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Sample records for activity recognition algorithm

  1. Human activity recognition based on feature selection in smart home using back-propagation algorithm.

    PubMed

    Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei

    2014-09-01

    In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM.

  2. User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm

    PubMed Central

    Bourobou, Serge Thomas Mickala; Yoo, Younghwan

    2015-01-01

    This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen’s temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home. PMID:26007738

  3. User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.

    PubMed

    Bourobou, Serge Thomas Mickala; Yoo, Younghwan

    2015-05-21

    This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

  4. Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people.

    PubMed

    Chernbumroong, Saisakul; Cang, Shuang; Yu, Hongnian

    2015-01-01

    Activity recognition of an elderly person can be used to provide information and intelligent services to health care professionals, carers, elderly people, and their families so that the elderly people can remain at homes independently. This study investigates the use and contribution of wrist-worn multisensors for activity recognition. We found that accelerometers are the most important sensors and heart rate data can be used to boost classification of activities with diverse heart rates. We propose a genetic algorithm-based fusion weight selection (GAFW) approach which utilizes GA to find fusion weights. For all possible classifier combinations and fusion methods, the study shows that 98% of times GAFW can achieve equal or higher accuracy than the best classifier within the group.

  5. Activity recognition in planetary navigation field tests using classification algorithms applied to accelerometer data.

    PubMed

    Song, Wen; Ade, Carl; Broxterman, Ryan; Barstow, Thomas; Nelson, Thomas; Warren, Steve

    2012-01-01

    Accelerometer data provide useful information about subject activity in many different application scenarios. For this study, single-accelerometer data were acquired from subjects participating in field tests that mimic tasks that astronauts might encounter in reduced gravity environments. The primary goal of this effort was to apply classification algorithms that could identify these tasks based on features present in their corresponding accelerometer data, where the end goal is to establish methods to unobtrusively gauge subject well-being based on sensors that reside in their local environment. In this initial analysis, six different activities that involve leg movement are classified. The k-Nearest Neighbors (kNN) algorithm was found to be the most effective, with an overall classification success rate of 90.8%.

  6. A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition.

    PubMed

    Saez, Yago; Baldominos, Alejandro; Isasi, Pedro

    2016-12-30

    Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances in wearable devices, such as smartwatches or physical activity wristbands, motion tracking sensors are becoming pervasive, which has led to an impressive growth in the amount of physical activity data available and an increasing interest in recognizing which specific activity a user is performing. Moreover, big data and machine learning are now cross-fertilizing each other in an approach called "deep learning", which consists of massive artificial neural networks able to detect complicated patterns from enormous amounts of input data to learn classification models. This work compares various state-of-the-art classification techniques for automatic cross-person activity recognition under different scenarios that vary widely in how much information is available for analysis. We have incorporated deep learning by using Google's TensorFlow framework. The data used in this study were acquired from PAMAP2 (Physical Activity Monitoring in the Ageing Population), a publicly available dataset containing physical activity data. To perform cross-person prediction, we used the leave-one-subject-out (LOSO) cross-validation technique. When working with large training sets, the best classifiers obtain very high average accuracies (e.g., 96% using extra randomized trees). However, when the data volume is drastically reduced (where available data are only 0.001% of the continuous data), deep neural networks performed the best, achieving 60% in overall prediction accuracy. We found that even when working with only approximately 22.67% of the full dataset, we can statistically obtain the same results as when working with the full dataset. This finding enables the design of more energy-efficient devices and facilitates cold

  7. A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition

    PubMed Central

    Saez, Yago; Baldominos, Alejandro; Isasi, Pedro

    2016-01-01

    Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances in wearable devices, such as smartwatches or physical activity wristbands, motion tracking sensors are becoming pervasive, which has led to an impressive growth in the amount of physical activity data available and an increasing interest in recognizing which specific activity a user is performing. Moreover, big data and machine learning are now cross-fertilizing each other in an approach called “deep learning”, which consists of massive artificial neural networks able to detect complicated patterns from enormous amounts of input data to learn classification models. This work compares various state-of-the-art classification techniques for automatic cross-person activity recognition under different scenarios that vary widely in how much information is available for analysis. We have incorporated deep learning by using Google’s TensorFlow framework. The data used in this study were acquired from PAMAP2 (Physical Activity Monitoring in the Ageing Population), a publicly available dataset containing physical activity data. To perform cross-person prediction, we used the leave-one-subject-out (LOSO) cross-validation technique. When working with large training sets, the best classifiers obtain very high average accuracies (e.g., 96% using extra randomized trees). However, when the data volume is drastically reduced (where available data are only 0.001% of the continuous data), deep neural networks performed the best, achieving 60% in overall prediction accuracy. We found that even when working with only approximately 22.67% of the full dataset, we can statistically obtain the same results as when working with the full dataset. This finding enables the design of more energy-efficient devices and facilitates cold

  8. Physical environment virtualization for human activities recognition

    NASA Astrophysics Data System (ADS)

    Poshtkar, Azin; Elangovan, Vinayak; Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen

    2015-05-01

    Human activity recognition research relies heavily on extensive datasets to verify and validate performance of activity recognition algorithms. However, obtaining real datasets are expensive and highly time consuming. A physics-based virtual simulation can accelerate the development of context based human activity recognition algorithms and techniques by generating relevant training and testing videos simulating diverse operational scenarios. In this paper, we discuss in detail the requisite capabilities of a virtual environment to aid as a test bed for evaluating and enhancing activity recognition algorithms. To demonstrate the numerous advantages of virtual environment development, a newly developed virtual environment simulation modeling (VESM) environment is presented here to generate calibrated multisource imagery datasets suitable for development and testing of recognition algorithms for context-based human activities. The VESM environment serves as a versatile test bed to generate a vast amount of realistic data for training and testing of sensor processing algorithms. To demonstrate the effectiveness of VESM environment, we present various simulated scenarios and processed results to infer proper semantic annotations from the high fidelity imagery data for human-vehicle activity recognition under different operational contexts.

  9. Statistical pattern recognition algorithms for autofluorescence imaging

    NASA Astrophysics Data System (ADS)

    Kulas, Zbigniew; Bereś-Pawlik, Elżbieta; Wierzbicki, Jarosław

    2009-02-01

    In cancer diagnostics the most important problems are the early identification and estimation of the tumor growth and spread in order to determine the area to be operated. The aim of the work was to design of statistical algorithms helping doctors to objectively estimate pathologically changed areas and to assess the disease advancement. In the research, algorithms for classifying endoscopic autofluorescence images of larynx and intestine were used. The results show that the statistical pattern recognition offers new possibilities for endoscopic diagnostics and can be of a tremendous help in assessing the area of the pathological changes.

  10. Apply lightweight recognition algorithms in optical music recognition

    NASA Astrophysics Data System (ADS)

    Pham, Viet-Khoi; Nguyen, Hai-Dang; Nguyen-Khac, Tung-Anh; Tran, Minh-Triet

    2015-02-01

    The problems of digitalization and transformation of musical scores into machine-readable format are necessary to be solved since they help people to enjoy music, to learn music, to conserve music sheets, and even to assist music composers. However, the results of existing methods still require improvements for higher accuracy. Therefore, the authors propose lightweight algorithms for Optical Music Recognition to help people to recognize and automatically play musical scores. In our proposal, after removing staff lines and extracting symbols, each music symbol is represented as a grid of identical M ∗ N cells, and the features are extracted and classified with multiple lightweight SVM classifiers. Through experiments, the authors find that the size of 10 ∗ 12 cells yields the highest precision value. Experimental results on the dataset consisting of 4929 music symbols taken from 18 modern music sheets in the Synthetic Score Database show that our proposed method is able to classify printed musical scores with accuracy up to 99.56%.

  11. Robust face recognition algorithm for identifition of disaster victims

    NASA Astrophysics Data System (ADS)

    Gevaert, Wouter J. R.; de With, Peter H. N.

    2013-02-01

    We present a robust face recognition algorithm for the identification of occluded, injured and mutilated faces with a limited training set per person. In such cases, the conventional face recognition methods fall short due to specific aspects in the classification. The proposed algorithm involves recursive Principle Component Analysis for reconstruction of afiected facial parts, followed by a feature extractor based on Gabor wavelets and uniform multi-scale Local Binary Patterns. As a classifier, a Radial Basis Neural Network is employed. In terms of robustness to facial abnormalities, tests show that the proposed algorithm outperforms conventional face recognition algorithms like, the Eigenfaces approach, Local Binary Patterns and the Gabor magnitude method. To mimic real-life conditions in which the algorithm would have to operate, specific databases have been constructed and merged with partial existing databases and jointly compiled. Experiments on these particular databases show that the proposed algorithm achieves recognition rates beyond 95%.

  12. Algorithm-development activities

    NASA Technical Reports Server (NTRS)

    Carder, Kendall L.

    1994-01-01

    The task of algorithm-development activities at USF continues. The algorithm for determining chlorophyll alpha concentration, (Chl alpha) and gelbstoff absorption coefficient for SeaWiFS and MODIS-N radiance data is our current priority.

  13. An improved HMM/SVM dynamic hand gesture recognition algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, Yi; Yao, Yuanyuan; Luo, Yuan

    2015-10-01

    In order to improve the recognition rate and stability of dynamic hand gesture recognition, for the low accuracy rate of the classical HMM algorithm in train the B parameter, this paper proposed an improved HMM/SVM dynamic gesture recognition algorithm. In the calculation of the B parameter of HMM model, this paper introduced the SVM algorithm which has the strong ability of classification. Through the sigmoid function converted the state output of the SVM into the probability and treat this probability as the observation state transition probability of the HMM model. After this, it optimized the B parameter of HMM model and improved the recognition rate of the system. At the same time, it also enhanced the accuracy and the real-time performance of the human-computer interaction. Experiments show that this algorithm has a strong robustness under the complex background environment and the varying illumination environment. The average recognition rate increased from 86.4% to 97.55%.

  14. Digital signal processing algorithms for automatic voice recognition

    NASA Technical Reports Server (NTRS)

    Botros, Nazeih M.

    1987-01-01

    The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms.

  15. A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors

    PubMed Central

    Han, Manhyung; Bang, Jae Hun; Nugent, Chris; McClean, Sally; Lee, Sungyoung

    2014-01-01

    Activity recognition for the purposes of recognizing a user's intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables the use of different sources of sensor data. In this paper, we propose a smartphone-based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user's activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%. PMID:25184486

  16. Performance Improvements of the Phoneme Recognition Algorithm.

    DTIC Science & Technology

    1984-06-01

    present time, there are commercially available speech recognition machines that perform limited speech recognition. There are still major drawbacks to...to recognize. Even though the training period has been made fairly painless to the user, it still severely limits the vocabulary the machine can...this information to perform the recognition routines. 47 ..- 7f Alterations to the templates’ spectrum file was limited to changing values in the

  17. A face recognition algorithm based on thermal and visible data

    NASA Astrophysics Data System (ADS)

    Sochenkov, Ilya; Tihonkih, Dmitrii; Vokhmintcev, Aleksandr; Melnikov, Andrey; Makovetskii, Artyom

    2016-09-01

    In this work we present an algorithm of fusing thermal infrared and visible imagery to identify persons. The proposed face recognition method contains several components. In particular this is rigid body image registration. The rigid registration is achieved by a modified variant of the iterative closest point (ICP) algorithm. We consider an affine transformation in three-dimensional space that preserves the angles between the lines. An algorithm of matching is inspirited by the recent results of neurophysiology of vision. Also we consider the ICP minimizing error metric stage for the case of an arbitrary affine transformation. Our face recognition algorithm also uses the localized-contouring algorithms to segment the subject's face; thermal matching based on partial least squares discriminant analysis. Thermal imagery face recognition methods are advantageous when there is no control over illumination or for detecting disguised faces. The proposed algorithm leads to good matching accuracies for different person recognition scenarios (near infrared, far infrared, thermal infrared, viewed sketch). The performance of the proposed face recognition algorithm in real indoor environments is presented and discussed.

  18. A novel fingerprint recognition algorithm based on VK-LLE

    NASA Astrophysics Data System (ADS)

    Luo, Jing; Lin, Shu-zhong; Ni, Jian-yun; Song, Li-mei

    2009-07-01

    It is a challenging problem to overcome shift and rotation and nonlinearity in fingerprint images. By analyzing the shortcoming of fingerprint recognition algorithm on shift or rotation images at present, manifold learning algorithm is introduced. A fingerprint recognition algorithm has been proposed based on locally linear embedding of variable neighbourhood k (VK-LLE). Firstly, approximate geodesic distance between any two points is computed by ISOMAP ( isometric feature mapping) and then the neighborhood is determined for each point by the relationship between its local estimated geodesic distance matrix and local Euclidean distance matrix. Secondly, the dimension of fingerprint image is reduced by nonlinear dimension-reduction method. And the best projected features of original fingerprint data of large dimension are acquired. By analyzing the changes of recognition accuracy with the neighborhood and embedding dimension, the neighborhood and embedding dimension is determined at last. Finally, fingerprint recognition is accomplished by Euclidean distance Classifier. The experimental results based on standard fingerprint datasets have verified the proposed algorithm had a better robustness to those fingerprint images of shift or rotation or nonlinearity than the algorithm using LLE, thus this method has some values in practice.

  19. Learning algorithm of environmental recognition in driving vehicle

    SciTech Connect

    Qiao, L.; Sato, M.; Takeda, H.

    1995-06-01

    We consider the problem of recognizing driving environments of a vehicle by using the information obtained from some sensors of the vehicle. Previously, we presented recognition algorithms based on a usual method of pattern matching by use of distance on a vector space and fuzzy reasoning. These algorithms can not be applied to meet the demands of nonstandard drivers and changes of vehicle properties, because the standard pattern or membership function for the pattern matching is always fixed. Then to cover such weakness we presented adaptive recognition algorithms with adaptive change of the standard pattern and membership function. In this work, we put forward a fuzzy supervisor in the learning process. Also we presented an algorithm into which a new learning method is introduced to improve the performance of the previous ones and to meet the above demands. 18 refs.

  20. Leukocyte Recognition Using EM-Algorithm

    NASA Astrophysics Data System (ADS)

    Colunga, Mario Chirinos; Siordia, Oscar Sánchez; Maybank, Stephen J.

    This document describes a method for classifying images of blood cells. Three different classes of cells are used: Band Neutrophils, Eosinophils and Lymphocytes. The image pattern is projected down to a lower dimensional sub space using PCA; the probability density function for each class is modeled with a Gaussian mixture using the EM-Algorithm. A new cell image is classified using the maximum a posteriori decision rule.

  1. Physical Human Activity Recognition Using Wearable Sensors.

    PubMed

    Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

    2015-12-11

    This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

  2. Physical Human Activity Recognition Using Wearable Sensors

    PubMed Central

    Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

    2015-01-01

    This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject. PMID:26690450

  3. Effectiveness of feature and classifier algorithms in character recognition systems

    NASA Astrophysics Data System (ADS)

    Wilson, Charles L.

    1993-04-01

    At the first Census Optical Character Recognition Systems Conference, NIST generated accuracy data for more than character recognition systems. Most systems were tested on the recognition of isolated digits and upper and lower case alphabetic characters. The recognition experiments were performed on sample sizes of 58,000 digits, and 12,000 upper and lower case alphabetic characters. The algorithms used by the 26 conference participants included rule-based methods, image-based methods, statistical methods, and neural networks. The neural network methods included Multi-Layer Perceptron's, Learned Vector Quantitization, Neocognitrons, and cascaded neural networks. In this paper 11 different systems are compared using correlations between the answers of different systems, comparing the decrease in error rate as a function of confidence of recognition, and comparing the writer dependence of recognition. This comparison shows that methods that used different algorithms for feature extraction and recognition performed with very high levels of correlation. This is true for neural network systems, hybrid systems, and statistically based systems, and leads to the conclusion that neural networks have not yet demonstrated a clear superiority to more conventional statistical methods. Comparison of these results with the models of Vapnick (for estimation problems), MacKay (for Bayesian statistical models), Moody (for effective parameterization), and Boltzmann models (for information content) demonstrate that as the limits of training data variance are approached, all classifier systems have similar statistical properties. The limiting condition can only be approached for sufficiently rich feature sets because the accuracy limit is controlled by the available information content of the training set, which must pass through the feature extraction process prior to classification.

  4. A study of speech emotion recognition based on hybrid algorithm

    NASA Astrophysics Data System (ADS)

    Zhu, Ju-xia; Zhang, Chao; Lv, Zhao; Rao, Yao-quan; Wu, Xiao-pei

    2011-10-01

    To effectively improve the recognition accuracy of the speech emotion recognition system, a hybrid algorithm which combines Continuous Hidden Markov Model (CHMM), All-Class-in-One Neural Network (ACON) and Support Vector Machine (SVM) is proposed. In SVM and ACON methods, some global statistics are used as emotional features, while in CHMM method, instantaneous features are employed. The recognition rate by the proposed method is 92.25%, with the rejection rate to be 0.78%. Furthermore, it obtains the relative increasing of 8.53%, 4.69% and 0.78% compared with ACON, CHMM and SVM methods respectively. The experiment result confirms the efficiency of distinguishing anger, happiness, neutral and sadness emotional states.

  5. Effect of severe image compression on face recognition algorithms

    NASA Astrophysics Data System (ADS)

    Zhao, Peilong; Dong, Jiwen; Li, Hengjian

    2015-10-01

    In today's information age, people will depend more and more on computers to obtain and make use of information, there is a big gap between the multimedia information after digitization that has large data and the current hardware technology that can provide the computer storage resources and network band width. For example, there is a large amount of image storage and transmission problem. Image compression becomes useful in cases when images need to be transmitted across networks in a less costly way by increasing data volume while reducing transmission time. This paper discusses image compression to effect on face recognition system. For compression purposes, we adopted the JPEG, JPEG2000, JPEG XR coding standard. The face recognition algorithms studied are SIFT. As a form of an extensive research, Experimental results show that it still maintains a high recognition rate under the high compression ratio, and JPEG XR standards is superior to other two kinds in terms of performance and complexity.

  6. Clustering algorithms for Stokes space modulation format recognition.

    PubMed

    Boada, Ricard; Borkowski, Robert; Monroy, Idelfonso Tafur

    2015-06-15

    Stokes space modulation format recognition (Stokes MFR) is a blind method enabling digital coherent receivers to infer modulation format information directly from a received polarization-division-multiplexed signal. A crucial part of the Stokes MFR is a clustering algorithm, which largely influences the performance of the detection process, particularly at low signal-to-noise ratios. This paper reports on an extensive study of six different clustering algorithms: k-means, expectation maximization, density-based DBSCAN and OPTICS, spectral clustering and maximum likelihood clustering, used for discriminating between dual polarization: BPSK, QPSK, 8-PSK, 8-QAM, and 16-QAM. We determine essential performance metrics for each clustering algorithm and modulation format under test: minimum required signal-to-noise ratio, detection accuracy and algorithm complexity.

  7. Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

    NASA Astrophysics Data System (ADS)

    Lee, Chien-Cheng; Huang, Shin-Sheng; Shih, Cheng-Yuan

    2010-12-01

    This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB) with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO) algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.

  8. Balanced 0, + or - Matrices. Part 2. Recognition Algorithm

    DTIC Science & Technology

    1994-01-22

    Matrices MAY 101994 Part II: Recognition Algorithm D Michele ConfortlI G6rard Cornu6j~ls2 Ajai Kapoor Krisina Vuskovi 2 January 22, 1994 Dipartimento...di Matematica Pura ed Applicata Universiti di Padova, Via Belzoni 7, 94-13892 35131 Padova, Italy I IIII In II ii I l1i III Graduate School of...for balanced 0, ± matrices . This algorithm is based on a decomposition theorem proved in a companion paper. Acce166 ýr7 NTIS CRA& D’BC TAB L 1 U

  9. Activity recognition with smartphone support.

    PubMed

    Guiry, John J; van de Ven, Pepijn; Nelson, John; Warmerdam, Lisanne; Riper, Heleen

    2014-06-01

    In this paper, the authors describe a method of accurately detecting human activity using a smartphone accelerometer paired with a dedicated chest sensor. The design, implementation, testing and validation of a custom mobility classifier are also presented. Offline analysis was carried out to compare this custom classifier to de-facto machine learning algorithms, including C4.5, CART, SVM, Multi-Layer Perceptrons, and Naïve Bayes. A series of trials were carried out in Ireland, initially involving N=6 individuals to test the feasibility of the system, before a final trial with N=24 subjects took place in the Netherlands. The protocol used and analysis of 1165min of recorded activities from these trials are described in detail in this paper. Analysis of collected data indicate that accelerometers placed in these locations, are capable of recognizing activities including sitting, standing, lying, walking, running and cycling with accuracies as high as 98%.

  10. A Multifaceted Independent Performance Analysis of Facial Subspace Recognition Algorithms

    PubMed Central

    Bajwa, Usama Ijaz; Taj, Imtiaz Ahmad; Anwar, Muhammad Waqas; Wang, Xuan

    2013-01-01

    Face recognition has emerged as the fastest growing biometric technology and has expanded a lot in the last few years. Many new algorithms and commercial systems have been proposed and developed. Most of them use Principal Component Analysis (PCA) as a base for their techniques. Different and even conflicting results have been reported by researchers comparing these algorithms. The purpose of this study is to have an independent comparative analysis considering both performance and computational complexity of six appearance based face recognition algorithms namely PCA, 2DPCA, A2DPCA, (2D)2PCA, LPP and 2DLPP under equal working conditions. This study was motivated due to the lack of unbiased comprehensive comparative analysis of some recent subspace methods with diverse distance metric combinations. For comparison with other studies, FERET, ORL and YALE databases have been used with evaluation criteria as of FERET evaluations which closely simulate real life scenarios. A comparison of results with previous studies is performed and anomalies are reported. An important contribution of this study is that it presents the suitable performance conditions for each of the algorithms under consideration. PMID:23451054

  11. Practical algorithms for algebraic and logical correction in precedent-based recognition problems

    NASA Astrophysics Data System (ADS)

    Ablameyko, S. V.; Biryukov, A. S.; Dokukin, A. A.; D'yakonov, A. G.; Zhuravlev, Yu. I.; Krasnoproshin, V. V.; Obraztsov, V. A.; Romanov, M. Yu.; Ryazanov, V. V.

    2014-12-01

    Practical precedent-based recognition algorithms relying on logical or algebraic correction of various heuristic recognition algorithms are described. The recognition problem is solved in two stages. First, an arbitrary object is recognized independently by algorithms from a group. Then a final collective solution is produced by a suitable corrector. The general concepts of the algebraic approach are presented, practical algorithms for logical and algebraic correction are described, and results of their comparison are given.

  12. Efficient detection and recognition algorithm of reference points in photogrammetry

    NASA Astrophysics Data System (ADS)

    Li, Weimin; Liu, Gang; Zhu, Lichun; Li, Xiaofeng; Zhang, Yuhai; Shan, Siyu

    2016-04-01

    In photogrammetry, an approach of automatic detection and recognition on reference points have been proposed to meet the requirements on detection and matching of reference points. The reference points used here are the CCT(circular coded target), which compose of two parts: the round target point in central region and the circular encoding band in surrounding region. Firstly, the contours of image are extracted, after that noises and disturbances of the image are filtered out by means of a series of criteria, such as the area of the contours, the correlation coefficient between two regions of contours etc. Secondly, the cubic spline interpolation is adopted to process the central contour region of the CCT. The contours of the interpolated image are extracted again, then the least square ellipse fitting is performed to calculate the center coordinates of the CCT. Finally, the encoded value is obtained by the angle information from the circular encoding band of the CCT. From the experiment results, the location precision of the CCT can be achieved to sub-pixel level of the algorithm presented. Meanwhile the recognition accuracy is pretty high, even if the background of the image is complex and full of disturbances. In addition, the property of the algorithm is robust. Furthermore, the runtime of the algorithm is fast.

  13. Pattern-Recognition Algorithm for Locking Laser Frequency

    NASA Technical Reports Server (NTRS)

    Karayan, Vahag; Klipstein, William; Enzer, Daphna; Yates, Philip; Thompson, Robert; Wells, George

    2006-01-01

    A computer program serves as part of a feedback control system that locks the frequency of a laser to one of the spectral peaks of cesium atoms in an optical absorption cell. The system analyzes a saturation absorption spectrum to find a target peak and commands a laser-frequency-control circuit to minimize an error signal representing the difference between the laser frequency and the target peak. The program implements an algorithm consisting of the following steps: Acquire a saturation absorption signal while scanning the laser through the frequency range of interest. Condition the signal by use of convolution filtering. Detect peaks. Match the peaks in the signal to a pattern of known spectral peaks by use of a pattern-recognition algorithm. Add missing peaks. Tune the laser to the desired peak and thereafter lock onto this peak. Finding and locking onto the desired peak is a challenging problem, given that the saturation absorption signal includes noise and other spurious signal components; the problem is further complicated by nonlinearity and shifting of the voltage-to-frequency correspondence. The pattern-recognition algorithm, which is based on Hausdorff distance, is what enables the program to meet these challenges.

  14. Implementation of pattern recognition algorithm based on RBF neural network

    NASA Astrophysics Data System (ADS)

    Bouchoux, Sophie; Brost, Vincent; Yang, Fan; Grapin, Jean Claude; Paindavoine, Michel

    2002-12-01

    In this paper, we present implementations of a pattern recognition algorithm which uses a RBF (Radial Basis Function) neural network. Our aim is to elaborate a quite efficient system which realizes real time faces tracking and identity verification in natural video sequences. Hardware implementations have been realized on an embedded system developed by our laboratory. This system is based on a DSP (Digital Signal Processor) TMS320C6x. The optimization of implementations allow us to obtain a processing speed of 4.8 images (240x320 pixels) per second with a correct rate of 95% of faces tracking and identity verification.

  15. Purposive recognition: an active and qualitative approach

    NASA Astrophysics Data System (ADS)

    Rivlin, Ehud; Aloimonos, Yiannis; Rosenfeld, Azriel

    1992-04-01

    We propose an alternative way to study the problem of visual recognition which is closer to the spirit emerging from Brooks' work on building robots than to Marr's reconstructive approach. Our theory is purposive in the sense that recognition is considered in the context of an agent performing it in an environment, along with the agent's intentions that translate into a set of behaviors; it is qualitative in the sense that only partial recovery is needed; it is active in the sense that various partial recovery tasks need for recognition are achieved through active vision; and it is opportunistic in the sense that every available cue is used.

  16. Pattern activation/recognition theory of mind

    PubMed Central

    du Castel, Bertrand

    2015-01-01

    In his 2012 book How to Create a Mind, Ray Kurzweil defines a “Pattern Recognition Theory of Mind” that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call “Pattern Activation/Recognition Theory of Mind.” While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation. PMID:26236228

  17. Pattern activation/recognition theory of mind.

    PubMed

    du Castel, Bertrand

    2015-01-01

    In his 2012 book How to Create a Mind, Ray Kurzweil defines a "Pattern Recognition Theory of Mind" that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call "Pattern Activation/Recognition Theory of Mind." While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation.

  18. Making Activity Recognition Robust against Deceptive Behavior.

    PubMed

    Saeb, Sohrab; Körding, Konrad; Mohr, David C

    2015-01-01

    Healthcare services increasingly use the activity recognition technology to track the daily activities of individuals. In some cases, this is used to provide incentives. For example, some health insurance companies offer discount to customers who are physically active, based on the data collected from their activity tracking devices. Therefore, there is an increasing motivation for individuals to cheat, by making activity trackers detect activities that increase their benefits rather than the ones they actually do. In this study, we used a novel method to make activity recognition robust against deceptive behavior. We asked 14 subjects to attempt to trick our smartphone-based activity classifier by making it detect an activity other than the one they actually performed, for example by shaking the phone while seated to make the classifier detect walking. If they succeeded, we used their motion data to retrain the classifier, and asked them to try to trick it again. The experiment ended when subjects could no longer cheat. We found that some subjects were not able to trick the classifier at all, while others required five rounds of retraining. While classifiers trained on normal activity data predicted true activity with ~38% accuracy, training on the data gathered during the deceptive behavior increased their accuracy to ~84%. We conclude that learning the deceptive behavior of one individual helps to detect the deceptive behavior of others. Thus, we can make current activity recognition robust to deception by including deceptive activity data from a few individuals.

  19. Making Activity Recognition Robust against Deceptive Behavior

    PubMed Central

    Saeb, Sohrab; Körding, Konrad; Mohr, David C.

    2015-01-01

    Healthcare services increasingly use the activity recognition technology to track the daily activities of individuals. In some cases, this is used to provide incentives. For example, some health insurance companies offer discount to customers who are physically active, based on the data collected from their activity tracking devices. Therefore, there is an increasing motivation for individuals to cheat, by making activity trackers detect activities that increase their benefits rather than the ones they actually do. In this study, we used a novel method to make activity recognition robust against deceptive behavior. We asked 14 subjects to attempt to trick our smartphone-based activity classifier by making it detect an activity other than the one they actually performed, for example by shaking the phone while seated to make the classifier detect walking. If they succeeded, we used their motion data to retrain the classifier, and asked them to try to trick it again. The experiment ended when subjects could no longer cheat. We found that some subjects were not able to trick the classifier at all, while others required five rounds of retraining. While classifiers trained on normal activity data predicted true activity with ~38% accuracy, training on the data gathered during the deceptive behavior increased their accuracy to ~84%. We conclude that learning the deceptive behavior of one individual helps to detect the deceptive behavior of others. Thus, we can make current activity recognition robust to deception by including deceptive activity data from a few individuals. PMID:26659118

  20. A correlation-based algorithm for recognition and tracking of partially occluded objects

    NASA Astrophysics Data System (ADS)

    Ruchay, Alexey; Kober, Vitaly

    2016-09-01

    In this work, a correlation-based algorithm consisting of a set of adaptive filters for recognition of occluded objects in still and dynamic scenes in the presence of additive noise is proposed. The designed algorithm is adaptive to the input scene, which may contain different fragments of the target, false objects, and background to be rejected. The algorithm output is high correlation peaks corresponding to pieces of the target in scenes. The proposed algorithm uses a bank of composite optimum filters. The performance of the proposed algorithm for recognition partially occluded objects is compared with that of common algorithms in terms of objective metrics.

  1. Optimal Recognition Method of Human Activities Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Oniga, Stefan; József, Sütő

    2015-12-01

    The aim of this research is an exhaustive analysis of the various factors that may influence the recognition rate of the human activity using wearable sensors data. We made a total of 1674 simulations on a publically released human activity database by a group of researcher from the University of California at Berkeley. In a previous research, we analyzed the influence of the number of sensors and their placement. In the present research we have examined the influence of the number of sensor nodes, the type of sensor node, preprocessing algorithms, type of classifier and its parameters. The final purpose is to find the optimal setup for best recognition rates with lowest hardware and software costs.

  2. Development of a two wheeled self balancing robot with speech recognition and navigation algorithm

    NASA Astrophysics Data System (ADS)

    Rahman, Md. Muhaimin; Ashik-E-Rasul, Haq, Nowab. Md. Aminul; Hassan, Mehedi; Hasib, Irfan Mohammad Al; Hassan, K. M. Rafidh

    2016-07-01

    This paper is aimed to discuss modeling, construction and development of navigation algorithm of a two wheeled self balancing mobile robot in an enclosure. In this paper, we have discussed the design of two of the main controller algorithms, namely PID algorithms, on the robot model. Simulation is performed in the SIMULINK environment. The controller is developed primarily for self-balancing of the robot and also it's positioning. As for the navigation in an enclosure, template matching algorithm is proposed for precise measurement of the robot position. The navigation system needs to be calibrated before navigation process starts. Almost all of the earlier template matching algorithms that can be found in the open literature can only trace the robot. But the proposed algorithm here can also locate the position of other objects in an enclosure, like furniture, tables etc. This will enable the robot to know the exact location of every stationary object in the enclosure. Moreover, some additional features, such as Speech Recognition and Object Detection, are added. For Object Detection, the single board Computer Raspberry Pi is used. The system is programmed to analyze images captured via the camera, which are then processed through background subtraction, followed by active noise reduction.

  3. An Improved Algorithm for Linear Inequalities in Pattern Recognition and Switching Theory.

    ERIC Educational Resources Information Center

    Geary, Leo C.

    This thesis presents a new iterative algorithm for solving an n by l solution vector w, if one exists, to a set of linear inequalities, A w greater than zero which arises in pattern recognition and switching theory. The algorithm is an extension of the Ho-Kashyap algorithm, utilizing the gradient descent procedure to minimize a criterion function…

  4. Pattern recognition and active vision in chickens.

    PubMed

    Dawkins, M S; Woodington, A

    2000-02-10

    Recognition of objects or environmental landmarks is problematic because appearance can vary widely depending on illumination, viewing distance, angle of view and so on. Storing a separate image or 'template' for every possible view requires vast numbers to be stored and scanned, has a high probability of recognition error and appears not to be the solution adopted by primates. However, some invertebrate template matching systems can achieve recognition by 'active vision' in which the animal's own behaviour is used to achieve a fit between template and object, for example by repeatedly following a set path. Recognition is thus limited to views from the set path but achieved with a minimal number of templates. Here we report the first evidence of similar active vision in a bird, in the form of locomotion and individually distinct head movements that give the eyes a similar series of views on different occasions. The hens' ability to recognize objects is also found to decrease when their normal paths are altered.

  5. Human body contour data based activity recognition.

    PubMed

    Myagmarbayar, Nergui; Yuki, Yoshida; Imamoglu, Nevrez; Gonzalez, Jose; Otake, Mihoko; Yu, Wenwei

    2013-01-01

    This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate.

  6. Active AU Based Patch Weighting for Facial Expression Recognition

    PubMed Central

    Xie, Weicheng; Shen, Linlin; Yang, Meng; Lai, Zhihui

    2017-01-01

    Facial expression has many applications in human-computer interaction. Although feature extraction and selection have been well studied, the specificity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on action unit (AU) weighting and patch weight optimization is proposed to represent the specificity of the expression triplet. The sparse representation-based approach is then proposed to detect the active AUs of the testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for the Jaffe and Cohn–Kanade (CK+) databases, respectively. Better cross-database performance has also been observed. PMID:28146094

  7. A robust face recognition algorithm under varying illumination using adaptive retina modeling

    NASA Astrophysics Data System (ADS)

    Cheong, Yuen Kiat; Yap, Vooi Voon; Nisar, Humaira

    2013-10-01

    Variation in illumination has a drastic effect on the appearance of a face image. This may hinder the automatic face recognition process. This paper presents a novel approach for face recognition under varying lighting conditions. The proposed algorithm uses adaptive retina modeling based illumination normalization. In the proposed approach, retina modeling is employed along with histogram remapping following normal distribution. Retina modeling is an approach that combines two adaptive nonlinear equations and a difference of Gaussians filter. Two databases: extended Yale B database and CMU PIE database are used to verify the proposed algorithm. For face recognition Gabor Kernel Fisher Analysis method is used. Experimental results show that the recognition rate for the face images with different illumination conditions has improved by the proposed approach. Average recognition rate for Extended Yale B database is 99.16%. Whereas, the recognition rate for CMU-PIE database is 99.64%.

  8. Visual pattern recognition network: its training algorithm and its optoelectronic architecture

    NASA Astrophysics Data System (ADS)

    Wang, Ning; Liu, Liren

    1996-07-01

    A visual pattern recognition network and its training algorithm are proposed. The network constructed of a one-layer morphology network and a two-layer modified Hamming net. This visual network can implement invariant pattern recognition with respect to image translation and size projection. After supervised learning takes place, the visual network extracts image features and classifies patterns much the same as living beings do. Moreover we set up its optoelectronic architecture for real-time pattern recognition.

  9. An arc-length warping algorithm for gesture recognition using quaternion representation.

    PubMed

    Cifuentes, Jenny; Pham, Minh Tu; Moreau, Richard; Prieto, Flavio; Boulanger, Pierre

    2013-01-01

    This paper presents a new algorithm, called Dynamic Arc-Length Warping algorithm (DALW) for hand gesture recognition based on the orientation data. In this algorithm, after calculating the quaternion for each orientation measurement, we use DALW algorithm to obtain a similarity measure between different trajectories. We present the benefits of using quaternion alongside the implementation of Dynamic Arc Length Warping to present an optimized tool for gesture recognition.We show the advantages of this approach compared with other techniques. This tool can be used to distinguish similar and different gestures. An experimental validation is carried out to classify a series of simple human gestures.

  10. Modeling words with subword units in an articulatorily constrained speech recognition algorithm

    SciTech Connect

    Hogden, J.

    1997-11-20

    The goal of speech recognition is to find the most probable word given the acoustic evidence, i.e. a string of VQ codes or acoustic features. Speech recognition algorithms typically take advantage of the fact that the probability of a word, given a sequence of VQ codes, can be calculated.

  11. Human suspicious activity recognition in thermal infrared video

    NASA Astrophysics Data System (ADS)

    Hossen, Jakir; Jacobs, Eddie; Chowdhury, Fahmida K.

    2014-10-01

    Detecting suspicious behaviors is important for surveillance and monitoring systems. In this paper, we investigate suspicious activity detection in thermal infrared imagery, where human motion can be easily detected from the background regardless of the lighting conditions and colors of the human clothing and surfaces. We use locally adaptive regression kernels (LARK) as patch descriptors, which capture the underlying local structure of the data exceedingly well, even in the presence of significant distortions. Patch descriptors are generated for each query patch and for each database patch. A statistical approach is used to match the query activity with the database to make the decision of suspicious activity. Human activity videos in different condition such as, walking, running, carrying a gun, crawling, and carrying backpack in different terrains were acquired using thermal infrared camera. These videos are used for training and performance evaluation of the algorithm. Experimental results show that the proposed approach achieves good performance in suspicious activity recognition.

  12. An algorithm for recognition and localization of rotated and scaled objects

    NASA Technical Reports Server (NTRS)

    Peli, T.

    1981-01-01

    An algorithm for recognition and localization of objects, which is invariant to displacement and rotation, is extended to the recognition and localization of differently scaled, rotated, and displaced objects. The proposed algorithm provides an optimum way to find if a match exists between two objects that are scaled, rotated, and displaced, while the number of computations is of the same order as for equally scaled objects.

  13. Recognition of human activities with wearable sensors

    NASA Astrophysics Data System (ADS)

    He, Weihua; Guo, Yongcai; Gao, Chao; Li, Xinke

    2012-12-01

    A novel approach for recognizing human activities with wearable sensors is investigated in this article. The key techniques of this approach include the generalized discriminant analysis (GDA) and the relevance vector machines (RVM). The feature vectors extracted from the measured signal are processed by GDA, with its dimension remarkably reduced from 350 to 12 while fully maintaining the most discriminative information. The reduced feature vectors are then classified by the RVM technique according to an extended multiclass model, which shows good convergence characteristic. Experimental results on the Wearable Action Recognition Dataset demonstrate that our approach achieves an encouraging recognition rate of 99.2%, true positive rate of 99.18% and false positive rate of 0.07%. Although in most cases, the support vector machines model has more than 70 support vectors, the number of relevance vectors related to different activities is always not more than 4, which implies a great simplicity in the classifier structure. Our approach is expected to have potential in real-time applications or solving problems with large-scale datasets, due to its perfect recognition performance, strong ability in feature reduction, and simple classifier structure.

  14. Implementation of The LDA Algorithm for Online Validation Based on Face Recognition

    NASA Astrophysics Data System (ADS)

    Zainuddin, Z.; Laswi, A. S.

    2017-01-01

    This paper report work in implementation of computer vision application in face recognition to the on-line validation for distance learning. Face recognition is chosen among many other alternatives of validation because its robustness. The problem with basic validation such as password cannot validate the student in distance learning. This cannot be accepted especially is distance examination. Face recognition algorithm used in this research is Linear Discriminant Analysis (LDA). By using this algorithm, the system capable of recognize the authorized persons about 93% and reject the unauthorized persons 100%.

  15. Active Behavior Recognition in Beyond Visual Range Air Combat

    DTIC Science & Technology

    2015-05-01

    Active Behavior Recognition in Beyond Visual Range Air Combat Ron Alford RONALD.ALFORD.CTR@NRL.NAVY.MIL ASEE Postdoctoral Fellow; Naval Research...planning and recognition, as well as its im- plementation in a beyond visual range air combat simulator. We found that it yields better behavior recognition...SUBTITLE Active Behavior Recognition in Beyond Visual Range Air Combat 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR

  16. Eye movement analysis for activity recognition using electrooculography.

    PubMed

    Bulling, Andreas; Ward, Jamie A; Gellersen, Hans; Tröster, Gerhard

    2011-04-01

    In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.

  17. Computational and performance aspects of PCA-based face-recognition algorithms.

    PubMed

    Moon, H; Phillips, P J

    2001-01-01

    Algorithms based on principal component analysis (PCA) form the basis of numerous studies in the psychological and algorithmic face-recognition literature. PCA is a statistical technique and its incorporation into a face-recognition algorithm requires numerous design decisions. We explicitly state the design decisions by introducing a generic modular PCA-algorithm. This allows us to investigate these decisions, including those not documented in the literature. We experimented with different implementations of each module, and evaluated the different implementations using the September 1996 FERET evaluation protocol (the de facto standard for evaluating face-recognition algorithms). We experimented with (i) changing the illumination normalization procedure; (ii) studying effects on algorithm performance of compressing images with JPEG and wavelet compression algorithms; (iii) varying the number of eigenvectors in the representation; and (iv) changing the similarity measure in the classification process. We performed two experiments. In the first experiment, we obtained performance results on the standard September 1996 FERET large-gallery image sets. In the second experiment, we examined the variability in algorithm performance on different sets of facial images. The study was performed on 100 randomly generated image sets (galleries) of the same size. Our two most significant results are (i) changing the similarity measure produced the greatest change in performance, and (ii) that difference in performance of +/- 10% is needed to distinguish between algorithms.

  18. Recognition of pharmaceuticals with compact mini-Raman-spectrometer and automized pattern recognition algorithms

    NASA Astrophysics Data System (ADS)

    Jähme, Hendrik; Di Florio, Giuseppe; Conti Nibali, Valeria; Esen, Cemal; Ostendorf, Andreas; Grafen, Markus; Henke, Erich; Soetebier, Jens; Brenner, Carsten; Havenith, Martina; Hofmann, Martin R.

    2016-04-01

    Robust classification of pharmaceuticals in an industrial process is an important step for validation of the final product. Especially for pharmaceuticals with similar visual appearance a quality control is only possible if a reliable algorithm based on easily obtainable spectroscopic data is available. We used Principal Component Analysis (PCA) and Support Vector Machines (SVM) on Raman spectroscopy data from a compact Raman system to classify several look-alike pharmaceuticals. This paper describes the data gathering and analysis process to robustly discriminate 19 different pharmaceuticals with similar visual appearance. With the described process we successfully identified all given pharmaceuticals which had a significant amount of active ingredients. Thus automatic validation of these pharmaceuticals in a process can be used to prevent wrong administration of look-alike drugs in an industrial setting, e.g. patient individual blistering.

  19. Performance Evaluation of Neuromorphic-Vision Object Recognition Algorithms

    DTIC Science & Technology

    2014-08-01

    Malibu Canyon Rd, Malibu, CA 90265, USA Lior Elazary, Randolph C. Voorhies, Daniel F. Parks, Laurent Itti University of Southern California 3641...and Pattern Recognition, CVPR’07, pp. 1-8. [12] Carpenter , G., Grossberg, S. and Reynolds. J.H. (1991), “ARTMAP: Supervised real-time learning and

  20. Hessian-regularized co-training for social activity recognition.

    PubMed

    Liu, Weifeng; Li, Yang; Lin, Xu; Tao, Dacheng; Wang, Yanjiang

    2014-01-01

    Co-training is a major multi-view learning paradigm that alternately trains two classifiers on two distinct views and maximizes the mutual agreement on the two-view unlabeled data. Traditional co-training algorithms usually train a learner on each view separately and then force the learners to be consistent across views. Although many co-trainings have been developed, it is quite possible that a learner will receive erroneous labels for unlabeled data when the other learner has only mediocre accuracy. This usually happens in the first rounds of co-training, when there are only a few labeled examples. As a result, co-training algorithms often have unstable performance. In this paper, Hessian-regularized co-training is proposed to overcome these limitations. Specifically, each Hessian is obtained from a particular view of examples; Hessian regularization is then integrated into the learner training process of each view by penalizing the regression function along the potential manifold. Hessian can properly exploit the local structure of the underlying data manifold. Hessian regularization significantly boosts the generalizability of a classifier, especially when there are a small number of labeled examples and a large number of unlabeled examples. To evaluate the proposed method, extensive experiments were conducted on the unstructured social activity attribute (USAA) dataset for social activity recognition. Our results demonstrate that the proposed method outperforms baseline methods, including the traditional co-training and LapCo algorithms.

  1. Hessian-Regularized Co-Training for Social Activity Recognition

    PubMed Central

    Liu, Weifeng; Li, Yang; Lin, Xu; Tao, Dacheng; Wang, Yanjiang

    2014-01-01

    Co-training is a major multi-view learning paradigm that alternately trains two classifiers on two distinct views and maximizes the mutual agreement on the two-view unlabeled data. Traditional co-training algorithms usually train a learner on each view separately and then force the learners to be consistent across views. Although many co-trainings have been developed, it is quite possible that a learner will receive erroneous labels for unlabeled data when the other learner has only mediocre accuracy. This usually happens in the first rounds of co-training, when there are only a few labeled examples. As a result, co-training algorithms often have unstable performance. In this paper, Hessian-regularized co-training is proposed to overcome these limitations. Specifically, each Hessian is obtained from a particular view of examples; Hessian regularization is then integrated into the learner training process of each view by penalizing the regression function along the potential manifold. Hessian can properly exploit the local structure of the underlying data manifold. Hessian regularization significantly boosts the generalizability of a classifier, especially when there are a small number of labeled examples and a large number of unlabeled examples. To evaluate the proposed method, extensive experiments were conducted on the unstructured social activity attribute (USAA) dataset for social activity recognition. Our results demonstrate that the proposed method outperforms baseline methods, including the traditional co-training and LapCo algorithms. PMID:25259945

  2. Fusion of Visible and Thermal Descriptors Using Genetic Algorithms for Face Recognition Systems.

    PubMed

    Hermosilla, Gabriel; Gallardo, Francisco; Farias, Gonzalo; San Martin, Cesar

    2015-07-23

    The aim of this article is to present a new face recognition system based on the fusion of visible and thermal features obtained from the most current local matching descriptors by maximizing face recognition rates through the use of genetic algorithms. The article considers a comparison of the performance of the proposed fusion methodology against five current face recognition methods and classic fusion techniques used commonly in the literature. These were selected by considering their performance in face recognition. The five local matching methods and the proposed fusion methodology are evaluated using the standard visible/thermal database, the Equinox database, along with a new database, the PUCV-VTF, designed for visible-thermal studies in face recognition and described for the first time in this work. The latter is created considering visible and thermal image sensors with different real-world conditions, such as variations in illumination, facial expression, pose, occlusion, etc. The main conclusions of this article are that two variants of the proposed fusion methodology surpass current face recognition methods and the classic fusion techniques reported in the literature, attaining recognition rates of over 97% and 99% for the Equinox and PUCV-VTF databases, respectively. The fusion methodology is very robust to illumination and expression changes, as it combines thermal and visible information efficiently by using genetic algorithms, thus allowing it to choose optimal face areas where one spectrum is more representative than the other.

  3. Study on recognition algorithm for paper currency numbers based on neural network

    NASA Astrophysics Data System (ADS)

    Li, Xiuyan; Liu, Tiegen; Li, Yuanyao; Zhang, Zhongchuan; Deng, Shichao

    2008-12-01

    Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.

  4. Fusion of Visible and Thermal Descriptors Using Genetic Algorithms for Face Recognition Systems

    PubMed Central

    Hermosilla, Gabriel; Gallardo, Francisco; Farias, Gonzalo; San Martin, Cesar

    2015-01-01

    The aim of this article is to present a new face recognition system based on the fusion of visible and thermal features obtained from the most current local matching descriptors by maximizing face recognition rates through the use of genetic algorithms. The article considers a comparison of the performance of the proposed fusion methodology against five current face recognition methods and classic fusion techniques used commonly in the literature. These were selected by considering their performance in face recognition. The five local matching methods and the proposed fusion methodology are evaluated using the standard visible/thermal database, the Equinox database, along with a new database, the PUCV-VTF, designed for visible-thermal studies in face recognition and described for the first time in this work. The latter is created considering visible and thermal image sensors with different real-world conditions, such as variations in illumination, facial expression, pose, occlusion, etc. The main conclusions of this article are that two variants of the proposed fusion methodology surpass current face recognition methods and the classic fusion techniques reported in the literature, attaining recognition rates of over 97% and 99% for the Equinox and PUCV-VTF databases, respectively. The fusion methodology is very robust to illumination and expression changes, as it combines thermal and visible information efficiently by using genetic algorithms, thus allowing it to choose optimal face areas where one spectrum is more representative than the other. PMID:26213932

  5. 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

  6. Algorithmic recognition of anomalous time intervals in sea-level observations

    NASA Astrophysics Data System (ADS)

    Getmanov, V. G.; Gvishiani, A. D.; Kamaev, D. A.; Kornilov, A. S.

    2016-03-01

    The problem of the algorithmic recognition of anomalous time intervals in the time series of the sea-level observations conducted by the Russian Tsunami Warning Survey (RTWS) is considered. The normal and anomalous sea-level observations are described. The polyharmonic models describing the sea-level fluctuations on the short time intervals are constructed, and sea-level forecasting based on these models is suggested. The algorithm for the recognition of anomalous time intervals is developed and its work is tested on the real RTWS data.

  7. Classifying performance impairment in response to sleep loss using pattern recognition algorithms on single session testing

    PubMed Central

    St. Hilaire, Melissa A.; Sullivan, Jason P.; Anderson, Clare; Cohen, Daniel A.; Barger, Laura K.; Lockley, Steven W.; Klerman, Elizabeth B.

    2012-01-01

    There is currently no “gold standard” marker of cognitive performance impairment resulting from sleep loss. We utilized pattern recognition algorithms to determine which features of data collected under controlled laboratory conditions could most reliably identify cognitive performance impairment in response to sleep loss using data from only one testing session, such as would occur in the “real world” or field conditions. A training set for testing the pattern recognition algorithms was developed using objective Psychomotor Vigilance Task (PVT) and subjective Karolinska Sleepiness Scale (KSS) data collected from laboratory studies during which subjects were sleep deprived for 26 – 52 hours. The algorithm was then tested in data from both laboratory and field experiments. The pattern recognition algorithm was able to identify performance impairment with a single testing session in individuals studied under laboratory conditions using PVT, KSS, length of time awake and time of day information with sensitivity and specificity as high as 82%. When this algorithm was tested on data collected under real-world conditions from individuals whose data were not in the training set, accuracy of predictions for individuals categorized with low performance impairment were as high as 98%. Predictions for medium and severe performance impairment were less accurate. We conclude that pattern recognition algorithms may be a promising method for identifying performance impairment in individuals using only current information about the individual’s behavior. Single testing features (e.g., number of PVT lapses) with high correlation with performance impairment in the laboratory setting may not be the best indicators of performance impairment under real-world conditions. Pattern recognition algorithms should be further tested for their ability to be used in conjunction with other assessments of sleepiness in real-world conditions to quantify performance impairment in

  8. An Efficient Algorithm for Recognition of Human Actions

    PubMed Central

    Khan, Yaser Daanial; Khan, Nabeel Sabir; Farooq, Shoaib; Khan, Sher Afzal; Ahmad, Farooq; Mahmood, M. Khalid

    2014-01-01

    Recognition of human actions is an emerging need. Various researchers have endeavored to provide a solution to this problem. Some of the current state-of-the-art solutions are either inaccurate or computationally intensive while others require human intervention. In this paper a sufficiently accurate while computationally inexpensive solution is provided for the same problem. Image moments which are translation, rotation, and scale invariant are computed for a frame. A dynamic neural network is used to identify the patterns within the stream of image moments and hence recognize actions. Experiments show that the proposed model performs better than other competitive models. PMID:25250389

  9. A Human Activity Recognition System Using Skeleton Data from RGBD Sensors

    PubMed Central

    Gasparrini, Samuele

    2016-01-01

    The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed. PMID:27069469

  10. A Human Activity Recognition System Using Skeleton Data from RGBD Sensors.

    PubMed

    Cippitelli, Enea; Gasparrini, Samuele; Gambi, Ennio; Spinsante, Susanna

    2016-01-01

    The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

  11. Multi-Dimensional Classification Algorithm for Automatic Modulation Recognition

    DTIC Science & Technology

    2007-03-01

    processing time. Simulation results for the MDCA algorithm demonstrate good potential. In particular, the MDCA consistently performed well (at SNR ...33 3.5.1 The Database . . . . . . . . . . . . . . . . . . . 33 3.5.2 SNR Approximation . . . . . . . . . . . . . . . 35 3.5.3 User...Ratio( SNR ) . . . . . . . . . . . 44 4.3 Simulation Overview . . . . . . . . . . . . . . . . . . . . 45 4.3.1 Database Generation

  12. ROCIT : a visual object recognition algorithm based on a rank-order coding scheme.

    SciTech Connect

    Gonzales, Antonio Ignacio; Reeves, Paul C.; Jones, John J.; Farkas, Benjamin D.

    2004-06-01

    This document describes ROCIT, a neural-inspired object recognition algorithm based on a rank-order coding scheme that uses a light-weight neuron model. ROCIT coarsely simulates a subset of the human ventral visual stream from the retina through the inferior temporal cortex. It was designed to provide an extensible baseline from which to improve the fidelity of the ventral stream model and explore the engineering potential of rank order coding with respect to object recognition. This report describes the baseline algorithm, the model's neural network architecture, the theoretical basis for the approach, and reviews the history of similar implementations. Illustrative results are used to clarify algorithm details. A formal benchmark to the 1998 FERET fafc test shows above average performance, which is encouraging. The report concludes with a brief review of potential algorithmic extensions for obtaining scale and rotational invariance.

  13. Iris recognition using image moments and k-means algorithm.

    PubMed

    Khan, Yaser Daanial; Khan, Sher Afzal; Ahmad, Farooq; Islam, Saeed

    2014-01-01

    This paper presents a biometric technique for identification of a person using the iris image. The iris is first segmented from the acquired image of an eye using an edge detection algorithm. The disk shaped area of the iris is transformed into a rectangular form. Described moments are extracted from the grayscale image which yields a feature vector containing scale, rotation, and translation invariant moments. Images are clustered using the k-means algorithm and centroids for each cluster are computed. An arbitrary image is assumed to belong to the cluster whose centroid is the nearest to the feature vector in terms of Euclidean distance computed. The described model exhibits an accuracy of 98.5%.

  14. An improved finger-vein recognition algorithm based on template matching

    NASA Astrophysics Data System (ADS)

    Liu, Yueyue; Di, Si; Jin, Jian; Huang, Daoping

    2016-10-01

    Finger-vein recognition has became the most popular biometric identify methods. The investigation on the recognition algorithms always is the key point in this field. So far, there are many applicable algorithms have been developed. However, there are still some problems in practice, such as the variance of the finger position which may lead to the image distortion and shifting; during the identification process, some matching parameters determined according to experience may also reduce the adaptability of algorithm. Focus on above mentioned problems, this paper proposes an improved finger-vein recognition algorithm based on template matching. In order to enhance the robustness of the algorithm for the image distortion, the least squares error method is adopted to correct the oblique finger. During the feature extraction, local adaptive threshold method is adopted. As regard as the matching scores, we optimized the translation preferences as well as matching distance between the input images and register images on the basis of Naoto Miura algorithm. Experimental results indicate that the proposed method can improve the robustness effectively under the finger shifting and rotation conditions.

  15. Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar.

    PubMed

    Shin, Young Hoon; Seo, Jiwon

    2016-10-29

    People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker's vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing.

  16. Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar

    PubMed Central

    Shin, Young Hoon; Seo, Jiwon

    2016-01-01

    People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker’s vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing. PMID:27801867

  17. A triboelectric motion sensor in wearable body sensor network for human activity recognition.

    PubMed

    Hui Huang; Xian Li; Ye Sun

    2016-08-01

    The goal of this study is to design a novel triboelectric motion sensor in wearable body sensor network for human activity recognition. Physical activity recognition is widely used in well-being management, medical diagnosis and rehabilitation. Other than traditional accelerometers, we design a novel wearable sensor system based on triboelectrification. The triboelectric motion sensor can be easily attached to human body and collect motion signals caused by physical activities. The experiments are conducted to collect five common activity data: sitting and standing, walking, climbing upstairs, downstairs, and running. The k-Nearest Neighbor (kNN) clustering algorithm is adopted to recognize these activities and validate the feasibility of this new approach. The results show that our system can perform physical activity recognition with a successful rate over 80% for walking, sitting and standing. The triboelectric structure can also be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion.

  18. Recognition of Human Activities Using Continuous Autoencoders with Wearable Sensors

    PubMed Central

    Wang, Lukun

    2016-01-01

    This paper provides an approach for recognizing human activities with wearable sensors. The continuous autoencoder (CAE) as a novel stochastic neural network model is proposed which improves the ability of model continuous data. CAE adds Gaussian random units into the improved sigmoid activation function to extract the features of nonlinear data. In order to shorten the training time, we propose a new fast stochastic gradient descent (FSGD) algorithm to update the gradients of CAE. The reconstruction of a swiss-roll dataset experiment demonstrates that the CAE can fit continuous data better than the basic autoencoder, and the training time can be reduced by an FSGD algorithm. In the experiment of human activities’ recognition, time and frequency domain feature extract (TFFE) method is raised to extract features from the original sensors’ data. Then, the principal component analysis (PCA) method is applied to feature reduction. It can be noticed that the dimension of each data segment is reduced from 5625 to 42. The feature vectors extracted from original signals are used for the input of deep belief network (DBN), which is composed of multiple CAEs. The training results show that the correct differentiation rate of 99.3% has been achieved. Some contrast experiments like different sensors combinations, sensor units at different positions, and training time with different epochs are designed to validate our approach. PMID:26861319

  19. Study on the classification algorithm of degree of arteriosclerosis based on fuzzy pattern recognition

    NASA Astrophysics Data System (ADS)

    Ding, Li; Zhou, Runjing; Liu, Guiying

    2010-08-01

    Pulse wave of human body contains large amount of physiological and pathological information, so the degree of arteriosclerosis classification algorithm is study based on fuzzy pattern recognition in this paper. Taking the human's pulse wave as the research object, we can extract the characteristic of time and frequency domain of pulse signal, and select the parameters with a better clustering effect for arteriosclerosis identification. Moreover, the validity of characteristic parameters is verified by fuzzy ISODATA clustering method (FISOCM). Finally, fuzzy pattern recognition system can quantitatively distinguish the degree of arteriosclerosis with patients. By testing the 50 samples in the built pulse database, the experimental result shows that the algorithm is practical and achieves a good classification recognition result.

  20. Modeling optical pattern recognition algorithms for object tracking based on nonlinear equivalent models and subtraction of frames

    NASA Astrophysics Data System (ADS)

    Krasilenko, Vladimir G.; Nikolskyy, Aleksandr I.; Lazarev, Alexander A.

    2015-12-01

    We have proposed and discussed optical pattern recognition algorithms for object tracking based on nonlinear equivalent models and subtraction of frames. Experimental results of suggested algorithms in Mathcad and LabVIEW are shown. Application of equivalent functions and difference of frames gives good results for recognition and tracking moving objects.

  1. Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems

    PubMed Central

    Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

    2015-01-01

    A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What’s more, the improved algorithm can enhance the accuracy of blind recognition obviously. PMID:26154439

  2. Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

    PubMed

    Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

    2015-01-01

    A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

  3. Active Processor Scheduling Using Evolutionary Algorithms

    DTIC Science & Technology

    2002-12-01

    xiii Active Processor Scheduling Using Evolutionary Algorithms I. Introduction A distributed system offers the ability to run applications across...calculations are made. This model is sometimes referred to as a form of the island model of evolutionary computation because each population is evolved... Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic Algorithms and Evolutionary Computation , New York: Kluwer Academic Publishers, 2002

  4. Face recognition: database acquisition, hybrid algorithms, and human studies

    NASA Astrophysics Data System (ADS)

    Gutta, Srinivas; Huang, Jeffrey R.; Singh, Dig; Wechsler, Harry

    1997-02-01

    One of the most important technologies absent in traditional and emerging frontiers of computing is the management of visual information. Faces are accessible `windows' into the mechanisms that govern our emotional and social lives. The corresponding face recognition tasks considered herein include: (1) Surveillance, (2) CBIR, and (3) CBIR subject to correct ID (`match') displaying specific facial landmarks such as wearing glasses. We developed robust matching (`classification') and retrieval schemes based on hybrid classifiers and showed their feasibility using the FERET database. The hybrid classifier architecture consist of an ensemble of connectionist networks--radial basis functions-- and decision trees. The specific characteristics of our hybrid architecture include (a) query by consensus as provided by ensembles of networks for coping with the inherent variability of the image formation and data acquisition process, and (b) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds. Experimental results, proving the feasibility of our approach, yield (i) 96% accuracy, using cross validation (CV), for surveillance on a data base consisting of 904 images (ii) 97% accuracy for CBIR tasks, on a database of 1084 images, and (iii) 93% accuracy, using CV, for CBIR subject to correct ID match tasks on a data base of 200 images.

  5. Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

    PubMed Central

    Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J.; Adatia, Ian

    2016-01-01

    We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p  < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral. PMID:27609672

  6. Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

    NASA Astrophysics Data System (ADS)

    Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y.; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J.; Adatia, Ian

    2016-09-01

    We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p  < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral.

  7. 34 CFR 602.30 - Activities covered by recognition procedures.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 34 Education 3 2010-07-01 2010-07-01 false Activities covered by recognition procedures. 602.30 Section 602.30 Education Regulations of the Offices of the Department of Education (Continued) OFFICE OF POSTSECONDARY EDUCATION, DEPARTMENT OF EDUCATION THE SECRETARY'S RECOGNITION OF ACCREDITING AGENCIES...

  8. Protein-fold recognition using an improved single-source K diverse shortest paths algorithm.

    PubMed

    Lhota, John; Xie, Lei

    2016-04-01

    Protein structure prediction, when construed as a fold recognition problem, is one of the most important applications of similarity search in bioinformatics. A new protein-fold recognition method is reported which combines a single-source K diverse shortest path (SSKDSP) algorithm with Enrichment of Network Topological Similarity (ENTS) algorithm to search a graphic feature space generated using sequence similarity and structural similarity metrics. A modified, more efficient SSKDSP algorithm is developed to improve the performance of graph searching. The new implementation of the SSKDSP algorithm empirically requires 82% less memory and 61% less time than the current implementation, allowing for the analysis of larger, denser graphs. Furthermore, the statistical significance of fold ranking generated from SSKDSP is assessed using ENTS. The reported ENTS-SSKDSP algorithm outperforms original ENTS that uses random walk with restart for the graph search as well as other state-of-the-art protein structure prediction algorithms HHSearch and Sparks-X, as evaluated by a benchmark of 600 query proteins. The reported methods may easily be extended to other similarity search problems in bioinformatics and chemoinformatics. The SSKDSP software is available at http://compsci.hunter.cuny.edu/~leixie/sskdsp.html.

  9. Las Vegas algorithms for gene recognition: Suboptimal and error-tolerant spliced alignment

    SciTech Connect

    Sze, Sing-Hoi; Pevzner, P.A.

    1997-12-01

    Recently, Gelfand, Mironov and Pevzner proposed a spliced alignment approach to gene recognition that provides 99% accurate recognition of human gene if a related mammalian protein is available. However, even 99% accurate gene predictions are insufficient for automated sequence annotation in large-scale sequencing projects and therefore have to be complemented by experimental gene verification. 100% accurate gene predictions would lead to a substantial reduction of experimental work on gene identification. Our goal is to develop an algorithm that either predicts an exon assembly with accuracy sufficient for sequence annotation or warns a biologist that the accuracy of a prediction is insufficient and further experimental work is required. We study suboptimal and error-tolerant spliced alignment problems as the first steps towards such an algorithm, and report an algorithm which provides 100% accurate recognition of human genes in 37% of cases (if a related mammalian protein is available). For 52% of genes, the algorithm predicts at least one exon with 100% accuracy. 30 refs., 1 fig., 3 tabs.

  10. Evaluation of synthetic aperture radar image segmentation algorithms in the context of automatic target recognition

    NASA Astrophysics Data System (ADS)

    Xue, Kefu; Power, Gregory J.; Gregga, Jason B.

    2002-11-01

    Image segmentation is a process to extract and organize information energy in the image pixel space according to a prescribed feature set. It is often a key preprocess in automatic target recognition (ATR) algorithms. In many cases, the performance of image segmentation algorithms will have significant impact on the performance of ATR algorithms. Due to the variations in feature set definitions and the innovations in the segmentation processes, there is large number of image segmentation algorithms existing in ATR world. Recently, the authors have investigated a number of measures to evaluate the performance of segmentation algorithms, such as Percentage Pixels Same (pps), Partial Directed Hausdorff (pdh) and Complex Inner Product (cip). In the research, we found that the combination of the three measures shows effectiveness in the evaluation of segmentation algorithms against truth data (human master segmentation). However, we still don't know what are the impact of those measures in the performance of ATR algorithms that are commonly measured by Probability of detection (PDet), Probability of false alarm (PFA), Probability of identification (PID), etc. In all practical situations, ATR boxes are implemented without human observer in the loop. The performance of synthetic aperture radar (SAR) image segmentation should be evaluated in the context of ATR rather than human observers. This research establishes a segmentation algorithm evaluation suite involving segmentation algorithm performance measures as well as the ATR algorithm performance measures. It provides a practical quantitative evaluation method to judge which SAR image segmentation algorithm is the best for a particular ATR application. The results are tabulated based on some baseline ATR algorithms and a typical image segmentation algorithm used in ATR applications.

  11. A Genetic-Algorithm-Based Explicit Description of Object Contour and its Ability to Facilitate Recognition.

    PubMed

    Wei, Hui; Tang, Xue-Song

    2015-11-01

    Shape representation is an extremely important and longstanding problem in the field of pattern recognition. Closed contour, which refers to shape contour, plays a crucial role in the comparison of shapes. Because shape contour is the most stable, distinguishable, and invariable feature of an object, it is useful to incorporate it into the recognition process. This paper proposes a method based on genetic algorithms. The proposed method can be used to identify the most common contour fragments, which can be used to represent the contours of a shape category. The common fragments clarify the particular logics included in the contours. This paper shows that the explicit representation of the shape contour contributes significantly to shape representation and object recognition.

  12. Parametric estimation of sample entropy for physical activity recognition.

    PubMed

    Aktaruzzaman, Md; Scarabottolo, Nello; Sassi, Roberto

    2015-08-01

    Insufficient amount of physical activity, and hence storage of calories may lead depression, obesity, cardiovascular diseases, and diabetes. The amount of consumed calorie depends on the type of activity. The recognition of physical activity is very important to estimate the amount of calories spent by a subject every day. There are some research works already published in the literature for activity recognition through accelerometers (body worn sensors). The accuracy of any recognition system depends on the robustness of selected features and classifiers. The typical features reported for most physical activities recognitions are autoregressive coefficients (ARcoeffs), signal magnitude area (SMA), tilt angle (TA), and standard deviation (STD). In this study, we have studied the feasibility of using single value of sample entropy estimated parametrically (SETH) of an AR model instead of ARcoeffs. After feasibility study, we also compared the recognition accuracies between two popular classifiers ı.e. artificial neural network (ANN) and support vector machines (SVM). The recognition accuracies using linear structure (where all types of activities are classified using a single classifier) and hierarchical structure (where activities are first divided into static and dynamic events, and then activities of each event are classified in the second stage). The study showed that the use of SETH provides similar recognition accuracy (69.82%) as provided by ARcoeffs (67.67%) using ANN. The linear structure of SVM performs better (average accuracy of SVM: 98.22%) than linear ANN (average accuracy with ANN: 94.78%). The use of hierarchical structure of ANN increases the average recognition accuracy of static activities to about 100%. However, no significant changes are observed using hierarchical SVM than the linear one.

  13. An improved CS-LSSVM algorithm-based fault pattern recognition of ship power equipments

    PubMed Central

    Yang, Yifei; Tan, Minjia; Dai, Yuewei

    2017-01-01

    A ship power equipments’ fault monitoring signal usually provides few samples and the data’s feature is non-linear in practical situation. This paper adopts the method of the least squares support vector machine (LSSVM) to deal with the problem of fault pattern identification in the case of small sample data. Meanwhile, in order to avoid involving a local extremum and poor convergence precision which are induced by optimizing the kernel function parameter and penalty factor of LSSVM, an improved Cuckoo Search (CS) algorithm is proposed for the purpose of parameter optimization. Based on the dynamic adaptive strategy, the newly proposed algorithm improves the recognition probability and the searching step length, which can effectively solve the problems of slow searching speed and low calculation accuracy of the CS algorithm. A benchmark example demonstrates that the CS-LSSVM algorithm can accurately and effectively identify the fault pattern types of ship power equipments. PMID:28182678

  14. Iris recognition using Gabor filters optimized by the particle swarm algorithm

    NASA Astrophysics Data System (ADS)

    Tsai, Chung-Chih; Taur, Jin-Shiuh; Tao, Chin-Wang

    2009-04-01

    An efficient feature extraction algorithm based on optimized Gabor filters and a relative variation analysis approach is proposed for iris recognition. The Gabor filters are optimized by using the particle swarm algorithm to adjust the parameters. Moreover, a sequential scheme is developed to determine the number of filters in the optimal Gabor filter bank. In the preprocessing step, the lower part of the iris image is unwrapped and normalized to a rectangular block that is then decomposed by the optimal Gabor filters. After that, a simple encoding method is adopted to generate a compact iris code. Experimental results show that with a smaller iris code size, the proposed method can produce comparable performance to that of the existing iris recognition systems.

  15. A survey of online activity recognition using mobile phones.

    PubMed

    Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J M

    2015-01-19

    Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.

  16. On the Design of Smart Homes: A Framework for Activity Recognition in Home Environment.

    PubMed

    Cicirelli, Franco; Fortino, Giancarlo; Giordano, Andrea; Guerrieri, Antonio; Spezzano, Giandomenico; Vinci, Andrea

    2016-09-01

    A smart home is a home environment enriched with sensing, actuation, communication and computation capabilities which permits to adapt it to inhabitants preferences and requirements. Establishing a proper strategy of actuation on the home environment can require complex computational tasks on the sensed data. This is the case of activity recognition, which consists in retrieving high-level knowledge about what occurs in the home environment and about the behaviour of the inhabitants. The inherent complexity of this application domain asks for tools able to properly support the design and implementation phases. This paper proposes a framework for the design and implementation of smart home applications focused on activity recognition in home environments. The framework mainly relies on the Cloud-assisted Agent-based Smart home Environment (CASE) architecture offering basic abstraction entities which easily allow to design and implement Smart Home applications. CASE is a three layered architecture which exploits the distributed multi-agent paradigm and the cloud technology for offering analytics services. Details about how to implement activity recognition onto the CASE architecture are supplied focusing on the low-level technological issues as well as the algorithms and the methodologies useful for the activity recognition. The effectiveness of the framework is shown through a case study consisting of a daily activity recognition of a person in a home environment.

  17. Contrast- and illumination-invariant object recognition from active sensation.

    PubMed

    Rentschler, Ingo; Osman, Erol; Jüttner, Martin

    2009-01-01

    It has been suggested that the deleterious effect of contrast reversal on visual recognition is unique to faces, not objects. Here we show from priming, supervised category learning, and generalization that there is no such thing as general invariance of recognition of non-face objects against contrast reversal and, likewise, changes in direction of illumination. However, when recognition varies with rendering conditions, invariance may be restored and effects of continuous learning may be reduced by providing prior object knowledge from active sensation. Our findings suggest that the degree of contrast invariance achieved reflects functional characteristics of object representations learned in a task-dependent fashion.

  18. Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition.

    PubMed

    Stallkamp, J; Schlipsing, M; Salmen, J; Igel, C

    2012-08-01

    Traffic signs are characterized by a wide variability in their visual appearance in real-world environments. For example, changes of illumination, varying weather conditions and partial occlusions impact the perception of road signs. In practice, a large number of different sign classes needs to be recognized with very high accuracy. Traffic signs have been designed to be easily readable for humans, who perform very well at this task. For computer systems, however, classifying traffic signs still seems to pose a challenging pattern recognition problem. Both image processing and machine learning algorithms are continuously refined to improve on this task. But little systematic comparison of such systems exist. What is the status quo? Do today's algorithms reach human performance? For assessing the performance of state-of-the-art machine learning algorithms, we present a publicly available traffic sign dataset with more than 50,000 images of German road signs in 43 classes. The data was considered in the second stage of the German Traffic Sign Recognition Benchmark held at IJCNN 2011. The results of this competition are reported and the best-performing algorithms are briefly described. Convolutional neural networks (CNNs) showed particularly high classification accuracies in the competition. We measured the performance of human subjects on the same data-and the CNNs outperformed the human test persons.

  19. Step detection and activity recognition accuracy of seven physical activity monitors.

    PubMed

    Storm, Fabio A; Heller, Ben W; Mazzà, Claudia

    2015-01-01

    The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications.

  20. Step Detection and Activity Recognition Accuracy of Seven Physical Activity Monitors

    PubMed Central

    Storm, Fabio A.; Heller, Ben W.; Mazzà, Claudia

    2015-01-01

    The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications. PMID:25789630

  1. Sub-OBB based object recognition and localization algorithm using range images

    NASA Astrophysics Data System (ADS)

    Hoang, Dinh-Cuong; Chen, Liang-Chia; Nguyen, Thanh-Hung

    2017-02-01

    This paper presents a novel approach to recognize and estimate pose of the 3D objects in cluttered range images. The key technical breakthrough of the developed approach can enable robust object recognition and localization under undesirable condition such as environmental illumination variation as well as optical occlusion to viewing the object partially. First, the acquired point clouds are segmented into individual object point clouds based on the developed 3D object segmentation for randomly stacked objects. Second, an efficient shape-matching algorithm called Sub-OBB based object recognition by using the proposed oriented bounding box (OBB) regional area-based descriptor is performed to reliably recognize the object. Then, the 3D position and orientation of the object can be roughly estimated by aligning the OBB of segmented object point cloud with OBB of matched point cloud in a database generated from CAD model and 3D virtual camera. To detect accurate pose of the object, the iterative closest point (ICP) algorithm is used to match the object model with the segmented point clouds. From the feasibility test of several scenarios, the developed approach is verified to be feasible for object pose recognition and localization.

  2. Low Energy Physical Activity Recognition System on Smartphones

    PubMed Central

    Morillo, Luis Miguel Soria; Gonzalez-Abril, Luis; Ramirez, Juan Antonio Ortega; de la Concepcion, Miguel Angel Alvarez

    2015-01-01

    An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little energy as possible. To this end, an innovative discretization and classification technique is presented based on the χ2 distribution. Furthermore, the entire recognition process is executed on the smartphone, which determines not only the activity performed, but also the frequency at which it is carried out. These techniques and the new classification system presented reduce energy consumption caused by the activity monitoring system. The energy saved increases smartphone usage time to more than 27 h without recharging while maintaining accuracy. PMID:25742171

  3. Weakly Supervised Recognition of Daily Life Activities with Wearable Sensors.

    PubMed

    Stikic, Maja; Larlus, Diane; Ebert, Sandra; Schiele, Bernt

    2011-12-01

    This paper considers scalable and unobtrusive activity recognition using on-body sensing for context awareness in wearable computing. Common methods for activity recognition rely on supervised learning requiring substantial amounts of labeled training data. Obtaining accurate and detailed annotations of activities is challenging, preventing the applicability of these approaches in real-world settings. This paper proposes new annotation strategies that substantially reduce the required amount of annotation. We explore two learning schemes for activity recognition that effectively leverage such sparsely labeled data together with more easily obtainable unlabeled data. Experimental results on two public data sets indicate that both approaches obtain results close to fully supervised techniques. The proposed methods are robust to the presence of erroneous labels occurring in real-world annotation data.

  4. A Joint Gaussian Process Model for Active Visual Recognition with Expertise Estimation in Crowdsourcing

    PubMed Central

    Long, Chengjiang; Hua, Gang; Kapoor, Ashish

    2015-01-01

    We present a noise resilient probabilistic model for active learning of a Gaussian process classifier from crowds, i.e., a set of noisy labelers. It explicitly models both the overall label noise and the expertise level of each individual labeler with two levels of flip models. Expectation propagation is adopted for efficient approximate Bayesian inference of our probabilistic model for classification, based on which, a generalized EM algorithm is derived to estimate both the global label noise and the expertise of each individual labeler. The probabilistic nature of our model immediately allows the adoption of the prediction entropy for active selection of data samples to be labeled, and active selection of high quality labelers based on their estimated expertise to label the data. We apply the proposed model for four visual recognition tasks, i.e., object category recognition, multi-modal activity recognition, gender recognition, and fine-grained classification, on four datasets with real crowd-sourced labels from the Amazon Mechanical Turk. The experiments clearly demonstrate the efficacy of the proposed model. In addition, we extend the proposed model with the Predictive Active Set Selection Method to speed up the active learning system, whose efficacy is verified by conducting experiments on the first three datasets. The results show our extended model can not only preserve a higher accuracy, but also achieve a higher efficiency. PMID:26924892

  5. Comparative evaluation of pattern recognition algorithms: statistical, neural, fuzzy, and neuro-fuzzy techniques

    NASA Astrophysics Data System (ADS)

    Mitra, Sunanda; Castellanos, Ramiro

    1998-10-01

    Pattern recognition by fuzzy, neural, and neuro-fuzzy approaches, has gained popularity partly because of intelligent decision processes involved in some of the above techniques, thus providing better classification and partly because of simplicity in computation required by these methods as opposed to traditional statistical approaches for complex data structures. However, the accuracy of pattern classification by various methods is often not considered. This paper considers the performance of major fuzzy, neural, and neuro-fuzzy pattern recognition algorithms and compares their performances with common statistical methods for the same data sets. For the specific data sets chosen namely the Iris data set, an the small Soybean data set, two neuro-fuzzy algorithms, AFLC and IAFC, outperform other well- known fuzzy, neural, and neuro-fuzzy algorithms in minimizing the classification error and equal the performance of the Bayesian classification. AFLC, and IAFC also demonstrate excellent learning vector quantization capability in generating optimal code books for coding and decoding of large color images at very low bit rates with exceptionally high visual fidelity.

  6. An adaptive Hidden Markov model for activity recognition based on a wearable multi-sensor device.

    PubMed

    Li, Zhen; Wei, Zhiqiang; Yue, Yaofeng; Wang, Hao; Jia, Wenyan; Burke, Lora E; Baranowski, Thomas; Sun, Mingui

    2015-05-01

    Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based on multi-sensor data is presented. In order to utilize these data efficiently and overcome the big data problem, an offline adaptive-Hidden Markov Model (HMM) is proposed. A sensor selection scheme is implemented based on an improved Viterbi algorithm. A new method is proposed that incorporates personal experience into the HMM model as a priori information. Experiments are conducted using a personal wearable computer eButton consisting of multiple sensors. Our comparative study with the standard HMM and other alternative methods in processing the eButton data have shown that our method is more robust and efficient, providing a useful tool to evaluate human activity and lifestyle.

  7. Fusion of smartphone motion sensors for physical activity recognition.

    PubMed

    Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J M

    2014-06-10

    For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role) and an accelerometer (in a lead role) has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized). We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible.

  8. Fusion of Smartphone Motion Sensors for Physical Activity Recognition

    PubMed Central

    Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J. M.

    2014-01-01

    For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role) and an accelerometer (in a lead role) has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized). We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible. PMID:24919015

  9. Comparison of passive ranging integral imaging and active imaging digital holography for three-dimensional object recognition.

    PubMed

    Frauel, Yann; Tajahuerce, Enrique; Matoba, Osamu; Castro, Albertina; Javidi, Bahram

    2004-01-10

    We present an overview of three-dimensional (3D) object recognition techniques that use active sensing by interferometric imaging (digital holography) and passive sensing by integral imaging. We describe how each technique can be used to retrieve the depth information of a 3D scene and how this information can then be used for 3D object recognition. We explore various algorithms for 3D recognition such as nonlinear correlation and target distortion tolerance. We also provide a comparison of the advantages and disadvantages of the two techniques.

  10. Implementation study of wearable sensors for activity recognition systems

    PubMed Central

    Ghassemian, Mona

    2015-01-01

    This Letter investigates and reports on a number of activity recognition methods for a wearable sensor system. The authors apply three methods for data transmission, namely ‘stream-based’, ‘feature-based’ and ‘threshold-based’ scenarios to study the accuracy against energy efficiency of transmission and processing power that affects the mote's battery lifetime. They also report on the impact of variation of sampling frequency and data transmission rate on energy consumption of motes for each method. This study leads us to propose a cross-layer optimisation of an activity recognition system for provisioning acceptable levels of accuracy and energy efficiency. PMID:26609413

  11. Human activity recognition based on Evolving Fuzzy Systems.

    PubMed

    Iglesias, Jose Antonio; Angelov, Plamen; Ledezma, Agapito; Sanchis, Araceli

    2010-10-01

    Environments equipped with intelligent sensors can be of much help if they can recognize the actions or activities of their users. If this activity recognition is done automatically, it can be very useful for different tasks such as future action prediction, remote health monitoring, or interventions. Although there are several approaches for recognizing activities, most of them do not consider the changes in how a human performs a specific activity. We present an automated approach to recognize daily activities from the sensor readings of an intelligent home environment. However, as the way to perform an activity is usually not fixed but it changes and evolves, we propose an activity recognition method based on Evolving Fuzzy Systems.

  12. Combining classifiers generated by multi-gene genetic programming for protein fold recognition using genetic algorithm.

    PubMed

    Bardsiri, Mahshid Khatibi; Eftekhari, Mahdi; Mousavi, Reza

    2015-01-01

    In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA. At the end of the first step, there is a separate binary classifier for each class. In the second stage, the obtained binary classifiers are combined via GA weighting in order to generate the overall classifier. The final obtained classifier is superior to the previous works found in the literature in terms of classification accuracy.

  13. Iris unwrapping using the Bresenham circle algorithm for real-time iris recognition

    NASA Astrophysics Data System (ADS)

    Carothers, Matthew T.; Ngo, Hau T.; Rakvic, Ryan N.; Broussard, Randy P.

    2015-02-01

    An efficient parallel architecture design for the iris unwrapping process in a real-time iris recognition system using the Bresenham Circle Algorithm is presented in this paper. Based on the characteristics of the model parameters this algorithm was chosen over the widely used polar conversion technique as the iris unwrapping model. The architecture design is parallelized to increase the throughput of the system and is suitable for processing an inputted image size of 320 × 240 pixels in real-time using Field Programmable Gate Array (FPGA) technology. Quartus software is used to implement, verify, and analyze the design's performance using the VHSIC Hardware Description Language. The system's predicted processing time is faster than the modern iris unwrapping technique used today∗.

  14. Human Activity Recognition in AAL Environments Using Random Projections

    PubMed Central

    Damaševičius, Robertas; Vasiljevas, Mindaugas; Šalkevičius, Justas; Woźniak, Marcin

    2016-01-01

    Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented. PMID:27413392

  15. Human Activity Recognition in AAL Environments Using Random Projections.

    PubMed

    Damaševičius, Robertas; Vasiljevas, Mindaugas; Šalkevičius, Justas; Woźniak, Marcin

    2016-01-01

    Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented.

  16. Zone Based Hybrid Feature Extraction Algorithm for Handwritten Numeral Recognition of South Indian Scripts

    NASA Astrophysics Data System (ADS)

    Rajashekararadhya, S. V.; Ranjan, P. Vanaja

    India is a multi-lingual multi script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we propose a zone based hybrid feature extraction algorithm scheme towards the recognition of off-line handwritten numerals of south Indian scripts. The character centroid is computed and the image (character/numeral) is further divided in to n equal zones. Average distance and Average angle from the character centroid to the pixels present in the zone are computed (two features). Similarly zone centroid is computed (two features). This procedure is repeated sequentially for all the zones/grids/boxes present in the numeral image. There could be some zones that are empty, and then the value of that particular zone image value in the feature vector is zero. Finally 4*n such features are extracted. Nearest neighbor classifier is used for subsequent classification and recognition purpose. We obtained 97.55 %, 94 %, 92.5% and 95.2 % recognition rate for Kannada, Telugu, Tamil and Malayalam numerals respectively.

  17. Analysis of image content recognition algorithm based on sparse coding and machine learning

    NASA Astrophysics Data System (ADS)

    Xiao, Yu

    2017-03-01

    This paper presents an image classification algorithm based on spatial sparse coding model and random forest. Firstly, SIFT feature extraction of the image; and then use the sparse encoding theory to generate visual vocabulary based on SIFT features, and using the visual vocabulary of SIFT features into a sparse vector; through the combination of regional integration and spatial sparse vector, the sparse vector gets a fixed dimension is used to represent the image; at last random forest classifier for image sparse vectors for training and testing, using the experimental data set for standard test Caltech-101 and Scene-15. The experimental results show that the proposed algorithm can effectively represent the features of the image and improve the classification accuracy. In this paper, we propose an innovative image recognition algorithm based on image segmentation, sparse coding and multi instance learning. This algorithm introduces the concept of multi instance learning, the image as a multi instance bag, sparse feature transformation by SIFT images as instances, sparse encoding model generation visual vocabulary as the feature space is mapped to the feature space through the statistics on the number of instances in bags, and then use the 1-norm SVM to classify images and generate sample weights to select important image features.

  18. Simulation and experiment research of face recognition with modified multi-method morphological correlation algorithm

    NASA Astrophysics Data System (ADS)

    Yang, Yu; Xuping, Zhang

    2007-03-01

    Morphological definition of similarity degree of gray-scale image and general definition of morphological correlation (GMC) are proposed. Hardware and software design for a compact joint transform correlator are presented in order to implement GMC. Two kinds of modified general morphological correlation algorithm are proposed. The gray-scale image is decomposed into a set of binary image slices in certain decomposition method. In the first algorithm, the edge of each binary joint image slice is detected, width adjustability of which is investigated, and the joint power spectrum of the edge is summed. In the second algorithm, the joint power spectrum of each pair is binarized or thinned and then summed in one situation, and the summation of the joint power spectrums of these pairs is binarized or thinned in the other situation. Computer-simulation results and real face image recognition results indicate that the modified algorithm can improve the discrimination capabilities with respect to the gray-scale face images of high similarity.

  19. Mid-level Features Improve Recognition of Interactive Activities

    DTIC Science & Technology

    2012-11-14

    Recognizing action as clouds of space-time interest points. In CVPR, 2009. [5] W. Brendel, A. Fern , and S. Todorovic. Probabilistic event logic for interval...context. In CVPR, 2009. [27] R. Messing, C. Pal, and H. Kautz. Activity recognition using the velocity histories of tracked keypoints. In ICCV, 2009

  20. Focus-of-attention for human activity recognition from UAVs

    NASA Astrophysics Data System (ADS)

    Burghouts, G. J.; van Eekeren, A. W. M.; Dijk, J.

    2014-10-01

    This paper presents a system to extract metadata about human activities from full-motion video recorded from a UAV. The pipeline consists of these components: tracking, motion features, representation of the tracks in terms of their motion features, and classification of each track as one of the human activities of interest. We consider these activities: walk, run, throw, dig, wave. Our contribution is that we show how a robust system can be constructed for human activity recognition from UAVs, and that focus-of-attention is needed. We find that tracking and human detection are essential for robust human activity recognition from UAVs. Without tracking, the human activity recognition deteriorates. The combination of tracking and human detection is needed to focus the attention on the relevant tracks. The best performing system includes tracking, human detection and a per-track analysis of the five human activities. This system achieves an average accuracy of 93%. A graphical user interface is proposed to aid the operator or analyst during the task of retrieving the relevant parts of video that contain particular human activities. Our demo is available on YouTube.

  1. An Online Continuous Human Action Recognition Algorithm Based on the Kinect Sensor

    PubMed Central

    Zhu, Guangming; Zhang, Liang; Shen, Peiyi; Song, Juan

    2016-01-01

    Continuous human action recognition (CHAR) is more practical in human-robot interactions. In this paper, an online CHAR algorithm is proposed based on skeletal data extracted from RGB-D images captured by Kinect sensors. Each human action is modeled by a sequence of key poses and atomic motions in a particular order. In order to extract key poses and atomic motions, feature sequences are divided into pose feature segments and motion feature segments, by use of the online segmentation method based on potential differences of features. Likelihood probabilities that each feature segment can be labeled as the extracted key poses or atomic motions, are computed in the online model matching process. An online classification method with variable-length maximal entropy Markov model (MEMM) is performed based on the likelihood probabilities, for recognizing continuous human actions. The variable-length MEMM method ensures the effectiveness and efficiency of the proposed CHAR method. Compared with the published CHAR methods, the proposed algorithm does not need to detect the start and end points of each human action in advance. The experimental results on public datasets show that the proposed algorithm is effective and highly-efficient for recognizing continuous human actions. PMID:26828497

  2. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

    PubMed

    Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

    2016-07-05

    Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

  3. Novel algorithms for improved pattern recognition using the US FDA Adverse Event Network Analyzer.

    PubMed

    Botsis, Taxiarchis; Scott, John; Goud, Ravi; Toman, Pamela; Sutherland, Andrea; Ball, Robert

    2014-01-01

    The medical review of adverse event reports for medical products requires the processing of "big data" stored in spontaneous reporting systems, such as the US Vaccine Adverse Event Reporting System (VAERS). VAERS data are not well suited to traditional statistical analyses so we developed the FDA Adverse Event Network Analyzer (AENA) and three novel network analysis approaches to extract information from these data. Our new approaches include a weighting scheme based on co-occurring triplets in reports, a visualization layout inspired by the islands algorithm, and a network growth methodology for the detection of outliers. We explored and verified these approaches by analysing the historical signal of Intussusception (IS) after the administration of RotaShield vaccine (RV) in 1999. We believe that our study supports the use of AENA for pattern recognition in medical product safety and other clinical data.

  4. Robust Indoor Human Activity Recognition Using Wireless Signals

    PubMed Central

    Wang, Yi; Jiang, Xinli; Cao, Rongyu; Wang, Xiyang

    2015-01-01

    Wireless signals–based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions’ CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds. PMID:26184231

  5. An Iris Segmentation Algorithm based on Edge Orientation for Off-angle Iris Recognition

    SciTech Connect

    Karakaya, Mahmut; Barstow, Del R; Santos-Villalobos, Hector J; Boehnen, Chris Bensing

    2013-01-01

    Iris recognition is known as one of the most accurate and reliable biometrics. However, the accuracy of iris recognition systems depends on the quality of data capture and is negatively affected by several factors such as angle, occlusion, and dilation. In this paper, we present a segmentation algorithm for off-angle iris images that uses edge detection, edge elimination, edge classification, and ellipse fitting techniques. In our approach, we first detect all candidate edges in the iris image by using the canny edge detector; this collection contains edges from the iris and pupil boundaries as well as eyelash, eyelids, iris texture etc. Edge orientation is used to eliminate the edges that cannot be part of the iris or pupil. Then, we classify the remaining edge points into two sets as pupil edges and iris edges. Finally, we randomly generate subsets of iris and pupil edge points, fit ellipses for each subset, select ellipses with similar parameters, and average to form the resultant ellipses. Based on the results from real experiments, the proposed method shows effectiveness in segmentation for off-angle iris images.

  6. An iris segmentation algorithm based on edge orientation for off-angle iris recognition

    NASA Astrophysics Data System (ADS)

    Karakaya, Mahmut; Barstow, Del; Santos-Villalobos, Hector; Boehnen, Christopher

    2013-03-01

    Iris recognition is known as one of the most accurate and reliable biometrics. However, the accuracy of iris recognition systems depends on the quality of data capture and is negatively affected by several factors such as angle, occlusion, and dilation. In this paper, we present a segmentation algorithm for off-angle iris images that uses edge detection, edge elimination, edge classification, and ellipse fitting techniques. In our approach, we first detect all candidate edges in the iris image by using the canny edge detector; this collection contains edges from the iris and pupil boundaries as well as eyelash, eyelids, iris texture etc. Edge orientation is used to eliminate the edges that cannot be part of the iris or pupil. Then, we classify the remaining edge points into two sets as pupil edges and iris edges. Finally, we randomly generate subsets of iris and pupil edge points, fit ellipses for each subset, select ellipses with similar parameters, and average to form the resultant ellipses. Based on the results from real experiments, the proposed method shows effectiveness in segmentation for off-angle iris images.

  7. Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

    NASA Astrophysics Data System (ADS)

    Hortos, William S.

    2008-04-01

    Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at

  8. Two phases of V1 activity for visual recognition of natural images.

    PubMed

    Camprodon, Joan A; Zohary, Ehud; Brodbeck, Verena; Pascual-Leone, Alvaro

    2010-06-01

    Present theories of visual recognition emphasize the role of interactive processing across populations of neurons within a given network, but the nature of these interactions remains unresolved. In particular, data describing the sufficiency of feedforward algorithms for conscious vision and studies revealing the functional relevance of feedback connections to the striate cortex seem to offer contradictory accounts of visual information processing. TMS is a good method to experimentally address this issue, given its excellent temporal resolution and its capacity to establish causal relations between brain function and behavior. We studied 20 healthy volunteers in a visual recognition task. Subjects were briefly presented with images of animals (birds or mammals) in natural scenes and were asked to indicate the animal category. MRI-guided stereotaxic single TMS pulses were used to transiently disrupt striate cortex function at different times after image onset (SOA). Visual recognition was significantly impaired when TMS was applied over the occipital pole at SOAs of 100 and 220 msec. The first interval has consistently been described in previous TMS studies and is explained as the interruption of the feedforward volley of activity. Given the late latency and discrete nature of the second peak, we hypothesize that it represents the disruption of a feedback projection to V1, probably from other areas in the visual network. These results provide causal evidence for the necessity of recurrent interactive processing, through feedforward and feedback connections, in visual recognition of natural complex images.

  9. An Indoor Pedestrian Positioning Method Using HMM with a Fuzzy Pattern Recognition Algorithm in a WLAN Fingerprint System.

    PubMed

    Ni, Yepeng; Liu, Jianbo; Liu, Shan; Bai, Yaxin

    2016-09-08

    With the rapid development of smartphones and wireless networks, indoor location-based services have become more and more prevalent. Due to the sophisticated propagation of radio signals, the Received Signal Strength Indicator (RSSI) shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, we present a novel method to improve the indoor pedestrian positioning accuracy by embedding a fuzzy pattern recognition algorithm into a Hidden Markov Model. The fuzzy pattern recognition algorithm follows the rule that the RSSI fading has a positive correlation to the distance between the measuring point and the AP location even during a dynamic positioning measurement. Through this algorithm, we use the RSSI variation trend to replace the specific RSSI value to achieve a fuzzy positioning. The transition probability of the Hidden Markov Model is trained by the fuzzy pattern recognition algorithm with pedestrian trajectories. Using the Viterbi algorithm with the trained model, we can obtain a set of hidden location states. In our experiments, we demonstrate that, compared with the deterministic pattern matching algorithm, our method can greatly improve the positioning accuracy and shows robust environmental adaptability.

  10. An Indoor Pedestrian Positioning Method Using HMM with a Fuzzy Pattern Recognition Algorithm in a WLAN Fingerprint System

    PubMed Central

    Ni, Yepeng; Liu, Jianbo; Liu, Shan; Bai, Yaxin

    2016-01-01

    With the rapid development of smartphones and wireless networks, indoor location-based services have become more and more prevalent. Due to the sophisticated propagation of radio signals, the Received Signal Strength Indicator (RSSI) shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, we present a novel method to improve the indoor pedestrian positioning accuracy by embedding a fuzzy pattern recognition algorithm into a Hidden Markov Model. The fuzzy pattern recognition algorithm follows the rule that the RSSI fading has a positive correlation to the distance between the measuring point and the AP location even during a dynamic positioning measurement. Through this algorithm, we use the RSSI variation trend to replace the specific RSSI value to achieve a fuzzy positioning. The transition probability of the Hidden Markov Model is trained by the fuzzy pattern recognition algorithm with pedestrian trajectories. Using the Viterbi algorithm with the trained model, we can obtain a set of hidden location states. In our experiments, we demonstrate that, compared with the deterministic pattern matching algorithm, our method can greatly improve the positioning accuracy and shows robust environmental adaptability. PMID:27618053

  11. An MRI myocarditis index defined by a PCA-based object recognition algorithm

    NASA Astrophysics Data System (ADS)

    Romano, Rocco; De Giorgi, Igino; Acernese, Fausto; Giordano, Gerardo; Orientale, Antonio; Babino, Giovanni; Barone, Fabrizio

    2015-03-01

    Magnetic Resonance Imaging (MRI) has shown promising results in diagnosing myocarditis that can be qualitatively observed as enhanced pixels on the cardiac muscles images. In this paper, a myocarditis index, defined as the ratio between enhanced pixels, representing an inflammation, and the total pixels of myocardial muscle, is presented. In order to recognize and quantify enhanced pixels, a PCA-based recognition algorithm is used. The algorithm, implemented in Matlab, was tested by examining a group of 10 patients, referred to MRI with presumptive, clinical diagnosis of myocarditis. To assess intra- and interobserver variability, two observers blindly analyzed data related to the 10 patients by delimiting myocardial region and selecting enhanced pixels. After 5 days the same observers redid the analysis. The obtained myocarditis indexes were compared to an ordinal variable (values in the 1 - 5 range) that represented the blind assessment of myocarditis seriousness given by two radiologists on the base of the patient case histories. Results show that there is a significant correlation (P < 0:001; r = 0:94) between myocarditis indexes and the radiologists' clinical judgments. Furthermore, a good intraobserver and interobserver reproducibility was obtained.

  12. Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition

    PubMed Central

    Subhi Al-batah, Mohammad; Mat Isa, Nor Ashidi; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi

    2014-01-01

    To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy. PMID:24707316

  13. Automatic recognition of harmonic bird sounds using a frequency track extraction algorithm.

    PubMed

    Heller, Jason R; Pinezich, John D

    2008-09-01

    This paper demonstrates automatic recognition of vocalizations of four common bird species (herring gull [Larus argentatus], blue jay [Cyanocitta cristata], Canada goose [Branta canadensis], and American crow [Corvus brachyrhynchos]) using an algorithm that extracts frequency track sets using track properties of importance and harmonic correlation. The main result is that a complex harmonic vocalization is rendered into a set of related tracks that is easily applied to statistical models of the actual bird vocalizations. For each vocalization type, a statistical model of the vocalization was created by transforming the training set frequency tracks into feature vectors. The extraction algorithm extracts sets of frequency tracks from test recordings that closely approximate harmonic sounds in the file being processed. Each extracted set in its final form is then compared with the statistical models generated during the training phase using Mahalanobis distance functions. If it matches one of the models closely, the recognizer declares the set an occurrence of the corresponding vocalization. The method was evaluated against a test set containing vocalizations of both the 4 target species and 16 additional species as well as background noise containing planes, cars, and various natural sounds.

  14. Quantitative MRI myocarditis analysis by a PCA-based object recognition algorithm

    NASA Astrophysics Data System (ADS)

    Romano, Rocco; Acernese, Fausto; Giordano, Gerardo; De Giorgi, Igino; Orientale, Antonio; Babino, Giovanni; Barone, Fabrizio

    2016-03-01

    Magnetic Resonance Imaging (MRI) has shown promising results in diagnosing myocarditis that can be qualitatively observed as enhanced pixels on the cardiac muscles images. In this paper, a quantitative MRI Myocarditis Analysis is proposed. Analysis consists in introducing a myocarditis index, defined as the ratio between enhanced pixels, representing an inflammation, and the total pixels of myocardial muscle. In order to recognize and quantify enhanced pixels, a PCA-based recognition algorithm is used. The algorithm, implemented in Matlab, was tested by examining a group of 12 patients, referred to MRI with presumptive, clinical diagnosis of myocarditis. To assess intra- and interobserver variability, two observers blindly analyzed data related to the 12 patients by delimiting myocardial region and selecting enhanced pixels. After 10 days the same observers redid the analysis. The obtained myocarditis indexes were compared to an ordinal variable (values in the 1 - 5 range) that represented the blind assessment of myocarditis seriousness given by two radiologists on the base of the patient case histories. Results show that there is a significant correlation (P < 0:001; r = 0:96) between myocarditis indexes and the radiologists' clinical judgments. Furthermore, a good intraobserver and interobserver reproducibility was obtained.

  15. Activity recognition in patients with lower limb impairments: do we need training data from each patient?

    PubMed

    Lonini, Luca; Gupta, Aakash; Kording, Konrad; Jayaraman, Arun

    2016-08-01

    Machine learning allows detecting specific physical activities using data from wearable sensors. Such a quantification of patient mobility over time promises to accurately inform clinical decisions for physical rehabilitation. There are two strategies of setting up the machine learning problem: detect one patient's activities using data from the same patient (personal model) or detect their activities using data from other patients (global model), and we currently do not know if personal models are necessary. Here we consider the problem of detecting physical activities from a waist-worn accelerometer in patients who use a knee-ankle-foot orthosis (KAFO) to walk. We show that while a model based on healthy subjects has low accuracy, the global model performs as well as the personal model. This is encouraging because it suggests that condition-specific activity recognition algorithms are sufficient and that no data from individual patients is necessary.

  16. Extract the Relational Information of Static Features and Motion Features for Human Activities Recognition in Videos

    PubMed Central

    2016-01-01

    Both static features and motion features have shown promising performance in human activities recognition task. However, the information included in these features is insufficient for complex human activities. In this paper, we propose extracting relational information of static features and motion features for human activities recognition. The videos are represented by a classical Bag-of-Word (BoW) model which is useful in many works. To get a compact and discriminative codebook with small dimension, we employ the divisive algorithm based on KL-divergence to reconstruct the codebook. After that, to further capture strong relational information, we construct a bipartite graph to model the relationship between words of different feature set. Then we use a k-way partition to create a new codebook in which similar words are getting together. With this new codebook, videos can be represented by a new BoW vector with strong relational information. Moreover, we propose a method to compute new clusters from the divisive algorithm's projective function. We test our work on the several datasets and obtain very promising results. PMID:27656199

  17. Multiview fusion for activity recognition using deep neural networks

    NASA Astrophysics Data System (ADS)

    Kavi, Rahul; Kulathumani, Vinod; Rohit, Fnu; Kecojevic, Vlad

    2016-07-01

    Convolutional neural networks (ConvNets) coupled with long short term memory (LSTM) networks have been recently shown to be effective for video classification as they combine the automatic feature extraction capabilities of a neural network with additional memory in the temporal domain. This paper shows how multiview fusion can be applied to such a ConvNet LSTM architecture. Two different fusion techniques are presented. The system is first evaluated in the context of a driver activity recognition system using data collected in a multicamera driving simulator. These results show significant improvement in accuracy with multiview fusion and also show that deep learning performs better than a traditional approach using spatiotemporal features even without requiring any background subtraction. The system is also validated on another publicly available multiview action recognition dataset that has 12 action classes and 8 camera views.

  18. A fast 3-D object recognition algorithm for the vision system of a special-purpose dexterous manipulator

    NASA Technical Reports Server (NTRS)

    Hung, Stephen H. Y.

    1989-01-01

    A fast 3-D object recognition algorithm that can be used as a quick-look subsystem to the vision system for the Special-Purpose Dexterous Manipulator (SPDM) is described. Global features that can be easily computed from range data are used to characterize the images of a viewer-centered model of an object. This algorithm will speed up the processing by eliminating the low level processing whenever possible. It may identify the object, reject a set of bad data in the early stage, or create a better environment for a more powerful algorithm to carry the work further.

  19. Melanomas non-invasive diagnosis application based on the ABCD rule and pattern recognition image processing algorithms.

    PubMed

    Isasi, A Gola; Zapirain, B García; Zorrilla, A Méndez

    2011-09-01

    In this paper an automated dermatological tool for the parameterization of melanomas is presented. The system is based on the standard ABCD Rule and dermatological Pattern Recognition protocols. On the one hand, a complete stack of algorithms for the asymmetry, border, color, and diameter parameterization were developed. On the other hand, three automatic algorithms for digital image processing have been developed in order to detect the appropriate patterns. These allow one to calculate certain quantitative features based on the aspect and inner patterns of the melanoma using simple-operation algorithms, in order to minimize response time. The database used consists of 160 500 x 500-pixel RGB images (20 images per pattern) cataloged by dermatologists, and the results have turned out to be successful according to assessment by medical experts. While the ABCD algorithms are mathematically reliable, the proposed algorithms for pattern recognition produced a remarkable rate of globular, reticular, and blue veil Pattern recognition, with an average above 85% of accuracy. It thus proves to be a reliable system when performing a diagnosis.

  20. Improving word recognition in noise among hearing-impaired subjects with a single-channel cochlear noise-reduction algorithm.

    PubMed

    Fink, Nir; Furst, Miriam; Muchnik, Chava

    2012-09-01

    A common complaint of the hearing impaired is the inability to understand speech in noisy environments even with their hearing assistive devices. Only a few single-channel algorithms have significantly improved speech intelligibility in noise for hearing-impaired listeners. The current study introduces a cochlear noise reduction algorithm. It is based on a cochlear representation of acoustic signals and real-time derivation of a binary speech mask. The contribution of the algorithm for enhancing word recognition in noise was evaluated on a group of 42 normal-hearing subjects, 35 hearing-aid users, 8 cochlear implant recipients, and 14 participants with bimodal devices. Recognition scores of Hebrew monosyllabic words embedded in Gaussian noise at several signal-to-noise ratios (SNRs) were obtained with processed and unprocessed signals. The algorithm was not effective among the normal-hearing participants. However, it yielded a significant improvement in some of the hearing-impaired subjects under different listening conditions. Its most impressive benefit appeared among cochlear implant recipients. More than 20% improvement in recognition score of noisy words was obtained by 12, 16, and 26 hearing-impaired at SNR of 30, 24, and 18 dB, respectively. The algorithm has a potential to improve speech intelligibility in background noise, yet further research is required to improve its performances.

  1. Physical Activity Recognition with Mobile Phones: Challenges, Methods, and Applications

    NASA Astrophysics Data System (ADS)

    Yang, Jun; Lu, Hong; Liu, Zhigang; Boda, Péter Pál

    In this book chapter, we present a novel system that recognizes and records the physical activity of a person using a mobile phone. The sensor data is collected by built-in accelerometer sensor that measures the motion intensity of the device. The system recognizes five everyday activities in real-time, i.e., stationary, walking, running, bicycling, and in vehicle. We first introduce the sensor's data format, sensor calibration, signal projection, feature extraction, and selection methods. Then we have a detailed discussion and comparison of different choices of feature sets and classifiers. The design and implementation of one prototype system is presented along with resource and performance benchmark on Nokia N95 platform. Results show high recognition accuracies for distinguishing the five activities. The last part of the chapter introduces one demo application built on top of our system, physical activity diary, and a selection of potential applications in mobile wellness, mobile social sharing and contextual user interface domains.

  2. Super Normal Vector for Human Activity Recognition with Depth Cameras.

    PubMed

    Yang, Xiaodong; Tian, YingLi

    2017-05-01

    The advent of cost-effectiveness and easy-operation depth cameras has facilitated a variety of visual recognition tasks including human activity recognition. This paper presents a novel framework for recognizing human activities from video sequences captured by depth cameras. We extend the surface normal to polynormal by assembling local neighboring hypersurface normals from a depth sequence to jointly characterize local motion and shape information. We then propose a general scheme of super normal vector (SNV) to aggregate the low-level polynormals into a discriminative representation, which can be viewed as a simplified version of the Fisher kernel representation. In order to globally capture the spatial layout and temporal order, an adaptive spatio-temporal pyramid is introduced to subdivide a depth video into a set of space-time cells. In the extensive experiments, the proposed approach achieves superior performance to the state-of-the-art methods on the four public benchmark datasets, i.e., MSRAction3D, MSRDailyActivity3D, MSRGesture3D, and MSRActionPairs3D.

  3. Activity Recognition and Semantic Description for Indoor Mobile Localization

    PubMed Central

    Guo, Sheng; Xiong, Hanjiang; Zheng, Xianwei; Zhou, Yan

    2017-01-01

    As a result of the rapid development of smartphone-based indoor localization technology, location-based services in indoor spaces have become a topic of interest. However, to date, the rich data resulting from indoor localization and navigation applications have not been fully exploited, which is significant for trajectory correction and advanced indoor map information extraction. In this paper, an integrated location acquisition method utilizing activity recognition and semantic information extraction is proposed for indoor mobile localization. The location acquisition method combines pedestrian dead reckoning (PDR), human activity recognition (HAR) and landmarks to acquire accurate indoor localization information. Considering the problem of initial position determination, a hidden Markov model (HMM) is utilized to infer the user’s initial position. To provide an improved service for further applications, the landmarks are further assigned semantic descriptions by detecting the user’s activities. The experiments conducted in this study confirm that a high degree of accuracy for a user’s indoor location can be obtained. Furthermore, the semantic information of a user’s trajectories can be extracted, which is extremely useful for further research into indoor location applications. PMID:28335555

  4. Personalized adherence activity recognition via model-driven sensor data assessment.

    PubMed

    Hsiao, Mark; Hsueh, Pei-Yun; Ramakrishnan, Sreeram

    2012-01-01

    Creation of a personalized adherence feedback loop is crucial for initiating and sustaining health behavior change. However, self reports are not sufficient to measure actual adherence. Recording and recognizing personal activities in a ubiquitous environment has thus emerged as a promising solution. In this work, we present a model-driven sensor data assessment mechanism capable of identifying high level adherence-related activity patterns from low level signals. The proposed intelligent sensing algorithm can learn from a population-based training data set and adapt quickly to an individual's exercise patterns using the acquired personal data. Upon the recognition of each activity, the system can further provide personalized feedback such as exercise coaching, fitness planning, and abnormal event detection. The resulted system demonstrates the feasibility of a portable real-time personalized adherence feedback system that could be used for advanced healthcare services.

  5. Illumination invariant face recognition and impostor rejection using different MINACE filter algorithms

    NASA Astrophysics Data System (ADS)

    Patnaik, Rohit; Casasent, David

    2005-03-01

    A face recognition system that functions in the presence of illumination variations is presented. It is based on the minimum noise and correlation energy (MINACE) filter. A separate MINACE filter is synthesized for each person using an automated filter-synthesis algorithm that uses a training set of illumination differences of that person and a validation set of a few faces of other persons to select the MINACE filter parameter c. The MINACE filter for each person is a combination of training images of only that person; no false-class training is done. Different formulations of the MINACE filter and the use of two different correlation plane metrics: correlation peak value and peak-to-correlation plane energy ratio (PCER), are examined. Performance results for face verification and identification are presented using images from the CMU Pose, Illumination, and Expression (PIE) database. All training and test set images are registered to remove tilt bias and scale variations. To evaluate the face verification and identification systems, a set of impostor images (non-database faces) is used to obtain false alarm scores (PFA).

  6. Independent component feature-based human activity recognition via Linear Discriminant Analysis and Hidden Markov Model.

    PubMed

    Uddin, Md; Lee, J J; Kim, T S

    2008-01-01

    In proactive computing, human activity recognition from image sequences is an active research area. This paper presents a novel approach of human activity recognition based on Linear Discriminant Analysis (LDA) of Independent Component (IC) features from shape information. With extracted features, Hidden Markov Model (HMM) is applied for training and recognition. The recognition performance using LDA of IC features has been compared to other approaches including Principle Component Analysis (PCA), LDA of PC, and ICA. The preliminary results show much improved performance in the recognition rate with our proposed method.

  7. A sensor and video based ontology for activity recognition in smart environments.

    PubMed

    Mitchell, D; Morrow, Philip J; Nugent, Chris D

    2014-01-01

    Activity recognition is used in a wide range of applications including healthcare and security. In a smart environment activity recognition can be used to monitor and support the activities of a user. There have been a range of methods used in activity recognition including sensor-based approaches, vision-based approaches and ontological approaches. This paper presents a novel approach to activity recognition in a smart home environment which combines sensor and video data through an ontological framework. The ontology describes the relationships and interactions between activities, the user, objects, sensors and video data.

  8. The Role of Active Exploration of 3D Face Stimuli on Recognition Memory of Facial Information

    ERIC Educational Resources Information Center

    Liu, Chang Hong; Ward, James; Markall, Helena

    2007-01-01

    Research on face recognition has mainly relied on methods in which observers are relatively passive viewers of face stimuli. This study investigated whether active exploration of three-dimensional (3D) face stimuli could facilitate recognition memory. A standard recognition task and a sequential matching task were employed in a yoked design.…

  9. A bacterial tyrosine phosphatase inhibits plant pattern recognition receptor activation.

    PubMed

    Macho, Alberto P; Schwessinger, Benjamin; Ntoukakis, Vardis; Brutus, Alexandre; Segonzac, Cécile; Roy, Sonali; Kadota, Yasuhiro; Oh, Man-Ho; Sklenar, Jan; Derbyshire, Paul; Lozano-Durán, Rosa; Malinovsky, Frederikke Gro; Monaghan, Jacqueline; Menke, Frank L; Huber, Steven C; He, Sheng Yang; Zipfel, Cyril

    2014-03-28

    Innate immunity relies on the perception of pathogen-associated molecular patterns (PAMPs) by pattern-recognition receptors (PRRs) located on the host cell's surface. Many plant PRRs are kinases. Here, we report that the Arabidopsis receptor kinase EF-TU RECEPTOR (EFR), which perceives the elf18 peptide derived from bacterial elongation factor Tu, is activated upon ligand binding by phosphorylation on its tyrosine residues. Phosphorylation of a single tyrosine residue, Y836, is required for activation of EFR and downstream immunity to the phytopathogenic bacterium Pseudomonas syringae. A tyrosine phosphatase, HopAO1, secreted by P. syringae, reduces EFR phosphorylation and prevents subsequent immune responses. Thus, host and pathogen compete to take control of PRR tyrosine phosphorylation used to initiate antibacterial immunity.

  10. Recombinant Human Peptidoglycan Recognition Proteins Reveal Antichlamydial Activity.

    PubMed

    Bobrovsky, Pavel; Manuvera, Valentin; Polina, Nadezhda; Podgorny, Oleg; Prusakov, Kirill; Govorun, Vadim; Lazarev, Vassili

    2016-07-01

    Peptidoglycan recognition proteins (PGLYRPs) are innate immune components that recognize the peptidoglycan and lipopolysaccharides of bacteria and exhibit antibacterial activity. Recently, the obligate intracellular parasite Chlamydia trachomatis was shown to have peptidoglycan. However, the antichlamydial activity of PGLYRPs has not yet been demonstrated. The aim of our study was to test whether PGLYRPs exhibit antibacterial activity against C. trachomatis Thus, we cloned the regions containing the human Pglyrp1, Pglyrp2, Pglyrp3, and Pglyrp4 genes for subsequent expression in human cell lines. We obtained stable HeLa cell lines that secrete recombinant human PGLYRPs into culture medium. We also generated purified recombinant PGLYRP1, -2, and -4 and confirmed their activities against Gram-positive (Bacillus subtilis) and Gram-negative (Escherichia coli) bacteria. Furthermore, we examined the activities of recombinant PGLYRPs against C. trachomatis and determined their MICs. We also observed a decrease in the infectious ability of chlamydial elementary bodies in the next generation after a single exposure to PGLYRPs. Finally, we demonstrated that PGLYRPs attach to C. trachomatis elementary bodies and activate the expression of the chlamydial two-component stress response system. Thus, PGLYRPs inhibit the development of chlamydial infection.

  11. Recombinant Human Peptidoglycan Recognition Proteins Reveal Antichlamydial Activity

    PubMed Central

    Manuvera, Valentin; Polina, Nadezhda; Podgorny, Oleg; Prusakov, Kirill; Govorun, Vadim; Lazarev, Vassili

    2016-01-01

    Peptidoglycan recognition proteins (PGLYRPs) are innate immune components that recognize the peptidoglycan and lipopolysaccharides of bacteria and exhibit antibacterial activity. Recently, the obligate intracellular parasite Chlamydia trachomatis was shown to have peptidoglycan. However, the antichlamydial activity of PGLYRPs has not yet been demonstrated. The aim of our study was to test whether PGLYRPs exhibit antibacterial activity against C. trachomatis. Thus, we cloned the regions containing the human Pglyrp1, Pglyrp2, Pglyrp3, and Pglyrp4 genes for subsequent expression in human cell lines. We obtained stable HeLa cell lines that secrete recombinant human PGLYRPs into culture medium. We also generated purified recombinant PGLYRP1, -2, and -4 and confirmed their activities against Gram-positive (Bacillus subtilis) and Gram-negative (Escherichia coli) bacteria. Furthermore, we examined the activities of recombinant PGLYRPs against C. trachomatis and determined their MICs. We also observed a decrease in the infectious ability of chlamydial elementary bodies in the next generation after a single exposure to PGLYRPs. Finally, we demonstrated that PGLYRPs attach to C. trachomatis elementary bodies and activate the expression of the chlamydial two-component stress response system. Thus, PGLYRPs inhibit the development of chlamydial infection. PMID:27160295

  12. Identity Recognition Algorithm Using Improved Gabor Feature Selection of Gait Energy Image

    NASA Astrophysics Data System (ADS)

    Chao, LIANG; Ling-yao, JIA; Dong-cheng, SHI

    2017-01-01

    This paper describes an effective gait recognition approach based on Gabor features of gait energy image. In this paper, the kernel Fisher analysis combined with kernel matrix is proposed to select dominant features. The nearest neighbor classifier based on whitened cosine distance is used to discriminate different gait patterns. The approach proposed is tested on the CASIA and USF gait databases. The results show that our approach outperforms other state of gait recognition approaches in terms of recognition accuracy and robustness.

  13. Feature integration with random forests for real-time human activity recognition

    NASA Astrophysics Data System (ADS)

    Kataoka, Hirokatsu; Hashimoto, Kiyoshi; Aoki, Yoshimitsu

    2015-02-01

    This paper presents an approach for real-time human activity recognition. Three different kinds of features (flow, shape, and a keypoint-based feature) are applied in activity recognition. We use random forests for feature integration and activity classification. A forest is created at each feature that performs as a weak classifier. The international classification of functioning, disability and health (ICF) proposed by WHO is applied in order to set the novel definition in activity recognition. Experiments on human activity recognition using the proposed framework show - 99.2% (Weizmann action dataset), 95.5% (KTH human actions dataset), and 54.6% (UCF50 dataset) recognition accuracy with a real-time processing speed. The feature integration and activity-class definition allow us to accomplish high-accuracy recognition match for the state-of-the-art in real-time.

  14. An Experimental Method for the Active Learning of Greedy Algorithms

    ERIC Educational Resources Information Center

    Velazquez-Iturbide, J. Angel

    2013-01-01

    Greedy algorithms constitute an apparently simple algorithm design technique, but its learning goals are not simple to achieve.We present a didacticmethod aimed at promoting active learning of greedy algorithms. The method is focused on the concept of selection function, and is based on explicit learning goals. It mainly consists of an…

  15. A study on target recognition fusion algorithm based on fuzzy theory

    NASA Astrophysics Data System (ADS)

    Han, Feng; Yang, WanHai

    2008-03-01

    In the process of the multi-sensors target recognition fusion, focused on the problem that it is difficult to determine the reliability of each sensor and how the data measured by different sensors are fused, a multi-sensor target recognition fusion method based on fuzzy theory is proposed. The mutual supportability of multiple sensors is obtained from the correlation function. Then by the membership function, the reliability of information provide by each sensor is gained. Finally, the supposed fusion result of multi-sensors target recognition can be produced on the basis of fuzzy integration function. The method is simple computationally and can objectively reflect the reliability of each sensor and interrelationship between these sensors. By applying the method to the target recognition, the simulation experiment shows that it can identify the target accurately and is an effective and feasible multi-sensors target recognition fusion method.

  16. An adaptive Hidden Markov Model for activity recognition based on a wearable multi-sensor device

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based o...

  17. A Novel Optimization Technique to Improve Gas Recognition by Electronic Noses Based on the Enhanced Krill Herd Algorithm

    PubMed Central

    Wang, Li; Jia, Pengfei; Huang, Tailai; Duan, Shukai; Yan, Jia; Wang, Lidan

    2016-01-01

    An electronic nose (E-nose) is an intelligent system that we will use in this paper to distinguish three indoor pollutant gases (benzene (C6H6), toluene (C7H8), formaldehyde (CH2O)) and carbon monoxide (CO). The algorithm is a key part of an E-nose system mainly composed of data processing and pattern recognition. In this paper, we employ support vector machine (SVM) to distinguish indoor pollutant gases and two of its parameters need to be optimized, so in order to improve the performance of SVM, in other words, to get a higher gas recognition rate, an effective enhanced krill herd algorithm (EKH) based on a novel decision weighting factor computing method is proposed to optimize the two SVM parameters. Krill herd (KH) is an effective method in practice, however, on occasion, it cannot avoid the influence of some local best solutions so it cannot always find the global optimization value. In addition its search ability relies fully on randomness, so it cannot always converge rapidly. To address these issues we propose an enhanced KH (EKH) to improve the global searching and convergence speed performance of KH. To obtain a more accurate model of the krill behavior, an updated crossover operator is added to the approach. We can guarantee the krill group are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iterations. The recognition results of EKH are compared with those of other optimization algorithms (including KH, chaotic KH (CKH), quantum-behaved particle swarm optimization (QPSO), particle swarm optimization (PSO) and genetic algorithm (GA)), and we can find that EKH is better than the other considered methods. The research results verify that EKH not only significantly improves the performance of our E-nose system, but also provides a good beginning and theoretical basis for further study about other improved krill algorithms’ applications in all E-nose application areas. PMID

  18. ICRPfinder: a fast pattern design algorithm for coding sequences and its application in finding potential restriction enzyme recognition sites

    PubMed Central

    Li, Chao; Li, Yuhua; Zhang, Xiangmin; Stafford, Phillip; Dinu, Valentin

    2009-01-01

    Background Restriction enzymes can produce easily definable segments from DNA sequences by using a variety of cut patterns. There are, however, no software tools that can aid in gene building -- that is, modifying wild-type DNA sequences to express the same wild-type amino acid sequences but with enhanced codons, specific cut sites, unique post-translational modifications, and other engineered-in components for recombinant applications. A fast DNA pattern design algorithm, ICRPfinder, is provided in this paper and applied to find or create potential recognition sites in target coding sequences. Results ICRPfinder is applied to find or create restriction enzyme recognition sites by introducing silent mutations. The algorithm is shown capable of mapping existing cut-sites but importantly it also can generate specified new unique cut-sites within a specified region that are guaranteed not to be present elsewhere in the DNA sequence. Conclusion ICRPfinder is a powerful tool for finding or creating specific DNA patterns in a given target coding sequence. ICRPfinder finds or creates patterns, which can include restriction enzyme recognition sites, without changing the translated protein sequence. ICRPfinder is a browser-based JavaScript application and it can run on any platform, in on-line or off-line mode. PMID:19747395

  19. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks

    PubMed Central

    Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

    2016-01-01

    Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods. PMID:27399696

  20. Cross-person activity recognition using reduced kernel extreme learning machine.

    PubMed

    Deng, Wan-Yu; Zheng, Qing-Hua; Wang, Zhong-Min

    2014-05-01

    Activity recognition based on mobile embedded accelerometer is very important for developing human-centric pervasive applications such as healthcare, personalized recommendation and so on. However, the distribution of accelerometer data is heavily affected by varying users. The performance will degrade when the model trained on one person is used to others. To solve this problem, we propose a fast and accurate cross-person activity recognition model, known as TransRKELM (Transfer learning Reduced Kernel Extreme Learning Machine) which uses RKELM (Reduced Kernel Extreme Learning Machine) to realize initial activity recognition model. In the online phase OS-RKELM (Online Sequential Reduced Kernel Extreme Learning Machine) is applied to update the initial model and adapt the recognition model to new device users based on recognition results with high confidence level efficiently. Experimental results show that, the proposed model can adapt the classifier to new device users quickly and obtain good recognition performance.

  1. Design of an efficient real-time algorithm using reduced feature dimension for recognition of speed limit signs.

    PubMed

    Cho, Hanmin; Han, Seungwha; Hwang, Sun-Young

    2013-01-01

    We propose a real-time algorithm for recognition of speed limit signs from a moving vehicle. Linear Discriminant Analysis (LDA) required for classification is performed by using Discrete Cosine Transform (DCT) coefficients. To reduce feature dimension in LDA, DCT coefficients are selected by a devised discriminant function derived from information obtained by training. Binarization and thinning are performed on a Region of Interest (ROI) obtained by preprocessing a detected ROI prior to DCT for further reduction of computation time in DCT. This process is performed on a sequence of image frames to increase the hit rate of recognition. Experimental results show that arithmetic operations are reduced by about 60%, while hit rates reach about 100% compared to previous works.

  2. Automated, long-range, night/day, active-SWIR face recognition system

    NASA Astrophysics Data System (ADS)

    Lemoff, Brian E.; Martin, Robert B.; Sluch, Mikhail; Kafka, Kristopher M.; Dolby, Andrew; Ice, Robert

    2014-06-01

    Covert, long-range, night/day identification of stationary human subjects using face recognition has been previously demonstrated using the active-SWIR Tactical Imager for Night/Day Extended-Range Surveillance (TINDERS) system. TINDERS uses an invisible, eye-safe, SWIR laser illuminator to produce high-quality facial imagery under conditions ranging from bright sunlight to total darkness. The recent addition of automation software to TINDERS has enabled the autonomous identification of moving subjects at distances greater than 100 m. Unlike typical cooperative, short range face recognition scenarios, where positive identification requires only a single face image, the SWIR wavelength, long distance, and uncontrolled conditions mean that positive identification requires fusing the face matching results from multiple captured images of a single subject. Automation software is required to initially detect a person, lock on and track the person as they move, and select video frames containing high-quality frontal face images for processing. Fusion algorithms are required to combine the matching results from multiple frames to produce a high-confidence match. These automation functions will be described, and results showing automated identification of moving subjects, night and day, at multiple distances will be presented.

  3. BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms

    PubMed Central

    2013-01-01

    Background Processing and pattern recognition of myoelectric signals have been at the core of prosthetic control research in the last decade. Although most studies agree on reporting the accuracy of predicting predefined movements, there is a significant amount of study-dependent variables that make high-resolution inter-study comparison practically impossible. As an effort to provide a common research platform for the development and evaluation of algorithms in prosthetic control, we introduce BioPatRec as open source software. BioPatRec allows a seamless implementation of a variety of algorithms in the fields of (1) Signal processing; (2) Feature selection and extraction; (3) Pattern recognition; and, (4) Real-time control. Furthermore, since the platform is highly modular and customizable, researchers from different fields can seamlessly benchmark their algorithms by applying them in prosthetic control, without necessarily knowing how to obtain and process bioelectric signals, or how to produce and evaluate physically meaningful outputs. Results BioPatRec is demonstrated in this study by the implementation of a relatively new pattern recognition algorithm, namely Regulatory Feedback Networks (RFN). RFN produced comparable results to those of more sophisticated classifiers such as Linear Discriminant Analysis and Multi-Layer Perceptron. BioPatRec is released with these 3 fundamentally different classifiers, as well as all the necessary routines for the myoelectric control of a virtual hand; from data acquisition to real-time evaluations. All the required instructions for use and development are provided in the online project hosting platform, which includes issue tracking and an extensive “wiki”. This transparent implementation aims to facilitate collaboration and speed up utilization. Moreover, BioPatRec provides a publicly available repository of myoelectric signals that allow algorithms benchmarking on common data sets. This is particularly useful for

  4. A comparative study of pattern recognition classifiers to predict physical activities using smartphones and wearable body sensors.

    PubMed

    Kouris, Ioannis; Koutsouris, Dimitris

    2012-01-01

    This paper presents a wireless body area network platform that performs physical activities recognition using accelerometers, biosignals and smartphones. Multiple classifiers and sensor combinations were examined to identify the classifier with the best recognition performance for the static and dynamic activities. The Functional Trees classifier proved to provide the best results among the classifiers evaluated (Naive Bayes, Bayesian Networks, Support Vector Machines and Decision Trees [C4.5, Random Forest]) and was used to train the model which was implemented for the real time activity recognition on the smartphone. The identified patterns of daily physical activities were used to examine conformance with medical advice, regarding physical activity guidelines. An algorithm based on Skip Chain Conditional Random Fields, received as inputs the recognized activities and data retrieved from the GPS receiver of the smartphone to develop dynamic daily patterns that enhance prediction results. The presented platform can be extended to be used in the prevention of short-term complications of metabolic diseases such as diabetes.

  5. Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition

    PubMed Central

    Munoz-Organero, Mario; Ruiz-Blazquez, Ramona

    2017-01-01

    Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware. PMID

  6. Modeling excitation-emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety.

    PubMed

    Azcarate, Silvana M; de Araújo Gomes, Adriano; Alcaraz, Mirta R; Ugulino de Araújo, Mário C; Camiña, José M; Goicoechea, Héctor C

    2015-10-01

    This paper reports the modeling of excitation-emission matrices for classification of Argentinean white wines according to the grape variety employing chemometric tools for pattern recognition. The discriminative power of the data was first investigated using Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC). The score plots showed strong overlapping between classes. A forty-one samples set was partitioned into training and test sets by the Kennard-Stone algorithm. The algorithms evaluated were SIMCA, N- and U-PLS-DA and SPA-LDA. The fit of the implemented models was assessed by mean of accuracy, sensitivity and specificity. These models were then used to assign the type of grape of the wines corresponding to the twenty samples test set. The best results were obtained for U-PLS-DA and SPA-LDA with 76% and 80% accuracy.

  7. Fast and accurate image recognition algorithms for fresh produce food safety sensing

    NASA Astrophysics Data System (ADS)

    Yang, Chun-Chieh; Kim, Moon S.; Chao, Kuanglin; Kang, Sukwon; Lefcourt, Alan M.

    2011-06-01

    This research developed and evaluated the multispectral algorithms derived from hyperspectral line-scan fluorescence imaging under violet LED excitation for detection of fecal contamination on Golden Delicious apples. The algorithms utilized the fluorescence intensities at four wavebands, 680 nm, 684 nm, 720 nm, and 780 nm, for computation of simple functions for effective detection of contamination spots created on the apple surfaces using four concentrations of aqueous fecal dilutions. The algorithms detected more than 99% of the fecal spots. The effective detection of feces showed that a simple multispectral fluorescence imaging algorithm based on violet LED excitation may be appropriate to detect fecal contamination on fast-speed apple processing lines.

  8. An algorithmic approach for static and dynamic gesture recognition utilising mechanical and biomechanical characteristics.

    PubMed

    Parvini, Farid; Shahabi, Cyrus

    2007-01-01

    We propose a novel approach for recognising static and dynamic hand gestures by analysing the raw data streams generated by the sensors attached to the human hands. We utilise the concept of 'range of motion' in the movement of fingers and exploit this characteristic to analyse the acquired data for recognising hand signs. Our approach for hand gesture recognition addresses two major problems: user-dependency and device-dependency. Furthermore, we show that our approach neither requires calibration nor involves training. We apply our approach for recognising American Sign Language (ASL) signs and show that more than 75% accuracy in sign recognition can be achieved.

  9. Exploring techniques for vision based human activity recognition: methods, systems, and evaluation.

    PubMed

    Xu, Xin; Tang, Jinshan; Zhang, Xiaolong; Liu, Xiaoming; Zhang, Hong; Qiu, Yimin

    2013-01-25

    With the wide applications of vision based intelligent systems, image and video analysis technologies have attracted the attention of researchers in the computer vision field. In image and video analysis, human activity recognition is an important research direction. By interpreting and understanding human activity, we can recognize and predict the occurrence of crimes and help the police or other agencies react immediately. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation towards the performance of human activity recognition.

  10. Evaluation of Image Segmentation and Object Recognition Algorithms for Image Parsing

    DTIC Science & Technology

    2013-09-01

    results for precision, recall, and F-measure indicate that the best approach to use for image segmentation is Sobel edge detection and to use Canny...or Sobel for object recognition. The process for this report would not work for a warfighter or analyst. It has poor performance. Additionally...1 2.1. Sobel Edge Detection

  11. Predicting mining activity with parallel genetic algorithms

    USGS Publications Warehouse

    Talaie, S.; Leigh, R.; Louis, S.J.; Raines, G.L.; Beyer, H.G.; O'Reilly, U.M.; Banzhaf, Arnold D.; Blum, W.; Bonabeau, C.; Cantu-Paz, E.W.; ,; ,

    2005-01-01

    We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance. Copyright 2005 ACM.

  12. The Roles of Spreading Activation and Retrieval Mode in Producing False Recognition in the DRM Paradigm

    ERIC Educational Resources Information Center

    Meade, Michelle L.; Watson, Jason M.; Balota, David A.; Roediger, Henry L., III

    2007-01-01

    The nature of persisting spreading activation from list presentation in eliciting false recognition in the Deese-Roediger-McDermott (DRM) paradigm was examined in two experiments. We compared the time course of semantic priming in the lexical decision task (LDT) and false alarms in speeded recognition under identical study and test conditions. The…

  13. Multi-View Human Activity Recognition in Distributed Camera Sensor Networks

    PubMed Central

    Mosabbeb, Ehsan Adeli; Raahemifar, Kaamran; Fathy, Mahmood

    2013-01-01

    With the increasing demand on the usage of smart and networked cameras in intelligent and ambient technology environments, development of algorithms for such resource-distributed networks are of great interest. Multi-view action recognition addresses many challenges dealing with view-invariance and occlusion, and due to the huge amount of processing and communicating data in real life applications, it is not easy to adapt these methods for use in smart camera networks. In this paper, we propose a distributed activity classification framework, in which we assume that several camera sensors are observing the scene. Each camera processes its own observations, and while communicating with other cameras, they come to an agreement about the activity class. Our method is based on recovering a low-rank matrix over consensus to perform a distributed matrix completion via convex optimization. Then, it is applied to the problem of human activity classification. We test our approach on IXMAS and MuHAVi datasets to show the performance and the feasibility of the method. PMID:23881136

  14. Impact of Sensor Misplacement on Dynamic Time Warping Based Human Activity Recognition using Wearable Computers

    PubMed Central

    Kale, Nimish; Lee, Jaeseong; Lotfian, Reza; Jafari, Roozbeh

    2017-01-01

    Daily living activity monitoring is important for early detection of the onset of many diseases and for improving quality of life especially in elderly. A wireless wearable network of inertial sensor nodes can be used to observe daily motions. Continuous stream of data generated by these sensor networks can be used to recognize the movements of interest. Dynamic Time Warping (DTW) is a widely used signal processing method for time-series pattern matching because of its robustness to variations in time and speed as opposed to other template matching methods. Despite this flexibility, for the application of activity recognition, DTW can only find the similarity between the template of a movement and the incoming samples, when the location and orientation of the sensor remains unchanged. Due to this restriction, small sensor misplacements can lead to a decrease in the classification accuracy. In this work, we adopt DTW distance as a feature for real-time detection of human daily activities like sit to stand in the presence of sensor misplacement. To measure this performance of DTW, we need to create a large number of sensor configurations while the sensors are rotated or misplaced. Creating a large number of closely spaced sensors is impractical. To address this problem, we use the marker based optical motion capture system and generate simulated inertial sensor data for different locations and orientations on the body. We study the performance of the DTW under these conditions to determine the worst-case sensor location variations that the algorithm can accommodate.

  15. Pattern Recognition Algorithm for High-Sensitivity Odorant Detection in Unknown Environments

    NASA Technical Reports Server (NTRS)

    Duong, Tuan A.

    2012-01-01

    In a realistic odorant detection application environment, the collected sensory data is a mix of unknown chemicals with unknown concentrations and noise. The identification of the odorants among these mixtures is a challenge in data recognition. In addition, deriving their individual concentrations in the mix is also a challenge. A deterministic analytical model was developed to accurately identify odorants and calculate their concentrations in a mixture with noisy data.

  16. Human activities recognition by head movement using partial recurrent neural network

    NASA Astrophysics Data System (ADS)

    Tan, Henry C. C.; Jia, Kui; De Silva, Liyanage C.

    2003-06-01

    Traditionally, human activities recognition has been achieved mainly by the statistical pattern recognition methods or the Hidden Markov Model (HMM). In this paper, we propose a novel use of the connectionist approach for the recognition of ten simple human activities: walking, sitting down, getting up, squatting down and standing up, in both lateral and frontal views, in an office environment. By means of tracking the head movement of the subjects over consecutive frames from a database of different color image sequences, and incorporating the Elman model of the partial recurrent neural network (RNN) that learns the sequential patterns of relative change of the head location in the images, the proposed system is able to robustly classify all the ten activities performed by unseen subjects from both sexes, of different race and physique, with a recognition rate as high as 92.5%. This demonstrates the potential of employing partial RNN to recognize complex activities in the increasingly popular human-activities-based applications.

  17. False Positives in Recognition Memory Produced by Cohort Activation.

    ERIC Educational Resources Information Center

    Wallace, William P.; And Others

    1995-01-01

    Undergraduates listened to a list of words and nonwords. They then listened to a list of items, some of which contained phonemic variations of items in the first list, and stated whether items had been presented previously. Subjects made more recognition errors to items that had phonemic variations occurring near the beginning rather than the end…

  18. A bacterial tyrosine phosphatase inhibits plant pattern recognition receptor activation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Perception of pathogen-associated molecular patterns (PAMPs) by surface-localised pattern-recognition receptors (PRRs) is a key component of plant innate immunity. Most known plant PRRs are receptor kinases and initiation of PAMP-triggered immunity (PTI) signalling requires phosphorylation of the PR...

  19. Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms

    PubMed Central

    Khan, Naveed; McClean, Sally; Zhang, Shuai; Nugent, Chris

    2016-01-01

    In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient’s vital signs in the medical domain or requesting activity labels for generating real-world labeled activity datasets. Our work focuses on change-point detection to identify a transition from one activity to another. Within this paper, we extend our previous work on multivariate exponentially weighted moving average (MEWMA) algorithm by using a genetic algorithm (GA) to identify the optimal set of parameters for online change-point detection. The proposed technique finds the maximum accuracy and F_measure by optimizing the different parameters of the MEWMA, which subsequently identifies the exact location of the change point from an existing activity to a new one. Optimal parameter selection facilitates an algorithm to detect accurate change points and minimize false alarms. Results have been evaluated based on two real datasets of accelerometer data collected from a set of different activities from two users, with a high degree of accuracy from 99.4% to 99.8% and F_measure of up to 66.7%. PMID:27792177

  20. Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors

    PubMed Central

    Ordóñez, Fco. Javier; de Toledo, Paula; Sanchis, Araceli

    2013-01-01

    Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network) and SVM (Support Vector Machines), within the framework of HMM (Hidden Markov Model) in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach. We evaluate our approach by comparing these hybrid models with other classical activity recognition methods using five real datasets. We show how the hybrid models achieve significantly better recognition performance, with significance level p < 0.05, proving that the hybrid approach is better suited for the addressed domain. PMID:23615583

  1. Goal- and retrieval-dependent activity in the striatum during memory recognition.

    PubMed

    Clos, Mareike; Schwarze, Ulrike; Gluth, Sebastian; Bunzeck, Nico; Sommer, Tobias

    2015-06-01

    The striatum has been associated with successful memory retrieval but the precise functional link still remains unclear. One hypothesis is that striatal activity reflects an active evaluation process of the retrieval outcome dependent on the current behavioral goals rather than being a consequence of memory reactivation. We have recently shown that the striatum also correlates with confidence in memory recognition, which could reflect high subjective value ascribed to high certainty decisions. To examine whether striatal activity during memory recognition reflects subjective value indeed, we conducted an fMRI study using a recognition memory paradigm in which the participants rated not only the recognition confidence but also indicated the pleasantness associated with the previous memory retrieval. The results demonstrated a high positive correlation between confidence and pleasantness both on the behavioral and brain activation level particularly in the striatum. As almost all of variance in the striatal confidence signal could be explained by experienced pleasantness, this part of the striatal memory recognition response probably corresponds to greater subjective value of high confidence responses. While perceived oldness was also strongly correlated with striatal activity, this activation pattern was clearly distinct from that associated with confidence and pleasantness and thus could not be explained by higher subjective value to detect "old" items. Together, these results show that at least two independent processes contribute to striatal activation in recognition memory: a more flexible evaluation response dependent on context and goals captured by memory confidence and a potentially retrieval-related response captured by perceived oldness.

  2. A probability of error-constrained sequential decision algorithm for data-rich automatic target recognition

    NASA Astrophysics Data System (ADS)

    Reyes, Irwin O.; DeVore, Michael D.; Beling, Peter A.; Horowitz, Barry M.

    2010-04-01

    This paper illustrates an approach to sequential hypothesis testing designed not to minimize the amount of data collected but to reduce the overall amount of processing required, while still guaranteeing pre-specified conditional probabilities of error. The approach is potentially useful when sensor data are plentiful but time and processing capability are constrained. The approach gradually reduces the number of target hypotheses under consideration as more sensor data are processed, proportionally allocating time and processing resources to the most likely target classes. The approach is demonstrated on a multi-class ladar-based target recognition problem and compared with uniform-computation tests.

  3. Human multimedia display interface based on human activity recognition

    NASA Astrophysics Data System (ADS)

    Shang, Yiting; Lee, Eung-Joo

    2011-06-01

    In this paper, we will propose a Human Multimedia Display Interface. The interface uses the tracking of human hand movements to control the IP-TV. This paper presents an improved CAMSHIFT algorithm to control an IP-TV system. The CAMSHIFT algorithm (Continuously Adaptive MeanShift) is a method of using color information[1]. It can do tracking with a specific color of the target. In some typical environmental constraints, it can obtain good tracking performance. However, as the question of noise, large area similar to the color interference and so on, only by CAM-SHIFT algorithm it is not competent. Against these issues we propose an improved CAMSHIFT algorithm[2].

  4. Evolutionary Metric-Learning-Based Recognition Algorithm for Online Isolated Persian/Arabic Characters, Reconstructed Using Inertial Pen Signals.

    PubMed

    Sepahvand, Majid; Abdali-Mohammadi, Fardin; Mardukhi, Farhad

    2016-12-13

    The development of sensors with the microelectromechanical systems technology expedites the emergence of new tools for human-computer interaction, such as inertial pens. These pens, which are used as writing tools, do not depend on a specific embedded hardware, and thus, they are inexpensive. Most of the available inertial pen character recognition approaches use the low-level features of inertial signals. This paper introduces a Persian/Arabic handwriting character recognition system for inertial-sensor-equipped pens. First, the motion trajectory of the inertial pen is reconstructed to estimate the position signals by using the theory of inertial navigation systems. The position signals are then used to extract high-level geometrical features. A new metric learning technique is then adopted to enhance the accuracy of character classification. To this end, a characteristic function is calculated for each character using a genetic programming algorithm. These functions form a metric kernel classifying all the characters. The experimental results show that the performance of the proposed method is superior to that of one of the state-of-the-art works in terms of recognizing Persian/Arabic handwriting characters.

  5. Team activity recognition in Association Football using a Bag-of-Words-based method.

    PubMed

    Montoliu, Raúl; Martín-Félez, Raúl; Torres-Sospedra, Joaquín; Martínez-Usó, Adolfo

    2015-06-01

    In this paper, a new methodology is used to perform team activity recognition and analysis in Association Football. It is based on pattern recognition and machine learning techniques. In particular, a strategy based on the Bag-of-Words (BoW) technique is used to characterize short Football video clips that are used to explain the team's performance and to train advanced classifiers in automatic recognition of team activities. In addition to the neural network-based classifier, three more classifier families are tested: the k-Nearest Neighbor, the Support Vector Machine and the Random Forest. The results obtained show that the proposed methodology is able to explain the most common movements of a team and to perform the team activity recognition task with high accuracy when classifying three Football actions: Ball Possession, Quick Attack and Set Piece. Random Forest is the classifier obtaining the best classification results.

  6. An active set algorithm for treatment planning optimization.

    PubMed

    Hristov, D H; Fallone, B G

    1997-09-01

    An active set algorithm for optimization of radiation therapy dose planning by intensity modulated beams has been developed. The algorithm employs a conjugate-gradient routine for subspace minimization in order to achieve a higher rate of convergence than the widely used constrained steepest-descent method at the expense of a negligible amount of overhead calculations. The performance of the new algorithm has been compared to that of the constrained steepest-descent method for various treatment geometries and two different objectives. The active set algorithm is found to be superior to the constrained steepest descent, both in terms of its convergence properties and the residual value of the cost functions at termination. Its use can significantly accelerate the design of conformal plans with intensity modulated beams by decreasing the number of time-consuming dose calculations.

  7. An efficient fitness function in genetic algorithm classifier for Landuse recognition on satellite images.

    PubMed

    Yang, Ming-Der; Yang, Yeh-Fen; Su, Tung-Ching; Huang, Kai-Siang

    2014-01-01

    Genetic algorithm (GA) is designed to search the optimal solution via weeding out the worse gene strings based on a fitness function. GA had demonstrated effectiveness in solving the problems of unsupervised image classification, one of the optimization problems in a large domain. Many indices or hybrid algorithms as a fitness function in a GA classifier are built to improve the classification accuracy. This paper proposes a new index, DBFCMI, by integrating two common indices, DBI and FCMI, in a GA classifier to improve the accuracy and robustness of classification. For the purpose of testing and verifying DBFCMI, well-known indices such as DBI, FCMI, and PASI are employed as well for comparison. A SPOT-5 satellite image in a partial watershed of Shihmen reservoir is adopted as the examined material for landuse classification. As a result, DBFCMI acquires higher overall accuracy and robustness than the rest indices in unsupervised classification.

  8. New perspectives in face correlation: discrimination enhancement in face recognition based on iterative algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Q.; Alfalou, A.; Brosseau, C.

    2016-04-01

    Here, we report a brief review on the recent developments of correlation algorithms. Several implementation schemes and specific applications proposed in recent years are also given to illustrate powerful applications of these methods. Following a discussion and comparison of the implementation of these schemes, we believe that all-numerical implementation is the most practical choice for application of the correlation method because the advantages of optical processing cannot compensate the technical and/or financial cost needed for an optical implementation platform. We also present a simple iterative algorithm to optimize the training images of composite correlation filters. By making use of three or four iterations, the peak-to-correlation energy (PCE) value of correlation plane can be significantly enhanced. A simulation test using the Pointing Head Pose Image Database (PHPID) illustrates the effectiveness of this statement. Our method can be applied in many composite filters based on linear composition of training images as an optimization means.

  9. Diazirine photocrosslinking recruits activated FTO demethylase complexes for specific N(6)-methyladenosine recognition.

    PubMed

    Jeong, Hyun Seok; Hayashi, Gosuke; Okamoto, Akimitsu

    2015-06-19

    N(6)-methyladenosine (m(6)A) is a prevalent modification of RNAs. m(6)A exists in mRNA and plays an important role in RNA biological pathways and in RNA epigenetic regulation. We applied diazirine photocrosslinking to the event of m(6)A recognition mediated by the fat mass and obesity associated (FTO) demethylase. A highly photoreactive diazirine adjacent to m(6)A on the RNA successfully recruited activated FTO complexes with an m(6)A preference. The process of recognition of m(6)A via FTO using diazirine photocrosslinking was controlled by the α-ketoglutarate (α-KG) cosubstrate and the Fe(II) cofactor, which are involved in m(6)A oxidative demethylation. In addition, FTO bound to ssRNAs prior to the m(6)A recognition process. Diazirine photocrosslinking contributes to increasing the chances of capturing activated FTO complexes with specific m(6)A recognition and provides new insights into the dynamic FTO oxidative demethylation process.

  10. Combining Users' Activity Survey and Simulators to Evaluate Human Activity Recognition Systems

    PubMed Central

    Azkune, Gorka; Almeida, Aitor; López-de-Ipiña, Diego; Chen, Liming

    2015-01-01

    Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant. PMID:25856329

  11. Combining users' activity survey and simulators to evaluate human activity recognition systems.

    PubMed

    Azkune, Gorka; Almeida, Aitor; López-de-Ipiña, Diego; Chen, Liming

    2015-04-08

    Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant.

  12. A Robust and Device-Free System for the Recognition and Classification of Elderly Activities

    PubMed Central

    Li, Fangmin; Al-qaness, Mohammed Abdulaziz Aide; Zhang, Yong; Zhao, Bihai; Luan, Xidao

    2016-01-01

    Human activity recognition, tracking and classification is an essential trend in assisted living systems that can help support elderly people with their daily activities. Traditional activity recognition approaches depend on vision-based or sensor-based techniques. Nowadays, a novel promising technique has obtained more attention, namely device-free human activity recognition that neither requires the target object to wear or carry a device nor install cameras in a perceived area. The device-free technique for activity recognition uses only the signals of common wireless local area network (WLAN) devices available everywhere. In this paper, we present a novel elderly activities recognition system by leveraging the fluctuation of the wireless signals caused by human motion. We present an efficient method to select the correct data from the Channel State Information (CSI) streams that were neglected in previous approaches. We apply a Principle Component Analysis method that exposes the useful information from raw CSI. Thereafter, Forest Decision (FD) is adopted to classify the proposed activities and has gained a high accuracy rate. Extensive experiments have been conducted in an indoor environment to test the feasibility of the proposed system with a total of five volunteer users. The evaluation shows that the proposed system is applicable and robust to electromagnetic noise. PMID:27916948

  13. An Improved Force Feedback Control Algorithm for Active Tendons

    PubMed Central

    Guo, Tieneng; Liu, Zhifeng; Cai, Ligang

    2012-01-01

    An active tendon, consisting of a displacement actuator and a co-located force sensor, has been adopted by many studies to suppress the vibration of large space flexible structures. The damping, provided by the force feedback control algorithm in these studies, is small and can increase, especially for tendons with low axial stiffness. This study introduces an improved force feedback algorithm, which is based on the idea of velocity feedback. The algorithm provides a large damping ratio for space flexible structures and does not require a structure model. The effectiveness of the algorithm is demonstrated on a structure similar to JPL-MPI. The results show that large damping can be achieved for the vibration control of large space structures. PMID:23112660

  14. Motion mode recognition and step detection algorithms for mobile phone users.

    PubMed

    Susi, Melania; Renaudin, Valérie; Lachapelle, Gérard

    2013-01-24

    Microelectromechanical Systems (MEMS) technology is playing a key role in the design of the new generation of smartphones. Thanks to their reduced size, reduced power consumption, MEMS sensors can be embedded in above mobile devices for increasing their functionalities. However, MEMS cannot allow accurate autonomous location without external updates, e.g., from GPS signals, since their signals are degraded by various errors. When these sensors are fixed on the user's foot, the stance phases of the foot can easily be determined and periodic Zero velocity UPdaTes (ZUPTs) are performed to bound the position error. When the sensor is in the hand, the situation becomes much more complex. First of all, the hand motion can be decoupled from the general motion of the user. Second, the characteristics of the inertial signals can differ depending on the carrying modes. Therefore, algorithms for characterizing the gait cycle of a pedestrian using a handheld device have been developed. A classifier able to detect motion modes typical for mobile phone users has been designed and implemented. According to the detected motion mode, adaptive step detection algorithms are applied. Success of the step detection process is found to be higher than 97% in all motion modes.

  15. Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users

    PubMed Central

    Susi, Melania; Renaudin, Valérie; Lachapelle, Gérard

    2013-01-01

    Microelectromechanical Systems (MEMS) technology is playing a key role in the design of the new generation of smartphones. Thanks to their reduced size, reduced power consumption, MEMS sensors can be embedded in above mobile devices for increasing their functionalities. However, MEMS cannot allow accurate autonomous location without external updates, e.g., from GPS signals, since their signals are degraded by various errors. When these sensors are fixed on the user's foot, the stance phases of the foot can easily be determined and periodic Zero velocity UPdaTes (ZUPTs) are performed to bound the position error. When the sensor is in the hand, the situation becomes much more complex. First of all, the hand motion can be decoupled from the general motion of the user. Second, the characteristics of the inertial signals can differ depending on the carrying modes. Therefore, algorithms for characterizing the gait cycle of a pedestrian using a handheld device have been developed. A classifier able to detect motion modes typical for mobile phone users has been designed and implemented. According to the detected motion mode, adaptive step detection algorithms are applied. Success of the step detection process is found to be higher than 97% in all motion modes. PMID:23348038

  16. A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

    PubMed Central

    Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

    2016-01-01

    Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches. PMID:27792136

  17. A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

    PubMed

    Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

    2016-10-25

    Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

  18. Recognition algorithm for assisting ovarian cancer diagnosis from coregistered ultrasound and photoacoustic images: ex vivo study

    NASA Astrophysics Data System (ADS)

    Alqasemi, Umar; Kumavor, Patrick; Aguirre, Andres; Zhu, Quing

    2012-12-01

    Unique features and the underlining hypotheses of how these features may relate to the tumor physiology in coregistered ultrasound and photoacoustic images of ex vivo ovarian tissue are introduced. The images were first compressed with wavelet transform. The mean Radon transform of photoacoustic images was then computed and fitted with a Gaussian function to find the centroid of a suspicious area for shift-invariant recognition process. Twenty-four features were extracted from a training set by several methods, including Fourier transform, image statistics, and different composite filters. The features were chosen from more than 400 training images obtained from 33 ex vivo ovaries of 24 patients, and used to train three classifiers, including generalized linear model, neural network, and support vector machine (SVM). The SVM achieved the best training performance and was able to exclusively separate cancerous from non-cancerous cases with 100% sensitivity and specificity. At the end, the classifiers were used to test 95 new images obtained from 37 ovaries of 20 additional patients. The SVM classifier achieved 76.92% sensitivity and 95.12% specificity. Furthermore, if we assume that recognizing one image as a cancer is sufficient to consider an ovary as malignant, the SVM classifier achieves 100% sensitivity and 87.88% specificity.

  19. [A peak recognition algorithm designed for chromatographic peaks of transformer oil].

    PubMed

    Ou, Linjun; Cao, Jian

    2014-09-01

    In the field of the chromatographic peak identification of the transformer oil, the traditional first-order derivative requires slope threshold to achieve peak identification. In terms of its shortcomings of low automation and easy distortion, the first-order derivative method was improved by applying the moving average iterative method and the normalized analysis techniques to identify the peaks. Accurate identification of the chromatographic peaks was realized through using multiple iterations of the moving average of signal curves and square wave curves to determine the optimal value of the normalized peak identification parameters, combined with the absolute peak retention times and peak window. The experimental results show that this algorithm can accurately identify the peaks and is not sensitive to the noise, the chromatographic peak width or the peak shape changes. It has strong adaptability to meet the on-site requirements of online monitoring devices of dissolved gases in transformer oil.

  20. Ocean feature recognition using genetic algorithms with fuzzy fitness functions (GA/F3)

    NASA Technical Reports Server (NTRS)

    Ankenbrandt, C. A.; Buckles, B. P.; Petry, F. E.; Lybanon, M.

    1990-01-01

    A model for genetic algorithms with semantic nets is derived for which the relationships between concepts is depicted as a semantic net. An organism represents the manner in which objects in a scene are attached to concepts in the net. Predicates between object pairs are continuous valued truth functions in the form of an inverse exponential function (e sub beta lxl). 1:n relationships are combined via the fuzzy OR (Max (...)). Finally, predicates between pairs of concepts are resolved by taking the average of the combined predicate values of the objects attached to the concept at the tail of the arc representing the predicate in the semantic net. The method is illustrated by applying it to the identification of oceanic features in the North Atlantic.

  1. An active set algorithm for tracing parametrized optima

    NASA Technical Reports Server (NTRS)

    Rakowska, J.; Haftka, R. T.; Watson, L. T.

    1991-01-01

    Optimization problems often depend on parameters that define constraints or objective functions. It is often necessary to know the effect of a change in a parameter on the optimum solution. An algorithm is presented here for tracking paths of optimal solutions of inequality constrained nonlinear programming problems as a function of a parameter. The proposed algorithm employs homotopy zero-curve tracing techniques to track segments where the set of active constraints is unchanged. The transition between segments is handled by considering all possible sets of active constraints and eliminating nonoptimal ones based on the signs of the Lagrange multipliers and the derivatives of the optimal solutions with respect to the parameter. A spring-mass problem is used to illustrate all possible kinds of transition events, and the algorithm is applied to a well-known ten-bar truss structural optimization problem.

  2. Influence of music with different volumes and styles on recognition activity in humans.

    PubMed

    Pavlygina, R A; Sakharov, D S; Davydov, V I; Avdonkin, A V

    2010-10-01

    The efficiency of the recognition of masked visual images (Arabic numerals) increased when accompanied by classical (62 dB) and rock music (25 dB). These changes were accompanied by increases in the coherence of potentials in the frontal areas seen on recognition without music. Changes in intercenter EEG relationships correlated with the formation a dominant at the behavioral level. When loud music (85 dB) and music of other styles was used, these changes in behavior and the EEG were not seen; however, the coherence of potentials in the temporal and motor cortex of the right hemisphere increased and the latent periods of motor reactions of the hands decreased. These results provide evidence that the "recognition" dominant is formed when there are particular ratios of the levels of excitation in the corresponding centers, which should be considered when there is a need to increase the efficiency of recognition activity in humans.

  3. PKC-epsilon activation is required for recognition memory in the rat.

    PubMed

    Zisopoulou, Styliani; Asimaki, Olga; Leondaritis, George; Vasilaki, Anna; Sakellaridis, Nikos; Pitsikas, Nikolaos; Mangoura, Dimitra

    2013-09-15

    Activation of PKCɛ, an abundant and developmentally regulated PKC isoform in the brain, has been implicated in memory throughout life and across species. Yet, direct evidence for a mechanistic role for PKCɛ in memory is still lacking. Hence, we sought to evaluate this in rats, using short-term treatments with two PKCɛ-selective peptides, the inhibitory ɛV1-2 and the activating ψɛRACK, and the novel object recognition task (NORT). Our results show that the PKCɛ-selective activator ψɛRACK, did not have a significant effect on recognition memory. In the short time frames used, however, inhibition of PKCɛ activation with the peptide inhibitor ɛV1-2 significantly impaired recognition memory. Moreover, when we addressed at the molecular level the immediate proximal signalling events of PKCɛ activation in acutely dissected rat hippocampi, we found that ψɛRACK increased in a time-dependent manner phosphorylation of MARCKS and activation of Src, Raf, and finally ERK1/2, whereas ɛV1-2 inhibited all basal activity of this pathway. Taken together, these findings present the first direct evidence that PKCɛ activation is an essential molecular component of recognition memory and point toward the use of systemically administered PKCɛ-regulating peptides as memory study tools and putative therapeutic agents.

  4. Pattern recognition in lithology classification: modeling using neural networks, self-organizing maps and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Sahoo, Sasmita; Jha, Madan K.

    2017-03-01

    Effective characterization of lithology is vital for the conceptualization of complex aquifer systems, which is a prerequisite for the development of reliable groundwater-flow and contaminant-transport models. However, such information is often limited for most groundwater basins. This study explores the usefulness and potential of a hybrid soft-computing framework; a traditional artificial neural network with gradient descent-momentum training (ANN-GDM) and a traditional genetic algorithm (GA) based ANN (ANN-GA) approach were developed and compared with a novel hybrid self-organizing map (SOM) based ANN (SOM-ANN-GA) method for the prediction of lithology at a basin scale. This framework is demonstrated through a case study involving a complex multi-layered aquifer system in India, where well-log sites were clustered on the basis of sand-layer frequencies; within each cluster, subsurface layers were reclassified into four depth classes based on the maximum drilling depth. ANN models for each depth class were developed using each of the three approaches. Of the three, the hybrid SOM-ANN-GA models were able to recognize incomplete geologic pattern more reasonably, followed by ANN-GA and ANN-GDM models. It is concluded that the hybrid soft-computing framework can serve as a promising tool for characterizing lithology in groundwater basins with missing lithologic patterns.

  5. Hierarchical Activity Recognition Using Smart Watches and RGB-Depth Cameras

    PubMed Central

    Li, Zhen; Wei, Zhiqiang; Huang, Lei; Zhang, Shugang; Nie, Jie

    2016-01-01

    Human activity recognition is important for healthcare and lifestyle evaluation. In this paper, a novel method for activity recognition by jointly considering motion sensor data recorded by wearable smart watches and image data captured by RGB-Depth (RGB-D) cameras is presented. A normalized cross correlation based mapping method is implemented to establish association between motion sensor data with corresponding image data from the same person in multi-person situations. Further, to improve the performance and accuracy of recognition, a hierarchical structure embedded with an automatic group selection method is proposed. Through this method, if the number of activities to be classified is changed, the structure will be changed correspondingly without interaction. Our comparative experiments against the single data source and single layer methods have shown that our method is more accurate and robust. PMID:27754458

  6. Optical pattern recognition architecture implementing the mean-square error correlation algorithm

    SciTech Connect

    Molley, P.A.

    1991-10-22

    This patent describes an optical architecture implementing the mean-square error correlation algorithm, MSE = {Sigma}(I {minus} R){sup 2} for discriminating the presence of a reference image R in an input image scene I by computing the mean-square-error between a time-varying reference image signal s{sub 1}(t) and a time-varying input image signal s{sub 2}(t) includes a laser diode light source which is temporally modulated by a double-sideband suppressed-carrier source modulation signal I{sub 1}(t) having the form I{sub 1}(t) = A{sub 1}(1 = sq. root 2m{sub 1}s{sub 1}(t)cos (2{pi} f{sub 0}t)) and the modulated light output from the laser diode source is diffracted by an acousto-optic deflector. The resultant intensity of the +1 diffracted order from the acousto-optic device is given by I{sub 2}(t) = A{sub 2}(+2m{sub 2}{sup 2}s{sub 2}{sup 2}(t) {minus} 2 sq. root 2m{sub 2}(t) cos (2{pi}f{sub 0}t)). The time integration of the two signals I{sub 1}(t) and I{sub 2}(t) on the CCD deflector plane produces the result R{tau} of the mean-square error having the form: R({tau}) = A{sub 1}A{sub 2}{l brace}(T) +(2m{sub 2}{sup 2 {integral} s}{sub 2}{sup 2}(t {minus} {tau})dt) {minus} (2m{sub 1}m{sub 2} cos (2{tau}f{sub 0}{tau}) {integral} s{sub 1}(t)s{sub 2}(t {minus} {tau}) dt){r brace}.

  7. An automated tool for face recognition using visual attention and active shape models analysis.

    PubMed

    Faro, A; Giordano, D; Spampinato, C

    2006-01-01

    An entirely automated approach for the recognition of the face of a people starting from her/his images is presented. The approach uses a computational attention module to find automatically the most relevant facial features using the Focus Of Attentions (FOA) These features are used to build the model of a face during the learning phase and for recognition during the testing phase. The landmarking of the features is performed by applying the active contour model (ACM) technique, whereas the active shape model (ASM) is adopted for constructing a flexible model of the selected facial features. The advantages of this approach and opportunities for further improvements are discussed.

  8. Investigation of Dynamic Algorithms for Pattern Recognition Identified in Cerebral Cortex

    DTIC Science & Technology

    1991-12-02

    oscillatory and possibly chaotic activity forin the actual cortical substrate of the diverse sensory, motor, and cognitive operations now studied in...September Neural Information Processing Systems - Natural and Synthetic, Denver, Colo., November 1989 U.C. San Diego, Cognitive Science Dept...Baird. Biologically applied neural networks may foster the co-evolution of neurobiology and cognitive psychology. Brain and Behavioral Sciences, 37

  9. Activity recognition using a mixture of vector fields.

    PubMed

    Nascimento, Jacinto C; Figueiredo, Mário A T; Marques, Jorge S

    2013-05-01

    The analysis of moving objects in image sequences (video) has been one of the major themes in computer vision. In this paper, we focus on video-surveillance tasks; more specifically, we consider pedestrian trajectories and propose modeling them through a small set of motion/vector fields together with a space-varying switching mechanism. Despite the diversity of motion patterns that can occur in a given scene, we show that it is often possible to find a relatively small number of typical behaviors, and model each of these behaviors by a "simple" motion field. We increase the expressiveness of the formulation by allowing the trajectories to switch from one motion field to another, in a space-dependent manner. We present an expectation-maximization algorithm to learn all the parameters of the model, and apply it to trajectory classification tasks. Experiments with both synthetic and real data support the claims about the performance of the proposed approach.

  10. Emotion Recognition of Weblog Sentences Based on an Ensemble Algorithm of Multi-label Classification and Word Emotions

    NASA Astrophysics Data System (ADS)

    Li, Ji; Ren, Fuji

    Weblogs have greatly changed the communication ways of mankind. Affective analysis of blog posts is found valuable for many applications such as text-to-speech synthesis or computer-assisted recommendation. Traditional emotion recognition in text based on single-label classification can not satisfy higher requirements of affective computing. In this paper, the automatic identification of sentence emotion in weblogs is modeled as a multi-label text categorization task. Experiments are carried out on 12273 blog sentences from the Chinese emotion corpus Ren_CECps with 8-dimension emotion annotation. An ensemble algorithm RAKEL is used to recognize dominant emotions from the writer's perspective. Our emotion feature using detailed intensity representation for word emotions outperforms the other main features such as the word frequency feature and the traditional lexicon-based feature. In order to deal with relatively complex sentences, we integrate grammatical characteristics of punctuations, disjunctive connectives, modification relations and negation into features. It achieves 13.51% and 12.49% increases for Micro-averaged F1 and Macro-averaged F1 respectively compared to the traditional lexicon-based feature. Result shows that multiple-dimension emotion representation with grammatical features can efficiently classify sentence emotion in a multi-label problem.

  11. Optical pattern recognition architecture implementing the mean-square error correlation algorithm

    DOEpatents

    Molley, Perry A.

    1991-01-01

    An optical architecture implementing the mean-square error correlation algorithm, MSE=.SIGMA.[I-R].sup.2 for discriminating the presence of a reference image R in an input image scene I by computing the mean-square-error between a time-varying reference image signal s.sub.1 (t) and a time-varying input image signal s.sub.2 (t) includes a laser diode light source which is temporally modulated by a double-sideband suppressed-carrier source modulation signal I.sub.1 (t) having the form I.sub.1 (t)=A.sub.1 [1+.sqroot.2m.sub.1 s.sub.1 (t)cos (2.pi.f.sub.o t)] and the modulated light output from the laser diode source is diffracted by an acousto-optic deflector. The resultant intensity of the +1 diffracted order from the acousto-optic device is given by: I.sub.2 (t)=A.sub.2 [+2m.sub.2.sup.2 s.sub.2.sup.2 (t)-2.sqroot.2m.sub.2 (t) cos (2.pi.f.sub.o t] The time integration of the two signals I.sub.1 (t) and I.sub.2 (t) on the CCD deflector plane produces the result R(.tau.) of the mean-square error having the form: R(.tau.)=A.sub.1 A.sub.2 {[T]+[2m.sub.2.sup.2.multidot..intg.s.sub.2.sup.2 (t-.tau.)dt]-[2m.sub.1 m.sub.2 cos (2.tau.f.sub.o .tau.).multidot..intg.s.sub.1 (t)s.sub.2 (t-.tau.)dt]} where: s.sub.1 (t) is the signal input to the diode modulation source: s.sub.2 (t) is the signal input to the AOD modulation source; A.sub.1 is the light intensity; A.sub.2 is the diffraction efficiency; m.sub.1 and m.sub.2 are constants that determine the signal-to-bias ratio; f.sub.o is the frequency offset between the oscillator at f.sub.c and the modulation at f.sub.c +f.sub.o ; and a.sub.o and a.sub.1 are constant chosen to bias the diode source and the acousto-optic deflector into their respective linear operating regions so that the diode source exhibits a linear intensity characteristic and the AOD exhibits a linear amplitude characteristic.

  12. Prestimulus default mode activity influences depth of processing and recognition in an emotional memory task.

    PubMed

    Soravia, Leila M; Witmer, Joëlle S; Schwab, Simon; Nakataki, Masahito; Dierks, Thomas; Wiest, Roland; Henke, Katharina; Federspiel, Andrea; Jann, Kay

    2016-03-01

    Low self-referential thoughts are associated with better concentration, which leads to deeper encoding and increases learning and subsequent retrieval. There is evidence that being engaged in externally rather than internally focused tasks is related to low neural activity in the default mode network (DMN) promoting open mind and the deep elaboration of new information. Thus, reduced DMN activity should lead to enhanced concentration, comprehensive stimulus evaluation including emotional categorization, deeper stimulus processing, and better long-term retention over one whole week. In this fMRI study, we investigated brain activation preceding and during incidental encoding of emotional pictures and on subsequent recognition performance. During fMRI, 24 subjects were exposed to 80 pictures of different emotional valence and subsequently asked to complete an online recognition task one week later. Results indicate that neural activity within the medial temporal lobes during encoding predicts subsequent memory performance. Moreover, a low activity of the default mode network preceding incidental encoding leads to slightly better recognition performance independent of the emotional perception of a picture. The findings indicate that the suppression of internally-oriented thoughts leads to a more comprehensive and thorough evaluation of a stimulus and its emotional valence. Reduced activation of the DMN prior to stimulus onset is associated with deeper encoding and enhanced consolidation and retrieval performance even one week later. Even small prestimulus lapses of attention influence consolidation and subsequent recognition performance.

  13. Smartphone-Based Patients' Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring.

    PubMed

    Guo, Junqi; Zhou, Xi; Sun, Yunchuan; Ping, Gong; Zhao, Guoxing; Li, Zhuorong

    2016-06-01

    Smartphone based activity recognition has recently received remarkable attention in various applications of mobile health such as safety monitoring, fitness tracking, and disease prediction. To achieve more accurate and simplified medical monitoring, this paper proposes a self-learning scheme for patients' activity recognition, in which a patient only needs to carry an ordinary smartphone that contains common motion sensors. After the real-time data collection though this smartphone, we preprocess the data using coordinate system transformation to eliminate phone orientation influence. A set of robust and effective features are then extracted from the preprocessed data. Because a patient may inevitably perform various unpredictable activities that have no apriori knowledge in the training dataset, we propose a self-learning activity recognition scheme. The scheme determines whether there are apriori training samples and labeled categories in training pools that well match with unpredictable activity data. If not, it automatically assembles these unpredictable samples into different clusters and gives them new category labels. These clustered samples combined with the acquired new category labels are then merged into the training dataset to reinforce recognition ability of the self-learning model. In experiments, we evaluate our scheme using the data collected from two postoperative patient volunteers, including six labeled daily activities as the initial apriori categories in the training pool. Experimental results demonstrate that the proposed self-learning scheme for activity recognition works very well for most cases. When there exist several types of unseen activities without any apriori information, the accuracy reaches above 80 % after the self-learning process converges.

  14. Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

    NASA Astrophysics Data System (ADS)

    Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

    2016-10-01

    Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

  15. Long-Term Activity Recognition from Wristwatch Accelerometer Data *

    PubMed Central

    Garcia-Ceja, Enrique; Brena, Ramon F.; Carrasco-Jimenez, Jose C.; Garrido, Leonardo

    2014-01-01

    With the development of wearable devices that have several embedded sensors, it is possible to collect data that can be analyzed in order to understand the user's needs and provide personalized services. Examples of these types of devices are smartphones, fitness-bracelets, smartwatches, just to mention a few. In the last years, several works have used these devices to recognize simple activities like running, walking, sleeping, and other physical activities. There has also been research on recognizing complex activities like cooking, sporting, and taking medication, but these generally require the installation of external sensors that may become obtrusive to the user. In this work we used acceleration data from a wristwatch in order to identify long-term activities. We compare the use of Hidden Markov Models and Conditional Random Fields for the segmentation task. We also added prior knowledge into the models regarding the duration of the activities by coding them as constraints and sequence patterns were added in the form of feature functions. We also performed subclassing in order to deal with the problem of intra-class fragmentation, which arises when the same label is applied to activities that are conceptually the same but very different from the acceleration point of view. PMID:25436652

  16. Long-term activity recognition from wristwatch accelerometer data.

    PubMed

    Garcia-Ceja, Enrique; Brena, Ramon F; Carrasco-Jimenez, Jose C; Garrido, Leonardo

    2014-11-27

    With the development of wearable devices that have several embedded sensors, it is possible to collect data that can be analyzed in order to understand the user's needs and provide personalized services. Examples of these types of devices are smartphones, fitness-bracelets, smartwatches, just to mention a few. In the last years, several works have used these devices to recognize simple activities like running, walking, sleeping, and other physical activities. There has also been research on recognizing complex activities like cooking, sporting, and taking medication, but these generally require the installation of external sensors that may become obtrusive to the user. In this work we used acceleration data from a wristwatch in order to identify long-term activities. We compare the use of Hidden Markov Models and Conditional Random Fields for the segmentation task. We also added prior knowledge into the models regarding the duration of the activities by coding them as constraints and sequence patterns were added in the form of feature functions. We also performed subclassing in order to deal with the problem of intra-class fragmentation, which arises when the same label is applied to activities that are conceptually the same but very different from the acceleration point of view.

  17. Effects of Cooperative Group Work Activities on Pre-School Children's Pattern Recognition Skills

    ERIC Educational Resources Information Center

    Tarim, Kamuran

    2015-01-01

    The aim of this research is twofold; to investigate the effects of cooperative group-based work activities on children's pattern recognition skills in pre-school and to examine the teachers' opinions about the implementation process. In line with this objective, for the study, 57 children (25 girls and 32 boys) were chosen from two private schools…

  18. Active Planning, Sensing and Recognition Using a Resource-Constrained Discriminant POMDP

    DTIC Science & Technology

    2014-06-28

    ADDRESS. William Marsh Rice University 6100 Main St., MS-16 Houston, TX 77005 -1827 ABSTRACT Active Planning, Sensing and Recognition Using a...Urbana, IL 61801 ‡Dept. of Computer Science, Rice University, Houston, TX 77005 §U.S. Army Research Laboratory, Adelphi, MD 20783 {wang308, zwang119

  19. Phonological Activation during Visual Word Recognition in Deaf and Hearing Children

    ERIC Educational Resources Information Center

    Ormel, Ellen; Hermans, Daan; Knoors, Harry; Hendriks, Angelique; Verhoeven, Ludo

    2010-01-01

    Purpose: Phonological activation during visual word recognition was studied in deaf and hearing children under two circumstances: (a) when the use of phonology was not required for task performance and might even hinder it and (b) when the use of phonology was critical for task performance. Method: Deaf children mastering written Dutch and Sign…

  20. Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition

    PubMed Central

    Banos, Oresti; Toth, Mate Attila; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

    2014-01-01

    Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements. PMID:24915181

  1. Activity recognition using Video Event Segmentation with Text (VEST)

    NASA Astrophysics Data System (ADS)

    Holloway, Hillary; Jones, Eric K.; Kaluzniacki, Andrew; Blasch, Erik; Tierno, Jorge

    2014-06-01

    Multi-Intelligence (multi-INT) data includes video, text, and signals that require analysis by operators. Analysis methods include information fusion approaches such as filtering, correlation, and association. In this paper, we discuss the Video Event Segmentation with Text (VEST) method, which provides event boundaries of an activity to compile related message and video clips for future interest. VEST infers meaningful activities by clustering multiple streams of time-sequenced multi-INT intelligence data and derived fusion products. We discuss exemplar results that segment raw full-motion video (FMV) data by using extracted commentary message timestamps, FMV metadata, and user-defined queries.

  2. Noninvasive imaging of sialyltransferase activity in living cells by chemoselective recognition

    NASA Astrophysics Data System (ADS)

    Bao, Lei; Ding, Lin; Yang, Min; Ju, Huangxian

    2015-06-01

    To elucidate the biological and pathological functions of sialyltransferases (STs), intracellular ST activity evaluation is necessary. Focusing on the lack of noninvasive methods for obtaining the dynamic activity information, this work designs a sensing platform for in situ FRET imaging of intracellular ST activity and tracing of sialylation process. The system uses tetramethylrhodamine isothiocyanate labeled asialofetuin (TRITC-AF) as a ST substrate and fluorescein isothiocyanate labeled 3-aminophenylboronic acid (FITC-APBA) as the chemoselective recognition probe of sialylation product, both of which are encapsulated in a liposome vesicle for cellular delivery. The recognition of FITC-APBA to sialylated TRITC-AF leads to the FRET signal that is analyzed by FRET efficiency images. This strategy has been used to evaluate the correlation of ST activity with malignancy and cell surface sialylation, and the sialylation inhibition activity of inhibitors. This work provides a powerful noninvasive tool for glycan biosynthesis mechanism research, cancer diagnostics and drug development.

  3. Automatic Activation of Orthography in Spoken Word Recognition: Pseudohomograph Priming

    ERIC Educational Resources Information Center

    Taft, Marcus; Castles, Anne; Davis, Chris; Lazendic, Goran; Nguyen-Hoan, Minh

    2008-01-01

    There is increasing evidence that orthographic information has an impact on spoken word processing. However, much of this evidence comes from tasks that are subject to strategic effects. In the three experiments reported here, we examined activation of orthographic information during spoken word processing within a paradigm that is unlikely to…

  4. The Activation of Embedded Words in Spoken Word Recognition.

    PubMed

    Zhang, Xujin; Samuel, Arthur G

    2015-01-01

    The current study investigated how listeners understand English words that have shorter words embedded in them. A series of auditory-auditory priming experiments assessed the activation of six types of embedded words (2 embedded positions × 3 embedded proportions) under different listening conditions. Facilitation of lexical decision responses to targets (e.g., pig) associated with words embedded in primes (e.g., hamster) indexed activation of the embedded words (e.g., ham). When the listening conditions were optimal, isolated embedded words (e.g., ham) primed their targets in all six conditions (Experiment 1a). Within carrier words (e.g., hamster), the same set of embedded words produced priming only when they were at the beginning or comprised a large proportion of the carrier word (Experiment 1b). When the listening conditions were made suboptimal by expanding or compressing the primes, significant priming was found for isolated embedded words (Experiment 2a), but no priming was produced when the carrier words were compressed/expanded (Experiment 2b). Similarly, priming was eliminated when the carrier words were presented with one segment replaced by noise (Experiment 3). When cognitive load was imposed, priming for embedded words was again found when they were presented in isolation (Experiment 4a), but not when they were embedded in the carrier words (Experiment 4b). The results suggest that both embedded position and proportion play important roles in the activation of embedded words, but that such activation only occurs under unusually good listening conditions.

  5. The Activation of Embedded Words in Spoken Word Recognition

    PubMed Central

    Zhang, Xujin; Samuel, Arthur G.

    2015-01-01

    The current study investigated how listeners understand English words that have shorter words embedded in them. A series of auditory-auditory priming experiments assessed the activation of six types of embedded words (2 embedded positions × 3 embedded proportions) under different listening conditions. Facilitation of lexical decision responses to targets (e.g., pig) associated with words embedded in primes (e.g., hamster) indexed activation of the embedded words (e.g., ham). When the listening conditions were optimal, isolated embedded words (e.g., ham) primed their targets in all six conditions (Experiment 1a). Within carrier words (e.g., hamster), the same set of embedded words produced priming only when they were at the beginning or comprised a large proportion of the carrier word (Experiment 1b). When the listening conditions were made suboptimal by expanding or compressing the primes, significant priming was found for isolated embedded words (Experiment 2a), but no priming was produced when the carrier words were compressed/expanded (Experiment 2b). Similarly, priming was eliminated when the carrier words were presented with one segment replaced by noise (Experiment 3). When cognitive load was imposed, priming for embedded words was again found when they were presented in isolation (Experiment 4a), but not when they were embedded in the carrier words (Experiment 4b). The results suggest that both embedded position and proportion play important roles in the activation of embedded words, but that such activation only occurs under unusually good listening conditions. PMID:25593407

  6. Multiplatform GPGPU implementation of the active contours without edges algorithm

    NASA Astrophysics Data System (ADS)

    Zavala-Romero, Olmo; Meyer-Baese, Anke; Meyer-Baese, Uwe

    2012-05-01

    An OpenCL implementation of the Active Contours Without Edges algorithm is presented. The proposed algorithm uses the General Purpose Computing on Graphics Processing Units (GPGPU) to accelerate the original model by parallelizing the two main steps of the segmentation process, the computation of the Signed Distance Function (SDF) and the evolution of the segmented curve. The proposed scheme for the computation of the SDF is based on the iterative construction of partial Voronoi diagrams of a reduced dimension and obtains the exact Euclidean distance in a time of order O(N/p), where N is the number of pixels and p the number of processors. With high resolution images the segmentation algorithm runs 10 times faster than its equivalent sequential implementation. This work is being done as an open source software that, being programmed in OpenCL, can be used in dierent platforms allowing a broad number of nal users and can be applied in dierent areas of computer vision, like medical imaging, tracking, robotics, etc. This work uses OpenGL to visualize the algorithm results in real time.

  7. Indirect DNA Sequence Recognition and Its Impact on Nuclease Cleavage Activity.

    PubMed

    Lambert, Abigail R; Hallinan, Jazmine P; Shen, Betty W; Chik, Jennifer K; Bolduc, Jill M; Kulshina, Nadia; Robins, Lori I; Kaiser, Brett K; Jarjour, Jordan; Havens, Kyle; Scharenberg, Andrew M; Stoddard, Barry L

    2016-06-07

    LAGLIDADG meganucleases are DNA cleaving enzymes used for genome engineering. While their cleavage specificity can be altered using several protein engineering and selection strategies, their overall targetability is limited by highly specific indirect recognition of the central four base pairs within their recognition sites. In order to examine the physical basis of indirect sequence recognition and to expand the number of such nucleases available for genome engineering, we have determined the target sites, DNA-bound structures, and central four cleavage fidelities of nine related enzymes. Subsequent crystallographic analyses of a meganuclease bound to two noncleavable target sites, each containing a single inactivating base pair substitution at its center, indicates that a localized slip of the mutated base pair causes a small change in the DNA backbone conformation that results in a loss of metal occupancy at one binding site, eliminating cleavage activity.

  8. Disrupting pre-SMA activity impairs facial happiness recognition: an event-related TMS study.

    PubMed

    Rochas, Vincent; Gelmini, Lauriane; Krolak-Salmon, Pierre; Poulet, Emmanuel; Saoud, Mohamed; Brunelin, Jerome; Bediou, Benoit

    2013-07-01

    It has been suggested that the left pre-supplementary motor area (pre-SMA) could be implicated in facial emotion expression and recognition, especially for laughter/happiness. To test this hypothesis, in a single-blind, randomized crossover study, we investigated the impact of transcranial magnetic stimulation (TMS) on performances of 18 healthy participants during a facial emotion recognition task. Using a neuronavigation system based on T1-weighted magnetic resonance imaging of each participant, TMS (5 pulses, 10 Hz) was delivered over the pre-SMA or the vertex (control condition) in an event-related fashion after the presentation of happy, fear, and angry faces. Compared with performances during vertex stimulation, we observed that TMS applied over the left pre-SMA specifically disrupted facial happiness recognition (FHR). No difference was observed between the 2 conditions neither for fear and anger recognition nor for reaction times (RT). Thus, interfering with pre-SMA activity with event-related TMS after stimulus presentation produced a selective impairment in the recognition of happy faces. These findings provide new insights into the functional implication of the pre-SMA in FHR, which may rely on the mirror properties of pre-SMA neurons.

  9. The activation of visual face memory and explicit face recognition are delayed in developmental prosopagnosia.

    PubMed

    Parketny, Joanna; Towler, John; Eimer, Martin

    2015-08-01

    Individuals with developmental prosopagnosia (DP) are strongly impaired in recognizing faces, but the causes of this deficit are not well understood. We employed event-related brain potentials (ERPs) to study the time-course of neural processes involved in the recognition of previously unfamiliar faces in DPs and in age-matched control participants with normal face recognition abilities. Faces of different individuals were presented sequentially in one of three possible views, and participants had to detect a specific Target Face ("Joe"). EEG was recorded during task performance to Target Faces, Nontarget Faces, or the participants' Own Face (which had to be ignored). The N250 component was measured as a marker of the match between a seen face and a stored representation in visual face memory. The subsequent P600f was measured as an index of attentional processes associated with the conscious awareness and recognition of a particular face. Target Faces elicited reliable N250 and P600f in the DP group, but both of these components emerged later in DPs than in control participants. This shows that the activation of visual face memory for previously unknown learned faces and the subsequent attentional processing and conscious recognition of these faces are delayed in DP. N250 and P600f components to Own Faces did not differ between the two groups, indicating that the processing of long-term familiar faces is less affected in DP. However, P600f components to Own Faces were absent in two participants with DP who failed to recognize their Own Face during the experiment. These results provide new evidence that face recognition deficits in DP may be linked to a delayed activation of visual face memory and explicit identity recognition mechanisms.

  10. The effect of gaze direction on three-dimensional face recognition in infant brain activity.

    PubMed

    Yamashita, Wakayo; Kanazawa, So; Yamaguchi, Masami K; Kakigi, Ryusuke

    2012-09-12

    In three-dimensional face recognition studies, it is well known that viewing rotating faces enhance face recognition. For infants, our previous study indicated that 8-month-old infants showed recognition of three-dimensional rotating faces with a direct gaze, and they did not learn with an averted gaze. This suggests that gaze direction may affect three-dimensional face recognition in infants. In this experiment, we used near-infrared spectroscopy to measure infants' hemodynamic responses to averted gaze and direct gaze. We hypothesized that infants would show different neural activity for averted and direct gazes. The responses were compared with the baseline activation during the presentation of non-face objects. We found that the concentration of oxyhemoglobin increased in the temporal cortex on both sides only during the presentation of averted gaze compared with that of the baseline period. This is the first study to show that infants' brain activity in three-dimensional face processing is different between averted gaze and direct gaze.

  11. Activity reductions in perirhinal cortex predict conceptual priming and familiarity-based recognition.

    PubMed

    Wang, Wei-Chun; Ranganath, Charan; Yonelinas, Andrew P

    2014-01-01

    Although it is well established that regions in the medial temporal lobes are critical for explicit memory, recent work has suggested that one medial temporal lobe subregion--the perirhinal cortex (PRC)--may also support conceptual priming, a form of implicit memory. Here, we sought to investigate whether activity reductions in PRC, previously linked to familiarity-based recognition, might also support conceptual implicit memory retrieval. Using a free association priming task, the current study tested the prediction that PRC indexes conceptual priming independent of contributions from perceptual and response repetition. Participants first completed an incidental semantic encoding task outside of the MRI scanner. Next, they were scanned during performance of a free association priming task, followed by a recognition memory test. Results indicated successful conceptual priming was associated with decreased PRC activity, and that an overlapping region within the PRC also exhibited activity reductions that covaried with familiarity during the recognition memory test. Our results demonstrate that the PRC contributes to both conceptual priming and familiarity-based recognition, which may reflect a common role of this region in implicit and explicit memory retrieval.

  12. Activity reductions in perirhinal cortex predict conceptual priming and familiarity-based recognition

    PubMed Central

    Wang, Wei-chun; Ranganath, Charan; Yonelinas, Andrew P

    2013-01-01

    Although it is well established that regions in the medial temporal lobes are critical for explicit memory, recent work has suggested that one medial temporal lobe subregion – the perirhinal cortex (PRC) – may also support conceptual priming, a form of implicit memory. Here, we sought to investigate whether activity reductions in PRC, previously linked to familiarity-based recognition, might also support conceptual implicit memory retrieval. Using a free association priming task, the current study tested the prediction that PRC indexes conceptual priming independent of contributions from perceptual and response repetition. Participants first completed an incidental semantic encoding task outside of the MRI scanner. Next, they were scanned during performance of a free association priming task, followed by a recognition memory test. Results indicated successful conceptual priming was associated with decreased PRC activity, and that an overlapping region within the PRC also exhibited activity reductions that covaried with familiarity during the recognition memory test. Our results demonstrate that the PRC contributes to both conceptual priming and familiarity-based recognition, which may reflect a common role of this region in implicit and explicit memory retrieval. PMID:24157537

  13. Sudden Event Recognition: A Survey

    PubMed Central

    Suriani, Nor Surayahani; Hussain, Aini; Zulkifley, Mohd Asyraf

    2013-01-01

    Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1) the importance of a sudden event over a general anomalous event; (2) frameworks used in sudden event recognition; (3) the requirements and comparative studies of a sudden event recognition system and (4) various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition. PMID:23921828

  14. Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

    PubMed Central

    Jurek, Anna; Nugent, Chris; Bi, Yaxin; Wu, Shengli

    2014-01-01

    Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks. PMID:25014095

  15. Clustering-based ensemble learning for activity recognition in smart homes.

    PubMed

    Jurek, Anna; Nugent, Chris; Bi, Yaxin; Wu, Shengli

    2014-07-10

    Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

  16. Health smart home for elders - a tool for automatic recognition of activities of daily living.

    PubMed

    Le, Xuan Hoa Binh; Di Mascolo, Maria; Gouin, Alexia; Noury, Norbert

    2008-01-01

    Elders live preferently in their own home, but with aging comes the loss of autonomy and associated risks. In order to help them live longer in safe conditions, we need a tool to automatically detect their loss of autonomy by assessing the degree of performance of activities of daily living. This article presents an approach enabling the activities recognition of an elder living alone in a home equipped with noninvasive sensors.

  17. Active recognition enhances the representation of behaviorally relevant information in single auditory forebrain neurons.

    PubMed

    Knudsen, Daniel P; Gentner, Timothy Q

    2013-04-01

    Sensory systems are dynamic. They must process a wide range of natural signals that facilitate adaptive behaviors in a manner that depends on an organism's constantly changing goals. A full understanding of the sensory physiology that underlies adaptive natural behaviors must therefore account for the activity of sensory systems in light of these behavioral goals. Here we present a novel technique that combines in vivo electrophysiological recording from awake, freely moving songbirds with operant conditioning techniques that allow control over birds' recognition of conspecific song, a widespread natural behavior in songbirds. We show that engaging in a vocal recognition task alters the response properties of neurons in the caudal mesopallium (CM), an avian analog of mammalian auditory cortex, in European starlings. Compared with awake, passive listening, active engagement of subjects in an auditory recognition task results in neurons responding to fewer song stimuli and a decrease in the trial-to-trial variability in their driven firing rates. Mean firing rates also change during active recognition, but not uniformly. Relative to nonengaged listening, active recognition causes increases in the driven firing rates in some neurons, decreases in other neurons, and stimulus-specific changes in other neurons. These changes lead to both an increase in stimulus selectivity and an increase in the information conveyed by the neurons about the animals' behavioral task. This study demonstrates the behavioral dependence of neural responses in the avian auditory forebrain and introduces the starling as a model for real-time monitoring of task-related neural processing of complex auditory objects.

  18. Statistical algorithms for target detection in coherent active polarimetric images.

    PubMed

    Goudail, F; Réfrégier, P

    2001-12-01

    We address the problem of small-target detection with a polarimetric imager that provides orthogonal state contrast images. Such active systems allow one to measure the degree of polarization of the light backscattered by purely depolarizing isotropic materials. To be independent of the spatial nonuniformities of the illumination beam, small-target detection on the orthogonal state contrast image must be performed without using the image of backscattered intensity. We thus propose and develop a simple and efficient target detection algorithm based on a nonlinear pointwise transformation of the orthogonal state contrast image followed by a maximum-likelihood algorithm optimal for additive Gaussian perturbations. We demonstrate the efficiency of this suboptimal technique in comparison with the optimal one, which, however, assumes a priori knowledge about the scene that is not available in practice. We illustrate the performance of this approach on both simulated and real polarimetric images.

  19. Parallel language activation and cognitive control during spoken word recognition in bilinguals

    PubMed Central

    Blumenfeld, Henrike K.; Marian, Viorica

    2013-01-01

    Accounts of bilingual cognitive advantages suggest an associative link between cross-linguistic competition and inhibitory control. We investigate this link by examining English-Spanish bilinguals’ parallel language activation during auditory word recognition and nonlinguistic Stroop performance. Thirty-one English-Spanish bilinguals and 30 English monolinguals participated in an eye-tracking study. Participants heard words in English (e.g., comb) and identified corresponding pictures from a display that included pictures of a Spanish competitor (e.g., conejo, English rabbit). Bilinguals with higher Spanish proficiency showed more parallel language activation and smaller Stroop effects than bilinguals with lower Spanish proficiency. Across all bilinguals, stronger parallel language activation between 300–500ms after word onset was associated with smaller Stroop effects; between 633–767ms, reduced parallel language activation was associated with smaller Stroop effects. Results suggest that bilinguals who perform well on the Stroop task show increased cross-linguistic competitor activation during early stages of word recognition and decreased competitor activation during later stages of word recognition. Findings support the hypothesis that cross-linguistic competition impacts domain-general inhibition. PMID:24244842

  20. Recognition of Activities of Daily Living with Egocentric Vision: A Review

    PubMed Central

    Nguyen, Thi-Hoa-Cuc; Nebel, Jean-Christophe; Florez-Revuelta, Francisco

    2016-01-01

    Video-based recognition of activities of daily living (ADLs) is being used in ambient assisted living systems in order to support the independent living of older people. However, current systems based on cameras located in the environment present a number of problems, such as occlusions and a limited field of view. Recently, wearable cameras have begun to be exploited. This paper presents a review of the state of the art of egocentric vision systems for the recognition of ADLs following a hierarchical structure: motion, action and activity levels, where each level provides higher semantic information and involves a longer time frame. The current egocentric vision literature suggests that ADLs recognition is mainly driven by the objects present in the scene, especially those associated with specific tasks. However, although object-based approaches have proven popular, object recognition remains a challenge due to the intra-class variations found in unconstrained scenarios. As a consequence, the performance of current systems is far from satisfactory. PMID:26751452

  1. Video-based convolutional neural networks for activity recognition from robot-centric videos

    NASA Astrophysics Data System (ADS)

    Ryoo, M. S.; Matthies, Larry

    2016-05-01

    In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.

  2. SALSA: a pattern recognition algorithm to detect electrophile-adducted peptides by automated evaluation of CID spectra in LC-MS-MS analyses.

    PubMed

    Hansen, B T; Jones, J A; Mason, D E; Liebler, D C

    2001-04-15

    A pattern recognition algorithm called SALSA (scoring algorithm for spectral analysis) has been developed to rapidly screen large numbers of peptide MS-MS spectra for fragmentation characteristics indicative of specific peptide modifications. The algorithm facilitates sensitive and specific detection of modified peptides at low abundance in an enzymatic protein digest. SALSA can simultaneously score multiple user-specified search criteria, including product ions, neutral losses, charged losses, and ion pairs that are diagnostic of specific peptide modifications. Application of SALSA to the detection of peptide adducts of the electrophiles dehydromonocrotaline, benzoquinone, and iodoacetic acid permitted their detection in a complex tryptic peptide digest mixture. SALSA provides superior detection of adducted peptides compared to conventional tandem MS precursor ion or neutral loss scans.

  3. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

    PubMed Central

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-01

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

  4. [Multi-Target Recognition of Internal and External Defects of Potato by Semi-Transmission Hyperspectral Imaging and Manifold Learning Algorithm].

    PubMed

    Huang, Tao; Li, Xiao-yu; Jin, Rui; Ku, Jing; Xu, Sen-miao; Xu, Meng-ling; Wu, Zhen-zhong; Kong, De-guo

    2015-04-01

    The present paper put forward a non-destructive detection method which combines semi-transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM) to recognize internal and external defects in potatoes simultaneously. Three hundred fifteen potatoes were bought in farmers market as research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes. In order to conform to the actual production, defect part is randomly put right, side and back to the acquisition probe when the hyperspectral images of external defects potatoes are acquired. The average spectrums (390-1,040 nm) were extracted from the region of interests for spectral preprocessing. Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data, including supervised locally linear embedding (SLLE), locally linear embedding (LLE) and isometric mapping (ISOMAP), the low-dimensional data gotten by manifold learning algorithms is used as model input, Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi-target classification model. By comparing and analyzing results of the three models, we concluded that SLLE is the optimal manifold learning dimension reduction algorithm, and the SLLE-LSSVM model is determined to get the best recognition rate for recognizing internal and external defects potatoes. For test set data, the single recognition rate of normal, bud, green rind and hollow heart potato reached 96.83%, 86.96%, 86.96% and 95% respectively, and he hybrid recognition rate was 93.02%. The results indicate that combining the semi-transmission hyperspectral imaging technology with SLLE-LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and

  5. Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring.

    PubMed

    Ghose, Soumya; Mitra, Jhimli; Karunanithi, Mohan; Dowling, Jason

    2015-01-01

    Home monitoring of chronically ill or elderly patient can reduce frequent hospitalisations and hence provide improved quality of care at a reduced cost to the community, therefore reducing the burden on the healthcare system. Activity recognition of such patients is of high importance in such a design. In this work, a system for automatic human physical activity recognition from smart-phone inertial sensors data is proposed. An ensemble of decision trees framework is adopted to train and predict the multi-class human activity system. A comparison of our proposed method with a multi-class traditional support vector machine shows significant improvement in activity recognition accuracies.

  6. Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients

    PubMed Central

    2015-01-01

    Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations. PMID:25885272

  7. Hierarchical classifier approach to physical activity recognition via wearable smartphone tri-axial accelerometer.

    PubMed

    Yusuf, Feridun; Maeder, Anthony; Basilakis, Jim

    2013-01-01

    Physical activity recognition has emerged as an active area of research which has drawn increasing interest from researchers in a variety of fields. It can support many different applications such as safety surveillance, fraud detection, and clinical management. Accelerometers have emerged as the most useful and extensive tool to capture and assess human physical activities in a continuous, unobtrusive and reliable manner. The need for objective physical activity data arises strongly in health related research. With the shift to a sedentary lifestyle, where work and leisure tend to be less physically demanding, research on the health effects of low physical activity has become a necessity. The increased availability of small, inexpensive components has led to the development of mobile devices such as smartphones, providing platforms for new opportunities in healthcare applications. In this study 3 subjects performed directed activity routines wearing a smartphone with a built in tri-axial accelerometer, attached on a belt around the waist. The data was collected to classify 11 basic physical activities such as sitting, lying, standing, walking, and the transitions in between them. A hierarchical classifier approach was utilised with Artificial Neural Networks integrated in a rule-based system, to classify the activities. Based on our evaluation, recognition accuracy of over 89.6% between subjects and over 91.5% within subject was achieved. These results show that activities such as these can be recognised with a high accuracy rate; hence the approach is promising for use in future work.

  8. Bulged Invader probes: activated duplexes for mixed-sequence dsDNA recognition with improved thermodynamic and kinetic profiles.

    PubMed

    Guenther, Dale C; Karmakar, Saswata; Hrdlicka, Patrick J

    2015-10-18

    Double-stranded oligonucleotides with +1 interstrand zipper arrangements of intercalator-functionalized nucleotides are energetically activated for recognition of mixed-sequence double-stranded DNA. Incorporation of nonyl (C9) bulges at specific positions of these probes, results in more highly affine (>5-fold), faster (>4-fold) and more persistent dsDNA recognition relative to conventional Invader probes.

  9. Recollection-related Hippocampal Activity during Continuous Recognition: a High-Resolution fMRI Study

    PubMed Central

    Suzuki, Maki; Johnson, Jeffrey D.; Rugg, Michael D.

    2010-01-01

    We used high-resolution functional magnetic resonance imaging (fMRI) to investigate whether successful recollection during continuous recognition is associated with relative enhancement of hippocampal activity, consistent with prior findings from experiments employing separate study and test phases. While being scanned, subjects discriminated between new and repeated pictures. Each picture, which was repeated once after an interval of between 10 and 30 items, was surrounded by a frame that was colored grey, blue, or orange. When an item repeated, its frame color determined the correct response. Repeated items surrounded by a grey frame always required an ‘old’ judgment. A repeated item surrounded by a blue or an orange frame required a different response depending whether it was re-presented in the same (Target) or a different (Nontarget) color from the first presentation. Consistent with the results from previous continuous recognition experiments, robust new > old effects were found in bilateral hippocampus. Additionally, an across-subjects correlational analysis identified a cluster of voxels in right hippocampus where recollection-related activity (operationalized by the contrast between correctly vs. incorrectly judged Nontargets) was positively correlated with recollection performance. Thus, successful recollection during continuous recognition is associated with a relative enhancement of hippocampal activity. PMID:20232398

  10. Age differences in hippocampal activation during gist-based false recognition.

    PubMed

    Paige, Laura E; Cassidy, Brittany S; Schacter, Daniel L; Gutchess, Angela H

    2016-10-01

    Age-related increases in reliance on gist-based processes can cause increased false recognition. Understanding the neural basis for this increase helps to elucidate a mechanism underlying this vulnerability in memory. We assessed age differences in gist-based false memory by increasing image set size at encoding, thereby increasing the rate of false alarms. False alarms during a recognition test elicited increased hippocampal activity for older adults as compared to younger adults for the small set sizes, whereas the age groups had similar hippocampal activation for items associated with larger set sizes. Interestingly, younger adults had stronger connectivity between the hippocampus and posterior temporal regions relative to older adults during false alarms for items associated with large versus small set sizes. With increased gist, younger adults might rely more on additional processes (e.g., semantic associations) during recognition than older adults. Parametric modulation revealed that younger adults had increased anterior cingulate activity than older adults with decreasing set size, perhaps indicating difficulty in using monitoring processes in error-prone situations.

  11. Apelin-13 exerts antidepressant-like and recognition memory improving activities in stressed rats.

    PubMed

    Li, E; Deng, Haifeng; Wang, Bo; Fu, Wan; You, Yong; Tian, Shaowen

    2016-03-01

    Apelin is the endogenous ligand for the G-protein-coupled receptor (APJ). The localization of APJ in limbic structures suggests a potential role for apelin in emotional processes. However, the role of apelin in the regulation of stress-induced responses such as depression and memory impairment is largely unknown. In the present study, we evaluated the role of apelin-13 in the regulation of stress-induced depression and memory impairment in rats. We report that repeated intracerebroventricular injections of apelin-13 reversed behavioral despair (immobility) in the forced swim (FS) test, a model widely used for the selection of new antidepressant agents. Apelin-13 also reversed behavioral deficits (escape failure) in the learned helplessness test. The magnitude of the antiimmobility and anti-escape failure effects of apelin-13 was comparable to that of imipramine, a classic antidepressant used as a positive control. Rats exposed to FS stress showed memory performance impairment in the novel object recognition test, and this impairment was improved by apelin-13 treatment. Apelin-13 did not affect recognition memory performance in non-stressed rats. Furthermore, the pretreatment of LY294002 (PI3K inhibitors) or PD98059 (ERK1/2 inhibitor) blocked apelin-13-mediated activities in FS-stressed rats. These findings suggest that apelin-13 exerts antidepressant-like and recognition memory improving activities through activating PI3K and ERK1/2 signaling pathways in stressed rats.

  12. When Action Observation Facilitates Visual Perception: Activation in Visuo-Motor Areas Contributes to Object Recognition.

    PubMed

    Sim, Eun-Jin; Helbig, Hannah B; Graf, Markus; Kiefer, Markus

    2015-09-01

    Recent evidence suggests an interaction between the ventral visual-perceptual and dorsal visuo-motor brain systems during the course of object recognition. However, the precise function of the dorsal stream for perception remains to be determined. The present study specified the functional contribution of the visuo-motor system to visual object recognition using functional magnetic resonance imaging and event-related potential (ERP) during action priming. Primes were movies showing hands performing an action with an object with the object being erased, followed by a manipulable target object, which either afforded a similar or a dissimilar action (congruent vs. incongruent condition). Participants had to recognize the target object within a picture-word matching task. Priming-related reductions of brain activity were found in frontal and parietal visuo-motor areas as well as in ventral regions including inferior and anterior temporal areas. Effective connectivity analyses suggested functional influences of parietal areas on anterior temporal areas. ERPs revealed priming-related source activity in visuo-motor regions at about 120 ms and later activity in the ventral stream at about 380 ms. Hence, rapidly initiated visuo-motor processes within the dorsal stream functionally contribute to visual object recognition in interaction with ventral stream processes dedicated to visual analysis and semantic integration.

  13. A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

    PubMed Central

    Janidarmian, Majid; Roshan Fekr, Atena; Radecka, Katarzyna; Zilic, Zeljko

    2017-01-01

    Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results. PMID:28272362

  14. Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors.

    PubMed

    Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J M

    2016-03-24

    The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2-30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available.

  15. Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors

    PubMed Central

    Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J. M.

    2016-01-01

    The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2–30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available. PMID:27023543

  16. Object discrimination through active electrolocation: Shape recognition and the influence of electrical noise.

    PubMed

    Schumacher, Sarah; Burt de Perera, Theresa; von der Emde, Gerhard

    2016-12-12

    The weakly electric fish Gnathonemus petersii can recognise objects using active electrolocation. Here, we tested two aspects of object recognition; first whether shape recognition might be influenced by movement of the fish, and second whether object discrimination is affected by the presence of electrical noise from conspecifics. (i) Unlike other object features, such as size or volume, no parameter within a single electrical image has been found that encodes object shape. We investigated whether shape recognition might be facilitated by movement-induced modulations (MIM) of the set of electrical images that are created as a fish swims past an object. Fish were trained to discriminate between pairs of objects that either created similar or dissimilar levels of MIM of the electrical images. As predicted, the fish were able to discriminate between objects up to a longer distance if there was a large difference in MIM between the objects than if there was a small difference. This supports an involvement of MIMs in shape recognition but the use of other cues cannot be excluded. (ii) Electrical noise might impair object recognition if the noise signals overlap with the EODs of an electrolocating fish. To avoid jamming, we predicted that fish might employ pulsing strategies to prevent overlaps. To investigate the influence of electrical noise on discrimination performance, two fish were tested either in the presence of a conspecific or of playback signals and the electric signals were recorded during the experiments. The fish were surprisingly immune to jamming by conspecifics: While the discrimination performance of one fish dropped to chance level when more than 22% of its EODs overlapped with the noise signals, the performance of the other fish was not impaired even when all its EODs overlapped. Neither of the fish changed their pulsing behaviour, suggesting that they did not use any kind of jamming avoidance strategy.

  17. Colocalization recognition-activated cascade signal amplification strategy for ultrasensitive detection of transcription factors.

    PubMed

    Zhu, Desong; Wang, Lei; Xu, Xiaowen; Jiang, Wei

    2017-03-15

    Transcription factors (TFs) bind to specific double-stranded DNA (dsDNA) sequences in the regulatory regions of genes to regulate the process of gene transcription. Their expression levels sensitively reflect cell developmental situation and disease state. TFs have become potential diagnostic markers and therapeutic targets of cancers and some other diseases. Hence, high sensitive detection of TFs is of vital importance for early diagnosis of diseases and drugs development. The traditional exonucleases-assisted signal amplification methods suffered from the false positives caused by incomplete digestion of excess recognition probes. Herein, based on a new recognition way-colocalization recognition (CR)-activated dual signal amplification, an ultrasensitive fluorescent detection strategy for TFs was developed. TFs-induced the colocalization of three split recognition components resulted in noticeable increases of local effective concentrations and hybridization of three split components, which activated the subsequent cascade signal amplification including strand displacement amplification (SDA) and exponential rolling circle amplification (ERCA). This strategy eliminated the false positive influence and achieved ultra-high sensitivity towards the purified NF-κB p50 with detection limit of 2.0×10(-13)M. Moreover, NF-κB p50 can be detected in as low as 0.21ngμL(-1) HeLa cell nuclear extracts. In addition, this proposed strategy could be used for the screening of NF-κB p50 activity inhibitors and potential anti-NF-κB p50 drugs. Finally, our proposed strategy offered a potential method for reliable detection of TFs in medical diagnosis and treatment research of cancers and other related diseases.

  18. Equivalent activation of the hippocampus by face-face and face-laugh paired associate learning and recognition.

    PubMed

    Holdstock, J S; Crane, J; Bachorowski, J-A; Milner, B

    2010-11-01

    The human hippocampus is known to play an important role in relational memory. Both patient lesion studies and functional-imaging studies have shown that it is involved in the encoding and retrieval from memory of arbitrary associations. Two recent patient lesion studies, however, have found dissociations between spared and impaired memory within the domain of relational memory. Recognition of associations between information of the same kind (e.g., two faces) was spared, whereas recognition of associations between information of different kinds (e.g., face-name or face-voice associations) was impaired by hippocampal lesions. Thus, recognition of associations between information of the same kind may not be mediated by the hippocampus. Few imaging studies have directly compared activation at encoding and recognition of associations between same and different types of information. Those that have have shown mixed findings and been open to alternative interpretation. We used fMRI to compare hippocampal activation while participants studied and later recognized face-face and face-laugh paired associates. We found no differences in hippocampal activation between our two types of stimulus materials during either study or recognition. Study of both types of paired associate activated the hippocampus bilaterally, but the hippocampus was not activated by either condition during recognition. Our findings suggest that the human hippocampus is normally engaged to a similar extent by study and recognition of associations between information of the same kind and associations between information of different kinds.

  19. Molecular crowding drives active Pin1 into nonspecific complexes with endogenous proteins prior to substrate recognition.

    PubMed

    Luh, Laura M; Hänsel, Robert; Löhr, Frank; Kirchner, Donata K; Krauskopf, Katharina; Pitzius, Susanne; Schäfer, Birgit; Tufar, Peter; Corbeski, Ivan; Güntert, Peter; Dötsch, Volker

    2013-09-18

    Proteins and nucleic acids maintain the crowded interior of a living cell and can reach concentrations in the order of 200-400 g/L which affects the physicochemical parameters of the environment, such as viscosity and hydrodynamic as well as nonspecific strong repulsive and weak attractive interactions. Dynamics, structure, and activity of macromolecules were demonstrated to be affected by these parameters. However, it remains controversially debated, which of these factors are the dominant cause for the observed alterations in vivo. In this study we investigated the globular folded peptidyl-prolyl isomerase Pin1 in Xenopus laevis oocytes and in native-like crowded oocyte extract by in-cell NMR spectroscopy. We show that active Pin1 is driven into nonspecific weak attractive interactions with intracellular proteins prior to substrate recognition. The substrate recognition site of Pin1 performs specific and nonspecific attractive interactions. Phosphorylation of the WW domain at Ser16 by PKA abrogates both substrate recognition and the nonspecific interactions with the endogenous proteins. Our results validate the hypothesis formulated by McConkey that the majority of globular folded proteins with surface charge properties close to neutral under physiological conditions reside in macromolecular complexes with other sticky proteins due to molecular crowding. In addition, we demonstrate that commonly used synthetic crowding agents like Ficoll 70 are not suitable to mimic the intracellular environment due to their incapability to simulate biologically important weak attractive interactions.

  20. Suspicious activity recognition in infrared imagery using Hidden Conditional Random Fields for outdoor perimeter surveillance

    NASA Astrophysics Data System (ADS)

    Rogotis, Savvas; Ioannidis, Dimosthenis; Tzovaras, Dimitrios; Likothanassis, Spiros

    2015-04-01

    The aim of this work is to present a novel approach for automatic recognition of suspicious activities in outdoor perimeter surveillance systems based on infrared video processing. Through the combination of size, speed and appearance based features, like the Center-Symmetric Local Binary Patterns, short-term actions are identified and serve as input, along with user location, for modeling target activities using the theory of Hidden Conditional Random Fields. HCRFs are used to directly link a set of observations to the most appropriate activity label and as such to discriminate high risk activities (e.g. trespassing) from zero risk activities (e.g loitering outside the perimeter). Experimental results demonstrate the effectiveness of our approach in identifying suspicious activities for video surveillance systems.

  1. Accelerometry-Based Activity Recognition and Assessment in Rheumatic and Musculoskeletal Diseases

    PubMed Central

    Billiet, Lieven; Swinnen, Thijs Willem; Westhovens, Rene; de Vlam, Kurt; Van Huffel, Sabine

    2016-01-01

    One of the important aspects to be considered in rheumatic and musculoskeletal diseases is the patient’s activity capacity (or performance), defined as the ability to perform a task. Currently, it is assessed by physicians or health professionals mainly by means of a patient-reported questionnaire, sometimes combined with the therapist’s judgment on performance-based tasks. This work introduces an approach to assess the activity capacity at home in a more objective, yet interpretable way. It offers a pilot study on 28 patients suffering from axial spondyloarthritis (axSpA) to demonstrate its efficacy. Firstly, a protocol is introduced to recognize a limited set of six transition activities in the home environment using a single accelerometer. To this end, a hierarchical classifier with the rejection of non-informative activity segments has been developed drawing on both direct pattern recognition and statistical signal features. Secondly, the recognized activities should be assessed, similarly to the scoring performed by patients themselves. This is achieved through the interval coded scoring (ICS) system, a novel method to extract an interpretable scoring system from data. The activity recognition reaches an average accuracy of 93.5%; assessment is currently 64.3% accurate. These results indicate the potential of the approach; a next step should be its validation in a larger patient study. PMID:27999255

  2. [Development of a wearable electrocardiogram monitor with recognition of physical activity scene].

    PubMed

    Wang, Zihong; Wu, Baoming; Yin, Jian; Gong, Yushun

    2012-10-01

    To overcome the problems of current electrocardiogram (ECG) tele-monitoring devices used for daily life, according to information fusion thought and by means of wearable technology, we developed a new type of wearable ECG monitor with the capability of physical activity recognition in this paper. The ECG monitor synchronously detected electrocardiogram signal and body acceleration signal, and recognized the scene information of physical activity, and finally determined the health status of the heart. With the advantages of accuracy for measurement, easy to use, comfort to wear, private feelings and long-term continuous in monitoring, this ECG monitor is quite fit for the heart-health monitoring in daily life.

  3. Theory of simple biochemical ``shape recognition'' via diffusion from activator coated nanoshapes

    NASA Astrophysics Data System (ADS)

    Daniels, D. R.

    2008-09-01

    Inspired by recent experiments, we model the shape sensitivity, via a typical threshold initiation response, of an underlying complex biochemical reaction network to activator coated nanoshapes. Our theory re-emphasizes that shape effects can be vitally important for the onset of functional behavior in nanopatches and nanoparticles. For certain critical or particular shapes, activator coated nanoshapes do not evoke a threshold response in a complex biochemical network setting, while for different critical or specific shapes, the threshold response is rapidly achieved. The model thus provides a general theoretical understanding for how activator coated nanoshapes can enable a chemical system to perform simple "shape recognition," with an associated "all or nothing" response. The novel and interesting cases of the chemical response due to a nanoshape that shrinks with time is additionally considered, as well as activator coated nanospheres. Possible important applications of this work include the initiation of blood clotting by nanoshapes, nanoshape effects in nanocatalysis, physiological toxicity to nanoparticles, as well as nanoshapes in nanomedicine, drug delivery, and T cell immunological response. The aim of the theory presented here is that it inspires further experimentation on simple biochemical shape recognition via diffusion from activator coated nanoshapes.

  4. Automatic Recognition of Solar Features for Developing Data Driven Prediction Models of Solar Activity and Space Weather

    DTIC Science & Technology

    2013-05-01

    Aschwanden, M. J. 2005, Physics of the Solar Corona . An Introduction with Problems and Solutions (2nd edition), ed. Aschwanden, M. J. Balasubramaniam, K...AFRL-OSR-VA-TR-2013-0020 Automatic Recognition of Solar Features for Developing Data Driven Prediction Models of Solar Activity...Automatic Recognition of Solar Features for Developing Data Driven Prediction Models of Solar Activity and Space Weather 5a. CONTRACT NUMBER FA9550-09

  5. The development of line-scan image recognition algorithms for the detection of frass on mature tomatoes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this research, a multispectral algorithm derived from hyperspectral line-scan fluorescence imaging under violet LED excitation was developed for the detection of frass contamination on mature tomatoes. The algorithm utilized the fluorescence intensities at two wavebands, 664 nm and 690 nm, for co...

  6. An Optically Active Polymer for Broad-Spectrum Enantiomeric Recognition of Chiral Acids.

    PubMed

    Yan, Jijun; Kang, Chuanqing; Bian, Zheng; Ma, Xiaoye; Jin, Rizhe; Du, Zhijun; Gao, Lianxun

    2017-03-14

    Recognition of enantiomers of chiral acids by anion-π or lone pair-π interactions has not yet been investigated but is a significant and attractive challenge. This study reports an optically active polymer-based supramolecular system with capabilities of discriminating enantiomers of various chiral acids. The polymer featuring alternate π-acidic naphthalenediimides (NDIs) and methyl l-phenylalaninates in the backbone exhibits an unprecedented slow self-assembly process that is susceptible to perturbation by various chiral acids. Thus, the combination of anion-π or lone pair-π interactions and sensitivity of the polymeric self-assembly process to external chiral species endows the system with recognition capabilities. This is the first time that anion-π or lone pair-π interactions have been applied in the recognition of enantiomers of various chiral acids with a single system. The results shed light on new strategies for material design by integrating π-acidic aromatic systems and chiral building blocks to afford relevant advanced functions.

  7. Soil type recognition as improved by genetic algorithm-based variable selection using near infrared spectroscopy and partial least squares discriminant analysis

    PubMed Central

    Xie, Hongtu; Zhao, Jinsong; Wang, Qiubing; Sui, Yueyu; Wang, Jingkuan; Yang, Xueming; Zhang, Xudong; Liang, Chao

    2015-01-01

    Soil types have traditionally been determined by soil physical and chemical properties, diagnostic horizons and pedogenic processes based on a given classification system. This is a laborious and time consuming process. Near infrared (NIR) spectroscopy can comprehensively characterize soil properties, and may provide a viable alternative method for soil type recognition. Here, we presented a partial least squares discriminant analysis (PLSDA) method based on the NIR spectra for the accurate recognition of the types of 230 soil samples collected from farmland topsoils (0–10 cm), representing 5 different soil classes (Albic Luvisols, Haplic Luvisols, Chernozems, Eutric Cambisols and Phaeozems) in northeast China. We found that the PLSDA had an internal validation accuracy of 89% and external validation accuracy of 83% on average, while variable selection with the genetic algorithm (GA and GA-PLSDA) improved this to 92% and 93%. Our results indicate that the GA variable selection technique can significantly improve the accuracy rate of soil type recognition using NIR spectroscopy, suggesting that the proposed methodology is a promising alternative for recognizing soil types using NIR spectroscopy. PMID:26086823

  8. A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm.

    PubMed

    Yuan, Tao; Zheng, Xinqi; Hu, Xuan; Zhou, Wei; Wang, Wei

    2014-01-01

    Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.

  9. Recognition of familiar food activates feeding via an endocrine serotonin signal in Caenorhabditis elegans.

    PubMed

    Song, Bo-Mi; Faumont, Serge; Lockery, Shawn; Avery, Leon

    2013-02-05

    Familiarity discrimination has a significant impact on the pattern of food intake across species. However, the mechanism by which the recognition memory controls feeding is unclear. Here, we show that the nematode Caenorhabditis elegans forms a memory of particular foods after experience and displays behavioral plasticity, increasing the feeding response when they subsequently recognize the familiar food. We found that recognition of familiar food activates the pair of ADF chemosensory neurons, which subsequently increase serotonin release. The released serotonin activates the feeding response mainly by acting humorally and directly activates SER-7, a type 7 serotonin receptor, in MC motor neurons in the feeding organ. Our data suggest that worms sense the taste and/or smell of novel bacteria, which overrides the stimulatory effect of familiar bacteria on feeding by suppressing the activity of ADF or its upstream neurons. Our study provides insight into the mechanism by which familiarity discrimination alters behavior.DOI:http://dx.doi.org/10.7554/eLife.00329.001.

  10. Active vibration control of smart composite plates using LQR algorithm

    NASA Astrophysics Data System (ADS)

    Suresh, R.; Venkateshwara Rao, G.

    2003-10-01

    The concept of using the actuators and sensors to form a self controlling and self monitoring smart system in advanced structural design has drawn considerable interest among the research community. The smart system has large number of active, light weight, distributed sensors and actuators either bonded or embedded in the structure for the purpose of vibration suppression, shape and acoustic controls as well as fault detection and mitigation. The present study addresses the issues related to the active vibration control schemes for the smart composite panels, with substrate as the fiber reinforced composite laminate and the piezo ceramic layers as the actuators and sensors, using LQR algorithm. The study involves the structural modelling, controller design, open and closed loop system response analysis. For this purpose, an eight noded isoparametric finite element with seven degrees of freedom, viz., three translations, two section rotations and two potential differences corresponding to the actuators and sensors is developed. The piezo-ceramic actuator and sensor layers are also considered as the load bearing components in the panel. The finite element equations are first transformed into the modal state space form and then are used to obtain the constant controller gains. These are used to obtain the closed loop responses.

  11. Recognition of pathogens and activation of immune responses in Drosophila and horseshoe crab innate immunity.

    PubMed

    Kurata, Shoichiro; Ariki, Shigeru; Kawabata, Shun-ichiro

    2006-01-01

    In innate immunity, pattern recognition receptors discriminate between self- and infectious non-self-matter. Mammalian homologs of the Drosophila Toll protein, which are collectively referred to as Toll-like receptors (TLRs), recognize pathogen-associated molecular patterns (PAMPs), including lipopolysaccharides (LPS) and lipoproteins, whereas the Drosophila Toll protein does not act as a PAMP receptor, but rather binds to Spätzle, an endogenous peptide. In Drosophila, innate immune surveillance is mediated by members of the peptidoglycan recognition protein (PGRP) family, which recognize diverse bacteria-derived peptidoglycans and initiate appropriate immune reactions including the release of antimicrobial peptides and the activation of the prophenoloxidase cascade, the latter effecting localized wound healing, melanization, and microbial phagocytosis. In the horseshoe crab, LPS induces hemocyte exocytotic degranulation, resulting in the secretion of various defense molecules, such as coagulation factors, antimicrobial peptides, and lectins. Recent studies have demonstrated that the zymogen form of the serine protease factor C, a major granular component of hemocyte, also exists on the hemocyte surface and functions as a biosensor for LPS. The proteolytic activity of activated factor C initiates hemocyte exocytosis via a G protein mediated signal transduction pathway. Furthermore, it has become clear that an endogenous mechanism for the feedback amplification of the innate immune response exists and is dependent upon a granular component of the horseshoe crab hemocyte.

  12. Feasibility of the MUSIC Algorithm for the Active Protection System

    DTIC Science & Technology

    2001-03-01

    methods of computing the doppler frequency the multiple signal classification ( MUSIC ) algorithm and power spectral density (PSD) with the use of fast...Fourier transform (1024-point FFT). Normally, MUSIC has been used to improve the resolution of multiple closely spaced targets. In this application, MUSIC ...assumed head-on projectile using FSD and the MUSIC algorithm; I wanted to determine whether the MUSIC algorithm performs better than PSD in terms of accuracy and processing time.

  13. Shape recognition for capacitive touch display

    NASA Astrophysics Data System (ADS)

    Guarneri, I.; Capra, A.; Farinella, G. M.; Battiato, S.

    2013-03-01

    In this paper we present a technique to classify five common classes of shapes acquired with a capacitive touch display: finger, ear, cheek, hand hold, half ear-half cheek. The need of algorithms able to discriminate among the aforementioned shapes comes from the growing diffusion of touch screen based consumer devices (e.g. smartphones, tablet, etc.). In this context, detection and the recognition of fingers are fundamental tasks in many touch based user applications (e.g., mobile games). Shape recognition algorithms are also extremely useful to identify accidental touches in order to avoid involuntary activation of the device functionalities (e.g., accidental calls). Our solution makes use of simple descriptors designed to capture discriminative information of the considered classes of shapes. The recognition is performed through a decision tree based approach whose parameters are learned on a set of labeled samples. Experimental results demonstrate that the proposed solution achieves good recognition accuracy.

  14. An fMRI comparison of neural activity associated with recognition of familiar melodies in younger and older adults

    PubMed Central

    Sikka, Ritu; Cuddy, Lola L.; Johnsrude, Ingrid S.; Vanstone, Ashley D.

    2015-01-01

    Several studies of semantic memory in non-musical domains involving recognition of items from long-term memory have shown an age-related shift from the medial temporal lobe structures to the frontal lobe. However, the effects of aging on musical semantic memory remain unexamined. We compared activation associated with recognition of familiar melodies in younger and older adults. Recognition follows successful retrieval from the musical lexicon that comprises a lifetime of learned musical phrases. We used the sparse-sampling technique in fMRI to determine the neural correlates of melody recognition by comparing activation when listening to familiar vs. unfamiliar melodies, and to identify age differences. Recognition-related cortical activation was detected in the right superior temporal, bilateral inferior and superior frontal, left middle orbitofrontal, bilateral precentral, and left supramarginal gyri. Region-of-interest analysis showed greater activation for younger adults in the left superior temporal gyrus and for older adults in the left superior frontal, left angular, and bilateral superior parietal regions. Our study provides powerful evidence for these musical memory networks due to a large sample (N = 40) that includes older adults. This study is the first to investigate the neural basis of melody recognition in older adults and to compare the findings to younger adults. PMID:26500480

  15. Estradiol enhances object recognition memory in Swiss female mice by activating hippocampal estrogen receptor α.

    PubMed

    Pereira, Luciana M; Bastos, Cristiane P; de Souza, Jéssica M; Ribeiro, Fabíola M; Pereira, Grace S

    2014-10-01

    In rodents, 17β-estradiol (E2) enhances hippocampal function and improves performance in several memory tasks. Regarding the object recognition paradigm, E2 commonly act as a cognitive enhancer. However, the types of estrogen receptor (ER) involved, as well as the underlying molecular mechanisms are still under investigation. In the present study, we asked whether E2 enhances object recognition memory by activating ERα and/or ERβ in the hippocampus of Swiss female mice. First, we showed that immediately post-training intraperitoneal (i.p.) injection of E2 (0.2 mg/kg) allowed object recognition memory to persist 48 h in ovariectomized (OVX) Swiss female mice. This result indicates that Swiss female mice are sensitive to the promnesic effects of E2 and is in accordance with other studies, which used C57/BL6 female mice. To verify if the activation of hippocampal ERα or ERβ would be sufficient to improve object memory, we used PPT and DPN, which are selective ERα and ERβ agonists, respectively. We found that PPT, but not DPN, improved object memory in Swiss female mice. However, DPN was able to improve memory in C57/BL6 female mice, which is in accordance with other studies. Next, we tested if the E2 effect on improving object memory depends on ER activation in the hippocampus. Thus, we tested if the infusion of intra-hippocampal TPBM and PHTPP, selective antagonists of ERα and ERβ, respectively, would block the memory enhancement effect of E2. Our results showed that TPBM, but not PHTPP, blunted the promnesic effect of E2, strongly suggesting that in Swiss female mice, the ERα and not the ERβ is the receptor involved in the promnesic effect of E2. It was already demonstrated that E2, as well as PPT and DPN, increase the phospho-ERK2 level in the dorsal hippocampus of C57/BL6 mice. Here we observed that PPT increased phospho-ERK1, while DPN decreased phospho-ERK2 in the dorsal hippocampus of Swiss female mice subjected to the object recognition sample phase

  16. Neural activity during emotion recognition after combined cognitive plus social cognitive training in schizophrenia.

    PubMed

    Hooker, Christine I; Bruce, Lori; Fisher, Melissa; Verosky, Sara C; Miyakawa, Asako; Vinogradov, Sophia

    2012-08-01

    Cognitive remediation training has been shown to improve both cognitive and social cognitive deficits in people with schizophrenia, but the mechanisms that support this behavioral improvement are largely unknown. One hypothesis is that intensive behavioral training in cognition and/or social cognition restores the underlying neural mechanisms that support targeted skills. However, there is little research on the neural effects of cognitive remediation training. This study investigated whether a 50 h (10-week) remediation intervention which included both cognitive and social cognitive training would influence neural function in regions that support social cognition. Twenty-two stable, outpatient schizophrenia participants were randomized to a treatment condition consisting of auditory-based cognitive training (AT) [Brain Fitness Program/auditory module ~60 min/day] plus social cognition training (SCT) which was focused on emotion recognition [~5-15 min per day] or a placebo condition of non-specific computer games (CG) for an equal amount of time. Pre and post intervention assessments included an fMRI task of positive and negative facial emotion recognition, and standard behavioral assessments of cognition, emotion processing, and functional outcome. There were no significant intervention-related improvements in general cognition or functional outcome. fMRI results showed the predicted group-by-time interaction. Specifically, in comparison to CG, AT+SCT participants had a greater pre-to-post intervention increase in postcentral gyrus activity during emotion recognition of both positive and negative emotions. Furthermore, among all participants, the increase in postcentral gyrus activity predicted behavioral improvement on a standardized test of emotion processing (MSCEIT: Perceiving Emotions). Results indicate that combined cognition and social cognition training impacts neural mechanisms that support social cognition skills.

  17. Recognition of human activity characteristics based on state transitions modeling technique

    NASA Astrophysics Data System (ADS)

    Elangovan, Vinayak; Shirkhodaie, Amir

    2012-06-01

    Human Activity Discovery & Recognition (HADR) is a complex, diverse and challenging task but yet an active area of ongoing research in the Department of Defense. By detecting, tracking, and characterizing cohesive Human interactional activity patterns, potential threats can be identified which can significantly improve situation awareness, particularly, in Persistent Surveillance Systems (PSS). Understanding the nature of such dynamic activities, inevitably involves interpretation of a collection of spatiotemporally correlated activities with respect to a known context. In this paper, we present a State Transition model for recognizing the characteristics of human activities with a link to a prior contextbased ontology. Modeling the state transitions between successive evidential events determines the activities' temperament. The proposed state transition model poses six categories of state transitions including: Human state transitions of Object handling, Visibility, Entity-entity relation, Human Postures, Human Kinematics and Distance to Target. The proposed state transition model generates semantic annotations describing the human interactional activities via a technique called Casual Event State Inference (CESI). The proposed approach uses a low cost kinect depth camera for indoor and normal optical camera for outdoor monitoring activities. Experimental results are presented here to demonstrate the effectiveness and efficiency of the proposed technique.

  18. Active destabilization of base pairs by a DNA glycosylase wedge initiates damage recognition.

    PubMed

    Kuznetsov, Nikita A; Bergonzo, Christina; Campbell, Arthur J; Li, Haoquan; Mechetin, Grigory V; de los Santos, Carlos; Grollman, Arthur P; Fedorova, Olga S; Zharkov, Dmitry O; Simmerling, Carlos

    2015-01-01

    Formamidopyrimidine-DNA glycosylase (Fpg) excises 8-oxoguanine (oxoG) from DNA but ignores normal guanine. We combined molecular dynamics simulation and stopped-flow kinetics with fluorescence detection to track the events in the recognition of oxoG by Fpg and its mutants with a key phenylalanine residue, which intercalates next to the damaged base, changed to either alanine (F110A) or fluorescent reporter tryptophan (F110W). Guanine was sampled by Fpg, as evident from the F110W stopped-flow traces, but less extensively than oxoG. The wedgeless F110A enzyme could bend DNA but failed to proceed further in oxoG recognition. Modeling of the base eversion with energy decomposition suggested that the wedge destabilizes the intrahelical base primarily through buckling both surrounding base pairs. Replacement of oxoG with abasic (AP) site rescued the activity, and calculations suggested that wedge insertion is not required for AP site destabilization and eversion. Our results suggest that Fpg, and possibly other DNA glycosylases, convert part of the binding energy into active destabilization of their substrates, using the energy differences between normal and damaged bases for fast substrate discrimination.

  19. Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data.

    PubMed

    Nef, Tobias; Urwyler, Prabitha; Büchler, Marcel; Tarnanas, Ioannis; Stucki, Reto; Cazzoli, Dario; Müri, René; Mosimann, Urs

    2015-05-21

    Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our

  20. Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

    PubMed Central

    Nef, Tobias; Urwyler, Prabitha; Büchler, Marcel; Tarnanas, Ioannis; Stucki, Reto; Cazzoli, Dario; Müri, René; Mosimann, Urs

    2012-01-01

    Smart homes for the aging population have recently started attracting the attention of the research community. The “health state” of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus

  1. Recognition of human activities using depth images of Kinect for biofied building

    NASA Astrophysics Data System (ADS)

    Ogawa, Ami; Mita, Akira

    2015-03-01

    These days, various functions in the living spaces are needed because of an aging society, promotion of energy conservation, and diversification of lifestyles. To meet this requirement, we propose "Biofied Building". The "Biofied Building" is the system learnt from living beings. The various information is accumulated in a database using small sensor agent robots as a key function of this system to control the living spaces. Among the various kinds of information about the living spaces, especially human activities can be triggers for lighting or air conditioning control. By doing so, customized space is possible. Human activities are divided into two groups, the activities consisting of single behavior and the activities consisting of multiple behaviors. For example, "standing up" or "sitting down" consists of a single behavior. These activities are accompanied by large motions. On the other hand "eating" consists of several behaviors, holding the chopsticks, catching the food, putting them in the mouth, and so on. These are continuous motions. Considering the characteristics of two types of human activities, we individually, use two methods, R transformation and variance. In this paper, we focus on the two different types of human activities, and propose the two methods of human activity recognition methods for construction of the database of living space for "Biofied Building". Finally, we compare the results of both methods.

  2. SVM-based multimodal classification of activities of daily living in Health Smart Homes: sensors, algorithms, and first experimental results.

    PubMed

    Fleury, Anthony; Vacher, Michel; Noury, Norbert

    2010-03-01

    By 2050, about one third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international activities of daily living (ADL) or the French Autonomie Gerontologie Groupes Iso-Ressources (AGGIR) scales, by automatically classifying the different ADL performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, infrared presence sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors are then used to classify each temporal frame into one of the ADL that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing/undressing). This is done using support vector machines. We performed a 1-h experimentation with 13 young and healthy subjects to determine the models of the different activities, and then we tested the classification algorithm (cross validation) with real data.

  3. Optimization of object region and boundary extraction by energy minimization for activity recognition

    NASA Astrophysics Data System (ADS)

    Albalooshi, Fatema A.; Asari, Vijayan K.

    2013-05-01

    Automatic video segmentation for human activity recognition has played an important role in several computer vision applications. Active contour model (ACM) has been used extensively for unsupervised adaptive segmentation and automatic object region and boundary extraction in video sequences. This paper presents optimizing Active Contour Model using recurrent architecture for automatic object region and boundary extraction in human activity video sequences. Taking advantage of the collective computational ability and energy convergence capability of the recurrent architecture, energy function of Active Contour Model is optimized with lower computational time. The system starts with initializing recurrent architecture state based on the initial boundary points and ends up with final contour which represent actual boundary points of human body region. The initial contour of the Active Contour Model is computed using background subtraction based on Gaussian Mixture Model (GMM) such that background model is built dynamically and regularly updated to overcome different challenges including illumination changes, camera oscillations, and changes in background geometry. The recurrent nature is useful for dealing with optimization problems due to its dynamic nature, thus, ensuring convergence of the system. The proposed boundary detection and region extraction can be used for real time processing. This method results in an effective segmentation that is less sensitive to noise and complex environments. Experiments on different databases of human activity show that our method is effective and can be used for real-time video segmentation.

  4. Reading as active sensing: a computational model of gaze planning in word recognition.

    PubMed

    Ferro, Marcello; Ognibene, Dimitri; Pezzulo, Giovanni; Pirrelli, Vito

    2010-01-01

    WE OFFER A COMPUTATIONAL MODEL OF GAZE PLANNING DURING READING THAT CONSISTS OF TWO MAIN COMPONENTS: a lexical representation network, acquiring lexical representations from input texts (a subset of the Italian CHILDES database), and a gaze planner, designed to recognize written words by mapping strings of characters onto lexical representations. The model implements an active sensing strategy that selects which characters of the input string are to be fixated, depending on the predictions dynamically made by the lexical representation network. We analyze the developmental trajectory of the system in performing the word recognition task as a function of both increasing lexical competence, and correspondingly increasing lexical prediction ability. We conclude by discussing how our approach can be scaled up in the context of an active sensing strategy applied to a robotic setting.

  5. Reading as Active Sensing: A Computational Model of Gaze Planning in Word Recognition

    PubMed Central

    Ferro, Marcello; Ognibene, Dimitri; Pezzulo, Giovanni; Pirrelli, Vito

    2010-01-01

    We offer a computational model of gaze planning during reading that consists of two main components: a lexical representation network, acquiring lexical representations from input texts (a subset of the Italian CHILDES database), and a gaze planner, designed to recognize written words by mapping strings of characters onto lexical representations. The model implements an active sensing strategy that selects which characters of the input string are to be fixated, depending on the predictions dynamically made by the lexical representation network. We analyze the developmental trajectory of the system in performing the word recognition task as a function of both increasing lexical competence, and correspondingly increasing lexical prediction ability. We conclude by discussing how our approach can be scaled up in the context of an active sensing strategy applied to a robotic setting. PMID:20577589

  6. Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping

    PubMed Central

    Probst, Yasmine; Nguyen, Duc Thanh; Tran, Minh Khoi; Li, Wanqing

    2015-01-01

    Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work. PMID:26225994

  7. Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping.

    PubMed

    Probst, Yasmine; Nguyen, Duc Thanh; Tran, Minh Khoi; Li, Wanqing

    2015-07-27

    Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work.

  8. Encoding-related brain activity dissociates between the recollective processes underlying successful recall and recognition: a subsequent-memory study.

    PubMed

    Sadeh, Talya; Maril, Anat; Goshen-Gottstein, Yonatan

    2012-07-01

    The subsequent-memory (SM) paradigm uncovers brain mechanisms that are associated with mnemonic activity during encoding by measuring participants' neural activity during encoding and classifying the encoding trials according to performance in the subsequent retrieval phase. The majority of these studies have converged on the notion that the mechanism supporting recognition is mediated by familiarity and recollection. The process of recollection is often assumed to be a recall-like process, implying that the active search for the memory trace is similar, if not identical, for recall and recognition. Here we challenge this assumption and hypothesize - based on previous findings obtained in our lab - that the recollective processes underlying recall and recognition might show dissociative patterns of encoding-related brain activity. To this end, our design controlled for familiarity, thereby focusing on contextual, recollective processes. We found evidence for dissociative neurocognitive encoding mechanisms supporting subsequent-recall and subsequent-recognition. Specifically, the contrast of subsequent-recognition versus subsequent-recall revealed activation in the Parahippocampal cortex (PHc) and the posterior hippocampus--regions associated with contextual processing. Implications of our findings and their relation to current cognitive models of recollection are discussed.

  9. The Application of Pattern Recognition to Screening Prospective Anti-Cancer Drugs: Adenocarcinoma 775 Biological Activity Test.

    DTIC Science & Technology

    A novel application of pattern recognition to the screening of potential anti-cancer drugs is presented. Structural features of 200 drugs previously tested by the National Cancer Institute for activity in the solid tumor adenocarcinoma 755 screening test are input to a master program of pattern recognition methods. The programs were 93.5% accurate in discriminating drugs with positive anti-neoplastic activity versus those with no anti-cancer activity. Extensions to a more rational approach to ’ drug design ’ are also discussed. (Author)

  10. Orthographic Activation in L2 Spoken Word Recognition Depends on Proficiency: Evidence from Eye-Tracking

    PubMed Central

    Veivo, Outi; Järvikivi, Juhani; Porretta, Vincent; Hyönä, Jukka

    2016-01-01

    The use of orthographic and phonological information in spoken word recognition was studied in a visual world task where L1 Finnish learners of L2 French (n = 64) and L1 French native speakers (n = 24) were asked to match spoken word forms with printed words while their eye movements were recorded. In Experiment 1, French target words were contrasted with competitors having a longer ( vs. ) or a shorter word initial phonological overlap ( vs. ) and an identical orthographic overlap. In Experiment 2, target words were contrasted with competitors of either longer ( vs. ) or shorter word initial orthographic overlap ( vs. ) and of an identical phonological overlap. A general phonological effect was observed in the L2 listener group but not in the L1 control group. No general orthographic effects were observed in the L2 or L1 groups, but a significant effect of proficiency was observed for orthographic overlap over time: higher proficiency L2 listeners used also orthographic information in the matching task in a time-window from 400 to 700 ms, whereas no such effect was observed for lower proficiency listeners. These results suggest that the activation of orthographic information in L2 spoken word recognition depends on proficiency in L2. PMID:27512381

  11. Mechanistic insights into metal ion activation and operator recognition by the ferric uptake regulator

    NASA Astrophysics Data System (ADS)

    Deng, Zengqin; Wang, Qing; Liu, Zhao; Zhang, Manfeng; Machado, Ana Carolina Dantas; Chiu, Tsu-Pei; Feng, Chong; Zhang, Qi; Yu, Lin; Qi, Lei; Zheng, Jiangge; Wang, Xu; Huo, Xinmei; Qi, Xiaoxuan; Li, Xiaorong; Wu, Wei; Rohs, Remo; Li, Ying; Chen, Zhongzhou

    2015-07-01

    Ferric uptake regulator (Fur) plays a key role in the iron homeostasis of prokaryotes, such as bacterial pathogens, but the molecular mechanisms and structural basis of Fur-DNA binding remain incompletely understood. Here, we report high-resolution structures of Magnetospirillum gryphiswaldense MSR-1 Fur in four different states: apo-Fur, holo-Fur, the Fur-feoAB1 operator complex and the Fur-Pseudomonas aeruginosa Fur box complex. Apo-Fur is a transition metal ion-independent dimer whose binding induces profound conformational changes and confers DNA-binding ability. Structural characterization, mutagenesis, biochemistry and in vivo data reveal that Fur recognizes DNA by using a combination of base readout through direct contacts in the major groove and shape readout through recognition of the minor-groove electrostatic potential by lysine. The resulting conformational plasticity enables Fur binding to diverse substrates. Our results provide insights into metal ion activation and substrate recognition by Fur that suggest pathways to engineer magnetotactic bacteria and antipathogenic drugs.

  12. Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement.

    PubMed

    Chiang, Shu-Yin; Kan, Yao-Chiang; Chen, Yun-Shan; Tu, Ying-Ching; Lin, Hsueh-Chun

    2016-12-03

    Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.

  13. Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement

    PubMed Central

    Chiang, Shu-Yin; Kan, Yao-Chiang; Chen, Yun-Shan; Tu, Ying-Ching; Lin, Hsueh-Chun

    2016-01-01

    Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC. PMID:27918482

  14. Two kinds of active impulsive noise control algorithms based on sigmoid transformation

    NASA Astrophysics Data System (ADS)

    Li, Pei; Bai, Xuefeng; Ma, Yongjian

    2017-01-01

    In this thesis, active noise control of symmetric α stable (SαS) distribution impulsive noise has been studied. Two kinds of algorithm based on Sigmoid transformation of error signal have been proposed. The convergence condition of algorithms also has been analyzed. It does not need the parameter selection and thresholds estimation. Computer simulations were carried out to validate algorithm. Simulation results have proven the effectiveness of the algorithm and achieved the expected control effect. Compared to the previous algorithm, the convergence speed is improved.

  15. A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments.

    PubMed

    Jalal, Ahmad; Kamal, Shaharyar; Kim, Daijin

    2014-07-02

    Recent advancements in depth video sensors technologies have made human activity recognition (HAR) realizable for elderly monitoring applications. Although conventional HAR utilizes RGB video sensors, HAR could be greatly improved with depth video sensors which produce depth or distance information. In this paper, a depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space. Initially, a depth imaging sensor is used to capture depth silhouettes. Based on these silhouettes, human skeletons with joint information are produced which are further used for activity recognition and generating their life logs. The life-logging system is divided into two processes. Firstly, the training system includes data collection using a depth camera, feature extraction and training for each activity via Hidden Markov Models. Secondly, after training, the recognition engine starts to recognize the learned activities and produces life logs. The system was evaluated using life logging features against principal component and independent component features and achieved satisfactory recognition rates against the conventional approaches. Experiments conducted on the smart indoor activity datasets and the MSRDailyActivity3D dataset show promising results. The proposed system is directly applicable to any elderly monitoring system, such as monitoring healthcare problems for elderly people, or examining the indoor activities of people at home, office or hospital.

  16. A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments

    PubMed Central

    Jalal, Ahmad; Kamal, Shaharyar; Kim, Daijin

    2014-01-01

    Recent advancements in depth video sensors technologies have made human activity recognition (HAR) realizable for elderly monitoring applications. Although conventional HAR utilizes RGB video sensors, HAR could be greatly improved with depth video sensors which produce depth or distance information. In this paper, a depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space. Initially, a depth imaging sensor is used to capture depth silhouettes. Based on these silhouettes, human skeletons with joint information are produced which are further used for activity recognition and generating their life logs. The life-logging system is divided into two processes. Firstly, the training system includes data collection using a depth camera, feature extraction and training for each activity via Hidden Markov Models. Secondly, after training, the recognition engine starts to recognize the learned activities and produces life logs. The system was evaluated using life logging features against principal component and independent component features and achieved satisfactory recognition rates against the conventional approaches. Experiments conducted on the smart indoor activity datasets and the MSRDailyActivity3D dataset show promising results. The proposed system is directly applicable to any elderly monitoring system, such as monitoring healthcare problems for elderly people, or examining the indoor activities of people at home, office or hospital. PMID:24991942

  17. Right frontopolar cortex activity correlates with reliability of retrospective rating of confidence in short-term recognition memory performance.

    PubMed

    Yokoyama, Osamu; Miura, Naoki; Watanabe, Jobu; Takemoto, Atsushi; Uchida, Shinya; Sugiura, Motoaki; Horie, Kaoru; Sato, Shigeru; Kawashima, Ryuta; Nakamura, Katsuki

    2010-11-01

    Human memory systems contain self-monitoring mechanisms for evaluating their progress. People can change their learning strategy on the basis of confidence in their performance at that time. However, it has not been fully understood how the brain is engaged in reliable rating of confidence in past recognition memory performance. We measured the brain activity by fMRI while healthy subjects performed a visual short-term recognition memory test and then rated their confidence in their answers as high, middle, or low. As shown previously, their behavioral performance in the confidence rating widely varied; some showed a positive confidence-recognition correlation (i.e., "rate reliably") while others did not. Among brain regions showing greater activity during rating their confidence relative to during a control, non-metamemory task (discriminating brightness of words), only a posterior-dorsal part of the right frontopolar cortex exhibited higher activity as the confidence level better correlated with actual recognition memory performance. These results suggest that activation in the right frontopolar cortex is key to a reliable, retrospective rating of confidence in short-term recognition memory performance.

  18. Electrocorticography reveals the temporal dynamics of posterior parietal cortical activity during recognition memory decisions

    PubMed Central

    Gonzalez, Alex; Hutchinson, J. Benjamin; Uncapher, Melina R.; Chen, Janice; LaRocque, Karen F.; Foster, Brett L.; Rangarajan, Vinitha; Parvizi, Josef; Wagner, Anthony D.

    2015-01-01

    Theories of the neurobiology of episodic memory predominantly focus on the contributions of medial temporal lobe structures, based on extensive lesion, electrophysiological, and imaging evidence. Against this backdrop, functional neuroimaging data have unexpectedly implicated left posterior parietal cortex (PPC) in episodic retrieval, revealing distinct activation patterns in PPC subregions as humans make memory-related decisions. To date, theorizing about the functional contributions of PPC has been hampered by the absence of information about the temporal dynamics of PPC activity as retrieval unfolds. Here, we leveraged electrocorticography to examine the temporal profile of high gamma power (HGP) in dorsal PPC subregions as participants made old/new recognition memory decisions. A double dissociation in memory-related HGP was observed, with activity in left intraparietal sulcus (IPS) and left superior parietal lobule (SPL) differing in time and sign for recognized old items (Hits) and correctly rejected novel items (CRs). Specifically, HGP in left IPS increased for Hits 300–700 ms poststimulus onset, and decayed to baseline ∼200 ms preresponse. By contrast, HGP in left SPL increased for CRs early after stimulus onset (200−300 ms) and late in the memory decision (from 700 ms to response). These memory-related effects were unique to left PPC, as they were not observed in right PPC. Finally, memory-related HGP in left IPS and SPL was sufficiently reliable to enable brain-based decoding of the participant’s memory state at the single-trial level, using multivariate pattern classification. Collectively, these data provide insights into left PPC temporal dynamics as humans make recognition memory decisions. PMID:26283375

  19. Hippocampal Activation of Rac1 Regulates the Forgetting of Object Recognition Memory.

    PubMed

    Liu, Yunlong; Du, Shuwen; Lv, Li; Lei, Bo; Shi, Wei; Tang, Yikai; Wang, Lianzhang; Zhong, Yi

    2016-09-12

    Forgetting is a universal feature for most types of memories. The best-defined and extensively characterized behaviors that depict forgetting are natural memory decay and interference-based forgetting [1, 2]. Molecular mechanisms underlying the active forgetting remain to be determined for memories in vertebrates. Recent progress has begun to unravel such mechanisms underlying the active forgetting [3-11] that is induced through the behavior-dependent activation of intracellular signaling pathways. In Drosophila, training-induced activation of the small G protein Rac1 mediates natural memory decay and interference-based forgetting of aversive conditioning memory [3]. In mice, the activation of photoactivable-Rac1 in recently potentiated spines in a motor learning task erases the motor memory [12]. These lines of evidence prompted us to investigate a role for Rac1 in time-based natural memory decay and interference-based forgetting in mice. The inhibition of Rac1 activity in hippocampal neurons through targeted expression of a dominant-negative Rac1 form extended object recognition memory from less than 72 hr to over 72 hr, whereas Rac1 activation accelerated memory decay within 24 hr. Interference-induced forgetting of this memory was correlated with Rac1 activation and was completely blocked by inhibition of Rac1 activity. Electrophysiological recordings of long-term potentiation provided independent evidence that further supported a role for Rac1 activation in forgetting. Thus, Rac1-dependent forgetting is evolutionarily conserved from invertebrates to vertebrates.

  20. Probabilistic learning from incomplete data for recognition of activities of daily living in smart homes.

    PubMed

    Zhang, Shuai; McClean, Sally I; Scotney, Bryan W

    2012-05-01

    Learning behavioral patterns for activities of daily living in a smart home environment can be challenged by the limited number of training data that may be available. This may be due to the infrequent repetition of routine activities (e.g., once daily), the expense of using observers to label activities, and the intrusion that would be caused by the presence of observers over long time periods. It is important, therefore, to make as much use of any labeled data that are collected, however, incomplete these data may be. In this paper, we propose an algorithm for learning behavioral patterns for multi-inhabitants living in a single smart home environment, by making full use of all limited labeled activities, including incomplete data resulting from unreliable low-level sensors in this environment. Through maximum-likelihood estimation, using Expectation-Maximization, we build a model that captures both environmental uncertainties from sensor readings and user uncertainties, including variations in how individuals carry out activities. Our algorithm outperforms models that cannot handle data incompleteness, with increasing performance gains as incompleteness increases. The approach also enables the impact of particular sensors to be assessed and can thus inform sensor maintenance and deployment.

  1. Nod2-mediated recognition of the microbiota is critical for mucosal adjuvant activity of cholera toxin

    PubMed Central

    Kim, Donghyun; Kim, Yun-Gi; Seo, Sang-Uk; Kim, Dong-Jae; Kamada, Nobuhiko; Prescott, Dave; Philpott, Dana J.; Rosenstiel, Philip; Inohara, Naohiro; Núñez, Gabriel

    2016-01-01

    Cholera toxin (CT) is a potent adjuvant for inducing mucosal immune responses. However, the mechanism by which CT induces adjuvant activity remains unclear. Here we show that the microbiota is critical for inducing antigen-specific IgG production after intranasal immunization. After mucosal vaccination with CT, both antibiotic-treated mice and germ-free (GF) had reduced antigen-specific IgG, recall-stimulated cytokine responses, an impaired follicular helper T (TFH) response and reduced plasma cells. Recognition of symbiotic bacteria via Nod2 in CD11c+ cells was required for the adjuvanticity of CT. Reconstitution of GF mice with a Nod2 agonist or Staphylococcus sciuri having high Nod2-stimulatory activity was sufficient to promote robust CT adjuvant activity whereas bacteria with low Nod2-stimulatory activity did not. Mechanistically, CT enhanced Nod2-mediated cytokine production in DCs via intracellular cAMP. These results show an important role for the microbiota and the intracellular receptor Nod2 in promoting the mucosal adjuvant activity of CT. PMID:27064448

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

    PubMed

    Zhu, Xiangbin; Qiu, Huiling

    2016-01-01

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

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

    PubMed Central

    2016-01-01

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

  4. Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

    PubMed Central

    Fong, Simon; Song, Wei; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K. L.

    2017-01-01

    In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called ‘shadow features’ are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research. PMID:28264470

  5. An active noise control algorithm for controlling multiple sinusoids.

    PubMed

    Lee, S M; Lee, H J; Yoo, C H; Youn, D H; Cha, I W

    1998-07-01

    The filtered-x LMS algorithm and its modified versions have been successfully applied in suppressing acoustic noise such as single and multiple tones and broadband random noise. This paper presents an adaptive algorithm based on the filtered-x LMS algorithm which may be applied in attenuating tonal acoustic noise. In the proposed method, the weights of the adaptive filter and estimation of the phase shift due to the acoustic path from a loudspeaker to a microphone are computed simultaneously for optimal control. The algorithm possesses advantages over other filtered-x LMS approaches in three aspects: (1) each frequency component is processed separately using an adaptive filter with two coefficients, (2) the convergence parameter for each sinusoid can be selected independently, and (3) the computational load can be reduced by eliminating the convolution process required to obtain the filtered reference signal. Simulation results for a single-input/single-output (SISO) environment demonstrate that the proposed method is robust to the changes of the acoustic path between the actuator and the microphone and outperforms the filtered-x LMS algorithm in simplicity and convergence speed.

  6. Multimodal eye recognition

    NASA Astrophysics Data System (ADS)

    Zhou, Zhi; Du, Yingzi; Thomas, N. L.; Delp, Edward J., III

    2010-04-01

    Multimodal biometrics use more than one means of biometric identification to achieve higher recognition accuracy, since sometimes a unimodal biometric is not good enough used to do identification and classification. In this paper, we proposed a multimodal eye recognition system, which can obtain both iris and sclera patterns from one color eye image. Gabor filter and 1-D Log-Gabor filter algorithms have been applied as the iris recognition algorithms. In sclera recognition, we introduced automatic sclera segmentation, sclera pattern enhancement, sclera pattern template generation, and sclera pattern matching. We applied kernelbased matching score fusion to improve the performance of the eye recognition system. The experimental results show that the proposed eye recognition method can achieve better performance compared to unimodal biometric identification, and the accuracy of our proposed kernel-based matching score fusion method is higher than two classic linear matching score fusion methods: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

  7. Implementation of FFT Algorithm using DSP TMS320F28335 for Shunt Active Power Filter

    NASA Astrophysics Data System (ADS)

    Patel, Pinkal Jashvantbhai; Patel, Rajesh M.; Patel, Vinod

    2016-07-01

    This work presents simulation, analysis and experimental verification of Fast Fourier Transform (FFT) algorithm for shunt active power filter based on three-level inverter. Different types of filters can be used for elimination of harmonics in the power system. In this work, FFT algorithm for reference current generation is discussed. FFT control algorithm is verified using PSIM simulation results with DLL block and C-code. Simulation results are compared with experimental results for FFT algorithm using DSP TMS320F28335 for shunt active power filter application.

  8. Embryonal cell surface recognition. Extraction of an active plasma membrane component.

    PubMed

    Merrell, R; Gottlieb, D I; Glaser, L

    1975-07-25

    Plasma membranes obtained from different neural regions of the chicken embryo have previously been shown to specifically bind to homotypic cells and prevent cell aggregation (Merrell, R., and Glaser, L. (1973) Proc. Natl. Acad. Sci. U. S. A. 70, 2794-2798). Proteins responsible for the specific inhibition of cell aggregation have been solubilized from the plasma membrane of neural retina and optic tectum by delipidation with acetone followed by extraction with lithium diiodosalicylate. The extracts show the same regional and temporal specificity as previously shown for plasma membrane recognition by the same cells (Gottlieb, D. I., Merrell, R., and Glaser, L. (1974) Proc. Natl. Acad. Sci. U. S. A. 71, 1800-1802). Two micrograms of the most purified protein fraction inhibits the aggregation of 2.5 times 10(-4) cells under standard assay conditions. This represents a 20-fold increase in specific activity compared to whole membranes.

  9. Promiscuous Substrate Recognition in Folding and Assembly Activities of the Trigger Factor Chaperone

    SciTech Connect

    Martinez-Hackert, E.; Hendrickson, W

    2009-01-01

    Trigger factor (TF) is a molecular chaperone that binds to bacterial ribosomes where it contacts emerging nascent chains, but TF is also abundant free in the cytosol where its activity is less well characterized. In vitro studies show that TF promotes protein refolding. We find here that ribosome-free TF stably associates with and rescues from misfolding a large repertoire of full-length proteins. We identify over 170 members of this cytosolic Escherichia coli TF substrate proteome, including ribosomal protein S7. We analyzed the biochemical properties of a TF:S7 complex from Thermotoga maritima and determined its crystal structure. Thereby, we obtained an atomic-level picture of a promiscuous chaperone in complex with a physiological substrate protein. The structure of the complex reveals the molecular basis of substrate recognition by TF, indicates how TF could accelerate protein folding, and suggests a role for TF in the biogenesis of protein complexes.

  10. Neural activities associated with emotion recognition observed in men and women.

    PubMed

    Lee, T M C; Liu, H-L; Chan, C C H; Fang, S-Y; Gao, J-H

    2005-05-01

    Previous studies have suggested that men and women process emotional stimuli differently. In this study, we examined if there would be any consistency in regions of activation in men and women when processing stimuli portraying happy or sad emotions presented in the form of facial expressions, scenes, and words. A blocked design BOLD functional magnetic resonance imaging paradigm was employed to monitor the neural activities of male and female healthy volunteers while they were presented with the experimental stimuli. The imaging data revealed that the right insula and left thalamus were consistently activated for men, but not women, during emotion recognition of all forms of stimuli studied. To further understand the imaging data acquired, we conducted the protocol analysis method to identify the cognitive processes engaged while the men and women were viewing the emotional stimuli and deciding whether they were happy or sad. The findings suggest that men rely on the recall of past emotional experiences to evaluate current emotional experiences. This may explain why the insula, a structure important for self-induced or internally generated recalled emotions, was consistently activated in men while processing emotional stimuli. Our findings suggest possible gender-related neural responses to emotional stimuli.

  11. The Johns Hopkins University multimodal dataset for human action recognition

    NASA Astrophysics Data System (ADS)

    Murray, Thomas S.; Mendat, Daniel R.; Pouliquen, Philippe O.; Andreou, Andreas G.

    2015-05-01

    The Johns Hopkins University MultiModal Action (JHUMMA) dataset contains a set of twenty-one actions recorded with four sensor systems in three different modalities. The data was collected with a data acquisition system that includes three independent active sonar devices at three different frequencies and a Microsoft Kinect sensor that provides both RGB and Depth data. We have developed algorithms for human action recognition from active acoustics and provide benchmark baseline recognition performance results.

  12. Recognition of Daily Activity in Living Space based on Indoor Ambient Atmosphere and Acquiring Localized Information for Improvement of Recognition Accuracy

    NASA Astrophysics Data System (ADS)

    Hirasawa, Kazuki; Sawada, Shinya; Saitoh, Atsushi

    The system watching over elder's life is very important in a super-aged society Japan. In this paper, we describe a method to recognize resident's daily activities by means of using the information of indoor ambient atmosphere changes. The measuring targets of environmental changes are of gas and smell, temperature, humidity, and brightness. Those changes have much relation with resident's daily activities. The measurement system with 7 sensors (4 gas sensors, a thermistor, humidity sensor, and CdS light sensor) was developed for getting indoor ambient atmosphere changes. Some measurements were done in a one-room type residential space. 21 dimensional activity vectors were composed for each daily activity from acquired data. Those vectors were classified into 9 categories that were main activities by using Self-Organizing Map (SOM) method. From the result, it was found that the recognition of main daily activities based on information on indoor ambient atmosphere changes is possible. Moreover, we also describe the method for getting information of local gas and smell environmental changes. Gas and smell environmental changes are related with daily activities, especially very important action, eating and drinking. And, local information enables the relation of the place and the activity. For such a purpose, a gas sensing module with the operation function that synchronizes with human detection signal was developed and evaluated. From the result, the sensor module had the ability to acquire and to emphasize local gas environment changes caused by the person's activity.

  13. Mycobacterium tuberculosis Activates Human Macrophage Peroxisome Proliferator-Activated Receptor γ Linking Mannose Receptor Recognition to Regulation of Immune Responses

    PubMed Central

    Rajaram, Murugesan V. S.; Brooks, Michelle N.; Morris, Jessica D.; Torrelles, Jordi B.; Azad, Abul K.; Schlesinger, Larry S.

    2010-01-01

    Mycobacterium tuberculosis enhances its survival in macrophages by suppressing immune responses in part through its complex cell wall structures. Peroxisome proliferator-activated receptor γ (PPARγ), a nuclear receptor superfamily member, is a transcriptional factor that regulates inflammation and has high expression in alternatively activated alveolar macrophages and macrophage-derived foam cells, both cell types relevant to tuberculosis pathogenesis. In this study, we show that virulent M. tuberculosis and its cell wall mannose-capped lipoarabinomannan induce PPARγ expression through a macrophage mannose receptor-dependent pathway. When activated, PPARγ promotes IL-8 and cyclooxygenase 2 expression, a process modulated by a PPARγ agonist or antagonist. Upstream, MAPK-p38 mediates cytosolic phospholipase A2 activation, which is required for PPARγ ligand production. The induced IL-8 response mediated by mannose-capped lipoarabinomannan and the mannose receptor is independent of TLR2 and NF-κB activation. In contrast, the attenuated Mycobacterium bovis bacillus Calmette-Guérin induces less PPARγ and preferentially uses the NF-κB–mediated pathway to induce IL-8 production. Finally, PPARγ knockdown in human macrophages enhances TNF production and controls the intracellular growth of M. tuberculosis. These data identify a new molecular pathway that links engagement of the mannose receptor, an important pattern recognition receptor for M. tuberculosis, with PPARγ activation, which regulates the macrophage inflammatory response, thereby playing a role in tuberculosis pathogenesis. PMID:20554962

  14. A Fuzzy Logic Prompting Mechanism Based on Pattern Recognition and Accumulated Activity Effective Index Using a Smartphone Embedded Sensor.

    PubMed

    Liu, Chung-Tse; Chan, Chia-Tai

    2016-08-19

    Sufficient physical activity can reduce many adverse conditions and contribute to a healthy life. Nevertheless, inactivity is prevalent on an international scale. Improving physical activity is an essential concern for public health. Reminders that help people change their health behaviors are widely applied in health care services. However, timed-based reminders deliver periodic prompts suffer from flexibility and dependency issues which may decrease prompt effectiveness. We propose a fuzzy logic prompting mechanism, Accumulated Activity Effective Index Reminder (AAEIReminder), based on pattern recognition and activity effective analysis to manage physical activity. AAEIReminder recognizes activity levels using a smartphone-embedded sensor for pattern recognition and analyzing the amount of physical activity in activity effective analysis. AAEIReminder can infer activity situations such as the amount of physical activity and days spent exercising through fuzzy logic, and decides whether a prompt should be delivered to a user. This prompting system was implemented in smartphones and was used in a short-term real-world trial by seventeenth participants for validation. The results demonstrated that the AAEIReminder is feasible. The fuzzy logic prompting mechanism can deliver prompts automatically based on pattern recognition and activity effective analysis. AAEIReminder provides flexibility which may increase the prompts' efficiency.

  15. Medial temporal lobe activity at recognition increases with the duration of mnemonic delay during an object working memory task.

    PubMed

    Picchioni, Marco; Matthiasson, Pall; Broome, Matthew; Giampietro, Vincent; Brammer, Mick; Mathes, Birgit; Fletcher, Paul; Williams, Steven; McGuire, Philip

    2007-11-01

    Object working memory (WM) engages a disseminated neural network, although the extent to which the length of time that data is held in WM influences regional activity within this network is unclear. We used functional magnetic resonance imaging to study a delayed matching to sample task in 14 healthy subjects, manipulating the duration of mnemonic delay. Across all lengths of delay, successful recognition was associated with the bilateral engagement of the inferior and middle frontal gyri and insula, the medial and inferior temporal, dorsal anterior cingulate and the posterior parietal cortices. As the length of time that data was held in WM increased, activation at recognition increased in the medial temporal, medial occipito-temporal, anterior cingulate and posterior parietal cortices. These results confirm the components of an object WM network required for successful recognition, and suggest that parts of this network, including the medial temporal cortex, are sensitive to the duration of mnemonic delay.

  16. A visual basic program for ridge axis picking on DEM data using the profile-recognition and polygon-breaking algorithm

    NASA Astrophysics Data System (ADS)

    Chang, Yet-Chung; Sinha, Gaurav

    2007-02-01

    For many scientists working with digital topographic data, extracting lineaments or linear features is an important step in structuring and analyzing raw data. A ridge axis, which represents the top a mountain ridge, is one of the most important topographic features used in a wide variety of applications. Algorithms and software for automating the extraction of ridges or ridge axes from DEMs are, however, still not easily available or not widely acceptable. In this paper, we present a user-friendly Visual Basic program that automates the extraction of the ridge axis system from DEM data, based on the profile-recognition and polygon-breaking algorithm (PPA). An important feature of PPA is that it takes a global approach, as opposed to the local neighborhood operators used in many other algorithms. Each segment detected by PPA considers not only relations with contiguous neighboring grid points, but also strives to preserve the continuity of the global trend. This is an attempt to simulate human operators, who always factor in the overall trend of the lineament before delineating its local parts. PPA starts by connecting all points in a neighborhood that can possibly lie on the ridge axis, thus forming a belt of polygons in the first step. Next, a polygon breaking process eliminates unwanted segments according to the assumption that a ridge segment cannot be the side of any closed polygon, and that the result should be a purely dendritic line pattern. Finally, a branch-reduction process is executed to eliminate all parallel false ridges that remained due to the conservative approach taken in the first step. Results indicate that PPA is reasonably successful in picking out ridges that would have been identified manually by experts. In addition to providing a detailed user interface for executing PPA, several modifications were made to significantly improve the computational efficiency of PPA, as compared to the original version published in 1998. The source codes are

  17. IDEEA activity monitor: validity of activity recognition for lying, reclining, sitting and standing.

    PubMed

    Jiang, Yuyu; Larson, Janet L

    2013-03-01

    Recent evidence demonstrates the independent negative effects of sedentary behavior on health, but there are few objective measures of sedentary behavior. Most instruments measure physical activity and are not validated as measures of sedentary behavior. The purpose of this study was to evaluate the validity of the IDEEA system's measures of sedentary and low-intensity physical activities: lying, reclining, sitting and standing. Thirty subjects, 14 men and 16 women, aged 23 to 77 years, body mass index (BMI) between 18 to 34 kg/m(2), participated in the study. IDEEA measures were compared to direct observation for 27 activities: 10 lying in bed, 3 lying on a sofa, 1 reclining in a lawn chair, 10 sitting and 3 standing. Two measures are reported, the percentage of activities accurately identified and the percentage of monitored time that was accurately labeled by the IDEEA system for all subjects. A total of 91.6% of all observed activities were accurately identified and 92.4% of the total monitored time was accurately labeled. The IDEEA system did not accurately differentiate between lying and reclining so the two activities were combined for calculating accuracy. Using this approach the IDEEA system accurately identified 96% of sitting activities for a total of 97% of the monitored sitting time, 99% and 99% for standing, 87% and 88% for lying in bed, 87% and 88% for lying on the sofa, and 83% and 83% for reclining on a lawn chair. We conclude that the IDEEA system accurately recognizes sitting and standing positions, but it is less accurate in identifying lying and reclining positions. We recommend combining the lying and reclining activities to improve accuracy. The IDEEA system enables researchers to monitor lying, reclining, sitting and standing with a reasonable level of accuracy and has the potential to advance the science of sedentary behaviors and low-intensity physical activities.

  18. Structure of the active form of human origin recognition complex and its ATPase motor module

    PubMed Central

    Tocilj, Ante; On, Kin Fan; Yuan, Zuanning; Sun, Jingchuan; Elkayam, Elad; Li, Huilin; Stillman, Bruce; Joshua-Tor, Leemor

    2017-01-01

    Binding of the Origin Recognition Complex (ORC) to origins of replication marks the first step in the initiation of replication of the genome in all eukaryotic cells. Here, we report the structure of the active form of human ORC determined by X-ray crystallography and cryo-electron microscopy. The complex is composed of an ORC1/4/5 motor module lobe in an organization reminiscent of the DNA polymerase clamp loader complexes. A second lobe contains the ORC2/3 subunits. The complex is organized as a double-layered shallow corkscrew, with the AAA+ and AAA+-like domains forming one layer, and the winged-helix domains (WHDs) forming a top layer. CDC6 fits easily between ORC1 and ORC2, completing the ring and the DNA-binding channel, forming an additional ATP hydrolysis site. Analysis of the ATPase activity of the complex provides a basis for understanding ORC activity as well as molecular defects observed in Meier-Gorlin Syndrome mutations. DOI: http://dx.doi.org/10.7554/eLife.20818.001 PMID:28112645

  19. Feature activation during word recognition: action, visual, and associative-semantic priming effects

    PubMed Central

    Lam, Kevin J. Y.; Dijkstra, Ton; Rueschemeyer, Shirley-Ann

    2015-01-01

    Embodied theories of language postulate that language meaning is stored in modality-specific brain areas generally involved in perception and action in the real world. However, the temporal dynamics of the interaction between modality-specific information and lexical-semantic processing remain unclear. We investigated the relative timing at which two types of modality-specific information (action-based and visual-form information) contribute to lexical-semantic comprehension. To this end, we applied a behavioral priming paradigm in which prime and target words were related with respect to (1) action features, (2) visual features, or (3) semantically associative information. Using a Go/No-Go lexical decision task, priming effects were measured across four different inter-stimulus intervals (ISI = 100, 250, 400, and 1000 ms) to determine the relative time course of the different features. Notably, action priming effects were found in ISIs of 100, 250, and 1000 ms whereas a visual priming effect was seen only in the ISI of 1000 ms. Importantly, our data suggest that features follow different time courses of activation during word recognition. In this regard, feature activation is dynamic, measurable in specific time windows but not in others. Thus the current study (1) demonstrates how multiple ISIs can be used within an experiment to help chart the time course of feature activation and (2) provides new evidence for embodied theories of language. PMID:26074836

  20. Contextual action recognition and target localization with an active allocation of attention on a humanoid robot.

    PubMed

    Ognibene, Dimitri; Chinellato, Eris; Sarabia, Miguel; Demiris, Yiannis

    2013-09-01

    Exploratory gaze movements are fundamental for gathering the most relevant information regarding the partner during social interactions. Inspired by the cognitive mechanisms underlying human social behaviour, we have designed and implemented a system for a dynamic attention allocation which is able to actively control gaze movements during a visual action recognition task exploiting its own action execution predictions. Our humanoid robot is able, during the observation of a partner's reaching movement, to contextually estimate the goal position of the partner's hand and the location in space of the candidate targets. This is done while actively gazing around the environment, with the purpose of optimizing the gathering of information relevant for the task. Experimental results on a simulated environment show that active gaze control, based on the internal simulation of actions, provides a relevant advantage with respect to other action perception approaches, both in terms of estimation precision and of time required to recognize an action. Moreover, our model reproduces and extends some experimental results on human attention during an action perception.

  1. Peptidoglycan recognition protein–peptidoglycan complexes increase monocyte/macrophage activation and enhance the inflammatory response

    PubMed Central

    De Marzi, Mauricio C; Todone, Marcos; Ganem, María B; Wang, Qian; Mariuzza, Roy A; Fernández, Marisa M; Malchiodi, Emilio L

    2015-01-01

    Peptidoglycan recognition proteins (PGRP) are pattern recognition receptors that can bind or hydrolyse peptidoglycan (PGN). Four human PGRP have been described: PGRP-S, PGRP-L, PGRP-Iα and PGRP-Iβ. Mammalian PGRP-S has been implicated in intracellular destruction of bacteria by polymorphonuclear cells, PGRP-Iα and PGRP-Iβ have been found in keratinocytes and epithelial cells, and PGRP-L is a serum protein that hydrolyses PGN. We have expressed recombinant human PGRP and observed that PGRP-S and PGRP-Iα exist as monomer and disulphide dimer proteins. The PGRP dimers maintain their biological functions. We detected the PGRP-S dimer in human serum and polymorphonuclear cells, from where it is secreted after degranulation; these cells being a possible source of serum PGRP-S. Recombinant PGRP do not act as bactericidal or bacteriostatic agents in the assayed conditions; however, PGRP-S and PGRP-Iα cause slight damage in the bacterial membrane. Monocytes/macrophages increase Staphylococcus aureus phagocytosis in the presence of PGRP-S, PGRP-Iα and PGRP-Iβ. All PGRP bind to monocyte/macrophage membranes and are endocytosed by them. In addition, all PGRP protect cells from PGN-induced apoptosis. PGRP increase THP-1 cell proliferation and enhance activation by PGN. PGRP-S–PGN complexes increase the membrane expression of CD14, CD80 and CD86, and enhance secretion of interleukin-8, interleukin-12 and tumour necrosis factor-α, but reduce interleukin-10, clearly inducing an inflammatory profile. PMID:25752767

  2. Peptidoglycan recognition protein-peptidoglycan complexes increase monocyte/macrophage activation and enhance the inflammatory response.

    PubMed

    De Marzi, Mauricio C; Todone, Marcos; Ganem, María B; Wang, Qian; Mariuzza, Roy A; Fernández, Marisa M; Malchiodi, Emilio L

    2015-07-01

    Peptidoglycan recognition proteins (PGRP) are pattern recognition receptors that can bind or hydrolyse peptidoglycan (PGN). Four human PGRP have been described: PGRP-S, PGRP-L, PGRP-Iα and PGRP-Iβ. Mammalian PGRP-S has been implicated in intracellular destruction of bacteria by polymorphonuclear cells, PGRP-Iα and PGRP-Iβ have been found in keratinocytes and epithelial cells, and PGRP-L is a serum protein that hydrolyses PGN. We have expressed recombinant human PGRP and observed that PGRP-S and PGRP-Iα exist as monomer and disulphide dimer proteins. The PGRP dimers maintain their biological functions. We detected the PGRP-S dimer in human serum and polymorphonuclear cells, from where it is secreted after degranulation; these cells being a possible source of serum PGRP-S. Recombinant PGRP do not act as bactericidal or bacteriostatic agents in the assayed conditions; however, PGRP-S and PGRP-Iα cause slight damage in the bacterial membrane. Monocytes/macrophages increase Staphylococcus aureus phagocytosis in the presence of PGRP-S, PGRP-Iα and PGRP-Iβ. All PGRP bind to monocyte/macrophage membranes and are endocytosed by them. In addition, all PGRP protect cells from PGN-induced apoptosis. PGRP increase THP-1 cell proliferation and enhance activation by PGN. PGRP-S-PGN complexes increase the membrane expression of CD14, CD80 and CD86, and enhance secretion of interleukin-8, interleukin-12 and tumour necrosis factor-α, but reduce interleukin-10, clearly inducing an inflammatory profile.

  3. A Wolf Pack Algorithm for Active and Reactive Power Coordinated Optimization in Active Distribution Network

    NASA Astrophysics Data System (ADS)

    Zhuang, H. M.; Jiang, X. J.

    2016-08-01

    This paper presents an active and reactive power dynamic optimization model for active distribution network (ADN), whose control variables include the output of distributed generations (DGs), charge or discharge power of energy storage system (ESS) and reactive power from capacitor banks. To solve the high-dimension nonlinear optimization model, a new heuristic swarm intelligent method, namely wolf pack algorithm (WPA) with better global convergence and computational robustness, is adapted so that the network loss minimization can be achieved. In this paper, the IEEE33-bus system is used to show the effectiveness of WPA technique compared with other techniques. Numerical tests on the modified IEEE 33-bus system show that WPA for active and reactive multi-period optimization of ADN is exact and effective.

  4. Hand-gesture extraction and recognition from the video sequence acquired by a dynamic camera using condensation algorithm

    NASA Astrophysics Data System (ADS)

    Luo, Dan; Ohya, Jun

    2009-01-01

    To achieve environments in which humans and mobile robots co-exist, technologies for recognizing hand gestures from the video sequence acquired by a dynamic camera could be useful for human-to-robot interface systems. Most of conventional hand gesture technologies deal with only still camera images. This paper proposes a very simple and stable method for extracting hand motion trajectories based on the Human-Following Local Coordinate System (HFLC System), which is obtained from the located human face and both hands. Then, we apply Condensation Algorithm to the extracted hand trajectories so that the hand motion is recognized. We demonstrate the effectiveness of the proposed method by conducting experiments on 35 kinds of sign language based hand gestures.

  5. DHARMA - Discriminant hyperplane abstracting residuals minimization algorithm for separating clusters with fuzzy boundaries. [data points pattern recognition technique

    NASA Technical Reports Server (NTRS)

    Dasarathy, B. V.

    1976-01-01

    Learning of discriminant hyperplanes in imperfectly supervised or unsupervised training sample sets with unreliably labeled samples along the fuzzy joint boundaries between sample clusters is discussed, with the discriminant hyperplane designed to be a least-squares fit to the unreliably labeled data points. (Samples along the fuzzy boundary jump back and forth from one cluster to the other in recursive cluster stabilization and are considered unreliably labeled.) Minimization of the distances of these unreliably labeled samples from the hyperplanes does not sacrifice the ability to discriminate between classes represented by reliably labeled subsets of samples. An equivalent unconstrained linear inequality problem is formulated and algorithms for its solution are indicated. Landsat earth sensing data were used in confirming the validity and computational feasibility of the approach, which should be useful in deriving discriminant hyperplanes separating clusters with fuzzy boundaries, given supervised training sample sets with unreliably labeled boundary samples.

  6. Structural basis of RNA recognition and activation by innate immune receptor RIG-I

    SciTech Connect

    Jiang, Fuguo; Ramanathan, Anand; Miller, Matthew T.; Tang, Guo-Qing; Gale, Jr., Michael; Patel, Smita S.; Marcotrigiano, Joseph

    2012-05-29

    Retinoic-acid-inducible gene-I (RIG-I; also known as DDX58) is a cytoplasmic pathogen recognition receptor that recognizes pathogen-associated molecular pattern (PAMP) motifs to differentiate between viral and cellular RNAs. RIG-I is activated by blunt-ended double-stranded (ds)RNA with or without a 5'-triphosphate (ppp), by single-stranded RNA marked by a 5'-ppp and by polyuridine sequences. Upon binding to such PAMP motifs, RIG-I initiates a signalling cascade that induces innate immune defences and inflammatory cytokines to establish an antiviral state. The RIG-I pathway is highly regulated and aberrant signalling leads to apoptosis, altered cell differentiation, inflammation, autoimmune diseases and cancer. The helicase and repressor domains (RD) of RIG-I recognize dsRNA and 5'-ppp RNA to activate the two amino-terminal caspase recruitment domains (CARDs) for signalling. Here, to understand the synergy between the helicase and the RD for RNA binding, and the contribution of ATP hydrolysis to RIG-I activation, we determined the structure of human RIG-I helicase-RD in complex with dsRNA and an ATP analogue. The helicase-RD organizes into a ring around dsRNA, capping one end, while contacting both strands using previously uncharacterized motifs to recognize dsRNA. Small-angle X-ray scattering, limited proteolysis and differential scanning fluorimetry indicate that RIG-I is in an extended and flexible conformation that compacts upon binding RNA. These results provide a detailed view of the role of helicase in dsRNA recognition, the synergy between the RD and the helicase for RNA binding and the organization of full-length RIG-I bound to dsRNA, and provide evidence of a conformational change upon RNA binding. The RIG-I helicase-RD structure is consistent with dsRNA translocation without unwinding and cooperative binding to RNA. The structure yields unprecedented insight into innate immunity and has a broader impact on other areas of biology, including RNA

  7. An Active Learning Algorithm for Control of Epidural Electrostimulation.

    PubMed

    Desautels, Thomas A; Choe, Jaehoon; Gad, Parag; Nandra, Mandheerej S; Roy, Roland R; Zhong, Hui; Tai, Yu-Chong; Edgerton, V Reggie; Burdick, Joel W

    2015-10-01

    Epidural electrostimulation has shown promise for spinal cord injury therapy. However, finding effective stimuli on the multi-electrode stimulating arrays employed requires a laborious manual search of a vast space for each patient. Widespread clinical application of these techniques would be greatly facilitated by an autonomous, algorithmic system which choses stimuli to simultaneously deliver effective therapy and explore this space. We propose a method based on GP-BUCB, a Gaussian process bandit algorithm. In n = 4 spinally transected rats, we implant epidural electrode arrays and examine the algorithm's performance in selecting bipolar stimuli to elicit specified muscle responses. These responses are compared with temporally interleaved intra-animal stimulus selections by a human expert. GP-BUCB successfully controlled the spinal electrostimulation preparation in 37 testing sessions, selecting 670 stimuli. These sessions included sustained autonomous operations (ten-session duration). Delivered performance with respect to the specified metric was as good as or better than that of the human expert. Despite receiving no information as to anatomically likely locations of effective stimuli, GP-BUCB also consistently discovered such a pattern. Further, GP-BUCB was able to extrapolate from previous sessions' results to make predictions about performance in new testing sessions, while remaining sufficiently flexible to capture temporal variability. These results provide validation for applying automated stimulus selection methods to the problem of spinal cord injury therapy.

  8. An Active Learning Algorithm for Control of Epidural Electrostimulation

    PubMed Central

    Desautels, Thomas A.; Nandra, Mandheerej S.; Roy, Roland R.; Zhong, Hui; Tai, Yu-Chong; Edgerton, V. Reggie; Burdick, Joel W.

    2015-01-01

    Epidural electrostimulation has shown promise for spinal cord injury therapy. However, finding effective stimuli on the multi-electrode stimulating arrays employed requires a laborious manual search of a vast space for each patient. Widespread clinical application of these techniques would be greatly facilitated by an autonomous, algorithmic system which choses stimuli to simultaneously deliver effective therapy and explore this space. We propose a method based on GP-BUCB, a Gaussian process bandit algorithm. In n = 4 spinally transected rats, we implant epidural electrode arrays and examine the algorithm's performance in selecting bipolar stimuli to elicit specified muscle responses. These responses are compared with temporally interleaved, intra-animal stimulus selections by a human expert. GP-BUCB successfully controlled the spinal electrostimulation preparation in 37 testing sessions, selecting 670 stimuli. These sessions included sustained, autonomous operations (10 session duration). Delivered performance with respect to the specified metric was as good as or better than that of the human expert. Despite receiving no information as to anatomically likely locations of effective stimuli, GP-BUCB also consistently discovered such a pattern. Further, GP-BUCB was able to extrapolate from previous sessions’ results to make predictions about performance in new testing sessions, while remaining sufficiently flexible to capture temporal variability. These results provide validation for applying automated stimulus selection methods to the problem of spinal cord injury therapy. PMID:25974925

  9. Lung nodule segmentation and recognition using SVM classifier and active contour modeling: a complete intelligent system.

    PubMed

    Keshani, Mohsen; Azimifar, Zohreh; Tajeripour, Farshad; Boostani, Reza

    2013-05-01

    In this paper, a novel method for lung nodule detection, segmentation and recognition using computed tomography (CT) images is presented. Our contribution consists of several steps. First, the lung area is segmented by active contour modeling followed by some masking techniques to transfer non-isolated nodules into isolated ones. Then, nodules are detected by the support vector machine (SVM) classifier using efficient 2D stochastic and 3D anatomical features. Contours of detected nodules are then extracted by active contour modeling. In this step all solid and cavitary nodules are accurately segmented. Finally, lung tissues are classified into four classes: namely lung wall, parenchyma, bronchioles and nodules. This classification helps us to distinguish a nodule connected to the lung wall and/or bronchioles (attached nodule) from the one covered by parenchyma (solitary nodule). At the end, performance of our proposed method is examined and compared with other efficient methods through experiments using clinical CT images and two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. Solid, non-solid and cavitary nodules are detected with an overall detection rate of 89%; the number of false positive is 7.3/scan and the location of all detected nodules are recognized correctly.

  10. Human facial neural activities and gesture recognition for machine-interfacing applications.

    PubMed

    Hamedi, M; Salleh, Sh-Hussain; Tan, T S; Ismail, K; Ali, J; Dee-Uam, C; Pavaganun, C; Yupapin, P P

    2011-01-01

    The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2-11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.

  11. Human facial neural activities and gesture recognition for machine-interfacing applications

    PubMed Central

    Hamedi, M; Salleh, Sh-Hussain; Tan, TS; Ismail, K; Ali, J; Dee-Uam, C; Pavaganun, C; Yupapin, PP

    2011-01-01

    The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human–machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2–11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers. PMID:22267930

  12. [Influence of music different in volume and style on human recognition activity].

    PubMed

    Pavlygina, R A; Sakharov, D S; Davydov, V I; Avdonkin, A V

    2009-01-01

    The efficiency of recognition of masked visual images (Arabic numerals) under conditions of listening to classical (intensity 62 dB) or rock music (25 dB) increased. Coherence of potential in the frontal cortical region characteristic of the masked image recognition increased under conditions of listening to music. The changes in intercenter EEG relations were correlated with the formation of "the recognition dominant" at the behavioral level. Such behavioral and EEG changes were not observed during listening to louder music (85 dB) and listening to music of other styles, however, the coherence between potentials of the temporal and motor areas of the right hemisphere increased, and the latency of hand motor reactions decreased. The results suggest that the "recognition dominant" is formed under conditions of establishment of certain relations between the levels of excitation in the corresponding centers. These findings should be taken into consideration in case if it were necessary to increase the efficiency of the recognition.

  13. Interaction investigations of crustacean β-GBP recognition toward pathogenic microbial cell membrane and stimulate upon prophenoloxidase activation.

    PubMed

    Sivakamavalli, Jeyachandran; Selvaraj, Chandrabose; Singh, Sanjeev Kumar; Vaseeharan, Baskaralingam

    2014-04-01

    In invertebrates, crustaceans' immune system consists of pattern recognition receptors (PRRs) instead of immunoglobulin's, which involves in the microbial recognition and initiates the protein-ligand interaction between hosts and pathogens. In the present study, PRRs namely β-1,3 glucan binding protein (β-GBP) from mangrove crab Episesarma tetragonum and its interactions with the pathogens such as bacterial and fungal outer membrane proteins (OMP) were investigated through microbial aggregation and computational interaction studies. Molecular recognition and microbial aggregation results of Episesarma tetragonum β-GBP showed the specific binding affinity toward the fungal β-1,3 glucan molecule when compared to other bacterial ligands. Because of this microbial recognition, prophenoloxidase activity was enhanced and triggers the innate immunity inside the host animal. Our findings disclose the role of β-GBP in molecular recognition, host-pathogen interaction through microbial aggregation, and docking analysis. In vitro results were concurred with the in silico docking, and molecular dynamics simulation analysis. This study would be helpful to understand the molecular mechanism of β-GBP and update the current knowledge on the PRRs of crustaceans.

  14. A split active site couples cap recognition by Dcp2 to activation

    PubMed Central

    Floor, Stephen N.; Jones, Brittnee N.; Hernandez, Gail A.; Gross, John D.

    2010-01-01

    Decapping by Dcp2 is an essential step in 5′-3′ mRNA decay. In yeast, decapping requires an open-to-closed transition in Dcp2, though the link between closure and catalysis remains elusive. Here we show using NMR that cap binds conserved residues on both the catalytic and regulatory domains of Dcp2. Lesions in the cap-binding site on the regulatory domain reduce the catalytic step two orders of magnitude and block formation of the closed state whereas Dcp1 enhances the catalytic step by a factor of ten and promotes closure. We conclude that closure occurs during the rate-limiting catalytic step of decapping, juxtaposing the cap-binding region of each domain to form a composite active site. This work suggests a model for regulation of decapping, where coactivators trigger decapping by stabilizing a labile composite active site. PMID:20711189

  15. Gray level co-occurrence matrix algorithm as pattern recognition biosensor for oxidopamine-induced changes in lymphocyte chromatin architecture.

    PubMed

    Pantic, Igor; Dimitrijevic, Draga; Nesic, Dejan; Petrovic, Danica

    2016-10-07

    We demonstrate that a proapoptotic chemical agent, oxidopamine, induces dose dependent changes in chromatin textural patterns which can be quantified using the Gray level co-occurrence matrix (GLCM) method. Peripheral blood (heparin-pretreated) samples were treated with oxidopamine (6-OHDA, 6-hydroxydopamine) to achieve effective concentrations of 100, 200 and 300µM. The samples were smeared on microscope slides and fixated in methanol. The smears were stained using a modification of Feulgen method for DNA visualization. For each stained smear, a sample of 30 lymphocyte chromatin structures were visualized and analyzed. This way, textural parameters for a total of 120 nuclei micrographs were calculated. For each chromatin structure, five different GLCM features were calculated: angular second moment, GLCM entropy, inverse difference moment, GLCM correlation, and GLCM variance. Oxidopamine induced the rise of the values of GLCM entropy and variance, and the reduction of angular second moment, correlation, and inverse difference moment. The trends for GLCM parameter changes were found to be highly significant (p<0.001). These results indicate that GLCM mathematical algorithm might be successfully used in detection and evaluation of discrete early apoptotic structural changes in Feulgen-stained chromatin of peripheral blood lymphocytes that are not detectable using conventional microscopy/cell biology techniques.

  16. Status report: Data management program algorithm evaluation activity at Marshall Space Flight Center

    NASA Technical Reports Server (NTRS)

    Jayroe, R. R., Jr.

    1977-01-01

    An algorithm evaluation activity was initiated to study the problems associated with image processing by assessing the independent and interdependent effects of registration, compression, and classification techniques on LANDSAT data for several discipline applications. The objective of the activity was to make recommendations on selected applicable image processing algorithms in terms of accuracy, cost, and timeliness or to propose alternative ways of processing the data. As a means of accomplishing this objective, an Image Coding Panel was established. The conduct of the algorithm evaluation is described.

  17. Structural recognition and functional activation of Fc[gamma]R by innate pentraxins

    SciTech Connect

    Lu, Jinghua; Marnell, Lorraine L.; Marjon, Kristopher D.; Mold, Carolyn; Du Clos, Terry W.; Sun, Peter D.

    2009-10-05

    Pentraxins are a family of ancient innate immune mediators conserved throughout evolution. The classical pentraxins include serum amyloid P component (SAP) and C-reactive protein, which are two of the acute-phase proteins synthesized in response to infection. Both recognize microbial pathogens and activate the classical complement pathway through C1q. More recently, members of the pentraxin family were found to interact with cell-surface Fc{gamma} receptors (Fc{gamma}R) and activate leukocyte-mediated phagocytosis. Here we describe the structural mechanism for pentraxin's binding to Fc{gamma}R and its functional activation of Fc{gamma}R-mediated phagocytosis and cytokine secretion. The complex structure between human SAP and Fc{gamma}RIIa reveals a diagonally bound receptor on each SAP pentamer with both D1 and D2 domains of the receptor contacting the ridge helices from two SAP subunits. The 1:1 stoichiometry between SAP and Fc{gamma}RIIa infers the requirement for multivalent pathogen binding for receptor aggregation. Mutational and binding studies show that pentraxins are diverse in their binding specificity for Fc{gamma}R isoforms but conserved in their recognition structure. The shared binding site for SAP and IgG results in competition for Fc{gamma}R binding and the inhibition of immune-complex-mediated phagocytosis by soluble pentraxins. These results establish antibody-like functions for pentraxins in the Fc{gamma}R pathway, suggest an evolutionary overlap between the innate and adaptive immune systems, and have new therapeutic implications for autoimmune diseases.

  18. An important base triple anchors the substrate helix recognition surface within the Tetrahymena ribozyme active site.

    PubMed

    Szewczak, A A; Ortoleva-Donnelly, L; Zivarts, M V; Oyelere, A K; Kazantsev, A V; Strobel, S A

    1999-09-28

    Key to understanding the structural biology of catalytic RNA is determining the underlying networks of interactions that stabilize RNA folding, substrate binding, and catalysis. Here we demonstrate the existence and functional importance of a Hoogsteen base triple (U300.A97-U277), which anchors the substrate helix recognition surface within the Tetrahymena group I ribozyme active site. Nucleotide analog interference suppression analysis of the interacting functional groups shows that the U300.A97-U277 triple forms part of a network of hydrogen bonds that connect the P3 helix, the J8/7 strand, and the P1 substrate helix. Product binding and substrate cleavage kinetics experiments performed on mutant ribozymes that lack this base triple (C A-U, U G-C) or replace it with the isomorphous C(+).G-C triple show that the A97 Hoogsteen triple contributes to the stabilization of both substrate helix docking and the conformation of the ribozyme's active site. The U300. A97-U277 base triple is not formed in the recently reported crystallographic model of a portion of the group I intron, despite the presence of J8/7 and P3 in the RNA construct [Golden, B. L., Gooding, A. R., Podell, E. R. & Cech, T. R. (1998) Science 282, 259-264]. This, along with other biochemical evidence, suggests that the active site in the crystallized form of the ribozyme is not fully preorganized and that substantial rearrangement may be required for substrate helix docking and catalysis.

  19. Real-time observation of signal recognition particle binding to actively translating ribosomes.

    PubMed

    Noriega, Thomas R; Chen, Jin; Walter, Peter; Puglisi, Joseph D

    2014-10-30

    The signal recognition particle (SRP) directs translating ribosome-nascent chain complexes (RNCs) that display a signal sequence to protein translocation channels in target membranes. All previous work on the initial step of the targeting reaction, when SRP binds to RNCs, used stalled and non-translating RNCs. This meant that an important dimension of the co-translational process remained unstudied. We apply single-molecule fluorescence measurements to observe directly and in real-time E. coli SRP binding to actively translating RNCs. We show at physiologically relevant SRP concentrations that SRP-RNC association and dissociation rates depend on nascent chain length and the exposure of a functional signal sequence outside the ribosome. Our results resolve a long-standing question: how can a limited, sub-stoichiometric pool of cellular SRP effectively distinguish RNCs displaying a signal sequence from those that are not? The answer is strikingly simple: as originally proposed, SRP only stably engages translating RNCs exposing a functional signal sequence.

  20. Activity Recognition Using Community Data to Complement Small Amounts of Labeled Instances †

    PubMed Central

    Garcia-Ceja, Enrique; Brena, Ramon F.

    2016-01-01

    Human Activity Recognition (HAR) is an important part of ambient intelligence systems since it can provide user-context information, thus allowing a greater personalization of services. One of the problems with HAR systems is that the labeling process for the training data is costly, which has hindered its practical application. A common approach is to train a general model with the aggregated data from all users. The problem is that for a new target user, this model can perform poorly because it is biased towards the majority type of users and does not take into account the particular characteristics of the target user. To overcome this limitation, a user-dependent model can be trained with data only from the target user that will be optimal for this particular user; however, this requires a considerable amount of labeled data, which is cumbersome to obtain. In this work, we propose a method to build a personalized model for a given target user that does not require large amounts of labeled data. Our method uses data already labeled by a community of users to complement the scarce labeled data of the target user. Our results showed that the personalized model outperformed the general and the user-dependent models when labeled data is scarce. PMID:27314355

  1. Transparent Stretchable Self-Powered Patchable Sensor Platform with Ultrasensitive Recognition of Human Activities.

    PubMed

    Hwang, Byeong-Ung; Lee, Ju-Hyuck; Trung, Tran Quang; Roh, Eun; Kim, Do-Il; Kim, Sang-Woo; Lee, Nae-Eung

    2015-09-22

    Monitoring of human activities can provide clinically relevant information pertaining to disease diagnostics, preventive medicine, care for patients with chronic diseases, rehabilitation, and prosthetics. The recognition of strains on human skin, induced by subtle movements of muscles in the internal organs, such as the esophagus and trachea, and the motion of joints, was demonstrated using a self-powered patchable strain sensor platform, composed on multifunctional nanocomposites of low-density silver nanowires with a conductive elastomer of poly(3,4-ethylenedioxythiophene):polystyrenesulfonate/polyurethane, with high sensitivity, stretchability, and optical transparency. The ultra-low-power consumption of the sensor, integrated with both a supercapacitor and a triboelectric nanogenerator into a single transparent stretchable platform based on the same nanocomposites, results in a self-powered monitoring system for skin strain. The capability of the sensor to recognize a wide range of strain on skin has the potential for use in new areas of invisible stretchable electronics for human monitoring. A new type of transparent, stretchable, and ultrasensitive strain sensor based on a AgNW/PEDOT:PSS/PU nanocomposite was developed. The concept of a self-powered patchable sensor system integrated with a supercapacitor and a triboelectric nanogenerator that can be used universally as an autonomous invisible sensor system was used to detect the wide range of strain on human skin.

  2. γ-Cyclodextrin capped silver nanoparticles for molecular recognition and enhancement of antibacterial activity of chloramphenicol.

    PubMed

    Gannimani, Ramesh; Ramesh, Muthusamy; Mtambo, Sphamandla; Pillay, Karen; Soliman, Mahmoud E; Govender, Patrick

    2016-04-01

    Computational studies were conducted to identify the favourable formation of the inclusion complex of chloramphenicol with cyclodextrins. The results of molecular docking and molecular dynamics predicted the strongest interaction of chloramphenicol with γ-cyclodextrin. Further, the inclusion complex of chloramphenicol with γ-cyclodextrin was experimentally prepared and a phenomenon of inclusion was verified by using different characterization techniques such as thermogravimetric analysis, differential scanning calorimetry, (1)H nuclear magnetic resonance (NMR) and two dimensional nuclear overhauser effect spectroscopy (NOESY) experiments. From these results it was concluded that γ-cyclodextrins could be an appropriate cyclodextrin polymer which can be used to functionalize chloramphenicol on the surface of silver nanoparticles. In addition, γ-cyclodextrin capped silver nanoparticles were synthesized and characterized using UV-visible spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray analysis (EDX), Fourier transform infrared spectroscopy (FTIR) and zeta potential analysis. Molecular recognition of chloramphenicol by these cyclodextrin capped silver nanoparticles was confirmed by surface enhanced raman spectroscopy (SERS) experiments. Synergistic antibacterial effect of chloramphenicol with γ-cyclodextrin capped silver nanoparticles was evaluated against Pseudomonas aeruginosa (ATCC 27853), Enterococcus faecalis (ATCC 5129), Klebsiella pneumoniae (ATCC 700603) and Staphylococcus aureus (ATCC 43300). The results from the antibacterial experiment were favourable thus allowing us to conclude that the approach of modifying organic drug molecules with cyclodextrin capped inorganic silver nanoparticles could help to enhance the antibacterial activity of them.

  3. Probabilistic image modeling with an extended chain graph for human activity recognition and image segmentation.

    PubMed

    Zhang, Lei; Zeng, Zhi; Ji, Qiang

    2011-09-01

    Chain graph (CG) is a hybrid probabilistic graphical model (PGM) capable of modeling heterogeneous relationships among random variables. So far, however, its application in image and video analysis is very limited due to lack of principled learning and inference methods for a CG of general topology. To overcome this limitation, we introduce methods to extend the conventional chain-like CG model to CG model with more general topology and the associated methods for learning and inference in such a general CG model. Specifically, we propose techniques to systematically construct a generally structured CG, to parameterize this model, to derive its joint probability distribution, to perform joint parameter learning, and to perform probabilistic inference in this model. To demonstrate the utility of such an extended CG, we apply it to two challenging image and video analysis problems: human activity recognition and image segmentation. The experimental results show improved performance of the extended CG model over the conventional directed or undirected PGMs. This study demonstrates the promise of the extended CG for effective modeling and inference of complex real-world problems.

  4. Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition

    PubMed Central

    Zhang, Zelun; Poslad, Stefan

    2013-01-01

    Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during travelling) and transportation mode simultaneously, and a relatively high computational complexity. Here, a new GPS and Foot-Force (GPS + FF) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with GPS, e.g., in a mobile phone. User mobility activities that can be recognised include both daily user postures and common transportation modes: sitting, standing, walking, cycling, bus passenger, car passenger (including private cars and taxis) and car driver. The novelty of this work is that our approach provides a more comprehensive recognition capability in terms of reliably recognising both human posture and transportation mode simultaneously during travel. In addition, by comparing the new GPS + FF method with both an ACC method (62% accuracy) and a GPS + ACC based method (70% accuracy) as baseline methods, it obtains a higher accuracy (95%) with less computational complexity, when tested on a dataset obtained from ten individuals. PMID:24189333

  5. Design and test of a hybrid foot force sensing and GPS system for richer user mobility activity recognition.

    PubMed

    Zhang, Zelun; Poslad, Stefan

    2013-11-01

    Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during travelling) and transportation mode simultaneously, and a relatively high computational complexity. Here, a new GPS and Foot-Force (GPS + FF) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with GPS, e.g., in a mobile phone. User mobility activities that can be recognised include both daily user postures and common transportation modes: sitting, standing, walking, cycling, bus passenger, car passenger (including private cars and taxis) and car driver. The novelty of this work is that our approach provides a more comprehensive recognition capability in terms of reliably recognising both human posture and transportation mode simultaneously during travel. In addition, by comparing the new GPS + FF method with both an ACC method (62% accuracy) and a GPS + ACC based method (70% accuracy) as baseline methods, it obtains a higher accuracy (95%) with less computational complexity, when tested on a dataset obtained from ten individuals.

  6. SciDAC Institute for Ultra-Scale Visualization: Activity Recognition for Ultra-Scale Visualization

    SciTech Connect

    Silver, Deborah

    2014-04-30

    Understanding the science behind ultra-scale simulations requires extracting meaning from data sets of hundreds of terabytes or more. Developing scalable parallel visualization algorithms is a key step enabling scientists to interact and visualize their data at this scale. However, at extreme scales, the datasets are so huge, there is not even enough time to view the data, let alone explore it with basic visualization methods. Automated tools are necessary for knowledge discovery -- to help sift through the information and isolate characteristic patterns, thereby enabling the scientist to study local interactions, the origin of features and their evolution in large volumes of data. These tools must be able to operate on data of this scale and work with the visualization process. In this project, we developed a framework for activity detection to allow scientists to model and extract spatio-temporal patterns from time-varying data.

  7. Effects of acute psychosocial stress on neural activity to emotional and neutral faces in a face recognition memory paradigm.

    PubMed

    Li, Shijia; Weerda, Riklef; Milde, Christopher; Wolf, Oliver T; Thiel, Christiane M

    2014-12-01

    Previous studies have shown that acute psychosocial stress impairs recognition of declarative memory and that emotional material is especially sensitive to this effect. Animal studies suggest a central role of the amygdala which modulates memory processes in hippocampus, prefrontal cortex and other brain areas. We used functional magnetic resonance imaging (fMRI) to investigate neural correlates of stress-induced modulation of emotional recognition memory in humans. Twenty-seven healthy, right-handed, non-smoker male volunteers performed an emotional face recognition task. During encoding, participants were presented with 50 fearful and 50 neutral faces. One hour later, they underwent either a stress (Trier Social Stress Test) or a control procedure outside the scanner which was followed immediately by the recognition session inside the scanner, where participants had to discriminate between 100 old and 50 new faces. Stress increased salivary cortisol, blood pressure and pulse, and decreased the mood of participants but did not impact recognition memory. BOLD data during recognition revealed a stress condition by emotion interaction in the left inferior frontal gyrus and right hippocampus which was due to a stress-induced increase of neural activity to fearful and a decrease to neutral faces. Functional connectivity analyses revealed a stress-induced increase in coupling between the right amygdala and the right fusiform gyrus, when processing fearful as compared to neutral faces. Our results provide evidence that acute psychosocial stress affects medial temporal and frontal brain areas differentially for neutral and emotional items, with a stress-induced privileged processing of emotional stimuli.

  8. Effects of activity and energy budget balancing algorithm on laboratory performance of a fish bioenergetics model

    USGS Publications Warehouse

    Madenjian, Charles P.; David, Solomon R.; Pothoven, Steven A.

    2012-01-01

    We evaluated the performance of the Wisconsin bioenergetics model for lake trout Salvelinus namaycush that were fed ad libitum in laboratory tanks under regimes of low activity and high activity. In addition, we compared model performance under two different model algorithms: (1) balancing the lake trout energy budget on day t based on lake trout energy density on day t and (2) balancing the lake trout energy budget on day t based on lake trout energy density on day t + 1. Results indicated that the model significantly underestimated consumption for both inactive and active lake trout when algorithm 1 was used and that the degree of underestimation was similar for the two activity levels. In contrast, model performance substantially improved when using algorithm 2, as no detectable bias was found in model predictions of consumption for inactive fish and only a slight degree of overestimation was detected for active fish. The energy budget was accurately balanced by using algorithm 2 but not by using algorithm 1. Based on the results of this study, we recommend the use of algorithm 2 to estimate food consumption by fish in the field. Our study results highlight the importance of accurately accounting for changes in fish energy density when balancing the energy budget; furthermore, these results have implications for the science of evaluating fish bioenergetics model performance and for more accurate estimation of food consumption by fish in the field when fish energy density undergoes relatively rapid changes.

  9. Detecting activity locations from raw GPS data: a novel kernel-based algorithm

    PubMed Central

    2013-01-01

    Background Health studies and mHealth applications are increasingly resorting to tracking technologies such as Global Positioning Systems (GPS) to study the relation between mobility, exposures, and health. GPS tracking generates large sets of geographic data that need to be transformed to be useful for health research. This paper proposes a method to test the performance of activity place detection algorithms, and compares the performance of a novel kernel-based algorithm with a more traditional time-distance cluster detection method. Methods A set of 750 artificial GPS tracks containing three stops each were generated, with various levels of noise.. A total of 9,000 tracks were processed to measure the algorithms’ capacity to detect stop locations and estimate stop durations, with varying GPS noise and algorithm parameters. Results The proposed kernel-based algorithm outperformed the traditional algorithm on most criteria associated to activity place detection, and offered a stronger resilience to GPS noise, managing to detect up to 92.3% of actual stops, and estimating stop duration within 5% error margins at all tested noise levels. Conclusions Capacity to detect activity locations is an important feature in a context of increasing use of GPS devices in health and place research. While further testing with real-life tracks is recommended, testing algorithms’ performance with artificial track sets for which characteristics are controlled is useful. The proposed novel algorithm outperformed the traditional algorithm under these conditions. PMID:23497213

  10. Activity Augmentation of Amphioxus Peptidoglycan Recognition Protein BbtPGRP3 via Fusion with a Chitin Binding Domain

    PubMed Central

    Wang, Wen-Jie; Cheng, Wang; Luo, Ming; Yan, Qingyu; Yu, Hong-Mei; Li, Qiong; Cao, Dong-Dong; Huang, Shengfeng; Xu, Anlong; Mariuzza, Roy A.; Chen, Yuxing; Zhou, Cong-Zhao

    2015-01-01

    Peptidoglycan recognition proteins (PGRPs), which have been identified in most animals, are pattern recognition molecules that involve antimicrobial defense. Resulting from extraordinary expansion of innate immune genes, the amphioxus encodes many PGRPs of diverse functions. For instance, three isoforms of PGRP encoded by Branchiostoma belcheri tsingtauense, termed BbtPGRP1~3, are fused with a chitin binding domain (CBD) at the N-terminus. Here we report the 2.7 Å crystal structure of BbtPGRP3, revealing an overall structure of an N-terminal hevein-like CBD followed by a catalytic PGRP domain. Activity assays combined with site-directed mutagenesis indicated that the individual PGRP domain exhibits amidase activity towards both DAP-type and Lys-type peptidoglycans (PGNs), the former of which is favored. The N-terminal CBD not only has the chitin-binding activity, but also enables BbtPGRP3 to gain a five-fold increase of amidase activity towards the Lys-type PGNs, leading to a significantly broadened substrate spectrum. Together, we propose that modular evolution via domain shuffling combined with gene horizontal transfer makes BbtPGRP1~3 novel PGRPs of augmented catalytic activity and broad recognition spectrum. PMID:26479246

  11. Moving human full body and body parts detection, tracking, and applications on human activity estimation, walking pattern and face recognition

    NASA Astrophysics Data System (ADS)

    Chen, Hai-Wen; McGurr, Mike

    2016-05-01

    We have developed a new way for detection and tracking of human full-body and body-parts with color (intensity) patch morphological segmentation and adaptive thresholding for security surveillance cameras. An adaptive threshold scheme has been developed for dealing with body size changes, illumination condition changes, and cross camera parameter changes. Tests with the PETS 2009 and 2014 datasets show that we can obtain high probability of detection and low probability of false alarm for full-body. Test results indicate that our human full-body detection method can considerably outperform the current state-of-the-art methods in both detection performance and computational complexity. Furthermore, in this paper, we have developed several methods using color features for detection and tracking of human body-parts (arms, legs, torso, and head, etc.). For example, we have developed a human skin color sub-patch segmentation algorithm by first conducting a RGB to YIQ transformation and then applying a Subtractive I/Q image Fusion with morphological operations. With this method, we can reliably detect and track human skin color related body-parts such as face, neck, arms, and legs. Reliable body-parts (e.g. head) detection allows us to continuously track the individual person even in the case that multiple closely spaced persons are merged. Accordingly, we have developed a new algorithm to split a merged detection blob back to individual detections based on the detected head positions. Detected body-parts also allow us to extract important local constellation features of the body-parts positions and angles related to the full-body. These features are useful for human walking gait pattern recognition and human pose (e.g. standing or falling down) estimation for potential abnormal behavior and accidental event detection, as evidenced with our experimental tests. Furthermore, based on the reliable head (face) tacking, we have applied a super-resolution algorithm to enhance

  12. Optimal placement of active material actuators using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Johnson, Terrence; Frecker, Mary I.

    2004-07-01

    Actuators based on smart materials generally exhibit a tradeoff between force and stroke. Researchers have surrounded piezoelectric materials (PZT"s) with complaint structures to magnify either their geometric or mechanical advantage. Most of these designs are literally built around a particular piezoelectric device, so the design space consists of only the compliant mechanism. Materials scientists researchers have demonstrated the ability to pole a PZT in an arbitrary direction, and some engineers have taken advantage of this to build "shear mode" actuators. The goal of this work is to determine if the performance of compliant mechanisms improves by the inclusion of the piezoelectric polarization as a design variable. The polarization vector is varied via transformation matrixes, and the compliant actuator is modeled using the SIMP (Solid Isotropic Material with Penalization) or "power-law method." The concept of mutual potential energy is used to form an objective function to measure the piezoelectric actuator"s performance. The optimal topology of the compliant mechanism and orientation of the polarization method are determined using a sequential linear programming algorithm. This paper presents a demonstration problem that shows small changes in the polarization vector have a marginal effect on the optimum topology of the mechanism, but improves actuation.

  13. Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity

    USGS Publications Warehouse

    Louis, S.J.; Raines, G.L.

    2003-01-01

    We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.

  14. An Optimal CDS Construction Algorithm with Activity Scheduling in Ad Hoc Networks.

    PubMed

    Penumalli, Chakradhar; Palanichamy, Yogesh

    2015-01-01

    A new energy efficient optimal Connected Dominating Set (CDS) algorithm with activity scheduling for mobile ad hoc networks (MANETs) is proposed. This algorithm achieves energy efficiency by minimizing the Broadcast Storm Problem [BSP] and at the same time considering the node's remaining energy. The Connected Dominating Set is widely used as a virtual backbone or spine in mobile ad hoc networks [MANETs] or Wireless Sensor Networks [WSN]. The CDS of a graph representing a network has a significant impact on an efficient design of routing protocol in wireless networks. Here the CDS is a distributed algorithm with activity scheduling based on unit disk graph [UDG]. The node's mobility and residual energy (RE) are considered as parameters in the construction of stable optimal energy efficient CDS. The performance is evaluated at various node densities, various transmission ranges, and mobility rates. The theoretical analysis and simulation results of this algorithm are also presented which yield better results.

  15. Active-passive correlation spectroscopy - A new technique for identifying ocean color algorithm spectral regions

    NASA Technical Reports Server (NTRS)

    Hoge, F. E.; Swift, R. N.

    1986-01-01

    A new active-passive airborne data correlation technique has been developed which allows the validation of existing in-water oceoan color algorithms and the rapid search, identification, and evaluation of new sensor band locations and algorithm wavelength intervals. Thus far, applied only in conjunction with the spectral curvature algorithm (SCA), the active-passive correlation spectroscopy (APCS) technique shows that (1) the usual 490-nm (center-band) chlorophyll SCA could satisfactorily be placed anywhere within the nominal 460-510-nm interval, and (2) two other spectral regions, 645-660 and 680-695 nm, show considerable promise for chlorophyll pigment measurement. Additionally, the APCS method reveals potentially useful wavelength regions (at 600 and about 670 nm) of very low chlorophyll-in-water spectral curvature into which accessory pigment algorithms for phycoerythrin might be carefully positioned. In combination, the APCS and SCA methods strongly suggest that significant information content resides within the seemingly featureless ocean color spectrum.

  16. Distance, shape and more: recognition of object features during active electrolocation in a weakly electric fish.

    PubMed

    von der Emde, Gerhard; Fetz, Steffen

    2007-09-01

    In the absence of light, the weakly electric fish Gnathonemus petersii detects and distinguishes objects in the environment through active electrolocation. In order to test which features of an object the fish use under these conditions to discriminate between differently shaped objects, we trained eight individuals in a food-rewarded, two-alternative, forced-choice procedure. All fish learned to discriminate between two objects of different shapes and volumes. When new object combinations were offered in non-rewarded test trials, fish preferred those objects that resembled the one they had been trained to (S+) and avoided objects resembling the one that had not been rewarded (S-). For a decision, fish paid attention to the relative differences between the two objects they had to discriminate. For discrimination, fish used several object features, the most important ones being volume, material and shape. The importance of shape was demonstrated by reducing the objects to their 3-dimensional contours, which sufficed for the fish to distinguish differently shaped objects. Our results also showed that fish attended strongly to the feature ;volume', because all individuals tended to avoid the larger one of two objects. When confronted with metal versus plastic objects, all fish avoided metal and preferred plastic objects, irrespective of training. In addition to volume, material and shape, fish attended to additional parameters, such as corners or rounded edges. When confronted with two unknown objects, fish weighed up the positive and negative properties of these novel objects and based their decision on the outcome of this comparison. Our results suggest that fish are able to link and assemble local features of an electrolocation pattern to construct a representation of an object, suggesting that some form of a feature extraction mechanism enables them to solve a complex object recognition task.

  17. NLRP7 and related inflammasome activating pattern recognition receptors and their function in host defense and disease.

    PubMed

    Radian, Alexander D; de Almeida, Lucia; Dorfleutner, Andrea; Stehlik, Christian

    2013-01-01

    Host defense requires the maturation and release of the pro-inflammatory cytokines interleukin (IL)-1β and IL-18 and the induction of pyroptotic cell death, which depends on the activation of inflammatory Caspases within inflammasomes by innate immune cells. Several cytosolic pattern recognition receptors (PRRs) have been implicated in this process in response to infectious and sterile agonists. Here we summarize the current knowledge on inflammasome-organizing PRRs, emphasizing the recently described NLRP7, and their implications in human disease.

  18. Automatic Recognition of Solar Features for Developing Data Driven Prediction Models of Solar Activity and Space Weather

    DTIC Science & Technology

    2012-07-06

    Ephemeral Brightening,” 2nd ATST – East Workshop In Solar Physics: Magnetic Fields From The Photosphere To The Corona , Washington D.C., Mar 2012. [6...AFRL-RV-PS- AFRL-RV-PS- TR-2012-0133 TR-2012-0133 AUTOMATIC RECOGNITION OF SOLAR FEATURES FOR DEVELOPING DATA DRIVEN PREDICTION MODELS OF... SOLAR ACTIVITY AND SPACE WEATHER Jason Jackiewicz New Mexico State University Department of Astronomy PO Box 30001, MSC 4500 Las

  19. Comparative study on semi-active control algorithms for piezoelectric friction dampers

    NASA Astrophysics Data System (ADS)

    Chen, Chaoqiang; Chen, Genda

    2004-07-01

    A semi-active Tri-D algorithm combining Coulomb, Reid and viscous damping mechanisms has recently been developed by the authors to drive piezoelectric friction dampers. The objective of this study is to analytically compare its performance with those of bang-bang control, clipped-optimal control, modulated homogeneous control, and a modified clipped-optimal control. Two far-field and two near-field historical earthquake records with various intensities and dominant frequencies were used in this study. All algorithms were evaluated with a ¼ scale 3-story frame structure in terms of reductions in peak inter-story drift ratio and peak floor acceleration. A piezoelectric friction damper was considered to be installed between a bracing support and the first floor of the frame structure. Both advantages and disadvantages of each control algorithm were discussed with numerical simulations. At near resonance, both bang-bang and clipped-optimal algorithms are more effective in drift reduction, and the modified clipped-optimal algorithm is more effective in acceleration reduction than both Tri-D and modulated homogeneous algorithms. But the latter requires less control force on the average. For a non-resonant case, the Tri-D and modulated homogeneous algorithms are more effective in acceleration reduction than others even with less control force required. Overall, the Tri-D and modulated homogeneous controls are effective in response reduction, adaptive, and robust to earthquake characteristics.

  20. Environmental Monitoring Networks Optimization Using Advanced Active Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Kanevski, Mikhail; Volpi, Michele; Copa, Loris

    2010-05-01

    The problem of environmental monitoring networks optimization (MNO) belongs to one of the basic and fundamental tasks in spatio-temporal data collection, analysis, and modeling. There are several approaches to this problem, which can be considered as a design or redesign of monitoring network by applying some optimization criteria. The most developed and widespread methods are based on geostatistics (family of kriging models, conditional stochastic simulations). In geostatistics the variance is mainly used as an optimization criterion which has some advantages and drawbacks. In the present research we study an application of advanced techniques following from the statistical learning theory (SLT) - support vector machines (SVM) and the optimization of monitoring networks when dealing with a classification problem (data are discrete values/classes: hydrogeological units, soil types, pollution decision levels, etc.) is considered. SVM is a universal nonlinear modeling tool for classification problems in high dimensional spaces. The SVM solution is maximizing the decision boundary between classes and has a good generalization property for noisy data. The sparse solution of SVM is based on support vectors - data which contribute to the solution with nonzero weights. Fundamentally the MNO for classification problems can be considered as a task of selecting new measurement points which increase the quality of spatial classification and reduce the testing error (error on new independent measurements). In SLT this is a typical problem of active learning - a selection of the new unlabelled points which efficiently reduce the testing error. A classical approach (margin sampling) to active learning is to sample the points closest to the classification boundary. This solution is suboptimal when points (or generally the dataset) are redundant for the same class. In the present research we propose and study two new advanced methods of active learning adapted to the solution of

  1. Active structured learning for cell tracking: algorithm, framework, and usability.

    PubMed

    Lou, Xinghua; Schiegg, Martin; Hamprecht, Fred A

    2014-04-01

    One distinguishing property of life is its temporal dynamics, and it is hence only natural that time lapse experiments play a crucial role in modern biomedical research areas such as signaling pathways, drug discovery or developmental biology. Such experiments yield a very large number of images that encode complex cellular activities, and reliable automated cell tracking emerges naturally as a prerequisite for further quantitative analysis. However, many existing cell tracking methods are restricted to using only a small number of features to allow for manual tweaking. In this paper, we propose a novel cell tracking approach that embraces a powerful machine learning technique to optimize the tracking parameters based on user annotated tracks. Our approach replaces the tedious parameter tuning with parameter learning and allows for the use of a much richer set of complex tracking features, which in turn affords superior prediction accuracy. Furthermore, we developed an active learning approach for efficient training data retrieval, which reduces the annotation effort to only 17%. In practical terms, our approach allows life science researchers to inject their expertise in a more intuitive and direct manner. This process is further facilitated by using a glyph visualization technique for ground truth annotation and validation. Evaluation and comparison on several publicly available benchmark sequences show significant performance improvement over recently reported approaches. Code and software tools are provided to the public.

  2. Understanding medial temporal activation in memory tasks: evidence from fMRI of encoding and recognition in a case of transient global amnesia.

    PubMed

    Westmacott, Robyn; Silver, Frank L; McAndrews, Mary Pat

    2008-01-01

    We used fMRI to examine the activation patterns of patient AE during encoding and recognition of visual scenes during an episode of transient global amnesia (TGA) and 3 months later. Controls (n = 5) showed bilateral (R > L) activation in parahippocampal and fusiform gyri during encoding and right-sided activation in the same regions associated with recognition of previously viewed scenes. AE showed a similar pattern at follow-up. During acute TGA, when performance was profoundly impaired, AE showed no medial temporal activation associated with encoding of new scenes or recognition of old scenes. In both contrasts, the percent signal change in relevant medial temporal regions was more than three standard deviations below the control sample mean. She did, however, show striking bilateral hippocampal activation for recognition attempts (old + new scenes > baseline) even though retrieval was unsuccessful (55% recognition accuracy). This finding was unique to AE on this occasion. This is the first study to document normalization of both encoding and recognition activation patterns in TGA. Furthermore, the strong hippocampal activation during unsuccessful retrieval highlights important issues in interpreting memory-related activations, particularly in dysfunctional systems.

  3. Computational State Space Models for Activity and Intention Recognition. A Feasibility Study

    PubMed Central

    Krüger, Frank; Nyolt, Martin; Yordanova, Kristina; Hein, Albert; Kirste, Thomas

    2014-01-01

    Background Computational state space models (CSSMs) enable the knowledge-based construction of Bayesian filters for recognizing intentions and reconstructing activities of human protagonists in application domains such as smart environments, assisted living, or security. Computational, i. e., algorithmic, representations allow the construction of increasingly complex human behaviour models. However, the symbolic models used in CSSMs potentially suffer from combinatorial explosion, rendering inference intractable outside of the limited experimental settings investigated in present research. The objective of this study was to obtain data on the feasibility of CSSM-based inference in domains of realistic complexity. Methods A typical instrumental activity of daily living was used as a trial scenario. As primary sensor modality, wearable inertial measurement units were employed. The results achievable by CSSM methods were evaluated by comparison with those obtained from established training-based methods (hidden Markov models, HMMs) using Wilcoxon signed rank tests. The influence of modeling factors on CSSM performance was analyzed via repeated measures analysis of variance. Results The symbolic domain model was found to have more than states, exceeding the complexity of models considered in previous research by at least three orders of magnitude. Nevertheless, if factors and procedures governing the inference process were suitably chosen, CSSMs outperformed HMMs. Specifically, inference methods used in previous studies (particle filters) were found to perform substantially inferior in comparison to a marginal filtering procedure. Conclusions Our results suggest that the combinatorial explosion caused by rich CSSM models does not inevitably lead to intractable inference or inferior performance. This means that the potential benefits of CSSM models (knowledge-based model construction, model reusability, reduced need for training data) are available without performance

  4. Medial temporal lobe activity for recognition of recent and remote famous names: an event-related fMRI study.

    PubMed

    Douville, Kelli; Woodard, John L; Seidenberg, Michael; Miller, Sarah K; Leveroni, Catherine L; Nielson, Kristy A; Franczak, Malgorzata; Antuono, Piero; Rao, Stephen M

    2005-01-01

    Previous neuroimaging studies examining recognition of famous faces have identified activation of an extensive bilateral neural network [Gorno Tempini, M. L., Price, C. J., Josephs, O., Vandenberghe, R., Cappa, S. F., Kapur, N. et al. (1998). The neural systems sustaining face and proper-name processing. Brain, 121, 2103-2118], including the medial temporal lobe (MTL) and specifically the hippocampal complex [Haist, F., Bowden, G. J., & Mao, H. (2001). Consolidation of human memory over decades revealed by functional magnetic resonance imaging. Nature Neuroscience, 4, 1139-1145; Leveroni, C. L., Seidenberg, M., Mayer, A. R., Mead, L. A., Binder, J. R., & Rao, S. M. (2000). Neural systems underlying the recognition of familiar and newly learned faces. Journal of Neuroscience, 20, 878-886]. One model of hippocampal functioning in autobiographical, episodic memory retrieval argues that the hippocampal complex remains active in retrieval tasks regardless of time or age of memory (multiple trace theory, MTT), whereas another proposal posits that the hippocampal complex plays a time-limited role in retrieval of autobiographical memories. The current event-related fMRI study focused on the medial temporal lobe and its response to recognition judgments of famous names from two distinct time epochs (1990s and 1950s) in 15 right-handed healthy older adults (mean age=70 years). A pilot study with an independent sample of young and older subjects ensured that the stimuli were representative of a recent and remote time period. Increased MR signal activity was observed on a bilateral basis for both the hippocampus and parahippocampal gyrus (PHG) during recognition of familiar names from both the recent and remote time periods when compared to non-famous names. However, the impulse response functions in the right hippocampus and right PHG demonstrated a differential response to stimuli from different time epochs, with the 1990s names showing the greatest MR signal intensity

  5. Beta-band activity in auditory pathways reflects speech localization and recognition in bilateral cochlear implant users.

    PubMed

    Senkowski, Daniel; Pomper, Ulrich; Fitzner, Inga; Engel, Andreas Karl; Kral, Andrej

    2014-07-01

    In normal-hearing listeners, localization of auditory speech involves stimulus processing in the postero-dorsal pathway of the auditory system. In quiet environments, bilateral cochlear implant (CI) users show high speech recognition performance, but localization of auditory speech is poor, especially when discriminating stimuli from the same hemifield. Whether this difficulty relates to the inability of the auditory system to translate binaural electrical cues into neural signals, or to a functional reorganization of auditory cortical pathways following long periods of binaural deprivation is unknown. In this electroencephalography study, we examined the processing of auditory syllables in postlingually deaf adults with bilateral CIs and in normal-hearing adults. Participants were instructed to either recognize ("recognition" task) or localize ("localization" task) the syllables. The analysis focused on event-related potentials and oscillatory brain responses. N1 amplitudes in CI users were larger in the localization compared with recognition task, suggesting an enhanced stimulus processing effort in the localization task. Linear beamforming of oscillatory activity in CI users revealed stronger suppression of beta-band activity after 200 ms in the postero-dorsal auditory pathway for the localization compared with the recognition task. In normal-hearing adults, effects for longer latency event-related potentials were found, but no effects were observed for N1 amplitudes or beta-band responses. Our study suggests that difficulties in speech localization in bilateral CI users are not reflected in a functional reorganization of cortical auditory pathways. New signal processing strategies of cochlear devices preserving unambiguous binaural cues may improve auditory localization performance in bilateral CI users.

  6. Efficient localization of synchronous EEG source activities using a modified RAP-MUSIC algorithm.

    PubMed

    Liu, Hesheng; Schimpf, Paul H

    2006-04-01

    Synchronization across different brain regions is suggested to be a possible mechanism for functional integration. Noninvasive analysis of the synchronization among cortical areas is possible if the electrical sources can be estimated by solving the electroencephalography inverse problem. Among various inverse algorithms, spatio-temporal dipole fitting methods such as RAP-MUSIC and R-MUSIC have demonstrated superior ability in the localization of a restricted number of independent sources, and also have the ability to reliably reproduce temporal waveforms. However, these algorithms experience difficulty in reconstructing multiple correlated sources. Accurate reconstruction of correlated brain activities is critical in synchronization analysis. In this study, we modified the well-known inverse algorithm RAP-MUSIC to a multistage process which analyzes the correlation of candidate sources and searches for independent topographies (ITs) among precorrelated groups. Comparative studies were carried out on both simulated data and clinical seizure data. The results demonstrated superior performance with the modified algorithm compared to the original RAP-MUSIC in recovering synchronous sources and localizing the epileptiform activity. The modified RAP-MUSIC algorithm, thus, has potential in neurological applications involving significant synchronous brain activities.

  7. Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

    PubMed Central

    Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

    2016-01-01

    Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works. PMID:27918414

  8. Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform.

    PubMed

    Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

    2016-12-02

    Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

  9. Aquarius geophysical model function and combined active passive algorithm for ocean surface salinity and wind retrieval

    NASA Astrophysics Data System (ADS)

    Yueh, Simon; Tang, Wenqing; Fore, Alexander; Hayashi, Akiko; Song, Yuhe T.; Lagerloef, Gary

    2014-08-01

    This paper describes the updated Combined Active-Passive (CAP) retrieval algorithm for simultaneous retrieval of surface salinity and wind from Aquarius' brightness temperature and radar backscatter. Unlike the algorithm developed by Remote Sensing Systems (RSS), implemented in the Aquarius Data Processing System (ADPS) to produce Aquarius standard products, the Jet Propulsion Laboratory's CAP algorithm does not require monthly climatology SSS maps for the salinity retrieval. Furthermore, the ADPS-RSS algorithm fully uses the National Center for Environmental Predictions (NCEP) wind for data correction, while the CAP algorithm uses the NCEP wind only as a constraint. The major updates to the CAP algorithm include the galactic reflection correction, Faraday rotation, Antenna Pattern Correction, and geophysical model functions of wind or wave impacts. Recognizing the limitation of geometric optics scattering, we improve the modeling of the reflection of galactic radiation; the results are better salinity accuracy and significantly reduced ascending-descending bias. We assess the accuracy of CAP's salinity by comparison with ARGO monthly gridded salinity products provided by the Asia-Pacific Data-Research Center (APDRC) and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). The RMS differences between Aquarius CAP and APDRC's or JAMSTEC's ARGO salinities are less than 0.2 psu for most parts of the ocean, except for the regions in the Intertropical Convergence Zone, near the outflow of major rivers and at high latitudes.

  10. Dual influences of early-life maternal deprivation on histone deacetylase activity and recognition memory in rats.

    PubMed

    Albuquerque Filho, Manoel Osório; de Freitas, Betânia Souza; Garcia, Rebeca Carvalho Lacerda; Crivelaro, Pedro Castilhos de Freitas; Schröder, Nadja; de Lima, Maria Noêmia Martins

    2017-03-06

    Exposure to stress early in life may negatively impact nervous system functioning, including increasing the proneness to learning and memory impairments later in life. Maternal deprivation, a model of early-life stress, hinders memory in adult rats and lessens brain-derived neurotrophic factor (BDNF) levels in the hippocampus in a very heterogeneous way among individuals. The main goal of the present study was to investigate the possible epigenetic modulation underlying recognition memory impairment and reduced BDNF levels in the hippocampus of adult maternally deprived rats. We also evaluated the potential ameliorating properties of the histone deacetylase (HDAC) inhibitor, sodium butyrate, on memory deficits and BDNF changes related to maternal deprivation. Maternally deprived animals were categorized as 'inferior learners' and 'superior learners' according to their performance in object recognition memory task in comparison to controls. Results indicated that HDAC activity was higher in individuals submitted to maternal deprivation with the worst cognitive performance (inferior learners). Acute administration of sodium butyrate increased histone H3 acetylation and BDNF levels, and restored recognition memory in maternally deprived animals with the worst cognitive performance. Moreover, we also showed that there is a positive correlation between BDNF levels and memory performance. Taken together, the results indicated that HDAC inhibitors could be considered as a possible therapeutic agent to improve cognitive performance in inferior learners. Further studies need to be conducted for a better comprehension of the mechanisms related to persistent alterations observed in adult life induced by early stressful circumstances and those leading to resilience.

  11. Does viotin activate violin more than viocin? On the use of visual cues during visual-word recognition.

    PubMed

    Perea, Manuel; Panadero, Victoria

    2014-01-01

    The vast majority of neural and computational models of visual-word recognition assume that lexical access is achieved via the activation of abstract letter identities. Thus, a word's overall shape should play no role in this process. In the present lexical decision experiment, we compared word-like pseudowords like viotín (same shape as its base word: violín) vs. viocín (different shape) in mature (college-aged skilled readers), immature (normally reading children), and immature/impaired (young readers with developmental dyslexia) word-recognition systems. Results revealed similar response times (and error rates) to consistent-shape and inconsistent-shape pseudowords for both adult skilled readers and normally reading children - this is consistent with current models of visual-word recognition. In contrast, young readers with developmental dyslexia made significantly more errors to viotín-like pseudowords than to viocín-like pseudowords. Thus, unlike normally reading children, young readers with developmental dyslexia are sensitive to a word's visual cues, presumably because of poor letter representations.

  12. Dopamine D1 receptor activity modulates object recognition memory consolidation in the perirhinal cortex but not in the hippocampus.

    PubMed

    Balderas, Israela; Moreno-Castilla, Perla; Bermudez-Rattoni, Federico

    2013-10-01

    It has been proposed that distributed neuronal networks in the medial temporal lobe process different characteristics of a recognition event; the hippocampus has been associated with contextual recollection while the perirhinal cortex has been linked with familiarity. Here we show that D1 dopamine receptor activity in these two structures participates differentially in object recognition memory consolidation. The D1 receptor antagonist SCH23390 was infused bilaterally 15 min before a 5 min sample phase in either rats' perirhinal cortex or dorsal hippocampus, and they were tested 90 min for short-term memory or 24 h later for long-term memory. SCH23390 impaired long-term memory when infused in the perirhinal cortex but not when infused in the hippocampus. Conversely, when the D1 receptor agonist SKF38393 was infused 10 min before a 3 min sample phase in the perirhinal cortex, long-term memory was enhanced, however, this was not observed when the D1 agonist was infused in the hippocampus. Short-term memory was spared when SCH23390 or SKF38393 were infused in the perirhinal cortex or the dorsal hippocampus suggesting that acquisition was unaffected. These results suggest that dopaminergic transmission in these medial temporal lobe structures have a differential involvement in object recognition memory consolidation.

  13. Fingerprints of Learned Object Recognition Seen in the fMRI Activation Patterns of Lateral Occipital Complex.

    PubMed

    Roth, Zvi N; Zohary, Ehud

    2015-09-01

    One feature of visual processing in the ventral stream is that cortical responses gradually depart from the physical aspects of the visual stimulus and become correlated with perceptual experience. Thus, unlike early retinotopic areas, the responses in the object-related lateral occipital complex (LOC) are typically immune to parameter changes (e.g., contrast, location, etc.) when these do not affect recognition. Here, we use a complementary approach to highlight changes in brain activity following a shift in the perceptual state (in the absence of any alteration in the physical image). Specifically, we focus on LOC and early visual cortex (EVC) and compare their functional magnetic resonance imaging (fMRI) responses to degraded object images, before and after fast perceptual learning that renders initially unrecognized objects identifiable. Using 3 complementary analyses, we find that, in LOC, unlike EVC, learned recognition is associated with a change in the multivoxel response pattern to degraded object images, such that the response becomes significantly more correlated with that evoked by the intact version of the same image. This provides further evidence that the coding in LOC reflects the recognition of visual objects.

  14. Raloxifene treatment enhances brain activation during recognition of familiar items: a pharmacological fMRI study in healthy elderly males.

    PubMed

    Goekoop, Rutger; Barkhof, Frederik; Duschek, Erik J J; Netelenbos, Coen; Knol, Dirk L; Scheltens, Philip; Rombouts, Serge A R B

    2006-07-01

    Raloxifene is a selective estrogen receptor modulator that may delay the onset of mild cognitive impairment in elderly women. Effects of raloxifene treatment on mental performance in males remain to be investigated. In a previous functional magnetic resonance imaging (fMRI) study, we showed that raloxifene treatment enhanced brain activation in elderly males during encoding of new information (faces) into memory. The current study used fMRI in the same group of subjects to screen for effects of raloxifene treatment on brain function during face recognition. Healthy elderly males (n=28; mean age 63.6 years, SD 2.4) were scanned at baseline and after 3 months of treatment with either raloxifene 120 mg (n=14) or placebo (n=14) in a randomized, double-blind, placebo-controlled study design. Functional data were analyzed in an event-related fashion with respect to correct hits and correct rejections using FSL software. Performance data were analyzed with respect to recognition accuracy, latency, and response bias. Functional effects of treatment were found on brain activation related to correct hits only. When compared to placebo treatment, raloxifene treatment enhanced brain activation in the left posterior parahippocampal area (Z=3.9) and right inferior prefrontal cortex (Z=3.5). Recognition accuracy scores remained stable in the raloxifene group, whereas the placebo group showed a small but significant decrease in accuracy scores (p=0.02). No significant effects were found on response bias or latency. In conclusion, raloxifene treatment affects brain function during memory performance in a way that may reflect increased arousal during initial encoding, with downstream effects on brain function during retrieval of information. Behaviorally, such neurofunctional effects may actively block decreased memory performance as a result of context-dependency. The validity of these predictions can be tested in large-scale clinical trials.

  15. Pathogen recognition by Toll-like receptor 2 activates Weibel-Palade body exocytosis in human aortic endothelial cells.

    PubMed

    Into, Takeshi; Kanno, Yosuke; Dohkan, Jun-ichi; Nakashima, Misako; Inomata, Megumi; Shibata, Ken-ichiro; Lowenstein, Charles J; Matsushita, Kenji

    2007-03-16

    The endothelial cell-specific granule Weibel-Palade body releases vasoactive substances capable of modulating vascular inflammation. Although innate recognition of pathogens by Toll-like receptors (TLRs) is thought to play a crucial role in promotion of inflammatory responses, the molecular basis for early-phase responses of endothelial cells to bacterial pathogens has not fully been understood. We here report that human aortic endothelial cells respond to bacterial lipoteichoic acid (LTA) and synthetic bacterial lipopeptides, but not lipopolysaccharide or peptidoglycan, to induce Weibel-Palade body exocytosis, accompanied by release or externalization of the storage components von Willebrand factor and P-selectin. LTA could activate rapid Weibel-Palade body exocytosis through a TLR2- and MyD88-dependent mechanism without de novo protein synthesis. This process was at least mediated through MyD88-dependent phosphorylation and activation of phospholipase Cgamma. Moreover, LTA activated interleukin-1 receptor-associated kinase-1-dependent delayed exocytosis with de novo protein synthesis and phospholipase Cgamma-dependent activation of the NF-kappaB pathway. Increased TLR2 expression by transfection or interferon-gamma treatment increased TLR2-mediated Weibel-Palade body exocytosis, whereas reduced TLR2 expression under laminar flow decreased the response. Thus, we propose a novel role for TLR2 in induction of a primary proinflammatory event in aortic endothelial cells through Weibel-Palade body exocytosis, which may be an important step for linking innate recognition of bacterial pathogens to vascular inflammation.

  16. Accuracy of Optimized Branched Algorithms to Assess Activity-Specific PAEE

    PubMed Central

    Edwards, Andy G.; Hill, James O.; Byrnes, William C.; Browning, Raymond C.

    2009-01-01

    PURPOSE To assess the activity-specific accuracy achievable by branched algorithm (BA) analysis of simulated daily-living physical activity energy expenditure (PAEE) within a sedentary population. METHODS Sedentary men (n=8) and women (n=8) first performed a treadmill calibration protocol, during which heart rate (HR), accelerometry (ACC), and PAEE were measured in 1-minute epochs. From these data, HR-PAEE, and ACC-PAEE regressions were constructed and used in each of six analytic models to predict PAEE from ACC and HR data collected during a subsequent simulated daily-living protocol. Criterion PAEE was measured during both protocols via indirect calorimetry. The accuracy achieved by each model was assessed by the root mean square of the difference between model-predicted daily–living PAEE and the criterion daily-living PAEE (expressed here as % of mean daily living PAEE). RESULTS Across the range of activities an unconstrained post hoc optimized branched algorithm best predicted criterion PAEE. Estimates using individual calibration were generally more accurate than those using group calibration (14 vs. 16 % error, respectively). These analyses also performed well within each of the six daily-living activities, but systematic errors appeared for several of those activities, which may be explained by an inability of the algorithm to simultaneously accommodate a heterogeneous range of activities. Analyses of between mean square error by subject and activity suggest that optimization involving minimization of RMS for total daily-living PAEE is associated with decreased error between subjects but increased error between activities. CONCLUSION The performance of post hoc optimized branched algorithms may be limited by heterogeneity in the daily-living activities being performed. PMID:19952842

  17. Extended target recognition in cognitive radar networks.

    PubMed

    Wei, Yimin; Meng, Huadong; Liu, Yimin; Wang, Xiqin

    2010-01-01

    We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.

  18. Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms

    PubMed Central

    Ellis, Katherine; Godbole, Suneeta; Marshall, Simon; Lanckriet, Gert; Staudenmayer, John; Kerr, Jacqueline

    2014-01-01

    Background: Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data. Methods: We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time. Results: The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%. Conclusion: Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel. PMID:24795875

  19. Two-stage neural algorithm for defect detection and characterization uses an active thermography

    NASA Astrophysics Data System (ADS)

    Dudzik, Sebastian

    2015-07-01

    In the paper a two-stage neural algorithm for defect detection and characterization is presented. In order to estimate the defect depth two neural networks trained on data obtained using an active thermography were employed. The first stage of the algorithm is developed to detect the defect by a classification neural network. Then the defects depth is estimated using a regressive neural network. In this work the results of experimental investigations and simulations are shown. Further, the sensitivity analysis of the presented algorithm was conducted and the impacts of emissivity error and the ambient temperature error on the depth estimation errors were studied. The results were obtained using a test sample made of material with a low thermal diffusivity.

  20. Differential AMP-activated Protein Kinase (AMPK) Recognition Mechanism of Ca2+/Calmodulin-dependent Protein Kinase Kinase Isoforms.

    PubMed

    Fujiwara, Yuya; Kawaguchi, Yoshinori; Fujimoto, Tomohito; Kanayama, Naoki; Magari, Masaki; Tokumitsu, Hiroshi

    2016-06-24

    Ca(2+)/calmodulin-dependent protein kinase kinase β (CaMKKβ) is a known activating kinase for AMP-activated protein kinase (AMPK). In vitro, CaMKKβ phosphorylates Thr(172) in the AMPKα subunit more efficiently than CaMKKα, with a lower Km (∼2 μm) for AMPK, whereas the CaMKIα phosphorylation efficiencies by both CaMKKs are indistinguishable. Here we found that subdomain VIII of CaMKK is involved in the discrimination of AMPK as a native substrate by measuring the activities of various CaMKKα/CaMKKβ chimera mutants. Site-directed mutagenesis analysis revealed that Leu(358) in CaMKKβ/Ile(322) in CaMKKα confer, at least in part, a distinct recognition of AMPK but not of CaMKIα.

  1. Document Form and Character Recognition using SVM

    NASA Astrophysics Data System (ADS)

    Park, Sang-Sung; Shin, Young-Geun; Jung, Won-Kyo; Ahn, Dong-Kyu; Jang, Dong-Sik

    2009-08-01

    Because of development of computer and information communication, EDI (Electronic Data Interchange) has been developing. There is OCR (Optical Character Recognition) of Pattern recognition technology for EDI. OCR contributed to changing many manual in the past into automation. But for the more perfect database of document, much manual is needed for excluding unnecessary recognition. To resolve this problem, we propose document form based character recognition method in this study. Proposed method is divided into document form recognition part and character recognition part. Especially, in character recognition, change character into binarization by using SVM algorithm and extract more correct feature value.

  2. Efficient parallel implementation of active appearance model fitting algorithm on GPU.

    PubMed

    Wang, Jinwei; Ma, Xirong; Zhu, Yuanping; Sun, Jizhou

    2014-01-01

    The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia's GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.

  3. How does Arabic orthographic connectivity modulate brain activity during visual word recognition: an ERP study.

    PubMed

    Taha, Haitham; Ibrahim, Raphiq; Khateb, Asaid

    2013-04-01

    One of the unique features of the Arabic orthography that differentiates it from many other alphabetical ones is the fact that most letters connect obligatorily to each other. Hence, these letters change their forms according to the location in the word (i.e. beginning, middle, or end), leading to the suggestion that connectivity adds a visual load which negatively impacts reading in Arabic. In this study, we investigated the effects of the orthographic connectivity on the time course of early brain electric responses during the visual word recognition. For this purpose, we collected event-related potentials (ERPs) from adult skilled readers while performing a lexical decision task using fully connected (Cw), partially connected and non-connected words (NCw). Reaction times variance was higher and accuracy was lower in NCw compared to Cw words. ERPs analysis revealed significant amplitude and latency differences between Cw and NCw at posterior electrodes during the N170 component which implied the temporo-occipital areas. Our findings show that instead of slowing down reading, orthographic connectivity in Arabic skilled readers seems to impact positively the reading process already during the early stages of word recognition. These results are discussed in relation to previous observations in the literature.

  4. The Effect of Involuntary Motor Activity on Myoelectric Pattern Recognition: A Case Study with Chronic Stroke Patients

    PubMed Central

    Zhang, Xu; Li, Yun; Chen, Xiang; Li, Guanglin; Rymer, William Zev; Zhou, Ping

    2013-01-01

    This study investigates the effect of involuntary motor activity of paretic-spastic muscles on classification of surface electromyography (EMG) signals. Two data collection sessions were designed for 8 stroke subjects to voluntarily perform 11 functional movements using their affected forearm and hand at a relatively slow and fast speed. For each stroke subject, the degree of involuntary motor activity present in voluntary surface EMG recordings was qualitatively described from such slow and fast experimental protocols. Myoelectric pattern recognition analysis was performed using different combinations of voluntary surface EMG data recorded from slow and fast sessions. Across all tested stroke subjects, our results revealed that when involuntary surface EMG was absent or present in both training and testing datasets, high accuracies (> 96%, > 98%, respectively, averaged over all the subjects) can be achieved in classification of different movements using surface EMG signals from paretic muscles. When involuntary surface EMG was solely involved in either training or testing datasets, the classification accuracies were dramatically reduced (< 89%, < 85%, respectively). However, if both training and testing datasets contained EMG signals with presence and absence of involuntary EMG interference, high accuracies were still achieved (> 97%). The findings of this study can be used to guide appropriate design and implementation of myoelectric pattern recognition based systems or devices toward promoting robot-aided therapy for stroke rehabilitation. PMID:23860192

  5. Effects of Active and Passive Hearing Protection Devices on Sound Source Localization, Speech Recognition, and Tone Detection

    PubMed Central

    Brown, Andrew D.; Beemer, Brianne T.; Greene, Nathaniel T.; Argo, Theodore; Meegan, G. Douglas; Tollin, Daniel J.

    2015-01-01

    Hearing protection devices (HPDs) such as earplugs offer to mitigate noise exposure and reduce the incidence of hearing loss among persons frequently exposed to intense sound. However, distortions of spatial acoustic information and reduced audibility of low-intensity sounds caused by many existing HPDs can make their use untenable in high-risk (e.g., military or law enforcement) environments where auditory situational awareness is imperative. Here we assessed (1) sound source localization accuracy using a head-turning paradigm, (2) speech-in-noise recognition using a modified version of the QuickSIN test, and (3) tone detection thresholds using a two-alternative forced-choice task. Subjects were 10 young normal-hearing males. Four different HPDs were tested (two active, two passive), including two new and previously untested devices. Relative to unoccluded (control) performance, all tested HPDs significantly degraded performance across tasks, although one active HPD slightly improved high-frequency tone detection thresholds and did not degrade speech recognition. Behavioral data were examined with respect to head-related transfer functions measured using a binaural manikin with and without tested HPDs in place. Data reinforce previous reports that HPDs significantly compromise a variety of auditory perceptual facilities, particularly sound localization due to distortions of high-frequency spectral cues that are important for the avoidance of front-back confusions. PMID:26313145

  6. The effect of involuntary motor activity on myoelectric pattern recognition: a case study with chronic stroke patients

    NASA Astrophysics Data System (ADS)

    Zhang, Xu; Li, Yun; Chen, Xiang; Li, Guanglin; Zev Rymer, William; Zhou, Ping

    2013-08-01

    Objective. This study investigates the effect of the involuntary motor activity of paretic-spastic muscles on the classification of surface electromyography (EMG) signals. Approach. Two data collection sessions were designed for 8 stroke subjects to voluntarily perform 11 functional movements using their affected forearm and hand at relatively slow and fast speeds. For each stroke subject, the degree of involuntary motor activity present in the voluntary surface EMG recordings was qualitatively described from such slow and fast experimental protocols. Myoelectric pattern recognition analysis was performed using different combinations of voluntary surface EMG data recorded from the slow and fast sessions. Main results. Across all tested stroke subjects, our results revealed that when involuntary surface EMG is absent or present in both the training and testing datasets, high accuracies (>96%, >98%, respectively, averaged over all the subjects) can be achieved in the classification of different movements using surface EMG signals from paretic muscles. When involuntary surface EMG was solely involved in either the training or testing datasets, the classification accuracies were dramatically reduced (<89%, <85%, respectively). However, if both the training and testing datasets contained EMG signals with the presence and absence of involuntary EMG interference, high accuracies were still achieved (>97%). Significance. The findings of this study can be used to guide the appropriate design and implementation of myoelectric pattern recognition based systems or devices toward promoting robot-aided therapy for stroke rehabilitation.

  7. Communication: Active space decomposition with multiple sites: Density matrix renormalization group algorithm

    SciTech Connect

    Parker, Shane M.; Shiozaki, Toru

    2014-12-07

    We extend the active space decomposition method, recently developed by us, to more than two active sites using the density matrix renormalization group algorithm. The fragment wave functions are described by complete or restricted active-space wave functions. Numerical results are shown on a benzene pentamer and a perylene diimide trimer. It is found that the truncation errors in our method decrease almost exponentially with respect to the number of renormalization states M, allowing for numerically exact calculations (to a few μE{sub h} or less) with M = 128 in both cases. This rapid convergence is because the renormalization steps are used only for the interfragment electron correlation.

  8. Recognition of protein-linked glycans as a determinant of peptidase activity.

    PubMed

    Noach, Ilit; Ficko-Blean, Elizabeth; Pluvinage, Benjamin; Stuart, Christopher; Jenkins, Meredith L; Brochu, Denis; Buenbrazo, Nakita; Wakarchuk, Warren; Burke, John E; Gilbert, Michel; Boraston, Alisdair B

    2017-01-31

    The vast majority of proteins are posttranslationally altered, with the addition of covalently linked sugars (glycosylation) being one of the most abundant modifications. However, despite the hydrolysis of protein peptide bonds by peptidases being a process essential to all life on Earth, the fundamental details of how peptidases accommodate posttranslational modifications, including glycosylation, has not been addressed. Through biochemical analyses and X-ray crystallographic structures we show that to hydrolyze their substrates, three structurally related metallopeptidases require the specific recognition of O-linked glycan modifications via carbohydrate-specific subsites immediately adjacent to their peptidase catalytic machinery. The three peptidases showed selectivity for different glycans, revealing protein-specific adaptations to particular glycan modifications, yet always cleaved the peptide bond immediately preceding the glycosylated residue. This insight builds upon the paradigm of how peptidases recognize substrates and provides a molecular understanding of glycoprotein degradation.

  9. Tuning sensitivity of CAR to EGFR density limits recognition of normal tissue while maintaining potent anti-tumor activity

    PubMed Central

    Caruso, Hillary G.; Hurton, Lenka V.; Najjar, Amer; Rushworth, David; Ang, Sonny; Olivares, Simon; Mi, Tiejuan; Switzer, Kirsten; Singh, Harjeet; Huls, Helen; Lee, Dean A.; Heimberger, Amy B.; Champlin, Richard E.; Cooper, Laurence J. N.

    2015-01-01

    Many tumors over express tumor-associated antigens relative to normal tissue, such as epidermal growth factor receptor (EGFR). This limits targeting by human T cells modified to express chimeric antigen receptors (CARs) due to potential for deleterious recognition of normal cells. We sought to generate CAR+ T cells capable of distinguishing malignant from normal cells based on the disparate density of EGFR expression by generating two CARs from monoclonal antibodies which differ in affinity. T cells with low affinity Nimo-CAR selectively targeted cells over-expressing EGFR, but exhibited diminished effector function as the density of EGFR decreased. In contrast, the activation of T cells bearing high affinity Cetux-CAR was not impacted by the density of EGFR. In summary, we describe the generation of CARs able to tune T-cell activity to the level of EGFR expression in which a CAR with reduced affinity enabled T cells to distinguish malignant from non-malignant cells. PMID:26330164

  10. Neural mechanisms of infant learning: differences in frontal theta activity during object exploration modulate subsequent object recognition

    PubMed Central

    Begus, Katarina; Southgate, Victoria; Gliga, Teodora

    2015-01-01

    Investigating learning mechanisms in infancy relies largely on behavioural measures like visual attention, which often fail to predict whether stimuli would be encoded successfully. This study explored EEG activity in the theta frequency band, previously shown to predict successful learning in adults, to directly study infants' cognitive engagement, beyond visual attention. We tested 11-month-old infants (N = 23) and demonstrated that differences in frontal theta-band oscillations, recorded during infants' object exploration, predicted differential subsequent recognition of these objects in a preferential-looking test. Given that theta activity is modulated by motivation to learn in adults, these findings set the ground for future investigation into the drivers of infant learning. PMID:26018832

  11. Character Recognition Using Genetically Trained Neural Networks

    SciTech Connect

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the amount of

  12. Model and algorithmic framework for detection and correction of cognitive errors.

    PubMed

    Feki, Mohamed Ali; Biswas, Jit; Tolstikov, Andrei

    2009-01-01

    This paper outlines an approach that we are taking for elder-care applications in the smart home, involving cognitive errors and their compensation. Our approach involves high level modeling of daily activities of the elderly by breaking down these activities into smaller units, which can then be automatically recognized at a low level by collections of sensors placed in the homes of the elderly. This separation allows us to employ plan recognition algorithms and systems at a high level, while developing stand-alone activity recognition algorithms and systems at a low level. It also allows the mixing and matching of multi-modality sensors of various kinds that go to support the same high level requirement. Currently our plan recognition algorithms are still at a conceptual stage, whereas a number of low level activity recognition algorithms and systems have been developed. Herein we present our model for plan recognition, providing a brief survey of the background literature. We also present some concrete results that we have achieved for activity recognition, emphasizing how these results are incorporated into the overall plan recognition system.

  13. Activity level classification algorithm using SHIMMER™ wearable sensors for individuals with rheumatoid arthritis.

    PubMed

    Fortune, Emma; Tierney, Marie; Scanaill, Cliodhna Ni; Bourke, Ala; Kennedy, Norelee; Nelson, John

    2011-01-01

    In rheumatoid arthritis (RA) it is believed that symptoms associated with the progression of the disease result in a reduction in the physical activity level of the patient. One of the key flaws of the research surrounding this hypothesis to date is the use of non-validated physical activity outcomes measures. In this study, an algorithm to estimate physical activity levels in patients as they perform a simulated protocol of typical activities of daily living using SHIMMER kinematic sensors, incorporating tri-axial gyroscopes and accelerometers, is proposed. The results are validated against simultaneously recorded energy expenditure data and the defined activity protocol and demonstrate that SHIMMER can be used to accurately estimate physical activity levels in RA populations.

  14. On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition

    PubMed Central

    Banos, Oresti; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

    2012-01-01

    The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise) imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered. PMID:22969386

  15. Ear recognition based on Gabor features and KFDA.

    PubMed

    Yuan, Li; Mu, Zhichun

    2014-01-01

    We propose an ear recognition system based on 2D ear images which includes three stages: ear enrollment, feature extraction, and ear recognition. Ear enrollment includes ear detection and ear normalization. The ear detection approach based on improved Adaboost algorithm detects the ear part under complex background using two steps: offline cascaded classifier training and online ear detection. Then Active Shape Model is applied to segment the ear part and normalize all the ear images to the same size. For its eminent characteristics in spatial local feature extraction and orientation selection, Gabor filter based ear feature extraction is presented in this paper. Kernel Fisher Discriminant Analysis (KFDA) is then applied for dimension reduction of the high-dimensional Gabor features. Finally distance based classifier is applied for ear recognition. Experimental results of ear recognition on two datasets (USTB and UND datasets) and the performance of the ear authentication system show the feasibility and effectiveness of the proposed approach.

  16. Infrared active polarimetric imaging system controlled by image segmentation algorithms: application to decamouflage

    NASA Astrophysics Data System (ADS)

    Vannier, Nicolas; Goudail, François; Plassart, Corentin; Boffety, Matthieu; Feneyrou, Patrick; Leviandier, Luc; Galland, Frédéric; Bertaux, Nicolas

    2016-05-01

    We describe an active polarimetric imager with laser illumination at 1.5 µm that can generate any illumination and analysis polarization state on the Poincar sphere. Thanks to its full polarization agility and to image analysis of the scene with an ultrafast active-contour based segmentation algorithm, it can perform adaptive polarimetric contrast optimization. We demonstrate the capacity of this imager to detect manufactured objects in different types of environments for such applications as decamouflage and hazardous object detection. We compare two imaging modes having different number of polarimetric degrees of freedom and underline the characteristics that a polarimetric imager aimed at this type of applications should possess.

  17. The parietal memory network activates similarly for true and associative false recognition elicited via the DRM procedure.

    PubMed

    McDermott, Kathleen B; Gilmore, Adrian W; Nelson, Steven M; Watson, Jason M; Ojemann, Jeffrey G

    2017-02-01

    Neuroimaging investigations of human memory encoding and retrieval have revealed that multiple regions of parietal cortex contribute to memory. Recently, a sparse network of regions within parietal cortex has been identified using resting state functional connectivity (MRI techniques). The regions within this network exhibit consistent task-related responses during memory formation and retrieval, leading to its being called the parietal memory network (PMN). Among its signature patterns are: deactivation during initial experience with an item (e.g., encoding); activation during subsequent repetitions (e.g., at retrieval); greater activation for successfully retrieved familiar words than novel words (e.g., hits relative to correctly-rejected lures). The question of interest here is whether novel words that are subjectively experienced as having been recently studied would elicit PMN activation similar to that of hits. That is, we compared old items correctly recognized to two types of novel items on a recognition test: those correctly identified as new and those incorrectly labeled as old due to their strong associative relation to the studied words (in the DRM false memory protocol). Subjective oldness plays a strong role in driving activation, as hits and false alarms activated similarly (and greater than correctly-rejected lures).

  18. A robust technique for semantic annotation of group activities based on recognition of extracted features in video streams

    NASA Astrophysics Data System (ADS)

    Elangovan, Vinayak; Shirkhodaie, Amir

    2013-05-01

    Recognition and understanding of group activities can significantly improve situational awareness in Surveillance Systems. To maximize reliability and effectiveness of Persistent Surveillance Systems, annotations of sequential images gathered from video streams (i.e. imagery and acoustic features) must be fused together to generate semantic messages describing group activities (GA). To facilitate efficient fusion of extracted features from any physical sensors a common structure will suffice to ease integration of processed data into new comprehension. In this paper, we describe a framework for extraction and management of pertinent features/attributes vital for annotation of group activities reliably. A robust technique is proposed for fusion of generated events and entities' attributes from video streams. A modified Transducer Markup Language (TML) is introduced for semantic annotation of events and entities attributes. By aggregation of multi-attribute TML messages, we have demonstrated that salient group activities can be spatiotemporal can be reliable annotated. This paper discusses our experimental results; our analysis of a set of simulated group activities performed under different contexts and demonstrates the efficiency and effectiveness of the proposed modified TML data structure which facilitates seamless fusion of extracted information from video streams.

  19. Sparsity-motivated automatic target recognition.

    PubMed

    Patel, Vishal M; Nasrabadi, Nasser M; Chellappa, Rama

    2011-04-01

    We present an automatic target recognition algorithm using the recently developed theory of sparse representations and compressive sensing. We show how sparsity can be helpful for efficient utilization of data for target recognition. We verify the efficacy of the proposed algorithm in terms of the recognition rate and confusion matrices on the well known Comanche (Boeing-Sikorsky, USA) forward-looking IR data set consisting of ten different military targets at different orientations.

  20. Dendritic cells and parasites: from recognition and activation to immune response instruction.

    PubMed

    Motran, Claudia Cristina; Ambrosio, Laura Fernanda; Volpini, Ximena; Celias, Daiana Pamela; Cervi, Laura

    2017-02-01

    The effective defense against parasite infections requires the ability to mount an appropriate and controlled specific immune response able to eradicate the invading pathogen while limiting the collateral damage to self-tissues. Dendritic cells are key elements for the development of immunity against parasites; they control the responses required to eliminate these pathogens while maintaining host homeostasis. Ligation of dendritic cell pattern recognition receptors by pathogen-associated molecular pattern present in the parasites initiates signaling pathways that lead to the production of surface and secreted proteins that are required, together with the antigen, to induce an appropriate and timely regulated immune response. There is evidence showing that parasites can influence and regulate dendritic cell functions in order to promote a more permissive environment for their survival. In this review, we will focus on new insights about the ability of protozoan and helminth parasites or their products to modify dendritic cell function and discuss how this interaction is crucial in shaping the host response.

  1. Learning to read shapes the activation of neural lexical representations in the speech recognition pathway.

    PubMed

    Schild, Ulrike; Röder, Brigitte; Friedrich, Claudia K

    2011-04-01

    It has been demonstrated that written and spoken language processing are tightly linked. Here we focus on the development of this relationship at the time children start reading and writing. We hypothesize that the newly acquired knowledge about graphemes shapes lexical access in neural spoken word recognition. A group of preliterate children (six years old) and two groups of beginning readers (six and eight years old) were tested in a spoken word identification task. Using word onset priming we compared behavioural and neural facilitation for target words in identical prime-target pairs (e.g., mon-monster) and in prime target pairs that varied in the first speech sound (e.g., non-monster, Variation condition). In both groups of beginning readers priming was less effective in the Variation condition than in the Identity condition. This was indexed by less behavioural facilitation and enhanced P350 amplitudes in the event related potentials (ERPs). In the group of preliterate children, by contrast, both conditions did not differ. Together these results reveal that lexical access in beginning readers is based on more acoustic detail than lexical access in preliterate children. The results are discussed in the light of bidirectional speech and print interactions in readers.

  2. Active and passive computed tomography algorithm with a constrained conjugate gradient solution

    SciTech Connect

    Goodman, D.; Jackson, J. A.; Martz, H. E.; Roberson, G. P.

    1998-10-01

    An active and passive computed tomographic technique (A&PCT) has been developed at the Lawrence Livermore National Laboratory (LLNL). The technique uses an external radioactive source and active tomography to map the attenuation within a waste drum as a function of mono-energetic gamma-ray energy. Passive tomography is used to localize and identify specific radioactive waste within the same container. The passive data is corrected for attenuation using the active data and this yields a quantitative assay of drum activity. A&PCT involves the development of a detailed system model that combines the data from the active scans with the geometry of the imaging system. Using the system model, iterative optimization techniques are used to reconstruct the image from the passive data. Requirements for high throughput yield measured emission levels in waste barrels that are too low to apply optimization techniques involving the usual Gaussian statistics. In this situation a Poisson distribution, typically used for cases with low counting statistics, is used to create an effective maximum likelihood estimation function. An optimization algorithm, Constrained Conjugate Gradient (CCG), is used to determine a solution for A&PCT quantitative assay. CCG, which was developed at LLNL, has proven to be an efficient and effective optimization method to solve limited-data problems. A detailed explanation of the algorithms used in developing the model and optimization codes is given.

  3. A novel fair active queue management algorithm based on traffic delay jitter

    NASA Astrophysics Data System (ADS)

    Wang, Xue-Shun; Yu, Shao-Hua; Dai, Jin-You; Luo, Ting

    2009-11-01

    In order to guarantee the quantity of data traffic delivered in the network, congestion control strategy is adopted. According to the study of many active queue management (AQM) algorithms, this paper proposes a novel active queue management algorithm named JFED. JFED can stabilize queue length at a desirable level by adjusting output traffic rate and adopting a reasonable calculation of packet drop probability based on buffer queue length and traffic jitter; and it support burst packet traffic through the packet delay jitter, so that it can traffic flow medium data. JFED impose effective punishment upon non-responsible flow with a full stateless method. To verify the performance of JFED, it is implemented in NS2 and is compared with RED and CHOKe with respect to different performance metrics. Simulation results show that the proposed JFED algorithm outperforms RED and CHOKe in stabilizing instantaneous queue length and in fairness. It is also shown that JFED enables the link capacity to be fully utilized by stabilizing the queue length at a desirable level, while not incurring excessive packet loss ratio.

  4. Comparing wearable devices with wet and textile electrodes for activity recognition.

    PubMed

    Lokare, Namita; Gonzalez, Laura; Lobaton, Edgar

    2016-08-01

    This paper explores the idea of identifying activities from muscle activation which is captured by wearable ECG recording devices that use wet and textile electrodes. Most of the devices available today filter out the high frequency components to retain only the signal related to an ECG. We explain how the high frequency components that correspond to muscle activation can be extracted from the recorded signal and can be used to identify activities. We notice that is possible to obtain good performance for both the wet and dry electrodes. However, we observed that signals from the dry textile electrodes introduce less artifacts associated with muscle activation.

  5. Hippocampal noradrenergic activation is necessary for object recognition memory consolidation and can promote BDNF increase and memory persistence.

    PubMed

    Mello-Carpes, Pâmela B; da Silva de Vargas, Liane; Gayer, Mateus Cristofari; Roehrs, Rafael; Izquierdo, Ivan

    2016-01-01

    Previously we showed that activation of the Nucleus of the Solitary Tract (NTS)-Nucleus Paragigantocellularis (PGi)-Locus coeruleus (LC) pathway, which theoretically culminates with norepinephrine (NE) release in dorsal hippocampus (CA1 region) and basolateral amygdala (BLA) is necessary for the consolidation of object recognition (OR) memory. Here we show that, while the microinjection of the beta-noradrenergic receptor blocker timolol into CA1 impairs OR memory consolidation, the microinjection of norepinephrine (NE) promotes the persistence of this type of memory. Further, we show that OR consolidation is attended by an increase of norepinephrine (NE) levels and of the expression of brain derived neurotrophic factor (BDNF) in hippocampus, which are impaired by inactivation of the NTS-PGi-LC pathway by the infusion of muscimol into the NTS.

  6. TLR9-dependent recognition of MCMV by IPC and DC generates coordinated cytokine responses that activate antiviral NK cell function.

    PubMed

    Krug, Anne; French, Anthony R; Barchet, Winfried; Fischer, Jens A A; Dzionek, Andrzej; Pingel, Jeanette T; Orihuela, Michael M; Akira, Shizuo; Yokoyama, Wayne M; Colonna, Marco

    2004-07-01

    Natural interferon-producing cells (IPC) respond to viruses by secreting type I interferon (IFN) and interleukin-12 (IL-12). Toll-like receptor (TLR) 9 mediates IPC recognition of some of these viruses in vitro. However, whether TLR9-induced activation of IPC is necessary for an effective antiviral response in vivo is not clear. Here, we demonstrate that IPC and dendritic cells (DC) recognize murine cytomegalovirus (MCMV) through TLR9. TLR9-mediated cytokine secretion promotes viral clearance by NK cells that express the MCMV-specific receptor Ly49H. Although depletion of IPC leads to a drastic reduction of the IFN-alpha response, this allows other cell types to secrete IL-12, ensuring normal IFN-gamma and NK cell responses to MCMV. We conclude that the TLR9/MyD88 pathway mediates antiviral cytokine responses by IPC, DC, and possibly other cell types, which are coordinated to promote effective NK cell function and MCMV clearance.

  7. Reinstatement of pain-related brain activation during the recognition of neutral images previously paired with nociceptive stimuli.

    PubMed

    Forkmann, Katarina; Wiech, Katja; Sommer, Tobias; Bingel, Ulrike

    2015-08-01

    Remembering an event partially reactivates cortical and subcortical brain regions that were engaged during its experience and encoding. Such reinstatement of neuronal activation has been observed in different sensory systems, including the visual, auditory, olfactory, and somatosensory domain. However, so far, this phenomenon of incidental memory has not been explored in the context of pain. In this functional magnetic resonance imaging study, we investigated the neural reinstatement of pain-related and tone-related activations during the recognition of neutral images that had been encoded during (1) painful stimulation, (2) auditory stimulation of comparable unpleasantness, or (3) no additional stimulation. Stimulus-specific reinstatement was tested in 24 healthy male and female participants who performed a visual categorization task (encoding) that was immediately followed by a surprise recognition task. Neural responses were acquired in both sessions. Our data show a partial reinstatement of brain regions frequently associated with pain processing, including the left posterior insula, bilateral putamen, and right operculum, during the presentation of images previously paired with painful heat. This effect was specific to painful stimuli. Moreover, the bilateral ventral striatum showed stronger responses for remembered pain-associated images as compared with tone-associated images, suggesting a higher behavioral relevance of remembering neutral pictures previously paired with pain. Our results support the biological relevance of pain in that only painful but not equally unpleasant auditory stimuli were able to "tag" neutral images during their simultaneous presentation and reactivate pain-related brain regions. Such mechanisms might contribute to the development or maintenance of chronic pain and deserve further investigation in clinical populations.

  8. Activation of G-protein-coupled receptor 30 is sufficient to enhance spatial recognition memory in ovariectomized rats.

    PubMed

    Hawley, Wayne R; Grissom, Elin M; Moody, Nicole M; Dohanich, Gary P; Vasudevan, Nandini

    2014-04-01

    In ovariectomized rats, administration of estradiol, or selective estrogen receptor agonists that activate either the α or β isoforms, have been shown to enhance spatial cognition on a variety of learning and memory tasks, including those that capitalize on the preference of rats to seek out novelty. Although the effects of the putative estrogen G-protein-coupled receptor 30 (GPR30) on hippocampus-based tasks have been reported using food-motivated tasks, the effects of activation of GPR30 receptors on tasks that depend on the preference of rats to seek out spatial novelty remain to be determined. Therefore, the aim of the current study was to determine if short-term treatment of ovariectomized rats with G-1, an agonist for GPR30, would mimic the effects on spatial recognition memory observed following short-term estradiol treatment. In Experiment 1, ovariectomized rats treated with a low dose (1 μg) of estradiol 48 h and 24 h prior to the information trial of a Y-maze task exhibited a preference for the arm associated with the novel environment on the retention trial conducted 48 h later. In Experiment 2, treatment of ovariectomized rats with G-1 (25 μg) 48 h and 24 h prior to the information trial of a Y-maze task resulted in a greater preference for the arm associated with the novel environment on the retention trial. Collectively, the results indicated that short-term treatment of ovariectomized rats with a GPR30 agonist was sufficient to enhance spatial recognition memory, an effect that also occurred following short-term treatment with a low dose of estradiol.

  9. Effect of length of molecular recognition moiety on enzymatic activity switching.

    PubMed

    Oshiba, Yuhei; Tamaki, Takanori; Ohashi, Hidenori; Hirakawa, Hidehiko; Yamaguchi, Satoshi; Nagamune, Teruyuki; Yamaguchi, Takeo

    2013-10-01

    We site-specifically conjugated biotin-PEG derivatives with spacer arms of different lengths to mutant P450cam (3mD) and evaluated the activity of and structural changes in the conjugates as a first step toward clarifying the mechanism whereby the activity of the 3mD conjugate is inhibited. 3mD was prepared by site-specific mutation to inhibit its enzymatic activity artificially, after which the derivative compounds were conjugated to the enzyme. 3mD has one cysteine on its surface with a reactive thiol group that can react with compounds near the active site, where a conformational change will be induced after conjugation. The activity of 3mD was retained in the biotin-PEG₂-3mD conjugate, but was dramatically reduced in the biotin-PEG₁₁-3mD conjugate. To investigate the effect of poly(ethylene glycol) (PEG) length on the enzymatic activity after conjugation, PEGs of different lengths, exceeding that in biotin-PEG₁₁, and whose termini were not biotin, were conjugated to 3mD. The activity of 3mD decreased in all these conjugates. This indicates that the activity of 3mD in these conjugates decreased after its conjugation with PEG molecules that exceeded a certain length. The biotin-PEG₂-3mD, which retains enzymatic activity after conjugation, showed avidin responsiveness; the enzymatic activity decreased after avidin binding.

  10. Seasonal variations of global lightning activity extracted from Schumann resonances using a genetic algorithm method

    NASA Astrophysics Data System (ADS)

    Yang, Heng; Pasko, Victor P.; SáTori, Gabriella

    2009-01-01

    A three-dimensional Finite Difference Time Domain (FDTD) model of the Earth-ionosphere cavity with a realistic conductivity profile is employed to study the global lightning activity using the observed intensity variations of Schumann resonances (SR). Comparison of the results derived from our FDTD model and the previous studies by other authors on related subjects shows that Schumann resonance is a good probe to indicate the seasonal variations of lightning activity in three main thunderstorm regions (Africa, southeast Asia, and South America). An inverse method based on genetic algorithms is developed to extract information on lightning intensity in these three regions from observed SR intensity data. Seasonal variations of the lightning activity in three thunderstorm centers are clearly observed in our results. Different SR frequency variations associated with seasonal variations of global lighting activity are also discussed.

  11. Comparison of a new rapid convergent adaptive control algorithm to least mean square on an active noise control system

    NASA Astrophysics Data System (ADS)

    Koshigoe, Shozo; Gordon, Alan; Teagle, Allen; Tsay, Ching-Hsu

    1995-04-01

    In this paper, an efficient rapid convergent control algorithm will be developed and will be compared with other adaptive control algorithms using an experimental active noise control system. Other control algorithms are Widrow's finite impulse response adaptive control algorithm, and a modified Godard's algorithm. Comparisons of the random noise attenuation capability, transient and convergence performance, and computational requirements of each algorithm will be made as the order of the controller and relevant convergence parameters are varied. The system used for these experiments is a test bed of noise suppression technology for expendable launch vehicles. It consists of a flexible plate backed by a rigid cavity. Piezoelectric actuators are mounted on the plate and polyvinylidene fluoride is used both for microphones and pressure sensors within the cavity. The plate is bombarded with an amplified random noise signal, and the control system is used to suppress the noise inside the cavity generated by the outside sound source.

  12. Dissociation of Active Working Memory and Passive Recognition in Rhesus Monkeys

    ERIC Educational Resources Information Center

    Basile, Benjamin M.; Hampton, Robert R.

    2013-01-01

    Active cognitive control of working memory is central in most human memory models, but behavioral evidence for such control in nonhuman primates is absent and neurophysiological evidence, while suggestive, is indirect. We present behavioral evidence that monkey memory for familiar images is under active cognitive control. Concurrent cognitive…

  13. Activation by SLAM Family Receptors Contributes to NK Cell Mediated "Missing-Self" Recognition.

    PubMed

    Alari-Pahissa, Elisenda; Grandclément, Camille; Jeevan-Raj, Beena; Leclercq, Georges; Veillette, André; Held, Werner

    2016-01-01

    Natural Killer (NK) cells attack normal hematopoietic cells that do not express inhibitory MHC class I (MHC-I) molecules, but the ligands that activate NK cells remain incompletely defined. Here we show that the expression of the Signaling Lymphocyte Activation Molecule (SLAM) family members CD48 and Ly9 (CD229) by MHC-I-deficient tumor cells significantly contributes to NK cell activation. When NK cells develop in the presence of T cells or B cells that lack inhibitory MHC-I but express activating CD48 and Ly9 ligands, the NK cells' ability to respond to MHC-I-deficient tumor cells is severely compromised. In this situation, NK cells express normal levels of the corresponding activation receptors 2B4 (CD244) and Ly9 but these receptors are non-functional. This provides a partial explanation for the tolerance of NK cells to MHC-I-deficient cells in vivo. Activating signaling via 2B4 is restored when MHC-I-deficient T cells are removed, indicating that interactions with MHC-I-deficient T cells dominantly, but not permanently, impair the function of the 2B4 NK cell activation receptor. These data identify an important role of SLAM family receptors for NK cell mediated "missing-self" reactivity and suggest that NK cell tolerance in MHC-I mosaic mice is in part explained by an acquired dysfunction of SLAM family receptors.

  14. Inferring Human Activity Recognition with Ambient Sound on Wireless Sensor Nodes

    PubMed Central

    Salomons, Etto L.; Havinga, Paul J. M.; van Leeuwen, Henk

    2016-01-01

    A wireless sensor network that consists of nodes with a sound sensor can be used to obtain context awareness in home environments. However, the limited processing power of wireless nodes offers a challenge when extracting features from the signal, and subsequently, classifying the source. Although multiple papers can be found on different methods of sound classification, none of these are aimed at limited hardware or take the efficiency of the algorithms into account. In this paper, we compare and evaluate several classification methods on a real sensor platform using different feature types and classifiers, in order to find an approach that results in a good classifier that can run on limited hardware. To be as realistic as possible, we trained our classifiers using sound waves from many different sources. We conclude that despite the fact that the classifiers are often of low quality due to the highly restricted hardware resources, sufficient performance can be achieved when (1) the window length for our classifiers is increased, and (2) if we apply a two-step approach that uses a refined classification after a global classification has been performed. PMID:27690026

  15. Structural basis for chemokine recognition and activation of a viral G protein-coupled receptor

    SciTech Connect

    Burg, John S.; Ingram, Jessica R.; Venkatakrishnan, A.J.; Jude, Kevin M.; Dukkipati, Abhiram; Feinberg, Evan N.; Angelini, Alessandro; Waghray, Deepa; Dror, Ron O.; Ploegh, Hidde L.; Garcia, K. Christopher

    2015-03-05

    Chemokines are small proteins that function as immune modulators through activation of chemokine G protein-coupled receptors (GPCRs). Several viruses also encode chemokines and chemokine receptors to subvert the host immune response. How protein ligands activate GPCRs remains unknown. We report the crystal structure at 2.9 angstrom resolution of the human cytomegalovirus GPCR US28 in complex with the chemokine domain of human CX3CL1 (fractalkine). The globular body of CX3CL1 is perched on top of the US28 extracellular vestibule, whereas its amino terminus projects into the central core of US28. The transmembrane helices of US28 adopt an active-state-like conformation. Atomic-level simulations suggest that the agonist-independent activity of US28 may be due to an amino acid network evolved in the viral GPCR to destabilize the receptor’s inactive state.

  16. ERP correlates of object recognition memory in Down syndrome: Do active and passive tasks measure the same thing?

    PubMed

    Van Hoogmoed, A H; Nadel, L; Spanò, G; Edgin, J O

    2016-02-01

    Event related potentials (ERPs) can help to determine the cognitive and neural processes underlying memory functions and are often used to study populations with severe memory impairment. In healthy adults, memory is typically assessed with active tasks, while in patient studies passive memory paradigms are generally used. In this study we examined whether active and passive continuous object recognition tasks measure the same underlying memory process in typically developing (TD) adults and in individuals with Down syndrome (DS), a population with known hippocampal impairment. We further explored how ERPs in these tasks relate to behavioral measures of memory. Data-driven analysis techniques revealed large differences in old-new effects in the active versus passive task in TD adults, but no difference between these tasks in DS. The group with DS required additional processing in the active task in comparison to the TD group in two ways. First, the old-new effect started 150 ms later. Second, more repetitions were required to show the old-new effect. In the group with DS, performance on a behavioral measure of object-location memory was related to ERP measures across both tasks. In total, our results suggest that active and passive ERP memory measures do not differ in DS and likely reflect the use of implicit memory, but not explicit processing, on both tasks. Our findings highlight the need for a greater understanding of the comparison between active and passive ERP paradigms before they are inferred to measure similar functions across populations (e.g., infants or intellectual disability).

  17. Adjunctive selective estrogen receptor modulator increases neural activity in the hippocampus and inferior frontal gyrus during emotional face recognition in schizophrenia

    PubMed Central

    Ji, E; Weickert, C S; Lenroot, R; Kindler, J; Skilleter, A J; Vercammen, A; White, C; Gur, R E; Weickert, T W

    2016-01-01

    Estrogen has been implicated in the development and course of schizophrenia with most evidence suggesting a neuroprotective effect. Treatment with raloxifene, a selective estrogen receptor modulator, can reduce symptom severity, improve cognition and normalize brain activity during learning in schizophrenia. People with schizophrenia are especially impaired in the identification of negative facial emotions. The present study was designed to determine the extent to which adjunctive raloxifene treatment would alter abnormal neural activity during angry facial emotion recognition in schizophrenia. Twenty people with schizophrenia (12 men, 8 women) participated in a 13-week, randomized, double-blind, placebo-controlled, crossover trial of adjunctive raloxifene treatment (120 mg per day orally) and performed a facial emotion recognition task during functional magnetic resonance imaging after each treatment phase. Two-sample t-tests in regions of interest selected a priori were performed to assess activation differences between raloxifene and placebo conditions during the recognition of angry faces. Adjunctive raloxifene significantly increased activation in the right hippocampus and left inferior frontal gyrus compared with the placebo condition (family-wise error, P<0.05). There was no significant difference in performance accuracy or reaction time between active and placebo conditions. To the best of our knowledge, this study provides the first evidence suggesting that adjunctive raloxifene treatment changes neural activity in brain regions associated with facial emotion recognition in schizophrenia. These findings support the hypothesis that estrogen plays a modifying role in schizophrenia and shows that adjunctive raloxifene treatment may reverse abnormal neural activity during facial emotion recognition, which is relevant to impaired social functioning in men and women with schizophrenia. PMID:27138794

  18. Adjunctive selective estrogen receptor modulator increases neural activity in the hippocampus and inferior frontal gyrus during emotional face recognition in schizophrenia.

    PubMed

    Ji, E; Weickert, C S; Lenroot, R; Kindler, J; Skilleter, A J; Vercammen, A; White, C; Gur, R E; Weickert, T W

    2016-05-03

    Estrogen has been implicated in the development and course of schizophrenia with most evidence suggesting a neuroprotective effect. Treatment with raloxifene, a selective estrogen receptor modulator, can reduce symptom severity, improve cognition and normalize brain activity during learning in schizophrenia. People with schizophrenia are especially impaired in the identification of negative facial emotions. The present study was designed to determine the extent to which adjunctive raloxifene treatment would alter abnormal neural activity during angry facial emotion recognition in schizophrenia. Twenty people with schizophrenia (12 men, 8 women) participated in a 13-week, randomized, double-blind, placebo-controlled, crossover trial of adjunctive raloxifene treatment (120 mg per day orally) and performed a facial emotion recognition task during functional magnetic resonance imaging after each treatment phase. Two-sample t-tests in regions of interest selected a priori were performed to assess activation differences between raloxifene and placebo conditions during the recognition of angry faces. Adjunctive raloxifene significantly increased activation in the right hippocampus and left inferior frontal gyrus compared with the placebo condition (family-wise error, P<0.05). There was no significant difference in performance accuracy or reaction time between active and placebo conditions. To the best of our knowledge, this study provides the first evidence suggesting that adjunctive raloxifene treatment changes neural activity in brain regions associated with facial emotion recognition in schizophrenia. These findings support the hypothesis that estrogen plays a modifying role in schizophrenia and shows that adjunctive raloxifene treatment may reverse abnormal neural activity during facial emotion recognition, which is relevant to impaired social functioning in men and women with schizophrenia.

  19. Sunspots and Coronal Bright Points Tracking using a Hybrid Algorithm of PSO and Active Contour Model

    NASA Astrophysics Data System (ADS)

    Dorotovic, I.; Shahamatnia, E.; Lorenc, M.; Rybansky, M.; Ribeiro, R. A.; Fonseca, J. M.

    2014-02-01

    In the last decades there has been a steady increase of high-resolution data, from ground-based and space-borne solar instruments, and also of solar data volume. These huge image archives require efficient automatic image processing software tools capable of detecting and tracking various features in the solar atmosphere. Results of application of such tools are essential for studies of solar activity evolution, climate change understanding and space weather prediction. The follow up of interplanetary and near-Earth phenomena requires, among others, automatic tracking algorithms that can determine where a feature is located, on successive images taken along the period of observation. Full-disc solar images, obtained both with the ground-based solar telescopes and the instruments onboard the satellites, provide essential observational material for solar physicists and space weather researchers for better understanding the Sun, studying the evolution of various features in the solar atmosphere, and also investigating solar differential rotation by tracking such features along time. Here we demonstrate and discuss the suitability of applying a hybrid Particle Swarm Optimization (PSO) algorithm and Active Contour model for tracking and determining the differential rotation of sunspots and coronal bright points (CBPs) on a set of selected solar images. The results obtained confirm that the proposed approach constitutes a promising tool for investigating the evolution of solar activity and also for automating tracking features on massive solar image archives.

  20. A novel algorithm for detecting active propulsion in wheelchair users following spinal cord injury.

    PubMed

    Popp, Werner L; Brogioli, Michael; Leuenberger, Kaspar; Albisser, Urs; Frotzler, Angela; Curt, Armin; Gassert, Roger; Starkey, Michelle L

    2016-03-01

    Physical activity in wheelchair-bound individuals can be assessed by monitoring their mobility as this is one of the most intense upper extremity activities they perform. Current accelerometer-based approaches for describing wheelchair mobility do not distinguish between self- and attendant-propulsion and hence may overestimate total physical activity. The aim of this study was to develop and validate an inertial measurement unit based algorithm to monitor wheel kinematics and the type of wheelchair propulsion (self- or attendant-) within a "real-world" situation. Different sensor set-ups were investigated, ranging from a high precision set-up including four sensor modules with a relatively short measurement duration of 24 h, to a less precise set-up with only one module attached at the wheel exceeding one week of measurement because the gyroscope of the sensor was turned off. The "high-precision" algorithm distinguished self- and attendant-propulsion with accuracy greater than 93% whilst the long-term measurement set-up showed an accuracy of 82%. The estimation accuracy of kinematic parameters was greater than 97% for both set-ups. The possibility of having different sensor set-ups allows the use of the inertial measurement units as high precision tools for researchers as well as unobtrusive and simple tools for manual wheelchair users.

  1. Cognitive object recognition system (CORS)

    NASA Astrophysics Data System (ADS)

    Raju, Chaitanya; Varadarajan, Karthik Mahesh; Krishnamurthi, Niyant; Xu, Shuli; Biederman, Irving; Kelley, Troy

    2010-04-01

    We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

  2. A Novel Wearable Device for Food Intake and Physical Activity Recognition

    PubMed Central

    Farooq, Muhammad; Sazonov, Edward

    2016-01-01

    Presence of speech and motion artifacts has been shown to impact the performance of wearable sensor systems used for automatic detection of food intake. This work presents a novel wearable device which can detect food intake even when the user is physically active and/or talking. The device consists of a piezoelectric strain sensor placed on the temporalis muscle, an accelerometer, and a data acquisition module connected to the temple of eyeglasses. Data from 10 participants was collected while they performed activities including quiet sitting, talking, eating while sitting, eating while walking, and walking. Piezoelectric strain sensor and accelerometer signals were divided into non-overlapping epochs of 3 s; four features were computed for each signal. To differentiate between eating and not eating, as well as between sedentary postures and physical activity, two multiclass classification approaches are presented. The first approach used a single classifier with sensor fusion and the second approach used two-stage classification. The best results were achieved when two separate linear support vector machine (SVM) classifiers were trained for food intake and activity detection, and their results were combined using a decision tree (two-stage classification) to determine the final class. This approach resulted in an average F1-score of 99.85% and area under the curve (AUC) of 0.99 for multiclass classification. With its ability to differentiate between food intake and activity level, this device may potentially be used for tracking both energy intake and energy expenditure. PMID:27409622

  3. Recognition of human-vehicle interactions in group activities via multi-attributed semantic message generation

    NASA Astrophysics Data System (ADS)

    Elangovan, Vinayak; Shirkhodaie, Amir

    2015-05-01

    Improved Situational awareness is a vital ongoing research effort for the U.S. Homeland Security for the past recent years. Many outdoor anomalous activities involve vehicles as their primary source of transportation to and from the scene where a plot is executed. Analysis of dynamics of Human-Vehicle Interaction (HVI) helps to identify correlated patterns of activities representing potential threats. The objective of this paper is bi-folded. Primarily, we discuss a method for temporal HVI events detection and verification for generation of HVI hypotheses. To effectively recognize HVI events, a Multi-attribute Vehicle Detection and Identification technique (MVDI) for detection and classification of stationary vehicles is presented. Secondly, we describe a method for identification of pertinent anomalous behaviors through analysis of state transitions between two successively detected events. Finally, we present a technique for generation of HVI semantic messages and present our experimental results to demonstrate the effectiveness of semantic messages for discovery of HVI in group activities.

  4. Divergent short- and long-term effects of acute stress in object recognition memory are mediated by endogenous opioid system activation.

    PubMed

    Nava-Mesa, Mauricio O; Lamprea, Marisol R; Múnera, Alejandro

    2013-11-01

    Acute stress induces short-term object recognition memory impairment and elicits endogenous opioid system activation. The aim of this study was thus to evaluate whether opiate system activation mediates the acute stress-induced object recognition memory changes. Adult male Wistar rats were trained in an object recognition task designed to test both short- and long-term memory. Subjects were randomly assigned to receive an intraperitoneal injection of saline, 1 mg/kg naltrexone or 3 mg/kg naltrexone, four and a half hours before the sample trial. Five minutes after the injection, half the subjects were submitted to movement restraint during four hours while the other half remained in their home cages. Non-stressed subjects receiving saline (control) performed adequately during the short-term memory test, while stressed subjects receiving saline displayed impaired performance. Naltrexone prevented such deleterious effect, in spite of the fact that it had no intrinsic effect on short-term object recognition memory. Stressed subjects receiving saline and non-stressed subjects receiving naltrexone performed adequately during the long-term memory test; however, control subjects as well as stressed subjects receiving a high dose of naltrexone performed poorly. Control subjects' dissociated performance during both memory tests suggests that the short-term memory test induced a retroactive interference effect mediated through light opioid system activation; such effect was prevented either by low dose naltrexone administration or by strongly activating the opioid system through acute stress. Both short-term memory retrieval impairment and long-term memory improvement observed in stressed subjects may have been mediated through strong opioid system activation, since they were prevented by high dose naltrexone administration. Therefore, the activation of the opioid system plays a dual modulating role in object recognition memory.

  5. Recognition and treatment of concurrent active and neurodegenerative langerhans cell histiocytosis: a case report.

    PubMed

    Ehrhardt, Matthew J; Karst, Jeffrey; Donohoue, Patricia A; Maheshwari, Mohit; McClain, Kenneth L; Bingen, Kristin; Kelly, Michael E

    2015-01-01

    Langerhans cell histiocytosis (LCH) is a disorder of dendritic cell proliferation with subsequent tissue damage often requiring chemotherapy. Neurodegenerative LCH presents with neuromuscular, cognitive, and behavioral alterations typically occurring years after diagnosis of active LCH. We present a male child with a 4-year history of growth arrest, polyuria, polydipsia, recurrent otitis media, and seborrheic dermatitis. Cutaneous biopsies confirmed LCH and chemotherapy was initiated. During treatment for active LCH he developed neuropsychiatric decline. White matter changes on brain MRI were consistent with neurodegenerative LCH. Treatment was changed to cytarabine and intravenous immunoglobulin. After 1 year of therapy the patient experienced neuropsychological improvement.

  6. Altered object-in-place recognition memory, prepulse inhibition, and locomotor activity in the offspring of rats exposed to a viral mimetic during pregnancy.

    PubMed

    Howland, J G; Cazakoff, B N; Zhang, Y

    2012-01-10

    Infection during pregnancy (i.e., prenatal infection) increases the risk of psychiatric illnesses such as schizophrenia and autism in the adult offspring. The present experiments examined the effects of prenatal immune challenge on behavior in three paradigms relevant to these disorders: prepulse inhibition (PPI) of the acoustic startle response, locomotor responses to an unfamiliar environment and the N-methyl-d-aspartate antagonist MK-801, and three forms of recognition memory. Pregnant Long-Evans rats were exposed to the viral mimetic polyinosinic-polycytidylic acid (PolyI:C; 4 mg/kg, i.v.) on gestational day 15. Offspring were tested for PPI and locomotor activity before puberty (postnatal days (PNDs)35 and 36) and during young adulthood (PNDs 56 and 57). Four prepulse-pulse intervals (30, 50, 80, and 140 ms) were employed in the PPI test. Recognition memory testing was performed using three different spontaneous novelty recognition tests (object, object location, and object-in-place recognition) after PND 60. Regardless of sex, offspring of PolyI:C-treated dams showed disrupted PPI at 50-, 80-, and 140-ms prepulse-pulse intervals. In the prepubescent rats, we observed prepulse facilitation for the 30-ms prepulse-pulse interval trials that was selectively retained in the adult PolyI:C-treated offspring. Locomotor responses to MK-801 were significantly reduced before puberty, whereas responses to an unfamiliar environment were increased in young adulthood. Both male and female PolyI:C-treated offspring showed intact object and object location recognition memory, whereas male PolyI:C-treated offspring displayed significantly impaired object-in-place recognition memory. Females were unable to perform the object-in-place test. The present results demonstrate that prenatal immune challenge during mid/late gestation disrupts PPI and locomotor behavior. In addition, the selective impairment of object-in-place recognition memory suggests tasks that depend on prefrontal

  7. DNA-recognition by a σ54 transcriptional activator from Aquifex aeolicus

    PubMed Central

    Vidangos, Natasha K.; Heideker, Johanna; Lyubimov, Artem; Lamers, Meindert; Huo, Yixin; Pelton, Jeffrey G.; Ton, Jimmy; Gralla, Jay; Berger, James; Wemmer, David E.

    2014-01-01

    Transcription initiation by bacterial σ54-polymerase requires the action of a transcriptional activator protein. Activators bind sequence-specifically upstream of the transcription initiation site via a DNA-binding domain. The structurally characterized DNA-binding domains from activators all belong to the Factor for Inversion Stimulation (Fis) family of helix-turn-helix DNA-binding proteins. We report here structures of the free and DNA-bound forms of the DNA-binding domain of NtrC4 (4DBD) from Aquifex aeolicus, a member of the NtrC family of σ54 activators. Two NtrC4 binding sites were identified upstream (−145 and −85 base pairs) from the start of the lpxC gene, which is responsible for the first committed step in Lipid A biosynthesis. This is the first experimental evidence for σ54 regulation in lpxC expression. 4DBD was crystallized both without DNA and in complex with the −145 binding site. The structures, together with biochemical data, indicate that NtrC4 binds to DNA in a manner that is similar to that of its close homologue, Fis. The greater sequence specificity for the binding of 4DBD relative to Fis seems to arise from a larger number of base specific contacts contributing to affinity than for Fis. PMID:25158097

  8. Automatic Association of Chats and Video Tracks for Activity Learning and Recognition in Aerial Video Surveillance

    PubMed Central

    Hammoud, Riad I.; Sahin, Cem S.; Blasch, Erik P.; Rhodes, Bradley J.; Wang, Tao

    2014-01-01

    We describe two advanced video analysis techniques, including video-indexed by voice annotations (VIVA) and multi-media indexing and explorer (MINER). VIVA utilizes analyst call-outs (ACOs) in the form of chat messages (voice-to-text) to associate labels with video target tracks, to designate spatial-temporal activity boundaries and to augment video tracking in challenging scenarios. Challenging scenarios include low-resolution sensors, moving targets and target trajectories obscured by natural and man-made clutter. MINER includes: (1) a fusion of graphical track and text data using probabilistic methods; (2) an activity pattern learning framework to support querying an index of activities of interest (AOIs) and targets of interest (TOIs) by movement type and geolocation; and (3) a user interface to support streaming multi-intelligence data processing. We also present an activity pattern learning framework that uses the multi-source associated data as training to index a large archive of full-motion videos (FMV). VIVA and MINER examples are demonstrated for wide aerial/overhead imagery over common data sets affording an improvement in tracking from video data alone, leading to 84% detection with modest misdetection/false alarm results due to the complexity of the scenario. The novel use of ACOs and chat messages in video tracking paves the way for user interaction, correction and preparation of situation awareness reports. PMID:25340453

  9. Automatic association of chats and video tracks for activity learning and recognition in aerial video surveillance.

    PubMed

    Hammoud, Riad I; Sahin, Cem S; Blasch, Erik P; Rhodes, Bradley J; Wang, Tao

    2014-10-22

    We describe two advanced video analysis techniques, including video-indexed by voice annotations (VIVA) and multi-media indexing and explorer (MINER). VIVA utilizes analyst call-outs (ACOs) in the form of chat messages (voice-to-text) to associate labels with video target tracks, to designate spatial-temporal activity boundaries and to augment video tracking in challenging scenarios. Challenging scenarios include low-resolution sensors, moving targets and target trajectories obscured by natural and man-made clutter. MINER includes: (1) a fusion of graphical track and text data using probabilistic methods; (2) an activity pattern learning framework to support querying an index of activities of interest (AOIs) and targets of interest (TOIs) by movement type and geolocation; and (3) a user interface to support streaming multi-intelligence data processing. We also present an activity pattern learning framework that uses the multi-source associated data as training to index a large archive of full-motion videos (FMV). VIVA and MINER examples are demonstrated for wide aerial/overhead imagery over common data sets affording an improvement in tracking from video data alone, leading to 84% detection with modest misdetection/false alarm results due to the complexity of the scenario. The novel use of ACOs and chat Sensors 2014, 14 19844 messages in video tracking paves the way for user interaction, correction and preparation of situation awareness reports.

  10. DNA Recognition by a σ54 Transcriptional Activator from Aquifex aeolicus

    SciTech Connect

    Vidangos, Natasha K.; Heideker, Johanna; Lyubimov, Artem; Lamers, Meindert; Huo, Yixin; Pelton, Jeffrey G.; Ton, Jimmy; Gralla, Jay; Berger, James; Wemmer, David E.

    2014-08-23

    Transcription initiation by bacterial σ54-polymerase requires the action of a transcriptional activator protein. Activators bind sequence-specifically upstream of the transcription initiation site via a DNA-binding domain. The structurally characterized DNA-binding domains from activators all belong to the Factor for Inversion Stimulation (Fis) family of helix-turn-helix DNA-binding proteins. We report here structures of the free and DNA-bound forms of the DNA-binding domain of NtrC4 (4DBD) from Aquifex aeolicus, a member of the NtrC family of σ54 activators. Two NtrC4 binding sites were identified upstream (-145 and -85 base pairs) from the start of the lpxC gene, which is responsible for the first committed step in Lipid A biosynthesis. This is the first experimental evidence for σ54 regulation in lpxC expression. 4DBD was crystallized both without DNA and in complex with the -145 binding site. The structures, together with biochemical data, indicate that NtrC4 binds to DNA in a manner that is similar to that of its close homologue, Fis. Ultimately, the greater sequence specificity for the binding of 4DBD relative to Fis seems to arise from a larger number of base specific contacts contributing to affinity than for Fis.

  11. DNA Recognition by a σ54 Transcriptional Activator from Aquifex aeolicus

    DOE PAGES

    Vidangos, Natasha K.; Heideker, Johanna; Lyubimov, Artem; ...

    2014-08-23

    Transcription initiation by bacterial σ54-polymerase requires the action of a transcriptional activator protein. Activators bind sequence-specifically upstream of the transcription initiation site via a DNA-binding domain. The structurally characterized DNA-binding domains from activators all belong to the Factor for Inversion Stimulation (Fis) family of helix-turn-helix DNA-binding proteins. We report here structures of the free and DNA-bound forms of the DNA-binding domain of NtrC4 (4DBD) from Aquifex aeolicus, a member of the NtrC family of σ54 activators. Two NtrC4 binding sites were identified upstream (-145 and -85 base pairs) from the start of the lpxC gene, which is responsible for themore » first committed step in Lipid A biosynthesis. This is the first experimental evidence for σ54 regulation in lpxC expression. 4DBD was crystallized both without DNA and in complex with the -145 binding site. The structures, together with biochemical data, indicate that NtrC4 binds to DNA in a manner that is similar to that of its close homologue, Fis. Ultimately, the greater sequence specificity for the binding of 4DBD relative to Fis seems to arise from a larger number of base specific contacts contributing to affinity than for Fis.« less

  12. Age-Related Differences in Brain Electrical Activity during Extended Continuous Face Recognition in Younger Children, Older Children and Adults

    ERIC Educational Resources Information Center

    Van Strien, Jan W.; Glimmerveen, Johanna C.; Franken, Ingmar H. A.; Martens, Vanessa E. G.; de Bruin, Eveline A.

    2011-01-01

    To examine the development of recognition memory in primary-school children, 36 healthy younger children (8-9 years old) and 36 healthy older children (11-12 years old) participated in an ERP study with an extended continuous face recognition task (Study 1). Each face of a series of 30 faces was shown randomly six times interspersed with…

  13. A comparative study of DIGNET, average, complete, single hierarchical and k-means clustering algorithms in 2D face image recognition

    NASA Astrophysics Data System (ADS)

    Thanos, Konstantinos-Georgios; Thomopoulos, Stelios C. A.

    2014-06-01

    The study in this paper belongs to a more general research of discovering facial sub-clusters in different ethnicity face databases. These new sub-clusters along with other metadata (such as race, sex, etc.) lead to a vector for each face in the database where each vector component represents the likelihood of participation of a given face to each cluster. This vector is then used as a feature vector in a human identification and tracking system based on face and other biometrics. The first stage in this system involves a clustering method which evaluates and compares the clustering results of five different clustering algorithms (average, complete, single hierarchical algorithm, k-means and DIGNET), and selects the best strategy for each data collection. In this paper we present the comparative performance of clustering results of DIGNET and four clustering algorithms (average, complete, single hierarchical and k-means) on fabricated 2D and 3D samples, and on actual face images from various databases, using four different standard metrics. These metrics are the silhouette figure, the mean silhouette coefficient, the Hubert test Γ coefficient, and the classification accuracy for each clustering result. The results showed that, in general, DIGNET gives more trustworthy results than the other algorithms when the metrics values are above a specific acceptance threshold. However when the evaluation results metrics have values lower than the acceptance threshold but not too low (too low corresponds to ambiguous results or false results), then it is necessary for the clustering results to be verified by the other algorithms.

  14. Multi-objective decoupling algorithm for active distance control of intelligent hybrid electric vehicle

    NASA Astrophysics Data System (ADS)

    Luo, Yugong; Chen, Tao; Li, Keqiang

    2015-12-01

    The paper presents a novel active distance control strategy for intelligent hybrid electric vehicles (IHEV) with the purpose of guaranteeing an optimal performance in view of the driving functions, optimum safety, fuel economy and ride comfort. Considering the complexity of driving situations, the objects of safety and ride comfort are decoupled from that of fuel economy, and a hierarchical control architecture is adopted to improve the real-time performance and the adaptability. The hierarchical control structure consists of four layers: active distance control object determination, comprehensive driving and braking torque calculation, comprehensive torque distribution and torque coordination. The safety distance control and the emergency stop algorithms are designed to achieve the safety and ride comfort goals. The optimal rule-based energy management algorithm of the hybrid electric system is developed to improve the fuel economy. The torque coordination control strategy is proposed to regulate engine torque, motor torque and hydraulic braking torque to improve the ride comfort. This strategy is verified by simulation and experiment using a forward simulation platform and a prototype vehicle. The results show that the novel control strategy can achieve the integrated and coordinated control of its multiple subsystems, which guarantees top performance of the driving functions and optimum safety, fuel economy and ride comfort.

  15. Nursing activity recognition using an inexpensive game controller: An application to infection control.

    PubMed

    Momen, Kaveh; Fernie, Geoff R

    2010-01-01

    It is estimated that 10% of the patients admitted to North American hospitals die of hospital acquired infections. Approximately half of these are thought to be a consequence of poor hand hygiene practices by the hospital staff. Electronic hand washing reminders that prompt caregivers to wash their hands before and after the patient/patient's environment contact may help to increase the hand hygiene compliance rate. However, the current systems fail to identify the nursing procedures happening around the patient to issue proper hand hygiene prompt. In this research we used the hardware of a low-cost wireless Sony game controller, which included a 3-axis accelerometer, to identify six nursing activities happening around a patient. We attached five sensors to eight nurses' left and right wrists, left and right upper arms, and the backs. Each nurse performed 10 trials of each nursing activity in sequence, followed by a combined nursing activities trial. We extracted mean, standard deviation, energy, and correlation among axes per sensor and compared the results of 1-Nearest Neighbour (1-NN), Decision Tree (J48), and Naïve Bayes classifiers. 1-NN classifier had the best performance and on average regardless of the sensor locations, we achieved 84% ± 2% accuracy.

  16. Automatic phonological activation during visual word recognition in bilingual children: A cross-language masked priming study in grades 3 and 5.

    PubMed

    Sauval, Karinne; Perre, Laetitia; Duncan, Lynne G; Marinus, Eva; Casalis, Séverine

    2017-02-01

    Previous masked priming research has shown automatic phonological activation during visual word recognition in monolingual skilled adult readers. Activation also occurs across languages in bilingual adult readers, suggesting that the activation of phonological representations is not language specific. Less is known about developing readers. First, it is unclear whether there is automatic phonological activation during visual word recognition among children in general. Second, no empirical data exist on whether the activation of phonological representations is language specific or not in bilingual children. The current study investigated these issues in bilingual third and fifth graders using cross-language phonological masked priming in a lexical decision task. Targets were French words, and primes were English pseudowords of three types: (a) phonological primes, which share phonological information with the target beginning (e.g., dee-DIMANCHE [Sunday], pronounced /di:/-/dimãʃ/); (b) orthographic control primes, which control for letters shared by the phonological prime and target (e.g., d) and their position (e.g., doo-DIMANCHE, pronounced /du:/-/dimãʃ/); and (c) unrelated primes, which share no phonological or orthographic information with the target beginning (e.g., pow-DIMANCHE, pronounced /paʊ/-/dimãʃ/). Significant phonological priming was observed, suggesting that (a) phonological representations are rapidly and automatically activated by print during visual word recognition from Grade 3 onward and that (b) the activation of phonological representations is not language specific in bilingual children.

  17. An ECG-based Algorithm for the Automatic Identification of Autonomic Activations Associated with Cortical Arousal

    PubMed Central

    Basner, Mathias; Griefahn, Barbara; Müller, Uwe; Plath, Gernot; Samel, Alexander

    2007-01-01

    Objectives: EEG arousals are associated with autonomic activations. Visual EEG arousal scoring is time consuming and suffers from low interobserver agreement. We hypothesized that information on changes in heart rate alone suffice to predict the occurrence of cortical arousal. Methods: Two visual AASM EEG arousal scorings of 56 healthy subject nights (mean age 37.0 ± 12.8 years, 26 male) were obtained. For each of 5 heartbeats following the onset of 3581 consensus EEG arousals and of an equal number of control conditions, differences to a moving median were calculated and used to estimate likelihood ratios (LRs) for 10 categories of heartbeat differences. Comparable to 5 consecutive diagnostic tests, these LRs were used to calculate the probability of heart rate responses being associated with cortical arousals. Results: EEG and ECG arousal indexes agreed well across a wide range of decision thresholds, resulting in a receiver operating characteristic (ROC) with an area under the curve of 0.91. For the decision threshold chosen for the final analyses, a sensitivity of 68.1% and a specificity of 95.2% were obtained. ECG and EEG arousal indexes were poorly correlated (r = 0.19, P <0.001, ICC = 0.186), which could in part be attributed to 3 outliers. The Bland-Altman plot showed an unbiased estimation of EEG arousal indexes by ECG arousal indexes with a standard deviation of ± 7.9 arousals per hour sleep. In about two-thirds of all cases, ECG arousal scoring was matched by at least one (22.2%) or by both (42.5%) of the visual scorings. Sensitivity of the algorithm increased with increasing duration of EEG arousals. The ECG algorithm was also successfully validated with 30 different nights of 10 subjects (mean age 35.3 ▯ 13.6 years, 5 male). Conclusions: In its current version, the ECG algorithm cannot replace visual EEG arousal scoring. Sensitivity for detecting <10-s EEG arousals needs to be improved. However, in a nonclinical population, it may be valuable to

  18. Selective molecular recognition, C-H bond activation, and catalysis in nanoscale reaction vessels

    SciTech Connect

    Fiedler, Dorothea; Leung, Dennis H.; Raymond, Kenneth N.; Bergman, Robert G.

    2004-11-27

    Supramolecular chemistry represents a way to mimic enzyme reactivity by using specially designed container molecules. We have shown that a chiral self-assembled M{sub 4}L{sub 6} supramolecular tetrahedron can encapsulate a variety of cationic guests, with varying degrees of stereoselectivity. Reactive iridium guests can be encapsulated and the C-H bond activation of aldehydes occurs, with the host cavity controlling the ability of substrates to interact with the metal center based upon size and shape. In addition, the host container can act as a catalyst by itself. By restricting reaction space and preorganizing the substrates into reactive conformations, it accelerates the sigmatropic rearrangement of enammonium cations.

  19. Recognition of enhancer element-specific histone methylation by TIP60 in transcriptional activation

    PubMed Central

    Jeong, Kwang Won; Kim, Kyunghwan; Situ, Alan Jialun; Ulmer, Tobias S.; An, Woojin; Stallcup, Michael R.

    2011-01-01

    Many coregulator proteins are recruited by DNA-bound transcription factors to remodel chromatin and activate transcription. However, mechanisms for coordinating actions of multiple coregulator proteins are poorly understood. We demonstrate that multiple protein-protein interactions by protein acetyltransferase TIP60 are required for estrogen-induced transcription of a subset of estrogen receptor (ER) α target genes in human cells. Estrogen-induced recruitment of TIP60 requires direct binding of TIP60 to ERα and the action of chromatin remodeling ATPase BRG1, leading to increased recruitment of histone methyltransferase MLL1 and increased monomethylation of histone H3 at Lys4. TIP60 recruitment also requires preferential binding of the TIP60 chromodomain to histone H3 containing monomethylated Lys4, which marks active and poised enhancer elements. After recruitment, TIP60 increases acetylation of histone H2A at Lys5. Thus, complex cooperation of TIP60 with ERα and other chromatin remodeling enzymes is required for estrogen-induced transcription. PMID:22081016

  20. Support for Recognition of Women and for Activities for Women in Mathematical Sciences at National Meetings

    SciTech Connect

    Lewis, Jennifer

    2015-07-31

    The Association for Women in Mathematics (AWM) seeks to advance the rates of participation by women in events at national mathematical sciences conference primarily in the U.S. The grant was funded from 8/1/2007 through 3/31/2015. The first component is the lecture series (Noether, Kovalevsky and Falconer Lectures) named after celebrated mathematicians, and featuring prominent women mathematicians, with the result that men, as well as women, will learn about the achievements of women in the mathematical sciences. 22 women mathematicians gave lectures at the annual JMM, SIAM Annual Meetings, and the MAA MathFest. The second component is AWM’s “Workshops for Women Graduate Students and Recent PhDs,” which select junior women to give research talks and research poster presentations at the SIAM Annual Meeting. The workshop activities allow wider recruitment of participants and increased attention to mentoring. 122 women gave mathematics research presentations. The third component is the AWM’s 40th Anniversary Research Symposium, 2011. 300 women and men attended the two-day symposium with 135 women presenting mathematics research. These activities have succeeded in increasing the number of women speakers and presenters at meetings and have brought more women attendees to the meetings.

  1. Performance evaluation of nonnegative matrix factorization algorithms to estimate task-related neuronal activities from fMRI data.

    PubMed

    Ding, Xiaoyu; Lee, Jong-Hwan; Lee, Seong-Whan

    2013-04-01

    Nonnegative matrix factorization (NMF) is a blind source separation (BSS) algorithm which is based on the distinct constraint of nonnegativity of the estimated parameters as well as on the measured data. In this study, according to the potential feasibility of NMF for fMRI data, the four most popular NMF algorithms, corresponding to the following two types of (1) least-squares based update [i.e., alternating least-squares NMF (ALSNMF) and projected gradient descent NMF] and (2) multiplicative update (i.e., NMF based on Euclidean distance and NMF based on divergence cost function), were investigated by using them to estimate task-related neuronal activities. These algorithms were applied firstly to individual data from a single subject and, subsequently, to group data sets from multiple subjects. On the single-subject level, although all four algorithms detected task-related activation from simulated data, the performance of multiplicative update NMFs was significantly deteriorated when evaluated using visuomotor task fMRI data, for which they failed in estimating any task-related neuronal activities. In group-level analysis on both simulated data and real fMRI data, ALSNMF outperformed the other three algorithms. The presented findings may suggest that ALSNMF appears to be the most promising option among the tested NMF algorithms to extract task-related neuronal activities from fMRI data.

  2. Recognition of wall materials through active thermography coupled with numerical simulations.

    PubMed

    Pietrarca, Francesca; Mameli, Mauro; Filippeschi, Sauro; Fantozzi, Fabio

    2016-09-01

    In the framework of historical buildings, wall thickness as well as wall constituents are not often known a priori, and active IR thermography can be exploited as a nonintrusive method for detecting what kind of material lies beneath the external plaster layer. In the present work, the wall of a historical building is subjected to a heating stimulus, and the surface temperature temporal trend is recorded by an IR camera. A hybrid numerical model is developed in order to simulate the transient thermal response of a wall made of different known materials underneath the plaster layer. When the numerical thermal contrast and the appearance time match with the experimental thermal images, the material underneath the plaster can be qualitatively identified.

  3. Age-related differences in brain electrical activity during extended continuous face recognition in younger children, older children and adults.

    PubMed

    Van Strien, Jan W; Glimmerveen, Johanna C; Franken, Ingmar H A; Martens, Vanessa E G; de Bruin, Eveline A

    2011-09-01

    To examine the development of recognition memory in primary-school children, 36 healthy younger children (8-9 years old) and 36 healthy older children (11-12 years old) participated in an ERP study with an extended continuous face recognition task (Study 1). Each face of a series of 30 faces was shown randomly six times interspersed with distracter faces. The children were required to make old vs. new decisions. Older children responded faster than younger children, but younger children exhibited a steeper decrease in latencies across the five repetitions. Older children exhibited better accuracy for new faces, but there were no age differences in recognition accuracy for repeated faces. For the N2, N400 and late positive complex (LPC), we analyzed the old/new effects (repetition 1 vs. new presentation) and the extended repetition effects (repetitions 1 through 5). Compared to older children, younger children exhibited larger frontocentral N2 and N400 old/new effects. For extended face repetitions, negativity of the N2 and N400 decreased in a linear fashion in both age groups. For the LPC, an ERP component thought to reflect recollection, no significant old/new or extended repetition effects were found. Employing the same face recognition paradigm in 20 adults (Study 2), we found a significant N400 old/new effect at lateral frontal sites and a significant LPC repetition effect at parietal sites, with LPC amplitudes increasing linearly with the number of repetitions. This study clearly demonstrates differential developmental courses for the N400 and LPC pertaining to recognition memory for faces. It is concluded that face recognition in children is mediated by early and probably more automatic than conscious recognition processes. In adults, the LPC extended repetition effect indicates that adult face recognition memory is related to a conscious and graded recollection process rather than to an automatic recognition process.

  4. Intracellular RNA recognition pathway activates strong anti-viral response in human mast cells.

    PubMed

    Lappalainen, J; Rintahaka, J; Kovanen, P T; Matikainen, S; Eklund, K K

    2013-04-01

    Mast cells have been implicated in the first line of defence against parasites and bacteria, but less is known about their role in anti-viral responses. Allergic diseases often exacerbate during viral infection, suggesting an increased activation of mast cells in the process. In this study we investigated human mast cell response to double-stranded RNA and viral infection. Cultured human mast cells were incubated with poly(I:C), a synthetic RNA analogue and live Sendai virus as a model of RNA parainfluenza virus infection, and analysed for their anti-viral response. Mast cells responded to intracellular poly(I:C) by inducing type 1 and type 3 interferons and TNF-α. In contrast, extracellular Toll-like receptor 3 (TLR)-3-activating poly(I:C) failed to induce such response. Infection of mast cells with live Sendai virus induced an anti-viral response similar to that of intracellular poly(I:C). Type 1, but not type 3 interferons, up-regulated the expression of melanoma differentiation-associated gene 5 (MDA-5) and retinoic acid-inducible gene-1 (RIG-1), and TLR-3, demonstrating that human mast cells do not express functional receptors for type 3 interferons. Furthermore, virus infection induced the anti-viral proteins MxA and IFIT3 in human mast cells. In conclusion, our results support the notion that mast cells can recognize an invading virus through intracellular virus sensors and produce high amounts of type 1 and type 3 interferons and the anti-viral proteins human myxovirus resistance gene A (MxA) and interferon-induced protein with tetratricopeptide repeats 3 (IFIT3) in response to the virus infection.

  5. New Combined L-band Active/Passive Soil Moisture Retrieval Algorithm Optimized for Argentine Plains

    NASA Astrophysics Data System (ADS)

    Bruscantini, C. A.; Grings, F. M.; Salvia, M.; Ferrazzoli, P.; Karszenbaum, H.

    2015-12-01

    The ability of L-band passive microwave satellite observations to provide soil moisture (mv) measurements is well known. Despite its high sensitivity to near-surface mv, radiometric technology suffers from having a relatively low spatial resolution. Conversely active microwave observations, although their finer resolution, are difficult to be interpreted for mv content due to the confounding effects of vegetation and roughness. There have been and there are strong motivations for the realization of satellite missions that carry passive and active microwave instruments on board. This has also led to important contributions in algorithm development. In this line of work, NASA-CONAE SAC-D/Aquarius mission had on board an L band radiometer and scatterometer. This was followed by the launch of NASA SMAP mission (Soil Moisture Active Passive), as well as several airborne campaigns that provide active and passive measurements. Within this frame, a new combined active/passive mv retrieval algorithm is proposed by deriving an analytical expression of brightness temperature and radar backscattering relation using explicit semi-empirical models. Simple models (i.e. that can be easily inverted and have relatively low amount of ancillary parameters) were selected: ω-τ model (Jackson et al., 1982, Water Resources Research) and radar-only model (Narvekar et al., 2015, IEEE Transactions on Geoscience and Remote Sensing). A major challenge involves coupling the active and passive models to be consistent with observations. Coupling equations can be derived using theoretical active/passive high-order radiative transfer models, such as 3D Numerical Method of Maxwell equations (Zhou et al., 2004, IEEE Transactions on Geoscience and Remote Sensing) and Tor Vergata (Ferrazzoli et al., 1995,Remote Sensing of Environment) models. In this context, different coupling equations can be optimized for different land covers using theoretical forward models with specific parametrization for each

  6. A robust active contour edge detection algorithm based on local Gaussian statistical model for oil slick remote sensing image

    NASA Astrophysics Data System (ADS)

    Jing, Yu; Wang, Yaxuan; Liu, Jianxin; Liu, Zhaoxia

    2015-08-01

    Edge detection is a crucial method for the location and quantity estimation of oil slick when oil spills on the sea. In this paper, we present a robust active contour edge detection algorithm for oil spill remote sensing images. In the proposed algorithm, we define a local Gaussian data fitting energy term with spatially varying means and variances, and this data fitting energy term is introduced into a global minimization active contour (GMAC) framework. The energy function minimization is achieved fast by a dual formulation of the weighted total variation norm. The proposed algorithm avoids the existence of local minima, does not require the definition of initial contour, and is robust to weak boundaries, high noise and severe intensity inhomogeneity exiting in oil slick remote sensing images. Furthermore, the edge detection of oil slick and the correction of intensity inhomogeneity are simultaneously achieved via the proposed algorithm. The experiment results have shown that a superior performance of proposed algorithm over state-of-the-art edge detection algorithms. In addition, the proposed algorithm can also deal with the special images with the object and background of the same intensity means but different variances.

  7. Transgenic Expression of the Dicotyledonous Pattern Recognition Receptor EFR in Rice Leads to Ligand-Dependent Activation of Defense Responses

    PubMed Central

    Thomas, Nicolas; Holton, Nicolas; Nekrasov, Vladimir; Ruan, Deling; Canlas, Patrick E.; Daudi, Arsalan; Petzold, Christopher J.; Singan, Vasanth R.; Kuo, Rita; Chovatia, Mansi; Daum, Christopher; Heazlewood, Joshua L.; Zipfel, Cyril; Ronald, Pamela C.

    2015-01-01

    Plant plasma membrane localized pattern recognition receptors (PRRs) detect extracellular pathogen-associated molecules. PRRs such as Arabidopsis EFR and rice XA21 are taxonomically restricted and are absent from most plant genomes. Here we show that rice plants expressing EFR or the chimeric receptor EFR::XA21, containing the EFR ectodomain and the XA21 intracellular domain, sense both Escherichia coli- and Xanthomonas oryzae pv. oryzae (Xoo)-derived elf18 peptides at sub-nanomolar concentrations. Treatment of EFR and EFR::XA21 rice leaf tissue with elf18 leads to MAP kinase activation, reactive oxygen production and defense gene expression. Although expression of EFR does not lead to robust enhanced resistance to fully virulent Xoo isolates, it does lead to quantitatively enhanced resistance to weakly virulent Xoo isolates. EFR interacts with OsSERK2 and the XA21 binding protein 24 (XB24), two key components of the rice XA21-mediated immune response. Rice-EFR plants silenced for OsSERK2, or overexpressing rice XB24 are compromised in elf18-induced reactive oxygen production and defense gene expression indicating that these proteins are also important for EFR-mediated signaling in transgenic rice. Taken together, our results demonstrate the potential feasibility of enhancing disease resistance in rice and possibly other monocotyledonous crop species by expression of dicotyledonous PRRs. Our results also suggest that Arabidopsis EFR utilizes at least a subset of the known endogenous rice XA21 signaling components. PMID:25821973

  8. Transgenic expression of the dicotyledonous pattern recognition receptor EFR in rice leads to ligand-dependent activation of defense responses

    DOE PAGES

    Schwessinger, Benjamin; Bahar, Ofir; Thomas, Nicolas; ...

    2015-03-30

    Plant plasma membrane localized pattern recognition receptors (PRRs) detect extracellular pathogen-associated molecules. PRRs such as Arabidopsis EFR and rice XA21 are taxonomically restricted and are absent from most plant genomes. Here we show that rice plants expressing EFR or the chimeric receptor EFR::XA21, containing the EFR ectodomain and the XA21 intracellular domain, sense both Escherichia coli- and Xanthomonas oryzae pv. oryzae (Xoo)-derived elf18 peptides at sub-nanomolar concentrations. Treatment of EFR and EFR::XA21 rice leaf tissue with elf18 leads to MAP kinase activation, reactive oxygen production and defense gene expression. Although expression of EFR does not lead to robust enhanced resistancemore » to fully virulent Xoo isolates, it does lead to quantitatively enhanced resistance to weakly virulent Xoo isolates. EFR interacts with OsSERK2 and the XA21 binding protein 24 (XB24), two key components of the rice XA21-mediated immune response. Rice-EFR plants silenced for OsSERK2, or overexpressing rice XB24 are compromised in elf18-induced reactive oxygen production and defense gene expression indicating that these proteins are also important for EFR-mediated signaling in transgenic rice. Taken together, our results demonstrate the potential feasibility of enhancing disease resistance in rice and possibly other monocotyledonous crop species by expression of dicotyledonous PRRs. Our results also suggest that Arabidopsis EFR utilizes at least a subset of the known endogenous rice XA21 signaling components.« less

  9. Tuning of temporo-occipital activity by frontal oscillations during virtual mirror exposure causes erroneous self-recognition.

    PubMed

    Serino, Andrea; Sforza, Anna Laura; Kanayama, Noriaki; van Elk, Michiel; Kaliuzhna, Mariia; Herbelin, Bruno; Blanke, Olaf

    2015-10-01

    Self-face recognition, a hallmark of self-awareness, depends on 'off-line' stored information about one's face and 'on-line' multisensory-motor face-related cues. The brain mechanisms of how on-line sensory-motor processes affect off-line neural self-face representations are unknown. This study used 3D virtual reality to create a 'virtual mirror' in which participants saw an avatar's face moving synchronously with their own face movements. Electroencephalographic (EEG) analysis during virtual mirror exposure revealed mu oscillations in sensory-motor cortex signalling on-line congruency between the avatar's and participants' movements. After such exposure and compatible with a change in their off-line self-face representation, participants were more prone to recognize the avatar's face as their own, and this was also reflected in the activation of face-specific regions in the inferotemporal cortex. Further EEG analysis showed that the on-line sensory-motor effects during virtual mirror exposure caused these off-line visual effects, revealing the brain mechanisms that maintain a coherent self-representation, despite our continuously changing appearance.

  10. The effect of encoding strategies on medial temporal lobe activations during the recognition of words: an event-related fMRI study.

    PubMed

    Tsukiura, Takashi; Mochizuki-Kawai, Hiroko; Fujii, Toshikatsu

    2005-04-01

    It is known that manipulation of the encoding strategy affects behavioral and activation data during later retrieval. In the present fMRI study, we examined brain activity during the recognition of words encoded using three different strategies formed by the combination of two factors of relational and self-performed processes. The first encoding strategy involved subjects learning words using both relational and self-performed processes (R+S+). In the second, subjects learned words using only a relational process (R+S-). In the third, subjects learned words without using either process (R-S-). During fMRI after encoding, subjects were randomly presented with words encoded previously and with new words (New) and were required to judge whether or not the word presented had been previously encoded. The fMRI experiment was performed with the event-related design. Compared to New, activation of the left medial temporal lobe (MTL) occurred during the recognition of words encoded using R+S+ and R+S-, whereas right MTL activations only occurred with the R+S+ strategy. ROI analysis for the bilateral hippocampus and parahippocampal gyrus showed a linear increase in left MTL activity (hippocampus and parahippocampal gyrus) during the recognition of words encoded with the R-S-, R+S-, to R+S+, whereas right MTL activity (parahippocampal gyrus) was only increased with the R+S+ strategy. The findings suggest that the left and right MTL structures may contribute differentially to the processes involved in the recognition of stimuli and that these differential activities may depend on the encoding strategies formed by the two factors of relational and self-performed processes.

  11. Self-face recognition shares brain regions active during proprioceptive illusion in the right inferior fronto-parietal superior longitudinal fasciculus III network.

    PubMed

    Morita, Tomoyo; Saito, Daisuke N; Ban, Midori; Shimada, Koji; Okamoto, Yuko; Kosaka, Hirotaka; Okazawa, Hidehiko; Asada, Minoru; Naito, Eiichi

    2017-04-21

    Proprioception is somatic sensation that allows us to sense and recognize position, posture, and their changes in our body parts. It pertains directly to oneself and may contribute to bodily awareness. Likewise, one's face is a symbol of oneself, so that visual self-face recognition directly contributes to the awareness of self as distinct from others. Recently, we showed that right-hemispheric dominant activity in the inferior fronto-parietal cortices, which are connected by the inferior branch of the superior longitudinal fasciculus (SLF III), is associated with proprioceptive illusion (awareness), in concert with sensorimotor activity. Herein, we tested the hypothesis that visual self-face recognition shares brain regions active during proprioceptive illusion in the right inferior fronto-parietal SLF III network. We scanned brain activity using functional magnetic resonance imaging while twenty-two right-handed healthy adults performed two tasks. One was a proprioceptive illusion task, where blindfolded participants experienced a proprioceptive illusion of right hand movement. The other was a visual self-face recognition task, where the participants judged whether an observed face was their own. We examined whether the self-face recognition and the proprioceptive illusion commonly activated the inferior fronto-parietal cortices connected by the SLF III in a right-hemispheric dominant manner. Despite the difference in sensory modality and in the body parts involved in the two tasks, both tasks activated the right inferior fronto-parietal cortices, which are likely connected by the SLF III, in a right-side dominant manner. Here we discuss possible roles for right inferior fronto-parietal activity in bodily awareness and self-awareness.

  12. Structural basis for glycogen recognition by AMP-activated protein kinase.

    PubMed

    Polekhina, Galina; Gupta, Abhilasha; van Denderen, Bryce J W; Feil, Susanne C; Kemp, Bruce E; Stapleton, David; Parker, Michael W

    2005-10-01

    AMP-activated protein kinase (AMPK) coordinates cellular metabolism in response to energy demand as well as to a variety of stimuli. The AMPK beta subunit acts as a scaffold for the alpha catalytic and gamma regulatory subunits and targets the AMPK heterotrimer to glycogen. We have determined the structure of the AMPK beta glycogen binding domain in complex with beta-cyclodextrin. The structure reveals a carbohydrate binding pocket that consolidates all known aspects of carbohydrate binding observed in starch binding domains into one site, with extensive contact between several residues and five glucose units. beta-cyclodextrin is held in a pincer-like grasp with two tryptophan residues cradling two beta-cyclodextrin glucose units and a leucine residue piercing the beta-cyclodextrin ring. Mutation of key beta-cyclodextrin binding residues either partially or completely prevents the glycogen binding domain from binding glycogen. Modeling suggests that this binding pocket enables AMPK to interact with glycogen anywhere across the carbohydrate's helical surface.

  13. Dynamics of activation of semantically similar concepts during spoken word recognition.

    PubMed

    Mirman, Daniel; Magnuson, James S

    2009-10-01

    Semantic similarity effects provide critical insight into the organization of semantic knowledge and the nature of semantic processing. In the present study, we examined the dynamics of semantic similarity effects by using the visual world eyetracking paradigm. Four objects were shown on a computer monitor, and participants were instructed to click on a named object, during which time their gaze position was recorded. The likelihood of fixating competitor objects was predicted by the degree of semantic similarity to the target concept. We found reliable, graded competition that depended on degree of target-competitor similarity, even for distantly related items for which priming has not been found in previous priming studies. Time course measures revealed a consistently earlier fixation peak for near semantic neighbors relative to targets. Computational investigations with an attractor dynamical model, a spreading activation model, and a decision model revealed that a combination of excitatory and inhibitory mechanisms is required to obtain such peak timing, providing new constraints on models of semantic processing.

  14. Recognition-mediated activation of therapeutic gold nanoparticles inside living cells

    NASA Astrophysics Data System (ADS)

    Kim, Chaekyu; Agasti, Sarit S.; Zhu, Zhengjiang; Isaacs, Lyle; Rotello, Vincent M.

    2010-11-01

    Supramolecular chemistry provides a versatile tool for the organization of molecular systems into functional structures and the actuation of these assemblies for applications through the reversible association between complementary components. Use of this methodology in living systems, however, represents a significant challenge owing to the chemical complexity of cellular environments and lack of selectivity of conventional supramolecular interactions. Herein, we present a host-guest system featuring diaminohexane-terminated gold nanoparticles (AuNP-NH2) and complementary cucurbit[7]uril (CB[7]). In this system, threading of CB[7] on the particle surface reduces the cytotoxicity of AuNP-NH2 through sequestration of the particle in endosomes. Intracellular triggering of the therapeutic effect of AuNP-NH2 was then achieved through the administration of 1-adamantylamine (ADA), removing CB[7] from the nanoparticle surface, causing the endosomal release and concomitant in situ cytotoxicity of AuNP-NH2. This supramolecular strategy for intracellular activation provides a new tool for potential therapeutic applications.

  15. An algorithm to detect fire activity using Meteosat: fine tuning and quality assesment

    NASA Astrophysics Data System (ADS)

    Amraoui, M.; DaCamara, C. C.; Ermida, S. L.

    2012-04-01

    Hot spot detection by means of sensors on-board geostationary satellites allows studying wildfire activity at hourly and even sub-hourly intervals, an advantage that cannot be met by polar orbiters. Since 1997, the Satellite Application Facility for Land Surface Analysis has been running an operational procedure that allows detecting active fires based on information from Meteosat-8/SEVIRI. This is the so-called Fire Detection and Monitoring (FD&M) product and the procedure takes advantage of the temporal resolution of SEVIRI (one image every 15 min), and relies on information from SEVIRI channels (namely 0.6, 0.8, 3.9, 10.8 and 12.0 μm) together with information on illumination angles. The method is based on heritage from contextual algorithms designed for polar, sun-synchronous instruments, namely NOAA/AVHRR and MODIS/TERRAAQUA. A potential fire pixel is compared with the neighboring ones and the decision is made based on relative thresholds as derived from the pixels in the neighborhood. Generally speaking, the observed fire incidence compares well against hot spots extracted from the global daily active fire product developed by the MODIS Fire Team. However, values of probability of detection (POD) tend to be quite low, a result that may be partially expected by the finer resolution of MODIS. The aim of the present study is to make a systematic assessment of the impacts on POD and False Alarm Ratio (FAR) of the several parameters that are set in the algorithms. Such parameters range from the threshold values of brightness temperature in the IR3.9 and 10.8 channels that are used to select potential fire pixels up to the extent of the background grid and thresholds used to statistically characterize the radiometric departures of a potential pixel from the respective background. The impact of different criteria to identify pixels contaminated by clouds, smoke and sun glint is also evaluated. Finally, the advantages that may be brought to the algorithm by adding

  16. Posttraining activation of CB1 cannabinoid receptors in the CA1 region of the dorsal hippocampus impairs object recognition long-term memory.

    PubMed

    Clarke, Julia R; Rossato, Janine I; Monteiro, Siomara; Bevilaqua, Lia R M; Izquierdo, Iván; Cammarota, Martín

    2008-09-01

    Evidence indicates that brain endocannabinoids are involved in memory processing. However, the participation of CB1 and CB2 cannabinoid receptors in recognition memory has not been yet conclusively determined. Therefore, we evaluated the effect of the posttraining activation of hippocampal cannabinoid receptors on the consolidation of object recognition memory. Rats with infusion cannulae stereotaxically aimed to the CA1 region of the dorsal hippocampus were trained in an object recognition learning task involving exposure to two different stimulus objects. Memory retention was assessed at different times after training. In the test sessions, one of the objects presented during training was replaced by a novel one. When infused in the CA1 region immediately after training, the non-selective cannabinoid receptor agonist WIN-55,212-2 and the endocannabinoid membrane transporter inhibitor VDM-11 blocked long-term memory retention in a dose-dependent manner without affecting short-term memory, exploratory behavior, anxiety state or the functionality of the hippocampus. The amnesic effect of WIN-55,212-2 and VDM-11 was not due to state-dependency and was completely reversed by co-infusion of the CB1 receptor antagonist AM-251 and mimicked by the CB1 receptor agonist ACEA but not by the CB2 receptor agonists JWH-015 and palmitoylethanolamide. Our data indicate that activation of hippocampal CB1 receptors early after training hampers consolidation of object recognition memory.

  17. Echo mapping of active galactic nuclei broad-line regions: Fundamental algorithms

    NASA Technical Reports Server (NTRS)

    Vio, Roberto; Horne, Keith; Wamsteker, Willem

    1994-01-01

    We formulate and test a series of algorithms for echo mapping the emission-line regions near active galactic nuclei from measurements of correlated variability in their line and continuum light curves. The linear regularization method (LRM) employs a direct inversion of evenly spaced light-curve data, with a regularization parameter that can be used to control the trade-off between noise and resolution. Matrix formulas express the formal solution as well as its variance and covariance in terms of uncertainties in the measurements. Unlike the maximum-entropy method (MEM), LRM applies to kernels with both positive and negative values, but the results are somewhat limited by ringing effects. A positivity constraint proves effective in controlling the ringing. MEM combines regularization and positivity in a natural way, but similar results are also found using positivity constraints with nonentropic regularization functions. Direct inversions of unevenly sampled light curves require interpolating the noisy data. In this case better results are found by solving for both the continuum light curve and kernel function in a simultaneous fit to the data. Our conclusion is that while echo mapping currently gives ambiguous results, the algorithms are not the limiting factor. Progress depends on efforts to increase the accuracy and completeness of sampling of the observed light curves.

  18. Refinement and evaluation of helicopter real-time self-adaptive active vibration controller algorithms

    NASA Technical Reports Server (NTRS)

    Davis, M. W.

    1984-01-01

    A Real-Time Self-Adaptive (RTSA) active vibration controller was used as the framework in developing a computer program for a generic controller that can be used to alleviate helicopter vibration. Based upon on-line identification of system parameters, the generic controller minimizes vibration in the fuselage by closed-loop implementation of higher harmonic control in the main rotor system. The new generic controller incorporates a set of improved algorithms that gives the capability to readily define many different configurations by selecting one of three different controller types (deterministic, cautious, and dual), one of two linear system models (local and global), and one or more of several methods of applying limits on control inputs (external and/or internal limits on higher harmonic pitch amplitude and rate). A helicopter rotor simulation analysis was used to evaluate the algorithms associated with the alternative controller types as applied to the four-bladed H-34 rotor mounted on the NASA Ames Rotor Test Apparatus (RTA) which represents the fuselage. After proper tuning all three controllers provide more effective vibration reduction and converge more quickly and smoothly with smaller control inputs than the initial RTSA controller (deterministic with external pitch-rate limiting). It is demonstrated that internal limiting of the control inputs a significantly improves the overall performance of the deterministic controller.

  19. Single-Sample Face Recognition with Image Corruption and Misalignment via Sparse Illumination Transfer

    DTIC Science & Technology

    2013-06-01

    1998. [5] T. Cootes, C. Taylor, and J. Graham . Active shape models – their training and application. CVIU, 61:38–59, 1995. [6] W. Deng, J. Hu, and J...1- minimization algorithms for robust face recognition. Techni- cal Report arXiv:1007.3753, University of California, Berke- ley , 2012. [30] M. Yang

  20. Recognition and activation of Rho GTPases by Vav1 and Vav2 guanine nucleotide exchange factors.

    PubMed

    Heo, Jongyun; Thapar, Roopa; Campbell, Sharon L

    2005-05-03

    Vav proteins are Rho GTPase-specific guanine nucleotide exchange factors (GEFs) that are distinguished by the tandem arrangement of Dbl homology (DH), Pleckstrin homology (PH), and cysteine rich domains (CRD). Whereas the tandem DH-PH arrangement is conserved among Rho GEFs, the presence of the CRD is unique to Vav family members and is required for efficient nucleotide exchange. We provide evidence that Vav2-mediated nucleotide exchange of Rho GTPases follows the Theorell-Chance mechanism in which the Vav2.Rho GTPase complex is the major species during the exchange process and the Vav2.GDP-Mg(2+).Rho GTPase ternary complex is present only transiently. The GTPase specificity for the DH-PH-CRD Vav2 in vitro follows this order: Rac1 > Cdc42 > RhoA. Results obtained from fluorescence anisotropy and NMR chemical shift mapping experiments indicate that the isolated Vav1 CRD is capable of directly associating with Rac1, and residues K116 and S83 that are in the proximity of the P-loop and the guanine base either are part of this binding interface or undergo a conformational change in response to CRD binding. The NMR studies are supported by kinetic measurements on Rac1 mutants S83A, K116A, and K116Q and Vav2 CRD mutant K533A in that these mutants affect both the initial binding event of Vav2 with Rac1 (k(on)) and the rate-limiting dissociation of Vav2 from the Vav2.Rac1 binary complex (thereby influencing the enzyme turnover number, k(cat)). The results suggest that the CRD domain in Vav proteins plays an active role, affecting both the k(on) and the k(cat) for Vav-mediated nucleotide exchange on Rho GTPases.

  1. Crocins, the active constituents of Crocus sativus L., counteracted apomorphine-induced performance deficits in the novel object recognition task, but not novel object location task, in rats.

    PubMed

    Pitsikas, Nikolaos; Tarantilis, Petros A

    2017-02-17

    Schizophrenia is a chronic mental disease that affects nearly 1% of the population worldwide. Several lines of evidence suggest that the dopaminergic (DAergic) system might be compromised in schizophrenia. Specifically, the mixed dopamine (DA) D1/D2 receptor agonist apomorphine induces schizophrenia-like symptoms in rodents, including disruption of memory abilities. Crocins are among the active components of saffron (dried stigmas of Crocus sativus L. plant) and their implication in cognition is well documented. The present study investigated whether crocins counteract non-spatial and spatial recognition memory deficits induced by apomorphine in rats. For this purpose, the novel object recognition task (NORT) and the novel object location task (NOLT) were used. The effects of compounds on mobility in a locomotor activity chamber were also investigated in rats. Post-training peripheral administration of crocins (15 and 30mg/kg) counteracted apomorphine (1mg/kg)-induced performance deficits in the NORT. Conversely, crocins did not attenuate spatial recognition memory deficits produced by apomorphine in the NOLT. The present data show that crocins reversed non-spatial recognition memory impairments produced by dysfunction of the DAergic system and modulate different aspects of memory components (storage and/or retrieval). The effects of compounds on recognition memory cannot be attributed to changes in locomotor activity. Further, our findings illustrate a functional interaction between crocins and the DAergic system that may be of relevance for schizophrenia-like behavioral deficits. Therefore, the utilization of crocins as an adjunctive agent, for the treatment of cognitive deficits observed in schizophrenic patients should be further investigated.

  2. GASAKe: forecasting landslide activations by a genetic-algorithms based hydrological model

    NASA Astrophysics Data System (ADS)

    Terranova, O. G.; Gariano, S. L.; Iaquinta, P.; Iovine, G. G. R.

    2015-02-01

    GASAKe is a new hydrological model aimed at forecasting the triggering of landslides. The model is based on genetic-algorithms and allows to obtaining thresholds of landslide activation from the set of historical occurrences and from the rainfall series. GASAKe can be applied to either single landslides or set of similar slope movements in a homogeneous environment. Calibration of the model is based on genetic-algorithms, and provides for families of optimal, discretized solutions (kernels) that maximize the fitness function. Starting from these latter, the corresponding mobility functions (i.e. the predictive tools) can be obtained through convolution with the rain series. The base time of the kernel is related to the magnitude of the considered slope movement, as well as to hydro-geological complexity of the site. Generally, smaller values are expected for shallow slope instabilities with respect to large-scale phenomena. Once validated, the model can be applied to estimate the timing of future landslide activations in the same study area, by employing recorded or forecasted rainfall series. Example of application of GASAKe to a medium-scale slope movement (the Uncino landslide at San Fili, in Calabria, Southern Italy) and to a set of shallow landslides (in the Sorrento Peninsula, Campania, Southern Italy) are discussed. In both cases, a successful calibration of the model has been achieved, despite unavoidable uncertainties concerning the dates of landslide occurrence. In particular, for the Sorrento Peninsula case, a fitness of 0.81 has been obtained by calibrating the model against 10 dates of landslide activation; in the Uncino case, a fitness of 1 (i.e. neither missing nor false alarms) has been achieved against 5 activations. As for temporal validation, the experiments performed by considering the extra dates of landslide activation have also proved satisfactory. In view of early-warning applications for civil protection purposes, the capability of the

  3. Recognition of Intensive Valence and Arousal Affective States via Facial Electromyographic Activity in Young and Senior Adults

    PubMed Central

    Li, Hang; Walter, Steffen; Hrabal, David; Rukavina, Stefanie; Limbrecht-Ecklundt, Kerstin; Hoffman, Holger; Traue, Harald C.

    2016-01-01

    Background Research suggests that interaction between humans and digital environments characterizes a form of companionship in addition to technical convenience. To this effect, humans have attempted to design computer systems able to demonstrably empathize with the human affective experience. Facial electromyography (EMG) is one such technique enabling machines to access to human affective states. Numerous studies have investigated the effects of valence emotions on facial EMG activity captured over the corrugator supercilii (frowning muscle) and zygomaticus major (smiling muscle). The arousal emotion, specifically, has not received much research attention, however. In the present study, we sought to identify intensive valence and arousal affective states via facial EMG activity. Methods Ten blocks of affective pictures were separated into five categories: neutral valence/low arousal (0VLA), positive valence/high arousal (PVHA), negative valence/high arousal (NVHA), positive valence/low arousal (PVLA), and negative valence/low arousal (NVLA), and the ability of each to elicit corresponding valence and arousal affective states was investigated at length. One hundred and thirteen participants were subjected to these stimuli and provided facial EMG. A set of 16 features based on the amplitude, frequency, predictability, and variability of signals was defined and classified using a support vector machine (SVM). Results We observed highly accurate classification rates based on the combined corrugator and zygomaticus EMG, ranging from 75.69% to 100.00% for the baseline and five affective states (0VLA, PVHA, PVLA, NVHA, and NVLA) in all individuals. There were significant differences in classification rate accuracy between senior and young adults, but there was no significant difference between female and male participants. Conclusion Our research provides robust evidences for recognition of intensive valence and arousal affective states in young and senior adults. These

  4. Use of particle filters in an active control algorithm for noisy nonlinear structural dynamical systems

    NASA Astrophysics Data System (ADS)

    Sajeeb, R.; Manohar, C. S.; Roy, D.

    2007-09-01

    The problem of active control of nonlinear structural dynamical systems, in the presence of both process and measurement noises, is considered. The focus of the study is on the use of particle filters for state estimation in feedback control algorithms for nonlinear structures, when a limited number of noisy output measurements are available. The control design is done using the state-dependent Riccati equation (SDRE) method. The stochastic differential equations (SDEs) governing the dynamical systems are discretized using explicit forms of Ito-Taylor expansions. The Bayesian bootstrap filter and that based on sequential important sampling (SIS) are employed for state estimation. The simulation results show the feasibility of using particle filters and SDRE techniques in control of nonlinear structural dynamical systems.

  5. Unfolding neutron energy spectra from foil activation detector measurements with the Gold algorithm

    NASA Astrophysics Data System (ADS)

    Seghour, A.; Seghour, F. Z.

    2001-01-01

    In this work, the Gold algorithm is applied to the unfolding of neutron reactor energy spectra from reaction rates data of multiple foil activation detectors. Such a method, which forms the basis of a developed unfolding computer program called SAYD, has the advantage of not requiring a priori knowledge on the spectrum in the unfolding process. The program SAYD is first illustrated by synthesized reaction rates data calculated using a semi-empirical formulation of a typical intermediate and fast neutron reactor spectrum. The demonstration of the unfolding program SAYD is next achieved using measured reaction rates of the Arkansas Nuclear One power plant (ANO) benchmark spectrum by comparing results of SAYD program with those obtained by STAYNL and MSANDB unfolding codes.

  6. An intelligent active force control algorithm to control an upper extremity exoskeleton for motor recovery

    NASA Astrophysics Data System (ADS)

    Hasbullah Mohd Isa, Wan; Taha, Zahari; Mohd Khairuddin, Ismail; Majeed, Anwar P. P. Abdul; Fikri Muhammad, Khairul; Abdo Hashem, Mohammed; Mahmud, Jamaluddin; Mohamed, Zulkifli

    2016-02-01

    This paper presents the modelling and control of a two degree of freedom upper extremity exoskeleton by means of an intelligent active force control (AFC) mechanism. The Newton-Euler formulation was used in deriving the dynamic modelling of both the anthropometry based human upper extremity as well as the exoskeleton that consists of the upper arm and the forearm. A proportional-derivative (PD) architecture is employed in this study to investigate its efficacy performing joint-space control objectives. An intelligent AFC algorithm is also incorporated into the PD to investigate the effectiveness of this hybrid system in compensating disturbances. The Mamdani Fuzzy based rule is employed to approximate the estimated inertial properties of the system to ensure the AFC loop responds efficiently. It is found that the IAFC-PD performed well against the disturbances introduced into the system as compared to the conventional PD control architecture in performing the desired trajectory tracking.

  7. Stereospecific recognition and quantitative structure-activity relationship between antibodies and enantiomers: ofloxacin as a model hapten.

    PubMed

    Mu, Hongtao; Wang, Baoling; Xu, Zhenlin; Sun, Yuanming; Huang, Xinan; Shen, Yudong; Eremin, Sergei A; Zherdev, Anatoly V; Dzantiev, Boris B; Lei, Hongtao

    2015-02-21

    In this study, ofloxacin stereoisomers were chosen as a simple model to investigate the stereospecific recognition of chiral haptens and antibodies. Three polyclonal antibodies were studied and showed a relatively high enantioselectivity and an excellent sensitivity. Comparative molecular field analysis and comparative molecular similarity indices analysis were employed to investigate the chiral recognition between the antibody and the ofloxacin enantiomer, and all the models yielded high correlation and predictive ability. It was found that the chiral discrimination was probably caused by steric hindrance; the antibody stereospecificity could be ascribed to the variation of the R1 and R3 groups of quinolones; the common structure of the quinolones is also essential in the hapten-antibody recognition. The recognition between the chiral haptens and the antibodies was co-affected by multiple interaction forces, and those forces were defined explicitly at the sub-structural level. An illustrative enhanced model with good simplicity and universality was also developed for a better understanding of the stereospecific recognition of ofloxacin enantiomers and antibodies for the first time. This work provides insights into the stereospecific recognition of chiral haptens and antibodies.

  8. Marginalised Stacked Denoising Autoencoders for Robust Representation of Real-Time Multi-View Action Recognition

    PubMed Central

    Gu, Feng; Flórez-Revuelta, Francisco; Monekosso, Dorothy; Remagnino, Paolo

    2015-01-01

    Multi-view action recognition has gained a great interest in video surveillance, human computer interaction, and multimedia retrieval, where multiple cameras of different types are deployed to provide a complementary field of views. Fusion of multiple camera views evidently leads to more robust decisions on both tracking multiple targets and analysing complex human activities, especially where there are occlusions. In this paper, we incorporate the marginalised stacked denoising autoencoders (mSDA) algorithm to further improve the bag of words (BoWs) representation in terms of robustness and usefulness for multi-view action recognition. The resulting representations are fed into three simple fusion strategies as well as a multiple kernel learning algorithm at the classification stage. Based on the internal evaluation, the codebook size of BoWs and the number of layers of mSDA may not significantly affect recognition performance. According to results on three multi-view benchmark datasets, the proposed framework improves recognition performance across all three datasets and outputs record recognition performance, beating the state-of-art algorithms in the literature. It is also capable of performing real-time action recognition at a frame rate ranging from 33 to 45, which could be further improved by using more powerful machines in future applications. PMID:26193271

  9. A new algorithmic approach for fingers detection and identification

    NASA Astrophysics Data System (ADS)

    Mubashar Khan, Arslan; Umar, Waqas; Choudhary, Taimoor; Hussain, Fawad; Haroon Yousaf, Muhammad

    2013-03-01

    Gesture recognition is concerned with the goal of interpreting human gestures through mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Hand gesture detection in a real time environment, where the time and memory are important issues, is a critical operation. Hand gesture recognition largely depends on the accurate detection of the fingers. This paper presents a new algorithmic approach to detect and identify fingers of human hand. The proposed algorithm does not depend upon the prior knowledge of the scene. It detects the active fingers and Metacarpophalangeal (MCP) of the inactive fingers from an already detected hand. Dynamic thresholding technique and connected component labeling scheme are employed for background elimination and hand detection respectively. Algorithm proposed a new approach for finger identification in real time environment keeping the memory and time constraint as low as possible.

  10. Investigating gait recognition in the short-wave infrared (SWIR) spectrum: dataset and challenges

    NASA Astrophysics Data System (ADS)

    DeCann, Brian; Ross, Arun; Dawson, Jeremy

    2013-05-01

    In the biometrics community, challenge datasets are often released to determine the robustness of state-of-the- art algorithms to conditions that can confound recognition accuracy. In the context of automated human gait recognition, evaluation has predominantly been conducted on video data acquired in the active visible spectral band, although recent literature has explored recognition in the passive thermal band. The advent of sophisticated sensors has piqued interest in performing gait recognition in other spectral bands such as short-wave infrared (SWIR), due to their use in military-based tactical applications and the possibility of operating in nighttime environments. Further, in many operational scenarios, the environmental variables are not controlled, thereby posing several challenges to traditional recognition schemes. In this work, we discuss the possibility of performing gait recognition in the SWIR spectrum by first assembling a dataset, referred to as the WVU Outdoor SWIR Gait (WOSG) Dataset, and then evaluate the performance of three gait recognition algorithms on the dataset. The dataset consists of 155 subjects and represents gait information acquired under multiple walking paths in an uncontrolled, outdoor environment. Detailed experimental analysis suggests the benefits of distributing this new challenging dataset to the broader research community. In particular, the following observations were made: (a) the importance of SWIR imagery in acquiring data covertly for surveillance applications; (b) the difficulty in extracting human silhouettes in low-contrast SWIR imagery; (c) the impact of silhouette quality on overall recognition accuracy; (d) the possibility of matching gait sequences pertaining to different walking trajectories; and (e) the need for developing sophisticated gait recognition algorithms to handle data acquired in unconstrained environments.

  11. Gamma-band activity as a signature for cross-modal priming of auditory object recognition by active haptic exploration.

    PubMed

    Schneider, Till R; Lorenz, Simone; Senkowski, Daniel; Engel, Andreas K

    2011-02-16

    When visual sensory information is restricted, we often rely on haptic and auditory information to recognize objects. Here we examined how haptic exploration of familiar objects affects neural processing of subsequently presented sounds of objects. Recent studies indicated that oscillatory responses, in particular in the gamma band (30-100 Hz), reflect cross-modal processing, but it is not clear which cortical networks are involved. In this high-density EEG study, we measured gamma-band activity (GBA) in humans performing a haptic-to-auditory priming paradigm. Haptic stimuli served as primes, and sounds of objects as targets. Haptic and auditory stimuli were either semantically congruent or incongruent, and participants were asked to categorize the objects represented by the sounds. Response times were shorter for semantically congruent compared with semantically incongruent inputs. This haptic-to-auditory priming effect was associated with enhanced total power GBA (250-350 ms) for semantically congruent inputs and additional effects of semantic congruency on evoked GBA (50-100 ms). Source reconstruction of total GBA using linear beamforming revealed effects of semantic congruency in the left lateral temporal lobe, possibly reflecting matching of information across modalities. For semantically incongruent inputs, total GBA was enhanced in middle frontal cortices, possibly indicating the processing or detection of conflicting information. Our findings demonstrate that semantic priming by haptic object exploration affects processing of auditory inputs in the lateral temporal lobe and suggest an important role of oscillatory activity for multisensory processing.

  12. Identification of mild cognitive impairment in ACTIVE: algorithmic classification and stability.

    PubMed

    Cook, Sarah E; Marsiske, Michael; Thomas, Kelsey R; Unverzagt, Frederick W; Wadley, Virginia G; Langbaum, Jessica B S; Crowe, Michael

    2013-01-01

    Rates of mild cognitive impairment (MCI) have varied substantially, depending on the criteria used and the samples surveyed. The present investigation used a psychometric algorithm for identifying MCI and its stability to determine if low cognitive functioning was related to poorer longitudinal outcomes. The Advanced Cognitive Training of Independent and Vital Elders (ACTIVE) study is a multi-site longitudinal investigation of long-term effects of cognitive training with older adults. ACTIVE exclusion criteria eliminated participants at highest risk for dementia (i.e., Mini-Mental State Examination < 23). Using composite normative for sample- and training-corrected psychometric data, 8.07% of the sample had amnestic impairment, while 25.09% had a non-amnestic impairment at baseline. Poorer baseline functional scores were observed in those with impairment at the first visit, including a higher rate of attrition, depressive symptoms, and self-reported physical functioning. Participants were then classified based upon the stability of their classification. Those who were stably impaired over the 5-year interval had the worst functional outcomes (e.g., Instrumental Activities of Daily Living performance), and inconsistency in classification over time also appeared to be associated increased risk. These findings suggest that there is prognostic value in assessing and tracking cognition to assist in identifying the critical baseline features associated with poorer outcomes.

  13. Appearance-based color face recognition with 3D model

    NASA Astrophysics Data System (ADS)

    Wang, Chengzhang; Bai, Xiaoming

    2013-03-01

    Appearance-based face recognition approaches explore color cues of face images, i.e. grey or color information for recognition task. They first encode color face images, and then extract facial features for classification. Similar to conventional singular value decomposition, hypercomplex matrix also exists singular value decomposition on hypercomplex field. In this paper, a novel color face recognition approach based on hypercomplex singular value decomposition is proposed. The approach employs hypercomplex to encode color face information of different channels simultaneously. Hypercomplex singular value decomposition is utilized then to compute the basis vectors of the color face subspace. To improve learning efficiency of the algorithm, 3D active deformable model is exploited to generate virtual face images. Color face samples are projected onto the subspace and projection coefficients are utilized as facial features. Experimental results on CMU PIE face database verify the effectiveness of the proposed approach.

  14. Towards automatic musical instrument timbre recognition

    NASA Astrophysics Data System (ADS)

    Park, Tae Hong

    This dissertation is comprised of two parts---focus on issues concerning research and development of an artificial system for automatic musical instrument timbre recognition and musical compositions. The technical part of the essay includes a detailed record of developed and implemented algorithms for feature extraction and pattern recognition. A review of existing literature introducing historical aspects surrounding timbre research, problems associated with a number of timbre definitions, and highlights of selected research activities that have had significant impact in this field are also included. The developed timbre recognition system follows a bottom-up, data-driven model that includes a pre-processing module, feature extraction module, and a RBF/EBF (Radial/Elliptical Basis Function) neural network-based pattern recognition module. 829 monophonic samples from 12 instruments have been chosen from the Peter Siedlaczek library (Best Service) and other samples from the Internet and personal collections. Significant emphasis has been put on feature extraction development and testing to achieve robust and consistent feature vectors that are eventually passed to the neural network module. In order to avoid a garbage-in-garbage-out (GIGO) trap and improve generality, extra care was taken in designing and testing the developed algorithms using various dynamics, different playing techniques, and a variety of pitches for each instrument with inclusion of attack and steady-state portions of a signal. Most of the research and development was conducted in Matlab. The compositional part of the essay includes brief introductions to "A d'Ess Are ," "Aboji," "48 13 N, 16 20 O," and "pH-SQ." A general outline pertaining to the ideas and concepts behind the architectural designs of the pieces including formal structures, time structures, orchestration methods, and pitch structures are also presented.

  15. Study on Information Fusion Based Check Recognition System

    NASA Astrophysics Data System (ADS)

    Wang, Dong

    Automatic check recognition techniques play an important role in financial systems, especially in risk management. This paper presents a novel check recognition system based on multi-cue information fusion theory. For Chinese bank check, the amount can be independently determined by legal amount, courtesy amount, or E13B code. The check recognition algorithm consists of four steps: preprocessing, check layout analysis, segmentation and recognition, and information fusion. For layout analysis, an adaptive template matching algorithm is presented to locate the target recognition regions on the check. The hidden markov model is used to segment and recognize legal amount. Courtesy and E13B code are recognized by artificial neural network method, respectively. Finally, D-S evidence theory is then introduced to fuse above three recognition results for better recognition performance. Experimental results demonstrate that the system can robustly recognize checks and the information fusion based algorithm improves the recognition rate by 5~10 percent.

  16. A Neural Network Based Speech Recognition System

    DTIC Science & Technology

    1990-02-01

    encoder and identifies individual words. This use of neural networks offers two advantages over conventional algorithmic detectors: the detection...environment. Keywords: Artificial intelligence; Neural networks : Back propagation; Speech recognition.

  17. Time-domain filtered-x-Newton narrowband algorithms for active isolation of frequency-fluctuating vibration

    NASA Astrophysics Data System (ADS)

    Li, Yan; He, Lin; Shuai, Chang-geng; Wang, Fei

    2016-04-01

    A time-domain filtered-x Newton narrowband algorithm (the Fx-Newton algorithm) is proposed to address three major problems in active isolation of machinery vibration: multiple narrowband components, MIMO coupling, and amplitude and frequency fluctuations. In this algorithm, narrowband components are extracted by narrowband-pass filters (NBPF) and independently controlled by multi-controllers, and fast convergence of the control algorithm is achieved by inverse secondary-path filtering of the extracted sinusoidal reference signal and its orthogonal component using L×L numbers of 2nd-order filters in the time domain. Controller adapting and control signal generation are also implemented in the time domain, to ensure good real-time performance. The phase shift caused by narrowband filter is compensated online to improve the robustness of control system to frequency fluctuations. A double-reference Fx-Newton algorithm is also proposed to control double sinusoids in the same frequency band, under the precondition of acquiring two independent reference signals. Experiments are conducted with an MIMO single-deck vibration isolation system on which a 200 kW ship diesel generator is mounted, and the algorithms are tested under the vibration alternately excited by the diesel generator and inertial shakers. The results of control over sinusoidal vibration excited by inertial shakers suggest that the Fx-Newton algorithm with NBPF have much faster convergence rate and better attenuation effect than the Fx-LMS algorithm. For swept, frequency-jumping, double, double frequency-swept and double frequency-jumping sinusoidal vibration, and multiple high-level harmonics in broadband vibration excited by the diesel generator, the proposed algorithms also demonstrate large vibration suppression at fast convergence rate, and good robustness to vibration with frequency fluctuations.

  18. Transgenic expression of the dicotyledonous pattern recognition receptor EFR in rice leads to ligand-dependent activation of defense responses

    SciTech Connect

    Schwessinger, Benjamin; Bahar, Ofir; Thomas, Nicolas; Holton, Nicolas; Nekrasov, Vladimir; Ruan, Deling; Canlas, Patrick E.; Daudi, Arsalan; Petzold, Christopher J.; Singan, Vasanth R.; Kuo, Rita; Chovatia, Mansi; Daum, Christopher; Heazlewood, Joshua L.; Zipfel, Cyril; Ronald, Pamela C.

    2015-03-30

    Plant plasma membrane localized pattern recognition receptors (PRRs) detect extracellular pathogen-associated molecules. PRRs such as Arabidopsis EFR and rice XA21 are taxonomically restricted and are absent from most plant genomes. Here we show that rice plants expressing EFR or the chimeric receptor EFR::XA21, containing the EFR ectodomain and the XA21 intracellular domain, sense both Escherichia coli- and Xanthomonas oryzae pv. oryzae (Xoo)-derived elf18 peptides at sub-nanomolar concentrations. Treatment of EFR and EFR::XA21 rice leaf tissue with elf18 leads to MAP kinase activation, reactive oxygen production and defense gene expression. Although expression of EFR does not lead to robust enhanced resistance to fully virulent Xoo isolates, it does lead to quantitatively enhanced resistance to weakly virulent Xoo isolates. EFR interacts with OsSERK2 and the XA21 binding protein 24 (XB24), two key components of the rice XA21-mediated immune response. Rice-EFR plants silenced for OsSERK2, or overexpressing rice XB24 are compromised in elf18-induced reactive oxygen production and defense gene expression indicating that these proteins are also important for EFR-mediated signaling in transgenic rice. Taken together, our results demonstrate the potential feasibility of enhancing disease resistance in rice and possibly other monocotyledonous crop species by expression of dicotyledonous PRRs. Our results also suggest that Arabidopsis EFR utilizes at least a subset of the known endogenous rice XA21 signaling components.

  19. CT liver volumetry using geodesic active contour segmentation with a level-set algorithm

    NASA Astrophysics Data System (ADS)

    Suzuki, Kenji; Epstein, Mark L.; Kohlbrenner, Ryan; Obajuluwa, Ademola; Xu, Jianwu; Hori, Masatoshi; Baron, Richard

    2010-03-01

    Automatic liver segmentation on CT images is challenging because the liver often abuts other organs of a similar density. Our purpose was to develop an accurate automated liver segmentation scheme for measuring liver volumes. We developed an automated volumetry scheme for the liver in CT based on a 5 step schema. First, an anisotropic smoothing filter was applied to portal-venous phase CT images to remove noise while preserving the liver structure, followed by an edge enhancer to enhance the liver boundary. By using the boundary-enhanced image as a speed function, a fastmarching algorithm generated an initial surface that roughly estimated the liver shape. A geodesic-active-contour segmentation algorithm coupled with level-set contour-evolution refined the initial surface so as to more precisely fit the liver boundary. The liver volume was calculated based on the refined liver surface. Hepatic CT scans of eighteen prospective liver donors were obtained under a liver transplant protocol with a multi-detector CT system. Automated liver volumes obtained were compared with those manually traced by a radiologist, used as "gold standard." The mean liver volume obtained with our scheme was 1,520 cc, whereas the mean manual volume was 1,486 cc, with the mean absolute difference of 104 cc (7.0%). CT liver volumetrics based on an automated scheme agreed excellently with "goldstandard" manual volumetrics (intra-class correlation coefficient was 0.95) with no statistically significant difference (p(F<=f)=0.32), and required substantially less completion time. Our automated scheme provides an efficient and accurate way of measuring liver volumes.

  20. Prolonged duration of isoflurane anesthesia impairs spatial recognition memory through the activation of JNK1/2 in the hippocampus of mice.

    PubMed

    Jiang, Shan; Miao, Bei; Chen, Ying

    2017-02-24

    Postoperative cognitive dysfunction is a frequent complication with surgery and anesthesia, and the underlying mechanism is unclear. Our aim was to investigate the effect of different durations of isoflurane anesthesia on spatial recognition memory and activation of JNK1/2 in the hippocampus of mice. In the present study, adult male mice were anesthetized with isoflurane for different durations (1.5% isoflurane for 1, 2, and 4 h). Spatial recognition memory was determined using spontaneous alternation and two-trial recognition memory in Y-maze at 24 h after anesthesia. The activation of JNK1/2 in the hippocampus was tested using western blot. Mice treated with isoflurane for 4 h showed significantly decreased spontaneous alternations and decreased exploration parameters compared with the no anesthesia group, but this was not observed in mice treated with isoflurane for 1 or 2 h. The protein levels of p-JNK1/2 in the hippocampus were significantly increased at 10 min after isoflurane anesthesia for 1, 2, and 4 h compared with no anesthesia. However, only isoflurane anesthesia for 4 h still increased JNK1/2 and p-JNK1/2 levels at 24 h after anesthesia. We concluded that prolonged duration of isoflurane anesthesia maintained the activation of JNK1/2, which led to memory impairment at 24 h after anesthesia.

  1. Active vibration control of structure by Active Mass Damper and Multi-Modal Negative Acceleration Feedback control algorithm

    NASA Astrophysics Data System (ADS)

    Yang, Don-Ho; Shin, Ji-Hwan; Lee, HyunWook; Kim, Seoug-Ki; Kwak, Moon K.

    2017-03-01

    In this study, an Active Mass Damper (AMD) consisting of an AC servo motor, a movable mass connected to the AC servo motor by a ball-screw mechanism, and an accelerometer as a sensor for vibration measurement were considered. Considering the capability of the AC servo motor which can follow the desired displacement accurately, the Negative Acceleration Feedback (NAF) control algorithm which uses the acceleration signal directly and produces the desired displacement for the active mass was proposed. The effectiveness of the NAF control was proved theoretically using a single-degree-of-freedom (SDOF) system. It was found that the stability condition for the NAF control is static and it can effectively increase the damping of the target natural mode without causing instability in the low frequency region. Based on the theoretical results of the SDOF system, the Multi-Modal NAF (MMNAF) control is proposed to suppress the many natural modes of multi-degree-of-freedom (MDOF) systems using a single AMD. It was proved both theoretically and experimentally that the MMNAF control can suppress vibrations of the MDOF system.

  2. Handling real-world context awareness, uncertainty and vagueness in real-time human activity tracking and recognition with a fuzzy ontology-based hybrid method.

    PubMed

    Díaz-Rodríguez, Natalia; Cadahía, Olmo León; Cuéllar, Manuel Pegalajar; Lilius, Johan; Calvo-Flores, Miguel Delgado

    2014-09-29

    Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and Sensors 2014, 14 18132 high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches.

  3. Handling Real-World Context Awareness, Uncertainty and Vagueness in Real-Time Human Activity Tracking and Recognition with a Fuzzy Ontology-Based Hybrid Method

    PubMed Central

    Díaz-Rodríguez, Natalia; Cadahía, Olmo León; Cuéllar, Manuel Pegalajar; Lilius, Johan; Calvo-Flores, Miguel Delgado

    2014-01-01

    Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches. PMID:25268914

  4. Features of Recently Transmitted HIV-1 Clade C Viruses that Impact Antibody Recognition: Implications for Active and Passi