentation are analyzed: non-ectopic beats (N), supra-ventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F) and unclassifiable and paced beats (U). The characterization ability of nonlinear features such as high order statistics and cumulants and nonlinear feature reduction methods such as independent component analysis are combined with linear features, namely, the principal component analysis of discrete wavelet transform coefficients. The features are tested for their ability to differentiate different classes of data using different classifiers, namely, the support vector machine and neural network methods with tenfold cross-validation. Our proposed method is able to classify the N, S, V, F and U arrhythmia classes with high accuracy (98.91%) using a combined support

Effect of current federal regulations on handgun safety features.

PubMed

Milne, John S; Hargarten, Stephen W; Kellermann, Arthur L; Wintemute, Garen J

2003-01-01

In the late 1960s, the Bureau of Alcohol, Tobacco, and Firearms implemented the "factoring criteria," a set of minimum size and safety standards required for any handgun imported into the United States. These standards, however, were not applied to guns manufactured domestically. We determine whether extending the factoring criteria to all handguns sold in the United States, as has been proposed in Congress, would increase the likelihood that safety devices would be included in new handgun designs. Imported and domestic handgun models produced in 1996 were examined to determine the prevalence of 4 passively acting safety devices on pistols and 1 passive safety device on revolvers. Domestic models were also scored against the factoring criteria. Compared with domestic pistol models, imported pistols were more likely to include a firing pin block (odds ratio [OR] 2.43; 95% confidence interval [CI] 1.54 to 3.85) and a loaded chamber indicator (OR 1.59; 95% CI 0.98 to 2.56). Domestic pistol models that already met the factoring criteria were more likely to include a loaded chamber indicator (OR 12.05; 95% CI 2.74 to 53.02), a grip safety (OR 24.12; 95% CI 7.8 to 74.33), and a firing pin block (OR 4.92; 95% CI 2.35 to 10.29) than domestic models that did not meet the criteria. Although pistol models that meet the factoring criteria are more likely to contain safety devices than those that do not, the net effect is modest. Thus, the factoring criteria alone are insufficient to ensure consistent incorporation of safety features into new handgun designs.

[Lithology feature extraction of CASI hyperspectral data based on fractal signal algorithm].

PubMed

Tang, Chao; Chen, Jian-Ping; Cui, Jing; Wen, Bo-Tao

2014-05-01

Hyperspectral data is characterized by combination of image and spectrum and large data volume dimension reduction is the main research direction. Band selection and feature extraction is the primary method used for this objective. In the present article, the authors tested methods applied for the lithology feature extraction from hyperspectral data. Based on the self-similarity of hyperspectral data, the authors explored the application of fractal algorithm to lithology feature extraction from CASI hyperspectral data. The "carpet method" was corrected and then applied to calculate the fractal value of every pixel in the hyperspectral data. The results show that fractal information highlights the exposed bedrock lithology better than the original hyperspectral data The fractal signal and characterized scale are influenced by the spectral curve shape, the initial scale selection and iteration step. At present, research on the fractal signal of spectral curve is rare, implying the necessity of further quantitative analysis and investigation of its physical implications.

Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features

PubMed Central

Morison, Gordon; Boreham, Philip

2018-01-01

Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to map multiple discharge source signals captured by EMI and labelled by experts, including PD, from the time domain to a feature space, which aids in the interpretation of subsequent fault information. Here, instead of using only one permutation entropy measure, a more robust measure, called Dispersion Entropy (DE), is added to the feature vector. Multi-Class Support Vector Machine (MCSVM) methods are utilized for classification of the different discharge sources. Results show an improved classification accuracy compared to previously proposed methods. This yields to a successful development of an expert’s knowledge-based intelligent system. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real-world application for EMI condition monitoring. PMID:29385030

Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

PubMed Central

Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

2015-01-01

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature. PMID:25799141

Serotonin 2C receptor antagonist improves fear discrimination and subsequent safety signal recall

PubMed Central

Foilb, Allison R.; Christianson, John P.

2015-01-01

The capacity to discriminate between safety and danger is fundamental for survival, but is disrupted in individuals with posttraumatic stress disorder (PTSD). Acute stressors cause a release of serotonin (5-HT) in the forebrain, which is one mechanism for enhanced fear and anxiety; these effects are mediated by the 5-HT2C receptor. Using a fear discrimination paradigm where a danger signal conditioned stimulus (CS+) coterminates with a mild footshock and a safety signal (CS-) indicates the absence of shock, we demonstrate that danger/safety discrimination and fear inhibition develops over the course of 4 daily conditioning sessions. Systemic administration of the 5-HT2C receptor antagonist SB 242084 (0.25 or 1.0 mg/kg) prior to conditioning reduced behavioral freezing during conditioning, improved learning and subsequent inhibition of fear by the safety signal. Discrimination was apparent in the first recall test, and discrimination during training was evident after 3 days of conditioning versus 5 days in the vehicle treated controls. These results suggest a novel therapeutic use for 5-HT2C receptor antagonists to improve learning under stressful circumstances. Potential anatomical loci for 5-HT2C receptor modulation of fear discrimination learning and cognitive performance enhancement are discussed. PMID:26344640

Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors

NASA Astrophysics Data System (ADS)

Chen, Xiang; Li, Jingchao; Han, Hui; Ying, Yulong

2018-05-01

Because of the limitations of the traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm is proposed. First, the radiation source signal was preprocessed, and a Hilbert transform was performed to obtain the instantaneous amplitude of the signal. Then, the improved fractal box-counting dimension of the signal instantaneous amplitude was extracted as the first eigenvector. At the same time, the improved fractal box-counting dimension of the signal without the Hilbert transform was extracted as the second eigenvector. Finally, the dual improved fractal box-counting dimension eigenvectors formed the multi-dimensional eigenvectors as signal subtle features, which were used for radiation source signal recognition by the grey relation algorithm. The experimental results show that, compared with the traditional fractal box-counting dimension algorithm and the single improved fractal box-counting dimension algorithm, the proposed dual improved fractal box-counting dimension algorithm can better extract the signal subtle distribution characteristics under different reconstruction phase space, and has a better recognition effect with good real-time performance.

Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface.

PubMed

Matsubara, Takamitsu; Morimoto, Jun

2013-08-01

In this study, we propose a multiuser myoelectric interface that can easily adapt to novel users. When a user performs different motions (e.g., grasping and pinching), different electromyography (EMG) signals are measured. When different users perform the same motion (e.g., grasping), different EMG signals are also measured. Therefore, designing a myoelectric interface that can be used by multiple users to perform multiple motions is difficult. To cope with this problem, we propose for EMG signals a bilinear model that is composed of two linear factors: 1) user dependent and 2) motion dependent. By decomposing the EMG signals into these two factors, the extracted motion-dependent factors can be used as user-independent features. We can construct a motion classifier on the extracted feature space to develop the multiuser interface. For novel users, the proposed adaptation method estimates the user-dependent factor through only a few interactions. The bilinear EMG model with the estimated user-dependent factor can extract the user-independent features from the novel user data. We applied our proposed method to a recognition task of five hand gestures for robotic hand control using four-channel EMG signals measured from subject forearms. Our method resulted in 73% accuracy, which was statistically significantly different from the accuracy of standard nonmultiuser interfaces, as the result of a two-sample t -test at a significance level of 1%.

LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

PubMed

Zhang, Tao; Chen, Wanzhong

2017-08-01

Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

Comparison of two drug safety signals in a pharmacovigilance data mining framework.

PubMed

Tubert-Bitter, Pascale; Bégaud, Bernard; Ahmed, Ismaïl

2016-04-01

Since adverse drug reactions are a major public health concern, early detection of drug safety signals has become a top priority for regulatory agencies and the pharmaceutical industry. Quantitative methods for analyzing spontaneous reporting material recorded in pharmacovigilance databases through data mining have been proposed in the last decades and are increasingly used to flag potential safety problems. While automated data mining is motivated by the usually huge size of pharmacovigilance databases, it does not systematically produce relevant alerts. Moreover, each detected signal requires appropriate assessment that may involve investigation of the whole therapeutic class. The goal of this article is to provide a methodology for comparing two detected signals. It is nested within the automated surveillance framework as (1) no extra information is required and (2) no simple inference on the actual risks can be extrapolated from spontaneous reporting data. We designed our methodology on the basis of two classical methods used for automated signal detection: the Bayesian Gamma Poisson Shrinker and the frequentist Proportional Reporting Ratio. A simulation study was conducted to assess the performances of both proposed methods. The latter were used to compare cardiovascular signals for two HIV treatments from the French pharmacovigilance database. © The Author(s) 2012.

A signal-detection-based diagnostic-feature-detection model of eyewitness identification.

PubMed

Wixted, John T; Mickes, Laura

2014-04-01

The theoretical understanding of eyewitness identifications made from a police lineup has long been guided by the distinction between absolute and relative decision strategies. In addition, the accuracy of identifications associated with different eyewitness memory procedures has long been evaluated using measures like the diagnosticity ratio (the correct identification rate divided by the false identification rate). Framed in terms of signal-detection theory, both the absolute/relative distinction and the diagnosticity ratio are mainly relevant to response bias while remaining silent about the key issue of diagnostic accuracy, or discriminability (i.e., the ability to tell the difference between innocent and guilty suspects in a lineup). Here, we propose a signal-detection-based model of eyewitness identification, one that encourages the use of (and helps to conceptualize) receiver operating characteristic (ROC) analysis to measure discriminability. Recent ROC analyses indicate that the simultaneous presentation of faces in a lineup yields higher discriminability than the presentation of faces in isolation, and we propose a diagnostic feature-detection hypothesis to account for that result. According to this hypothesis, the simultaneous presentation of faces allows the eyewitness to appreciate that certain facial features (viz., those that are shared by everyone in the lineup) are non-diagnostic of guilt. To the extent that those non-diagnostic features are discounted in favor of potentially more diagnostic features, the ability to discriminate innocent from guilty suspects will be enhanced.

Traffic signal design and simulation for vulnerable road users safety and bus preemption

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

Lo, Shih-Ching; Huang, Hsieh-Chu

Mostly, pedestrian car accidents occurred at a signalized interaction is because pedestrians cannot across the intersection safely within the green light. From the viewpoint of pedestrian, there might have two reasons. The first one is pedestrians cannot speed up to across the intersection, such as the elders. The other reason is pedestrians do not sense that the signal phase is going to change and their right-of-way is going to be lost. Developing signal logic to protect pedestrian, who is crossing an intersection is the first purpose of this study. In addition, to improve the reliability and reduce delay of publicmore » transportation service is the second purpose. Therefore, bus preemption is also considered in the designed signal logic. In this study, the traffic data of the intersection of Chong-Qing North Road and Min-Zu West Road, Taipei, Taiwan, is employed to calibrate and validate the signal logic by simulation. VISSIM 5.20, which is a microscopic traffic simulation software, is employed to simulate the signal logic. From the simulated results, the signal logic presented in this study can protect pedestrians crossing the intersection successfully. The design of bus preemption can reduce the average delay. However, the pedestrian safety and bus preemption signal will influence the average delay of cars largely. Thus, whether applying the pedestrian safety and bus preemption signal logic to an intersection or not should be evaluated carefully.« less

Operational safety assessment of turbo generators with wavelet Rényi entropy from sensor-dependent vibration signals.

PubMed

Zhang, Xiaoli; Wang, Baojian; Chen, Xuefeng

2015-04-16

With the rapid development of sensor technology, various professional sensors are installed on modern machinery to monitor operational processes and assure operational safety, which play an important role in industry and society. In this work a new operational safety assessment approach with wavelet Rényi entropy utilizing sensor-dependent vibration signals is proposed. On the basis of a professional sensor and the corresponding system, sensor-dependent vibration signals are acquired and analyzed by a second generation wavelet package, which reflects time-varying operational characteristic of individual machinery. Derived from the sensor-dependent signals' wavelet energy distribution over the observed signal frequency range, wavelet Rényi entropy is defined to compute the operational uncertainty of a turbo generator, which is then associated with its operational safety degree. The proposed method is applied in a 50 MW turbo generator, whereupon it is proved to be reasonable and effective for operation and maintenance.

Joint Spatial-Spectral Feature Space Clustering for Speech Activity Detection from ECoG Signals

PubMed Central

Kanas, Vasileios G.; Mporas, Iosif; Benz, Heather L.; Sgarbas, Kyriakos N.; Bezerianos, Anastasios; Crone, Nathan E.

2014-01-01

Brain machine interfaces for speech restoration have been extensively studied for more than two decades. The success of such a system will depend in part on selecting the best brain recording sites and signal features corresponding to speech production. The purpose of this study was to detect speech activity automatically from electrocorticographic signals based on joint spatial-frequency clustering of the ECoG feature space. For this study, the ECoG signals were recorded while a subject performed two different syllable repetition tasks. We found that the optimal frequency resolution to detect speech activity from ECoG signals was 8 Hz, achieving 98.8% accuracy by employing support vector machines (SVM) as a classifier. We also defined the cortical areas that held the most information about the discrimination of speech and non-speech time intervals. Additionally, the results shed light on the distinct cortical areas associated with the two syllable repetition tasks and may contribute to the development of portable ECoG-based communication. PMID:24658248

Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals.

PubMed

Ebrahimi, Farideh; Setarehdan, Seyed-Kamaledin; Ayala-Moyeda, Jose; Nazeran, Homer

2013-10-01

The conventional method for sleep staging is to analyze polysomnograms (PSGs) recorded in a sleep lab. The electroencephalogram (EEG) is one of the most important signals in PSGs but recording and analysis of this signal presents a number of technical challenges, especially at home. Instead, electrocardiograms (ECGs) are much easier to record and may offer an attractive alternative for home sleep monitoring. The heart rate variability (HRV) signal proves suitable for automatic sleep staging. Thirty PSGs from the Sleep Heart Health Study (SHHS) database were used. Three feature sets were extracted from 5- and 0.5-min HRV segments: time-domain features, nonlinear-dynamics features and time-frequency features. The latter was achieved by using empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods. Normalized energies in important frequency bands of HRV signals were computed using time-frequency methods. ANOVA and t-test were used for statistical evaluations. Automatic sleep staging was based on HRV signal features. The ANOVA followed by a post hoc Bonferroni was used for individual feature assessment. Most features were beneficial for sleep staging. A t-test was used to compare the means of extracted features in 5- and 0.5-min HRV segments. The results showed that the extracted features means were statistically similar for a small number of features. A separability measure showed that time-frequency features, especially EMD features, had larger separation than others. There was not a sizable difference in separability of linear features between 5- and 0.5-min HRV segments but separability of nonlinear features, especially EMD features, decreased in 0.5-min HRV segments. HRV signal features were classified by linear discriminant (LD) and quadratic discriminant (QD) methods. Classification results based on features from 5-min segments surpassed those obtained from 0.5-min segments. The best result was obtained from features using 5-min HRV

A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer.

PubMed

Khan, Adil Mehmood; Lee, Young-Koo; Lee, Sungyoung Y; Kim, Tae-Seong

2010-09-01

Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.

Selecting Power-Efficient Signal Features for a Low-Power Fall Detector.

PubMed

Wang, Changhong; Redmond, Stephen J; Lu, Wei; Stevens, Michael C; Lord, Stephen R; Lovell, Nigel H

2017-11-01

Falls are a serious threat to the health of older people. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or an emergency response service so they may deliver immediate assistance, improving the chances of recovering from fall-related injuries. One constraint of such a wearable technology is its limited battery life. Thus, minimization of power consumption is an important design concern, all the while maintaining satisfactory accuracy of the fall detection algorithms implemented on the wearable device. This paper proposes an approach for selecting power-efficient signal features such that the minimum desirable fall detection accuracy is assured. Using data collected in simulated falls, simulated activities of daily living, and real free-living trials, all using young volunteers, the proposed approach selects four features from a set of ten commonly used features, providing a power saving of 75.3%, while limiting the error rate of a binary classification decision tree fall detection algorithm to 7.1%.Falls are a serious threat to the health of older people. A wearable fall detector can automatically detect the occurrence of a fall and alert a caregiver or an emergency response service so they may deliver immediate assistance, improving the chances of recovering from fall-related injuries. One constraint of such a wearable technology is its limited battery life. Thus, minimization of power consumption is an important design concern, all the while maintaining satisfactory accuracy of the fall detection algorithms implemented on the wearable device. This paper proposes an approach for selecting power-efficient signal features such that the minimum desirable fall detection accuracy is assured. Using data collected in simulated falls, simulated activities of daily living, and real free-living trials, all using young volunteers, the proposed approach selects four features from a set of ten commonly used features, providing

Efficient and robust computation of PDF features from diffusion MR signal.

PubMed

Assemlal, Haz-Edine; Tschumperlé, David; Brun, Luc

2009-10-01

We present a method for the estimation of various features of the tissue micro-architecture using the diffusion magnetic resonance imaging. The considered features are designed from the displacement probability density function (PDF). The estimation is based on two steps: first the approximation of the signal by a series expansion made of Gaussian-Laguerre and Spherical Harmonics functions; followed by a projection on a finite dimensional space. Besides, we propose to tackle the problem of the robustness to Rician noise corrupting in-vivo acquisitions. Our feature estimation is expressed as a variational minimization process leading to a variational framework which is robust to noise. This approach is very flexible regarding the number of samples and enables the computation of a large set of various features of the local tissues structure. We demonstrate the effectiveness of the method with results on both synthetic phantom and real MR datasets acquired in a clinical time-frame.

Modeling crash injury severity by road feature to improve safety.

PubMed

Penmetsa, Praveena; Pulugurtha, Srinivas S

2018-01-02

The objective of this research is 2-fold: to (a) model and identify critical road features (or locations) based on crash injury severity and compare it with crash frequency and (b) model and identify drivers who are more likely to contribute to crashes by road feature. Crash data from 2011 to 2013 were obtained from the Highway Safety Information System (HSIS) for the state of North Carolina. Twenty-three different road features were considered, analyzed, and compared with each other as well as no road feature. A multinomial logit (MNL) model was developed and odds ratios were estimated to investigate the effect of road features on crash injury severity. Among the many road features, underpass, end or beginning of a divided highway, and on-ramp terminal on crossroad are the top 3 critical road features. Intersection crashes are frequent but are not highly likely to result in severe injuries compared to critical road features. Roundabouts are least likely to result in both severe and moderate injuries. Female drivers are more likely to be involved in crashes at intersections (4-way and T) compared to male drivers. Adult drivers are more likely to be involved in crashes at underpasses. Older drivers are 1.6 times more likely to be involved in a crash at the end or beginning of a divided highway. The findings from this research help to identify critical road features that need to be given priority. As an example, additional advanced warning signs and providing enlarged or highly retroreflective signs that grab the attention of older drivers may help in making locations such as end or beginning of a divided highway much safer. Educating drivers about the necessary skill sets required at critical road features in addition to engineering solutions may further help them adopt safe driving behaviors on the road.

MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression

NASA Astrophysics Data System (ADS)

Martínez-Torteya, Antonio; Rodríguez-Rojas, Juan; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Treviño, Victor; Tamez-Peña, José G.

2014-03-01

An early diagnosis of Alzheimer's disease (AD) confers many benefits. Several biomarkers from different information modalities have been proposed for the prediction of MCI to AD progression, where features extracted from MRI have played an important role. However, studies have focused almost exclusively in the morphological characteristics of the images. This study aims to determine whether features relating to the signal and texture of the image could add predictive power. Baseline clinical, biological and PET information, and MP-RAGE images for 62 subjects from the Alzheimer's Disease Neuroimaging Initiative were used in this study. Images were divided into 83 regions and 50 features were extracted from each one of these. A multimodal database was constructed, and a feature selection algorithm was used to obtain an accurate and small logistic regression model, which achieved a cross-validation accuracy of 0.96. These model included six features, five of them obtained from the MP-RAGE image, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index, showing that both groups are statistically different (p-value of 2.04e-11). The results demonstrate that MRI features related to both signal and texture, add MCI to AD predictive power, and support the idea that multimodal biomarkers outperform single-modality biomarkers.

An Automated System Combining Safety Signal Detection and Prioritization from Healthcare Databases: A Pilot Study.

PubMed

Arnaud, Mickael; Bégaud, Bernard; Thiessard, Frantz; Jarrion, Quentin; Bezin, Julien; Pariente, Antoine; Salvo, Francesco

2018-04-01

Signal detection from healthcare databases is possible, but is not yet used for routine surveillance of drug safety. One challenge is to develop methods for selecting signals that should be assessed with priority. The aim of this study was to develop an automated system combining safety signal detection and prioritization from healthcare databases and applicable to drugs used in chronic diseases. Patients present in the French EGB healthcare database for at least 1 year between 2005 and 2015 were considered. Noninsulin glucose-lowering drugs (NIGLDs) were selected as a case study, and hospitalization data were used to select important medical events (IME). Signal detection was performed quarterly from 2008 to 2015 using sequence symmetry analysis. NIGLD/IME associations were screened if one or more exposed case was identified in the quarter, and three or more exposed cases were identified in the population at the date of screening. Detected signals were prioritized using the Longitudinal-SNIP (L-SNIP) algorithm based on strength (S), novelty (N), and potential impact of signal (I), and pattern of drug use (P). Signals scored in the top 10% were identified as of high priority. A reference set was built based on NIGLD summaries of product characteristics (SPCs) to compute the performance of the developed system. A total of 815 associations were screened and 241 (29.6%) were detected as signals; among these, 58 (24.1%) were prioritized. The performance for signal detection was sensitivity = 47%; specificity = 80%; positive predictive value (PPV) 33%; negative predictive value = 82%. The use of the L-SNIP algorithm increased the early identification of positive controls, restricted to those mentioned in the SPCs after 2008: PPV = 100% versus PPV = 14% with its non-use. The system revealed a strong new signal with dipeptidylpeptidase-4 inhibitors and venous thromboembolism. The developed system seems promising for the routine use of healthcare data for safety

The effects of the non-contingent presentation of safety signals on the elimination of safety behaviors: An experimental comparison between individuals with low and high obsessive-compulsive profiles.

PubMed

Angelakis, Ioannis; Austin, Jennifer L

2018-06-01

Safety behaviors, defined as engagement in avoidance within safe environments, are a key symptom of obsessive-compulsive and related disorders. They may interfere with daily functioning and as such their emission should be reduced. The purpose of the current study is to investigate the effects of the non-contingent presentation of safety signals (cues produced by safety behaviors) on reducing safety behaviors in participants self-reporting low and high OCD profiles. In total, 32 participants were asked to play a game to gain points and avoid their loss. After having developed avoidance behavior, evidenced by maintaining all of their earned points, they were exposed to safe environments where no point loss was programmed. In Test 1, safety cues (blue bar) were produced contingent on performing safety behaviors. In Test 2, safety cues were presented continuously without any response requirement. Findings demonstrated that high OCD group displayed higher rates of safety behaviors than low OCD group. However, exposure to the non-contingent presentation of safety signals eliminated their emission in both groups. Future studies need to evaluate the effects of different non-contingent schedules on the suppression of safety behaviors. These findings contribute to the literature by demonstrating that non-contingent introduction of safety signals eliminated safety behaviors completely, even in high OCD participants, who performed safety behavior at higher rates. Such a treatment protocol may ameliorate exposure therapy in which response prevention constitutes a key element and is generally associated with increased drop-out rates. Copyright © 2017 Elsevier Ltd. All rights reserved.

A UWB Radar Signal Processing Platform for Real-Time Human Respiratory Feature Extraction Based on Four-Segment Linear Waveform Model.

PubMed

Hsieh, Chi-Hsuan; Chiu, Yu-Fang; Shen, Yi-Hsiang; Chu, Ta-Shun; Huang, Yuan-Hao

2016-02-01

This paper presents an ultra-wideband (UWB) impulse-radio radar signal processing platform used to analyze human respiratory features. Conventional radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory signal information is available that has not been explored using radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB signal processing platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB radar signal processing platform including a radar front-end chip and an FPGA chip. The proposed radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.

A robust indicator based on singular value decomposition for flaw feature detection from noisy ultrasonic signals

NASA Astrophysics Data System (ADS)

Cui, Ximing; Wang, Zhe; Kang, Yihua; Pu, Haiming; Deng, Zhiyang

2018-05-01

Singular value decomposition (SVD) has been proven to be an effective de-noising tool for flaw echo signal feature detection in ultrasonic non-destructive evaluation (NDE). However, the uncertainty in the arbitrary manner of the selection of an effective singular value weakens the robustness of this technique. Improper selection of effective singular values will lead to bad performance of SVD de-noising. What is more, the computational complexity of SVD is too large for it to be applied in real-time applications. In this paper, to eliminate the uncertainty in SVD de-noising, a novel flaw indicator, named the maximum singular value indicator (MSI), based on short-time SVD (STSVD), is proposed for flaw feature detection from a measured signal in ultrasonic NDE. In this technique, the measured signal is first truncated into overlapping short-time data segments to put feature information of a transient flaw echo signal in local field, and then the MSI can be obtained from the SVD of each short-time data segment. Research shows that this indicator can clearly indicate the location of ultrasonic flaw signals, and the computational complexity of this STSVD-based indicator is significantly reduced with the algorithm proposed in this paper. Both simulation and experiments show that this technique is very efficient for real-time application in flaw detection from noisy data.

Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection.

PubMed

Xu, Rong; Wang, QuanQiu

2014-01-15

Independent data sources can be used to augment post-marketing drug safety signal detection. The vast amount of publicly available biomedical literature contains rich side effect information for drugs at all clinical stages. In this study, we present a large-scale signal boosting approach that combines over 4 million records in the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and over 21 million biomedical articles. The datasets are comprised of 4,285,097 records from FAERS and 21,354,075 MEDLINE articles. We first extracted all drug-side effect (SE) pairs from FAERS. Our study implemented a total of seven signal ranking algorithms. We then compared these different ranking algorithms before and after they were boosted with signals from MEDLINE sentences or abstracts. Finally, we manually curated all drug-cardiovascular (CV) pairs that appeared in both data sources and investigated whether our approach can detect many true signals that have not been included in FDA drug labels. We extracted a total of 2,787,797 drug-SE pairs from FAERS with a low initial precision of 0.025. The ranking algorithm combined signals from both FAERS and MEDLINE, significantly improving the precision from 0.025 to 0.371 for top-ranked pairs, representing a 13.8 fold elevation in precision. We showed by manual curation that drug-SE pairs that appeared in both data sources were highly enriched with true signals, many of which have not yet been included in FDA drug labels. We have developed an efficient and effective drug safety signal ranking and strengthening approach We demonstrate that large-scale combining information from FAERS and biomedical literature can significantly contribute to drug safety surveillance.

Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection

PubMed Central

2014-01-01

Background Independent data sources can be used to augment post-marketing drug safety signal detection. The vast amount of publicly available biomedical literature contains rich side effect information for drugs at all clinical stages. In this study, we present a large-scale signal boosting approach that combines over 4 million records in the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and over 21 million biomedical articles. Results The datasets are comprised of 4,285,097 records from FAERS and 21,354,075 MEDLINE articles. We first extracted all drug-side effect (SE) pairs from FAERS. Our study implemented a total of seven signal ranking algorithms. We then compared these different ranking algorithms before and after they were boosted with signals from MEDLINE sentences or abstracts. Finally, we manually curated all drug-cardiovascular (CV) pairs that appeared in both data sources and investigated whether our approach can detect many true signals that have not been included in FDA drug labels. We extracted a total of 2,787,797 drug-SE pairs from FAERS with a low initial precision of 0.025. The ranking algorithm combined signals from both FAERS and MEDLINE, significantly improving the precision from 0.025 to 0.371 for top-ranked pairs, representing a 13.8 fold elevation in precision. We showed by manual curation that drug-SE pairs that appeared in both data sources were highly enriched with true signals, many of which have not yet been included in FDA drug labels. Conclusions We have developed an efficient and effective drug safety signal ranking and strengthening approach We demonstrate that large-scale combining information from FAERS and biomedical literature can significantly contribute to drug safety surveillance. PMID:24428898

A novel framework to evaluate pedestrian safety at non-signalized locations.

PubMed

Fu, Ting; Miranda-Moreno, Luis; Saunier, Nicolas

2018-02-01

This paper proposes a new framework to evaluate pedestrian safety at non-signalized crosswalk locations. In the proposed framework, the yielding maneuver of a driver in response to a pedestrian is split into the reaction and braking time. Hence, the relationship of the distance required for a yielding maneuver and the approaching vehicle speed depends on the reaction time of the driver and deceleration rate that the vehicle can achieve. The proposed framework is represented in the distance-velocity (DV) diagram and referred as the DV model. The interactions between approaching vehicles and pedestrians showing the intention to cross are divided in three categories: i) situations where the vehicle cannot make a complete stop, ii) situations where the vehicle's ability to stop depends on the driver reaction time, and iii) situations where the vehicle can make a complete stop. Based on these classifications, non-yielding maneuvers are classified as "non-infraction non-yielding" maneuvers, "uncertain non-yielding" maneuvers and "non-yielding" violations, respectively. From the pedestrian perspective, crossing decisions are classified as dangerous crossings, risky crossings and safe crossings accordingly. The yielding compliance and yielding rate, as measures of the yielding behavior, are redefined based on these categories. Time to crossing and deceleration rate required for the vehicle to stop are used to measure the probability of collision. Finally, the framework is demonstrated through a case study in evaluating pedestrian safety at three different types of non-signalized crossings: a painted crosswalk, an unprotected crosswalk, and a crosswalk controlled by stop signs. Results from the case study suggest that the proposed framework works well in describing pedestrian-vehicle interactions which helps in evaluating pedestrian safety at non-signalized crosswalk locations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Operational Safety Assessment of Turbo Generators with Wavelet Rényi Entropy from Sensor-Dependent Vibration Signals

PubMed Central

Zhang, Xiaoli; Wang, Baojian; Chen, Xuefeng

2015-01-01

With the rapid development of sensor technology, various professional sensors are installed on modern machinery to monitor operational processes and assure operational safety, which play an important role in industry and society. In this work a new operational safety assessment approach with wavelet Rényi entropy utilizing sensor-dependent vibration signals is proposed. On the basis of a professional sensor and the corresponding system, sensor-dependent vibration signals are acquired and analyzed by a second generation wavelet package, which reflects time-varying operational characteristic of individual machinery. Derived from the sensor-dependent signals’ wavelet energy distribution over the observed signal frequency range, wavelet Rényi entropy is defined to compute the operational uncertainty of a turbo generator, which is then associated with its operational safety degree. The proposed method is applied in a 50 MW turbo generator, whereupon it is proved to be reasonable and effective for operation and maintenance. PMID:25894934

Specific features of medicines safety and pharmacovigilance in Africa

PubMed Central

Pal, Shanthi N.; Olsson, Sten; Dodoo, Alexander; Bencheikh, Rachida Soulayami

2012-01-01

The thalidomide tragedy in the late 1950s and early 1960s served as a wakeup call and raised questions about the safety of medicinal products. The developed countries rose to the challenge putting in place systems to ensure the safety of medicines. However, this was not the case for low-resource settings because of prevailing factors inherent in them. This paper reviews some of these features and the current status of pharmacovigilance in Africa. The health systems in most of the 54 countries of Africa are essentially weak, lacking in basic infrastructure, personnel, equipment and facilities. The recent mass deployment of medicines to address diseases of public health significance in Africa poses additional challenges to the health system with notable safety concerns. Other safety issues of note include substandard and counterfeit medicines, medication errors and quality of medicinal products. The first national pharmacovigilance centres established in Africa with membership of the World Health Organization (WHO) international drug monitoring programme were in Morocco and South Africa in 1992. Of the 104 full member countries in the programme, there are now 24 African countries with a further nine countries as associate members. The pharmacovigilance systems operational in African countries are based essentially on spontaneous reporting facilitated by the introduction of the new tool Vigiflow. The individual case safety reports committed to the WHO global database (Vigibase) attest to the growth of pharmacovigilance in Africa with the number of reports rising from 2695 in 2000 to over 25,000 in 2010. There is need to engage the various identified challenges of the weak pharmacovigilance systems in the African setting and to focus efforts on how to provide resources, infrastructure and expertise. Raising the level of awareness among healthcare providers, developing training curricula for healthcare professionals, provisions for paediatric and geriatric

Identification of features of electronic prescribing systems to support quality and safety in primary care using a modified Delphi process.

PubMed

Sweidan, Michelle; Williamson, Margaret; Reeve, James F; Harvey, Ken; O'Neill, Jennifer A; Schattner, Peter; Snowdon, Teri

2010-04-15

Electronic prescribing is increasingly being used in primary care and in hospitals. Studies on the effects of e-prescribing systems have found evidence for both benefit and harm. The aim of this study was to identify features of e-prescribing software systems that support patient safety and quality of care and that are useful to the clinician and the patient, with a focus on improving the quality use of medicines. Software features were identified by a literature review, key informants and an expert group. A modified Delphi process was used with a 12-member multidisciplinary expert group to reach consensus on the expected impact of the features in four domains: patient safety, quality of care, usefulness to the clinician and usefulness to the patient. The setting was electronic prescribing in general practice in Australia. A list of 114 software features was developed. Most of the features relate to the recording and use of patient data, the medication selection process, prescribing decision support, monitoring drug therapy and clinical reports. The expert group rated 78 of the features (68%) as likely to have a high positive impact in at least one domain, 36 features (32%) as medium impact, and none as low or negative impact. Twenty seven features were rated as high positive impact across 3 or 4 domains including patient safety and quality of care. Ten features were considered "aspirational" because of a lack of agreed standards and/or suitable knowledge bases. This study defines features of e-prescribing software systems that are expected to support safety and quality, especially in relation to prescribing and use of medicines in general practice. The features could be used to develop software standards, and could be adapted if necessary for use in other settings and countries.

Identification of features of electronic prescribing systems to support quality and safety in primary care using a modified Delphi process

PubMed Central

2010-01-01

Background Electronic prescribing is increasingly being used in primary care and in hospitals. Studies on the effects of e-prescribing systems have found evidence for both benefit and harm. The aim of this study was to identify features of e-prescribing software systems that support patient safety and quality of care and that are useful to the clinician and the patient, with a focus on improving the quality use of medicines. Methods Software features were identified by a literature review, key informants and an expert group. A modified Delphi process was used with a 12-member multidisciplinary expert group to reach consensus on the expected impact of the features in four domains: patient safety, quality of care, usefulness to the clinician and usefulness to the patient. The setting was electronic prescribing in general practice in Australia. Results A list of 114 software features was developed. Most of the features relate to the recording and use of patient data, the medication selection process, prescribing decision support, monitoring drug therapy and clinical reports. The expert group rated 78 of the features (68%) as likely to have a high positive impact in at least one domain, 36 features (32%) as medium impact, and none as low or negative impact. Twenty seven features were rated as high positive impact across 3 or 4 domains including patient safety and quality of care. Ten features were considered "aspirational" because of a lack of agreed standards and/or suitable knowledge bases. Conclusions This study defines features of e-prescribing software systems that are expected to support safety and quality, especially in relation to prescribing and use of medicines in general practice. The features could be used to develop software standards, and could be adapted if necessary for use in other settings and countries. PMID:20398294

Reporting of methodological features in observational studies of pre-harvest food safety.

PubMed

Sargeant, Jan M; O'Connor, Annette M; Renter, David G; Kelton, David F; Snedeker, Kate; Wisener, Lee V; Leonard, Erin K; Guthrie, Alessia D; Faires, Meredith

2011-02-01

Observational studies in pre-harvest food safety may be useful for identifying risk factors and for evaluating potential mitigation strategies to reduce foodborne pathogens. However, there are no structured reporting guidelines for these types of study designs in livestock species. Our objective was to evaluate the reporting of observational studies in the pre-harvest food safety literature using guidelines modified from the human healthcare literature. We identified 100 pre-harvest food safety studies published between 1999 and 2009. Each study was evaluated independently by two reviewers using a structured checklist. Of the 38 studies that explicitly stated the observational study design, 27 were described as cross-sectional studies, eight as case-control studies, and three as cohort studies. Study features reported in over 75% of the selected studies included: description of the geographic location of the studies, definitions and sources of data for outcomes, organizational level and source of data for independent variables, description of statistical methods and results, number of herds enrolled in the study and included in the analysis, and sources of study funding. However, other features were not consistently reported, including details related to eligibility criteria for groups (such as barn, room, or pen) and individuals, numbers of groups and individuals included in various stages of the study, identification of primary outcomes, the distinction between putative risk factors and confounding variables, the identification of a primary exposure variable, the referent level for evaluation of categorical variable associations, methods of controlling confounding variables and missing variables, model fit, details of subset analysis, demographic information at the sampling unit level, and generalizability of the study results. Improvement in reporting of observational studies of pre-harvest food safety will aid research readers and reviewers in interpreting and

Serotonin 2C receptor antagonist improves fear discrimination and subsequent safety signal recall.

PubMed

Foilb, Allison R; Christianson, John P

2016-02-04

The capacity to discriminate between safety and danger is fundamental for survival, but is disrupted in individuals with posttraumatic stress disorder (PTSD). Acute stressors cause a release of serotonin (5-HT) in the forebrain, which is one mechanism for enhanced fear and anxiety; these effects are mediated by the 5-HT2Creceptor. Using a fear discrimination paradigm where a danger signal conditioned stimulus (CS+) co-terminates with a mild footshock and a safety signal (CS-) indicates the absence of shock, we demonstrate that danger/safety discrimination and fear inhibition develop over the course of 4 daily conditioning sessions. Systemic administration of the 5-HT2Creceptor antagonist SB 242084 (0.25 or 1.0mg/kg) prior to conditioning reduced behavioral freezing during conditioning, and improved learning and subsequent inhibition of fear by the safety signal. Discrimination was apparent in the first recall test, and discrimination during training was evident after 3days of conditioning versus 5days in the vehicle treated controls. These results suggest a novel therapeutic use for 5-HT2Creceptor antagonists to improve learning under stressful circumstances. Potential anatomical loci for 5-HT2Creceptor modulation of fear discrimination learning and cognitive performance enhancement are discussed. John P. Christianson and Allison R. Foilb, the authors, verify that animal research was carried out in accordance with the National Institute of Health Guide for the Care and Use of Laboratory Animals (NIH Publications No. 80-23) and all procedures involving animals were reviewed and approved by the Boston College Animal Care and Use Committee. All efforts were made to limit the number of animals used and their suffering. Copyright © 2015 Elsevier Inc. All rights reserved.

Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization.

PubMed

Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan

2014-01-01

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

PubMed Central

Adam, Asrul; Mohd Tumari, Mohd Zaidi; Mohamad, Mohd Saberi

2014-01-01

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236

Evaluating safety and operation of high-speed signalized intersections : final report, March 2010.

DOT National Transportation Integrated Search

2010-03-01

This Final Report reviews a research effort to evaluate the safety and operations of high-speed intersections in the State of : Oregon. In particular, this research effort focuses on four-leg, signalized intersections with speed limits of 45 mph or :...

Frequency-feature based antistrong-disturbance signal processing method and system for vortex flowmeter with single sensor

NASA Astrophysics Data System (ADS)

Xu, Ke-Jun; Luo, Qing-Lin; Wang, Gang; Liu, San-Shan; Kang, Yi-Bo

2010-07-01

Digital signal processing methods have been applied to vortex flowmeter for extracting the useful information from noisy output of the vortex flow sensor. But these approaches are unavailable when the power of the mechanical vibration noise is larger than that of the vortex flow signal. In order to solve this problem, an antistrong-disturbance signal processing method is proposed based on frequency features of the vortex flow signal and mechanical vibration noise for the vortex flowmeter with single sensor. The frequency bandwidth of the vortex flow signal is different from that of the mechanical vibration noise. The autocorrelation function can represent bandwidth features of the signal and noise. The output of the vortex flow sensor is processed by the spectrum analysis, filtered by bandpass filters, and calculated by autocorrelation function at the fixed delaying time and at τ =0 to obtain ratios. The frequency corresponding to the minimal ratio is regarded as the vortex flow frequency. With an ultralow-power microcontroller, a digital signal processing system is developed to implement the antistrong-disturbance algorithm, and at the same time to ensure low-power and two-wire mode for meeting the requirement of process instrumentation. The water flow-rate calibration and vibration test experiments are conducted, and the experimental results show that both the algorithm and system are effective.

Frequency-feature based antistrong-disturbance signal processing method and system for vortex flowmeter with single sensor.

PubMed

Xu, Ke-Jun; Luo, Qing-Lin; Wang, Gang; Liu, San-Shan; Kang, Yi-Bo

2010-07-01

Digital signal processing methods have been applied to vortex flowmeter for extracting the useful information from noisy output of the vortex flow sensor. But these approaches are unavailable when the power of the mechanical vibration noise is larger than that of the vortex flow signal. In order to solve this problem, an antistrong-disturbance signal processing method is proposed based on frequency features of the vortex flow signal and mechanical vibration noise for the vortex flowmeter with single sensor. The frequency bandwidth of the vortex flow signal is different from that of the mechanical vibration noise. The autocorrelation function can represent bandwidth features of the signal and noise. The output of the vortex flow sensor is processed by the spectrum analysis, filtered by bandpass filters, and calculated by autocorrelation function at the fixed delaying time and at tau=0 to obtain ratios. The frequency corresponding to the minimal ratio is regarded as the vortex flow frequency. With an ultralow-power microcontroller, a digital signal processing system is developed to implement the antistrong-disturbance algorithm, and at the same time to ensure low-power and two-wire mode for meeting the requirement of process instrumentation. The water flow-rate calibration and vibration test experiments are conducted, and the experimental results show that both the algorithm and system are effective.

An empirical Bayes safety evaluation of tram/streetcar signal and lane priority measures in Melbourne.

PubMed

Naznin, Farhana; Currie, Graham; Sarvi, Majid; Logan, David

2016-01-01

Streetcars/tram systems are growing worldwide, and many are given priority to increase speed and reliability performance in mixed traffic conditions. Research related to the road safety impact of tram priority is limited. This study explores the road safety impacts of tram priority measures including lane and intersection/signal priority measures. A before-after crash study was conducted using the empirical Bayes (EB) method to provide more accurate crash impact estimates by accounting for wider crash trends and regression to the mean effects. Before-after crash data for 29 intersections with tram signal priority and 23 arterials with tram lane priority in Melbourne, Australia, were analyzed to evaluate the road safety impact of tram priority. The EB before-after analysis results indicated a statistically significant adjusted crash reduction rate of 16.4% after implementation of tram priority measures. Signal priority measures were found to reduce crashes by 13.9% and lane priority by 19.4%. A disaggregate level simple before-after analysis indicated reductions in total and serious crashes as well as vehicle-, pedestrian-, and motorcycle-involved crashes. In addition, reductions in on-path crashes, pedestrian-involved crashes, and collisions among vehicles moving in the same and opposite directions and all other specific crash types were found after tram priority implementation. Results suggest that streetcar/tram priority measures result in safety benefits for all road users, including vehicles, pedestrians, and cyclists. Policy implications and areas for future research are discussed.

Feature-selective attention enhances color signals in early visual areas of the human brain.

PubMed

Müller, M M; Andersen, S; Trujillo, N J; Valdés-Sosa, P; Malinowski, P; Hillyard, S A

2006-09-19

We used an electrophysiological measure of selective stimulus processing (the steady-state visual evoked potential, SSVEP) to investigate feature-specific attention to color cues. Subjects viewed a display consisting of spatially intermingled red and blue dots that continually shifted their positions at random. The red and blue dots flickered at different frequencies and thereby elicited distinguishable SSVEP signals in the visual cortex. Paying attention selectively to either the red or blue dot population produced an enhanced amplitude of its frequency-tagged SSVEP, which was localized by source modeling to early levels of the visual cortex. A control experiment showed that this selection was based on color rather than flicker frequency cues. This signal amplification of attended color items provides an empirical basis for the rapid identification of feature conjunctions during visual search, as proposed by "guided search" models.

Evaluating the safety risk of roadside features for rural two-lane roads using reliability analysis.

PubMed

Jalayer, Mohammad; Zhou, Huaguo

2016-08-01

The severity of roadway departure crashes mainly depends on the roadside features, including the sideslope, fixed-object density, offset from fixed objects, and shoulder width. Common engineering countermeasures to improve roadside safety include: cross section improvements, hazard removal or modification, and delineation. It is not always feasible to maintain an object-free and smooth roadside clear zone as recommended in design guidelines. Currently, clear zone width and sideslope are used to determine roadside hazard ratings (RHRs) to quantify the roadside safety of rural two-lane roadways on a seven-point pictorial scale. Since these two variables are continuous and can be treated as random, probabilistic analysis can be applied as an alternative method to address existing uncertainties. Specifically, using reliability analysis, it is possible to quantify roadside safety levels by treating the clear zone width and sideslope as two continuous, rather than discrete, variables. The objective of this manuscript is to present a new approach for defining the reliability index for measuring roadside safety on rural two-lane roads. To evaluate the proposed approach, we gathered five years (2009-2013) of Illinois run-off-road (ROR) crash data and identified the roadside features (i.e., clear zone widths and sideslopes) of 4500 300ft roadway segments. Based on the obtained results, we confirm that reliability indices can serve as indicators to gauge safety levels, such that the greater the reliability index value, the lower the ROR crash rate. Copyright © 2016 Elsevier Ltd. All rights reserved.

Experimental features of natural thermally assisted OSL (NTA-OSL) signal in various quartz samples; preliminary results

NASA Astrophysics Data System (ADS)

Polymeris, George S.; Şahiner, Eren; Meriç, Niyazi; Kitis, George

2015-04-01

The access to the OSL signals from very deep traps is achieved by an alternative experimental method which comprises combined action of thermal and optical stimulation, termed as thermally assisted OSL (TA-OSL). This experimental technique was suggested in order to not only measure the signal of the deep traps without heating the sample to temperatures greater than 500 °C, but also use the former for dosimetry purposes as well, due to exhibiting a number of interesting properties which could be effectively used towards dosimetry purposes, especially for large accumulated artificial doses. The present study provides for the first time in the literature with preliminary results towards the feasibility study of the naturally occurring TA-OSL signal in coarse grains of natural quartz towards its effective application to geological dating. The samples subjected to the present study were collected from fault lines in Kütahya-Simav, Western Anatolia Region, Turkey; independent luminescence approaches yielded an equivalent dose larger than 100 Gy. Several experimental luminescence features were studied, such as sensitivity, reproducibility, TA-OSL curve shape as well as the correlation between NTA-OSL and NTL/NOSL. Nevertheless, special emphasis was addressed towards optimizing the measuring conditions of the TA-OSL signal. The high intensity of the OSL signal confirms the existence of a transfer phenomenon from deep electron traps. The increase of the integrated TA-OSL signal as a function of temperature is monitored for temperatures up to 180 °C, indicating the later as the most effective stimulation temperature. At all temperatures of the studied temperature range between 75 and 260 °C, the shape of the signal resembles much the shape of a typical CW-OSL curve. However, a long-lived, residual NTA-OSL component was monitored after the primary, initial NTA-OSL measured at 180 °C; the intensity of this component increases with increasing stimulation temperature. The

Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal.

PubMed

Mohebbi, Maryam; Ghassemian, Hassan

2011-08-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of stroke. Predicting the onset of paroxysmal AF (PAF), based on noninvasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize risks for the patients. In this paper, we propose an effective PAF predictor which is based on the analysis of the RR-interval signal. This method consists of three steps: preprocessing, feature extraction and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the RR-interval signal is extracted. In the next step, the recurrence plot (RP) of the RR-interval signal is obtained and five statistically significant features are extracted to characterize the basic patterns of the RP. These features consist of the recurrence rate, length of longest diagonal segments (L(max )), average length of the diagonal lines (L(mean)), entropy, and trapping time. Recurrence quantification analysis can reveal subtle aspects of dynamics not easily appreciated by other methods and exhibits characteristic patterns which are caused by the typical dynamical behavior. In the final step, a support vector machine (SVM)-based classifier is used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which consists of both 30 min ECG recordings that end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, positive predictivity and negative predictivity were 97%, 100%, 100%, and 96%, respectively. The proposed methodology presents better results than other existing approaches.

Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

PubMed

Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

2013-03-01

Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Passive Safety Features Evaluation of KIPT Neutron Source Facility

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

Zhong, Zhaopeng; Gohar, Yousry

2016-06-01

Argonne National Laboratory (ANL) of the United States and Kharkov Institute of Physics and Technology (KIPT) of Ukraine have cooperated on the development, design, and construction of a neutron source facility. The facility was constructed at Kharkov, Ukraine and its commissioning process is underway. It will be used to conduct basic and applied nuclear research, produce medical isotopes, and train young nuclear specialists. The facility has an electron accelerator-driven subcritical assembly. The electron beam power is 100 kW using 100 MeV electrons. Tungsten or natural uranium is the target material for generating neutrons driving the subcritical assembly. The subcritical assemblymore » is composed of WWR-M2 - Russian fuel assemblies with U-235 enrichment of 19.7 wt%, surrounded by beryllium reflector assembles and graphite blocks. The subcritical assembly is seated in a water tank, which is a part of the primary cooling loop. During normal operation, the water coolant operates at room temperature and the total facility power is ~300 KW. The passive safety features of the facility are discussed in in this study. Monte Carlo computer code MCNPX was utilized in the analyses with ENDF/B-VII.0 nuclear data libraries. Negative reactivity temperature feedback was consistently observed, which is important for the facility safety performance. Due to the design of WWR-M2 fuel assemblies, slight water temperature increase and the corresponding water density decrease produce large reactivity drop, which offset the reactivity gain by mistakenly loading an additional fuel assembly. The increase of fuel temperature also causes sufficiently large reactivity decrease. This enhances the facility safety performance because fuel temperature increase provides prompt negative reactivity feedback. The reactivity variation due to an empty fuel position filled by water during the fuel loading process is examined. Also, the loading mistakes of removing beryllium reflector assemblies

Safety first: Instrumentality for reaching safety determines attention allocation under threat.

PubMed

Vogt, Julia; Koster, Ernst H W; De Houwer, Jan

2017-04-01

Theories of attention to emotional information suggest that attentional processes prioritize threatening information. In this article, we suggest that attention will prioritize the events that are most instrumental to a goal in any given context, which in threatening situations is typically reaching safety. To test our hypotheses, we used an attentional cueing paradigm that contained cues signaling imminent threat (i.e., aversive noises) as well as cues that allowed participants to avoid threat (instrumental safety signals). Correct reactions to instrumental safety signals seemingly allowed participants to lower the presentation rate of the threat. Experiment 1 demonstrates that attention prioritizes instrumental safety signals over threat signals. Experiment 2 replicates this finding and additionally compares instrumental safety signals to other action-relevant signals controlling for action relevance as cause of the effects. Experiment 3 demonstrates that when actions toward threat signals permit to avoid threat, attention prioritizes threat signals. Taken together, these results support the view that instrumentality for reaching safety determines the allocation of attention under threat. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition

PubMed Central

Zhao, Yu-Xiang; Chou, Chien-Hsing

2016-01-01

In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters. PMID:27314346

Designing Crane Controls with Applied Mechanical and Electrical Safety Features

NASA Technical Reports Server (NTRS)

Lytle, Bradford P.; Walczak, Thomas A.

2002-01-01

The use of overhead traveling bridge cranes in many varied applications is common practice. In particular, the use of cranes in the nuclear, military, commercial, aerospace, and other industries can involve safety critical situations. Considerations for Human Injury or Casualty, Loss of Assets, Endangering the Environment, or Economic Reduction must be addressed. Traditionally, in order to achieve additional safety in these applications, mechanical systems have been augmented with a variety of devices. These devices assure that a mechanical component failure shall reduce the risk of a catastrophic loss of the correct and/or safe load carrying capability. ASME NOG-1-1998, (Rules for Construction of Overhead and Gantry Cranes, Top Running Bridge, and Multiple Girder), provides design standards for cranes in safety critical areas. Over and above the minimum safety requirements of todays design standards, users struggle with obtaining a higher degree of reliability through more precise functional specifications while attempting to provide "smart" safety systems. Electrical control systems also may be equipped with protective devices similar to the mechanical design features. Demands for improvement of the cranes "control system" is often recognized, but difficult to quantify for this traditionally "mechanically" oriented market. Finite details for each operation must be examined and understood. As an example, load drift (or small motions) at close tolerances can be unacceptable (and considered critical). To meet these high functional demands encoders and other devices are independently added to control systems to provide motion and velocity feedback to the control drive. This paper will examine the implementation of Programmable Electronic Systems (PES). PES is a term this paper will use to describe any control system utilizing any programmable electronic device such as Programmable Logic Controllers (PLC), or an Adjustable Frequency Drive (AID) 'smart' programmable

Endogenous cortisol reactivity moderates the relationship between fear inhibition to safety signals and posttraumatic stress disorder symptoms.

PubMed

Zuj, Daniel V; Palmer, Matthew A; Malhi, Gin S; Bryant, Richard A; Felmingham, Kim L

2017-04-01

Posttraumatic stress symptoms (PTSS) are commonly associated with impairments in extinguishing fear to signals previously associated with danger, and also with inhibiting fear to safety signals. Previous studies indicate that PTSS are associated with low cortisol activity, and cortisol is shown to facilitate fear extinction. Few studies have examined the influence of cortisol reactivity on fear extinction in PTSS. We used a standardized fear conditioning and extinction paradigm to investigate the relationship between fear extinction and endogenous salivary cortisol activity in participants with high PTSS (n=18), trauma-exposed controls (n=33), and non-trauma-exposed controls (n=27). Skin conductance response (SCR) was used as an index of conditioned responding. Saliva samples were collected at baseline, and 20min post-fear acquisition for basal and reactive cortisol levels, respectively. PTSS participants demonstrated a slower rate of extinction learning during the early extinction phase. A moderation analysis revealed that cortisol reactivity was a significant moderator between fear inhibition to the safety signal (CS-) during early extinction and PTSS, but not to the threat signal (CS+). Specifically, this interaction was significant in two ways: (1) participants with elevated cortisol reactivity showed lower PTSS as fear inhibition improved; and (2) participants with low cortisol reactivity showed higher PTSS as fear inhibition improved. The findings of the present study show that the relationship between fear inhibition and cortisol reactivity is complex, and suggest that cortisol reactivity shapes safety signal learning in PTSS. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dysphagia Screening: Contributions of Cervical Auscultation Signals and Modern Signal-Processing Techniques

PubMed Central

Dudik, Joshua M.; Coyle, James L.

2015-01-01

Cervical auscultation is the recording of sounds and vibrations caused by the human body from the throat during swallowing. While traditionally done by a trained clinician with a stethoscope, much work has been put towards developing more sensitive and clinically useful methods to characterize the data obtained with this technique. The eventual goal of the field is to improve the effectiveness of screening algorithms designed to predict the risk that swallowing disorders pose to individual patients’ health and safety. This paper provides an overview of these signal processing techniques and summarizes recent advances made with digital transducers in hopes of organizing the highly varied research on cervical auscultation. It investigates where on the body these transducers are placed in order to record a signal as well as the collection of analog and digital filtering techniques used to further improve the signal quality. It also presents the wide array of methods and features used to characterize these signals, ranging from simply counting the number of swallows that occur over a period of time to calculating various descriptive features in the time, frequency, and phase space domains. Finally, this paper presents the algorithms that have been used to classify this data into ‘normal’ and ‘abnormal’ categories. Both linear as well as non-linear techniques are presented in this regard. PMID:26213659

Survey of Current Practice in the Fitting and Fine-Tuning of Common Signal-Processing Features in Hearing Aids for Adults.

PubMed

Anderson, Melinda C; Arehart, Kathryn H; Souza, Pamela E

2018-02-01

Current guidelines for adult hearing aid fittings recommend the use of a prescriptive fitting rationale with real-ear verification that considers the audiogram for the determination of frequency-specific gain and ratios for wide dynamic range compression. However, the guidelines lack recommendations for how other common signal-processing features (e.g., noise reduction, frequency lowering, directional microphones) should be considered during the provision of hearing aid fittings and fine-tunings for adult patients. The purpose of this survey was to identify how audiologists make clinical decisions regarding common signal-processing features for hearing aid provision in adults. An online survey was sent to audiologists across the United States. The 22 survey questions addressed four primary topics including demographics of the responding audiologists, factors affecting selection of hearing aid devices, the approaches used in the fitting of signal-processing features, and the strategies used in the fine-tuning of these features. A total of 251 audiologists who provide hearing aid fittings to adults completed the electronically distributed survey. The respondents worked in a variety of settings including private practice, physician offices, university clinics, and hospitals/medical centers. Data analysis was based on a qualitative analysis of the question responses. The survey results for each of the four topic areas (demographics, device selection, hearing aid fitting, and hearing aid fine-tuning) are summarized descriptively. Survey responses indicate that audiologists vary in the procedures they use in fitting and fine-tuning based on the specific feature, such that the approaches used for the fitting of frequency-specific gain differ from other types of features (i.e., compression time constants, frequency lowering parameters, noise reduction strength, directional microphones, feedback management). Audiologists commonly rely on prescriptive fitting formulas and

Sensor-Based Vibration Signal Feature Extraction Using an Improved Composite Dictionary Matching Pursuit Algorithm

PubMed Central

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-01-01

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm

Sensor-based vibration signal feature extraction using an improved composite dictionary matching pursuit algorithm.

PubMed

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-09-09

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm

Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals.

PubMed

Sudarshan, Vidya K; Acharya, U Rajendra; Oh, Shu Lih; Adam, Muhammad; Tan, Jen Hong; Chua, Chua Kuang; Chua, Kok Poo; Tan, Ru San

2017-04-01

Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments. Copyright © 2017 Elsevier Ltd. All rights reserved.

29 CFR 1926.1422 - Signals-hand signal chart.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 29 Labor 8 2011-07-01 2011-07-01 false Signals-hand signal chart. 1926.1422 Section 1926.1422 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR (CONTINUED) SAFETY AND HEALTH REGULATIONS FOR CONSTRUCTION Cranes and Derricks in...

Acquisition and extinction of human avoidance behavior: attenuating effect of safety signals and associations with anxiety vulnerabilities.

PubMed

Sheynin, Jony; Beck, Kevin D; Servatius, Richard J; Myers, Catherine E

2014-01-01

While avoidance behavior is often an adaptive strategy, exaggerated avoidance can be detrimental and result in the development of psychopathologies, such as anxiety disorders. A large animal literature shows that the acquisition and extinction of avoidance behavior in rodents depends on individual differences (e.g., sex, strain) and might be modulated by the presence of environmental cues. However, there is a dearth of such reports in human literature, mainly due to the lack of adequate experimental paradigms. In the current study, we employed a computer-based task, where participants control a spaceship and attempt to gain points by shooting an enemy spaceship that appears on the screen. Warning signals predict on-screen aversive events; the participants can learn a protective response to escape or avoid these events. This task has been recently used to reveal facilitated acquisition of avoidance behavior in individuals with anxiety vulnerability due to female sex or inhibited personality. Here, we extended the task to include an extinction phase, and tested the effect of signals that appeared during "safe" periods. Healthy young adults (n = 122) were randomly assigned to a testing condition with or without such signals. Results showed that the addition of safety signals during the acquisition phase impaired acquisition (in females) and facilitated extinction of the avoidance behavior. We also replicated our recent finding of an association between female sex and longer avoidance duration and further showed that females continued to demonstrate more avoidance behavior even on extinction trials when the aversive events no longer occurred. This study is the first to show sex differences on the acquisition and extinction of human avoidance behavior and to demonstrate the role of safety signals in such behavior, highlighting the potential relevance of safety signals for cognitive therapies that focus on extinction learning to treat anxiety symptoms.

Acquisition and Extinction of Human Avoidance Behavior: Attenuating Effect of Safety Signals and Associations with Anxiety Vulnerabilities

PubMed Central

Sheynin, Jony; Beck, Kevin D.; Servatius, Richard J.; Myers, Catherine E.

2014-01-01

While avoidance behavior is often an adaptive strategy, exaggerated avoidance can be detrimental and result in the development of psychopathologies, such as anxiety disorders. A large animal literature shows that the acquisition and extinction of avoidance behavior in rodents depends on individual differences (e.g., sex, strain) and might be modulated by the presence of environmental cues. However, there is a dearth of such reports in human literature, mainly due to the lack of adequate experimental paradigms. In the current study, we employed a computer-based task, where participants control a spaceship and attempt to gain points by shooting an enemy spaceship that appears on the screen. Warning signals predict on-screen aversive events; the participants can learn a protective response to escape or avoid these events. This task has been recently used to reveal facilitated acquisition of avoidance behavior in individuals with anxiety vulnerability due to female sex or inhibited personality. Here, we extended the task to include an extinction phase, and tested the effect of signals that appeared during “safe” periods. Healthy young adults (n = 122) were randomly assigned to a testing condition with or without such signals. Results showed that the addition of safety signals during the acquisition phase impaired acquisition (in females) and facilitated extinction of the avoidance behavior. We also replicated our recent finding of an association between female sex and longer avoidance duration and further showed that females continued to demonstrate more avoidance behavior even on extinction trials when the aversive events no longer occurred. This study is the first to show sex differences on the acquisition and extinction of human avoidance behavior and to demonstrate the role of safety signals in such behavior, highlighting the potential relevance of safety signals for cognitive therapies that focus on extinction learning to treat anxiety symptoms. PMID

Specific features of goal setting in road traffic safety

NASA Astrophysics Data System (ADS)

Kolesov, V. I.; Danilov, O. F.; Petrov, A. I.

2017-10-01

Road traffic safety (RTS) management is inherently a branch of cybernetics and therefore requires clear formalization of the task. The paper aims at identification of the specific features of goal setting in RTS management under the system approach. The paper presents the results of cybernetic modeling of the cause-to-effect mechanism of a road traffic accident (RTA); in here, the mechanism itself is viewed as a complex system. A designed management goal function is focused on minimizing the difficulty in achieving the target goal. Optimization of the target goal has been performed using the Lagrange principle. The created working algorithms have passed the soft testing. The key role of the obtained solution in the tactical and strategic RTS management is considered. The dynamics of the management effectiveness indicator has been analyzed based on the ten-year statistics for Russia.

A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery

NASA Astrophysics Data System (ADS)

Zhao, Ming; Jia, Xiaodong

2017-09-01

Singular value decomposition (SVD), as an effective signal denoising tool, has been attracting considerable attention in recent years. The basic idea behind SVD denoising is to preserve the singular components (SCs) with significant singular values. However, it is shown that the singular values mainly reflect the energy of decomposed SCs, therefore traditional SVD denoising approaches are essentially energy-based, which tend to highlight the high-energy regular components in the measured signal, while ignoring the weak feature caused by early fault. To overcome this issue, a reweighted singular value decomposition (RSVD) strategy is proposed for signal denoising and weak feature enhancement. In this work, a novel information index called periodic modulation intensity is introduced to quantify the diagnostic information in a mechanical signal. With this index, the decomposed SCs can be evaluated and sorted according to their information levels, rather than energy. Based on that, a truncated linear weighting function is proposed to control the contribution of each SC in the reconstruction of the denoised signal. In this way, some weak but informative SCs could be highlighted effectively. The advantages of RSVD over traditional approaches are demonstrated by both simulated signals and real vibration/acoustic data from a two-stage gearbox as well as train bearings. The results demonstrate that the proposed method can successfully extract the weak fault feature even in the presence of heavy noise and ambient interferences.

Signal detection of adverse events with imperfect confirmation rates in vaccine safety studies using self-controlled case series design.

PubMed

Xu, Stanley; Newcomer, Sophia; Nelson, Jennifer; Qian, Lei; McClure, David; Pan, Yi; Zeng, Chan; Glanz, Jason

2014-05-01

The Vaccine Safety Datalink project captures electronic health record data including vaccinations and medically attended adverse events on 8.8 million enrollees annually from participating managed care organizations in the United States. While the automated vaccination data are generally of high quality, a presumptive adverse event based on diagnosis codes in automated health care data may not be true (misclassification). Consequently, analyses using automated health care data can generate false positive results, where an association between the vaccine and outcome is incorrectly identified, as well as false negative findings, where a true association or signal is missed. We developed novel conditional Poisson regression models and fixed effects models that accommodate misclassification of adverse event outcome for self-controlled case series design. We conducted simulation studies to evaluate their performance in signal detection in vaccine safety hypotheses generating (screening) studies. We also reanalyzed four previously identified signals in a recent vaccine safety study using the newly proposed models. Our simulation studies demonstrated that (i) outcome misclassification resulted in both false positive and false negative signals in screening studies; (ii) the newly proposed models reduced both the rates of false positive and false negative signals. In reanalyses of four previously identified signals using the novel statistical models, the incidence rate ratio estimates and statistical significances were similar to those using conventional models and including only medical record review confirmed cases. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An investigation of pre-launch and in-flight STS range safety radio signal degradation and dropout

NASA Technical Reports Server (NTRS)

Mcdonald, Malcolm W.

1991-01-01

The range safety system (RSS) transmitters operate at a frequency of 416.500 MHz. The transmitting antennas transmit left circularly polarized waves, and the shuttle range safety system (SRSS) receiving antennas onboard the shuttle vehicle receive left circular polarization. Preliminary explanations are proposed for many of the observed fluctuations in signal levels. It is recommended that experiments and further investigation be performed to test the validity of certain of these explanations.

Signal Feature Analysis Using Neural Networks & Psychoacoustics

DTIC Science & Technology

1993-05-01

large class file on the DAT recording . This processing produced signals which ranged in length from 13200 and 39650 points. The extractions produced ... recorded . This signal set, denoted as "Air" signals , lacked the parameter of angle but added the parameter of striker (metal, plastic, and wood...the subjects were recorded . These became r.4 w data for confusion matrices which described how often a subject confused the class of a signal

Filtering of the Radon transform to enhance linear signal features via wavelet pyramid decomposition

NASA Astrophysics Data System (ADS)

Meckley, John R.

1995-09-01

The information content in many signal processing applications can be reduced to a set of linear features in a 2D signal transform. Examples include the narrowband lines in a spectrogram, ship wakes in a synthetic aperture radar image, and blood vessels in a medical computer-aided tomography scan. The line integrals that generate the values of the projections of the Radon transform can be characterized as a bank of matched filters for linear features. This localization of energy in the Radon transform for linear features can be exploited to enhance these features and to reduce noise by filtering the Radon transform with a filter explicitly designed to pass only linear features, and then reconstructing a new 2D signal by inverting the new filtered Radon transform (i.e., via filtered backprojection). Previously used methods for filtering the Radon transform include Fourier based filtering (a 2D elliptical Gaussian linear filter) and a nonlinear filter ((Radon xfrm)**y with y >= 2.0). Both of these techniques suffer from the mismatch of the filter response to the true functional form of the Radon transform of a line. The Radon transform of a line is not a point but is a function of the Radon variables (rho, theta) and the total line energy. This mismatch leads to artifacts in the reconstructed image and a reduction in achievable processing gain. The Radon transform for a line is computed as a function of angle and offset (rho, theta) and the line length. The 2D wavelet coefficients are then compared for the Haar wavelets and the Daubechies wavelets. These filter responses are used as frequency filters for the Radon transform. The filtering is performed on the wavelet pyramid decomposition of the Radon transform by detecting the most likely positions of lines in the transform and then by convolving the local area with the appropriate response and zeroing the pyramid coefficients outside of the response area. The response area is defined to contain 95% of the total

The 2D analytic signal for envelope detection and feature extraction on ultrasound images.

PubMed

Wachinger, Christian; Klein, Tassilo; Navab, Nassir

2012-08-01

The fundamental property of the analytic signal is the split of identity, meaning the separation of qualitative and quantitative information in form of the local phase and the local amplitude, respectively. Especially the structural representation, independent of brightness and contrast, of the local phase is interesting for numerous image processing tasks. Recently, the extension of the analytic signal from 1D to 2D, covering also intrinsic 2D structures, was proposed. We show the advantages of this improved concept on ultrasound RF and B-mode images. Precisely, we use the 2D analytic signal for the envelope detection of RF data. This leads to advantages for the extraction of the information-bearing signal from the modulated carrier wave. We illustrate this, first, by visual assessment of the images, and second, by performing goodness-of-fit tests to a Nakagami distribution, indicating a clear improvement of statistical properties. The evaluation is performed for multiple window sizes and parameter estimation techniques. Finally, we show that the 2D analytic signal allows for an improved estimation of local features on B-mode images. Copyright © 2012 Elsevier B.V. All rights reserved.

Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features.

PubMed

Li, Yang; Cui, Weigang; Luo, Meilin; Li, Ke; Wang, Lina

2018-01-25

The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.

Structural health monitoring feature design by genetic programming

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Todd, Michael D.

2014-09-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.

Annotation analysis for testing drug safety signals using unstructured clinical notes

PubMed Central

2012-01-01

Background The electronic surveillance for adverse drug events is largely based upon the analysis of coded data from reporting systems. Yet, the vast majority of electronic health data lies embedded within the free text of clinical notes and is not gathered into centralized repositories. With the increasing access to large volumes of electronic medical data—in particular the clinical notes—it may be possible to computationally encode and to test drug safety signals in an active manner. Results We describe the application of simple annotation tools on clinical text and the mining of the resulting annotations to compute the risk of getting a myocardial infarction for patients with rheumatoid arthritis that take Vioxx. Our analysis clearly reveals elevated risks for myocardial infarction in rheumatoid arthritis patients taking Vioxx (odds ratio 2.06) before 2005. Conclusions Our results show that it is possible to apply annotation analysis methods for testing hypotheses about drug safety using electronic medical records. PMID:22541596

A Smartphone-Based Driver Safety Monitoring System Using Data Fusion

PubMed Central

Lee, Boon-Giin; Chung, Wan-Young

2012-01-01

This paper proposes a method for monitoring driver safety levels using a data fusion approach based on several discrete data types: eye features, bio-signal variation, in-vehicle temperature, and vehicle speed. The driver safety monitoring system was developed in practice in the form of an application for an Android-based smartphone device, where measuring safety-related data requires no extra monetary expenditure or equipment. Moreover, the system provides high resolution and flexibility. The safety monitoring process involves the fusion of attributes gathered from different sensors, including video, electrocardiography, photoplethysmography, temperature, and a three-axis accelerometer, that are assigned as input variables to an inference analysis framework. A Fuzzy Bayesian framework is designed to indicate the driver’s capability level and is updated continuously in real-time. The sensory data are transmitted via Bluetooth communication to the smartphone device. A fake incoming call warning service alerts the driver if his or her safety level is suspiciously compromised. Realistic testing of the system demonstrates the practical benefits of multiple features and their fusion in providing a more authentic and effective driver safety monitoring. PMID:23247416

Traffic signal phasing at intersections to improve safety for alcohol-affected pedestrians.

PubMed

Lenné, Michael G; Corben, Bruce F; Stephan, Karen

2007-07-01

Alcohol-affected pedestrians are among the highest-risk groups involved in pedestrian casualty crashes. This paper investigates the opportunities to use a modified form of traffic signal operation during high-risk periods and at high-risk locations to reduce alcohol-affected pedestrian crashes and the severity of injuries that might otherwise occur. The 'Dwell-on-Red' treatment involves displaying a red traffic signal to all vehicle directions during periods when no vehicular traffic is detected, so that drivers approach high-risk intersections at a lower speed than if a green signal were displayed. Vehicle speed data were collected before and after treatment activation at both a control and treatment site. Speed data were collected both 30 m prior to and at the intersection stop line. The treatment was associated with a reduction in mean vehicle speeds of 3.9 kph (9%) and 11.0 kph (28%) at 30 m and stop line collection points, respectively, and substantial reductions in the proportion of vehicles travelling at threatening speeds with regard to the severity of pedestrian injury. Other important road safety concerns may also benefit from this form of traffic signal modification, and it is recommended that other areas of application be explored, including the other severe trauma categories typically concentrated around signalised intersections.

Range safety signal propagation through the SRM exhaust plume of the space shuttle

NASA Technical Reports Server (NTRS)

Boynton, F. P.; Davies, A. R.; Rajasekhar, P. S.; Thompson, J. A.

1977-01-01

Theoretical predictions of plume interference for the space shuttle range safety system by solid rocket booster exhaust plumes are reported. The signal propagation was calculated using a split operator technique based upon the Fresnel-Kirchoff integral, using fast Fourier transforms to evaluate the convolution and treating the plume as a series of absorbing and phase-changing screens. Talanov's lens transformation was applied to reduce aliasing problems caused by ray divergence.

Evaluating the Safety Profile of Non-Active Implantable Medical Devices Compared with Medicines.

PubMed

Pane, Josep; Coloma, Preciosa M; Verhamme, Katia M C; Sturkenboom, Miriam C J M; Rebollo, Irene

2017-01-01

Recent safety issues involving non-active implantable medical devices (NAIMDs) have highlighted the need for better pre-market and post-market evaluation. Some stakeholders have argued that certain features of medicine safety evaluation should also be applied to medical devices. Our objectives were to compare the current processes and methodologies for the assessment of NAIMD safety profiles with those for medicines, identify potential gaps, and make recommendations for the adoption of new methodologies for the ongoing benefit-risk monitoring of these devices throughout their entire life cycle. A literature review served to examine the current tools for the safety evaluation of NAIMDs and those for medicines. We searched MEDLINE using these two categories. We supplemented this search with Google searches using the same key terms used in the MEDLINE search. Using a comparative approach, we summarized the new product design, development cycle (preclinical and clinical phases), and post-market phases for NAIMDs and drugs. We also evaluated and compared the respective processes to integrate and assess safety data during the life cycle of the products, including signal detection, signal management, and subsequent potential regulatory actions. The search identified a gap in NAIMD safety signal generation: no global program exists that collects and analyzes adverse events and product quality issues. Data sources in real-world settings, such as electronic health records, need to be effectively identified and explored as additional sources of safety information, particularly in some areas such as the EU and USA where there are plans to implement the unique device identifier (UDI). The UDI and other initiatives will enable more robust follow-up and assessment of long-term patient outcomes. The safety evaluation system for NAIMDs differs in many ways from those for drugs, but both systems face analogous challenges with respect to monitoring real-world usage. Certain features

Component isolation for multi-component signal analysis using a non-parametric gaussian latent feature model

NASA Astrophysics Data System (ADS)

Yang, Yang; Peng, Zhike; Dong, Xingjian; Zhang, Wenming; Clifton, David A.

2018-03-01

A challenge in analysing non-stationary multi-component signals is to isolate nonlinearly time-varying signals especially when they are overlapped in time and frequency plane. In this paper, a framework integrating time-frequency analysis-based demodulation and a non-parametric Gaussian latent feature model is proposed to isolate and recover components of such signals. The former aims to remove high-order frequency modulation (FM) such that the latter is able to infer demodulated components while simultaneously discovering the number of the target components. The proposed method is effective in isolating multiple components that have the same FM behavior. In addition, the results show that the proposed method is superior to generalised demodulation with singular-value decomposition-based method, parametric time-frequency analysis with filter-based method and empirical model decomposition base method, in recovering the amplitude and phase of superimposed components.

GPS Signal Feature Analysis to Detect Volcanic Plume on Mount Etna

NASA Astrophysics Data System (ADS)

Cannavo', Flavio; Aranzulla, Massimo; Scollo, Simona; Puglisi, Giuseppe; Imme', Giuseppina

2014-05-01

Volcanic ash produced during explosive eruptions can cause disruptions to aviation operations and to population living around active volcanoes. Thus, detection of volcanic plume becomes a crucial issue to reduce troubles connected to its presence. Nowadays, the volcanic plume detection is carried out by using different approaches such as satellites, radars and lidars. Recently, the capability of GPS to retrieve volcanic plumes has been also investigated and some tests applied to explosive activity of Etna have demonstrated that also the GPS may give useful information. In this work, we use the permanent and continuous GPS network of the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (Italy) that consists of 35 stations located all around volcano flanks. Data are processed by the GAMIT package developed by Massachusetts Institute of Technology. Here we investigate the possibility to quantify the volcanic plume through the GPS signal features and to estimate its spatial distribution by means of a tomographic inversion algorithm. The method is tested on volcanic plumes produced during the lava fountain of 4-5 September 2007, already used to confirm if weak explosive activity may or may not affect the GPS signals.

Comparative performance of two quantitative safety signalling methods: implications for use in a pharmacovigilance department.

PubMed

Almenoff, June S; LaCroix, Karol K; Yuen, Nancy A; Fram, David; DuMouchel, William

2006-01-01

There is increasing interest in using disproportionality-based signal detection methods to support postmarketing safety surveillance activities. Two commonly used methods, empirical Bayes multi-item gamma Poisson shrinker (MGPS) and proportional reporting ratio (PRR), perform differently with respect to the number and types of signals detected. The goal of this study was to compare and analyse the performance characteristics of these two methods, to understand why they differ and to consider the practical implications of these differences for a large, industry-based pharmacovigilance department. We compared the numbers and types of signals of disproportionate reporting (SDRs) obtained with MGPS and PRR using two postmarketing safety databases and a simulated database. We recorded signal counts and performed a qualitative comparison of the drug-event combinations signalled by the two methods as well as a sensitivity analysis to better understand how the thresholds commonly used for these methods impact their performance. PRR detected more SDRs than MGPS. We observed that MGPS is less subject to confounding by demographic factors because it employs stratification and is more stable than PRR when report counts are low. Simulation experiments performed using published empirical thresholds demonstrated that PRR detected false-positive signals at a rate of 1.1%, while MGPS did not detect any statistical false positives. In an attempt to separate the effect of choice of signal threshold from more fundamental methodological differences, we performed a series of experiments in which we modified the conventional threshold values for each method so that each method detected the same number of SDRs for the example drugs studied. This analysis, which provided quantitative examples of the relationship between the published thresholds for the two methods, demonstrates that the signalling criterion published for PRR has a higher signalling frequency than that published for MGPS

[Some Features of Sound Signal Envelope by the Frog's Cochlear Nucleus Neurons].

PubMed

Bibikov, N G

2015-01-01

The responses of single neurons in the medullar auditory center of the grass frog were recorded extracellularly under the action of long tonal signals of the characteristic frequency modulated by repeating fragments of low-frequency (0-15 Hz, 0-50 Hz or 0-150 Hz) noise. Correlation method was used for evaluating the efficacy of different envelope fragments to ensure generation of a neuron pulse discharge. Carrying out these evaluations at different time intervals between a signal and a response the maximum delays were assessed. Two important envelope fragments were revealed. In majority of units the most effective was the time interval of the amplitude rise from mean value to maximum, and the fragment where the amplitude fall from maximum to mean value was the second by the efficacy. This type of response was observed in the vast majority of cells in the range of the envelope frequency bands 0-150 and 0-50 Hz. These cells performed half-wave rectification of such type of the envelope. However, in some neurons we observed more strong preference toward a time interval with growing amplitude, including even those where the amplitude value was smaller than the mean one. These properties were observed mainly for low-frequency (0-15 Hz) modulated signals at high modulation depth. The data show that even in medulla oblongata specialization of neural elements of the auditory pathway occurs with respect to time interval features of sound stimulus. This diversity is most evident for signals with a relatively slowly varying amplitude.

Extracting time-frequency feature of single-channel vastus medialis EMG signals for knee exercise pattern recognition.

PubMed

Zhang, Yi; Li, Peiyang; Zhu, Xuyang; Su, Steven W; Guo, Qing; Xu, Peng; Yao, Dezhong

2017-01-01

The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited. In this study, three types of knee exercises, i.e., flexion of the leg up (standing), hip extension from a sitting position (sitting) and gait (walking) are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded. Four types of lower-limb motions including standing, sitting, stance phase of walking, and swing phase of walking, are segmented. The Wavelet Transform (WT) based Singular Value Decomposition (SVD) approach is proposed for the classification of four lower-limb motions using a single-channel EMG signal from the muscle group of vastus medialis. Based on lower-limb motions from all subjects, the combination of five-level wavelet decomposition and SVD is used to comprise the feature vector. The Support Vector Machine (SVM) is then configured to build a multiple-subject classifier for which the subject independent accuracy will be given across all subjects for the classification of four types of lower-limb motions. In order to effectively indicate the classification performance, EMG features from time-domain (e.g., Mean Absolute Value (MAV), Root-Mean-Square (RMS), integrated EMG (iEMG), Zero Crossing (ZC)) and frequency-domain (e.g., Mean Frequency (MNF) and Median Frequency (MDF)) are also used to classify lower-limb motions. The five-fold cross validation is performed and it repeats fifty times in order to acquire the robust subject independent accuracy. Results show that the proposed WT-based SVD approach has the classification accuracy of 91.85%±0.88% which

29 CFR 1926.1422 - Signals-hand signal chart.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 29 Labor 8 2014-07-01 2014-07-01 false Signals-hand signal chart. 1926.1422 Section 1926.1422 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION... Construction § 1926.1422 Signals—hand signal chart. Hand signal charts must be either posted on the equipment...

29 CFR 1926.1422 - Signals-hand signal chart.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 29 Labor 8 2013-07-01 2013-07-01 false Signals-hand signal chart. 1926.1422 Section 1926.1422 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION... Construction § 1926.1422 Signals—hand signal chart. Hand signal charts must be either posted on the equipment...

29 CFR 1926.1422 - Signals-hand signal chart.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 29 Labor 8 2012-07-01 2012-07-01 false Signals-hand signal chart. 1926.1422 Section 1926.1422 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION... Construction § 1926.1422 Signals—hand signal chart. Hand signal charts must be either posted on the equipment...

Complex extreme learning machine applications in terahertz pulsed signals feature sets.

PubMed

Yin, X-X; Hadjiloucas, S; Zhang, Y

2014-11-01

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed

Signal analysis of the female singing voice: Features for perceptual singer identity

NASA Astrophysics Data System (ADS)

Mellody, Maureen

2001-07-01

Individual singing voices tend to be easy for a listener to identify, particularly when compared to the difficulty of identifying the performer of any other musical instrument. What cues does a listener use to identify a particular singing voice? This work seeks to identify a set of features with which one can synthesize notes with the vocal quality of a particular singer. Such analysis and synthesis influences computer music (in the creation of synthetic sounds with different timbre), vocal pedagogy (as a training tool to help singers understand properties of their own voice as well as different professional-quality voices), and vocal health (to identify improper behavior in vocal production). The problem of singer identification is approached in three phases: signal analysis, the development of low- order representations, and perceptual evaluation. To perform the signal analysis, a high-resolution time- frequency distribution is applied to vowel tokens from sopranos and mezzo-sopranos. From these results, low- order representations are created for each singer's notes, which are used to synthesize sounds with the timbral quality of that singer. Finally, these synthesized sounds, along with original recordings, are evaluated by trained listeners in a variety of perceptual experiments to determine the extent to which the vocal quality of the desired singer is captured. Results from the signal analysis show that amplitude and frequency estimates extracted from the time-frequency signal analysis can be used to re-create each signal with little degradation in quality and no loss of perceptual identity. Low-order representations derived from the signal analysis are used in clustering and classification, which successfully clusters signals with corresponding singer identity. Finally, perceptual results indicate that trained listeners are, surprisingly, only modestly successful at correctly identifying the singer of a recording, and find the task to be particularly

Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

NASA Astrophysics Data System (ADS)

Jegadeeshwaran, R.; Sugumaran, V.

2015-02-01

Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.

A Heckman selection model for the safety analysis of signalized intersections

PubMed Central

Wong, S. C.; Zhu, Feng; Pei, Xin; Huang, Helai; Liu, Youjun

2017-01-01

Purpose The objective of this paper is to provide a new method for estimating crash rate and severity simultaneously. Methods This study explores a Heckman selection model of the crash rate and severity simultaneously at different levels and a two-step procedure is used to investigate the crash rate and severity levels. The first step uses a probit regression model to determine the sample selection process, and the second step develops a multiple regression model to simultaneously evaluate the crash rate and severity for slight injury/kill or serious injury (KSI), respectively. The model uses 555 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during two years. Results The results of the proposed two-step Heckman selection model illustrate the necessity of different crash rates for different crash severity levels. Conclusions A comparison with the existing approaches suggests that the Heckman selection model offers an efficient and convenient alternative method for evaluating the safety performance at signalized intersections. PMID:28732050

Digital Signal Processing Methods for Safety Systems Employed in Nuclear Power Industry

NASA Astrophysics Data System (ADS)

Popescu, George

Some of the major safety concerns in the nuclear power industry focus on the readiness of nuclear power plant safety systems to respond to an abnormal event, the security of special nuclear materials in used nuclear fuels, and the need for physical security to protect personnel and reactor safety systems from an act of terror. Routine maintenance and tests of all nuclear reactor safety systems are performed on a regular basis to confirm the ability of these systems to operate as expected. However, these tests do not determine the reliability of these safety systems and whether the systems will perform for the duration of an accident and whether they will perform their tasks without failure after being engaged. This research has investigated the progression of spindle asynchronous error motion determined from spindle accelerations to predict bearings failure onset. This method could be applied to coolant pumps that are essential components of emergency core cooling systems at all nuclear power plants. Recent security upgrades mandated by the Nuclear Regulatory Commission and the Department of Homeland Security have resulted in implementation of multiple physical security barriers around all of the commercial and research nuclear reactors in the United States. A second part of this research attempts to address an increased concern about illegal trafficking of Special Nuclear Materials (SNM). This research describes a multi element scintillation detector system designed for non - invasive (passive) gamma ray surveillance for concealed SNM that may be within an area or sealed in a package, vehicle or shipping container. Detection capabilities of the system were greatly enhanced through digital signal processing, which allows the combination of two very powerful techniques: 1) Compton Suppression (CS) and 2) Pulse Shape Discrimination (PSD) with less reliance on complicated analog instrumentation.

Extinction of avoidance behavior by safety learning depends on endocannabinoid signaling in the hippocampus.

PubMed

Micale, Vincenzo; Stepan, Jens; Jurik, Angela; Pamplona, Fabricio A; Marsch, Rudolph; Drago, Filippo; Eder, Matthias; Wotjak, Carsten T

2017-07-01

The development of exaggerated avoidance behavior is largely responsible for the decreased quality of life in patients suffering from anxiety disorders. Studies using animal models have contributed to the understanding of the neural mechanisms underlying the acquisition of avoidance responses. However, much less is known about its extinction. Here we provide evidence in mice that learning about the safety of an environment (i.e., safety learning) rather than repeated execution of the avoided response in absence of negative consequences (i.e., response extinction) allowed the animals to overcome their avoidance behavior in a step-down avoidance task. This process was context-dependent and could be blocked by pharmacological (3 mg/kg, s.c.; SR141716) or genetic (lack of cannabinoid CB1 receptors in neurons expressing dopamine D1 receptors) inactivation of CB1 receptors. In turn, the endocannabinoid reuptake inhibitor AM404 (3 mg/kg, i.p.) facilitated safety learning in a CB1-dependent manner and attenuated the relapse of avoidance behavior 28 days after conditioning. Safety learning crucially depended on endocannabinoid signaling at level of the hippocampus, since intrahippocampal SR141716 treatment impaired, whereas AM404 facilitated safety learning. Other than AM404, treatment with diazepam (1 mg/kg, i.p.) impaired safety learning. Drug effects on behavior were directly mirrored by drug effects on evoked activity propagation through the hippocampal trisynaptic circuit in brain slices: As revealed by voltage-sensitive dye imaging, diazepam impaired whereas AM404 facilitated activity propagation to CA1 in a CB1-dependent manner. In line with this, systemic AM404 enhanced safety learning-induced expression of Egr1 at level of CA1. Together, our data render it likely that AM404 promotes safety learning by enhancing information flow through the trisynaptic circuit to CA1. Copyright © 2017 Elsevier Ltd. All rights reserved.

A new adaptive algorithm for automated feature extraction in exponentially damped signals for health monitoring of smart structures

NASA Astrophysics Data System (ADS)

Qarib, Hossein; Adeli, Hojjat

2015-12-01

In this paper authors introduce a new adaptive signal processing technique for feature extraction and parameter estimation in noisy exponentially damped signals. The iterative 3-stage method is based on the adroit integration of the strengths of parametric and nonparametric methods such as multiple signal categorization, matrix pencil, and empirical mode decomposition algorithms. The first stage is a new adaptive filtration or noise removal scheme. The second stage is a hybrid parametric-nonparametric signal parameter estimation technique based on an output-only system identification technique. The third stage is optimization of estimated parameters using a combination of the primal-dual path-following interior point algorithm and genetic algorithm. The methodology is evaluated using a synthetic signal and a signal obtained experimentally from transverse vibrations of a steel cantilever beam. The method is successful in estimating the frequencies accurately. Further, it estimates the damping exponents. The proposed adaptive filtration method does not include any frequency domain manipulation. Consequently, the time domain signal is not affected as a result of frequency domain and inverse transformations.

Nuclear safety

NASA Technical Reports Server (NTRS)

Buden, D.

1991-01-01

Topics dealing with nuclear safety are addressed which include the following: general safety requirements; safety design requirements; terrestrial safety; SP-100 Flight System key safety requirements; potential mission accidents and hazards; key safety features; ground operations; launch operations; flight operations; disposal; safety concerns; licensing; the nuclear engine for rocket vehicle application (NERVA) design philosophy; the NERVA flight safety program; and the NERVA safety plan.

Fatigue Level Estimation of Bill Based on Acoustic Signal Feature by Supervised SOM

NASA Astrophysics Data System (ADS)

Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

Fatigued bills have harmful influence on daily operation of Automated Teller Machine(ATM). To make the fatigued bills classification more efficient, development of an automatic fatigued bill classification method is desired. We propose a new method to estimate bending rigidity of bill from acoustic signal feature of banking machines. The estimated bending rigidities are used as continuous fatigue level for classification of fatigued bill. By using the supervised Self-Organizing Map(supervised SOM), we estimate the bending rigidity from only the acoustic energy pattern effectively. The experimental result with real bill samples shows the effectiveness of the proposed method.

A new feature extraction method for signal classification applied to cord dorsum potentials detection

PubMed Central

Vidaurre, D.; Rodríguez, E. E.; Bielza, C.; Larrañaga, P.; Rudomin, P.

2012-01-01

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods. PMID:22929924

A new feature extraction method for signal classification applied to cord dorsum potential detection.

PubMed

Vidaurre, D; Rodríguez, E E; Bielza, C; Larrañaga, P; Rudomin, P

2012-10-01

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

Extracting diagnostic stromal organization features based on intrinsic two-photon excited fluorescence and second-harmonic generation signals

NASA Astrophysics Data System (ADS)

Zhuo, Shuangmu; Chen, Jianxin; Xie, Shusen; Hong, Zhibin; Jiang, Xingshan

2009-03-01

Intrinsic two-photon excited fluorescence (TPEF) and second-harmonic generation (SHG) signals are shown to differentiate between normal and neoplastic human esophageal stroma. It was found that TPEF and SHG signals from normal and neoplastic stroma exhibit different organization features, providing quantitative information about the biomorphology and biochemistry of tissue. By comparing normal with neoplastic stroma, there were significant differences in collagen-related changes, elastin-related changes, and alteration in proportions of matrix molecules, giving insight into the stromal changes associated with cancer progression and providing substantial potential to be applied in vivo to the clinical diagnosis of epithelial precancers and cancers.

Radar signal return from near-shore surface and shallow subsurface features, Darien Province, Panama

NASA Technical Reports Server (NTRS)

Hanson, B. C.; Dellwig, L. F.

1973-01-01

The AN/APQ-97 radar imagery over eastern Panama is analyzed. The imagery was directed toward extraction of geologic and engineering data and the establishment of operational parameters. Subsequent investigations emphasized landform identification and vegetation distribution. The parameters affecting the observed return signal strength from such features are considered. Near-shore ocean phenomena were analyzed. Tidal zone features such as mud flats and reefs were identified in the near range, but were not detectable in the far range. Surface roughness dictated the nature of reflected energy (specular or diffuse). In surf zones, changes in wave train orientation relative to look direction, the slope of the surface, and the physical character of the wave must be considered. It is concluded that the establishment of the areal extent of the tidal flats, distributary channels, and reefs is practical only in the near to intermediate range under minimal low tide conditions.

Does patient reporting lead to earlier detection of drug safety signals? A retrospective comparison of time to reporting between patients and healthcare professionals in a global database.

PubMed

Rolfes, Leàn; van Hunsel, Florence; Caster, Ola; Taavola, Henric; Taxis, Katja; van Puijenbroek, Eugène

2018-03-09

To explore if there is a difference between patients and healthcare professionals (HCPs) in time to reporting drug-adverse drug reaction (ADR) associations that led to drug safety signals. This was a retrospective comparison of time to reporting selected drug-ADR associations which led to drug safety signals between patients and HCPs. ADR reports were selected from the World Health Organization Global database of individual case safety reports, VigiBase. Reports were selected based on drug-ADR associations of actual drug safety signals. Primary outcome was the difference in time to reporting between patients and HCPs. The date of the first report for each individual signal was used as time zero. The difference in time between the date of the reports and time zero was calculated. Statistical differences in timing were analysed on the corresponding survival curves using a Mann-Whitney U test. In total, 2822 reports were included, of which 52.7% were patient reports, with a median of 25% for all included signals. For all signals, median time to signal detection was 10.4 years. Overall, HCPs reported earlier than patients: median 7.0 vs. 8.3 years (P < 0.001). Patients contributed a large proportion of reports on drug-ADR pairs that eventually became signals. HCPs reported 1.3 year earlier than patients. These findings strengthen the evidence on the value of patient reporting in signal detection and highlight an opportunity to encourage patients to report suspected ADRs even earlier in the future. © 2018 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.

Non-linear feature extraction from HRV signal for mortality prediction of ICU cardiovascular patient.

PubMed

Karimi Moridani, Mohammad; Setarehdan, Seyed Kamaledin; Motie Nasrabadi, Ali; Hajinasrollah, Esmaeil

2016-01-01

Intensive care unit (ICU) patients are at risk of in-ICU morbidities and mortality, making specific systems for identifying at-risk patients a necessity for improving clinical care. This study presents a new method for predicting in-hospital mortality using heart rate variability (HRV) collected from the times of a patient's ICU stay. In this paper, a HRV time series processing based method is proposed for mortality prediction of ICU cardiovascular patients. HRV signals were obtained measuring R-R time intervals. A novel method, named return map, is then developed that reveals useful information from the HRV time series. This study also proposed several features that can be extracted from the return map, including the angle between two vectors, the area of triangles formed by successive points, shortest distance to 45° line and their various combinations. Finally, a thresholding technique is proposed to extract the risk period and to predict mortality. The data used to evaluate the proposed algorithm obtained from 80 cardiovascular ICU patients, from the first 48 h of the first ICU stay of 40 males and 40 females. This study showed that the angle feature has on average a sensitivity of 87.5% (with 12 false alarms), the area feature has on average a sensitivity of 89.58% (with 10 false alarms), the shortest distance feature has on average a sensitivity of 85.42% (with 14 false alarms) and, finally, the combined feature has on average a sensitivity of 92.71% (with seven false alarms). The results showed that the last half an hour before the patient's death is very informative for diagnosing the patient's condition and to save his/her life. These results confirm that it is possible to predict mortality based on the features introduced in this paper, relying on the variations of the HRV dynamic characteristics.

Clonal B cells in Waldenström's macroglobulinemia exhibit functional features of chronic active B-cell receptor signaling

PubMed Central

Argyropoulos, K V; Vogel, R; Ziegler, C; Altan-Bonnet, G; Velardi, E; Calafiore, M; Dogan, A; Arcila, M; Patel, M; Knapp, K; Mallek, C; Hunter, Z R; Treon, S P; van den Brink, M R M; Palomba, M L

2016-01-01

Waldenström's macroglobulinemia (WM) is a B-cell non-Hodgkin's lymphoma (B-NHL) characterized by immunoglobulin M (IgM) monoclonal gammopathy and the medullary expansion of clonal lymphoplasmacytic cells. Neoplastic transformation has been partially attributed to hyperactive MYD88 signaling, secondary to the MYD88 L265P mutation, occurring in the majority of WM patients. Nevertheless, the presence of chronic active B-cell receptor (BCR) signaling, a feature of multiple IgM+ B-NHL, remains a subject of speculation in WM. Here, we interrogated the BCR signaling capacity of primary WM cells by utilizing multiparametric phosphoflow cytometry and found heightened basal phosphorylation of BCR-related signaling proteins, and augmented phosphoresponses on surface IgM (sIgM) crosslinking, compared with normal B cells. In support of those findings we observed high sIgM expression and loss of phosphatase activity in WM cells, which could both lead to signaling potentiation in clonal cells. Finally, led by the high-signaling heterogeneity among WM samples, we generated patient-specific phosphosignatures, which subclassified patients into a ‘high' and a ‘healthy-like' signaling group, with the second corresponding to patients with a more indolent clinical phenotype. These findings support the presence of chronic active BCR signaling in WM while providing a link between differential BCR signaling utilization and distinct clinical WM subgroups. PMID:26867669

A comparison of safety benefits of pedestrian countdown signals with and without pushbuttons in Michigan.

PubMed

Boateng, Richard Atta; Kwigizile, Valerian; Oh, Jun-Seok

2018-04-11

This study evaluated the safety impacts of PCSs with and without pushbuttons based on pedestrian crashes and pedestrian injuries in Michigan. This study used ten years of intersection data: five years before PCSs were installed and five years after they were installed, along with a comparison group, to evaluate the crash impacts of PCSs: at 107 intersections the PCS had a pushbutton and at 96 it did not. At these intersections, and at their comparison sites (where no PCS was installed), crash data (from 2004-2016) were examined, along with traffic and geometric characteristics, population, education and poverty levels data. Intersections where PCSs with pushbuttons have been installed showed a 29 percent reduction in total pedestrian crashes and a 30 percent reduction in fatal/injury pedestrian crashes. Further, when considering only pedestrians age 65 and below, these respective reductions are 33 and 35 percent. Intersections with PCSs but without pushbuttons did not show any significant change in any type of pedestrian crash. Although the Manual of Uniform Traffic Control Devices (FHWA, 2009) requires the use of PCSs at new traffic signal installations, this study suggests a safety benefit of installing PCSs with pushbutton at signals where a PCS without a pushbutton is present.

Application of Mls Data to the Assessment of Safety-Related Features in the Surrounding Area of Automatically Detected Pedestrian Crossings

NASA Astrophysics Data System (ADS)

Soilán, M.; Riveiro, B.; Sánchez-Rodríguez, A.; González-deSantos, L. M.

2018-05-01

During the last few years, there has been a huge methodological development regarding the automatic processing of 3D point cloud data acquired by both terrestrial and aerial mobile mapping systems, motivated by the improvement of surveying technologies and hardware performance. This paper presents a methodology that, in a first place, extracts geometric and semantic information regarding the road markings within the surveyed area from Mobile Laser Scanning (MLS) data, and then employs it to isolate street areas where pedestrian crossings are found and, therefore, pedestrians are more likely to cross the road. Then, different safety-related features can be extracted in order to offer information about the adequacy of the pedestrian crossing regarding its safety, which can be displayed in a Geographical Information System (GIS) layer. These features are defined in four different processing modules: Accessibility analysis, traffic lights classification, traffic signs classification, and visibility analysis. The validation of the proposed methodology has been carried out in two different cities in the northwest of Spain, obtaining both quantitative and qualitative results for pedestrian crossing classification and for each processing module of the safety assessment on pedestrian crossing environments.

Safety impacts of red light cameras at signalized intersections based on cellular automata models.

PubMed

Chai, C; Wong, Y D; Lum, K M

2015-01-01

This study applies a simulation technique to evaluate the hypothesis that red light cameras (RLCs) exert important effects on accident risks. Conflict occurrences are generated by simulation and compared at intersections with and without RLCs to assess the impact of RLCs on several conflict types under various traffic conditions. Conflict occurrences are generated through simulating vehicular interactions based on an improved cellular automata (CA) model. The CA model is calibrated and validated against field observations at approaches with and without RLCs. Simulation experiments are conducted for RLC and non-RLC intersections with different geometric layouts and traffic demands to generate conflict occurrences that are analyzed to evaluate the hypothesis that RLCs exert important effects on road safety. The comparison of simulated conflict occurrences show favorable safety impacts of RLCs on crossing conflicts and unfavorable impacts for rear-end conflicts during red/amber phases. Corroborative results are found from broad analysis of accident occurrence. RLCs are found to have a mixed effect on accident risk at signalized intersections: crossing collisions are reduced, whereas rear-end collisions may increase. The specially developed CA model is found to be a feasible safety assessment tool.

Cosmetics as a feature of the extended human phenotype: modulation of the perception of biologically important facial signals.

PubMed

Etcoff, Nancy L; Stock, Shannon; Haley, Lauren E; Vickery, Sarah A; House, David M

2011-01-01

Research on the perception of faces has focused on the size, shape, and configuration of inherited features or the biological phenotype, and largely ignored the effects of adornment, or the extended phenotype. Research on the evolution of signaling has shown that animals frequently alter visual features, including color cues, to attract, intimidate or protect themselves from conspecifics. Humans engage in conscious manipulation of visual signals using cultural tools in real time rather than genetic changes over evolutionary time. Here, we investigate one tool, the use of color cosmetics. In two studies, we asked viewers to rate the same female faces with or without color cosmetics, and we varied the style of makeup from minimal (natural), to moderate (professional), to dramatic (glamorous). Each look provided increasing luminance contrast between the facial features and surrounding skin. Faces were shown for 250 ms or for unlimited inspection time, and subjects rated them for attractiveness, competence, likeability and trustworthiness. At 250 ms, cosmetics had significant positive effects on all outcomes. Length of inspection time did not change the effect for competence or attractiveness. However, with longer inspection time, the effect of cosmetics on likability and trust varied by specific makeup looks, indicating that cosmetics could impact automatic and deliberative judgments differently. The results suggest that cosmetics can create supernormal facial stimuli, and that one way they may do so is by exaggerating cues to sexual dimorphism. Our results provide evidence that judgments of facial trustworthiness and attractiveness are at least partially separable, that beauty has a significant positive effect on judgment of competence, a universal dimension of social cognition, but has a more nuanced effect on the other universal dimension of social warmth, and that the extended phenotype significantly influences perception of biologically important signals at first

Cosmetics as a Feature of the Extended Human Phenotype: Modulation of the Perception of Biologically Important Facial Signals

PubMed Central

Etcoff, Nancy L.; Stock, Shannon; Haley, Lauren E.; Vickery, Sarah A.; House, David M.

2011-01-01

Research on the perception of faces has focused on the size, shape, and configuration of inherited features or the biological phenotype, and largely ignored the effects of adornment, or the extended phenotype. Research on the evolution of signaling has shown that animals frequently alter visual features, including color cues, to attract, intimidate or protect themselves from conspecifics. Humans engage in conscious manipulation of visual signals using cultural tools in real time rather than genetic changes over evolutionary time. Here, we investigate one tool, the use of color cosmetics. In two studies, we asked viewers to rate the same female faces with or without color cosmetics, and we varied the style of makeup from minimal (natural), to moderate (professional), to dramatic (glamorous). Each look provided increasing luminance contrast between the facial features and surrounding skin. Faces were shown for 250 ms or for unlimited inspection time, and subjects rated them for attractiveness, competence, likeability and trustworthiness. At 250 ms, cosmetics had significant positive effects on all outcomes. Length of inspection time did not change the effect for competence or attractiveness. However, with longer inspection time, the effect of cosmetics on likability and trust varied by specific makeup looks, indicating that cosmetics could impact automatic and deliberative judgments differently. The results suggest that cosmetics can create supernormal facial stimuli, and that one way they may do so is by exaggerating cues to sexual dimorphism. Our results provide evidence that judgments of facial trustworthiness and attractiveness are at least partially separable, that beauty has a significant positive effect on judgment of competence, a universal dimension of social cognition, but has a more nuanced effect on the other universal dimension of social warmth, and that the extended phenotype significantly influences perception of biologically important signals at first

Feature-based attention elicits surround suppression in feature space.

PubMed

Störmer, Viola S; Alvarez, George A

2014-09-08

It is known that focusing attention on a particular feature (e.g., the color red) facilitates the processing of all objects in the visual field containing that feature [1-7]. Here, we show that such feature-based attention not only facilitates processing but also actively inhibits processing of similar, but not identical, features globally across the visual field. We combined behavior and electrophysiological recordings of frequency-tagged potentials in human observers to measure this inhibitory surround in feature space. We found that sensory signals of an attended color (e.g., red) were enhanced, whereas sensory signals of colors similar to the target color (e.g., orange) were suppressed relative to colors more distinct from the target color (e.g., yellow). Importantly, this inhibitory effect spreads globally across the visual field, thus operating independently of location. These findings suggest that feature-based attention comprises an excitatory peak surrounded by a narrow inhibitory zone in color space to attenuate the most distracting and potentially confusable stimuli during visual perception. This selection profile is akin to what has been reported for location-based attention [8-10] and thus suggests that such center-surround mechanisms are an overarching principle of attention across different domains in the human brain. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automated detection and location of indications in eddy current signals

DOEpatents

Brudnoy, David M.; Oppenlander, Jane E.; Levy, Arthur J.

2000-01-01

A computer implemented information extraction process that locates and identifies eddy current signal features in digital point-ordered signals, signals representing data from inspection of test materials, by enhancing the signal features relative to signal noise, detecting features of the signals, verifying the location of the signal features that can be known in advance, and outputting information about the identity and location of all detected signal features.

Space engine safety system

NASA Technical Reports Server (NTRS)

Maul, William A.; Meyer, Claudia M.

1991-01-01

A rocket engine safety system was designed to initiate control procedures to minimize damage to the engine or vehicle or test stand in the event of an engine failure. The features and the implementation issues associated with rocket engine safety systems are discussed, as well as the specific concerns of safety systems applied to a space-based engine and long duration space missions. Examples of safety system features and architectures are given, based on recent safety monitoring investigations conducted for the Space Shuttle Main Engine and for future liquid rocket engines. Also, the general design and implementation process for rocket engine safety systems is presented.

Research on vibration signal analysis and extraction method of gear local fault

NASA Astrophysics Data System (ADS)

Yang, X. F.; Wang, D.; Ma, J. F.; Shao, W.

2018-02-01

Gear is the main connection parts and power transmission parts in the mechanical equipment. If the fault occurs, it directly affects the running state of the whole machine and even endangers the personal safety. So it has important theoretical significance and practical value to study on the extraction of the gear fault signal and fault diagnosis of the gear. In this paper, the gear local fault as the research object, set up the vibration model of gear fault vibration mechanism, derive the vibration mechanism of the gear local fault and analyzes the similarities and differences of the vibration signal between the gear non fault and the gears local faults. In the MATLAB environment, the wavelet transform algorithm is used to denoise the fault signal. Hilbert transform is used to demodulate the fault vibration signal. The results show that the method can denoise the strong noise mechanical vibration signal and extract the local fault feature information from the fault vibration signal..

Multiple feature extraction and classification of electroencephalograph signal for Alzheimers' with spectrum and bispectrum

NASA Astrophysics Data System (ADS)

Wang, Ruofan; Wang, Jiang; Li, Shunan; Yu, Haitao; Deng, Bin; Wei, Xile

2015-01-01

In this paper, we have combined experimental neurophysiologic recording and statistical analysis to investigate the nonlinear characteristic and the cognitive function of the brain. Spectrum and bispectrum analyses are proposed to extract multiple effective features of electroencephalograph (EEG) signals from Alzheimer's disease (AD) patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared to the control group, the relative power spectral density of AD group is significantly higher in the theta frequency band, while lower in the alpha frequency bands. In addition, median frequency of spectrum is decreased, and spectral entropy ratio of these two frequency bands undergoes drastic changes at the P3 electrode in the central-parietal brain region, implying that the electrophysiological behavior in AD brain is much slower and less irregular. In order to explore the nonlinear high order information, bispectral analysis which measures the complexity of phase-coupling is further applied to P3 electrode in the whole frequency band. It is demonstrated that less bispectral peaks appear and the amplitudes of peaks fall, suggesting a decrease of non-Gaussianity and nonlinearity of EEG in ADs. Notably, the application of this method to five brain regions shows higher concentration of the weighted center of bispectrum and lower complexity reflecting phase-coupling by bispectral entropy. Based on spectrum and bispectrum analyses, six efficient features are extracted and then applied to discriminate AD from the normal in the five brain regions. The classification results indicate that all these features could differentiate AD patients from the normal controls with a maximum accuracy of 90.2%. Particularly, different brain regions are sensitive to different features. Moreover, the optimal combination of

Multiple feature extraction and classification of electroencephalograph signal for Alzheimers' with spectrum and bispectrum.

PubMed

Wang, Ruofan; Wang, Jiang; Li, Shunan; Yu, Haitao; Deng, Bin; Wei, Xile

2015-01-01

In this paper, we have combined experimental neurophysiologic recording and statistical analysis to investigate the nonlinear characteristic and the cognitive function of the brain. Spectrum and bispectrum analyses are proposed to extract multiple effective features of electroencephalograph (EEG) signals from Alzheimer's disease (AD) patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared to the control group, the relative power spectral density of AD group is significantly higher in the theta frequency band, while lower in the alpha frequency bands. In addition, median frequency of spectrum is decreased, and spectral entropy ratio of these two frequency bands undergoes drastic changes at the P3 electrode in the central-parietal brain region, implying that the electrophysiological behavior in AD brain is much slower and less irregular. In order to explore the nonlinear high order information, bispectral analysis which measures the complexity of phase-coupling is further applied to P3 electrode in the whole frequency band. It is demonstrated that less bispectral peaks appear and the amplitudes of peaks fall, suggesting a decrease of non-Gaussianity and nonlinearity of EEG in ADs. Notably, the application of this method to five brain regions shows higher concentration of the weighted center of bispectrum and lower complexity reflecting phase-coupling by bispectral entropy. Based on spectrum and bispectrum analyses, six efficient features are extracted and then applied to discriminate AD from the normal in the five brain regions. The classification results indicate that all these features could differentiate AD patients from the normal controls with a maximum accuracy of 90.2%. Particularly, different brain regions are sensitive to different features. Moreover, the optimal combination of

Human Amygdala Tracks a Feature-Based Valence Signal Embedded within the Facial Expression of Surprise.

PubMed

Kim, M Justin; Mattek, Alison M; Bennett, Randi H; Solomon, Kimberly M; Shin, Jin; Whalen, Paul J

2017-09-27

Human amygdala function has been traditionally associated with processing the affective valence (negative vs positive) of an emotionally charged event, especially those that signal fear or threat. However, this account of human amygdala function can be explained by alternative views, which posit that the amygdala might be tuned to either (1) general emotional arousal (activation vs deactivation) or (2) specific emotion categories (fear vs happy). Delineating the pure effects of valence independent of arousal or emotion category is a challenging task, given that these variables naturally covary under many circumstances. To circumvent this issue and test the sensitivity of the human amygdala to valence values specifically, we measured the dimension of valence within the single facial expression category of surprise. Given the inherent valence ambiguity of this category, we show that surprised expression exemplars are attributed valence and arousal values that are uniquely and naturally uncorrelated. We then present fMRI data from both sexes, showing that the amygdala tracks these consensus valence values. Finally, we provide evidence that these valence values are linked to specific visual features of the mouth region, isolating the signal by which the amygdala detects this valence information. SIGNIFICANCE STATEMENT There is an open question as to whether human amygdala function tracks the valence value of cues in the environment, as opposed to either a more general emotional arousal value or a more specific emotion category distinction. Here, we demonstrate the utility of surprised facial expressions because exemplars within this emotion category take on valence values spanning the dimension of bipolar valence (positive to negative) at a consistent level of emotional arousal. Functional neuroimaging data showed that amygdala responses tracked the valence of surprised facial expressions, unconfounded by arousal. Furthermore, a machine learning classifier identified

47 CFR 80.327 - Urgency signals and messages.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 5 2010-10-01 2010-10-01 false Urgency signals and messages. 80.327 Section 80... Safety Procedures § 80.327 Urgency signals and messages. (a) The urgency signal indicates that the... vehicle, or the safety of a person. The urgency signal must be sent only on the authority of the master or...

47 CFR 80.327 - Urgency signals and messages.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 5 2011-10-01 2011-10-01 false Urgency signals and messages. 80.327 Section 80... Safety Procedures § 80.327 Urgency signals and messages. (a) The urgency signal indicates that the... vehicle, or the safety of a person. The urgency signal must be sent only on the authority of the master or...

47 CFR 80.327 - Urgency signals and messages.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 47 Telecommunication 5 2012-10-01 2012-10-01 false Urgency signals and messages. 80.327 Section 80... Safety Procedures § 80.327 Urgency signals and messages. (a) The urgency signal indicates that the... vehicle, or the safety of a person. The urgency signal must be sent only on the authority of the master or...

47 CFR 80.327 - Urgency signals and messages.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 47 Telecommunication 5 2013-10-01 2013-10-01 false Urgency signals and messages. 80.327 Section 80... Safety Procedures § 80.327 Urgency signals and messages. (a) The urgency signal indicates that the... vehicle, or the safety of a person. The urgency signal must be sent only on the authority of the master or...

47 CFR 80.327 - Urgency signals and messages.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 5 2014-10-01 2014-10-01 false Urgency signals and messages. 80.327 Section 80... Safety Procedures § 80.327 Urgency signals and messages. (a) The urgency signal indicates that the... vehicle, or the safety of a person. The urgency signal must be sent only on the authority of the master or...

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

Data Exploration using Unsupervised Feature Extraction for Mixed Micro-Seismic Signals

NASA Astrophysics Data System (ADS)

Meyer, Matthias; Weber, Samuel; Beutel, Jan

2017-04-01

We present a system for the analysis of data originating in a multi-sensor and multi-year experiment focusing on slope stability and its underlying processes in fractured permafrost rock walls undertaken at 3500m a.s.l. on the Matterhorn Hörnligrat, (Zermatt, Switzerland). This system incorporates facilities for the transmission, management and storage of large-scales of data ( 7 GB/day), preprocessing and aggregation of multiple sensor types, machine-learning based automatic feature extraction for micro-seismic and acoustic emission data and interactive web-based visualization of the data. Specifically, a combination of three types of sensors are used to profile the frequency spectrum from 1 Hz to 80 kHz with the goal to identify the relevant destructive processes (e.g. micro-cracking and fracture propagation) leading to the eventual destabilization of large rock masses. The sensors installed for this profiling experiment (2 geophones, 1 accelerometers and 2 piezo-electric sensors for detecting acoustic emission), are further augmented with sensors originating from a previous activity focusing on long-term monitoring of temperature evolution and rock kinematics with the help of wireless sensor networks (crackmeters, cameras, weather station, rock temperature profiles, differential GPS) [Hasler2012]. In raw format, the data generated by the different types of sensors, specifically the micro-seismic and acoustic emission sensors, is strongly heterogeneous, in part unsynchronized and the storage and processing demand is large. Therefore, a purpose-built signal preprocessing and event-detection system is used. While the analysis of data from each individual sensor follows established methods, the application of all these sensor types in combination within a field experiment is unique. Furthermore, experience and methods from using such sensors in laboratory settings cannot be readily transferred to the mountain field site setting with its scale and full exposure to

Validating a driving simulator using surrogate safety measures.

PubMed

Yan, Xuedong; Abdel-Aty, Mohamed; Radwan, Essam; Wang, Xuesong; Chilakapati, Praveen

2008-01-01

Traffic crash statistics and previous research have shown an increased risk of traffic crashes at signalized intersections. How to diagnose safety problems and develop effective countermeasures to reduce crash rate at intersections is a key task for traffic engineers and researchers. This study aims at investigating whether the driving simulator can be used as a valid tool to assess traffic safety at signalized intersections. In support of the research objective, this simulator validity study was conducted from two perspectives, a traffic parameter (speed) and a safety parameter (crash history). A signalized intersection with as many important features (including roadway geometries, traffic control devices, intersection surroundings, and buildings) was replicated into a high-fidelity driving simulator. A driving simulator experiment with eight scenarios at the intersection were conducted to determine if the subjects' speed behavior and traffic risk patterns in the driving simulator were similar to what were found at the real intersection. The experiment results showed that speed data observed from the field and in the simulator experiment both follow normal distributions and have equal means for each intersection approach, which validated the driving simulator in absolute terms. Furthermore, this study used an innovative approach of using surrogate safety measures from the simulator to contrast with the crash analysis for the field data. The simulator experiment results indicated that compared to the right-turn lane with the low rear-end crash history record (2 crashes), subjects showed a series of more risky behaviors at the right-turn lane with the high rear-end crash history record (16 crashes), including higher deceleration rate (1.80+/-1.20 m/s(2) versus 0.80+/-0.65 m/s(2)), higher non-stop right-turn rate on red (81.67% versus 57.63%), higher right-turn speed as stop line (18.38+/-8.90 km/h versus 14.68+/-6.04 km/h), shorter following distance (30

Safety evaluation of signal installation with and without left turn lanes on two lane roads in rural and suburban areas.

DOT National Transportation Integrated Search

2014-10-01

Data from 117 intersections on two lane roads in rural and suburban areas in North Carolina were used to determine the safety : effect of signalization with and without left turn lanes. This was a before-after study that was conducted using the empir...

Features of the Phosphatidylinositol Cycle and its Role in Signal Transduction.

PubMed

Epand, Richard M

2017-08-01

The phosphatidylinositol cycle (PI-cycle) has a central role in cell signaling. It is the major pathway for the synthesis of phosphatidylinositol and its phosphorylated forms. In addition, some lipid intermediates of the PI-cycle, including diacylglycerol and phosphatidic acid, are also important lipid signaling agents. The PI-cycle has some features that are important for the understanding of its role in the cell. As a cycle, the intermediates will be regenerated. The PI-cycle requires a large amount of metabolic energy. There are different steps of the cycle that occur in two different membranes, the plasma membrane and the endoplasmic reticulum. In order to complete the PI-cycle lipid must be transferred between the two membranes. The role of the Nir proteins in the process has recently been elucidated. The lipid intermediates of the PI-cycle are normally highly enriched with 1-stearoyl-2-arachidonoyl molecular species in mammals. This enrichment will be retained as long as the intermediates are segregated from other lipids of the cell. However, there is a significant fraction (>15 %) of lipids in the PI-cycle of normal cells that have other acyl chains. Phosphatidylinositol largely devoid of arachidonoyl chains are found in cancer cells. Phosphatidylinositol species with less unsaturation will not be as readily converted to phosphatidylinositol-3,4,5-trisphosphate, the lipid required for the activation of Akt with resulting effects on cell proliferation. Thus, the cyclical nature of the PI-cycle, its dependence on acyl chain composition and its requirement for lipid transfer between two membranes, explain many of the biological properties of this cycle.

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing

PubMed Central

Wen, Tailai; Huang, Daoyu; Lu, Kun; Deng, Changjian; Zeng, Tanyue; Yu, Song; He, Zhiyi

2018-01-01

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods. PMID:29382146

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing.

PubMed

Wen, Tailai; Yan, Jia; Huang, Daoyu; Lu, Kun; Deng, Changjian; Zeng, Tanyue; Yu, Song; He, Zhiyi

2018-01-29

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors' responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose's classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods.

ECG Identification System Using Neural Network with Global and Local Features

ERIC Educational Resources Information Center

Tseng, Kuo-Kun; Lee, Dachao; Chen, Charles

2016-01-01

This paper proposes a human identification system via extracted electrocardiogram (ECG) signals. Two hierarchical classification structures based on global shape feature and local statistical feature is used to extract ECG signals. Global shape feature represents the outline information of ECG signals and local statistical feature extracts the…

Training Strategies and Materials : Use of Flashing Yellow Operations to Improve Safety at Signals with Protected-Permissive Left Turn (PPLT) Operations

DOT National Transportation Integrated Search

2012-08-01

TxDOT project 0-6568 Use of Flashing Yellow Operations to Improve Safety at Signals with : Protected-Permissive Left Turn (PPLT) Operations has developed guidelines for : implementation of FYA PPLT displays including general guidelines on the F...

Visual Feature Integration Indicated by pHase-Locked Frontal-Parietal EEG Signals

PubMed Central

Phillips, Steven; Takeda, Yuji; Singh, Archana

2012-01-01

The capacity to integrate multiple sources of information is a prerequisite for complex cognitive ability, such as finding a target uniquely identifiable by the conjunction of two or more features. Recent studies identified greater frontal-parietal synchrony during conjunctive than non-conjunctive (feature) search. Whether this difference also reflects greater information integration, rather than just differences in cognitive strategy (e.g., top-down versus bottom-up control of attention), or task difficulty is uncertain. Here, we examine the first possibility by parametrically varying the number of integrated sources from one to three and measuring phase-locking values (PLV) of frontal-parietal EEG electrode signals, as indicators of synchrony. Linear regressions, under hierarchical false-discovery rate control, indicated significant positive slopes for number of sources on PLV in the 30–38 Hz, 175–250 ms post-stimulus frequency-time band for pairs in the sagittal plane (i.e., F3-P3, Fz-Pz, F4-P4), after equating conditions for behavioural performance (to exclude effects due to task difficulty). No such effects were observed for pairs in the transverse plane (i.e., F3-F4, C3-C4, P3-P4). These results provide support for the idea that anterior-posterior phase-locking in the lower gamma-band mediates integration of visual information. They also provide a potential window into cognitive development, seen as developing the capacity to integrate more sources of information. PMID:22427847

Visual feature integration indicated by pHase-locked frontal-parietal EEG signals.

PubMed

Phillips, Steven; Takeda, Yuji; Singh, Archana

2012-01-01

The capacity to integrate multiple sources of information is a prerequisite for complex cognitive ability, such as finding a target uniquely identifiable by the conjunction of two or more features. Recent studies identified greater frontal-parietal synchrony during conjunctive than non-conjunctive (feature) search. Whether this difference also reflects greater information integration, rather than just differences in cognitive strategy (e.g., top-down versus bottom-up control of attention), or task difficulty is uncertain. Here, we examine the first possibility by parametrically varying the number of integrated sources from one to three and measuring phase-locking values (PLV) of frontal-parietal EEG electrode signals, as indicators of synchrony. Linear regressions, under hierarchical false-discovery rate control, indicated significant positive slopes for number of sources on PLV in the 30-38 Hz, 175-250 ms post-stimulus frequency-time band for pairs in the sagittal plane (i.e., F3-P3, Fz-Pz, F4-P4), after equating conditions for behavioural performance (to exclude effects due to task difficulty). No such effects were observed for pairs in the transverse plane (i.e., F3-F4, C3-C4, P3-P4). These results provide support for the idea that anterior-posterior phase-locking in the lower gamma-band mediates integration of visual information. They also provide a potential window into cognitive development, seen as developing the capacity to integrate more sources of information.

Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones.

PubMed

Khan, Adil Mehmood; Siddiqi, Muhammad Hameed; Lee, Seok-Won

2013-09-27

Smartphone-based activity recognition (SP-AR) recognizes users' activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification) is performed on the device. Most of these online systems use either a high sampling rate (SR) or long data-window (DW) to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR) process, and an accurate AR-model in this case can be built using a low SR (20 Hz) and a small DW (3 s). The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.

Social signals of safety and risk confer utility and have asymmetric effects on observers' choices.

PubMed

Chung, Dongil; Christopoulos, George I; King-Casas, Brooks; Ball, Sheryl B; Chiu, Pearl H

2015-06-01

Individuals' risk attitudes are known to guide choices about uncertain options. However, in the presence of others' decisions, these choices can be swayed and manifest as riskier or safer behavior than one would express alone. To test the mechanisms underlying effective social 'nudges' in human decision-making, we used functional neuroimaging and a task in which participants made choices about gambles alone and after observing others' selections. Against three alternative explanations, we found that observing others' choices of gambles increased the subjective value (utility) of those gambles for the observer. This 'other-conferred utility' was encoded in ventromedial prefrontal cortex, and these neural signals predicted conformity. We further identified a parametric interaction with individual risk preferences in anterior cingulate cortex and insula. These data provide a neuromechanistic account of how information from others is integrated with individual preferences that may explain preference-congruent susceptibility to social signals of safety and risk.

Escalator design features evaluation

NASA Technical Reports Server (NTRS)

Zimmerman, W. F.; Deshpande, G. K.

1982-01-01

Escalators are available with design features such as dual speed (90 and 120 fpm), mat operation and flat steps. These design features were evaluated based on the impact of each on capital and operating costs, traffic flow, and safety. A human factors engineering model was developed to analyze the need for flat steps at various speeds. Mat operation of escalators was found to be cost effective in terms of energy savings. Dual speed operation of escalators with the higher speed used during peak hours allows for efficient operation. A minimum number of flat steps required as a function of escalator speed was developed to ensure safety for the elderly.

Pulsatile Hormonal Signaling to Extracellular Signal-regulated Kinase

PubMed Central

Perrett, Rebecca M.; Voliotis, Margaritis; Armstrong, Stephen P.; Fowkes, Robert C.; Pope, George R.; Tsaneva-Atanasova, Krasimira; McArdle, Craig A.

2014-01-01

Gonadotropin-releasing hormone (GnRH) is secreted in brief pulses that stimulate synthesis and secretion of pituitary gonadotropin hormones and thereby mediate control of reproduction. It acts via G-protein-coupled receptors to stimulate effectors, including ERK. Information could be encoded in GnRH pulse frequency, width, amplitude, or other features of pulse shape, but the relative importance of these features is unknown. Here we examine this using automated fluorescence microscopy and mathematical modeling, focusing on ERK signaling. The simplest scenario is one in which the system is linear, and response dynamics are relatively fast (compared with the signal dynamics). In this case integrated system output (ERK activation or ERK-driven transcription) will be roughly proportional to integrated input, but we find that this is not the case. Notably, we find that relatively slow response kinetics lead to ERK activity beyond the GnRH pulse, and this reduces sensitivity to pulse width. More generally, we show that the slowing of response kinetics through the signaling cascade creates a system that is robust to pulse width. We, therefore, show how various levels of response kinetics synergize to dictate system sensitivity to different features of pulsatile hormone input. We reveal the mathematical and biochemical basis of a dynamic GnRH signaling system that is robust to changes in pulse amplitude and width but is sensitive to changes in receptor occupancy and frequency, precisely the features that are tightly regulated and exploited to exert physiological control in vivo. PMID:24482225

The contribution of direct patient reported ADRs to drug safety signals in the Netherlands from 2010 to 2015.

PubMed

van Hunsel, Florence; de Waal, Susan; Härmark, Linda

2017-08-01

The purpose of this study was to investigate the contribution of patient reports to signals sent by the Netherlands Pharmacovigilance Centre Lareb to the Dutch Medicines Evaluation Board and to determine if there are certain types of signals where patient report add a distinct contribution. All signals from 2010 until 2015 were included. First, we investigated how many patient reports were present in the signals and the characteristics of these reports compared to the health care professional and marketing authorization holders' reports. In addition to source, the analysis included ATC code of the drug, MedDRA® system organ class and preferred term for the adverse drug reaction (ADR), seriousness of the ADR, and 7 other factors like reports on over-the-counter medication, and how often an ADR listed in the important medical event terms list was present. Secondly, we determined the proportion of reports submitted by the individual groups to signals, in a cross-sectional manner. A total of 150 signals were included, including 1691 ADR reports. Our results show that 26.3% of all ADR reports in Dutch drug safety signals were reported by patients, and 30.5% of the patient reports in the signals contained one or more terms listed as important medical events. The proportion of reports by patients which were included the signals was 2% and 3.9% for health care professional reports and 0.2% for marketing authorization holders reports. Patients had an important contribution to signals overall, but especially for ADRs related to generic drug substitution and psychiatric ADRs. Copyright © 2017 John Wiley & Sons, Ltd.

Slow feature analysis: unsupervised learning of invariances.

PubMed

Wiskott, Laurenz; Sejnowski, Terrence J

2002-04-01

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features.

PubMed

Hassan, Ahnaf Rashik; Bhuiyan, Mohammed Imamul Hassan

2016-09-15

Automatic sleep scoring is essential owing to the fact that conventionally a large volume of data have to be analyzed visually by the physicians which is onerous, time-consuming and error-prone. Therefore, there is a dire need of an automated sleep staging scheme. In this work, we decompose sleep-EEG signal segments using tunable-Q factor wavelet transform (TQWT). Various spectral features are then computed from TQWT sub-bands. The performance of spectral features in the TQWT domain has been determined by intuitive and graphical analyses, statistical validation, and Fisher criteria. Random forest is used to perform classification. Optimal choices and the effects of TQWT and random forest parameters have been determined and expounded. Experimental outcomes manifest the efficacy of our feature generation scheme in terms of p-values of ANOVA analysis and Fisher criteria. The proposed scheme yields 90.38%, 91.50%, 92.11%, 94.80%, 97.50% for 6-stage to 2-stage classification of sleep states on the benchmark Sleep-EDF data-set. In addition, its performance on DREAMS Subjects Data-set is also promising. The performance of the proposed method is significantly better than the existing ones in terms of accuracy and Cohen's kappa coefficient. Additionally, the proposed scheme gives high detection accuracy for sleep stages non-REM 1 and REM. Spectral features in the TQWT domain can discriminate sleep-EEG signals corresponding to various sleep states efficaciously. The proposed scheme will alleviate the burden of the physicians, speed-up sleep disorder diagnosis, and expedite sleep research. Copyright © 2016 Elsevier B.V. All rights reserved.

Social signals of safety and risk confer utility and have asymmetric effects on observers’ choices

PubMed Central

Chung, Dongil; Ball, Sheryl B; Chiu, Pearl H

2015-01-01

Individuals’ risk attitudes are known to guide choices about uncertain options. However, in the presence of others’ decisions, these choices can be swayed and manifest as riskier or safer behavior than one would express alone. To test the mechanisms underlying effective social ‘nudges’ in human decision-making, we used functional neuroimaging and a task in which participants made choices about gambles alone and after observing others’ selections. Against three alternative explanations, we found that observing others’ choices of gambles increased the subjective value (utility) of those gambles for the observer. This ‘other-conferred utility’ was encoded in ventromedial prefrontal cortex, and these neural signals predicted conformity. We further identified a parametric interaction with individual risk preferences in anterior cingulate cortex and insula. These data provide a neuromechanistic account of how information from others is integrated with individual preferences that may explain preference-congruent susceptibility to social signals of safety and risk. PMID:25984890

Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer's disease progression.

PubMed

Martinez-Torteya, Antonio; Rodriguez-Rojas, Juan; Celaya-Padilla, José M; Galván-Tejada, Jorge I; Treviño, Victor; Tamez-Peña, Jose

2014-10-01

Early diagnoses of Alzheimer's disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different ([Formula: see text]). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones.

Range safety signal attenuation by the Space Shuttle main engine exhaust plumes

NASA Technical Reports Server (NTRS)

Pearce, B. E.

1983-01-01

An analysis of attenuation of the range safety signal at 416.5 MHz observed after SRB separation and ending at hand over to Bermuda, during which transmission must pass through the LOX/H2 propelled main engine exhaust plumes, is summarized. Absorption by free electrons in the exhaust plume can account for the nearly constant magnitude of the observed attenuation during this period; it does not explain the short term transient increases that occur at one or more times during this portion of the flight. It is necessary to assume that a trace amount (about 0.5 ppm) of easily ionizable impurity must be present in the exhaust flow. Other mechanisms of attenuation, such as scattering by turbulent fluctuations of both free and bound electrons and absorption by water vapor, were examined but found to be inadequate to explain the observations.

Six Tips for College Health and Safety

MedlinePlus

... Emergency Preparedness & Response Environmental Health Healthy Living Injury, Violence & Safety Life Stages & Populations Travelers’ Health Workplace Safety & Health Features Media Sign up for Features Get Email Updates To ...

Aircraft signal definition for flight safety system monitoring system

NASA Technical Reports Server (NTRS)

Gibbs, Michael (Inventor); Omen, Debi Van (Inventor)

2003-01-01

A system and method compares combinations of vehicle variable values against known combinations of potentially dangerous vehicle input signal values. Alarms and error messages are selectively generated based on such comparisons. An aircraft signal definition is provided to enable definition and monitoring of sets of aircraft input signals to customize such signals for different aircraft. The input signals are compared against known combinations of potentially dangerous values by operational software and hardware of a monitoring function. The aircraft signal definition is created using a text editor or custom application. A compiler receives the aircraft signal definition to generate a binary file that comprises the definition of all the input signals used by the monitoring function. The binary file also contains logic that specifies how the inputs are to be interpreted. The file is then loaded into the monitor function, where it is validated and used to continuously monitor the condition of the aircraft.

Roadway Safety Guide

DOT National Transportation Integrated Search

2001-01-01

Roadway safety refers to that portion of overall highway safety that is determined by the roadway's physical features such as road design, roadway signs, pavement markings, operating conditions, roadside objects (such as utility poles, signs, trees, ...

Featured Article: Evaluating Smartphone-Based Virtual Reality to Improve Chinese Schoolchildren's Pedestrian Safety: A Nonrandomized Trial.

PubMed

Schwebel, David C; Wu, Yue; Li, Peng; Severson, Joan; He, Yefei; Xiang, Henry; Hu, Guoqing

2018-06-01

This nonrandomized trial evaluated whether classroom-based training in a smartphone-based virtual reality (VR) pedestrian environment (a) teaches schoolchildren to cross streets safely, and (b) increases their self-efficacy for street-crossing. Fifty-six children, aged 8-10 years, attending primary school in Changsha, China participated. Baseline pedestrian safety assessment occurred in the VR environment and through unobtrusive observation of a subsample crossing a street for 11 days outside school. Self-efficacy was assessed through both self-report and observation. Following baseline, children engaged in the VR for 12 days in their classrooms, honing complex cognitive-perceptual skills required to engage safely in traffic. Follow-up assessment replicated baseline. Probability of crash in the VR decreased posttraining (0.40 vs. 0.09), and observational data found the odds of looking at oncoming traffic while crossing the first lane of traffic increased (odds ratio [OR] = 2.4). Self-efficacy increases occurred in self-report (proportional OR = 4.7 crossing busy streets) and observation of following crossing-guard signals (OR = 0.2, first lane). Pedestrian safety training via smartphone-based VR provides children the repeated practice needed to learn the complex skills required to cross streets safely, and also helps them improve self-efficacy to cross streets. Given rapid motorization and global smartphone penetration, plus epidemiological findings that about 75,000 children die annually worldwide in pedestrian crashes, smartphone-based VR could supplement existing policy and prevention efforts to improve global child pedestrian safety.

Feature Integration Theory Revisited: Dissociating Feature Detection and Attentional Guidance in Visual Search

ERIC Educational Resources Information Center

Chan, Louis K. H.; Hayward, William G.

2009-01-01

In feature integration theory (FIT; A. Treisman & S. Sato, 1990), feature detection is driven by independent dimensional modules, and other searches are driven by a master map of locations that integrates dimensional information into salience signals. Although recent theoretical models have largely abandoned this distinction, some observed…

Improving Pedestrian Safety at Signalized Intersections

DOT National Transportation Integrated Search

2012-06-01

Our research investigated methods and technologies to make signalized intersections safer for pedestrians using the capabilities : of accessible pedestrian systems. Our research focused on three technologies: acoustic beaconing, passive pedestrian de...

Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer’s disease progression

PubMed Central

Martinez-Torteya, Antonio; Rodriguez-Rojas, Juan; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Treviño, Victor; Tamez-Peña, Jose

2014-01-01

Abstract. Early diagnoses of Alzheimer’s disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different (p-value=2.04e−11). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones. PMID:26158047

Using micro-simulation to investigate the safety impacts of transit design alternatives at signalized intersections.

PubMed

Li, Lu; Persaud, Bhagwant; Shalaby, Amer

2017-03-01

This study investigates the use of crash prediction models and micro-simulation to develop an effective surrogate safety assessment measure at the intersection level. With the use of these tools, hypothetical scenarios can be developed and explored to evaluate the safety impacts of design alternatives in a controlled environment, in which factors not directly associated with the design alternatives can be fixed. Micro-simulation models are developed, calibrated, and validated. Traffic conflicts in the micro-simulation models are estimated and linked with observed crash frequency, which greatly alleviates the lengthy time needed to collect sufficient crash data for evaluating alternatives, due to the rare and infrequent nature of crash events. A set of generalized linear models with negative binomial error structure is developed to correlate the simulated conflicts with the observed crash frequency in Toronto, Ontario, Canada. Crash prediction models are also developed for crashes of different impact types and for transit-involved crashes. The resulting statistical significance and the goodness-of-fit of the models suggest adequate predictive ability. Based on the established correlation between simulated conflicts and observed crashes, scenarios are developed in the micro-simulation models to investigate the safety effects of individual transit line elements by making hypothetical modifications to such elements and estimating changes in crash frequency from the resulting changes in conflicts. The findings imply that the existing transit signal priority schemes can have a negative effect on safety performance, and that the existing near-side stop positioning and streetcar transit type can be safer at their current state than if they were to be replaced by their respective counterparts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Safety Concerns Reported by Patients Identified in a Collaborative Signal Detection Workshop using VigiBase: Results and Reflections from Lareb and Uppsala Monitoring Centre.

PubMed

Watson, Sarah; Chandler, Rebecca E; Taavola, Henric; Härmark, Linda; Grundmark, Birgitta; Zekarias, Alem; Star, Kristina; van Hunsel, Florence

2018-02-01

Patient reporting in pharmacovigilance is important and contributes to signal detection. However, descriptions of methodologies for using patient reports in signal detection are scarce, and published experiences of how patient reports are used in pharmacovigilance are limited to a few individual countries. Our objective was to explore the contribution of patient reports to global signal detection in VigiBase. Data were retrieved from VigiBase in September 2016. Drug-event-combination series were restricted to those with >50% patient reports, defined as reporter type "Consumer/non-health professional" per E2B reporting standard. vigiRank was applied to patient reports to prioritize combinations for assessment. Product information for healthcare professionals (HCPs) as well as patient information leaflets (PILs) were used as reference for information on adverse drug reactions (ADRs). Staff from the Uppsala Monitoring Centre and the Netherlands Pharmacovigilance Centre Lareb categorized the combinations. Potential signals proceeded to a more in-depth clinical review to determine whether the safety concern should be communicated as a "signal." Of the 212 combinations assessed, 20 (9%) resulted in eight signals communicated within the World Health Organization (WHO) programme for international drug monitoring. Review of PILs revealed insufficient ADR descriptions for patients and examples of poor consistency with product information for HCPs. Patient narratives provided details regarding the experience and impact of ADRs and evidence that patients make causality and personal risk assessments. Safety concerns described in patient reports can be identified in a global database including previously unknown ADRs as well as new aspects of known ADRs. Patient reports provide unique information valuable in signal assessment and should be included in signal detection. Novel approaches to highlighting patient reports in statistical signal detection can further improve the

Anomaly Detection of Electromyographic Signals.

PubMed

Ijaz, Ahsan; Choi, Jongeun

2018-04-01

In this paper, we provide a robust framework to detect anomalous electromyographic (EMG) signals and identify contamination types. As a first step for feature selection, optimally selected Lawton wavelets transform is applied. Robust principal component analysis (rPCA) is then performed on these wavelet coefficients to obtain features in a lower dimension. The rPCA based features are used for constructing a self-organizing map (SOM). Finally, hierarchical clustering is applied on the SOM that separates anomalous signals residing in the smaller clusters and breaks them into logical units for contamination identification. The proposed methodology is tested using synthetic and real world EMG signals. The synthetic EMG signals are generated using a heteroscedastic process mimicking desired experimental setups. A sub-part of these synthetic signals is introduced with anomalies. These results are followed with real EMG signals introduced with synthetic anomalies. Finally, a heterogeneous real world data set is used with known quality issues under an unsupervised setting. The framework provides recall of 90% (± 3.3) and precision of 99%(±0.4).

Improved statistical signal detection in pharmacovigilance by combining multiple strength-of-evidence aspects in vigiRank.

PubMed

Caster, Ola; Juhlin, Kristina; Watson, Sarah; Norén, G Niklas

2014-08-01

Detection of unknown risks with marketed medicines is key to securing the optimal care of individual patients and to reducing the societal burden from adverse drug reactions. Large collections of individual case reports remain the primary source of information and require effective analytics to guide clinical assessors towards likely drug safety signals. Disproportionality analysis is based solely on aggregate numbers of reports and naively disregards report quality and content. However, these latter features are the very fundament of the ensuing clinical assessment. Our objective was to develop and evaluate a data-driven screening algorithm for emerging drug safety signals that accounts for report quality and content. vigiRank is a predictive model for emerging safety signals, here implemented with shrinkage logistic regression to identify predictive variables and estimate their respective contributions. The variables considered for inclusion capture different aspects of strength of evidence, including quality and clinical content of individual reports, as well as trends in time and geographic spread. A reference set of 264 positive controls (historical safety signals from 2003 to 2007) and 5,280 negative controls (pairs of drugs and adverse events not listed in the Summary of Product Characteristics of that drug in 2012) was used for model fitting and evaluation; the latter used fivefold cross-validation to protect against over-fitting. All analyses were performed on a reconstructed version of VigiBase(®) as of 31 December 2004, at around which time most safety signals in our reference set were emerging. The following aspects of strength of evidence were selected for inclusion into vigiRank: the numbers of informative and recent reports, respectively; disproportional reporting; the number of reports with free-text descriptions of the case; and the geographic spread of reporting. vigiRank offered a statistically significant improvement in area under the receiver

A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays

NASA Technical Reports Server (NTRS)

Eckstein, M. P.; Thomas, J. P.; Palmer, J.; Shimozaki, S. S.

2000-01-01

Recently, quantitative models based on signal detection theory have been successfully applied to the prediction of human accuracy in visual search for a target that differs from distractors along a single attribute (feature search). The present paper extends these models for visual search accuracy to multidimensional search displays in which the target differs from the distractors along more than one feature dimension (conjunction, disjunction, and triple conjunction displays). The model assumes that each element in the display elicits a noisy representation for each of the relevant feature dimensions. The observer combines the representations across feature dimensions to obtain a single decision variable, and the stimulus with the maximum value determines the response. The model accurately predicts human experimental data on visual search accuracy in conjunctions and disjunctions of contrast and orientation. The model accounts for performance degradation without resorting to a limited-capacity spatially localized and temporally serial mechanism by which to bind information across feature dimensions.

Nozzle Extension for Safety Air Gun

NASA Technical Reports Server (NTRS)

Zumbrun, H. N.; Croom, Delwin R., Jr.

1986-01-01

New nozzle-extension design overcomes problems and incorporates original commercial nozzle, retaining intrinsic safety features. Components include extension tube, length of which made to suit application; adaptor fitting, and nozzle adaptor repinned to maintain original safety features. Design moves conical airstream to end of extension to blow machine chips away from operator. Nozzle-extension modification allows safe and efficient operation of machine tools while maintaining integrity of orginial safety-air-gun design.

The architecture of safety: hospital design.

PubMed

Joseph, Anjali; Rashid, Mahbub

2007-12-01

This paper reviews recent research literature reporting the effects of hospital design on patient safety. Features of hospital design that are linked to patient safety in the literature include noise, air quality, lighting conditions, patient room design, unit layout, and several other interior design features. Some of these features act as latent conditions for adverse events, and impact safety outcomes directly and indirectly by impacting staff working conditions. Others act as barriers to adverse events by providing hospital staff with opportunities for preventing accidents before they occur. Although the evidence linking hospital design to patient safety is growing, much is left to be done in this area of research. Nevertheless, the evidence reported in the literature may already be sufficient to have a positive impact on hospital design.

U18 : Traffic signal safety (phase B).

DOT National Transportation Integrated Search

2009-08-01

Efficiently scheduling traffic, particularly heavy vehicles, remains a key challenge in transportation engineering. This project has focused on the development of a novel trafficsignal-control methodology to improve the safety of heavy vehicles on...

Signal processing and control challenges for smart vehicles

NASA Astrophysics Data System (ADS)

Zhang, Hui; Braun, Simon G.

2017-03-01

Smart phones have changed not only the mobile phone market but also our society during the past few years. Could the next potential intelligent device may be the vehicle? Judging by the visibility, in all media, of the numerous attempts to develop autonomous vehicles, this is certainly one of the logical outcomes. Smart vehicles would be equipped with an advanced operating system such that the vehicles could communicate with others, optimize the operation to reduce fuel consumption and emissions, enhance safety, or even become self-driving. These combined new features of vehicles require instrumentation and hardware developments, fast signal processing/fusion, decision making and online optimization. Meanwhile, the inevitable increasing system complexity would certainly challenges the control unit design.

Features selection and classification to estimate elbow movements

NASA Astrophysics Data System (ADS)

Rubiano, A.; Ramírez, J. L.; El Korso, M. N.; Jouandeau, N.; Gallimard, L.; Polit, O.

2015-11-01

In this paper, we propose a novel method to estimate the elbow motion, through the features extracted from electromyography (EMG) signals. The features values are normalized and then compared to identify potential relationships between the EMG signal and the kinematic information as angle and angular velocity. We propose and implement a method to select the best set of features, maximizing the distance between the features that correspond to flexion and extension movements. Finally, we test the selected features as inputs to a non-linear support vector machine in the presence of non-idealistic conditions, obtaining an accuracy of 99.79% in the motion estimation results.

Safety for the Elementary Grades: A Multimedia Roundup.

ERIC Educational Resources Information Center

Mandell, Phyllis Levy; Rosenthal, Shiri

1980-01-01

Presents abstracts of films and cassettes for the elementary school dealing with basic safety, bicycle safety, electrical safety, emergencies and how to deal with them, fire and holiday safety, playground safety, poisons, school and bus safety, signs and signals, skateboard and water safety. (CS)

Latency features of SafetyNet ground systems architecture for the National Polar-orbiting Operational Environmental Satellite System (NPOESS)

NASA Astrophysics Data System (ADS)

Duda, James L.; Mulligan, Joseph; Valenti, James; Wenkel, Michael

2005-01-01

A key feature of the National Polar-orbiting Operational Environmental Satellite System (NPOESS) is the Northrop Grumman Space Technology patent-pending innovative data routing and retrieval architecture called SafetyNetTM. The SafetyNetTM ground system architecture for the National Polar-orbiting Operational Environmental Satellite System (NPOESS), combined with the Interface Data Processing Segment (IDPS), will together provide low data latency and high data availability to its customers. The NPOESS will cut the time between observation and delivery by a factor of four when compared with today's space-based weather systems, the Defense Meteorological Satellite Program (DMSP) and NOAA's Polar-orbiting Operational Environmental Satellites (POES). SafetyNetTM will be a key element of the NPOESS architecture, delivering near real-time data over commercial telecommunications networks. Scattered around the globe, the 15 unmanned ground receptors are linked by fiber-optic systems to four central data processing centers in the U. S. known as Weather Centrals. The National Environmental Satellite, Data and Information Service; Air Force Weather Agency; Fleet Numerical Meteorology and Oceanography Center, and the Naval Oceanographic Office operate the Centrals. In addition, this ground system architecture will have unused capacity attendant with an infrastructure that can accommodate additional users.

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification

PubMed Central

Wen, Tingxi; Zhang, Zhongnan

2017-01-01

Abstract In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy. PMID:28489789

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification.

PubMed

Wen, Tingxi; Zhang, Zhongnan

2017-05-01

In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy.

Suppression effects in feature-based attention

PubMed Central

Wang, Yixue; Miller, James; Liu, Taosheng

2015-01-01

Attending to a feature enhances visual processing of that feature, but it is less clear what occurs to unattended features. Single-unit recording studies in middle temporal (MT) have shown that neuronal modulation is a monotonic function of the difference between the attended and neuron's preferred direction. Such a relationship should predict a monotonic suppressive effect in psychophysical performance. However, past research on suppressive effects of feature-based attention has remained inconclusive. We investigated the suppressive effect for motion direction, orientation, and color in three experiments. We asked participants to detect a weak signal among noise and provided a partially valid feature cue to manipulate attention. We measured performance as a function of the offset between the cued and signal feature. We also included neutral trials where no feature cues were presented to provide a baseline measure of performance. Across three experiments, we consistently observed enhancement effects when the target feature and cued feature coincided and suppression effects when the target feature deviated from the cued feature. The exact profile of suppression was different across feature dimensions: Whereas the profile for direction exhibited a “rebound” effect, the profiles for orientation and color were monotonic. These results demonstrate that unattended features are suppressed during feature-based attention, but the exact suppression profile depends on the specific feature. Overall, the results are largely consistent with neurophysiological data and support the feature-similarity gain model of attention. PMID:26067533

Feature in Antenna Pattern for Pointing and Orientation Determination

NASA Technical Reports Server (NTRS)

Rochblatt, David (Inventor)

2016-01-01

Systems and methods for antenna pointing are disclosed. A transmit antenna system having an adjustable boresight transmits a signal exhibiting a far-field pattern including a feature (e.g. a V-Notch) in a polarization of the signal disposed at a fixed position off a beam peak of the far-field pattern of the signal. A receive antenna system scans across the far-field pattern of the signal in the polarization to locate the feature and determine a pointing error of the adjustable boresight therefrom. The system may be applied to a cross-polarization of the signal where a co-polarization of the signal is simultaneously used for telecommunication.

Speech recognition features for EEG signal description in detection of neonatal seizures.

PubMed

Temko, A; Boylan, G; Marnane, W; Lightbody, G

2010-01-01

In this work, features which are usually employed in automatic speech recognition (ASR) are used for the detection of neonatal seizures in newborn EEG. Three conventional ASR feature sets are compared to the feature set which has been previously developed for this task. The results indicate that the thoroughly-studied spectral envelope based ASR features perform reasonably well on their own. Additionally, the SVM Recursive Feature Elimination routine is applied to all extracted features pooled together. It is shown that ASR features consistently appear among the top-rank features.

Regulator Loss Functions and Hierarchical Modeling for Safety Decision Making.

PubMed

Hatfield, Laura A; Baugh, Christine M; Azzone, Vanessa; Normand, Sharon-Lise T

2017-07-01

Regulators must act to protect the public when evidence indicates safety problems with medical devices. This requires complex tradeoffs among risks and benefits, which conventional safety surveillance methods do not incorporate. To combine explicit regulator loss functions with statistical evidence on medical device safety signals to improve decision making. In the Hospital Cost and Utilization Project National Inpatient Sample, we select pediatric inpatient admissions and identify adverse medical device events (AMDEs). We fit hierarchical Bayesian models to the annual hospital-level AMDE rates, accounting for patient and hospital characteristics. These models produce expected AMDE rates (a safety target), against which we compare the observed rates in a test year to compute a safety signal. We specify a set of loss functions that quantify the costs and benefits of each action as a function of the safety signal. We integrate the loss functions over the posterior distribution of the safety signal to obtain the posterior (Bayes) risk; the preferred action has the smallest Bayes risk. Using simulation and an analysis of AMDE data, we compare our minimum-risk decisions to a conventional Z score approach for classifying safety signals. The 2 rules produced different actions for nearly half of hospitals (45%). In the simulation, decisions that minimize Bayes risk outperform Z score-based decisions, even when the loss functions or hierarchical models are misspecified. Our method is sensitive to the choice of loss functions; eliciting quantitative inputs to the loss functions from regulators is challenging. A decision-theoretic approach to acting on safety signals is potentially promising but requires careful specification of loss functions in consultation with subject matter experts.

Cardiotoxic and Cardioprotective Features of Chronic β-adrenergic Signaling

PubMed Central

Zhang, Xiaoying; Szeto, Christopher; Gao, Erhe; Tang, Mingxin; Jin, Jianguo; Fu, Qin; Makarewich, Catherine; Ai, Xiaojie; Li, Ying; Tang, Allen; Wang, Jenny; Gao, Hui; Wang, Fang; Ge, Xinyi Joy; Kunapuli, Satya P.; Zhou, Lin; Zeng, Chunyu; Xiang, Kevin Yang; Chen, Xiongwen

2012-01-01

Rationale In the failing heart, persistent β-adrenergic receptor (βAR) activation is thought to induce myocyte death by protein kinase A (PKA)-dependent and PKA-independent activation of calcium/calmodulin-dependent kinase II (CaMKII). β-Adrenergic signaling pathways are also capable of activating cardioprotective mechanisms. Objective This study used a novel PKA inhibitor peptide (PKI) to inhibit PKA activity to test the hypothesis that βAR signaling causes cell death through PKA-dependent pathways and cardioprotection through PKA-independent pathways. Methods and Results In PKI transgenic mice, chronic isoproterenol (ISO) failed to induce cardiac hypertrophy, fibrosis, myocyte apoptosis and depressed cardiac function. In cultured adult feline ventricular myocytes (AFVMs), PKA inhibition protected myocytes from death induced by β1-AR agonists by preventing cytosolic and SR Ca2+ overload and CaMKII activation. PKA inhibition revealed a cardioprotective role of β-adrenergic signaling via cAMP/EPAC /Rap1/Rac/ERK pathway. Selective PKA inhibition causes protection in the heart after myocardial infarction (MI) that was superior to β-blocker therapy. Conclusion These results suggest that selective block of PKA could be a novel heart failure therapy. PMID:23104882

30 CFR 56.14219 - Brakeman signals.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Brakeman signals. 56.14219 Section 56.14219... Safety Practices and Operational Procedures § 56.14219 Brakeman signals. When a train is under the direction of a brakeman and the train operator cannot clearly recognize the brakeman's signals, the train...

30 CFR 56.14219 - Brakeman signals.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Brakeman signals. 56.14219 Section 56.14219... Safety Practices and Operational Procedures § 56.14219 Brakeman signals. When a train is under the direction of a brakeman and the train operator cannot clearly recognize the brakeman's signals, the train...

30 CFR 57.14219 - Brakeman signals.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Brakeman signals. 57.14219 Section 57.14219... Equipment Safety Practices and Operational Procedures § 57.14219 Brakeman signals. When a train is under the direction of a brakeman and the train operator cannot clearly recognize the brakeman's signals, the train...

30 CFR 57.14219 - Brakeman signals.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Brakeman signals. 57.14219 Section 57.14219... Equipment Safety Practices and Operational Procedures § 57.14219 Brakeman signals. When a train is under the direction of a brakeman and the train operator cannot clearly recognize the brakeman's signals, the train...

30 CFR 56.14219 - Brakeman signals.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Brakeman signals. 56.14219 Section 56.14219... Safety Practices and Operational Procedures § 56.14219 Brakeman signals. When a train is under the direction of a brakeman and the train operator cannot clearly recognize the brakeman's signals, the train...

30 CFR 56.14219 - Brakeman signals.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Brakeman signals. 56.14219 Section 56.14219... Safety Practices and Operational Procedures § 56.14219 Brakeman signals. When a train is under the direction of a brakeman and the train operator cannot clearly recognize the brakeman's signals, the train...

30 CFR 56.14219 - Brakeman signals.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Brakeman signals. 56.14219 Section 56.14219... Safety Practices and Operational Procedures § 56.14219 Brakeman signals. When a train is under the direction of a brakeman and the train operator cannot clearly recognize the brakeman's signals, the train...

30 CFR 57.14219 - Brakeman signals.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Brakeman signals. 57.14219 Section 57.14219... Equipment Safety Practices and Operational Procedures § 57.14219 Brakeman signals. When a train is under the direction of a brakeman and the train operator cannot clearly recognize the brakeman's signals, the train...

30 CFR 57.14219 - Brakeman signals.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Brakeman signals. 57.14219 Section 57.14219... Equipment Safety Practices and Operational Procedures § 57.14219 Brakeman signals. When a train is under the direction of a brakeman and the train operator cannot clearly recognize the brakeman's signals, the train...

30 CFR 57.14219 - Brakeman signals.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Brakeman signals. 57.14219 Section 57.14219... Equipment Safety Practices and Operational Procedures § 57.14219 Brakeman signals. When a train is under the direction of a brakeman and the train operator cannot clearly recognize the brakeman's signals, the train...

Seniors' perceptions of vehicle safety risks and needs.

PubMed

Shaw, Lynn; Polgar, Jan Miller; Vrkljan, Brenda; Jacobson, Jill

2010-01-01

The investigation of vehicle safety needs for older drivers and passengers is integral for their safe transportation. A program of research on safe transportation for seniors was launched through AUTO21, a Canadian Network of Centres of Excellence. This national research network focuses on a wide range of automotive issues, from materials and design to safety and societal issues. An inductive qualitative inquiry of seniors' driving experiences, safety feature use, and strategies to prevent injury and manage risks was a first step in this program. We conducted interviews and focus groups with 58 seniors without disabilities and 9 seniors with disabilities. We identified a lack of congruity between the vehicle and safety feature design and seniors' needs. Seniors described strategies to manage their safety and that of others. Specific aspects of vehicle design, safety features, and action strategies that support safer use and operation of a vehicle by seniors are outlined.

30 CFR 57.12012 - Bare signal wires.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Bare signal wires. 57.12012 Section 57.12012 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-UNDERGROUND METAL AND NONMETAL MINES Electricity Surface...

Monitoring product safety in the postmarketing environment.

PubMed

Sharrar, Robert G; Dieck, Gretchen S

2013-10-01

The safety profile of a medicinal product may change in the postmarketing environment. Safety issues not identified in clinical development may be seen and need to be evaluated. Methods of evaluating spontaneous adverse experience reports and identifying new safety risks include a review of individual reports, a review of a frequency distribution of a list of the adverse experiences, the development and analysis of a case series, and various ways of examining the database for signals of disproportionality, which may suggest a possible association. Regulatory agencies monitor product safety through a variety of mechanisms including signal detection of the adverse experience safety reports in databases and by requiring and monitoring risk management plans, periodic safety update reports and postauthorization safety studies. The United States Food and Drug Administration is working with public, academic and private entities to develop methods for using large electronic databases to actively monitor product safety. Important identified risks will have to be evaluated through observational studies and registries.

Low-cost safety enhancements for stop-controlled and signalized intersections

DOT National Transportation Integrated Search

2009-05-01

The purpose of this document is to present information on suggested effective, low-cost intersection countermeasures developed using intersection safety research results and input from an intersection safety expert panel. These low-cost countermeasur...

Creating a highway information system for safety roadway features.

DOT National Transportation Integrated Search

2015-12-01

Roadway departures are the leading cause of roadside fatalities. The Kentucky Transportation Cabinet (KYTC) has : undertaken a number of roadside safety measures to reduce roadway departures. Specifically, KYTC has installed : several low-cost, syste...

Modified DCTNet for audio signals classification

NASA Astrophysics Data System (ADS)

Xian, Yin; Pu, Yunchen; Gan, Zhe; Lu, Liang; Thompson, Andrew

2016-10-01

In this paper, we investigate DCTNet for audio signal classification. Its output feature is related to Cohen's class of time-frequency distributions. We introduce the use of adaptive DCTNet (A-DCTNet) for audio signals feature extraction. The A-DCTNet applies the idea of constant-Q transform, with its center frequencies of filterbanks geometrically spaced. The A-DCTNet is adaptive to different acoustic scales, and it can better capture low frequency acoustic information that is sensitive to human audio perception than features such as Mel-frequency spectral coefficients (MFSC). We use features extracted by the A-DCTNet as input for classifiers. Experimental results show that the A-DCTNet and Recurrent Neural Networks (RNN) achieve state-of-the-art performance in bird song classification rate, and improve artist identification accuracy in music data. They demonstrate A-DCTNet's applicability to signal processing problems.

Acoustic⁻Seismic Mixed Feature Extraction Based on Wavelet Transform for Vehicle Classification in Wireless Sensor Networks.

PubMed

Zhang, Heng; Pan, Zhongming; Zhang, Wenna

2018-06-07

An acoustic⁻seismic mixed feature extraction method based on the wavelet coefficient energy ratio (WCER) of the target signal is proposed in this study for classifying vehicle targets in wireless sensor networks. The signal was decomposed into a set of wavelet coefficients using the à trous algorithm, which is a concise method used to implement the wavelet transform of a discrete signal sequence. After the wavelet coefficients of the target acoustic and seismic signals were obtained, the energy ratio of each layer coefficient was calculated as the feature vector of the target signals. Subsequently, the acoustic and seismic features were merged into an acoustic⁻seismic mixed feature to improve the target classification accuracy after the acoustic and seismic WCER features of the target signal were simplified using the hierarchical clustering method. We selected the support vector machine method for classification and utilized the data acquired from a real-world experiment to validate the proposed method. The calculated results show that the WCER feature extraction method can effectively extract the target features from target signals. Feature simplification can reduce the time consumption of feature extraction and classification, with no effect on the target classification accuracy. The use of acoustic⁻seismic mixed features effectively improved target classification accuracy by approximately 12% compared with either acoustic signal or seismic signal alone.

Feature extraction in MFL signals of machined defects in steel tubes

NASA Astrophysics Data System (ADS)

Perazzo, R.; Pignotti, A.; Reich, S.; Stickar, P.

2001-04-01

Thirty defects of various shapes were machined on the external and internal wall surfaces of a 177 mm diameter ferromagnetic steel pipe. MFL signals were digitized and recorded at a frequency of 4 Khz. Various magnetizing currents and relative tube-probe velocities of the order of 2m/s were used. The identification of the location of the defect by a principal component/neural network analysis of the signal is shown to be more effective than the standard procedure of classification based on the average signal frequency.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

PubMed Central

Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang

2018-01-01

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407

30 CFR 56.12012 - Bare signal wires.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Bare signal wires. 56.12012 Section 56.12012 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-SURFACE METAL AND NONMETAL MINES Electricity § 56.12012...

30 CFR 56.12012 - Bare signal wires.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Bare signal wires. 56.12012 Section 56.12012 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-SURFACE METAL AND NONMETAL MINES Electricity § 56.12012...

30 CFR 56.12012 - Bare signal wires.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Bare signal wires. 56.12012 Section 56.12012 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-SURFACE METAL AND NONMETAL MINES Electricity § 56.12012...

30 CFR 56.12012 - Bare signal wires.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Bare signal wires. 56.12012 Section 56.12012 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-SURFACE METAL AND NONMETAL MINES Electricity § 56.12012...

30 CFR 56.12012 - Bare signal wires.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Bare signal wires. 56.12012 Section 56.12012 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-SURFACE METAL AND NONMETAL MINES Electricity § 56.12012...

[Safety evaluation and risk control measures of Cassiae Semen].

PubMed

Zhao, Yi-Meng; Wu, Li; Zhang, Shuo; Zhang, Li; Gao, Xue-Min; Sun, Xiao-Bo; Wang, Chun

2017-11-01

In this study, the authors reviewed domestic and foreign literatures, conducted the textual research on origin and development of Cassia Semen, studied records in ancient books and ancient and modern literatures, clinical adverse reactions and relevant experimental studies in recent years, and summarized the clinical features and influencing factors related to the safety of Cassiae Semen. According to the findings,Cassia Semen's safety risks are mainly liver and kidney system damages, with the main clinical features of fatigue, anorexia, disgusting of oil, yellow urine and gray stool; digestive system injury, with the main clinical features of diarrhea, abdominal distension, nausea and loose stool; reproductive system damage, with the main clinical features of vaginal bleeding. Allergic reactions and clinical adverse events, with the main clinical features for numb mouth, itching skin, nausea and vomiting, diarrhea, wheezing and lip cyanosis were also reported. The toxicological studies on toxic components of Cassiae Semen obtusifolia were carried out through acute toxicity test, subacute toxicity test, subchronic toxicity test and chronic toxicity test. Risk factors might include patients, compatibility and physicians. Physicians should strictly abide by the medication requirements in the Pharmacopoeia, pay attention to rational compatibility, appropriate dosage,correct usage and appropriate processing, control the dosage below 15 g to avoid excessive intake, strictly control the course of treatment to avoid accumulated poisoning caused by long-term administration. At the same time, clinicians should pay attention to the latest research progress, update the knowledge structure, quickly find the latest and useful materials from clinical practice, scientific research and drug information and other literatures, make evaluation and judgment for the materials, establish a traditional Chinese medicine intelligence information library, and strengthen the control over

Evaluating the Performance of the NASA LaRC CMF Motion Base Safety Devices

NASA Technical Reports Server (NTRS)

Gupton, Lawrence E.; Bryant, Richard B., Jr.; Carrelli, David J.

2006-01-01

This paper describes the initial measured performance results of the previously documented NASA Langley Research Center (LaRC) Cockpit Motion Facility (CMF) motion base hardware safety devices. These safety systems are required to prevent excessive accelerations that could injure personnel and damage simulator cockpits or the motion base structure. Excessive accelerations may be caused by erroneous commands or hardware failures driving an actuator to the end of its travel at high velocity, stepping a servo valve, or instantly reversing servo direction. Such commands may result from single order failures of electrical or hydraulic components within the control system itself, or from aggressive or improper cueing commands from the host simulation computer. The safety systems must mitigate these high acceleration events while minimizing the negative performance impacts. The system accomplishes this by controlling the rate of change of valve signals to limit excessive commanded accelerations. It also aids hydraulic cushion performance by limiting valve command authority as the actuator approaches its end of travel. The design takes advantage of inherent motion base hydraulic characteristics to implement all safety features using hardware only solutions.

Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features

PubMed Central

Mousavi Kahaki, Seyed Mostafa; Nordin, Md Jan; Ashtari, Amir H.; J. Zahra, Sophia

2016-01-01

An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics—such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient—are insufficient for achieving adequate results under different image deformations. Thus, new descriptor’s similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence. PMID:26985996

Longitudinal medical records as a complement to routine drug safety signal analysis†

PubMed Central

Watson, Sarah; Sandberg, Lovisa; Johansson, Jeanette; Edwards, I. Ralph

2015-01-01

Abstract Purpose To explore whether and how longitudinal medical records could be used as a source of reference in the early phases of signal detection and analysis of novel adverse drug reactions (ADRs) in a global pharmacovigilance database. Methods Drug and ADR combinations from the routine signal detection process of VigiBase® in 2011 were matched to combinations in The Health Improvement Network (THIN). The number and type of drugs and ADRs from the data sets were investigated. For unlabelled combinations, graphical display of longitudinal event patterns (chronographs) in THIN was inspected to determine if the pattern supported the VigiBase combination. Results Of 458 combinations in the VigiBase data set, 190 matched to corresponding combinations in THIN (after excluding drugs with less than 100 prescriptions in THIN). Eighteen percent of the VigiBase and 9% of the matched THIN combinations referred to new drugs reported with serious reactions. Of the 112 unlabelled combinations matched to THIN, 52 chronographs were inconclusive mainly because of lack of data; 34 lacked any outstanding pattern around the time of prescription; 24 had an elevation of events in the pre‐prescription period, hence weakened the suspicion of a drug relationship; two had an elevated pattern of events exclusively in the post‐prescription period that, after review of individual patient histories, did not support an association. Conclusions Longitudinal medical records were useful in understanding the clinical context around a drug and suspected ADR combination and the probability of a causal relationship. A drawback was the paucity of data for newly marketed drugs with serious reactions. © 2015 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd. PMID:25623045

Probiotics: Safety and Side Effects

MedlinePlus

... of this page please turn JavaScript on. Feature: Probiotics Safety and Side Effects Past Issues / Winter 2016 ... Says About the Safety and Side Effects of Probiotics Whether probiotics are likely to be safe for ...

Relationship between grasping force and features of single-channel intramuscular EMG signals.

PubMed

Kamavuako, Ernest Nlandu; Farina, Dario; Yoshida, Ken; Jensen, Winnie

2009-12-15

The surface electromyographic (sEMG) signal can be used for force prediction and control in prosthetic devices. Because of technological advances on implantable sensors, the use of intramuscular EMG (iEMG) is becoming a potential alternative to sEMG for the control of multiple degrees-of-freedom (DOF). An invasive system is not affected by crosstalk, typical of sEMG, and provides more stable and independent control sites. However, intramuscular recordings provide more local information because of their high selectivity, and may thus be less representative of the global muscle activity with respect to sEMG. This study investigates the capacity of selective single-channel iEMG recordings to represent the grasping force with respect to the use of sEMG with the aim of assessing if iEMG can be an effective method for proportional myoelectric control. sEMG and iEMG were recorded concurrently from 10 subjects who exerted six grasping force profiles from 0 to 25/50N. The linear correlation coefficient between features extracted from iEMG and force was approximately 0.9 and was not significantly different from the degree of correlation between sEMG and force. This result indicates that a selective iEMG recording is representative of the applied grasping force and can be used for proportional control.

Driver face recognition as a security and safety feature

NASA Astrophysics Data System (ADS)

Vetter, Volker; Giefing, Gerd-Juergen; Mai, Rudolf; Weisser, Hubert

1995-09-01

We present a driver face recognition system for comfortable access control and individual settings of automobiles. The primary goals are the prevention of car thefts and heavy accidents caused by unauthorized use (joy-riders), as well as the increase of safety through optimal settings, e.g. of the mirrors and the seat position. The person sitting on the driver's seat is observed automatically by a small video camera in the dashboard. All he has to do is to behave cooperatively, i.e. to look into the camera. A classification system validates his access. Only after a positive identification, the car can be used and the driver-specific environment (e.g. seat position, mirrors, etc.) may be set up to ensure the driver's comfort and safety. The driver identification system has been integrated in a Volkswagen research car. Recognition results are presented.

Nuclear Reactor Safety--The APS Submits its Report

ERIC Educational Resources Information Center

Physics Today, 1975

1975-01-01

Presents the summary section of the American Physical Society (APS) report on the safety features of the light-water reactor, reviews the design, construction, and operation of a reactor and outlines the primary engineered safety features. Summarizes the major recommendations of the study group. (GS)

How important is vehicle safety in the new vehicle purchase process?

PubMed

Koppel, Sjaanie; Charlton, Judith; Fildes, Brian; Fitzharris, Michael

2008-05-01

Whilst there has been a significant increase in the amount of consumer interest in the safety performance of privately owned vehicles, the role that it plays in consumers' purchase decisions is poorly understood. The aims of the current study were to determine: how important vehicle safety is in the new vehicle purchase process; what importance consumers place on safety options/features relative to other convenience and comfort features, and how consumers conceptualise vehicle safety. In addition, the study aimed to investigate the key parameters associated with ranking 'vehicle safety' as the most important consideration in the new vehicle purchase. Participants recruited in Sweden and Spain completed a questionnaire about their new vehicle purchase. The findings from the questionnaire indicated that participants ranked safety-related factors (e.g., EuroNCAP (or other) safety ratings) as more important in the new vehicle purchase process than other vehicle factors (e.g., price, reliability etc.). Similarly, participants ranked safety-related features (e.g., advanced braking systems, front passenger airbags etc.) as more important than non-safety-related features (e.g., route navigation systems, air-conditioning etc.). Consistent with previous research, most participants equated vehicle safety with the presence of specific vehicle safety features or technologies rather than vehicle crash safety/test results or crashworthiness. The key parameters associated with ranking 'vehicle safety' as the most important consideration in the new vehicle purchase were: use of EuroNCAP, gender and education level, age, drivers' concern about crash involvement, first vehicle purchase, annual driving distance, person for whom the vehicle was purchased, and traffic infringement history. The findings from this study are important for policy makers, manufacturers and other stakeholders to assist in setting priorities with regard to the promotion and publicity of vehicle safety features

Feature Visibility Limits in the Non-Linear Enhancement of Turbid Images

NASA Technical Reports Server (NTRS)

Jobson, Daniel J.; Rahman, Zia-ur; Woodell, Glenn A.

2003-01-01

The advancement of non-linear processing methods for generic automatic clarification of turbid imagery has led us from extensions of entirely passive multiscale Retinex processing to a new framework of active measurement and control of the enhancement process called the Visual Servo. In the process of testing this new non-linear computational scheme, we have identified that feature visibility limits in the post-enhancement image now simplify to a single signal-to-noise figure of merit: a feature is visible if the feature-background signal difference is greater than the RMS noise level. In other words, a signal-to-noise limit of approximately unity constitutes a lower limit on feature visibility.

Gender differences in injury severity risks in crashes at signalized intersections.

PubMed

Obeng, K

2011-07-01

This paper analyzes gender differences in crash risk severities using data for signalized intersections. It estimates gender models for injury severity risks and finds that driver condition, type of crash, type of vehicle driven and vehicle safety features have different effects on females' and males' injury severity risks. Also, it finds some variables which are significantly related to females' injury severity risks but not males' and others which affect males' injury severity risks but not females'. It concludes that better and more in-depth information about gender differences in injury severity risks is gained by estimating separate models for females and males. Copyright © 2011 Elsevier Ltd. All rights reserved.

30 CFR 56.19095 - Location of signal devices.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Location of signal devices. 56.19095 Section 56.19095 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL... Signaling § 56.19095 Location of signal devices. Hoisting signal devices shall be positioned within easy...

30 CFR 57.19095 - Location of signal devices.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Location of signal devices. 57.19095 Section 57.19095 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL... Hoisting Signaling § 57.19095 Location of signal devices. Hoisting signal devices shall be positioned...

30 CFR 56.19095 - Location of signal devices.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Location of signal devices. 56.19095 Section 56.19095 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL... Signaling § 56.19095 Location of signal devices. Hoisting signal devices shall be positioned within easy...

30 CFR 57.19095 - Location of signal devices.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Location of signal devices. 57.19095 Section 57.19095 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL... Hoisting Signaling § 57.19095 Location of signal devices. Hoisting signal devices shall be positioned...

30 CFR 56.19095 - Location of signal devices.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Location of signal devices. 56.19095 Section 56.19095 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL... Signaling § 56.19095 Location of signal devices. Hoisting signal devices shall be positioned within easy...

47 CFR 80.1129 - Locating and homing signals.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 47 Telecommunication 5 2012-10-01 2012-10-01 false Locating and homing signals. 80.1129 Section 80... for Distress and Safety Communications § 80.1129 Locating and homing signals. (a) Locating signals are... survivors. These signals include those transmitted by searching units and those transmitted by the mobile...

30 CFR 57.19095 - Location of signal devices.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Location of signal devices. 57.19095 Section 57.19095 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL... Hoisting Signaling § 57.19095 Location of signal devices. Hoisting signal devices shall be positioned...

30 CFR 57.19095 - Location of signal devices.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Location of signal devices. 57.19095 Section 57.19095 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL... Hoisting Signaling § 57.19095 Location of signal devices. Hoisting signal devices shall be positioned...

30 CFR 56.19095 - Location of signal devices.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Location of signal devices. 56.19095 Section 56.19095 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL... Signaling § 56.19095 Location of signal devices. Hoisting signal devices shall be positioned within easy...

47 CFR 80.1129 - Locating and homing signals.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 5 2014-10-01 2014-10-01 false Locating and homing signals. 80.1129 Section 80... for Distress and Safety Communications § 80.1129 Locating and homing signals. (a) Locating signals are... survivors. These signals include those transmitted by searching units and those transmitted by the mobile...

30 CFR 56.19095 - Location of signal devices.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Location of signal devices. 56.19095 Section 56.19095 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL... Signaling § 56.19095 Location of signal devices. Hoisting signal devices shall be positioned within easy...

47 CFR 80.1129 - Locating and homing signals.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 47 Telecommunication 5 2013-10-01 2013-10-01 false Locating and homing signals. 80.1129 Section 80... for Distress and Safety Communications § 80.1129 Locating and homing signals. (a) Locating signals are... survivors. These signals include those transmitted by searching units and those transmitted by the mobile...

30 CFR 57.19095 - Location of signal devices.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Location of signal devices. 57.19095 Section 57.19095 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL... Hoisting Signaling § 57.19095 Location of signal devices. Hoisting signal devices shall be positioned...

Mother-child conversations about safety: implications for socializing safety values in children.

PubMed

O'Neal, Elizabeth E; Plumert, Jodie M

2014-05-01

This study examined how mothers socialize their children about safety through conversations about potentially unsafe activities. Mothers and their 8- and 10-year-old children discussed and rated the safety of 12 photographs depicting another same-gender child engaged in potentially dangerous activities. Conversations usually unfolded with children giving the first rating or rationale, followed by additional discussion between the mother and child. Mothers and children relied on 2 main types of rationales to justify their ratings: potential outcomes of the activity and specific features of the situation (dangerous and nondangerous). Mothers (but not children) used dangerous feature rationales more often than dangerous outcome rationales. When disagreements arose, mothers typically guided children to adopt their own rating rather than the child's rating. Additionally, children who used more nondangerous feature and outcome rationales had experienced more injuries requiring medical attention. Mothers' focus on dangerous features appears to reflect their efforts to help children make causal connections between dangerous elements of the situation and adverse outcomes that might result.

47 CFR 80.329 - Safety signals and messages.

Code of Federal Regulations, 2010 CFR

2010-10-01

... cyclones, dangerous ice, dangerous wrecks, or any other imminent danger to marine navigation must be... is about to transmit a message concerning the safety of navigation or giving important meteorological...

47 CFR 80.329 - Safety signals and messages.

Code of Federal Regulations, 2011 CFR

2011-10-01

... cyclones, dangerous ice, dangerous wrecks, or any other imminent danger to marine navigation must be... is about to transmit a message concerning the safety of navigation or giving important meteorological...

Monitoring circuit for reactor safety systems

DOEpatents

Keefe, Donald J.

1976-01-01

The ratio between the output signals of a pair of reactor safety channels is monitored. When ratio falls outside of a predetermined range, it indicates that one or more of the safety channels has malfunctioned.

Scaling and application of commercial, feature-rich, modular mixed-signal technology platforms for large format ROICs

NASA Astrophysics Data System (ADS)

Kar-Roy, Arjun; Racanelli, Marco; Howard, David; Miyagi, Glenn; Bowler, Mark; Jordan, Scott; Zhang, Tao; Krieger, William

2010-04-01

Today's modular, mixed-signal CMOS process platforms are excellent choices for manufacturing of highly integrated, large-format read out integrated circuits (ROICs). Platform features, that can be used for both cooled and un-cooled ROIC applications, can include (1) quality passives such as 4fFμm2 stacked MIM capacitors for linearity and higher density capacitance per pixel, 1kOhm high-value poly-silicon resistors, 2.8μm thick metals for efficient power distribution and reduced I-R drop; (2) analog active devices such as low noise single gate 3.3V, and 1.8V/3.3V or 1.8V/5V dual gate configurations, 40V LDMOS FETs, and NPN and PNP devices, deep n-well for substrate isolation for analog blocks and digital logic; (3) tools to assist the circuit designer such as models for cryogenic temperatures, CAD assistance for metal density uniformity determination, statistical, X-sigma and PCM-based models for corner validation and to simulate design sensitivity, and (4) sub-field stitching for large die. The TowerJazz platform of technology for 0.50μm, 0.25μm and 0.18μm CMOS nodes, with features as described above, is described in detail in this paper.

30 CFR 57.19096 - Familiarity with signal code.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 57.19096 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-UNDERGROUND METAL AND NONMETAL MINES Personnel... signals for cages, skips, and mantrips when persons or materials are being transported shall be familiar...

30 CFR 56.19096 - Familiarity with signal code.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 56.19096 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-SURFACE METAL AND NONMETAL MINES Personnel... signals for cages, skips, and mantrips when persons or materials are being transported shall be familiar...

Reference set for performance testing of pediatric vaccine safety signal detection methods and systems.

PubMed

Brauchli Pernus, Yolanda; Nan, Cassandra; Verstraeten, Thomas; Pedenko, Mariia; Osokogu, Osemeke U; Weibel, Daniel; Sturkenboom, Miriam; Bonhoeffer, Jan

2016-12-12

Safety signal detection in spontaneous reporting system databases and electronic healthcare records is key to detection of previously unknown adverse events following immunization. Various statistical methods for signal detection in these different datasources have been developed, however none are geared to the pediatric population and none specifically to vaccines. A reference set comprising pediatric vaccine-adverse event pairs is required for reliable performance testing of statistical methods within and across data sources. The study was conducted within the context of the Global Research in Paediatrics (GRiP) project, as part of the seventh framework programme (FP7) of the European Commission. Criteria for the selection of vaccines considered in the reference set were routine and global use in the pediatric population. Adverse events were primarily selected based on importance. Outcome based systematic literature searches were performed for all identified vaccine-adverse event pairs and complemented by expert committee reports, evidence based decision support systems (e.g. Micromedex), and summaries of product characteristics. Classification into positive (PC) and negative control (NC) pairs was performed by two independent reviewers according to a pre-defined algorithm and discussed for consensus in case of disagreement. We selected 13 vaccines and 14 adverse events to be included in the reference set. From a total of 182 vaccine-adverse event pairs, we classified 18 as PC, 113 as NC and 51 as unclassifiable. Most classifications (91) were based on literature review, 45 were based on expert committee reports, and for 46 vaccine-adverse event pairs, an underlying pathomechanism was not plausible classifying the association as NC. A reference set of vaccine-adverse event pairs was developed. We propose its use for comparing signal detection methods and systems in the pediatric population. Published by Elsevier Ltd.

33 CFR 175.130 - Visual distress signals accepted.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 2 2012-07-01 2012-07-01 false Visual distress signals accepted... (CONTINUED) BOATING SAFETY EQUIPMENT REQUIREMENTS Visual Distress Signals § 175.130 Visual distress signals accepted. (a) Any of the following signals, when carried in the number required, can be used to meet the...

33 CFR 175.130 - Visual distress signals accepted.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 2 2013-07-01 2013-07-01 false Visual distress signals accepted... (CONTINUED) BOATING SAFETY EQUIPMENT REQUIREMENTS Visual Distress Signals § 175.130 Visual distress signals accepted. (a) Any of the following signals, when carried in the number required, can be used to meet the...

33 CFR 175.130 - Visual distress signals accepted.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 2 2014-07-01 2014-07-01 false Visual distress signals accepted... (CONTINUED) BOATING SAFETY EQUIPMENT REQUIREMENTS Visual Distress Signals § 175.130 Visual distress signals accepted. (a) Any of the following signals, when carried in the number required, can be used to meet the...

46 CFR 108.659 - Lifesaving signal instructions.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 4 2010-10-01 2010-10-01 false Lifesaving signal instructions. 108.659 Section 108.659... AND EQUIPMENT Equipment Markings and Instructions § 108.659 Lifesaving signal instructions. On all... signals set forth in regulation 16, chapter V, of the International Convention for Safety of Life at Sea...

46 CFR 108.659 - Lifesaving signal instructions.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 4 2011-10-01 2011-10-01 false Lifesaving signal instructions. 108.659 Section 108.659... AND EQUIPMENT Equipment Markings and Instructions § 108.659 Lifesaving signal instructions. On all... signals set forth in regulation 16, chapter V, of the International Convention for Safety of Life at Sea...

30 CFR 57.12012 - Bare signal wires.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Bare signal wires. 57.12012 Section 57.12012 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE... and Underground § 57.12012 Bare signal wires. The potential on bare signal wires accessible to contact...

30 CFR 57.12012 - Bare signal wires.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Bare signal wires. 57.12012 Section 57.12012 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE... and Underground § 57.12012 Bare signal wires. The potential on bare signal wires accessible to contact...

30 CFR 57.12012 - Bare signal wires.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Bare signal wires. 57.12012 Section 57.12012 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE... and Underground § 57.12012 Bare signal wires. The potential on bare signal wires accessible to contact...

46 CFR 108.659 - Lifesaving signal instructions.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 46 Shipping 4 2012-10-01 2012-10-01 false Lifesaving signal instructions. 108.659 Section 108.659... AND EQUIPMENT Equipment Markings and Instructions § 108.659 Lifesaving signal instructions. On all... signals set forth in regulation 16, chapter V, of the International Convention for Safety of Life at Sea...

46 CFR 108.659 - Lifesaving signal instructions.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 46 Shipping 4 2013-10-01 2013-10-01 false Lifesaving signal instructions. 108.659 Section 108.659... AND EQUIPMENT Equipment Markings and Instructions § 108.659 Lifesaving signal instructions. On all... signals set forth in regulation 16, chapter V, of the International Convention for Safety of Life at Sea...

46 CFR 108.659 - Lifesaving signal instructions.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 46 Shipping 4 2014-10-01 2014-10-01 false Lifesaving signal instructions. 108.659 Section 108.659... AND EQUIPMENT Equipment Markings and Instructions § 108.659 Lifesaving signal instructions. On all... signals set forth in regulation 16, chapter V, of the International Convention for Safety of Life at Sea...

30 CFR 57.12012 - Bare signal wires.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Bare signal wires. 57.12012 Section 57.12012 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE... and Underground § 57.12012 Bare signal wires. The potential on bare signal wires accessible to contact...

Modeling pedestrian crossing speed profiles considering speed change behavior for the safety assessment of signalized intersections.

PubMed

Iryo-Asano, Miho; Alhajyaseen, Wael K M

2017-11-01

Pedestrian safety is one of the most challenging issues in road networks. Understanding how pedestrians maneuver across an intersection is the key to applying countermeasures against traffic crashes. It is known that the behaviors of pedestrians at signalized crosswalks are significantly different from those in ordinary walking spaces, and they are highly influenced by signal indication, potential conflicts with vehicles, and intersection geometries. One of the most important characteristics of pedestrian behavior at crosswalks is the possible sudden speed change while crossing. Such sudden behavioral change may not be expected by conflicting vehicles, which may lead to hazardous situations. This study aims to quantitatively model the sudden speed changes of pedestrians as they cross signalized crosswalks under uncongested conditions. Pedestrian speed profiles are collected from empirical data and speed change events are extracted assuming that the speed profiles are stepwise functions. The occurrence of speed change events is described by a discrete choice model as a function of the necessary walking speed to complete crossing before the red interval ends, current speed, and the presence of turning vehicles in the conflict area. The amount of speed change before and after the event is modeled using regression analysis. A Monte Carlo simulation is applied for the entire speed profile of the pedestrians. The results show that the model can represent the pedestrian travel time distribution more accurately than the constant speed model. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identification of particle-laden flow features from wavelet decomposition

NASA Astrophysics Data System (ADS)

Jackson, A.; Turnbull, B.

2017-12-01

A wavelet decomposition based technique is applied to air pressure data obtained from laboratory-scale powder snow avalanches. This technique is shown to be a powerful tool for identifying both repeatable and chaotic features at any frequency within the signal. Additionally, this technique is demonstrated to be a robust method for the removal of noise from the signal as well as being capable of removing other contaminants from the signal. Whilst powder snow avalanches are the focus of the experiments analysed here, the features identified can provide insight to other particle-laden gravity currents and the technique described is applicable to a wide variety of experimental signals.

33 CFR 175.110 - Visual distress signals required.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 2 2011-07-01 2011-07-01 false Visual distress signals required... (CONTINUED) BOATING SAFETY EQUIPMENT REQUIREMENTS Visual Distress Signals § 175.110 Visual distress signals... signals selected from the list in § 175.130 or the alternatives in § 175.135, in the number required, are...

33 CFR 175.110 - Visual distress signals required.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 2 2013-07-01 2013-07-01 false Visual distress signals required... (CONTINUED) BOATING SAFETY EQUIPMENT REQUIREMENTS Visual Distress Signals § 175.110 Visual distress signals... signals selected from the list in § 175.130 or the alternatives in § 175.135, in the number required, are...

33 CFR 175.110 - Visual distress signals required.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 2 2014-07-01 2014-07-01 false Visual distress signals required... (CONTINUED) BOATING SAFETY EQUIPMENT REQUIREMENTS Visual Distress Signals § 175.110 Visual distress signals... signals selected from the list in § 175.130 or the alternatives in § 175.135, in the number required, are...

33 CFR 175.110 - Visual distress signals required.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 2 2012-07-01 2012-07-01 false Visual distress signals required... (CONTINUED) BOATING SAFETY EQUIPMENT REQUIREMENTS Visual Distress Signals § 175.110 Visual distress signals... signals selected from the list in § 175.130 or the alternatives in § 175.135, in the number required, are...

Motor Vehicle Safety - Multiple Languages

MedlinePlus

... Are Here: Home → Multiple Languages → All Health Topics → Motor Vehicle Safety URL of this page: https://medlineplus.gov/languages/ ... V W XYZ List of All Topics All Motor Vehicle Safety - Multiple Languages To use the sharing features on ...

Systems, methods and apparatus for quiesence of autonomic safety devices with self action

NASA Technical Reports Server (NTRS)

Hinchey, Michael G. (Inventor); Sterritt, Roy (Inventor)

2011-01-01

Systems, methods and apparatus are provided through which in some embodiments an autonomic environmental safety device may be quiesced. In at least one embodiment, a method for managing an autonomic safety device, such as a smoke detector, based on functioning state and operating status of the autonomic safety device includes processing received signals from the autonomic safety device to obtain an analysis of the condition of the autonomic safety device, generating one or more stay-awake signals based on the functioning status and the operating state of the autonomic safety device, transmitting the stay-awake signal, transmitting self health/urgency data, and transmitting environment health/urgency data. A quiesce component of an autonomic safety device can render the autonomic safety device inactive for a specific amount of time or until a challenging situation has passed.

Multiscale Feature Analysis of Salivary Gland Branching Morphogenesis

PubMed Central

Baydil, Banu; Daley, William P.; Larsen, Melinda; Yener, Bülent

2012-01-01

Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We

Could the RTS,S/AS01 meningitis safety signal really be a protective effect of rabies vaccine?

PubMed

Gessner, Bradford D; Knobel, Darryn L; Conan, Anne; Finn, Adam

2017-02-01

The RTS,S/AS01 malaria vaccine has been associated with meningitis and cerebral malaria safety signals. Key characteristics of the meningitis signal include presence, in the 5-17month but not the 6-12week age group, of delayed and variable meningitis onset after vaccination, and multiple etiologies. For both meningitis and cerebral malaria, the 5-17month old age group control arm had abnormally low incidences while other arms in both age groups had meningitis and cerebral malaria incidences similar to background rates. No single hypothesis postulating an adverse effect from RTS,S/AS01 unites these observations. Unlike the 6-12week group, the control population in the 5-17month old age group received rabies vaccine. This raises the possibility that non-specific rabies vaccine effects had a protective effect against central nervous system infection, a hypothesis consistent with the epidemiologic data. The lack of a confirmed biologic mechanism for such an effect emphasizes the need for additional studies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Safety and Crashworthiness of Dual Mode Vehicles

DOT National Transportation Integrated Search

1974-01-01

Safety features and the degree of safety expected of dual-mode systems are reviewed. Some of the inherent advantages and disadvantages of dual-mode transportation are also outlined. Possible categories of vehicle safety are defined to aid in developi...

Critical features of an auditable management system for an ISO 9000-compatible occupational health and safety standard.

PubMed

Levine, S; Dyjack, D T

1997-04-01

An International Organization for Standardization (ISO) 9001: 1994-harmonized occupational health and safety (OHS) management system has been written at the University of Michigan, and reviewed, revised, and accepted under the direction of the American Industrial Hygiene Association (AIHA) Occupational Health and Safety Management Systems (OHSMS) Task Force and the Board of Directors. This system is easily adaptable to the ISO 14001 format and to both OHS and environmental management system applications. As was the case with ISO 9001: 1994, this system is expected to be compatible with current production quality and OHS quality systems and standards, have forward compatibility for new applications, and forward flexibility, with new features added as needed. Since ISO 9001: 1987 and 9001: 1994 have been applied worldwide, the incorporation of harmonized OHS and environmental management system components should be acceptable to business units already performing first-party (self-) auditing, and second-party (contract qualification) auditing. This article explains the basis of this OHS management system, its relationship to ISO 9001 and 14001 standards, the philosophy and methodology of an ISO-harmonized system audit, the relationship of these systems to traditional OHS audit systems, and the authors' vision of the future for application of such systems.

ECG feature extraction and disease diagnosis.

PubMed

Bhyri, Channappa; Hamde, S T; Waghmare, L M

2011-01-01

An important factor to consider when using findings on electrocardiograms for clinical decision making is that the waveforms are influenced by normal physiological and technical factors as well as by pathophysiological factors. In this paper, we propose a method for the feature extraction and heart disease diagnosis using wavelet transform (WT) technique and LabVIEW (Laboratory Virtual Instrument Engineering workbench). LabVIEW signal processing tools are used to denoise the signal before applying the developed algorithm for feature extraction. First, we have developed an algorithm for R-peak detection using Haar wavelet. After 4th level decomposition of the ECG signal, the detailed coefficient is squared and the standard deviation of the squared detailed coefficient is used as the threshold for detection of R-peaks. Second, we have used daubechies (db6) wavelet for the low resolution signals. After cross checking the R-peak location in 4th level, low resolution signal of daubechies wavelet P waves and T waves are detected. Other features of diagnostic importance, mainly heart rate, R-wave width, Q-wave width, T-wave amplitude and duration, ST segment and frontal plane axis are also extracted and scoring pattern is applied for the purpose of heart disease diagnosis. In this study, detection of tachycardia, bradycardia, left ventricular hypertrophy, right ventricular hypertrophy and myocardial infarction have been considered. In this work, CSE ECG data base which contains 5000 samples recorded at a sampling frequency of 500 Hz and the ECG data base created by the S.G.G.S. Institute of Engineering and Technology, Nanded (Maharashtra) have been used.

Subauditory Speech Recognition based on EMG/EPG Signals

NASA Technical Reports Server (NTRS)

Jorgensen, Charles; Lee, Diana Dee; Agabon, Shane; Lau, Sonie (Technical Monitor)

2003-01-01

Sub-vocal electromyogram/electro palatogram (EMG/EPG) signal classification is demonstrated as a method for silent speech recognition. Recorded electrode signals from the larynx and sublingual areas below the jaw are noise filtered and transformed into features using complex dual quad tree wavelet transforms. Feature sets for six sub-vocally pronounced words are trained using a trust region scaled conjugate gradient neural network. Real time signals for previously unseen patterns are classified into categories suitable for primitive control of graphic objects. Feature construction, recognition accuracy and an approach for extension of the technique to a variety of real world application areas are presented.

Massively Parallel Signal Processing using the Graphics Processing Unit for Real-Time Brain-Computer Interface Feature Extraction.

PubMed

Wilson, J Adam; Williams, Justin C

2009-01-01

The clock speeds of modern computer processors have nearly plateaued in the past 5 years. Consequently, neural prosthetic systems that rely on processing large quantities of data in a short period of time face a bottleneck, in that it may not be possible to process all of the data recorded from an electrode array with high channel counts and bandwidth, such as electrocorticographic grids or other implantable systems. Therefore, in this study a method of using the processing capabilities of a graphics card [graphics processing unit (GPU)] was developed for real-time neural signal processing of a brain-computer interface (BCI). The NVIDIA CUDA system was used to offload processing to the GPU, which is capable of running many operations in parallel, potentially greatly increasing the speed of existing algorithms. The BCI system records many channels of data, which are processed and translated into a control signal, such as the movement of a computer cursor. This signal processing chain involves computing a matrix-matrix multiplication (i.e., a spatial filter), followed by calculating the power spectral density on every channel using an auto-regressive method, and finally classifying appropriate features for control. In this study, the first two computationally intensive steps were implemented on the GPU, and the speed was compared to both the current implementation and a central processing unit-based implementation that uses multi-threading. Significant performance gains were obtained with GPU processing: the current implementation processed 1000 channels of 250 ms in 933 ms, while the new GPU method took only 27 ms, an improvement of nearly 35 times.

Research on oral test modeling based on multi-feature fusion

NASA Astrophysics Data System (ADS)

Shi, Yuliang; Tao, Yiyue; Lei, Jun

2018-04-01

In this paper, the spectrum of speech signal is taken as an input of feature extraction. The advantage of PCNN in image segmentation and other processing is used to process the speech spectrum and extract features. And a new method combining speech signal processing and image processing is explored. At the same time of using the features of the speech map, adding the MFCC to establish the spectral features and integrating them with the features of the spectrogram to further improve the accuracy of the spoken language recognition. Considering that the input features are more complicated and distinguishable, we use Support Vector Machine (SVM) to construct the classifier, and then compare the extracted test voice features with the standard voice features to achieve the spoken standard detection. Experiments show that the method of extracting features from spectrograms using PCNN is feasible, and the fusion of image features and spectral features can improve the detection accuracy.

Child safety seat and safety belt use among urban travelers.

DOT National Transportation Integrated Search

1984-01-01

During nine days in June 1977 and nine in June 1983, four major metropolitan areas of Virginia were surveyed to determine whether safety restraints were being used by urban travelers. Observers stationed at selected signalized intersections displayed...

PyEEG: an open source Python module for EEG/MEG feature extraction.

PubMed

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

PubMed Central

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction. PMID:21512582

Natural and Artificial Playing Fields: Characteristics and Safety Features.

ERIC Educational Resources Information Center

Schmidt, Roger C., Ed.; Hoerner, Earl F., Ed.; Milner, Edward M., Ed.; Morehouse, C. A., Ed.

These papers are on the subjects of playing field standards, surface traction, testing and correlation to actual field experience, and state-of-the-art natural and artificial surfaces. The papers, presented at the Symposium on the Characteristics and Safety of Playing Surfaces (Artificial and Natural) for Field Sports in 1998, cover the…

Biosensor method and system based on feature vector extraction

DOEpatents

Greenbaum, Elias [Knoxville, TN; Rodriguez, Jr., Miguel; Qi, Hairong [Knoxville, TN; Wang, Xiaoling [San Jose, CA

2012-04-17

A method of biosensor-based detection of toxins comprises the steps of providing at least one time-dependent control signal generated by a biosensor in a gas or liquid medium, and obtaining a time-dependent biosensor signal from the biosensor in the gas or liquid medium to be monitored or analyzed for the presence of one or more toxins selected from chemical, biological or radiological agents. The time-dependent biosensor signal is processed to obtain a plurality of feature vectors using at least one of amplitude statistics and a time-frequency analysis. At least one parameter relating to toxicity of the gas or liquid medium is then determined from the feature vectors based on reference to the control signal.

Biosensor method and system based on feature vector extraction

DOEpatents

Greenbaum, Elias; Rodriguez, Jr., Miguel; Qi, Hairong; Wang, Xiaoling

2013-07-02

A system for biosensor-based detection of toxins includes providing at least one time-dependent control signal generated by a biosensor in a gas or liquid medium, and obtaining a time-dependent biosensor signal from the biosensor in the gas or liquid medium to be monitored or analyzed for the presence of one or more toxins selected from chemical, biological or radiological agents. The time-dependent biosensor signal is processed to obtain a plurality of feature vectors using at least one of amplitude statistics and a time-frequency analysis. At least one parameter relating to toxicity of the gas or liquid medium is then determined from the feature vectors based on reference to the control signal.

Some dynamics of signaling games.

PubMed

Huttegger, Simon; Skyrms, Brian; Tarrès, Pierre; Wagner, Elliott

2014-07-22

Information transfer is a basic feature of life that includes signaling within and between organisms. Owing to its interactive nature, signaling can be investigated by using game theory. Game theoretic models of signaling have a long tradition in biology, economics, and philosophy. For a long time the analyses of these games has mostly relied on using static equilibrium concepts such as Pareto optimal Nash equilibria or evolutionarily stable strategies. More recently signaling games of various types have been investigated with the help of game dynamics, which includes dynamical models of evolution and individual learning. A dynamical analysis leads to more nuanced conclusions as to the outcomes of signaling interactions. Here we explore different kinds of signaling games that range from interactions without conflicts of interest between the players to interactions where their interests are seriously misaligned. We consider these games within the context of evolutionary dynamics (both infinite and finite population models) and learning dynamics (reinforcement learning). Some results are specific features of a particular dynamical model, whereas others turn out to be quite robust across different models. This suggests that there are certain qualitative aspects that are common to many real-world signaling interactions.

Some dynamics of signaling games

PubMed Central

Huttegger, Simon; Skyrms, Brian; Tarrès, Pierre; Wagner, Elliott

2014-01-01

Information transfer is a basic feature of life that includes signaling within and between organisms. Owing to its interactive nature, signaling can be investigated by using game theory. Game theoretic models of signaling have a long tradition in biology, economics, and philosophy. For a long time the analyses of these games has mostly relied on using static equilibrium concepts such as Pareto optimal Nash equilibria or evolutionarily stable strategies. More recently signaling games of various types have been investigated with the help of game dynamics, which includes dynamical models of evolution and individual learning. A dynamical analysis leads to more nuanced conclusions as to the outcomes of signaling interactions. Here we explore different kinds of signaling games that range from interactions without conflicts of interest between the players to interactions where their interests are seriously misaligned. We consider these games within the context of evolutionary dynamics (both infinite and finite population models) and learning dynamics (reinforcement learning). Some results are specific features of a particular dynamical model, whereas others turn out to be quite robust across different models. This suggests that there are certain qualitative aspects that are common to many real-world signaling interactions. PMID:25024209

Pilot study of a novel tool for input-free automated identification of transition zone prostate tumors using T2- and diffusion-weighted signal and textural features.

PubMed

Stember, Joseph N; Deng, Fang-Ming; Taneja, Samir S; Rosenkrantz, Andrew B

2014-08-01

To present results of a pilot study to develop software that identifies regions suspicious for prostate transition zone (TZ) tumor, free of user input. Eight patients with TZ tumors were used to develop the model by training a Naïve Bayes classifier to detect tumors based on selection of most accurate predictors among various signal and textural features on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps. Features tested as inputs were: average signal, signal standard deviation, energy, contrast, correlation, homogeneity and entropy (all defined on T2WI); and average ADC. A forward selection scheme was used on the remaining 20% of training set supervoxels to identify important inputs. The trained model was tested on a different set of ten patients, half with TZ tumors. In training cases, the software tiled the TZ with 4 × 4-voxel "supervoxels," 80% of which were used to train the classifier. Each of 100 iterations selected T2WI energy and average ADC, which therefore were deemed the optimal model input. The two-feature model was applied blindly to the separate set of test patients, again without operator input of suspicious foci. The software correctly predicted presence or absence of TZ tumor in all test patients. Furthermore, locations of predicted tumors corresponded spatially with locations of biopsies that had confirmed their presence. Preliminary findings suggest that this tool has potential to accurately predict TZ tumor presence and location, without operator input. © 2013 Wiley Periodicals, Inc.

Note on evaluating safety performance of road infrastructure to motivate safety competition.

PubMed

Han, Sangjin

2016-01-01

Road infrastructures are usually developed and maintained by governments or public sectors. There is no competitor in the market of their jurisdiction. This monopolic feature discourages road authorities from improving the level of safety with proactive motivation. This study suggests how to apply a principle of competition for roads, in particular by means of performance evaluation. It first discusses why road infrastructure has been slow in safety oriented development and management in respect of its business model. Then it suggests some practical ways of how to promote road safety between road authorities, particularly by evaluating safety performance of road infrastructure. These are summarized as decision of safety performance indicators, classification of spatial boundaries, data collection, evaluation, and reporting. Some consideration points are also discussed to make safety performance evaluation on road infrastructure lead to better road safety management.

An evaluation of safety culture initiatives at BNSF Railway

DOT National Transportation Integrated Search

2015-04-01

Major safety culture (SC) initiatives initiated in the FRA Office of Research, Technology and Development (RT&D), such as Clear Signal for Action (CSA), the Investigation of Safety Related Occurrences Protocol (ISROP), the Participative Safety Rules ...

Safety evaluation of driver cognitive failures and driving errors on right-turn filtering movement at signalized road intersections based on Fuzzy Cellular Automata (FCA) model.

PubMed

Chai, Chen; Wong, Yiik Diew; Wang, Xuesong

2017-07-01

This paper proposes a simulation-based approach to estimate safety impact of driver cognitive failures and driving errors. Fuzzy Logic, which involves linguistic terms and uncertainty, is incorporated with Cellular Automata model to simulate decision-making process of right-turn filtering movement at signalized intersections. Simulation experiments are conducted to estimate the relationships between cognitive failures and driving errors with safety performance. Simulation results show Different types of cognitive failures are found to have varied relationship with driving errors and safety performance. For right-turn filtering movement, cognitive failures are more likely to result in driving errors with denser conflicting traffic stream. Moreover, different driving errors are found to have different safety impacts. The study serves to provide a novel approach to linguistically assess cognitions and replicate decision-making procedures of the individual driver. Compare to crash analysis, the proposed FCA model allows quantitative estimation of particular cognitive failures, and the impact of cognitions on driving errors and safety performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Research notes : safety at high-speed intersections.

DOT National Transportation Integrated Search

2010-04-01

A 2010 study for ODOT by researchers at the Oregon State University School of Civil and Construction Engineering titled, Evaluating Safety and Operations of High-Speed Signalized Intersections, examined effective means for improving safety at isolate...

Steering Kids to Traffic Safety.

ERIC Educational Resources Information Center

PTA Today, 1991

1991-01-01

Guidelines to help parents explain traffic safety to children cover the following: school bus safety (e.g., remain seated, do not shout); walking (e.g., obey traffic signals, cross at crosswalks); driving (e.g., wear seatbelts, enter and exit from the curb side); and biking (e.g., wear helmets, do not ride at night). (SM)

Mitigating Motion Base Safety Issues: The NASA LaRC CMF Implementation

NASA Technical Reports Server (NTRS)

Bryant, Richard B., Jr.; Grupton, Lawrence E.; Martinez, Debbie; Carrelli, David J.

2005-01-01

The NASA Langley Research Center (LaRC), Cockpit Motion Facility (CMF) motion base design has taken advantage of inherent hydraulic characteristics to implement safety features using hardware solutions only. Motion system safety has always been a concern and its implementation is addressed differently by each organization. Some approaches rely heavily on software safety features. Software which performs safety functions is subject to more scrutiny making its approval, modification, and development time consuming and expensive. The NASA LaRC's CMF motion system is used for research and, as such, requires that the software be updated or modified frequently. The CMF's customers need the ability to update the simulation software frequently without the associated cost incurred with safety critical software. This paper describes the CMF engineering team's approach to achieving motion base safety by designing and implementing all safety features in hardware, resulting in applications software (including motion cueing and actuator dynamic control) being completely independent of the safety devices. This allows the CMF safety systems to remain intact and unaffected by frequent research system modifications.

Patient Safety: Ten Things You Can Do to Be a Safe Patient

MedlinePlus

... Emergency Preparedness & Response Environmental Health Healthy Living Injury, Violence & Safety Life Stages & Populations Travelers’ Health Workplace Safety & Health Features Media Sign up for Features Get Email Updates To ...

2011 Annual Meeting of the Safety Pharmacology Society: an overview.

PubMed

Cavero, Icilio

2012-03-01

The keynote address of 2011 Annual Meeting of the Safety Pharmacology Society examined the known and the still to be known on drug-induced nephrotoxicity. The nominee of the Distinguished Service Award Lecture gave an account of his career achievements particularly on the domain of chronically instrumented animals for assessing cardiovascular safety. The value of Safety Pharmacology resides in the benefits delivered to Pharma organizations, regulators, payers and patients. Meticulous due diligence concerning compliance of Safety Pharmacology studies to best practices is an effective means to ensure that equally stringent safety criteria are applied to both in-licensed and in-house compounds. Innovative technologies of great potential for Safety Pharmacology presented at the meeting are organs on chips (lung, heart, intestine) displaying mechanical and biochemical features of native organs, electrical field potential (MEA) or impedance (xCELLigence Cardio) measurements in human induced pluripotent stem cell-derived cardiomyocytes for unveiling cardiac electrophysiological and mechanical liabilities, functional human airway epithelium (MucilAir™) preparations with unique 1-year shelf-life for acute and chronic in vitro evaluation of drug efficacy and toxicity. Custom-designed in silico and in vitro assay platforms defining the receptorome space occupied by chemical entities facilitate, throughout the drug discovery phase, the selection of candidates with optimized safety profile on organ function. These approaches can now be complemented by advanced computational analysis allowing the identification of compounds with receptorome, or clinically adverse effect profiles, similar to those of the drug candidate under scrutiny for extending the safety assessment to potential liability targets not captured by classical approaches. Nonclinical data supporting safety can be quite reassuring for drugs with a discovered signal of risk. However, for marketing authorization

Signal Waveform Generator Performance Specifications and Certification Requirements

DOT National Transportation Integrated Search

1998-01-01

This report provides important information for users of the National Highway Traffic Safety Administration (NHTSA) signal waveform generator (SWG) and for those organizations that would perform testing to certify the accuracy of SWG signals. The perf...

The implementation of physical safety system in bunker of the electron beam accelerator

NASA Astrophysics Data System (ADS)

Ahmad, M. A.; Hashim, S. A.; Ahmad, A.; Leo, K. W.; Chulan, R. M.; Dalim, Y.; Baijan, A. H.; Zain, M. F.; Ros, R. C.

2017-01-01

This paper describes the implementation of physical safety system for the new low energy electron beam (EB) accelerator installed at Block 43T Nuclear Malaysia. The low energy EB is a locally designed and developed with a target energy of 300 keV. The issues on radiation protection have been addressed by the installation of radiation shielding in the form of a bunker and installation radiation monitors. Additional precaution is needed to ensure that personnel are not exposed to radiation and other physical hazards. Unintentional access to the radiation room can cause serious hazard and hence safety features must be installed to prevent such events. In this work we design and built a control and monitoring system for the shielding door. The system provides signals to the EB control panel to allow or prevent operation. The design includes limit switches, key-activated switches and emergency stop button and surveillance camera. Entry procedure is also developed as written record and for information purposes. As a result, through this safety implementation human error will be prevented, increase alertness during operation and minimizing unnecessary radiation exposure.

Feature Sampling in Detection: Implications for the Measurement of Perceptual Independence

ERIC Educational Resources Information Center

Macho, Siegfried

2007-01-01

The article presents the feature sampling signal detection (FS-SDT) model, an extension of the multivariate signal detection (SDT) model. The FS-SDT model assumes that, because of attentional shifts, different subsets of features are sampled for different presentations of the same multidimensional stimulus. Contrary to the SDT model, the FS-SDT…

The formation method of the feature space for the identification of fatigued bills

NASA Astrophysics Data System (ADS)

Kang, Dongshik; Oshiro, Ayumu; Ozawa, Kenji; Mitsui, Ikugo

2014-10-01

Fatigued bills make a trouble such as the paper jam in a bill handling machine. In the discrimination of fatigued bills using an acoustic signal, the variation of an observed bill sound is considered to be one of causes in misclassification. Therefore a technique has demanded in order to make the classification of fatigued bills more efficient. In this paper, we proposed the algorithm that extracted feature quantity of bill sound from acoustic signal using the frequency difference, and carried out discrimination experiment of fatigued bill money by Support Vector Machine(SVM). The feature quantity of frequency difference can represent the frequency components of an acoustic signal is varied by the fatigued degree of bill money. The generalization performance of SVM does not depend on the size of dimensions of the feature space, even in a high dimensional feature space such as bill-acoustic signals. Furthermore, SVM can induce an optimal classifier which considers the combination of features by the virtue of polynomial kernel functions.

Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine

NASA Astrophysics Data System (ADS)

Zhou, Peng; Peng, Zhike; Chen, Shiqian; Yang, Yang; Zhang, Wenming

2018-06-01

With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time-frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.

Active drug safety surveillance: a tool to improve public health.

PubMed

Platt, Richard; Madre, Leanne; Reynolds, Robert; Tilson, Hugh

2008-12-01

Ensuring that drugs have an acceptable safety profile and are used safely is a major public health priority. The Centers for Education and Research on Therapeutics (CERTs) convened experts from academia, government, and industry to assess strategies to increase the speed and predictive value of generating and evaluating safety signals, and to identify next steps to improve the US system for identifying and evaluating potential safety signals. The CERTs convened a think tank comprising representatives of the groups noted above to address these goals. Participants observed that, with the increasing availability of electronic health data, opportunities have emerged to more accurately characterize and confirm potential safety issues. The gain for public health from a highly coordinated network of population-based databases for active surveillance is great and within reach, although operational questions remain. A collaborative network must create a working definition of a safety signal, screening algorithms, and criteria and strategies to confirm or refute a signal once identified through screening. Guidelines are needed for when and how to communicate a signal exists and is being evaluated, as well as the outcome of that evaluation. A public-private partnership to create a network of government and private databases to routinely evaluate and prioritize safety questions is in the public interest. Better methods are needed, and a knowledgeable workforce is required to conduct the surveillance and understand how to interpret the results. The international community will benefit from the availability of better methods and more experts. Copyright (c) 2008 John Wiley & Sons, Ltd.

Diagnosis of multiple sclerosis from EEG signals using nonlinear methods.

PubMed

Torabi, Ali; Daliri, Mohammad Reza; Sabzposhan, Seyyed Hojjat

2017-12-01

EEG signals have essential and important information about the brain and neural diseases. The main purpose of this study is classifying two groups of healthy volunteers and Multiple Sclerosis (MS) patients using nonlinear features of EEG signals while performing cognitive tasks. EEG signals were recorded when users were doing two different attentional tasks. One of the tasks was based on detecting a desired change in color luminance and the other task was based on detecting a desired change in direction of motion. EEG signals were analyzed in two ways: EEG signals analysis without rhythms decomposition and EEG sub-bands analysis. After recording and preprocessing, time delay embedding method was used for state space reconstruction; embedding parameters were determined for original signals and their sub-bands. Afterwards nonlinear methods were used in feature extraction phase. To reduce the feature dimension, scalar feature selections were done by using T-test and Bhattacharyya criteria. Then, the data were classified using linear support vector machines (SVM) and k-nearest neighbor (KNN) method. The best combination of the criteria and classifiers was determined for each task by comparing performances. For both tasks, the best results were achieved by using T-test criterion and SVM classifier. For the direction-based and the color-luminance-based tasks, maximum classification performances were 93.08 and 79.79% respectively which were reached by using optimal set of features. Our results show that the nonlinear dynamic features of EEG signals seem to be useful and effective in MS diseases diagnosis.

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals.

PubMed

Batres-Mendoza, Patricia; Montoro-Sanjose, Carlos R; Guerra-Hernandez, Erick I; Almanza-Ojeda, Dora L; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene J; Ibarra-Manzano, Mario A

2016-03-05

Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states.

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals

PubMed Central

Batres-Mendoza, Patricia; Montoro-Sanjose, Carlos R.; Guerra-Hernandez, Erick I.; Almanza-Ojeda, Dora L.; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene J.; Ibarra-Manzano, Mario A.

2016-01-01

Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states. PMID:26959029

Are signalized intersections with cycle tracks safer? A case-control study based on automated surrogate safety analysis using video data.

PubMed

Zangenehpour, Sohail; Strauss, Jillian; Miranda-Moreno, Luis F; Saunier, Nicolas

2016-01-01

Cities in North America have been building bicycle infrastructure, in particular cycle tracks, with the intention of promoting urban cycling and improving cyclist safety. These facilities have been built and expanded but very little research has been done to investigate the safety impacts of cycle tracks, in particular at intersections, where cyclists interact with turning motor-vehicles. Some safety research has looked at injury data and most have reached the conclusion that cycle tracks have positive effects of cyclist safety. The objective of this work is to investigate the safety effects of cycle tracks at signalized intersections using a case-control study. For this purpose, a video-based method is proposed for analyzing the post-encroachment time as a surrogate measure of the severity of the interactions between cyclists and turning vehicles travelling in the same direction. Using the city of Montreal as the case study, a sample of intersections with and without cycle tracks on the right and left sides of the road were carefully selected accounting for intersection geometry and traffic volumes. More than 90h of video were collected from 23 intersections and processed to obtain cyclist and motor-vehicle trajectories and interactions. After cyclist and motor-vehicle interactions were defined, ordered logit models with random effects were developed to evaluate the safety effects of cycle tracks at intersections. Based on the extracted data from the recorded videos, it was found that intersection approaches with cycle tracks on the right are safer than intersection approaches with no cycle track. However, intersections with cycle tracks on the left compared to no cycle tracks seem to be significantly safer. Results also identify that the likelihood of a cyclist being involved in a dangerous interaction increases with increasing turning vehicle flow and decreases as the size of the cyclist group arriving at the intersection increases. The results highlight the

Speech Music Discrimination Using Class-Specific Features

DTIC Science & Technology

2004-08-01

Speech Music Discrimination Using Class-Specific Features Thomas Beierholm...between speech and music . Feature extraction is class-specific and can therefore be tailored to each class meaning that segment size, model orders...interest. Some of the applications of audio signal classification are speech/ music classification [1], acoustical environmental classification [2][3

What vehicle features are considered important when buying an automobile? An examination of driver preferences by age and gender.

PubMed

Vrkljan, Brenda H; Anaby, Dana

2011-02-01

Certain vehicle features can help drivers avoid collisions and/or protect occupants in the event of a crash, and therefore, might play an important role when deciding which vehicle to purchase. The objective of this study was to examine the importance attributed to key vehicle features (including safety) that drivers consider when buying a car and its association with age and gender. A sample of 2,002 Canadian drivers aged 18 years and older completed a survey that asked them to rank the importance of eight vehicle features if they were to purchase a vehicle (storage, mileage, safety, price, comfort, performance, design, and reliability). ANOVA tests were performed to: (a) determine if there were differences in the level of importance between features and; (b) examine the effect of age and gender on the importance attributed to these features. Of the features examined, safety and reliability were the most highly rated in terms of importance, whereas design and performance had the lowest rating. Differences in safety and performance across age groups were dependent on gender. This effect was most evident in the youngest and oldest age groups. Safety and reliability were considered the most important features. Age and gender play a significant role in explaining the importance of certain features. Targeted efforts for translating safety-related information to the youngest and oldest consumers should be emphasized due to their high collision, injury, and fatality rates. Copyright © 2011 National Safety Council and Elsevier Ltd. All rights reserved.

Orthogonal system of fractural and integrated diagnostic features in vibration analysis

NASA Astrophysics Data System (ADS)

Kostyukov, V. N.; Boychenko, S. N.

2017-08-01

The paper presents the results obtained in the studies of the orthogonality of the vibration diagnostic features system comprising the integrated features, particularly - root mean square values of vibration acceleration, vibration velocity, vibration displacement and fractal feature (Hurst exponent). To diagnose the condition of the equipment by the vibration signal, the orthogonality of the vibration diagnostic features is important. The fact of orthogonality shows that the system of features is not superfluous and allows the maximum coverage of the state space of the object being diagnosed. This, in turn, increases reliability of the machinery condition monitoring results. The studies were carried out on the models of vibration signals using the programming language R.

Timing issues for traffic signals interconnected with highway-railroad grade crossings.

DOT National Transportation Integrated Search

2013-02-01

The coordination of highway-railroad grade crossing warning signals with nearby traffic signals is of vital : importance due to potential safety consequences. Interconnections between traffic signals in close : proximity to railroad crossings provide...

Zero-inflated Poisson model based likelihood ratio test for drug safety signal detection.

PubMed

Huang, Lan; Zheng, Dan; Zalkikar, Jyoti; Tiwari, Ram

2017-02-01

In recent decades, numerous methods have been developed for data mining of large drug safety databases, such as Food and Drug Administration's (FDA's) Adverse Event Reporting System, where data matrices are formed by drugs such as columns and adverse events as rows. Often, a large number of cells in these data matrices have zero cell counts and some of them are "true zeros" indicating that the drug-adverse event pairs cannot occur, and these zero counts are distinguished from the other zero counts that are modeled zero counts and simply indicate that the drug-adverse event pairs have not occurred yet or have not been reported yet. In this paper, a zero-inflated Poisson model based likelihood ratio test method is proposed to identify drug-adverse event pairs that have disproportionately high reporting rates, which are also called signals. The maximum likelihood estimates of the model parameters of zero-inflated Poisson model based likelihood ratio test are obtained using the expectation and maximization algorithm. The zero-inflated Poisson model based likelihood ratio test is also modified to handle the stratified analyses for binary and categorical covariates (e.g. gender and age) in the data. The proposed zero-inflated Poisson model based likelihood ratio test method is shown to asymptotically control the type I error and false discovery rate, and its finite sample performance for signal detection is evaluated through a simulation study. The simulation results show that the zero-inflated Poisson model based likelihood ratio test method performs similar to Poisson model based likelihood ratio test method when the estimated percentage of true zeros in the database is small. Both the zero-inflated Poisson model based likelihood ratio test and likelihood ratio test methods are applied to six selected drugs, from the 2006 to 2011 Adverse Event Reporting System database, with varying percentages of observed zero-count cells.

Extraction of Rice Heavy Metal Stress Signal Features Based on Long Time Series Leaf Area Index Data Using Ensemble Empirical Mode Decomposition

PubMed Central

Liu, Xiangnan; Zhang, Biyao; Liu, Ming; Wu, Ling

2017-01-01

The use of remote sensing technology to diagnose heavy metal stress in crops is of great significance for environmental protection and food security. However, in the natural farmland ecosystem, various stressors could have a similar influence on crop growth, therefore making heavy metal stress difficult to identify accurately, so this is still not a well resolved scientific problem and a hot topic in the field of agricultural remote sensing. This study proposes a method that uses Ensemble Empirical Mode Decomposition (EEMD) to obtain the heavy metal stress signal features on a long time scale. The method operates based on the Leaf Area Index (LAI) simulated by the Enhanced World Food Studies (WOFOST) model, assimilated with remotely sensed data. The following results were obtained: (i) the use of EEMD was effective in the extraction of heavy metal stress signals by eliminating the intra-annual and annual components; (ii) LAIdf (The first derivative of the sum of the interannual component and residual) can preferably reflect the stable feature responses to rice heavy metal stress. LAIdf showed stability with an R2 of greater than 0.9 in three growing stages, and the stability is optimal in June. This study combines the spectral characteristics of the stress effect with the time characteristics, and confirms the potential of long-term remotely sensed data for improving the accuracy of crop heavy metal stress identification. PMID:28878147

Safety climate and mindful safety practices in the oil and gas industry.

PubMed

Dahl, Øyvind; Kongsvik, Trond

2018-02-01

The existence of a positive association between safety climate and the safety behavior of sharp-end workers in high-risk organizations is supported by a considerable body of research. Previous research has primarily analyzed two components of safety behavior, namely safety compliance and safety participation. The present study extends previous research by looking into the relationship between safety climate and another component of safety behavior, namely mindful safety practices. Mindful safety practices are defined as the ability to be aware of critical factors in the environment and to act appropriately when dangers arise. Regression analysis was used to examine whether mindful safety practices are, like compliance and participation, promoted by a positive safety climate, in a questionnaire-based study of 5712 sharp-end workers in the oil and gas industry. The analysis revealed that a positive safety climate promotes mindful safety practices. The regression model accounted for roughly 31% of the variance in mindful safety practices. The most important safety climate factor was safety leadership. The findings clearly demonstrate that mindful safety practices are highly context-dependent, hence, manageable and susceptible to change. In order to improve safety climate in a direction which is favorable for mindful safety practices, the results demonstrate that it is important to give the fundamental features of safety climate high priority and in particular that of safety leadership. Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.

Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

PubMed Central

Mala, S.; Latha, K.

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. PMID:25574185

Feature selection in classification of eye movements using electrooculography for activity recognition.

PubMed

Mala, S; Latha, K

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

A novel automated spike sorting algorithm with adaptable feature extraction.

PubMed

Bestel, Robert; Daus, Andreas W; Thielemann, Christiane

2012-10-15

To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach. Copyright © 2012 Elsevier B.V. All rights reserved.

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning

PubMed Central

Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

2016-01-01

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches. PMID:26927111

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning.

PubMed

Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

2016-02-25

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches.

Modeling of the ground-to-SSFMB link networking features using SPW

NASA Technical Reports Server (NTRS)

Watson, John C.

1993-01-01

This report describes the modeling and simulation of the networking features of the ground-to-Space Station Freedom manned base (SSFMB) link using COMDISCO signal processing work-system (SPW). The networking features modeled include the implementation of Consultative Committee for Space Data Systems (CCSDS) protocols in the multiplexing of digitized audio and core data into virtual channel data units (VCDU's) in the control center complex and the demultiplexing of VCDU's in the onboard baseband signal processor. The emphasis of this work has been placed on techniques for modeling the CCSDS networking features using SPW. The objectives for developing the SPW models are to test the suitability of SPW for modeling networking features and to develop SPW simulation models of the control center complex and space station baseband signal processor for use in end-to-end testing of the ground-to-SSFMB S-band single access forward (SSAF) link.

Terrain-Moisture Classification Using GPS Surface-Reflected Signals

NASA Technical Reports Server (NTRS)

Grant, Michael S.; Acton, Scott T.; Katzberg, Stephen J.

2006-01-01

In this study we present a novel method of land surface classification using surface-reflected GPS signals in combination with digital imagery. Two GPS-derived classification features are merged with visible image data to create terrain-moisture (TM) classes, defined here as visibly identifiable terrain or landcover classes containing a surface/soil moisture component. As compared to using surface imagery alone, classification accuracy is significantly improved for a number of visible classes when adding the GPS-based signal features. Since the strength of the reflected GPS signal is proportional to the amount of moisture in the surface, use of these GPS features provides information about the surface that is not obtainable using visible wavelengths alone. Application areas include hydrology, precision agriculture, and wetlands mapping.

29 CFR 1926.1419 - Signals-general requirements.

Code of Federal Regulations, 2011 CFR

2011-07-01

..., DEPARTMENT OF LABOR (CONTINUED) SAFETY AND HEALTH REGULATIONS FOR CONSTRUCTION Cranes and Derricks in... person may give signals to a crane/derrick at a time, except in circumstances covered by paragraph (j) of... multiple cranes/derricks. Where a signal person(s) is in communication with more than one crane/derrick, a...

Plume interference with space shuttle range safety signals

NASA Technical Reports Server (NTRS)

Boynton, F. P.; Rajaseknar, P. S.

1979-01-01

The computational procedure for signal propagation in the presence of an exhaust plume is presented. Comparisons with well-known analytic diffraction solutions indicate that accuracy suffers when mesh spacing is inadequate to resolve the first unobstructed Fresnel zone at the plume edge. Revisions to the procedure to improve its accuracy without requiring very large arrays are discussed. Comparisons to field measurements during a shuttle solid rocket motor (SRM) test firing suggest that the plume is sharper edged than one would expect on the basis of time averaged electron density calculations. The effects, both of revisions to the computational procedure and of allowing for a sharper plume edge, are to raise the signal level near tail aspect. The attenuation levels then predicted are still high enough to be of concern near SRM burnout for northerly launches of the space shuttle.

Toward a model for lexical access based on acoustic landmarks and distinctive features

NASA Astrophysics Data System (ADS)

Stevens, Kenneth N.

2002-04-01

This article describes a model in which the acoustic speech signal is processed to yield a discrete representation of the speech stream in terms of a sequence of segments, each of which is described by a set (or bundle) of binary distinctive features. These distinctive features specify the phonemic contrasts that are used in the language, such that a change in the value of a feature can potentially generate a new word. This model is a part of a more general model that derives a word sequence from this feature representation, the words being represented in a lexicon by sequences of feature bundles. The processing of the signal proceeds in three steps: (1) Detection of peaks, valleys, and discontinuities in particular frequency ranges of the signal leads to identification of acoustic landmarks. The type of landmark provides evidence for a subset of distinctive features called articulator-free features (e.g., [vowel], [consonant], [continuant]). (2) Acoustic parameters are derived from the signal near the landmarks to provide evidence for the actions of particular articulators, and acoustic cues are extracted by sampling selected attributes of these parameters in these regions. The selection of cues that are extracted depends on the type of landmark and on the environment in which it occurs. (3) The cues obtained in step (2) are combined, taking context into account, to provide estimates of ``articulator-bound'' features associated with each landmark (e.g., [lips], [high], [nasal]). These articulator-bound features, combined with the articulator-free features in (1), constitute the sequence of feature bundles that forms the output of the model. Examples of cues that are used, and justification for this selection, are given, as well as examples of the process of inferring the underlying features for a segment when there is variability in the signal due to enhancement gestures (recruited by a speaker to make a contrast more salient) or due to overlap of gestures from

How important is vehicle safety for older consumers in the vehicle purchase process?

PubMed

Koppel, Sjaan; Clark, Belinda; Hoareau, Effie; Charlton, Judith L; Newstead, Stuart V

2013-01-01

This study aimed to investigate the importance of vehicle safety to older consumers in the vehicle purchase process. Older (n = 102), middle-aged (n = 791), and younger (n = 109) participants throughout the eastern Australian states of Victoria, New South Wales, and Queensland who had recently purchased a new or used vehicle completed an online questionnaire about their vehicle purchase process. When asked to list the 3 most important considerations in the vehicle purchase process (in an open-ended format), older consumers were mostly likely to list price as their most important consideration (43%). Similarly, when presented with a list of vehicle factors (such as price, design, Australasian New Car Assessment Program [ANCAP] rating), older consumers were most likely to identify price as the most important vehicle factor (36%). When presented with a list of vehicle features (such as automatic transmission, braking, air bags), older consumers in the current study were most likely to identify an antilock braking system (41%) as the most important vehicle feature, and 50 percent of older consumers identified a safety-related vehicle feature as the highest priority vehicle feature (50%). When asked to list up to 3 factors that make a vehicle safe, older consumers in the current study were most likely to list braking systems (35%), air bags (22%), and the driver's behavior or skill (11%). When asked about the influence of safety in the new vehicle purchase process, one third of older consumers reported that all new vehicles are safe (33%) and almost half of the older consumers rated their vehicle as safer than average (49%). A logistic regression model was developed to predict the profile of older consumers more likely to assign a higher priority to safety features in the vehicle purchasing process. The model predicted that the importance of safety-related features was influenced by several variables, including older consumers' beliefs that they could protect themselves

Taking ownership of safety. What are the active ingredients of safety coaching and how do they impact safety outcomes in critical offshore working environments?

PubMed

Krauesslar, Victoria; Avery, Rachel E; Passmore, Jonathan

2015-01-01

Safety coaching interventions have become a common feature in the safety critical offshore working environments of the North Sea. Whilst the beneficial impact of coaching as an organizational tool has been evidenced, there remains a question specifically over the use of safety coaching and its impact on behavioural change and producing safe working practices. A series of 24 semi-structured interviews were conducted with three groups of experts in the offshore industry: safety coaches, offshore managers and HSE directors. Using a thematic analysis approach, several significant themes were identified across the three expert groups including connecting with and creating safety ownership in the individual, personal significance and humanisation, ingraining safety and assessing and measuring a safety coach's competence. Results suggest clear utility of safety coaching when applied by safety coaches with appropriate coach training and understanding of safety issues in an offshore environment. The current work has found that the use of safety coaching in the safety critical offshore oil and gas industry is a powerful tool in managing and promoting a culture of safety and care.

A Biological Signal-Based Stress Monitoring Framework for Children Using Wearable Devices.

PubMed

Choi, Yerim; Jeon, Yu-Mi; Wang, Lin; Kim, Kwanho

2017-08-23

The safety of children has always been an important issue, and several studies have been conducted to determine the stress state of a child to ensure the safety. Audio signals and biological signals including heart rate are known to be effective for stress state detection. However, collecting those data requires specialized equipment, which is not appropriate for the constant monitoring of children, and advanced data analysis is required for accurate detection. In this regard, we propose a stress state detection framework which utilizes both audio signal and heart rate collected from wearable devices, and adopted machine learning methods for the detection. Experiments using real-world data were conducted to compare detection performances across various machine learning methods and noise levels of audio signal. Adopting the proposed framework in the real-world will contribute to the enhancement of child safety.

Maintenance of safety behaviors via response-produced stimuli.

PubMed

Angelakis, Ioannis; Austin, Jennifer L

2015-11-01

Animal studies suggest that safety behaviors may be maintained by internally or externally produced safety signals, which function as positive reinforcers. We designed two experiments to test this phenomenon with humans. Participants played a computerized game in which they could earn or lose treasures by clicking on a map. In baseline, losses could be postponed by pressing a pedal that also produced a blue bar at the bottom of the screen. During test conditions, no losses were programmed, and pedal presses turned the bar from yellow to blue (Test 1) or blue to yellow (Test 2). In Experiment 2, new participants were exposed to the same conditions but were given information about the safety of the test environment. In both experiments, participants engaged in high rates of pedal pressing when presses were followed by blue bars, suggesting the bar functioned as a safety signal. We discuss how these findings may relate to safety behaviors commonly observed in certain mental health disorders. © The Author(s) 2015.

Sources of Safety Data and Statistical Strategies for Design and Analysis: Postmarket Surveillance.

PubMed

Izem, Rima; Sanchez-Kam, Matilde; Ma, Haijun; Zink, Richard; Zhao, Yueqin

2018-03-01

Safety data are continuously evaluated throughout the life cycle of a medical product to accurately assess and characterize the risks associated with the product. The knowledge about a medical product's safety profile continually evolves as safety data accumulate. This paper discusses data sources and analysis considerations for safety signal detection after a medical product is approved for marketing. This manuscript is the second in a series of papers from the American Statistical Association Biopharmaceutical Section Safety Working Group. We share our recommendations for the statistical and graphical methodologies necessary to appropriately analyze, report, and interpret safety outcomes, and we discuss the advantages and disadvantages of safety data obtained from passive postmarketing surveillance systems compared to other sources. Signal detection has traditionally relied on spontaneous reporting databases that have been available worldwide for decades. However, current regulatory guidelines and ease of reporting have increased the size of these databases exponentially over the last few years. With such large databases, data-mining tools using disproportionality analysis and helpful graphics are often used to detect potential signals. Although the data sources have many limitations, analyses of these data have been successful at identifying safety signals postmarketing. Experience analyzing these dynamic data is useful in understanding the potential and limitations of analyses with new data sources such as social media, claims, or electronic medical records data.

Lighting: Traffic Safety Tips

DOT National Transportation Integrated Search

1996-01-01

This fact sheet, NHTSA Facts: Summer 1996, discusses traffic safety tips on motor vehicle lighting, including headlights, tail lights, and signal lights. It details the cleaning of headlamps, proper aiming of headlamps, and replacement of burned out ...

Automatic measurement and representation of prosodic features

NASA Astrophysics Data System (ADS)

Ying, Goangshiuan Shawn

Effective measurement and representation of prosodic features of the acoustic signal for use in automatic speech recognition and understanding systems is the goal of this work. Prosodic features-stress, duration, and intonation-are variations of the acoustic signal whose domains are beyond the boundaries of each individual phonetic segment. Listeners perceive prosodic features through a complex combination of acoustic correlates such as intensity, duration, and fundamental frequency (F0). We have developed new tools to measure F0 and intensity features. We apply a probabilistic global error correction routine to an Average Magnitude Difference Function (AMDF) pitch detector. A new short-term frequency-domain Teager energy algorithm is used to measure the energy of a speech signal. We have conducted a series of experiments performing lexical stress detection on words in continuous English speech from two speech corpora. We have experimented with two different approaches, a segment-based approach and a rhythm unit-based approach, in lexical stress detection. The first approach uses pattern recognition with energy- and duration-based measurements as features to build Bayesian classifiers to detect the stress level of a vowel segment. In the second approach we define rhythm unit and use only the F0-based measurement and a scoring system to determine the stressed segment in the rhythm unit. A duration-based segmentation routine was developed to break polysyllabic words into rhythm units. The long-term goal of this work is to develop a system that can effectively detect the stress pattern for each word in continuous speech utterances. Stress information will be integrated as a constraint for pruning the word hypotheses in a word recognition system based on hidden Markov models.

Curcumin effectively inhibits oncogenic NF-kB signaling and restrains stemness features in liver cancer

PubMed Central

Marquardt, Jens U.; Gomez-Quiroz, Luis; Camacho, Lucrecia O. Arreguin; Pinna, Federico; Lee, Yun-Han; Kitade, Mitsuteru; Domínguez, Mayrel Palestino; Castven, Darko; Breuhahn, Kai; Conner, Elizabeth A.; Galle, Peter R.; Andersen, Jesper B.; Factor, Valentina M.; Thorgeirsson, Snorri S.

2015-01-01

Background & Aims The cancer stem cells (CSCs) have important therapeutic implications for multi-resistant cancers including hepatocellular carcinoma (HCC). Among the key pathways frequently activated in liver CSCs is NF-kB signaling. Methods We evaluated the CSCs-depleting potential of NF-kB inhibition in liver cancer achieved by the IKK inhibitor curcumin, RNAi and specific peptide SN50. The effects on CSCs were assessed by analysis of Side Population (SP), sphere formation and tumorigenicity. Molecular changes were determined by RT-qPCR, global gene expression microarray, EMSA, and Western blotting. Results HCC cell lines exposed to curcumin exhibited differential responses to curcumin and were classified as sensitive and resistant. In sensitive lines, curcumin-mediated induction of cell death was directly related to the extent of NF-kB inhibition. The treatment also led to a selective CSC-depletion as evidenced by a reduced SP size, decreased sphere formation, down-regulation of CSC markers and suppressed tumorigenicity. Similarly, NF-kB inhibition by SN50 and siRNA against p65 suppressed tumor cell growth. In contrast, curcumin-resistant cells displayed a paradoxical increase in proliferation and expression of CSC markers. Mechanistically, an important component of the CSC-depleting activity of curcumin could be attributed to a NF-kB-mediated HDAC inhibition. Co-administration of the class I/II HDAC inhibitor trichostatine sensitized resistant cells to curcumin. Further, integration of a predictive signature of curcumin sensitivity with human HCC database indicated that HCCs with poor prognosis and progenitor features are most likely to benefit from NF-kB inhibition. Conclusions These results demonstrate that blocking NF-kB can specifically target CSC populations and suggest a potential for combined inhibition of NF-kB and HDAC signaling for treatment of liver cancer patients with poor prognosis. PMID:25937435

Curcumin effectively inhibits oncogenic NF-κB signaling and restrains stemness features in liver cancer.

PubMed

Marquardt, Jens U; Gomez-Quiroz, Luis; Arreguin Camacho, Lucrecia O; Pinna, Federico; Lee, Yun-Han; Kitade, Mitsuteru; Domínguez, Mayrel Palestino; Castven, Darko; Breuhahn, Kai; Conner, Elizabeth A; Galle, Peter R; Andersen, Jesper B; Factor, Valentina M; Thorgeirsson, Snorri S

2015-09-01

The cancer stem cells (CSCs) have important therapeutic implications for multi-resistant cancers including hepatocellular carcinoma (HCC). Among the key pathways frequently activated in liver CSCs is NF-κB signaling. We evaluated the CSCs-depleting potential of NF-κB inhibition in liver cancer achieved by the IKK inhibitor curcumin, RNAi and specific peptide SN50. The effects on CSCs were assessed by analysis of side population (SP), sphere formation and tumorigenicity. Molecular changes were determined by RT-qPCR, global gene expression microarray, EMSA, and Western blotting. HCC cell lines exposed to curcumin exhibited differential responses to curcumin and were classified as sensitive and resistant. In sensitive lines, curcumin-mediated induction of cell death was directly related to the extent of NF-κB inhibition. The treatment also led to a selective CSC-depletion as evidenced by a reduced SP size, decreased sphere formation, down-regulation of CSC markers and suppressed tumorigenicity. Similarly, NF-κB inhibition by SN50 and siRNA against p65 suppressed tumor cell growth. In contrast, curcumin-resistant cells displayed a paradoxical increase in proliferation and expression of CSC markers. Mechanistically, an important component of the CSC-depleting activity of curcumin could be attributed to a NF-κB-mediated HDAC inhibition. Co-administration of the class I/II HDAC inhibitor trichostatine sensitized resistant cells to curcumin. Further, integration of a predictive signature of curcumin sensitivity with human HCC database indicated that HCCs with poor prognosis and progenitor features are most likely to benefit from NF-κB inhibition. These results demonstrate that blocking NF-κB can specifically target CSC populations and suggest a potential for combined inhibition of NF-κB and HDAC signaling for treatment of liver cancer patients with poor prognosis. Copyright © 2015 European Association for the Study of the Liver. All rights reserved.

Smart Roadside System for Driver Assistance and Safety Warnings: Framework and Applications

PubMed Central

Jang, Jeong Ah; Kim, Hyun Suk; Cho, Han Byeog

2011-01-01

The use of newly emerging sensor technologies in traditional roadway systems can provide real-time traffic services to drivers through Telematics and Intelligent Transport Systems (ITSs). This paper introduces a smart roadside system that utilizes various sensors for driver assistance and traffic safety warnings. This paper shows two road application models for a smart roadside system and sensors: a red-light violation warning system for signalized intersections, and a speed advisory system for highways. Evaluation results for the two services are then shown using a micro-simulation method. In the given real-time applications for drivers, the framework and certain algorithms produce a very efficient solution with respect to the roadway type features and sensor type use. PMID:22164025

Next generation safety performance monitoring at signalized intersections using connected vehicle technology.

DOT National Transportation Integrated Search

2014-08-01

Crash-based safety evaluation is often hampered by randomness, lack of timeliness, and rarity of crash : occurrences. This is particularly the case for technology-driven safety improvement projects that are : frequently updated or replaced by newer o...

Permanency analysis on human electroencephalogram signals for pervasive Brain-Computer Interface systems.

PubMed

Sadeghi, Koosha; Junghyo Lee; Banerjee, Ayan; Sohankar, Javad; Gupta, Sandeep K S

2017-07-01

Brain-Computer Interface (BCI) systems use some permanent features of brain signals to recognize their corresponding cognitive states with high accuracy. However, these features are not perfectly permanent, and BCI system should be continuously trained over time, which is tedious and time consuming. Thus, analyzing the permanency of signal features is essential in determining how often to repeat training. In this paper, we monitor electroencephalogram (EEG) signals, and analyze their behavior through continuous and relatively long period of time. In our experiment, we record EEG signals corresponding to rest state (eyes open and closed) from one subject everyday, for three and a half months. The results show that signal features such as auto-regression coefficients remain permanent through time, while others such as power spectral density specifically in 5-7 Hz frequency band are not permanent. In addition, eyes open EEG data shows more permanency than eyes closed data.

The Vocal Repertoire of the Domesticated Zebra Finch: a Data Driven Approach to Decipher the Information-bearing Acoustic Features of Communication Signals

PubMed Central

Elie, Julie E.; Theunissen, Frédéric E.

2018-01-01

Although a universal code for the acoustic features of animal vocal communication calls may not exist, the thorough analysis of the distinctive acoustical features of vocalization categories is important not only to decipher the acoustical code for a specific species but also to understand the evolution of communication signals and the mechanisms used to produce and understand them. Here, we recorded more than 8,000 examples of almost all the vocalizations of the domesticated zebra finch, Taeniopygia guttata: vocalizations produced to establish contact, to form and maintain pair bonds, to sound an alarm, to communicate distress or to advertise hunger or aggressive intents. We characterized each vocalization type using complete representations that avoided any a priori assumptions on the acoustic code, as well as classical bioacoustics measures that could provide more intuitive interpretations. We then used these acoustical features to rigorously determine the potential information-bearing acoustical features for each vocalization type using both a novel regularized classifier and an unsupervised clustering algorithm. Vocalization categories are discriminated by the shape of their frequency spectrum and by their pitch saliency (noisy to tonal vocalizations) but not particularly by their fundamental frequency. Notably, the spectral shape of zebra finch vocalizations contains peaks or formants that vary systematically across categories and that would be generated by active control of both the vocal organ (source) and the upper vocal tract (filter). PMID:26581377

Crutches and children - proper fit and safety tips

MedlinePlus

... 000640.htm Crutches and children - proper fit and safety tips To use the sharing features on this ... the crutch, then extended when taking a step. Safety Tips Teach your child to: Always keep crutches ...

Common Day Care Safety Renovations: Descriptions, Explanations and Cost Estimates.

ERIC Educational Resources Information Center

Spack, Stan

This booklet explains some of the day care safety features specified by the new Massachusetts State Building Code (January 1, 1975) which must be met before a new day care center can be licensed. The safety features described are those which most often require renovation to meet the building code standards. Best estimates of the costs involved in…

Treatment recommendations for DSM-5-defined mixed features.

PubMed

Rosenblat, Joshua D; McIntyre, Roger S

2017-04-01

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) mixed features specifier provides a less restrictive definition of mixed mood states, compared to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR), including mood episodes that manifest with subthreshold symptoms of the opposite mood state. A limited number of studies have assessed the efficacy of treatments specifically for DSM-5-defined mixed features in mood disorders. As such, there is currently an inadequate amount of data to appropriately inform evidence-based treatment guidelines of DSM-5 defined mixed features. However, given the high prevalence and morbidity of mixed features, treatment recommendations based on the currently available evidence along with expert opinion may be of benefit. This article serves to provide these interim treatment recommendations while humbly acknowledging the limited amount of evidence currently available. Second-generation antipsychotics (SGAs) appear to have the greatest promise in the treatment of bipolar disorder (BD) with mixed features. Conventional mood stabilizing agents (ie, lithium and divalproex) may also be of benefit; however, they have been inadequately studied. In the treatment of major depressive disorder (MDD) with mixed features, the comparable efficacy of antidepressants versus other treatments, such as SGAs, remains unknown. As such, antidepressants remain first-line treatment of MDD with or without mixed features; however, there are significant safety concerns associated with antidepressant monotherapy when mixed features are present, which merits increased monitoring. Lurasidone is the only SGA monotherapy that has been shown to be efficacious specifically in the treatment of MDD with mixed features. Further research is needed to accurately determine the efficacy, safety, and tolerability of treatments specifically for mood episodes with mixed features to adequately inform

Extraction of ECG signal with adaptive filter for hearth abnormalities detection

NASA Astrophysics Data System (ADS)

Turnip, Mardi; Saragih, Rijois. I. E.; Dharma, Abdi; Esti Kusumandari, Dwi; Turnip, Arjon; Sitanggang, Delima; Aisyah, Siti

2018-04-01

This paper demonstrates an adaptive filter method for extraction ofelectrocardiogram (ECG) feature in hearth abnormalities detection. In particular, electrocardiogram (ECG) is a recording of the heart's electrical activity by capturing a tracingof cardiac electrical impulse as it moves from the atrium to the ventricles. The applied algorithm is to evaluate and analyze ECG signals for abnormalities detection based on P, Q, R and S peaks. In the first phase, the real-time ECG data is acquired and pre-processed. In the second phase, the procured ECG signal is subjected to feature extraction process. The extracted features detect abnormal peaks present in the waveform. Thus the normal and abnormal ECG signal could be differentiated based on the features extracted.

Fault diagnosis of helical gearbox using acoustic signal and wavelets

NASA Astrophysics Data System (ADS)

Pranesh, SK; Abraham, Siju; Sugumaran, V.; Amarnath, M.

2017-05-01

The efficient transmission of power in machines is needed and gears are an appropriate choice. Faults in gears result in loss of energy and money. The monitoring and fault diagnosis are done by analysis of the acoustic and vibrational signals which are generally considered to be unwanted by products. This study proposes the usage of machine learning algorithm for condition monitoring of a helical gearbox by using the sound signals produced by the gearbox. Artificial faults were created and subsequently signals were captured by a microphone. An extensive study using different wavelet transformations for feature extraction from the acoustic signals was done, followed by waveletselection and feature selection using J48 decision tree and feature classification was performed using K star algorithm. Classification accuracy of 100% was obtained in the study

U-Turns at signalized intersections.

DOT National Transportation Integrated Search

2004-06-01

The objectives of the study were to examine the safety consequences from the installation of U-turns at signalized intersections in Kentucky and to develop a set of guidelines for using this alternative in the future. The literature review indicated ...

Safety analysis of urban arterials at the meso level.

PubMed

Li, Jia; Wang, Xuesong

2017-11-01

Urban arterials form the main structure of street networks. They typically have multiple lanes, high traffic volume, and high crash frequency. Classical crash prediction models investigate the relationship between arterial characteristics and traffic safety by treating road segments and intersections as isolated units. This micro-level analysis does not work when examining urban arterial crashes because signal spacing is typically short for urban arterials, and there are interactions between intersections and road segments that classical models do not accommodate. Signal spacing also has safety effects on both intersections and road segments that classical models cannot fully account for because they allocate crashes separately to intersections and road segments. In addition, classical models do not consider the impact on arterial safety of the immediately surrounding street network pattern. This study proposes a new modeling methodology that will offer an integrated treatment of intersections and road segments by combining signalized intersections and their adjacent road segments into a single unit based on road geometric design characteristics and operational conditions. These are called meso-level units because they offer an analytical approach between micro and macro. The safety effects of signal spacing and street network pattern were estimated for this study based on 118 meso-level units obtained from 21 urban arterials in Shanghai, and were examined using CAR (conditional auto regressive) models that corrected for spatial correlation among the units within individual arterials. Results showed shorter arterial signal spacing was associated with higher total and PDO (property damage only) crashes, while arterials with a greater number of parallel roads were associated with lower total, PDO, and injury crashes. The findings from this study can be used in the traffic safety planning, design, and management of urban arterials. Copyright © 2017 Elsevier Ltd. All

Non-conventional features of peripheral serotonin signalling - the gut and beyond.

PubMed

Spohn, Stephanie N; Mawe, Gary M

2017-07-01

Serotonin was first discovered in the gut, and its conventional actions as an intercellular signalling molecule in the intrinsic and extrinsic enteric reflexes are well recognized, as are a number of serotonin signalling pharmacotherapeutic targets for treatment of nausea, diarrhoea or constipation. The latest discoveries have greatly broadened our understanding of non-conventional actions of peripheral serotonin within the gastrointestinal tract and in a number of other tissues. For example, it is now clear that bacteria within the lumen of the bowel influence serotonin synthesis and release by enterochromaffin cells. Also, serotonin can act both as a pro-inflammatory and anti-inflammatory signalling molecule in the intestinal mucosa via activation of serotonin receptors (5-HT 7 or 5-HT 4 receptors, respectively). For decades, serotonin receptors have been known to exist in a variety of tissues other than the gut, but studies have now provided strong evidence for physiological roles of serotonin in several important processes, including haematopoiesis, metabolic homeostasis and bone metabolism. Furthermore, evidence for serotonin synthesis in peripheral tissues outside of the gut is emerging. In this Review, we expand the discussion beyond gastrointestinal functions to highlight the roles of peripheral serotonin in colitis, haematopoiesis, energy and bone metabolism, and how serotonin is influenced by the gut microbiota.

Time-Frequency Distribution of Seismocardiographic Signals: A Comparative Study

PubMed Central

Taebi, Amirtaha; Mansy, Hansen A.

2017-01-01

Accurate estimation of seismocardiographic (SCG) signal features can help successful signal characterization and classification in health and disease. This may lead to new methods for diagnosing and monitoring heart function. Time-frequency distributions (TFD) were often used to estimate the spectrotemporal signal features. In this study, the performance of different TFDs (e.g., short-time Fourier transform (STFT), polynomial chirplet transform (PCT), and continuous wavelet transform (CWT) with different mother functions) was assessed using simulated signals, and then utilized to analyze actual SCGs. The instantaneous frequency (IF) was determined from TFD and the error in estimating IF was calculated for simulated signals. Results suggested that the lowest IF error depended on the TFD and the test signal. STFT had lower error than CWT methods for most test signals. For a simulated SCG, Morlet CWT more accurately estimated IF than other CWTs, but Morlet did not provide noticeable advantages over STFT or PCT. PCT had the most consistently accurate IF estimations and appeared more suited for estimating IF of actual SCG signals. PCT analysis showed that actual SCGs from eight healthy subjects had multiple spectral peaks at 9.20 ± 0.48, 25.84 ± 0.77, 50.71 ± 1.83 Hz (mean ± SEM). These may prove useful features for SCG characterization and classification. PMID:28952511

Signal Transduction in Cancer

PubMed Central

Sever, Richard; Brugge, Joan S.

2015-01-01

SUMMARY Cancer is driven by genetic and epigenetic alterations that allow cells to overproliferate and escape mechanisms that normally control their survival and migration. Many of these alterations map to signaling pathways that control cell growth and division, cell death, cell fate, and cell motility, and can be placed in the context of distortions of wider signaling networks that fuel cancer progression, such as changes in the tumor microenvironment, angiogenesis, and inflammation. Mutations that convert cellular proto-oncogenes to oncogenes can cause hyperactivation of these signaling pathways, whereas inactivation of tumor suppressors eliminates critical negative regulators of signaling. An examination of the PI3K-Akt and Ras-ERK pathways illustrates how such alterations dysregulate signaling in cancer and produce many of the characteristic features of tumor cells. PMID:25833940

Safety assessment in plant layout design using indexing approach: implementing inherent safety perspective. Part 1 - guideword applicability and method description.

PubMed

Tugnoli, Alessandro; Khan, Faisal; Amyotte, Paul; Cozzani, Valerio

2008-12-15

Layout planning plays a key role in the inherent safety performance of process plants since this design feature controls the possibility of accidental chain-events and the magnitude of possible consequences. A lack of suitable methods to promote the effective implementation of inherent safety in layout design calls for the development of new techniques and methods. In the present paper, a safety assessment approach suitable for layout design in the critical early phase is proposed. The concept of inherent safety is implemented within this safety assessment; the approach is based on an integrated assessment of inherent safety guideword applicability within the constraints typically present in layout design. Application of these guidewords is evaluated along with unit hazards and control devices to quantitatively map the safety performance of different layout options. Moreover, the economic aspects related to safety and inherent safety are evaluated by the method. Specific sub-indices are developed within the integrated safety assessment system to analyze and quantify the hazard related to domino effects. The proposed approach is quick in application, auditable and shares a common framework applicable in other phases of the design lifecycle (e.g. process design). The present work is divided in two parts: Part 1 (current paper) presents the application of inherent safety guidelines in layout design and the index method for safety assessment; Part 2 (accompanying paper) describes the domino hazard sub-index and demonstrates the proposed approach with a case study, thus evidencing the introduction of inherent safety features in layout design.

The Analysis of Surface EMG Signals with the Wavelet-Based Correlation Dimension Method

PubMed Central

Zhang, Yanyan; Wang, Jue

2014-01-01

Many attempts have been made to effectively improve a prosthetic system controlled by the classification of surface electromyographic (SEMG) signals. Recently, the development of methodologies to extract the effective features still remains a primary challenge. Previous studies have demonstrated that the SEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time series analysis and the time-frequency domain methods, we proposed the wavelet-based correlation dimension method to extract the effective features of SEMG signals. The SEMG signals were firstly analyzed by the wavelet transform and the correlation dimension was calculated to obtain the features of the SEMG signals. Then, these features were used as the input vectors of a Gustafson-Kessel clustering classifier to discriminate four types of forearm movements. Our results showed that there are four separate clusters corresponding to different forearm movements at the third resolution level and the resulting classification accuracy was 100%, when two channels of SEMG signals were used. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of SEMG signals and is suitable for classifying different types of forearm movements. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy. PMID:24868240

Evaluation of Short-Term Cepstral Based Features for Detection of Parkinson’s Disease Severity Levels through Speech signals

NASA Astrophysics Data System (ADS)

Oung, Qi Wei; Nisha Basah, Shafriza; Muthusamy, Hariharan; Vijean, Vikneswaran; Lee, Hoileong

2018-03-01

Parkinson’s disease (PD) is one type of progressive neurodegenerative disease known as motor system syndrome, which is due to the death of dopamine-generating cells, a region of the human midbrain. PD normally affects people over 60 years of age, which at present has influenced a huge part of worldwide population. Lately, many researches have shown interest into the connection between PD and speech disorders. Researches have revealed that speech signals may be a suitable biomarker for distinguishing between people with Parkinson’s (PWP) from healthy subjects. Therefore, early diagnosis of PD through the speech signals can be considered for this aim. In this research, the speech data are acquired based on speech behaviour as the biomarker for differentiating PD severity levels (mild and moderate) from healthy subjects. Feature extraction algorithms applied are Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), and Weighted Linear Prediction Cepstral Coefficients (WLPCC). For classification, two types of classifiers are used: k-Nearest Neighbour (KNN) and Probabilistic Neural Network (PNN). The experimental results demonstrated that PNN classifier and KNN classifier achieve the best average classification performance of 92.63% and 88.56% respectively through 10-fold cross-validation measures. Favourably, the suggested techniques have the possibilities of becoming a new choice of promising tools for the PD detection with tremendous performance.

Nuclear Powerplant Safety: Design and Planning.

ERIC Educational Resources Information Center

Department of Energy, Washington, DC. Nuclear Energy Office.

The most important concern in the design, construction and operation of nuclear powerplants is safety. Nuclear power is one of the major contributors to the nation's supply of electricity; therefore, it is important to assure its safe use. Each different type of powerplant has special design features and systems to protect health and safety. One…

Threat as a feature in visual semantic object memory.

PubMed

Calley, Clifford S; Motes, Michael A; Chiang, H-Sheng; Buhl, Virginia; Spence, Jeffrey S; Abdi, Hervé; Anand, Raksha; Maguire, Mandy; Estevez, Leonardo; Briggs, Richard; Freeman, Thomas; Kraut, Michael A; Hart, John

2013-08-01

Threatening stimuli have been found to modulate visual processes related to perception and attention. The present functional magnetic resonance imaging (fMRI) study investigated whether threat modulates visual object recognition of man-made and naturally occurring categories of stimuli. Compared with nonthreatening pictures, threatening pictures of real items elicited larger fMRI BOLD signal changes in medial visual cortices extending inferiorly into the temporo-occipital (TO) "what" pathways. This region elicited greater signal changes for threatening items compared to nonthreatening from both the natural-occurring and man-made stimulus supraordinate categories, demonstrating a featural component to these visual processing areas. Two additional loci of signal changes within more lateral inferior TO areas (bilateral BA18 and 19 as well as the right ventral temporal lobe) were detected for a category-feature interaction, with stronger responses to man-made (category) threatening (feature) stimuli than to natural threats. The findings are discussed in terms of visual recognition of processing efficiently or rapidly groups of items that confer an advantage for survival. Copyright © 2012 Wiley Periodicals, Inc.

Features extraction algorithm about typical railway perimeter intrusion event

NASA Astrophysics Data System (ADS)

Zhou, Jieyun; Wang, Chaodong; Liu, Lihai

2017-10-01

Research purposes: Optical fiber vibration sensing system has been widely used in the oil, gas, frontier defence, prison and power industries. But, there are few reports about the application in railway defence. That is because the surrounding environment is complicated and there are many challenges to be overcomed in the optical fiber vibration sensing system application. For example, how to eliminate the effects of vibration caused by train, the natural environments such as wind and rain and how to identify and classify the intrusion events. In order to solve these problems, the feature signals of these events should be extracted firstly. Research conclusions: (1) In optical fiber vibration sensing system based on Sagnac interferometer, the peak-to-peak value, peak-to-average ratio, standard deviation, zero-crossing rate, short-term energy and kurtosis may serve as feature signals. (2) The feature signals of resting state, climbing concrete fence, breaking barbed wire, knocking concrete fence and rainstorm have been extracted, which shows significant difference among each other. (3) The research conclusions can be used in the identification and classification of intrusion events.

To signal or not to signal: that should not be the question.

PubMed

Faw, Harold W

2013-10-01

The purpose of the present research was to examine rates of turn signal use, a positive and potentially valuable means by which drivers can communicate. A second purpose was to explore factors that might impact these rates, including the modeling influence of other drivers. A series of observations involving more than 5600 vehicles making turns were recorded at a variety of intersections in British Columbia, Canada. Though the occurrence of signal use varied widely, ranging from a low of 54% to a high of 95%, the overall rate was 76%. Drivers used turn signals significantly less often when making right as compared with left turns, when traffic volume was higher, and when a designated turning lane was provided. In addition, compared with drivers following another vehicle not using signals, those following a vehicle with turn signals on were significantly more likely to activate their turn signals, suggesting a possible modeling effect. Both internal and external influences on turn signal use by drivers were considered. External factors explored in this research included direction of turn, traffic volume, intersection configuration, and the example of other drivers. It was concluded that the practice of signaling turns merits more research attention, since consistent use of signals is a potential contributor to enhanced safety for all road users. Copyright © 2013 Elsevier Ltd. All rights reserved.

NASA's Software Safety Standard

NASA Technical Reports Server (NTRS)

Ramsay, Christopher M.

2005-01-01

NASA (National Aeronautics and Space Administration) relies more and more on software to control, monitor, and verify its safety critical systems, facilities and operations. Since the 1960's there has hardly been a spacecraft (manned or unmanned) launched that did not have a computer on board that provided vital command and control services. Despite this growing dependence on software control and monitoring, there has been no consistent application of software safety practices and methodology to NASA's projects with safety critical software. Led by the NASA Headquarters Office of Safety and Mission Assurance, the NASA Software Safety Standard (STD-18l9.13B) has recently undergone a significant update in an attempt to provide that consistency. This paper will discuss the key features of the new NASA Software Safety Standard. It will start with a brief history of the use and development of software in safety critical applications at NASA. It will then give a brief overview of the NASA Software Working Group and the approach it took to revise the software engineering process across the Agency.

Proposed patient motion monitoring system using feature point tracking with a web camera.

PubMed

Miura, Hideharu; Ozawa, Shuichi; Matsuura, Takaaki; Yamada, Kiyoshi; Nagata, Yasushi

2017-12-01

Patient motion monitoring systems play an important role in providing accurate treatment dose delivery. We propose a system that utilizes a web camera (frame rate up to 30 fps, maximum resolution of 640 × 480 pixels) and an in-house image processing software (developed using Microsoft Visual C++ and OpenCV). This system is simple to use and convenient to set up. The pyramidal Lucas-Kanade method was applied to calculate motions for each feature point by analysing two consecutive frames. The image processing software employs a color scheme where the defined feature points are blue under stable (no movement) conditions and turn red along with a warning message and an audio signal (beeping alarm) for large patient movements. The initial position of the marker was used by the program to determine the marker positions in all the frames. The software generates a text file that contains the calculated motion for each frame and saves it as a compressed audio video interleave (AVI) file. We proposed a patient motion monitoring system using a web camera, which is simple and convenient to set up, to increase the safety of treatment delivery.

Visual Prediction Error Spreads Across Object Features in Human Visual Cortex

PubMed Central

Summerfield, Christopher; Egner, Tobias

2016-01-01

Visual cognition is thought to rely heavily on contextual expectations. Accordingly, previous studies have revealed distinct neural signatures for expected versus unexpected stimuli in visual cortex. However, it is presently unknown how the brain combines multiple concurrent stimulus expectations such as those we have for different features of a familiar object. To understand how an unexpected object feature affects the simultaneous processing of other expected feature(s), we combined human fMRI with a task that independently manipulated expectations for color and motion features of moving-dot stimuli. Behavioral data and neural signals from visual cortex were then interrogated to adjudicate between three possible ways in which prediction error (surprise) in the processing of one feature might affect the concurrent processing of another, expected feature: (1) feature processing may be independent; (2) surprise might “spread” from the unexpected to the expected feature, rendering the entire object unexpected; or (3) pairing a surprising feature with an expected feature might promote the inference that the two features are not in fact part of the same object. To formalize these rival hypotheses, we implemented them in a simple computational model of multifeature expectations. Across a range of analyses, behavior and visual neural signals consistently supported a model that assumes a mixing of prediction error signals across features: surprise in one object feature spreads to its other feature(s), thus rendering the entire object unexpected. These results reveal neurocomputational principles of multifeature expectations and indicate that objects are the unit of selection for predictive vision. SIGNIFICANCE STATEMENT We address a key question in predictive visual cognition: how does the brain combine multiple concurrent expectations for different features of a single object such as its color and motion trajectory? By combining a behavioral protocol that

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram.

PubMed

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-09-13

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features.

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram

PubMed Central

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-01-01

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features. PMID:27649171

[A novel method of multi-channel feature extraction combining multivariate autoregression and multiple-linear principal component analysis].

PubMed

Wang, Jinjia; Zhang, Yanna

2015-02-01

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.

Temporal resolution for the perception of features and conjunctions.

PubMed

Bodelón, Clara; Fallah, Mazyar; Reynolds, John H

2007-01-24

The visual system decomposes stimuli into their constituent features, represented by neurons with different feature selectivities. How the signals carried by these feature-selective neurons are integrated into coherent object representations is unknown. To constrain the set of possible integrative mechanisms, we quantified the temporal resolution of perception for color, orientation, and conjunctions of these two features. We find that temporal resolution is measurably higher for each feature than for their conjunction, indicating that time is required to integrate features into a perceptual whole. This finding places temporal limits on the mechanisms that could mediate this form of perceptual integration.

Acoustic emission signal processing for rolling bearing running state assessment using compressive sensing

NASA Astrophysics Data System (ADS)

Liu, Chang; Wu, Xing; Mao, Jianlin; Liu, Xiaoqin

2017-07-01

In the signal processing domain, there has been growing interest in using acoustic emission (AE) signals for the fault diagnosis and condition assessment instead of vibration signals, which has been advocated as an effective technique for identifying fracture, crack or damage. The AE signal has high frequencies up to several MHz which can avoid some signals interference, such as the parts of bearing (i.e. rolling elements, ring and so on) and other rotating parts of machine. However, acoustic emission signal necessitates advanced signal sampling capabilities and requests ability to deal with large amounts of sampling data. In this paper, compressive sensing (CS) is introduced as a processing framework, and then a compressive features extraction method is proposed. We use it for extracting the compressive features from compressively-sensed data directly, and also prove the energy preservation properties. First, we study the AE signals under the CS framework. The sparsity of AE signal of the rolling bearing is checked. The observation and reconstruction of signal is also studied. Second, we present a method of extraction AE compressive feature (AECF) from compressively-sensed data directly. We demonstrate the energy preservation properties and the processing of the extracted AECF feature. We assess the running state of the bearing using the AECF trend. The AECF trend of the running state of rolling bearings is consistent with the trend of traditional features. Thus, the method is an effective way to evaluate the running trend of rolling bearings. The results of the experiments have verified that the signal processing and the condition assessment based on AECF is simpler, the amount of data required is smaller, and the amount of computation is greatly reduced.

Autonomous system for launch vehicle range safety

NASA Astrophysics Data System (ADS)

Ferrell, Bob; Haley, Sam

2001-02-01

The Autonomous Flight Safety System (AFSS) is a launch vehicle subsystem whose ultimate goal is an autonomous capability to assure range safety (people and valuable resources), flight personnel safety, flight assets safety (recovery of valuable vehicles and cargo), and global coverage with a dramatic simplification of range infrastructure. The AFSS is capable of determining current vehicle position and predicting the impact point with respect to flight restriction zones. Additionally, it is able to discern whether or not the launch vehicle is an immediate threat to public safety, and initiate the appropriate range safety response. These features provide for a dramatic cost reduction in range operations and improved reliability of mission success. .

Calcium signalling silencing in atrial fibrillation.

PubMed

Greiser, Maura

2017-06-15

Subcellular calcium signalling silencing is a novel and distinct cellular and molecular adaptive response to rapid cardiac activation. Calcium signalling silencing develops during short-term sustained rapid atrial activation as seen clinically during paroxysmal atrial fibrillation (AF). It is the first 'anti-arrhythmic' adaptive response in the setting of AF and appears to counteract the maladaptive changes that lead to intracellular Ca 2+ signalling instability and Ca 2+ -based arrhythmogenicity. Calcium signalling silencing results in a failed propagation of the [Ca 2+ ] i signal to the myocyte centre both in patients with AF and in a rabbit model. This adaptive mechanism leads to a substantial reduction in the expression levels of calcium release channels (ryanodine receptors, RyR2) in the sarcoplasmic reticulum, and the frequency of Ca 2+ sparks and arrhythmogenic Ca 2+ waves remains low. Less Ca 2+ release per [Ca 2+ ] i transient, increased fast Ca 2+ buffering strength, shortened action potentials and reduced L-type Ca 2+ current contribute to a substantial reduction of intracellular [Na + ]. These features of Ca 2+ signalling silencing are distinct and in contrast to the changes attributed to Ca 2+ -based arrhythmogenicity. Some features of Ca 2+ signalling silencing prevail in human AF suggesting that the Ca 2+ signalling 'phenotype' in AF is a sum of Ca 2+ stabilizing (Ca 2+ signalling silencing) and Ca 2+ destabilizing (arrhythmogenic unstable Ca 2+ signalling) factors. Calcium signalling silencing is a part of the mechanisms that contribute to the natural progression of AF and may limit the role of Ca 2+ -based arrhythmogenicity after the onset of AF. © 2017 The Authors. The Journal of Physiology © 2017 The Physiological Society.

Two level approach to safety planning incorporating the Highway Safety Manual (HSM) network screening.

DOT National Transportation Integrated Search

2014-04-01

Compared to microscopic safety studies, macroscopic-focused research is more efficient at integrating zonal-level features into crash prediction models and identifying hot zones. However, macroscopic screening has accuracy limitations. Thus, this stu...

Channel and feature selection in multifunction myoelectric control.

PubMed

Khushaba, Rami N; Al-Jumaily, Adel

2007-01-01

Real time controlling devices based on myoelectric singles (MES) is one of the challenging research problems. This paper presents a new approach to reduce the computational cost of real time systems driven by Myoelectric signals (MES) (a.k.a Electromyography--EMG). The new approach evaluates the significance of feature/channel selection on MES pattern recognition. Particle Swarm Optimization (PSO), an evolutionary computational technique, is employed to search the feature/channel space for important subsets. These important subsets will be evaluated using a multilayer perceptron trained with back propagation neural network (BPNN). Practical results acquired from tests done on six subjects' datasets of MES signals measured in a noninvasive manner using surface electrodes are presented. It is proved that minimum error rates can be achieved by considering the correct combination of features/channels, thus providing a feasible system for practical implementation purpose for rehabilitation of patients.

Signals of ENPEMF Used in Earthquake Prediction

NASA Astrophysics Data System (ADS)

Hao, G.; Dong, H.; Zeng, Z.; Wu, G.; Zabrodin, S. M.

2012-12-01

The signals of Earth's natural pulse electromagnetic field (ENPEMF) is a combination of the abnormal crustal magnetic field pulse affected by the earthquake, the induced field of earth's endogenous magnetic field, the induced magnetic field of the exogenous variation magnetic field, geomagnetic pulsation disturbance and other energy coupling process between sun and earth. As an instantaneous disturbance of the variation field of natural geomagnetism, ENPEMF can be used to predict earthquakes. This theory was introduced by A.A Vorobyov, who expressed a hypothesis that pulses can arise not only in the atmosphere but within the Earth's crust due to processes of tectonic-to-electric energy conversion (Vorobyov, 1970; Vorobyov, 1979). The global field time scale of ENPEMF signals has specific stability. Although the wave curves may not overlap completely at different regions, the smoothed diurnal ENPEMF patterns always exhibit the same trend per month. The feature is a good reference for observing the abnormalities of the Earth's natural magnetic field in a specific region. The frequencies of the ENPEMF signals generally locate in kilo Hz range, where frequencies within 5-25 kilo Hz range can be applied to monitor earthquakes. In Wuhan, the best observation frequency is 14.5 kilo Hz. Two special devices are placed in accordance with the S-N and W-E direction. Dramatic variation from the comparison between the pulses waveform obtained from the instruments and the normal reference envelope diagram should indicate high possibility of earthquake. The proposed detection method of earthquake based on ENPEMF can improve the geodynamic monitoring effect and can enrich earthquake prediction methods. We suggest the prospective further researches are about on the exact sources composition of ENPEMF signals, the distinction between noise and useful signals, and the effect of the Earth's gravity tide and solid tidal wave. This method may also provide a promising application in

Method and apparatus for automatically detecting patterns in digital point-ordered signals

DOEpatents

Brudnoy, David M.

1998-01-01

The present invention is a method and system for detecting a physical feature of a test piece by detecting a pattern in a signal representing data from inspection of the test piece. The pattern is detected by automated additive decomposition of a digital point-ordered signal which represents the data. The present invention can properly handle a non-periodic signal. A physical parameter of the test piece is measured. A digital point-ordered signal representative of the measured physical parameter is generated. The digital point-ordered signal is decomposed into a baseline signal, a background noise signal, and a peaks/troughs signal. The peaks/troughs from the peaks/troughs signal are located and peaks/troughs information indicating the physical feature of the test piece is output.

Direct evidence for attention-dependent influences of the frontal eye-fields on feature-responsive visual cortex.

PubMed

Heinen, Klaartje; Feredoes, Eva; Weiskopf, Nikolaus; Ruff, Christian C; Driver, Jon

2014-11-01

Voluntary selective attention can prioritize different features in a visual scene. The frontal eye-fields (FEF) are one potential source of such feature-specific top-down signals, but causal evidence for influences on visual cortex (as was shown for "spatial" attention) has remained elusive. Here, we show that transcranial magnetic stimulation (TMS) applied to right FEF increased the blood oxygen level-dependent (BOLD) signals in visual areas processing "target feature" but not in "distracter feature"-processing regions. TMS-induced BOLD signals increase in motion-responsive visual cortex (MT+) when motion was attended in a display with moving dots superimposed on face stimuli, but in face-responsive fusiform area (FFA) when faces were attended to. These TMS effects on BOLD signal in both regions were negatively related to performance (on the motion task), supporting the behavioral relevance of this pathway. Our findings provide new causal evidence for the human FEF in the control of nonspatial "feature"-based attention, mediated by dynamic influences on feature-specific visual cortex that vary with the currently attended property. © The Author 2013. Published by Oxford University Press.

New reactor cavity cooling system having passive safety features using novel shape for HTGRs and VHTRs

DOE PAGES

Takamatsu, Kuniyoshi; Hu, Rui

2014-11-27

A new, highly efficient reactor cavity cooling system (RCCS) with passive safety features without a requirement for electricity and mechanical drive is proposed for high temperature gas cooled reactors (HTGRs) and very high temperature reactors (VHTRs). The RCCS design consists of continuous closed regions; one is an ex-reactor pressure vessel (RPV) region and another is a cooling region having heat transfer area to ambient air assumed at 40 (°C). The RCCS uses a novel shape to efficiently remove the heat released from the RPV with radiation and natural convection. Employing the air as the working fluid and the ambient airmore » as the ultimate heat sink, the new RCCS design strongly reduces the possibility of losing the heat sink for decay heat removal. Therefore, HTGRs and VHTRs adopting the new RCCS design can avoid core melting due to overheating the fuels. The simulation results from a commercial CFD code, STAR-CCM+, show that the temperature distribution of the RCCS is within the temperature limits of the structures, such as the maximum operating temperature of the RPV, 713.15 (K) = 440 (°C), and the heat released from the RPV could be removed safely, even during a loss of coolant accident (LOCA). Finally, when the RCCS can remove 600 (kW) of the rated nominal state even during LOCA, the safety review for building the HTTR could confirm that the temperature distribution of the HTTR is within the temperature limits of the structures to secure structures and fuels after the shutdown because the large heat capacity of the graphite core can absorb heat from the fuel in a short period. Therefore, the capacity of the new RCCS design would be sufficient for decay heat removal.« less

Campus Fire Safety Today.

ERIC Educational Resources Information Center

Thompson, Mike

2001-01-01

Reviews information on recent college and university dormitory fire fatalities, and highlights five examples of building features reported to be major contributing factors in residence-hall fires. Explains how public awareness and expectations are affecting school dormitory safety. (GR)

Stem Cells and Calcium Signaling

PubMed Central

Tonelli, Fernanda M.P.; Santos, Anderson K.; Gomes, Dawidson A.; da Silva, Saulo L.; Gomes, Katia N.; Ladeira, Luiz O.

2014-01-01

The increasing interest in stem cell research is linked to the promise of developing treatments for many lifethreatening, debilitating diseases, and for cell replacement therapies. However, performing these therapeutic innovations with safety will only be possible when an accurate knowledge about the molecular signals that promote the desired cell fate is reached. Among these signals are transient changes in intracellular Ca2+ concentration [Ca2+]i. Acting as an intracellular messenger, Ca2+ has a key role in cell signaling pathways in various differentiation stages of stem cells. The aim of this chapter is to present a broad overview of various moments in which Ca2+-mediated signaling is essential for the maintenance of stem cells and for promoting their development and differentiation, also focusing on their therapeutic potential. PMID:22453975

Stem cells and calcium signaling.

PubMed

Tonelli, Fernanda M P; Santos, Anderson K; Gomes, Dawidson A; da Silva, Saulo L; Gomes, Katia N; Ladeira, Luiz O; Resende, Rodrigo R

2012-01-01

The increasing interest in stem cell research is linked to the promise of developing treatments for many lifethreatening, debilitating diseases, and for cell replacement therapies. However, performing these therapeutic innovations with safety will only be possible when an accurate knowledge about the molecular signals that promote the desired cell fate is reached. Among these signals are transient changes in intracellular Ca(2+) concentration [Ca(2+)](i). Acting as an intracellular messenger, Ca(2+) has a key role in cell signaling pathways in various differentiation stages of stem cells. The aim of this chapter is to present a broad overview of various moments in which Ca(2+)-mediated signaling is essential for the maintenance of stem cells and for promoting their development and differentiation, also focusing on their therapeutic potential.

Synthesis Study on Transitions in Signal Infrastructure and Control Algorithms for Connected and Automated Transportation

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

Aziz, H. M. Abdul; Wang, Hong; Young, Stan

Documenting existing state of practice is an initial step in developing future control infrastructure to be co-deployed for heterogeneous mix of connected and automated vehicles with human drivers while leveraging benefits to safety, congestion, and energy. With advances in information technology and extensive deployment of connected and automated vehicle technology anticipated over the coming decades, cities globally are making efforts to plan and prepare for these transitions. CAVs not only offer opportunities to improve transportation systems through enhanced safety and efficient operations of vehicles. There are also significant needs in terms of exploring how best to leverage vehicle-to-vehicle (V2V) technology,more » vehicle-to-infrastructure (V2I) technology and vehicle-to-everything (V2X) technology. Both Connected Vehicle (CV) and Connected and Automated Vehicle (CAV) paradigms feature bi-directional connectivity and share similar applications in terms of signal control algorithm and infrastructure implementation. The discussion in our synthesis study assumes the CAV/CV context where connectivity exists with or without automated vehicles. Our synthesis study explores the current state of signal control algorithms and infrastructure, reports the completed and newly proposed CV/CAV deployment studies regarding signal control schemes, reviews the deployment costs for CAV/AV signal infrastructure, and concludes with a discussion on the opportunities such as detector free signal control schemes and dynamic performance management for intersections, and challenges such as dependency on market adaptation and the need to build a fault-tolerant signal system deployment in a CAV/CV environment. The study will serve as an initial critical assessment of existing signal control infrastructure (devices, control instruments, and firmware) and control schemes (actuated, adaptive, and coordinated-green wave). Also, the report will help to identify the future needs for the

Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

PubMed

Bascil, M Serdar; Tesneli, Ahmet Y; Temurtas, Feyzullah

2016-09-01

Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.

Progress in Arc Safety System Based on Harmonics Detection for ICRH Antennae

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

Berger-By, G.; Beaumont, B.; Lombard, G.

2007-09-28

The arc detection systems based on harmonics detection have been tested n USA (TFTR, DIII, Alcator C-mod) and Germany (Asdex). These systems have some advantages in comparison with traditonal securities which use a threshold on the Vr/Vf (Reflected to Forward voltage ratio) calculation and are ITER relevant. On Tore Supra (TS) 3 systems have been built using this principle with some improvements and new features to increase the protection of the 3 ICRH generators and antennae. On JET 2 arc safety systems based on the TS principle wil also be used to mprove the JET ITER-like antenna safety. In ordermore » to have the maximum security level on the TS ICRH system, the 3 antennae are used with these systems during all plasma shots n redundancy with the other systems. This TS RF principle and ts electronic interactions with the VME control of the generator are described. The results on the TS ICRH transmitter feeding the 3 antennae are summarized and some typical signals are given.« less

10 CFR 32.23 - Same: Safety criteria.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 1 2010-01-01 2010-01-01 false Same: Safety criteria. 32.23 Section 32.23 Energy NUCLEAR REGULATORY COMMISSION SPECIFIC DOMESTIC LICENSES TO MANUFACTURE OR TRANSFER CERTAIN ITEMS CONTAINING..., shielding, or other safety features of the product from wear and abuse likely to occur in normal handling...

10 CFR 32.27 - Same: Safety criteria.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 1 2010-01-01 2010-01-01 false Same: Safety criteria. 32.27 Section 32.27 Energy NUCLEAR REGULATORY COMMISSION SPECIFIC DOMESTIC LICENSES TO MANUFACTURE OR TRANSFER CERTAIN ITEMS CONTAINING..., shielding, or other safety features of the product from wear and abuse likely to occur in normal handling...

Wire bonding quality monitoring via refining process of electrical signal from ultrasonic generator

NASA Astrophysics Data System (ADS)

Feng, Wuwei; Meng, Qingfeng; Xie, Youbo; Fan, Hong

2011-04-01

In this paper, a technique for on-line quality detection of ultrasonic wire bonding is developed. The electrical signals from the ultrasonic generator supply, namely, voltage and current, are picked up by a measuring circuit and transformed into digital signals by a data acquisition system. A new feature extraction method is presented to characterize the transient property of the electrical signals and further evaluate the bond quality. The method includes three steps. First, the captured voltage and current are filtered by digital bandpass filter banks to obtain the corresponding subband signals such as fundamental signal, second harmonic, and third harmonic. Second, each subband envelope is obtained using the Hilbert transform for further feature extraction. Third, the subband envelopes are, respectively, separated into three phases, namely, envelope rising, stable, and damping phases, to extract the tiny waveform changes. The different waveform features are extracted from each phase of these subband envelopes. The principal components analysis (PCA) method is used for the feature selection in order to remove the relevant information and reduce the dimension of original feature variables. Using the selected features as inputs, an artificial neural network (ANN) is constructed to identify the complex bond fault pattern. By analyzing experimental data with the proposed feature extraction method and neural network, the results demonstrate the advantages of the proposed feature extraction method and the constructed artificial neural network in detecting and identifying bond quality.