Science.gov

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

    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

    2015-01-01

    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.

  4. Better-than-the-best fusion algorithm with application in human activity recognition

    NASA Astrophysics Data System (ADS)

    Najjar, Nayeff; Gupta, Shalabh

    2015-05-01

    This paper introduces the Better-than-the-Best Fusion (BB-Fus) algorithm. The BB-Fus algorithm is a simple and effective information fusion algorithm that combines the information from different sources (be it sensors, features or classifiers) to improve the Correct Classification Rate (CCR). It can be observed that in most classification problems, different sensors or features might have different classification accuracies in separating different classes. Therefore, this paper constructs an optimal decision tree that isolates one class at a time with the best sensor to separate that particular class. The paper shows that the decision tree improves the overall CCR as compared to the use of any single sensor or feature for any 3-class classification problem. The efficiency of the BB-Fus algorithm is validated on the Opportunity data set to solve the human activity recognition problem where a set of 56 sensors (including a localization system, accelerometers, inertial measurement units and magnetic sensors mounted on various body parts; besides, accelerometers and gyroscopes mounted on different objects) are used. The CCR resulting from the BB-Fus algorithm is 96% while the best sensor achieved 94% CCR.

  5. Personalization algorithm for real-time activity recognition using PDA, wireless motion bands, and binary decision tree.

    PubMed

    Pärkkä, Juha; Cluitmans, Luc; Ermes, Miikka

    2010-09-01

    Inactive and sedentary lifestyle is a major problem in many industrialized countries today. Automatic recognition of type of physical activity can be used to show the user the distribution of his daily activities and to motivate him into more active lifestyle. In this study, an automatic activity-recognition system consisting of wireless motion bands and a PDA is evaluated. The system classifies raw sensor data into activity types online. It uses a decision tree classifier, which has low computational cost and low battery consumption. The classifier parameters can be personalized online by performing a short bout of an activity and by telling the system which activity is being performed. Data were collected with seven volunteers during five everyday activities: lying, sitting/standing, walking, running, and cycling. The online system can detect these activities with overall 86.6% accuracy and with 94.0% accuracy after classifier personalization.

  6. 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.

  7. Algorithms for adaptive nonlinear pattern recognition

    NASA Astrophysics Data System (ADS)

    Schmalz, Mark S.; Ritter, Gerhard X.; Hayden, Eric; Key, Gary

    2011-09-01

    In Bayesian pattern recognition research, static classifiers have featured prominently in the literature. A static classifier is essentially based on a static model of input statistics, thereby assuming input ergodicity that is not realistic in practice. Classical Bayesian approaches attempt to circumvent the limitations of static classifiers, which can include brittleness and narrow coverage, by training extensively on a data set that is assumed to cover more than the subtense of expected input. Such assumptions are not realistic for more complex pattern classification tasks, for example, object detection using pattern classification applied to the output of computer vision filters. In contrast, we have developed a two step process, that can render the majority of static classifiers adaptive, such that the tracking of input nonergodicities is supported. Firstly, we developed operations that dynamically insert (or resp. delete) training patterns into (resp. from) the classifier's pattern database, without requiring that the classifier's internal representation of its training database be completely recomputed. Secondly, we developed and applied a pattern replacement algorithm that uses the aforementioned pattern insertion/deletion operations. This algorithm is designed to optimize the pattern database for a given set of performance measures, thereby supporting closed-loop, performance-directed optimization. This paper presents theory and algorithmic approaches for the efficient computation of adaptive linear and nonlinear pattern recognition operators that use our pattern insertion/deletion technology - in particular, tabular nearest-neighbor encoding (TNE) and lattice associative memories (LAMs). Of particular interest is the classification of nonergodic datastreams that have noise corruption with time-varying statistics. The TNE and LAM based classifiers discussed herein have been successfully applied to the computation of object classification in hyperspectral

  8. New Algorithms For Automated Symmetry Recognition

    NASA Astrophysics Data System (ADS)

    Paul, Jody; Kilgore, Tammy Elaine; Klinger, Allen

    1988-02-01

    In this paper we present new methods for computer-based symmetry identification that combine elements of group theory and pattern recognition. Detection of symmetry has diverse applications including: the reduction of image data to a manageable subset with minimal information loss, the interpretation of sensor data,1 such as the x-ray diffraction patterns which sparked the recent discovery of a new "quasicrystal" phase of solid matter,2 and music analysis and composition.3,4,5 Our algorithms are expressed as parallel operations on the data using the matrix representation and manipulation features of the APL programming language. We demonstrate the operation of programs that characterize symmetric and nearly-symmetric patterns by determining the degree of invariance with respect to candidate symmetry transformations. The results are completely general; they may be applied to pattern data of arbitrary dimension and from any source.

  9. 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%.

  10. Robust facial expression recognition algorithm based on local metric learning

    NASA Astrophysics Data System (ADS)

    Jiang, Bin; Jia, Kebin

    2016-01-01

    In facial expression recognition tasks, different facial expressions are often confused with each other. Motivated by the fact that a learned metric can significantly improve the accuracy of classification, a facial expression recognition algorithm based on local metric learning is proposed. First, k-nearest neighbors of the given testing sample are determined from the total training data. Second, chunklets are selected from the k-nearest neighbors. Finally, the optimal transformation matrix is computed by maximizing the total variance between different chunklets and minimizing the total variance of instances in the same chunklet. The proposed algorithm can find the suitable distance metric for every testing sample and improve the performance on facial expression recognition. Furthermore, the proposed algorithm can be used for vector-based and matrix-based facial expression recognition. Experimental results demonstrate that the proposed algorithm could achieve higher recognition rates and be more robust than baseline algorithms on the JAFFE, CK, and RaFD databases.

  11. 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.

  12. 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%.

  13. 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.

  14. 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

  15. Recognition of Short Time-Paired Activities

    NASA Astrophysics Data System (ADS)

    Chaminda, Hapugahage Thilak; Klyuev, Vitaly; Naruse, Keitaro; Osano, Minetada

    We undertake numerous activities in our daily life and for some of those we forget to complete the action as originally intended. Significant aspects while performing most of these actions might be: “pairing of both hands simultaneously” and “short time consumption”. In this work an attempt has been made to recognize those kinds of Paired Activities (PAs), which are easy to forget, and to provide a method to remind about uncompleted PAs. To represent PAs, a study was done on opening and closing of various bottles. A model to define PAs, which simulated the paired behavior of both hands, is proposed, called “Paired Activity Model” (PAM). To recognize PAs using PAM, Paired Activity Recognition Algorithm (PARA) was implemented. Paired motion capturing was done by accelerometers, which were worn by subjects on the wrist areas of both hands. Individual and correlative behavior of both hands was used to recognize exact PA among other activities. Artificial Neural Network (ANN) algorithm was used for data categorization in PARA. ANN significantly outperformed the support vector machine algorithm in real time evaluations. In the user-independent case, PARA achieved recognition rates of 96% for only target PAs and 91% for target PAs undertaken amidst unrelated activities.

  16. A Palmprint Recognition Algorithm Using Phase-Only Correlation

    NASA Astrophysics Data System (ADS)

    Ito, Koichi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

    This paper presents a palmprint recognition algorithm using Phase-Only Correlation (POC). The use of phase components in 2D (two-dimensional) discrete Fourier transforms of palmprint images makes it possible to achieve highly robust image registration and matching. In the proposed algorithm, POC is used to align scaling, rotation and translation between two palmprint images, and evaluate similarity between them. Experimental evaluation using a palmprint image database clearly demonstrates efficient matching performance of the proposed algorithm.

  17. Analysis of an algorithm for distributed recognition and accountability

    SciTech Connect

    Ko, C.; Frincke, D.A.; Goan, T. Jr.; Heberlein, L.T.; Levitt, K.; Mukherjee, B.; Wee, C.

    1993-08-01

    Computer and network systems are available to attacks. Abandoning the existing huge infrastructure of possibly-insecure computer and network systems is impossible, and replacing them by totally secure systems may not be feasible or cost effective. A common element in many attacks is that a single user will often attempt to intrude upon multiple resources throughout a network. Detecting the attack can become significantly easier by compiling and integrating evidence of such intrusion attempts across the network rather than attempting to assess the situation from the vantage point of only a single host. To solve this problem, we suggest an approach for distributed recognition and accountability (DRA), which consists of algorithms which ``process,`` at a central location, distributed and asynchronous ``reports`` generated by computers (or a subset thereof) throughout the network. Our highest-priority objectives are to observe ways by which an individual moves around in a network of computers, including changing user names to possibly hide his/her true identity, and to associate all activities of multiple instance of the same individual to the same network-wide user. We present the DRA algorithm and a sketch of its proof under an initial set of simplifying albeit realistic assumptions. Later, we relax these assumptions to accommodate pragmatic aspects such as missing or delayed ``reports,`` clock slew, tampered ``reports,`` etc. We believe that such algorithms will have widespread applications in the future, particularly in intrusion-detection system.

  18. A Random Forest-based ensemble method for activity recognition.

    PubMed

    Feng, Zengtao; Mo, Lingfei; Li, Meng

    2015-01-01

    This paper presents a multi-sensor ensemble approach to human physical activity (PA) recognition, using random forest. We designed an ensemble learning algorithm, which integrates several independent Random Forest classifiers based on different sensor feature sets to build a more stable, more accurate and faster classifier for human activity recognition. To evaluate the algorithm, PA data collected from the PAMAP (Physical Activity Monitoring for Aging People), which is a standard, publicly available database, was utilized to train and test. The experimental results show that the algorithm is able to correctly recognize 19 PA types with an accuracy of 93.44%, while the training is faster than others. The ensemble classifier system based on the RF (Random Forest) algorithm can achieve high recognition accuracy and fast calculation. PMID:26737432

  19. Implementation of perceptual aspects in a face recognition algorithm

    NASA Astrophysics Data System (ADS)

    Crenna, F.; Zappa, E.; Bovio, L.; Testa, R.; Gasparetto, M.; Rossi, G. B.

    2013-09-01

    Automatic face recognition is a biometric technique particularly appreciated in security applications. In fact face recognition presents the opportunity to operate at a low invasive level without the collaboration of the subjects under tests, with face images gathered either from surveillance systems or from specific cameras located in strategic points. The automatic recognition algorithms perform a measurement, on the face images, of a set of specific characteristics of the subject and provide a recognition decision based on the measurement results. Unfortunately several quantities may influence the measurement of the face geometry such as its orientation, the lighting conditions, the expression and so on, affecting the recognition rate. On the other hand human recognition of face is a very robust process far less influenced by the surrounding conditions. For this reason it may be interesting to insert perceptual aspects in an automatic facial-based recognition algorithm to improve its robustness. This paper presents a first study in this direction investigating the correlation between the results of a perception experiment and the facial geometry, estimated by means of the position of a set of repere points.

  20. 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.

  1. 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.

  2. DNA pattern recognition using canonical correlation algorithm.

    PubMed

    Sarkar, B K; Chakraborty, Chiranjib

    2015-10-01

    We performed canonical correlation analysis as an unsupervised statistical tool to describe related views of the same semantic object for identifying patterns. A pattern recognition technique based on canonical correlation analysis (CCA) was proposed for finding required genetic code in the DNA sequence. Two related but different objects were considered: one was a particular pattern, and other was test DNA sequence. CCA found correlations between two observations of the same semantic pattern and test sequence. It is concluded that the relationship possesses maximum value in the position where the pattern exists. As a case study, the potential of CCA was demonstrated on the sequence found from HIV-1 preferred integration sites. The subsequences on the left and right flanking from the integration site were considered as the two views, and statistically significant relationships were established between these two views to elucidate the viral preference as an important factor for the correlation. PMID:26564973

  3. Recognition of plant parts with problem-specific algorithms

    NASA Astrophysics Data System (ADS)

    Schwanke, Joerg; Brendel, Thorsten; Jensch, Peter F.; Megnet, Roland

    1994-06-01

    Automatic micropropagation is necessary to produce cost-effective high amounts of biomass. Juvenile plants are dissected in clean- room environment on particular points on the stem or the leaves. A vision-system detects possible cutting points and controls a specialized robot. This contribution is directed to the pattern- recognition algorithms to detect structural parts of the plant.

  4. 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.

  5. 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

  6. Physical Human Activity Recognition Using Wearable Sensors.

    PubMed

    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

  7. Active Finger Recognition from Surface EMG Signal Using Bayesian Filter

    NASA Astrophysics Data System (ADS)

    Araki, Nozomu; Hoashi, Yuki; Konishi, Yasuo; Mabuchi, Kunihiko; Ishigaki, Hiroyuki

    This paper proposed an active finger recognition method using Bayesian filter in order to control a myoelectric hand. We have previously proposed a finger joint angle estimation method based on measured surface electromyography (EMG) signals and a linear model. However, when we estimate 2 or more finger angles by this estimation method, the estimation angle of the inactive finger is not accurate. This is caused by interference of surface EMG signal. To solve this interference problem, we proposed active finger recognition method from the amplitude spectrum of surface EMG signal using Bayesian filter. To confirm the effectiveness of this recognition method, we developed a myoelectric hand simulator that implements proposed recognition algorithm and carried out real-time recognition experiment.

  8. Object recognition by active fusion

    NASA Astrophysics Data System (ADS)

    Prantl, Manfred; Kopp-Borotschnig, Hermann; Ganster, Harald; Sinclair, David; Pinz, Axel J.

    1996-10-01

    Today's computer vision applications often have to deal with multiple, uncertain, and incomplete visual information. In this paper, we apply a new method, termed 'active fusion', to the problem of generic object recognition. Active fusion provides a common framework for active selection and combination of information from multiple sources in order to arrive at a reliable result at reasonable costs. In our experimental setup we use a camera mounted on a 2m by 1.5m x/z-table observing objects placed on a rotating table. Zoom, pan, tilt, and aperture setting of the camera can be controlled by the system. We follow a part-based approach, trying to decompose objects into parts, which are modeled as geons. The active fusion system starts from an initial view of the objects placed on the table and is continuously trying to refine its current object hypotheses by requesting additional views. The implementation of active fusion on the basis of probability theory, Dempster-Shafer's theory of evidence and fuzzy set theory is discussed. First results demonstrating segmentation improvements by active fusion are presented.

  9. 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.

  10. Improvements on EMG-based handwriting recognition with DTW algorithm.

    PubMed

    Li, Chengzhang; Ma, Zheren; Yao, Lin; Zhang, Dingguo

    2013-01-01

    Previous works have shown that Dynamic Time Warping (DTW) algorithm is a proper method of feature extraction for electromyography (EMG)-based handwriting recognition. In this paper, several modifications are proposed to improve the classification process and enhance recognition accuracy. A two-phase template making approach has been introduced to generate templates with more salient features, and modified Mahalanobis Distance (mMD) approach is used to replace Euclidean Distance (ED) in order to minimize the interclass variance. To validate the effectiveness of such modifications, experiments were conducted, in which four subjects wrote lowercase letters at a normal speed and four-channel EMG signals from forearms were recorded. Results of offline analysis show that the improvements increased the average recognition accuracy by 9.20%.

  11. 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.

  12. New algorithm for iris recognition based on video sequences

    NASA Astrophysics Data System (ADS)

    Bourennane, Salah; Fossati, Caroline; Ketchantang, William

    2010-07-01

    Among existing biometrics, iris recognition systems are among the most accurate personal biometric identification systems. However, the acquisition of a workable iris image requires strict cooperation of the user; otherwise, the image will be rejected by a verification module because of its poor quality, inducing a high false reject rate (FRR). The FRR may also increase when iris localization fails or when the pupil is too dilated. To improve the existing methods, we propose to use video sequences acquired in real time by a camera. In order to keep the same computational load to identify the iris, we propose a new method to estimate the iris characteristics. First, we propose a new iris texture characterization based on Fourier-Mellin transform, which is less sensitive to pupil dilatations than previous methods. Then, we develop a new iris localization algorithm that is robust to variations of quality (partial occlusions due to eyelids and eyelashes, light reflects, etc.), and finally, we introduce a fast and new criterion of suitable image selection from an iris video sequence for an accurate recognition. The accuracy of each step of the algorithm in the whole proposed recognition process is tested and evaluated using our own iris video database and several public image databases, such as CASIA, UBIRIS, and BATH.

  13. 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.

  14. Visual Empirical Region of Influence (VERI) Pattern Recognition Algorithms

    2002-05-01

    We developed new pattern recognition (PR) algorithms based on a human visual perception model. We named these algorithms Visual Empirical Region of Influence (VERI) algorithms. To compare the new algorithm's effectiveness against othe PR algorithms, we benchmarked their clustering capabilities with a standard set of two-dimensional data that is well known in the PR community. The VERI algorithm succeeded in clustering all the data correctly. No existing algorithm had previously clustered all the pattens inmore » the data set successfully. The commands to execute VERI algorithms are quite difficult to master when executed from a DOS command line. The algorithm requires several parameters to operate correctly. From our own experiences we realized that if we wanted to provide a new data analysis tool to the PR community we would have to provide a new data analysis tool to the PR community we would have to make the tool powerful, yet easy and intuitive to use. That was our motivation for developing graphical user interfaces (GUI's) to the VERI algorithms. We developed GUI's to control the VERI algorithm in a single pass mode and in an optimization mode. We also developed a visualization technique that allows users to graphically animate and visually inspect multi-dimensional data after it has been classified by the VERI algorithms. The visualization technique that allows users to graphically animate and visually inspect multi-dimensional data after it has been classified by the VERI algorithms. The visualization package is integrated into the single pass interface. Both the single pass interface and optimization interface are part of the PR software package we have developed and make available to other users. The single pass mode only finds PR results for the sets of features in the data set that are manually requested by the user. The optimization model uses a brute force method of searching through the cominations of features in a data set for features that produce

  15. Window Size Impact in Human Activity Recognition

    PubMed Central

    Banos, Oresti; Galvez, Juan-Manuel; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

    2014-01-01

    Signal segmentation is a crucial stage in the activity recognition process; however, this has been rarely and vaguely characterized so far. Windowing approaches are normally used for segmentation, but no clear consensus exists on which window size should be preferably employed. In fact, most designs normally rely on figures used in previous works, but with no strict studies that support them. Intuitively, decreasing the window size allows for a faster activity detection, as well as reduced resources and energy needs. On the contrary, large data windows are normally considered for the recognition of complex activities. In this work, we present an extensive study to fairly characterize the windowing procedure, to determine its impact within the activity recognition process and to help clarify some of the habitual assumptions made during the recognition system design. To that end, some of the most widely used activity recognition procedures are evaluated for a wide range of window sizes and activities. From the evaluation, the interval 1–2 s proves to provide the best trade-off between recognition speed and accuracy. The study, specifically intended for on-body activity recognition systems, further provides designers with a set of guidelines devised to facilitate the system definition and configuration according to the particular application requirements and target activities. PMID:24721766

  16. Window size impact in human activity recognition.

    PubMed

    Banos, Oresti; Galvez, Juan-Manuel; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

    2014-01-01

    Signal segmentation is a crucial stage in the activity recognition process; however, this has been rarely and vaguely characterized so far. Windowing approaches are normally used for segmentation, but no clear consensus exists on which window size should be preferably employed. In fact, most designs normally rely on figures used in previous works, but with no strict studies that support them. Intuitively, decreasing the window size allows for a faster activity detection, as well as reduced resources and energy needs. On the contrary, large data windows are normally considered for the recognition of complex activities. In this work, we present an extensive study to fairly characterize the windowing procedure, to determine its impact within the activity recognition process and to help clarify some of the habitual assumptions made during the recognition system design. To that end, some of the most widely used activity recognition procedures are evaluated for a wide range of window sizes and activities. From the evaluation, the interval 1-2 s proves to provide the best trade-off between recognition speed and accuracy. The study, specifically intended for on-body activity recognition systems, further provides designers with a set of guidelines devised to facilitate the system definition and configuration according to the particular application requirements and target activities. PMID:24721766

  17. A Computationally Efficient Mel-Filter Bank VAD Algorithm for Distributed Speech Recognition Systems

    NASA Astrophysics Data System (ADS)

    Vlaj, Damjan; Kotnik, Bojan; Horvat, Bogomir; Kačič, Zdravko

    2005-12-01

    This paper presents a novel computationally efficient voice activity detection (VAD) algorithm and emphasizes the importance of such algorithms in distributed speech recognition (DSR) systems. When using VAD algorithms in telecommunication systems, the required capacity of the speech transmission channel can be reduced if only the speech parts of the signal are transmitted. A similar objective can be adopted in DSR systems, where the nonspeech parameters are not sent over the transmission channel. A novel approach is proposed for VAD decisions based on mel-filter bank (MFB) outputs with the so-called Hangover criterion. Comparative tests are presented between the presented MFB VAD algorithm and three VAD algorithms used in the G.729, G.723.1, and DSR (advanced front-end) Standards. These tests were made on the Aurora 2 database, with different signal-to-noise (SNRs) ratios. In the speech recognition tests, the proposed MFB VAD outperformed all the three VAD algorithms used in the standards by [InlineEquation not available: see fulltext.] relative (G.723.1 VAD), by [InlineEquation not available: see fulltext.] relative (G.729 VAD), and by [InlineEquation not available: see fulltext.] relative (DSR VAD) in all SNRs.

  18. Particle recognition in microfluidic applications using a template matching algorithm

    NASA Astrophysics Data System (ADS)

    Girault, Mathias; Odaka, Masao; Kim, Hyonchol; Matsuura, Kenji; Terazono, Hideyuki; Yasuda, Kenji

    2016-06-01

    We herein examined the ability of a template matching algorithm to recognize particles with diameters ranging from 1 to 20 µm in a microfluidic channel. The algorithm consisted of measurements of the distance between the templates and the images captured with a high-speed camera in order to search for the presence of the desired particle. The results obtained indicated that the effects of blur and diffraction rings observed around the particle are important phenomena that limit the recognition of a target. Owing to the effects of diffraction rings, the distance between a template and an image is not exclusively linked to the position of the focus plane; it is also linked to the size of the particle being searched for. By using a set of three templates captured at different Z focuses and an 800× magnification, the template matching algorithm has the ability to recognize beads ranging in diameter from 1.7 to 20 µm with a resolution between 0.3 and 1 µm.

  19. Assessing the performance of a covert automatic target recognition algorithm

    NASA Astrophysics Data System (ADS)

    Ehrman, Lisa M.; Lanterman, Aaron D.

    2005-05-01

    Passive radar systems exploit illuminators of opportunity, such as TV and FM radio, to illuminate potential targets. Doing so allows them to operate covertly and inexpensively. Our research seeks to enhance passive radar systems by adding automatic target recognition (ATR) capabilities. In previous papers we proposed conducting ATR by comparing the radar cross section (RCS) of aircraft detected by a passive radar system to the precomputed RCS of aircraft in the target class. To effectively model the low-frequency setting, the comparison is made via a Rician likelihood model. Monte Carlo simulations indicate that the approach is viable. This paper builds on that work by developing a method for quickly assessing the potential performance of the ATR algorithm without using exhaustive Monte Carlo trials. This method exploits the relation between the probability of error in a binary hypothesis test under the Bayesian framework to the Chernoff information. Since the data are well-modeled as Rician, we begin by deriving a closed-form approximation for the Chernoff information between two Rician densities. This leads to an approximation for the probability of error in the classification algorithm that is a function of the number of available measurements. We conclude with an application that would be particularly cumbersome to accomplish via Monte Carlo trials, but that can be quickly addressed using the Chernoff information approach. This application evaluates the length of time that an aircraft must be tracked before the probability of error in the ATR algorithm drops below a desired threshold.

  20. 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.

  1. 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

  2. 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. PMID:26236228

  3. Activity recognition from video using layered approach

    NASA Astrophysics Data System (ADS)

    McPherson, Charles A.; Irvine, John M.; Young, Mon; Stefanidis, Anthony

    2012-01-01

    The adversary in current threat situations can no longer be identified by what they are, but by what they are doing. This has lead to a large increase in the use of video surveillance systems for security and defense applications. With the quantity of video surveillance at the disposal of organizations responsible for protecting military and civilian lives comes issues regarding the storage and screening the data for events and activities of interest. Activity recognition from video for such applications seeks to develop automated screening of video based upon the recognition of activities of interest rather than merely the presence of specific persons or vehicle classes developed for the Cold War problem of "Find the T72 Tank". This paper explores numerous approaches to activity recognition, all of which examine heuristic, semantic, and syntactic methods based upon tokens derived from the video. The proposed architecture discussed herein uses a multi-level approach that divides the problem into three or more tiers of recognition, each employing different techniques according to their appropriateness to strengths at each tier using heuristics, syntactic recognition, and HMM's of token strings to form higher level interpretations.

  4. 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. PMID:26659118

  5. 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

  6. 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.

  7. Active place recognition using image signatures

    NASA Astrophysics Data System (ADS)

    Engelson, Sean P.

    1992-11-01

    For reliable navigation, a mobile robot needs to be able to recognize where it is in the world. We previously described an efficient and effective image-based representation of perceptual information for place recognition. Each place is associated with a set of stored image signatures, each a matrix of numbers derived by evaluating some measurement functions over large blocks of pixels. One difficulty, though, is the large number of inherently ambiguous signatures which bloats the database and makes recognition more difficult. Furthermore, since small differences in orientation can produce very different images, reliable recognition requires many images. These problems can be ameliorated by using active methods to select the best signatures to use for the recognition. Two criteria for good images are distinctiveness (is the scene distinguishable from others?) and stability (how much do small viewpoint motions change image recognizability?). We formulate several heuristic distinctiveness metrics which are good predictors of real image distinctiveness. These functions are then used to direct the motion of the camera to find locally distinctive views for use in recognition. This method also produces some modicum of stability, since it uses a form of local optimization. We present the results of applying this method with a camera mounted on a pan-tilt platform.

  8. 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.

  9. Rate-invariant recognition of humans and their activities.

    PubMed

    Veeraraghavan, Ashok; Srivastava, Anuj; Roy-Chowdhury, Amit K; Chellappa, Rama

    2009-06-01

    Pattern recognition in video is a challenging task because of the multitude of spatio-temporal variations that occur in different videos capturing the exact same event. While traditional pattern-theoretic approaches account for the spatial changes that occur due to lighting and pose, very little has been done to address the effect of temporal rate changes in the executions of an event. In this paper, we provide a systematic model-based approach to learn the nature of such temporal variations (time warps) while simultaneously allowing for the spatial variations in the descriptors. We illustrate our approach for the problem of action recognition and provide experimental justification for the importance of accounting for rate variations in action recognition. The model is composed of a nominal activity trajectory and a function space capturing the probability distribution of activity-specific time warping transformations. We use the square-root parameterization of time warps to derive geodesics, distance measures, and probability distributions on the space of time warping functions. We then design a Bayesian algorithm which treats the execution rate function as a nuisance variable and integrates it out using Monte Carlo sampling, to generate estimates of class posteriors. This approach allows us to learn the space of time warps for each activity while simultaneously capturing other intra- and interclass variations. Next, we discuss a special case of this approach which assumes a uniform distribution on the space of time warping functions and show how computationally efficient inference algorithms may be derived for this special case. We discuss the relative advantages and disadvantages of both approaches and show their efficacy using experiments on gait-based person identification and activity recognition. PMID:19398409

  10. Advancing from offline to online activity recognition with wearable sensors.

    PubMed

    Ermes, Miikka; Parkka, Juha; Cluitmans, Luc

    2008-01-01

    Activity recognition with wearable sensors could motivate people to perform a variety of different sports and other physical exercises. We have earlier developed algorithms for offline analysis of activity data collected with wearable sensors. In this paper, we present our current progress in advancing the platform for the existing algorithms to an online version, onto a PDA. Acceleration data are obtained from wireless motion bands which send the 3D raw acceleration signals via a Bluetooth link to the PDA which then performs the data collection, feature extraction and activity classification. As a proof-of-concept, the online activity system was tested with three subjects. All of them performed at least 5 minutes of each of the following activities: lying, sitting, standing, walking, running and cycling with an exercise bike. The average second-by-second classification accuracies for the subjects were 99%, 97%, and 82 %. These results suggest that earlier developed offline analysis methods for the acceleration data obtained from wearable sensors can be successfully implemented in an online activity recognition application. PMID:19163702

  11. Tracking and activity recognition through consensus in distributed camera networks.

    PubMed

    Song, Bi; Kamal, Ahmed T; Soto, Cristian; Ding, Chong; Farrell, Jay A; Roy-Chowdhury, Amit K

    2010-10-01

    Camera networks are being deployed for various applications like security and surveillance, disaster response and environmental modeling. However, there is little automated processing of the data. Moreover, most methods for multicamera analysis are centralized schemes that require the data to be present at a central server. In many applications, this is prohibitively expensive, both technically and economically. In this paper, we investigate distributed scene analysis algorithms by leveraging upon concepts of consensus that have been studied in the context of multiagent systems, but have had little applications in video analysis. Each camera estimates certain parameters based upon its own sensed data which is then shared locally with the neighboring cameras in an iterative fashion, and a final estimate is arrived at in the network using consensus algorithms. We specifically focus on two basic problems-tracking and activity recognition. For multitarget tracking in a distributed camera network, we show how the Kalman-Consensus algorithm can be adapted to take into account the directional nature of video sensors and the network topology. For the activity recognition problem, we derive a probabilistic consensus scheme that combines the similarity scores of neighboring cameras to come up with a probability for each action at the network level. Thorough experimental results are shown on real data along with a quantitative analysis.

  12. 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. PMID:24111015

  13. 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.

  14. Adaptive Activity and Environment Recognition for Mobile Phones

    PubMed Central

    Parviainen, Jussi; Bojja, Jayaprasad; Collin, Jussi; Leppänen, Jussi; Eronen, Antti

    2014-01-01

    In this paper, an adaptive activity and environment recognition algorithm running on a mobile phone is presented. The algorithm makes inferences based on sensor and radio receiver data provided by the phone. A wide set of features that can be extracted from these data sources were investigated, and a Bayesian maximum a posteriori classifier was used for classifying between several user activities and environments. The accuracy of the method was evaluated on a dataset collected in a real-life trial. In addition, comparison to other state-of-the-art classifiers, namely support vector machines and decision trees, was performed. To make the system adaptive for individual user characteristics, an adaptation algorithm for context model parameters was designed. Moreover, a confidence measure for the classification correctness was designed. The proposed adaptation algorithm and confidence measure were evaluated on a second dataset obtained from another real-life trial, where the users were requested to provide binary feedback on the classification correctness. The results show that the proposed adaptation algorithm is effective at improving the classification accuracy. PMID:25372620

  15. Adaptive activity and environment recognition for mobile phones.

    PubMed

    Parviainen, Jussi; Bojja, Jayaprasad; Collin, Jussi; Leppänen, Jussi; Eronen, Antti

    2014-11-03

    In this paper, an adaptive activity and environment recognition algorithm running on a mobile phone is presented. The algorithm makes inferences based on sensor and radio receiver data provided by the phone. A wide set of features that can be extracted from these data sources were investigated, and a Bayesian maximum a posteriori classifier was used for classifying between several user activities and environments. The accuracy of the method was evaluated on a dataset collected in a real-life trial. In addition, comparison to other state-of-the-art classifiers, namely support vector machines and decision trees, was performed. To make the system adaptive for individual user characteristics, an adaptation algorithm for context model parameters was designed. Moreover, a confidence measure for the classification correctness was designed. The proposed adaptation algorithm and confidence measure were evaluated on a second dataset obtained from another real-life trial, where the users were requested to provide binary feedback on the classification correctness. The results show that the proposed adaptation algorithm is effective at improving the classification accuracy.

  16. 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. PMID:24111168

  17. 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.

  18. 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.

  19. Tracheal activity recognition based on acoustic signals.

    PubMed

    Olubanjo, Temiloluwa; Ghovanloo, Maysam

    2014-01-01

    Tracheal activity recognition can play an important role in continuous health monitoring for wearable systems and facilitate the advancement of personalized healthcare. Neck-worn systems provide access to a unique set of health-related data that other wearable devices simply cannot obtain. Activities including breathing, chewing, clearing the throat, coughing, swallowing, speech and even heartbeat can be recorded from around the neck. In this paper, we explore tracheal activity recognition using a combination of promising acoustic features from related work and apply simplistic classifiers including K-NN and Naive Bayes. For wearable systems in which low power consumption is of primary concern, we show that with a sub-optimal sampling rate of 16 kHz, we have achieved average classification results in the range of 86.6% to 87.4% using 1-NN, 3-NN, 5-NN and Naive Bayes. All classifiers obtained the highest recognition rate in the range of 97.2% to 99.4% for speech classification. This is promising to mitigate privacy concerns associated with wearable systems interfering with the user's conversations.

  20. 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.

  1. Multimodal Physical Activity Recognition by Fusing Temporal and Cepstral Information

    PubMed Central

    Li, Ming; Rozgić, Viktor; Thatte, Gautam; Lee, Sangwon; Emken, Adar; Annavaram, Murali; Mitra, Urbashi; Spruijt-Metz, Donna; Narayanan, Shrikanth

    2015-01-01

    A physical activity (PA) recognition algorithm for a wearable wireless sensor network using both ambulatory electrocardiogram (ECG) and accelerometer signals is proposed. First, in the time domain, the cardiac activity mean and the motion artifact noise of the ECG signal are modeled by a Hermite polynomial expansion and principal component analysis, respectively. A set of time domain accelerometer features is also extracted. A support vector machine (SVM) is employed for supervised classification using these time domain features. Second, motivated by their potential for handling convolutional noise, cepstral features extracted from ECG and accelerometer signals based on a frame level analysis are modeled using Gaussian mixture models (GMMs). Third, to reduce the dimension of the tri-axial accelerometer cepstral features which are concatenated and fused at the feature level, heteroscedastic linear discriminant analysis is performed. Finally, to improve the overall recognition performance, fusion of the multi-modal (ECG and accelerometer) and multidomain (time domain SVM and cepstral domain GMM) subsystems at the score level is performed. The classification accuracy ranges from 79.3% to 97.3% for various testing scenarios and outperforms the state-of-the-art single accelerometer based PA recognition system by over 24% relative error reduction on our nine-category PA database. PMID:20699202

  2. Heuristic algorithm for optical character recognition of Arabic script

    NASA Astrophysics Data System (ADS)

    Yarman-Vural, Fatos T.; Atici, A.

    1996-02-01

    In this paper, a heuristic method is developed for segmentation, feature extraction and recognition of the Arabic script. The study is part of a large project for the transcription of the documents in Ottoman Archives. A geometrical and topological feature analysis method is developed for segmentation and feature extraction stages. Chain code transformation is applied to main strokes of the characters which are then classified by the hidden Markov model (HMM) in the recognition stage. Experimental results indicate that the performance of the proposed method is impressive, provided that the thinning process does not yield spurious branches.

  3. Optimized face recognition algorithm using radial basis function neural networks and its practical applications.

    PubMed

    Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold

    2015-09-01

    In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. PMID:26163042

  4. Optimized face recognition algorithm using radial basis function neural networks and its practical applications.

    PubMed

    Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold

    2015-09-01

    In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate.

  5. 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.

  6. Noise robust speech recognition with support vector learning algorithms

    NASA Astrophysics Data System (ADS)

    Namarvar, Hassan H.; Berger, Theodore W.

    2001-05-01

    We propose a new noise robust speech recognition system using time-frequency domain analysis and radial basis function (RBF) support vector machines (SVM). Here, we ignore the effects of correlative and nonstationary noise and only focus on continuous additive Gaussian white noise. We then develop an isolated digit/command recognizer and compare its performance to two other systems, in which the SVM classifier has been replaced by multilayer perceptron (MLP) and RBF neural networks. All systems are trained under the low signal-to-noise ratio (SNR) condition. We obtained the best correct classification rate of 83% and 52% for digit recognition on the TI-46 corpus for the SVM and MLP systems, respectively under the SNR=0 (dB), while we could not train the RBF network for the same dataset. The newly developed speech recognition system seems to be noise robust for medium size speech recognition problems under continuous, stationary background noise. However, it is still required to test the system under realistic noisy environment to observe whether the system keeps its adaptability and robustness under such conditions. [Work supported in part by grants from DARPA CBS, NASA, and ONR.

  7. Parallel algorithm for target recognition using a multiclass hash database

    NASA Astrophysics Data System (ADS)

    Uddin, Mosleh; Myler, Harley R.

    1998-07-01

    A method for recognition of unknown targets using large databases of model targets is discussed. Our approach is based on parallel processing of multi-class hash databases that are generated off-line. A geometric hashing technique is used on feature points of model targets to create each class database. Bit level coding is then performed to represent the models in an image format. Parallelism is achieved during the recognition phase. Feature points of an unknown target are passed to parallel processors each accessing an individual class database. Each processor reads a particular class of hash data base and indexes feature points of the unknown target. A simple voting technique is applied to determine the best match model with the unknown. The paper discusses our technique and the results from testing with unknown FLIR targets.

  8. 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

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

    PubMed

    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

  10. 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

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

    PubMed

    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.

  12. 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.

  13. Dynamic detection of window starting positions and its implementation within an activity recognition framework.

    PubMed

    Ni, Qin; Patterson, Timothy; Cleland, Ian; Nugent, Chris

    2016-08-01

    Activity recognition is an intrinsic component of many pervasive computing and ambient intelligent solutions. This has been facilitated by an explosion of technological developments in the area of wireless sensor network, wearable and mobile computing. Yet, delivering robust activity recognition, which could be deployed at scale in a real world environment, still remains an active research challenge. Much of the existing literature to date has focused on applying machine learning techniques to pre-segmented data collected in controlled laboratory environments. Whilst this approach can provide valuable ground truth information from which to build recognition models, these techniques often do not function well when implemented in near real time applications. This paper presents the application of a multivariate online change detection algorithm to dynamically detect the starting position of windows for the purposes of activity recognition. PMID:27392647

  14. An efficient algorithm for recognition of human actions.

    PubMed

    Khan, Yaser Daanial; Khan, Nabeel Sabir; Farooq, Shoaib; Abid, Adnan; 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.

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

    PubMed

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

    2013-01-01

    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 26-52h. 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 response to sleep loss.

  16. 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

  17. A survey of fuzzy clustering algorithms for pattern recognition. II.

    PubMed

    Baraldi, A; Blonda, P

    1999-01-01

    For pt.I see ibid., p.775-85. In part I an equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms is proposed on the basis of the existing literature. Moreover, a set of functional attributes is selected for use as dictionary entries in the comparison of clustering algorithms. In this paper, five clustering algorithms taken from the literature are reviewed, assessed and compared on the basis of the selected properties of interest. These clustering models are (1) self-organizing map (SOM); (2) fuzzy learning vector quantization (FLVQ); (3) fuzzy adaptive resonance theory (fuzzy ART); (4) growing neural gas (GNG); (5) fully self-organizing simplified adaptive resonance theory (FOSART). Although our theoretical comparison is fairly simple, it yields observations that may appear parodoxical. First, only FLVQ, fuzzy ART, and FOSART exploit concepts derived from fuzzy set theory (e.g., relative and/or absolute fuzzy membership functions). Secondly, only SOM, FLVQ, GNG, and FOSART employ soft competitive learning mechanisms, which are affected by asymptotic misbehaviors in the case of FLVQ, i.e., only SOM, GNG, and FOSART are considered effective fuzzy clustering algorithms. PMID:18252358

  18. 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

  19. 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.

  20. Egocentric daily activity recognition via multitask clustering.

    PubMed

    Yan, Yan; Ricci, Elisa; Liu, Gaowen; Sebe, Nicu

    2015-10-01

    Recognizing human activities from videos is a fundamental research problem in computer vision. Recently, there has been a growing interest in analyzing human behavior from data collected with wearable cameras. First-person cameras continuously record several hours of their wearers' life. To cope with this vast amount of unlabeled and heterogeneous data, novel algorithmic solutions are required. In this paper, we propose a multitask clustering framework for activity of daily living analysis from visual data gathered from wearable cameras. Our intuition is that, even if the data are not annotated, it is possible to exploit the fact that the tasks of recognizing everyday activities of multiple individuals are related, since typically people perform the same actions in similar environments, e.g., people working in an office often read and write documents). In our framework, rather than clustering data from different users separately, we propose to look for clustering partitions which are coherent among related tasks. In particular, two novel multitask clustering algorithms, derived from a common optimization problem, are introduced. Our experimental evaluation, conducted both on synthetic data and on publicly available first-person vision data sets, shows that the proposed approach outperforms several single-task and multitask learning methods. PMID:26067371

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

    PubMed

    Wang, Lukun

    2016-02-04

    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.

  2. 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%.

  3. 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%. PMID:24977221

  4. 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

  5. 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.

  6. An Activation-Verification Model for Letter and Word Recognition: The Word-Superiority Effect.

    ERIC Educational Resources Information Center

    Paap, Kenneth R.; And Others

    1982-01-01

    An encoding algorithm uses empirically determined confusion matrices to activate units in an alphabetum and a lexicon to predict performance of word, orthographically regular nonword, or irregular nonword recognition. Performance is enhanced when decisions are based on lexical information which constrains test letter identity. Word prediction…

  7. Definition and basic properties of one modification of computational schema for recognition algorithms

    SciTech Connect

    Okhtilev, M.Yu.

    1992-09-01

    This article presents one form of computational schema for recognition algorithms in the form of modified Petri nets. Associated concepts are introduced and a description is given of the fundamental properties of the proposed nets that make it possible to treat them as schema for programs for artificial intelligence systems. 13 refs., 4 figs.

  8. 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.

  9. Field testing of a 3D automatic target recognition and pose estimation algorithm

    NASA Astrophysics Data System (ADS)

    Ruel, Stephane; English, Chad E.; Melo, Len; Berube, Andrew; Aikman, Doug; Deslauriers, Adam M.; Church, Philip M.; Maheux, Jean

    2004-09-01

    Neptec Design Group Ltd. has developed a 3D Automatic Target Recognition (ATR) and pose estimation technology demonstrator in partnership with the Canadian DND. The system prototype was deployed for field testing at Defence Research and Development Canada (DRDC)-Valcartier. This paper discusses the performance of the developed algorithm using 3D scans acquired with an imaging LIDAR. 3D models of civilian and military vehicles were built using scans acquired with a triangulation laser scanner. The models were then used to generate a knowledge base for the recognition algorithm. A commercial imaging LIDAR was used to acquire test scans of the target vehicles with varying range, pose and degree of occlusion. Recognition and pose estimation results are presented for at least 4 different poses of each vehicle at each test range. Results obtained with targets partially occluded by an artificial plane, vegetation and military camouflage netting are also presented. Finally, future operational considerations are discussed.

  10. 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.

  11. 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

  12. 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

  13. A quick scan and lane recognition algorithm based on positional distribution and edge features

    NASA Astrophysics Data System (ADS)

    Wang, Jian; Zhang, Yuan; Chen, Xiaomin; Shi, Xiaoying

    2010-08-01

    With the growing number of vehicles on the road, the automatic guided vehicles (AGV) vision system for intelligent vehicles has been given more and more attention. Lane recognition is an important component in the automatic guided vehicles (AGV) vision system for intelligent vehicles. To improve the speed and accuracy of lane recognition, this paper proposed an image segmentation algorithm based on the normalized histogram matching and a specific image scan algorithm based on positional distribution of lanes to reduce runtime. The purpose of image segmentation is extracting useful road information and the algorithm is segmenting the image by calculating the similarity of Cumulative Distribution Function (CDF) of normalization histogram. The main idea of image scan algorithm proposed in this paper is regarding the lanes that have been found as starting points and looking for the new lanes. Then we use a novel lane screen algorithm based on the left and right edges of lanes' geometric feature to remove invalid information and improve the accuracy and promote efficiency effectively. At last, a lane prediction algorithm is proposed to predict the farther lanes which may be lost due to treating as noises. After our tests, this algorithm has better robustness and higher efficiency.

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

    PubMed

    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

  15. 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

  16. 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.

  17. Application of support vector machine and quantum genetic algorithm in infrared target recognition

    NASA Astrophysics Data System (ADS)

    Wang, Hongliang; Huang, Yangwen; Ding, Haifei

    2010-08-01

    In this paper, a kind of classifier based on support vector machine (SVM) is designed for infrared target recognition. In allusion to the problem how to choose kernel parameter and error penalty factor, quantum genetic algorithm (QGA) is used to optimize the parameters of SVM model, it overcomes the shortcoming of determining its parameters after trial and error in the past. Classification experiments of infrared target features extracted by this method show that the convergence speed is fast and the rate of accurate recognition is high.

  18. KD-tree based clustering algorithm for fast face recognition on large-scale data

    NASA Astrophysics Data System (ADS)

    Wang, Yuanyuan; Lin, Yaping; Yang, Junfeng

    2015-07-01

    This paper proposes an acceleration method for large-scale face recognition system. When dealing with a large-scale database, face recognition is time-consuming. In order to tackle this problem, we employ the k-means clustering algorithm to classify face data. Specifically, the data in each cluster are stored in the form of the kd-tree, and face feature matching is conducted with the kd-tree based nearest neighborhood search. Experiments on CAS-PEAL and self-collected database show the effectiveness of our proposed method.

  19. Hierarchical Vision-based Algorithm for Vehicle Model Type Recognition from Time-sequence Road Images

    NASA Astrophysics Data System (ADS)

    Zheng, Mingxie; Gotoh, Toshiyuki; Shiohara, Morito

    This paper describes a vision-based algorithm for recognizing the vehicle model type from time-sequence road images. Many types of vehicle models are offered commercially, and some of them are resemble in shape. This prevents us to discriminate their model types from the others easily. To solve these problems, we proposes a hierarchical recognition method with training process, in which the resemble model groups are firstly generated and the effective features to discriminate the models in the each group are then selected using the subspace method in training. In the recognition process, a front area is firstly detected from each frame of the input time-sequence images, then a hierarchical recognition which consists of a group and a category discrimination is performed. Finally, the results of frame recognition are integrated to realize stable recognition. The experimental results using time-sequence road images show the proposed method is effective: the recognition rate for the registered model types is more than 99%, and the rejection rate for unregistered vehicle type is more than 92%.

  20. Passive and active recognition of one's own face.

    PubMed

    Sugiura, M; Kawashima, R; Nakamura, K; Okada, K; Kato, T; Nakamura, A; Hatano, K; Itoh, K; Kojima, S; Fukuda, H

    2000-01-01

    Facial identity recognition has been studied mainly with explicit discrimination requirement and faces of social figures in previous human brain imaging studies. We performed a PET activation study with normal volunteers in facial identity recognition tasks using the subject's own face as visual stimulus. Three tasks were designed so that the activation of the visual representation of the face and the effect of sustained attention to the representation could be separately examined: a control-face recognition task (C), a passive own-face recognition task (no explicit discrimination was required) (P), and an active own-face recognition task (explicit discrimination was required) (A). Increased skin conductance responses during recognition of own face were seen in both task P and task A, suggesting the occurrence of psychophysiological changes during recognition of one's own face. The left fusiform gyrus, the right supramarginal gyrus, the left putamen, and the right hypothalamus were activated in tasks P and A compared with task C. The left fusiform gyrus and the right supramarginal gyrus are considered to be involved in the representation of one's own face. The activation in the right supramarginal gyrus may be associated with the representation of one's own face as a part of one's own body. The prefrontal cortices, the right anterior cingulate, the right presupplementary motor area, and the left insula were specifically activated during task A compared with tasks C and P, indicating that these regions may be involved in the sustained attention to the representation of one's own face. PMID:10686115

  1. 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.

  2. A new approach for modulation recognition based on ant colony algorithm

    NASA Astrophysics Data System (ADS)

    Liu, Shu; Wang, Hongyuan

    2007-11-01

    A New Approach based on ant colony algorithm for the automatic modulation recognition of communications signals is presented. This approach can discriminate between continuous wave (CW), Amplitude Modulation (AM), Frequency Modulation (FM), Frequency Shift Keying (FSK), Binary Phase Shift Keying (BPSK) and Quaternary Phase Shift Keying (QPSK) modulations. Requirements for a priori knowledge of the signals are minimized by the inclusion of an efficient carrier frequency estimator and low sensitivity to variations in the sampling epochs. Computer simulations indicate good performance on an AWGN channel, even at signal-to-noise ratios as low as 5 dB. This compares favorably with the performance obtained with most algorithms based on pattern recognition techniques.

  3. Automatic target recognition algorithm based on statistical dispersion of infrared multispectral image

    NASA Astrophysics Data System (ADS)

    Zhang, Wei; Cao, Le-lin; Wu, Chun-feng; Hou, Qing-yu

    2009-07-01

    A novel automatic target recognition algorithm based on statistical dispersion of infrared multispectral images(SDOIMI) is proposed. Firstly, infrared multispectral characteristic matrix of the scenario is constructed based on infrared multispectral characteristic information (such as radiation intensity and spectral distribution etc.) of targets, background and decoys. Then the infrared multispectral characteristic matrix of targets is reconstructed after segmenting image by maximum distance method and fusing spatial and spectral information. Finally, an statistical dispersion of infrared multispectral images(SDOIMI) recognition criteria is formulated in terms of spectral radiation difference of interesting targets. In simulation, nine sub-bands multispectral images of real ship target and shipborne aerosol infrared decoy modulated by laser simulating ship geometry appearance are obtained via using spectral radiation curves. Digital simulation experiment result verifies that the algorithm is effective and feasible.

  4. 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.

  5. A Survey of Online Activity Recognition Using Mobile Phones

    PubMed Central

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

    2015-01-01

    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. PMID:25608213

  6. 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. PMID:27468841

  7. 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.

  8. 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. PMID:22394690

  9. 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.

  10. 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. PMID:25789630

  11. 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

  12. Face recognition algorithm in hyperspectral imagery by employing the K-means method and the Mahalanobis distance

    NASA Astrophysics Data System (ADS)

    Elbakary, M. I.; Alam, M. S.; Aslan, M. S.

    2007-09-01

    Recently, spectral information is introduced into face recognition applications to improve the detection performance for different conditions. Besides the changes in scale, orientation, and rotation of facial images, expression, occlusion and lighting conditions change the overall appearance of faces and recognition results. To eliminate these difficulties, we introduced a new face recognition technique by using the spectral signature of facial tissues. Unlike alternate algorithms, the proposed algorithm classifies the hyperspectral imagery corresponding to each face into clusters to automatically recognize the desired face and to eliminate the user intervention in the data set. The K-means clustering algorithm is employed to accomplish the clustering and then Mahalanobis distance is computed between the clusters to identify the closest cluster in the data with respect to the reference cluster. By identifying a cluster in the data, the face that contains that cluster is identified by the proposed algorithm. Test results using real life hyperspectral imagery shows the effectiveness of the proposed algorithm.

  13. N-methyl-D-aspartate recognition site ligands modulate activity at the coupled glycine recognition site.

    PubMed

    Hood, W F; Compton, R P; Monahan, J B

    1990-03-01

    In synaptic plasma membranes from rat forebrain, the potencies of glycine recognition site agonists and antagonists for modulating [3H]1-[1-(2-thienyl)cyclohexyl]piperidine ([3H]TCP) binding and for displacing strychnine-insensitive [3H]glycine binding are altered in the presence of N-methyl-D-aspartate (NMDA) recognition site ligands. The NMDA competitive antagonist, cis-4-phosphonomethyl-2-piperidine carboxylate (CGS 19755), reduces [3H]glycine binding, and the reduction can be fully reversed by the NMDA recognition site agonist, L-glutamate. Scatchard analysis of [3H]glycine binding shows that in the presence of CGS 19755 there is no change in Bmax (8.81 vs. 8.79 pmol/mg of protein), but rather a decrease in the affinity of glycine (KD of 0.202 microM vs. 0.129 microM). Similar decreases in affinity are observed for the glycine site agonists, D-serine and 1-aminocyclopropane-1-carboxylate, in the presence of CGS 19755. In contrast, the affinity of glycine antagonists, 1-hydroxy-3-amino-2-pyrrolidone and 1-aminocyclobutane-1-carboxylate, at this [3H]glycine recognition site increases in the presence of CGS 19755. The functional consequence of this change in affinity was addressed using the modulation of [3H]TCP binding. In the presence of L-glutamate, the potency of glycine agonists for the stimulation of [3H]TCP binding increases, whereas the potency of glycine antagonists decreases. These data are consistent with NMDA recognition site ligands, through their interactions at the NMDA recognition site, modulating activity at the associated glycine recognition site.

  14. 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

  15. An automatic geo-spatial object recognition algorithm for high resolution satellite images

    NASA Astrophysics Data System (ADS)

    Ergul, Mustafa; Alatan, A. Aydın.

    2013-10-01

    This paper proposes a novel automatic geo-spatial object recognition algorithm for high resolution satellite imaging. The proposed algorithm consists of two main steps; a hypothesis generation step with a local feature-based algorithm and a verification step with a shape-based approach. In the hypothesis generation step, a set of hypothesis for possible object locations is generated, aiming lower missed detections and higher false-positives by using a Bag of Visual Words type approach. In the verification step, the foreground objects are first extracted by a semi-supervised image segmentation algorithm, utilizing detection results from the previous step, and then, the shape descriptors for segmented objects are utilized to prune out the false positives. Based on simulation results, it can be argued that the proposed algorithm achieves both high precision and high recall rates as a result of taking advantage of both the local feature-based and the shape-based object detection approaches. The superiority of the proposed method is due to the ability of minimization of false alarm rate and since most of the object shapes contain more characteristic and discriminative information about their identity and functionality.

  16. New approach for automatic recognition of melanoma in profilometry: optimized feature selection using genetic algorithms

    NASA Astrophysics Data System (ADS)

    Handels, Heinz; Ross, Th; Kreusch, J.; Wolff, H. H.; Poeppl, S. J.

    1998-06-01

    A new approach to computer supported recognition of melanoma and naevocytic naevi based on high resolution skin surface profiles is presented. Profiles are generated by sampling an area of 4 X 4 mm2 at a resolution of 125 sample points per mm with a laser profilometer at a vertical resolution of 0.1 micrometers . With image analysis algorithms Haralick's texture parameters, Fourier features and features based on fractal analysis are extracted. In order to improve classification performance, a subsequent feature selection process is applied to determine the best possible subset of features. Genetic algorithms are optimized for the feature selection process, and results of different approaches are compared. As quality measure for feature subsets, the error rate of the nearest neighbor classifier estimated with the leaving-one-out method is used. In comparison to heuristic strategies and greedy algorithms, genetic algorithms show the best results for the feature selection problem. After feature selection, several architectures of feed forward neural networks with error back-propagation are evaluated. Classification performance of the neural classifier is optimized using different topologies, learning parameters and pruning algorithms. The best neural classifier achieved an error rate of 4.5% and was found after network pruning. The best result in all with an error rate of 2.3% was obtained with the nearest neighbor classifier.

  17. 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.

  18. 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.

  19. 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.

  20. 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∗.

  1. 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

  2. Implementation study of wearable sensors for activity recognition systems.

    PubMed

    Rezaie, Hamed; Ghassemian, Mona

    2015-08-01

    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.

  3. A new FOD recognition algorithm based on multi-source information fusion and experiment analysis

    NASA Astrophysics Data System (ADS)

    Li, Yu; Xiao, Gang

    2011-08-01

    Foreign Object Debris (FOD) is a kind of substance, debris or article alien to an aircraft or system, which would potentially cause huge damage when it appears on the airport runway. Due to the airport's complex circumstance, quick and precise detection of FOD target on the runway is one of the important protections for airplane's safety. A multi-sensor system including millimeter-wave radar and Infrared image sensors is introduced and a developed new FOD detection and recognition algorithm based on inherent feature of FOD is proposed in this paper. Firstly, the FOD's location and coordinate can be accurately obtained by millimeter-wave radar, and then according to the coordinate IR camera will take target images and background images. Secondly, in IR image the runway's edges which are straight lines can be extracted by using Hough transformation method. The potential target region, that is, runway region, can be segmented from the whole image. Thirdly, background subtraction is utilized to localize the FOD target in runway region. Finally, in the detailed small images of FOD target, a new characteristic is discussed and used in target classification. The experiment results show that this algorithm can effectively reduce the computational complexity, satisfy the real-time requirement and possess of high detection and recognition probability.

  4. 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

  5. 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. PMID:27413392

  6. Sum Product Networks for Activity Recognition.

    PubMed

    Amer, Mohamed R; Todorovic, Sinisa

    2016-04-01

    This paper addresses detection and localization of human activities in videos. We focus on activities that may have variable spatiotemporal arrangements of parts, and numbers of actors. Such activities are represented by a sum-product network (SPN). A product node in SPN represents a particular arrangement of parts, and a sum node represents alternative arrangements. The sums and products are hierarchically organized, and grounded onto space-time windows covering the video. The windows provide evidence about the activity classes based on the Counting Grid (CG) model of visual words. This evidence is propagated bottom-up and top-down to parse the SPN graph for the explanation of the video. The node connectivity and model parameters of SPN and CG are jointly learned under two settings, weakly supervised, and supervised. For evaluation, we use our new Volleyball dataset, along with the benchmark datasets VIRAT, UT-Interactions, KTH, and TRECVID MED 2011. Our video classification and activity localization are superior to those of the state of the art on these datasets.

  7. Recognition of military-specific physical activities with body-fixed sensors.

    PubMed

    Wyss, Thomas; Mäder, Urs

    2010-11-01

    The purpose of this study was to develop and validate an algorithm for recognizing military-specific, physically demanding activities using body-fixed sensors. To develop the algorithm, the first group of study participants (n = 15) wore body-fixed sensors capable of measuring acceleration, step frequency, and heart rate while completing six military-specific activities: walking, marching with backpack, lifting and lowering loads, lifting and carrying loads, digging, and running. The accuracy of the algorithm was tested in these isolated activities in a laboratory setting (n = 18) and in the context of daily military training routine (n = 24). The overall recognition rates during isolated activities and during daily military routine activities were 87.5% and 85.5%, respectively. We conclude that the algorithm adequately recognized six military-specific physical activities based on sensor data alone both in a laboratory setting and in the military training environment. By recognizing type of physical activities this objective method provides additional information on military-job descriptions. PMID:21121495

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

    PubMed

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

    2014-06-01

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

  9. 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.

  10. Recognition of Protein-coding Genes Based on Z-curve Algorithms.

    PubMed

    -Biao Guo, Feng; Lin, Yan; -Ling Chen, Ling

    2014-04-01

    Recognition of protein-coding genes, a classical bioinformatics issue, is an absolutely needed step for annotating newly sequenced genomes. The Z-curve algorithm, as one of the most effective methods on this issue, has been successfully applied in annotating or re-annotating many genomes, including those of bacteria, archaea and viruses. Two Z-curve based ab initio gene-finding programs have been developed: ZCURVE (for bacteria and archaea) and ZCURVE_V (for viruses and phages). ZCURVE_C (for 57 bacteria) and Zfisher (for any bacterium) are web servers for re-annotation of bacterial and archaeal genomes. The above four tools can be used for genome annotation or re-annotation, either independently or combined with the other gene-finding programs. In addition to recognizing protein-coding genes and exons, Z-curve algorithms are also effective in recognizing promoters and translation start sites. Here, we summarize the applications of Z-curve algorithms in gene finding and genome annotation.

  11. An incremental learning algorithm based on Support Vector Machine for pattern recognition

    NASA Astrophysics Data System (ADS)

    Zou, Lamei; Zhang, Tianxu; Cao, Zhiguo

    2009-10-01

    With the advent of information age, especially with the rapid development of network, "information explosion" problem has emerged. How to improve the classifier's training precision steadily with accumulation of the samples is the original idea of the incremental learning. Support Vector Machine (SVM) has been successfully applied in many pattern recognition fields. While its complex computation is the bottle-neck to deal with large-scale data. It's important to do researches on the SVM's incremental learning. This article proposes a SVM's incremental learning algorithm based on the filtering fixed partition of the data set. This article firstly presents "Two-class problem"s algorithm and then generalizes it to the "Multiclass problem" algorithm by the One-vs-One method. The experimental results on three types of data sets' classification show that the proposed incremental learning technique can greatly improve the efficiency of SVM learning. SVM Incremental learning can not only ensure the correct identification rate but also speedup the training process.

  12. It may be possible to use Speech Recognition Algorithms to sort through Particle Detection

    NASA Astrophysics Data System (ADS)

    Kriske, Richard

    There are some similarities between recognizing speech and written language and in recognizing Particle interaction and decays. In the Viterbi Algorithm or speech recognition, a target word is recursively compared with the unknown utterance. Say one remembered the word Motion in a song and wanted to find that song. First the letter M is typed in and the most common words with M show up say it is the word ''Menards'', then an ''O'' is typed in and statistically the most common word is now ''Movies'', now the ''t'' is typed in and the most common word is ''Motley Crue'' finally all the letters are typed in and the song that matches is ''Motion Lyrics''. We all recognize the Algorithm and perhaps a few have realized that this Algorithm could also be applied to Decay Chains in Particle Scattering and Detection. Also there may come a day when perhaps Neutrinos where transmitted with the purpose of Communication, one system would be to use a type of ''Morse Code'', but another could be to use Decay Chains themselves. Perhaps the sender could tune the Energy such that the information received would rely on the Energy being transmitted, since it may be that only a few of the particles are received, too few for ''Morse Code'' to work.

  13. A unified classifier for robust face recognition based on combining multiple subspace algorithms

    NASA Astrophysics Data System (ADS)

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

    2012-10-01

    Face recognition being the fastest growing biometric technology has expanded manifold in the last few years. Various new algorithms and commercial systems have been proposed and developed. However, none of the proposed or developed algorithm is a complete solution because it may work very well on one set of images with say illumination changes but may not work properly on another set of image variations like expression variations. This study is motivated by the fact that any single classifier cannot claim to show generally better performance against all facial image variations. To overcome this shortcoming and achieve generality, combining several classifiers using various strategies has been studied extensively also incorporating the question of suitability of any classifier for this task. The study is based on the outcome of a comprehensive comparative analysis conducted on a combination of six subspace extraction algorithms and four distance metrics on three facial databases. The analysis leads to the selection of the most suitable classifiers which performs better on one task or the other. These classifiers are then combined together onto an ensemble classifier by two different strategies of weighted sum and re-ranking. The results of the ensemble classifier show that these strategies can be effectively used to construct a single classifier that can successfully handle varying facial image conditions of illumination, aging and facial expressions.

  14. 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

  15. Recognition of Protein-coding Genes Based on Z-curve Algorithms

    PubMed Central

    -Biao Guo, Feng; Lin, Yan; -Ling Chen, Ling

    2014-01-01

    Recognition of protein-coding genes, a classical bioinformatics issue, is an absolutely needed step for annotating newly sequenced genomes. The Z-curve algorithm, as one of the most effective methods on this issue, has been successfully applied in annotating or re-annotating many genomes, including those of bacteria, archaea and viruses. Two Z-curve based ab initio gene-finding programs have been developed: ZCURVE (for bacteria and archaea) and ZCURVE_V (for viruses and phages). ZCURVE_C (for 57 bacteria) and Zfisher (for any bacterium) are web servers for re-annotation of bacterial and archaeal genomes. The above four tools can be used for genome annotation or re-annotation, either independently or combined with the other gene-finding programs. In addition to recognizing protein-coding genes and exons, Z-curve algorithms are also effective in recognizing promoters and translation start sites. Here, we summarize the applications of Z-curve algorithms in gene finding and genome annotation. PMID:24822027

  16. Speech-cue transmission by an algorithm to increase consonant recognition in noise for hearing-impaired listeners

    PubMed Central

    Healy, Eric W.; Yoho, Sarah E.; Wang, Yuxuan; Apoux, Frédéric; Wang, DeLiang

    2014-01-01

    Consonant recognition was assessed following extraction of speech from noise using a more efficient version of the speech-segregation algorithm described in Healy, Yoho, Wang, and Wang [(2013) J. Acoust. Soc. Am. 134, 3029–3038]. Substantial increases in recognition were observed following algorithm processing, which were significantly larger for hearing-impaired (HI) than for normal-hearing (NH) listeners in both speech-shaped noise and babble backgrounds. As observed previously for sentence recognition, older HI listeners having access to the algorithm performed as well or better than young NH listeners in conditions of identical noise. It was also found that the binary masks estimated by the algorithm transmitted speech features to listeners in a fashion highly similar to that of the ideal binary mask (IBM), suggesting that the algorithm is estimating the IBM with substantial accuracy. Further, the speech features associated with voicing, manner of articulation, and place of articulation were all transmitted with relative uniformity and at relatively high levels, indicating that the algorithm and the IBM transmit speech cues without obvious deficiency. Because the current implementation of the algorithm is much more efficient, it should be more amenable to real-time implementation in devices such as hearing aids and cochlear implants. PMID:25480077

  17. 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. PMID:25160375

  18. 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.

  19. A robust algorithm for automated target recognition using precomputed radar cross sections

    NASA Astrophysics Data System (ADS)

    Ehrman, Lisa M.; Lanterman, Aaron D.

    2004-09-01

    Passive radar is an emerging technology that offers a number of unique benefits, including covert operation. Many such systems are already capable of detecting and tracking aircraft. The goal of this work is to develop a robust algorithm for adding automated target recognition (ATR) capabilities to existing passive radar systems. In previous papers, we proposed conducting ATR by comparing the precomputed RCS of known targets to that of detected targets. To make the precomputed RCS as accurate as possible, a coordinated flight model is used to estimate aircraft orientation. Once the aircraft's position and orientation are known, it is possible to determine the incident and observed angles on the aircraft, relative to the transmitter and receiver. This makes it possible to extract the appropriate radar cross section (RCS) from our simulated database. This RCS is then scaled to account for propagation losses and the receiver's antenna gain. A Rician likelihood model compares these expected signals from different targets to the received target profile. We have previously employed Monte Carlo runs to gauge the probability of error in the ATR algorithm; however, generation of a statistically significant set of Monte Carlo runs is computationally intensive. As an alternative to Monte Carlo runs, we derive the relative entropy (also known as Kullback-Liebler distance) between two Rician distributions. Since the probability of Type II error in our hypothesis testing problem can be expressed as a function of the relative entropy via Stein's Lemma, this provides us with a computationally efficient method for determining an upper bound on our algorithm's performance. It also provides great insight into the types of classification errors we can expect from our algorithm. This paper compares the numerically approximated probability of Type II error with the results obtained from a set of Monte Carlo runs.

  20. 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.

  1. 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.

  2. Learning person-person interaction in collective activity recognition.

    PubMed

    Chang, Xiaobin; Zheng, Wei-Shi; Zhang, Jianguo

    2015-06-01

    Collective activity is a collection of atomic activities (individual person's activity) and can hardly be distinguished by an atomic activity in isolation. The interactions among people are important cues for recognizing collective activity. In this paper, we concentrate on modeling the person-person interactions for collective activity recognition. Rather than relying on hand-craft description of the person-person interaction, we propose a novel learning-based approach that is capable of computing the class-specific person-person interaction patterns. In particular, we model each class of collective activity by an interaction matrix, which is designed to measure the connection between any pair of atomic activities in a collective activity instance. We then formulate an interaction response (IR) model by assembling all these measurements and make the IR class specific and distinct from each other. A multitask IR is further proposed to jointly learn different person-person interaction patterns simultaneously in order to learn the relation between different person-person interactions and keep more distinct activity-specific factor for each interaction at the same time. Our model is able to exploit discriminative low-rank representation of person-person interaction. Experimental results on two challenging data sets demonstrate our proposed model is comparable with the state-of-the-art models and show that learning person-person interactions plays a critical role in collective activity recognition. PMID:25769156

  3. 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

  4. 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.

  5. An algorithm for automatic target recognition using passive radar and an EKF for estimating aircraft orientation

    NASA Astrophysics Data System (ADS)

    Ehrman, Lisa M.

    2005-07-01

    Rather than emitting pulses, passive radar systems rely on "illuminators of opportunity," such as TV and FM radio, to illuminate potential targets. These systems are attractive since they allow receivers to operate without emitting energy, rendering them covert. Until recently, most of the research regarding passive radar has focused on detecting and tracking targets. This dissertation focuses on extending the capabilities of passive radar systems to include automatic target recognition. The target recognition algorithm described in this dissertation uses the radar cross section (RCS) of potential targets, collected over a short period of time, as the key information for target recognition. To make the simulated RCS as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. An extended Kalman filter (EKF) estimates the target's orientation (and uncertainty in the estimate) from velocity measurements obtained from the passive radar tracker. Coupling the aircraft orientation and state with the known antenna locations permits computation of the incident and observed azimuth and elevation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of potential target classes as a function of these angles. Thus, the approximated incident and observed angles allow the appropriate RCS to be extracted from a database of FISC results. Using this process, the RCS of each aircraft in the target class is simulated as though each is executing the same maneuver as the target detected by the system. Two additional scaling processes are required to transform the RCS into a power profile (magnitude only) simulating the signal in the receiver. First, the RCS is scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. Then, the Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern

  6. 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

  7. 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.

  8. 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

    2016-01-01

    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

  9. 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

  10. Activity and function recognition for moving and static objects in urban environments from wide-area persistent surveillance inputs

    NASA Astrophysics Data System (ADS)

    Levchuk, Georgiy; Bobick, Aaron; Jones, Eric

    2010-04-01

    In this paper, we describe results from experimental analysis of a model designed to recognize activities and functions of moving and static objects from low-resolution wide-area video inputs. Our model is based on representing the activities and functions using three variables: (i) time; (ii) space; and (iii) structures. The activity and function recognition is achieved by imposing lexical, syntactic, and semantic constraints on the lower-level event sequences. In the reported research, we have evaluated the utility and sensitivity of several algorithms derived from natural language processing and pattern recognition domains. We achieved high recognition accuracy for a wide range of activity and function types in the experiments using Electro-Optical (EO) imagery collected by Wide Area Airborne Surveillance (WAAS) platform.

  11. Ontology-based improvement to human activity recognition

    NASA Astrophysics Data System (ADS)

    Tahmoush, David; Bonial, Claire

    2016-05-01

    Human activity recognition has often prioritized low-level features extracted from imagery or video over higher-level class attributes and ontologies because they have traditionally been more effective on small datasets. However, by including knowledge-driven associations between actions and attributes while recognizing the lower-level attributes with their temporal relationships, we can attempt a hybrid approach that is more easily extensible to much larger datasets. We demonstrate a combination of hard and soft features with a comparison factor that prioritizes one approach over the other with a relative weight. We then exhaustively search over the comparison factor to evaluate the performance of a hybrid human activity recognition approach in comparison to the base hard approach at 84% accuracy and the current state-of-the-art.

  12. 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.

  13. 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

  14. Multiple adaptive neuro-fuzzy inference system with automatic features extraction algorithm for cervical cancer recognition.

    PubMed

    Al-batah, Mohammad Subhi; Isa, Nor Ashidi Mat; 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

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. GA-fisher: A new LDA-based face recognition algorithm with selection of principal components.

    PubMed

    Zheng, Wei-Shi; Lai, Jian-Huang; Yuen, Pong C

    2005-10-01

    This paper addresses the dimension reduction problem in Fisherface for face recognition. When the number of training samples is less than the image dimension (total number of pixels), the within-class scatter matrix (Sw) in Linear Discriminant Analysis (LDA) is singular, and Principal Component Analysis (PCA) is suggested to employ in Fisherface for dimension reduction of Sw so that it becomes nonsingular. The popular method is to select the largest nonzero eigenvalues and the corresponding eigenvectors for LDA. To attenuate the illumination effect, some researchers suggested removing the three eigenvectors with the largest eigenvalues and the performance is improved. However, as far as we know, there is no systematic way to determine which eigenvalues should be used. Along this line, this paper proposes a theorem to interpret why PCA can be used in LDA and an automatic and systematic method to select the eigenvectors to be used in LDA using a Genetic Algorithm (GA). A GA-PCA is then developed. It is found that some small eigenvectors should also be used as part of the basis for dimension reduction. Using the GA-PCA to reduce the dimension, a GA-Fisher method is designed and developed. Comparing with the traditional Fisherface method, the proposed GA-Fisher offers two additional advantages. First, optimal bases for dimensionality reduction are derived from GA-PCA. Second, the computational efficiency of LDA is improved by adding a whitening procedure after dimension reduction. The Face Recognition Technology (FERET) and Carnegie Mellon University Pose, Illumination, and Expression (CMU PIE) databases are used for evaluation. Experimental results show that almost 5 % improvement compared with Fisherface can be obtained, and the results are encouraging.

  20. Chemical sensing system using plasma polymer films and pattern recognition algorithm

    SciTech Connect

    Nakamura, M.; Sugimoto, I.; Kuwano, H.

    1994-05-01

    A chemical sensing system using a sensor array with sensitive but durable plasma polymer films is developed. Plasma polymer films have unsaturated bonds and radical sites which cause several unique characteristics. These films contain high concentrations of unsaturated bonds and radical sites, which act as interactive sites. These sites, scattered throughout an inert fluorocarbon framework, are believed to induce specific interactions with small molecules through pi and spin interactions. We have tried to apply our knowledge of these interactions to molecular recognition. For sensing small molecules, these films are deposited on both sides of an AT-cut quartz crystal microbalance (QCM) with a resonant frequency of 9 MHz by radio-frequency (RF) sputtering of polymers such as polychlorotrifluoroethylene. The QCM is connected to an oscillator circuit and its resonant shift is proportional to the mass of the adsorbed molecules. The affinity of plasma polymer films can be shifted by changing sputtering conditions such as the target materials, temperature, or RF power. The chemical sensing system studied here uses a sensor array having modified films with various sensitivities. Because the sensor films have an affinity for several kinds of gases, a pattern recognition algorithm is needed to discern unique gas information from sensors that have overlapping selectivities. The equilibrium mass of adsorbed gas and a time constant are first extracted from the time-dependent sensor outputs, which show that the adsorption process resembles Langmuir adsorption, and then the parameters are mapped to a classification space and used for classification. The addition of a time constant increases the selectivity of our sensor system for single-gas analysis and mixture analysis. 12 refs.

  1. Real-time intelligent pattern recognition algorithm for surface EMG signals

    PubMed Central

    Khezri, Mahdi; Jahed, Mehran

    2007-01-01

    Background Electromyography (EMG) is the study of muscle function through the inquiry of electrical signals that the muscles emanate. EMG signals collected from the surface of the skin (Surface Electromyogram: sEMG) can be used in different applications such as recognizing musculoskeletal neural based patterns intercepted for hand prosthesis movements. Current systems designed for controlling the prosthetic hands either have limited functions or can only be used to perform simple movements or use excessive amount of electrodes in order to achieve acceptable results. In an attempt to overcome these problems we have proposed an intelligent system to recognize hand movements and have provided a user assessment routine to evaluate the correctness of executed movements. Methods We propose to use an intelligent approach based on adaptive neuro-fuzzy inference system (ANFIS) integrated with a real-time learning scheme to identify hand motion commands. For this purpose and to consider the effect of user evaluation on recognizing hand movements, vision feedback is applied to increase the capability of our system. By using this scheme the user may assess the correctness of the performed hand movement. In this work a hybrid method for training fuzzy system, consisting of back-propagation (BP) and least mean square (LMS) is utilized. Also in order to optimize the number of fuzzy rules, a subtractive clustering algorithm has been developed. To design an effective system, we consider a conventional scheme of EMG pattern recognition system. To design this system we propose to use two different sets of EMG features, namely time domain (TD) and time-frequency representation (TFR). Also in order to decrease the undesirable effects of the dimension of these feature sets, principle component analysis (PCA) is utilized. Results In this study, the myoelectric signals considered for classification consists of six unique hand movements. Features chosen for EMG signal are time and time

  2. The research of multi-frame target recognition based on laser active imaging

    NASA Astrophysics Data System (ADS)

    Wang, Can-jin; Sun, Tao; Wang, Tin-feng; Chen, Juan

    2013-09-01

    Laser active imaging is fit to conditions such as no difference in temperature between target and background, pitch-black night, bad visibility. Also it can be used to detect a faint target in long range or small target in deep space, which has advantage of high definition and good contrast. In one word, it is immune to environment. However, due to the affect of long distance, limited laser energy and atmospheric backscatter, it is impossible to illuminate the whole scene at the same time. It means that the target in every single frame is unevenly or partly illuminated, which make the recognition more difficult. At the same time the speckle noise which is common in laser active imaging blurs the images . In this paper we do some research on laser active imaging and propose a new target recognition method based on multi-frame images . Firstly, multi pulses of laser is used to obtain sub-images for different parts of scene. A denoising method combined homomorphic filter with wavelet domain SURE is used to suppress speckle noise. And blind deconvolution is introduced to obtain low-noise and clear sub-images. Then these sub-images are registered and stitched to combine a completely and uniformly illuminated scene image. After that, a new target recognition method based on contour moments is proposed. Firstly, canny operator is used to obtain contours. For each contour, seven invariant Hu moments are calculated to generate the feature vectors. At last the feature vectors are input into double hidden layers BP neural network for classification . Experiments results indicate that the proposed algorithm could achieve a high recognition rate and satisfactory real-time performance for laser active imaging.

  3. Human activity recognition based on human shape dynamics

    NASA Astrophysics Data System (ADS)

    Cheng, Zhiqing; Mosher, Stephen; Cheng, Huaining; Webb, Timothy

    2013-05-01

    Human activity recognition based on human shape dynamics was investigated in this paper. The shape dynamics describe the spatial-temporal shape deformation of a human body during its movement and thus provide important information about the identity of a human subject and the motions performed by the subject. The dynamic shapes of four subjects in five activities (digging, jogging, limping, throwing, and walking) were created via 3-D motion replication. The Paquet Shape Descriptor (PSD) was used to describe subject shapes in each frame. The principal component analysis was performed on the calculated PSDs and principal components (PCs) were used to characterize PSDs. The PSD calculation was then reasonably approximated by its significant projections in the eigen-space formed by PCs and represented by the corresponding projection coefficients. As such, the dynamic human shapes for each activity were described by these projection coefficients, which in turn, along with their derivatives were used to form the feature vectors (attribute sets) for activity classification. Data mining technology was employed with six classification methods used. Seven attribute sets were evaluated with high classification accuracy attained for most of them. The results from this investigation illustrate the great potential of human shape dynamics for activity recognition.

  4. An early illness recognition framework using a temporal Smith Waterman algorithm and NLP.

    PubMed

    Hajihashemi, Zahra; Popescu, Mihail

    2013-01-01

    In this paper we propose a framework for detecting health patterns based on non-wearable sensor sequence similarity and natural language processing (NLP). In TigerPlace, an aging in place facility from Columbia, MO, we deployed 47 sensor networks together with a nursing electronic health record (EHR) system to provide early illness recognition. The proposed framework utilizes sensor sequence similarity and NLP on EHR nursing comments to automatically notify the physician when health problems are detected. The reported methodology is inspired by genomic sequence annotation using similarity algorithms such as Smith Waterman (SW). Similarly, for each sensor sequence, we associate health concepts extracted from the nursing notes using Metamap, a NLP tool provided by Unified Medical Language System (UMLS). Since sensor sequences, unlike genomics ones, have an associated time dimension we propose a temporal variant of SW (TSW) to account for time. The main challenges presented by our framework are finding the most suitable time sequence similarity and aggregation of the retrieved UMLS concepts. On a pilot dataset from three Tiger Place residents, with a total of 1685 sensor days and 626 nursing records, we obtained an average precision of 0.64 and a recall of 0.37.

  5. Alignment of three-dimensional molecules using an image recognition algorithm.

    PubMed

    Richmond, Nicola J; Willett, Peter; Clark, Robert D

    2004-10-01

    This paper describes a novel approach, based on image recognition in two dimensions, for the atom-based alignment of two rigid molecules in three dimensions. The atoms are characterised by their partial charges and their positions relative to the remaining atoms in the molecule. Based on this information, a cost of matching a pair of atoms, one from each molecule, is assigned to all possible pairs. A preliminary set of intermolecular atom equivalences that minimises the total atom matching cost is then determined using an algorithm for solving the linear assignment problem. Several geometric heuristics are described that aim to reduce the number of atom equivalences that are inconsistent with the 3D structures. Those that remain are used to calculate an alignment transformation that achieves an optimal superposition of atoms that have a similar local geometry and partial charge. This alignment is then refined by calculating a new set of equivalences consisting of atom pairs that are approximately overlaid, irrespective of partial charge. A range of examples is provided to demonstrate the efficiency and effectiveness of the method.

  6. A multi-environment dataset for activity of daily living recognition in video streams.

    PubMed

    Borreo, Alessandro; Onofri, Leonardo; Soda, Paolo

    2015-08-01

    Public datasets played a key role in the increasing level of interest that vision-based human action recognition has attracted in last years. While the production of such datasets has been influenced by the variability introduced by various actors performing the actions, the different modalities of interactions with the environment introduced by the variation of the scenes around the actors has been scarcely took into account. As a consequence, public datasets do not provide a proper test-bed for recognition algorithms that aim at achieving high accuracy, irrespective of the environment where actions are performed. This is all the more so, when systems are designed to recognize activities of daily living (ADL), which are characterized by a high level of human-environment interaction. For that reason, we present in this manuscript the MEA dataset, a new multi-environment ADL dataset, which permitted us to show how the change of scenario can affect the performances of state-of-the-art approaches for action recognition.

  7. A zero-training algorithm for EEG single-trial classification applied to a face recognition ERP experiment.

    PubMed

    Lage-Castellanos, Agustin; Nieto, Juan I; Quiñones, Ileana; Martinez-Montes, Eduardo

    2010-01-01

    This paper proposes a machine learning based approach to discriminate between EEG single trials of two experimental conditions in a face recognition experiment. The algorithm works using a single-trial EEG database of multiple subjects and thus does not require subject-specific training data. This approach supports the idea that zero-training classification and on-line detection Brain Computer Interface (BCI) systems are areas with a significant amount of potential.

  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. Automated Detection of Selective Logging in Amazon Forests Using Airborne Lidar Data and Pattern Recognition Algorithms

    NASA Astrophysics Data System (ADS)

    Keller, M. M.; d'Oliveira, M. N.; Takemura, C. M.; Vitoria, D.; Araujo, L. S.; Morton, D. C.

    2012-12-01

    Selective logging, the removal of several valuable timber trees per hectare, is an important land use in the Brazilian Amazon and may degrade forests through long term changes in structure, loss of forest carbon and species diversity. Similar to deforestation, the annual area affected by selected logging has declined significantly in the past decade. Nonetheless, this land use affects several thousand km2 per year in Brazil. We studied a 1000 ha area of the Antimary State Forest (FEA) in the State of Acre, Brazil (9.304 ○S, 68.281 ○W) that has a basal area of 22.5 m2 ha-1 and an above-ground biomass of 231 Mg ha-1. Logging intensity was low, approximately 10 to 15 m3 ha-1. We collected small-footprint airborne lidar data using an Optech ALTM 3100EA over the study area once each in 2010 and 2011. The study area contained both recent and older logging that used both conventional and technologically advanced logging techniques. Lidar return density averaged over 20 m-2 for both collection periods with estimated horizontal and vertical precision of 0.30 and 0.15 m. A relative density model comparing returns from 0 to 1 m elevation to returns in 1-5 m elevation range revealed the pattern of roads and skid trails. These patterns were confirmed by ground-based GPS survey. A GIS model of the road and skid network was built using lidar and ground data. We tested and compared two pattern recognition approaches used to automate logging detection. Both segmentation using commercial eCognition segmentation and a Frangi filter algorithm identified the road and skid trail network compared to the GIS model. We report on the effectiveness of these two techniques.

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

    PubMed

    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

  12. 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…

  13. Parallel algorithms for isolated and connected word recognition. Volumes I and II

    SciTech Connect

    Yoder, M.A.

    1984-01-01

    For years researchers have worked toward finding a way to allow people to talk to machines in the same manner a person communicates to another person. This verbal man to machine interface, called speech recognition, can be grouped into three types: isolated word recognition, connected word recognition, and continuous speech recognition. Isolated word recognizers recognize single words with distinctive pauses before and after them. Continuous speech recognizers recognize speech spoken as one person speaks to another, continuously without pauses. Connected word recognition is an extension of isolated word recognition which recognizes groups of words spoken continuously. A group of words must have distinctive pauses before and after it, and the number of words in a group is limited to some small value (typically less than six). If these types of recognition systems are to be successful in the real world, they must be speaker independent and support a large vocabulary. They also must be able to recognize the speech input accurately and in real time. Currently there is no system which can meet all of these criteria because a vast amount of computations are needed. This thesis examines the use of parallel processing to reduce the computation time for speech recognition.

  14. 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

  15. 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...

  16. New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks

    NASA Astrophysics Data System (ADS)

    Morillot, Olivier; Likforman-Sulem, Laurence; Grosicki, Emmanuèle

    2013-04-01

    Many preprocessing techniques have been proposed for isolated word recognition. However, recently, recognition systems have dealt with text blocks and their compound text lines. In this paper, we propose a new preprocessing approach to efficiently correct baseline skew and fluctuations. Our approach is based on a sliding window within which the vertical position of the baseline is estimated. Segmentation of text lines into subparts is, thus, avoided. Experiments conducted on a large publicly available database (Rimes), with a BLSTM (bidirectional long short-term memory) recurrent neural network recognition system, show that our baseline correction approach highly improves performance.

  17. Improving human activity recognition and its application in early stroke diagnosis.

    PubMed

    Villar, José R; González, Silvia; Sedano, Javier; Chira, Camelia; Trejo-Gabriel-Galan, Jose M

    2015-06-01

    The development of efficient stroke-detection methods is of significant importance in today's society due to the effects and impact of stroke on health and economy worldwide. This study focuses on Human Activity Recognition (HAR), which is a key component in developing an early stroke-diagnosis tool. An overview of the proposed global approach able to discriminate normal resting from stroke-related paralysis is detailed. The main contributions include an extension of the Genetic Fuzzy Finite State Machine (GFFSM) method and a new hybrid feature selection (FS) algorithm involving Principal Component Analysis (PCA) and a voting scheme putting the cross-validation results together. Experimental results show that the proposed approach is a well-performing HAR tool that can be successfully embedded in devices.

  18. Physical Activity Recognition Based on Motion in Images Acquired by a Wearable Camera.

    PubMed

    Zhang, Hong; Li, Lu; Jia, Wenyan; Fernstrom, John D; Sclabassi, Robert J; Mao, Zhi-Hong; Sun, Mingui

    2011-06-01

    A new technique to extract and evaluate physical activity patterns from image sequences captured by a wearable camera is presented in this paper. Unlike standard activity recognition schemes, the video data captured by our device do not include the wearer him/herself. The physical activity of the wearer, such as walking or exercising, is analyzed indirectly through the camera motion extracted from the acquired video frames. Two key tasks, pixel correspondence identification and motion feature extraction, are studied to recognize activity patterns. We utilize a multiscale approach to identify pixel correspondences. When compared with the existing methods such as the Good Features detector and the Speed-up Robust Feature (SURF) detector, our technique is more accurate and computationally efficient. Once the pixel correspondences are determined which define representative motion vectors, we build a set of activity pattern features based on motion statistics in each frame. Finally, the physical activity of the person wearing a camera is determined according to the global motion distribution in the video. Our algorithms are tested using different machine learning techniques such as the K-Nearest Neighbor (KNN), Naive Bayesian and Support Vector Machine (SVM). The results show that many types of physical activities can be recognized from field acquired real-world video. Our results also indicate that, with a design of specific motion features in the input vectors, different classifiers can be used successfully with similar performances.

  19. Physical Activity Recognition Based on Motion in Images Acquired by a Wearable Camera

    PubMed Central

    Zhang, Hong; Li, Lu; Jia, Wenyan; Fernstrom, John D.; Sclabassi, Robert J.; Mao, Zhi-Hong; Sun, Mingui

    2011-01-01

    A new technique to extract and evaluate physical activity patterns from image sequences captured by a wearable camera is presented in this paper. Unlike standard activity recognition schemes, the video data captured by our device do not include the wearer him/herself. The physical activity of the wearer, such as walking or exercising, is analyzed indirectly through the camera motion extracted from the acquired video frames. Two key tasks, pixel correspondence identification and motion feature extraction, are studied to recognize activity patterns. We utilize a multiscale approach to identify pixel correspondences. When compared with the existing methods such as the Good Features detector and the Speed-up Robust Feature (SURF) detector, our technique is more accurate and computationally efficient. Once the pixel correspondences are determined which define representative motion vectors, we build a set of activity pattern features based on motion statistics in each frame. Finally, the physical activity of the person wearing a camera is determined according to the global motion distribution in the video. Our algorithms are tested using different machine learning techniques such as the K-Nearest Neighbor (KNN), Naive Bayesian and Support Vector Machine (SVM). The results show that many types of physical activities can be recognized from field acquired real-world video. Our results also indicate that, with a design of specific motion features in the input vectors, different classifiers can be used successfully with similar performances. PMID:21779142

  20. Accelerometer's position independent physical activity recognition system for long-term activity monitoring in the elderly.

    PubMed

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

    2010-12-01

    Mobility is a good indicator of health status and thus objective mobility data could be used to assess the health status of elderly patients. Accelerometry has emerged as an effective means for long-term physical activity monitoring in the elderly. However, the output of an accelerometer varies at different positions on a subject's body, even for the same activity, resulting in high within-class variance. Existing accelerometer-based activity recognition systems thus require firm attachment of the sensor to a subject's body. This requirement makes them impractical for long-term activity monitoring during unsupervised free-living as it forces subjects into a fixed life pattern and impede their daily activities. Therefore, we introduce a novel single-triaxial-accelerometer-based activity recognition system that reduces the high within-class variance significantly and allows subjects to carry the sensor freely in any pocket without its firm attachment. We validated our system using seven activities: resting (lying/sitting/standing), walking, walking-upstairs, walking-downstairs, running, cycling, and vacuuming, recorded from five positions: chest pocket, front left trousers pocket, front right trousers pocket, rear trousers pocket, and inner jacket pocket. Its simplicity, ability to perform activities unimpeded, and an average recognition accuracy of 94% make our system a practical solution for continuous long-term activity monitoring in the elderly.

  1. 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

  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

    2016-01-01

    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

  3. 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.

  4. 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. PMID:24513850

  5. 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.

  6. 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.

  7. On the Use of Evolutionary Algorithms to Improve the Robustness of Continuous Speech Recognition Systems in Adverse Conditions

    NASA Astrophysics Data System (ADS)

    Selouani, Sid-Ahmed; O'Shaughnessy, Douglas

    2003-12-01

    Limiting the decrease in performance due to acoustic environment changes remains a major challenge for continuous speech recognition (CSR) systems. We propose a novel approach which combines the Karhunen-Loève transform (KLT) in the mel-frequency domain with a genetic algorithm (GA) to enhance the data representing corrupted speech. The idea consists of projecting noisy speech parameters onto the space generated by the genetically optimized principal axis issued from the KLT. The enhanced parameters increase the recognition rate for highly interfering noise environments. The proposed hybrid technique, when included in the front-end of an HTK-based CSR system, outperforms that of the conventional recognition process in severe interfering car noise environments for a wide range of signal-to-noise ratios (SNRs) varying from 16 dB to[InlineEquation not available: see fulltext.] dB. We also showed the effectiveness of the KLT-GA method in recognizing speech subject to telephone channel degradations.

  8. ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning.

    PubMed

    Gandola, Emanuele; Antonioli, Manuela; Traficante, Alessio; Franceschini, Simone; Scardi, Michele; Congestri, Roberta

    2016-05-01

    Toxigenic cyanobacteria are one of the main health risks associated with water resources worldwide, as their toxins can affect humans and fauna exposed via drinking water, aquaculture and recreation. Microscopy monitoring of cyanobacteria in water bodies and massive growth systems is a routine operation for cell abundance and growth estimation. Here we present ACQUA (Automated Cyanobacterial Quantification Algorithm), a new fully automated image analysis method designed for filamentous genera in Bright field microscopy. A pre-processing algorithm has been developed to highlight filaments of interest from background signals due to other phytoplankton and dust. A spline-fitting algorithm has been designed to recombine interrupted and crossing filaments in order to perform accurate morphometric analysis and to extract the surface pattern information of highlighted objects. In addition, 17 specific pattern indicators have been developed and used as input data for a machine-learning algorithm dedicated to the recognition between five widespread toxic or potentially toxic filamentous genera in freshwater: Aphanizomenon, Cylindrospermopsis, Dolichospermum, Limnothrix and Planktothrix. The method was validated using freshwater samples from three Italian volcanic lakes comparing automated vs. manual results. ACQUA proved to be a fast and accurate tool to rapidly assess freshwater quality and to characterize cyanobacterial assemblages in aquatic environments. PMID:27012737

  9. Exploring Techniques for Vision Based Human Activity Recognition: Methods, Systems, and Evaluation

    PubMed Central

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

    2013-01-01

    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 activities, 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 of the performance of human activity recognition. PMID:23353144

  10. 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.

  11. 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…

  12. Subsurface biological activity zone detection using genetic search algorithms

    SciTech Connect

    Mahinthakumar, G.; Gwo, J.P.; Moline, G.R.; Webb, O.F.

    1999-12-01

    Use of generic search algorithms for detection of subsurface biological activity zones (BAZ) is investigated through a series of hypothetical numerical biostimulation experiments. Continuous injection of dissolved oxygen and methane with periodically varying concentration stimulates the cometabolism of indigenous methanotropic bacteria. The observed breakthroughs of methane are used to deduce possible BAZ in the subsurface. The numerical experiments are implemented in a parallel computing environment to make possible the large number of simultaneous transport simulations required by the algorithm. The results show that genetic algorithms are very efficient in locating multiple activity zones, provided the observed signals adequately sample the BAZ.

  13. 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.

  14. Recognition of user's activity for adaptive cooperative assistance in robotic surgery.

    PubMed

    Nessi, Federico; Beretta, Elisa; Ferrigno, Giancarlo; De Momi, Elena

    2015-01-01

    During hands-on robotic surgery it is advisable to know how and when to provide the surgeon with different assistance levels with respect to the current performed activity. Gesteme-based on-line classification requires the definition of a complete set of primitives and the observation of large signal percentage. In this work an on-line, gesteme-free activity recognition method is addressed. The algorithm models the guidance forces and the resulting trajectory of the manipulator with 26 low-level components of a Gaussian Mixture Model (GMM). Temporal switching among the components is modeled with a Hidden Markov Model (HMM). Tests are performed in a simplified scenario over a pool of 5 non-surgeon users. Classification accuracy resulted higher than 89% after the observation of a 300 ms-long signal. Future work will address the use of the current detected activity to on-line trigger different strategies to control the manipulator and adapt the level of assistance. PMID:26737482

  15. Function-based classification of carbohydrate-active enzymes by recognition of short, conserved peptide motifs.

    PubMed

    Busk, Peter Kamp; Lange, Lene

    2013-06-01

    Functional prediction of carbohydrate-active enzymes is difficult due to low sequence identity. However, similar enzymes often share a few short motifs, e.g., around the active site, even when the overall sequences are very different. To exploit this notion for functional prediction of carbohydrate-active enzymes, we developed a simple algorithm, peptide pattern recognition (PPR), that can divide proteins into groups of sequences that share a set of short conserved sequences. When this method was used on 118 glycoside hydrolase 5 proteins with 9% average pairwise identity and representing four characterized enzymatic functions, 97% of the proteins were sorted into groups correlating with their enzymatic activity. Furthermore, we analyzed 8,138 glycoside hydrolase 13 proteins including 204 experimentally characterized enzymes with 28 different functions. There was a 91% correlation between group and enzyme activity. These results indicate that the function of carbohydrate-active enzymes can be predicted with high precision by finding short, conserved motifs in their sequences. The glycoside hydrolase 61 family is important for fungal biomass conversion, but only a few proteins of this family have been functionally characterized. Interestingly, PPR divided 743 glycoside hydrolase 61 proteins into 16 subfamilies useful for targeted investigation of the function of these proteins and pinpointed three conserved motifs with putative importance for enzyme activity. Furthermore, the conserved sequences were useful for cloning of new, subfamily-specific glycoside hydrolase 61 proteins from 14 fungi. In conclusion, identification of conserved sequence motifs is a new approach to sequence analysis that can predict carbohydrate-active enzyme functions with high precision. PMID:23524681

  16. Function-based classification of carbohydrate-active enzymes by recognition of short, conserved peptide motifs.

    PubMed

    Busk, Peter Kamp; Lange, Lene

    2013-06-01

    Functional prediction of carbohydrate-active enzymes is difficult due to low sequence identity. However, similar enzymes often share a few short motifs, e.g., around the active site, even when the overall sequences are very different. To exploit this notion for functional prediction of carbohydrate-active enzymes, we developed a simple algorithm, peptide pattern recognition (PPR), that can divide proteins into groups of sequences that share a set of short conserved sequences. When this method was used on 118 glycoside hydrolase 5 proteins with 9% average pairwise identity and representing four characterized enzymatic functions, 97% of the proteins were sorted into groups correlating with their enzymatic activity. Furthermore, we analyzed 8,138 glycoside hydrolase 13 proteins including 204 experimentally characterized enzymes with 28 different functions. There was a 91% correlation between group and enzyme activity. These results indicate that the function of carbohydrate-active enzymes can be predicted with high precision by finding short, conserved motifs in their sequences. The glycoside hydrolase 61 family is important for fungal biomass conversion, but only a few proteins of this family have been functionally characterized. Interestingly, PPR divided 743 glycoside hydrolase 61 proteins into 16 subfamilies useful for targeted investigation of the function of these proteins and pinpointed three conserved motifs with putative importance for enzyme activity. Furthermore, the conserved sequences were useful for cloning of new, subfamily-specific glycoside hydrolase 61 proteins from 14 fungi. In conclusion, identification of conserved sequence motifs is a new approach to sequence analysis that can predict carbohydrate-active enzyme functions with high precision.

  17. 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...

  18. Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

    NASA Astrophysics Data System (ADS)

    Mishra, Puneet; Singla, Sunil Kumar

    2013-01-01

    In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

  19. An algorithm to improve speech recognition in noise for hearing-impaired listeners

    PubMed Central

    Healy, Eric W.; Yoho, Sarah E.; Wang, Yuxuan; Wang, DeLiang

    2013-01-01

    Despite considerable effort, monaural (single-microphone) algorithms capable of increasing the intelligibility of speech in noise have remained elusive. Successful development of such an algorithm is especially important for hearing-impaired (HI) listeners, given their particular difficulty in noisy backgrounds. In the current study, an algorithm based on binary masking was developed to separate speech from noise. Unlike the ideal binary mask, which requires prior knowledge of the premixed signals, the masks used to segregate speech from noise in the current study were estimated by training the algorithm on speech not used during testing. Sentences were mixed with speech-shaped noise and with babble at various signal-to-noise ratios (SNRs). Testing using normal-hearing and HI listeners indicated that intelligibility increased following processing in all conditions. These increases were larger for HI listeners, for the modulated background, and for the least-favorable SNRs. They were also often substantial, allowing several HI listeners to improve intelligibility from scores near zero to values above 70%. PMID:24116438

  20. Hough transform algorithm for real-time pattern recognition using an artificial retina camera

    NASA Astrophysics Data System (ADS)

    Lin, Xin; Otobe, Kazunori

    2001-04-01

    An artificial retina camera (ARC) is employed for real-time preprocessing of images. And the algorithm of Hough transform is advanced for detecting the biology-images with approximate circle edge-information in the two-dimension space. This method also works in parallel for processing multiple input and partial input patterns.

  1. 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.

  2. 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.

  3. An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images

    PubMed Central

    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. PMID:24701151

  4. Pattern recognition algorithm reveals how birds evolve individual egg pattern signatures.

    PubMed

    Stoddard, Mary Caswell; Kilner, Rebecca M; Town, Christopher

    2014-01-01

    Pattern-based identity signatures are commonplace in the animal kingdom, but how they are recognized is poorly understood. Here we develop a computer vision tool for analysing visual patterns, NATUREPATTERNMATCH, which breaks new ground by mimicking visual and cognitive processes known to be involved in recognition tasks. We apply this tool to a long-standing question about the evolution of recognizable signatures. The common cuckoo (Cuculus canorus) is a notorious cheat that sneaks its mimetic eggs into nests of other species. Can host birds fight back against cuckoo forgery by evolving highly recognizable signatures? Using NATUREPATTERNMATCH, we show that hosts subjected to the best cuckoo mimicry have evolved the most recognizable egg pattern signatures. Theory predicts that effective pattern signatures should be simultaneously replicable, distinctive and complex. However, our results reveal that recognizable signatures need not incorporate all three of these features. Moreover, different hosts have evolved effective signatures in diverse ways. PMID:24939367

  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. Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets.

    PubMed

    Khan, A M; Lee, Y K; Kim, T S

    2008-01-01

    Automatic recognition of human activities is one of the important and challenging research areas in proactive and ubiquitous computing. In this work, we present some preliminary results of recognizing human activities using augmented features extracted from the activity signals measured using a single triaxial accelerometer sensor and artificial neural nets. The features include autoregressive (AR) modeling coefficients of activity signals, signal magnitude areas (SMA), and title angles (TA). We have recognized four human activities using AR coefficients (ARC) only, ARC with SMA, and ARC with SMA and TA. With the last augmented features, we have achieved the recognition rate above 99% for all four activities including lying, standing, walking, and running. With our proposed technique, real time recognition of some human activities is possible.

  7. Using fuzzy logic to confirm the integrity of a pattern recognition algorithm for long genomic sequences: the W-curve.

    PubMed

    Cork, Douglas J; Toguem, Andre

    2002-12-01

    The W-curve is a numerical mapping algorithm that provides tertiary information content of long and short genomic sequences. The most popular genomic pattern recognition algorithms depend on string matching of the primary information content of short genomic sequences. Herein, we describe a way to define the fuzzy properties of the W-curve. This approach improves a distance (dissimilarity) between two or more homologous long genomic sequences. Fourier analysis of W-curves delivers a smoother function for gap-stripped regions. Calculation of respective Fourier energies may improve the accuracy of the distance metric used to generate a phylogenetic tree of analyzed genomic sequences. This is especially the case for long genomic sequences that have been gap-stripped and aligned with the aid of previously published heuristic methods. These previous methods involved W-curve alignments used in concert with such programs as Clustal that use linear dynamic programming to align multiple gap-stripped W-curves. PMID:12594080

  8. Evidence for altered amygdala activation in schizophrenia in an adaptive emotion recognition task.

    PubMed

    Mier, Daniela; Lis, Stefanie; Zygrodnik, Karina; Sauer, Carina; Ulferts, Jens; Gallhofer, Bernd; Kirsch, Peter

    2014-03-30

    Deficits in social cognition seem to present an intermediate phenotype for schizophrenia, and are known to be associated with an altered amygdala response to faces. However, current results are heterogeneous with respect to whether this altered amygdala response in schizophrenia is hypoactive or hyperactive in nature. The present study used functional magnetic resonance imaging to investigate emotion-specific amygdala activation in schizophrenia using a novel adaptive emotion recognition paradigm. Participants comprised 11 schizophrenia outpatients and 16 healthy controls who viewed face stimuli expressing emotions of anger, fear, happiness, and disgust, as well as neutral expressions. The adaptive emotion recognition approach allows the assessment of group differences in both emotion recognition performance and associated neuronal activity while also ensuring a comparable number of correctly recognized emotions between groups. Schizophrenia participants were slower and had a negative bias in emotion recognition. In addition, they showed reduced differential activation during recognition of emotional compared with neutral expressions. Correlation analyses revealed an association of a negative bias with amygdala activation for neutral facial expressions that was specific to the patient group. We replicated previous findings of affected emotion recognition in schizophrenia. Furthermore, we demonstrated that altered amygdala activation in the patient group was associated with the occurrence of a negative bias. These results provide further evidence for impaired social cognition in schizophrenia and point to a central role of the amygdala in negative misperceptions of facial stimuli in schizophrenia.

  9. Changes in brain electrical activity during extended continuous word recognition.

    PubMed

    Van Strien, Jan W; Hagenbeek, Rogier E; Stam, Cornelis J; Rombouts, Serge A R B; Barkhof, Frederik

    2005-07-01

    Twenty healthy subjects (10 men, 10 women) participated in an EEG study with an extended continuous recognition memory task, in which each of 30 words was randomly shown 10 times and subjects were required to make old vs. new decisions. Both event-related brain potentials (ERPs) and induced band power (IBP) were investigated. We hypothesized that repeated presentations affect recollection rather than familiarity. For the 300- to 500-ms time window, an 'old/new' ERP effect was found for the first vs. second word presentations. The correct recognition of an 'old' word was associated with a more positive waveform than the correct identification of a new word. The old/new effect was most pronounced at and around the midline parietal electrode position. For the 500- to 800-ms time window, a linear repetition effect was found for multiple word repetitions. Correct recognition after an increasing number of repetitions was associated with increasing positivity. The multiple repetitions effect was most pronounced at the midline central (Cz) and fronto-central (FCz) electrode positions and reflects a graded recollection process: the stronger the memory trace grows, the more positive the ERP in the 500- to 800-ms time window. The ERP results support a dual-processing model, with familiarity being discernable from a more graded recollection state that depends on memory strengths. For IBP, we found 'old/new' effects for the lower-2 alpha, theta, and delta bands, with higher bandpower during 'old' words. The lower-2 alpha 'old/new' effect most probably reflects attentional processes, whereas the theta and delta effects reflect encoding and retrieval processes. Upon repeated word presentations, the magnitude of induced delta power in the 375- to 750-ms time window diminished linearly. Correlation analysis suggests that decreased delta power is moderately associated with faster decision speed and higher accuracy.

  10. 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.

  11. Real-time and simultaneous control of artificial limbs based on pattern recognition algorithms.

    PubMed

    Ortiz-Catalan, Max; Håkansson, Bo; Brånemark, Rickard

    2014-07-01

    The prediction of simultaneous limb motions is a highly desirable feature for the control of artificial limbs. In this work, we investigate different classification strategies for individual and simultaneous movements based on pattern recognition of myoelectric signals. Our results suggest that any classifier can be potentially employed in the prediction of simultaneous movements if arranged in a distributed topology. On the other hand, classifiers inherently capable of simultaneous predictions, such as the multi-layer perceptron (MLP), were found to be more cost effective, as they can be successfully employed in their simplest form. In the prediction of individual movements, the one-vs-one (OVO) topology was found to improve classification accuracy across different classifiers and it was therefore used to benchmark the benefits of simultaneous control. As opposed to previous work reporting only offline accuracy, the classification performance and the resulting controllability are evaluated in real time using the motion test and target achievement control (TAC) test, respectively. We propose a simultaneous classification strategy based on MLP that outperformed a top classifier for individual movements (LDA-OVO), thus improving the state-of-the-art classification approach. Furthermore, all the presented classification strategies and data collected in this study are freely available in BioPatRec, an open source platform for the development of advanced prosthetic control strategies.

  12. 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

  13. Combining soft decision algorithms and scale-sequential hypotheses pruning for object recognition

    SciTech Connect

    Kumar, V.P.; Manolakos, E.S.

    1996-12-31

    This paper describes a system that exploits the synergy of Hierarchical Mixture Density (HMD) estimation with multiresolution decomposition based hypothesis pruning to perform efficiently joint segmentation and labeling of partially occluded objects in images. First we present the overall structure of the HMD estimation algorithm in the form of a recurrent neural network which generates the posterior probabilities of the various hypotheses associated with the image. Then in order to reduce the large memory and computation requirement we propose a hypothesis pruning scheme making use of the orthonormal discrete wavelet transform for dimensionality reduction. We provide an intuitive justification for the validity of this scheme and present experimental results and performance analysis on real and synthetic images to verify our claims.

  14. 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.

  15. Communities recognition in the Chesapeake Bay ecosystem by dynamical clustering algorithms based on different oscillators systems

    NASA Astrophysics Data System (ADS)

    Pluchino, A.; Rapisarda, A.; Latora, V.

    2008-10-01

    We have recently introduced [Phys. Rev. E 75, 045102(R) (2007); AIP Conference Proceedings 965, 2007, p. 323] an efficient method for the detection and identification of modules in complex networks, based on the de-synchronization properties (dynamical clustering) of phase oscillators. In this paper we apply the dynamical clustering tecnique to the identification of communities of marine organisms living in the Chesapeake Bay food web. We show that our algorithm is able to perform a very reliable classification of the real communities existing in this ecosystem by using different kinds of dynamical oscillators. We compare also our results with those of other methods for the detection of community structures in complex networks.

  16. 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.

    2016-10-01

    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.

  17. Real-Time Very Large-Scale Integration Recognition System with an On-Chip Adaptive K-Means Learning Algorithm

    NASA Astrophysics Data System (ADS)

    Hou, Zuoxun; Ma, Yitao; Zhu, Hongbo; Zheng, Nanning; Shibata, Tadashi

    2013-04-01

    A very large-scale integration (VLSI) recognition system equipped with an on-chip learning capability has been developed for real-time processing applications. This system can work in two functional modes of operation: adaptive K-means learning mode and recognition mode. In the adaptive K-means learning mode, the variance ratio criterion (VRC) has been employed to evaluate the quality of K-means classification results, and the evaluation algorithm has been implemented on the chip. As a result, it has become possible for the system to autonomously determine the optimum number of clusters (K). In the recognition mode, the nearest-neighbor search algorithm is very efficiently carried out by the fully parallel architecture employed in the chip. In both modes of operation, many hardware resources are shared and the functionality is flexibly altered by the system controller designed as a finite-state machine (FSM). The chip is implemented on Altera Cyclone II FPGA with 46K logic cells. Its operating clock is 25 MHz and the processing times for adaptive learning and recognition with 256 64-dimension feature vectors are about 0.42 ms and 4 µs, respectively. Both adaptive K-means learning and recognition functions have been verified by experiments using the image data from the COIL-100 (Columbia University Object Image Library) database.

  18. 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.

  19. Automatic recognition of myeloma cells in microscopic images using bottleneck algorithm, modified watershed and SVM classifier.

    PubMed

    Saeedizadeh, Z; Mehri Dehnavi, A; Talebi, A; Rabbani, H; Sarrafzadeh, O; Vard, A

    2015-01-01

    Plasma cells are developed from B lymphocytes, a type of white blood cells that is generated in the bone marrow. The plasma cells produce antibodies to fight with bacteria and viruses and stop infection and disease. Multiple myeloma is a cancer of plasma cells that collections of abnormal plasma cells (myeloma cells) accumulate in the bone marrow. The definitive diagnosis of multiple myeloma is done by searching for myeloma cells in the bone marrow slides through a microscope. Diagnosis of myeloma cells from bone marrow smears is a subjective and time-consuming task for pathologists. Also, because of depending on final decision on human eye and opinion, error risk in decision may occur. Sometimes, existence of infection in body causes plasma cell's increment which could be diagnosed wrongly as multiple myeloma. The computer diagnostic process will reduce the diagnostic time and also can be worked as a second opinion for pathologists. This study presents a computer-aided diagnostic method for myeloma cells diagnosis from bone marrow smears. At first, white blood cells consist of plasma cells and other marrow cells are separated from the red blood cells and background. Then, plasma cells are detected from other marrow cells by feature extraction and series of decision rules. Finally, normal plasma cells and myeloma cells could be classified easily by a classifier. This algorithm is applied on 50 digital images that are provided from bone marrow aspiration smears. These images contain 678 cells: 132 normal plasma cells, 256 myeloma cells and 290 other types of marrow cells. Applying the computer-aided diagnostic method for identifying myeloma cells on provided database showed a sensitivity of 96.52%; specificity of 93.04% and precision of 95.28%. PMID:26457371

  20. Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment.

    PubMed

    Lemaire, Edward D; Tundo, Marco D; Baddour, Natalie

    2015-01-01

    An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated. The mobile versus immobile classification set had a sensitivity of 86.30% ± 7.2% and specificity of 98.96% ± 0.6%, while the second prediction set had a sensitivity of 88.35% ± 7.80% and specificity of 98.51% ± 0.62%. For the third classification set, sensitivity was 84.92% ± 6.38% and specificity was 98.17 ± 0.62. F1 scores for the first, second and third classification sets were 86.17 ± 6.3, 80.19 ± 6.36, and 78.42 ± 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity

  1. 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.

  2. 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.

  3. 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.

  4. 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. PMID:23193235

  5. ERK Pathway Activation Bidirectionally Affects Visual Recognition Memory and Synaptic Plasticity in the Perirhinal Cortex

    PubMed Central

    Silingardi, Davide; Angelucci, Andrea; De Pasquale, Roberto; Borsotti, Marco; Squitieri, Giovanni; Brambilla, Riccardo; Putignano, Elena; Pizzorusso, Tommaso; Berardi, Nicoletta

    2011-01-01

    ERK 1,2 pathway mediates experience-dependent gene transcription in neurons and several studies have identified its pivotal role in experience-dependent synaptic plasticity and in forms of long term memory involving hippocampus, amygdala, or striatum. The perirhinal cortex (PRHC) plays an essential role in familiarity-based object recognition memory. It is still unknown whether ERK activation in PRHC is necessary for recognition memory consolidation. Most important, it is unknown whether by modulating the gain of the ERK pathway it is possible to bidirectionally affect visual recognition memory and PRHC synaptic plasticity. We have first pharmacologically blocked ERK activation in the PRHC of adult mice and found that this was sufficient to impair long term recognition memory in a familiarity-based task, the object recognition task (ORT). We have then tested performance in the ORT in Ras-GRF1 knock-out (KO) mice, which exhibit a reduced activation of ERK by neuronal activity, and in ERK1 KO mice, which have an increased activation of ERK2 and exhibit enhanced striatal plasticity and striatal mediated memory. We found that Ras-GRF1 KO mice have normal short term memory but display a long term memory deficit; memory reconsolidation is also impaired. On the contrary, ERK1 KO mice exhibit a better performance than WT mice at 72 h retention interval, suggesting a longer lasting recognition memory. In parallel with behavioral data, LTD was strongly reduced and LTP was significantly smaller in PRHC slices from Ras-GRF1 KO than in WT mice while enhanced LTP and LTD were found in PRHC slices from ERK1 KO mice. PMID:22232579

  6. An efficient algorithm to identify coordinately activated transcription factors.

    PubMed

    Hu, Haiyan

    2010-03-01

    Identification of transcription factor (TF) activities associated with a certain physiological/experimental condition is one of the preliminary steps to reconstruct transcriptional regulatory networks and to identify signal transduction pathways. TF activities are often indicated by the activities of its target genes. Existing studies on identifying TF activities through target genes usually assume the equivalence between co-regulation and co-expression. However, genes with correlated expression profiles may not be co-regulated. In the mean time, although multiple TFs can be activated coordinately, there is a lack of efficient methods to identify coordinately activated TFs. In this paper, we propose an efficient algorithm embedding a dynamic programming procedure to identify a subset of TFs that are potentially coordinately activated under a given condition by utilizing ranked lists of differentially expressed target genes. Applying our algorithm to microarray expression data sets for a number of diseases, our approach found subsets of TFs that are highly likely associated with the given disease processes. PMID:20060041

  7. Cueing vocabulary during sleep increases theta activity during later recognition testing.

    PubMed

    Schreiner, Thomas; Göldi, Maurice; Rasch, Björn

    2015-11-01

    Neural oscillations in the theta band have repeatedly been implicated in successful memory encoding and retrieval. Several recent studies have shown that memory retrieval can be facilitated by reactivating memories during their consolidation during sleep. However, it is still unknown whether reactivation during sleep also enhances subsequent retrieval-related neural oscillations. We have recently demonstrated that foreign vocabulary cues presented during sleep improve later recall of the associated translations. Here, we examined the effect of cueing foreign vocabulary during sleep on oscillatory activity during subsequent recognition testing after sleep. We show that those words that were replayed during sleep after learning (cued words) elicited stronger centroparietal theta activity during recognition as compared to noncued words. The reactivation-induced increase in theta oscillations during later recognition testing might reflect a strengthening of individual memory traces and the integration of the newly learned words into the mental lexicon by cueing during sleep.

  8. 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.

  9. 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. PMID:27106584

  10. 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.

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

    PubMed

    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

  12. 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.

  13. 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…

  14. Dealing with the effects of sensor displacement in wearable activity recognition.

    PubMed

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

    2014-06-06

    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.

  15. 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

  16. 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.

  17. 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

  18. Classifying Volcanic Activity Using an Empirical Decision Making Algorithm

    NASA Astrophysics Data System (ADS)

    Junek, W. N.; Jones, W. L.; Woods, M. T.

    2012-12-01

    Detection and classification of developing volcanic activity is vital to eruption forecasting. Timely information regarding an impending eruption would aid civil authorities in determining the proper response to a developing crisis. In this presentation, volcanic activity is characterized using an event tree classifier and a suite of empirical statistical models derived through logistic regression. Forecasts are reported in terms of the United States Geological Survey (USGS) volcano alert level system. The algorithm employs multidisciplinary data (e.g., seismic, GPS, InSAR) acquired by various volcano monitoring systems and source modeling information to forecast the likelihood that an eruption, with a volcanic explosivity index (VEI) > 1, will occur within a quantitatively constrained area. Logistic models are constructed from a sparse and geographically diverse dataset assembled from a collection of historic volcanic unrest episodes. Bootstrapping techniques are applied to the training data to allow for the estimation of robust logistic model coefficients. Cross validation produced a series of receiver operating characteristic (ROC) curves with areas ranging between 0.78-0.81, which indicates the algorithm has good predictive capabilities. The ROC curves also allowed for the determination of a false positive rate and optimum detection for each stage of the algorithm. Forecasts for historic volcanic unrest episodes in North America and Iceland were computed and are consistent with the actual outcome of the events.

  19. A Variance Based Active Learning Approach for Named Entity Recognition

    NASA Astrophysics Data System (ADS)

    Hassanzadeh, Hamed; Keyvanpour, Mohammadreza

    The cost of manually annotating corpora is one of the significant issues in many text based tasks such as text mining, semantic annotation and generally information extraction. Active Learning is an approach that deals with reduction of labeling costs. In this paper we proposed an effective active learning approach based on minimal variance that reduces manual annotation cost by using a small number of manually labeled examples. In our approach we use a confidence measure based on the model's variance that reaches a considerable accuracy for annotating entities. Conditional Random Field (CRF) is chosen as the underlying learning model due to its promising performance in many sequence labeling tasks. The experiments show that the proposed method needs considerably fewer manual labeled samples to produce a desirable result.

  20. Human activities recognition with RGB-Depth camera using HMM.

    PubMed

    Dubois, Amandine; Charpillet, François

    2013-01-01

    Fall detection remains today an open issue for improving elderly people security. It is all the more pertinent today when more and more elderly people stay longer and longer at home. In this paper, we propose a method to detect fall using a system made up of RGB-Depth cameras. The major benefit of our approach is its low cost and the fact that the system is easy to distribute and install. In few words, the method is based on the detection in real time of the center of mass of any mobile object or person accurately determining its position in the 3D space and its velocity. We demonstrate in this paper that this information is adequate and robust enough for labeling the activity of a person among 8 possible situations. An evaluation has been conducted within a real smart environment with 26 subjects which were performing any of the eight activities (sitting, walking, going up, squatting, lying on a couch, falling, bending and lying down). Seven out of these eight activities were correctly detected among which falling which was detected without false positives.

  1. Bilingual Word Recognition in Deaf and Hearing Signers: Effects of Proficiency and Language Dominance on Cross-Language Activation

    ERIC Educational Resources Information Center

    Morford, Jill P.; Kroll, Judith F.; Piñar, Pilar; Wilkinson, Erin

    2014-01-01

    Recent evidence demonstrates that American Sign Language (ASL) signs are active during print word recognition in deaf bilinguals who are highly proficient in both ASL and English. In the present study, we investigate whether signs are active during print word recognition in two groups of unbalanced bilinguals: deaf ASL-dominant and hearing…

  2. 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.

  3. 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. PMID:26169316

  4. 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.

  5. 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

  6. 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.

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

    PubMed

    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.

  8. 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

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

    PubMed Central

    Knudsen, Daniel P.

    2013-01-01

    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. PMID:23303858

  10. 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

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

    PubMed

    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

  12. The Painful Face - Pain Expression Recognition Using Active Appearance Models.

    PubMed

    Ashraf, Ahmed Bilal; Lucey, Simon; Cohn, Jeffrey F; Chen, Tsuhan; Ambadar, Zara; Prkachin, Kenneth M; Solomon, Patricia E

    2009-10-01

    Pain is typically assessed by patient self-report. Self-reported pain, however, is difficult to interpret and may be impaired or in some circumstances (i.e., young children and the severely ill) not even possible. To circumvent these problems behavioral scientists have identified reliable and valid facial indicators of pain. Hitherto, these methods have required manual measurement by highly skilled human observers. In this paper we explore an approach for automatically recognizing acute pain without the need for human observers. Specifically, our study was restricted to automatically detecting pain in adult patients with rotator cuff injuries. The system employed video input of the patients as they moved their affected and unaffected shoulder. Two types of ground truth were considered. Sequence-level ground truth consisted of Likert-type ratings by skilled observers. Frame-level ground truth was calculated from presence/absence and intensity of facial actions previously associated with pain. Active appearance models (AAM) were used to decouple shape and appearance in the digitized face images. Support vector machines (SVM) were compared for several representations from the AAM and of ground truth of varying granularity. We explored two questions pertinent to the construction, design and development of automatic pain detection systems. First, at what level (i.e., sequence- or frame-level) should datasets be labeled in order to obtain satisfactory automatic pain detection performance? Second, how important is it, at both levels of labeling, that we non-rigidly register the face?

  13. Physical activity recognition based on rotated acceleration data using quaternion in sedentary behavior: a preliminary study.

    PubMed

    Shin, Y E; Choi, W H; Shin, T M

    2014-01-01

    This paper suggests a physical activity assessment method based on quaternion. To reduce user inconvenience, we measured the activity using a mobile device which is not put on fixed position. Recognized results were verified with various machine learning algorithms, such as neural network (multilayer perceptron), decision tree (J48), SVM (support vector machine) and naive bayes classifier. All algorithms have shown over 97% accuracy including decision tree (J48), which recognized the activity with 98.35% accuracy. As a result, physical activity assessment method based on rotated acceleration using quaternion can classify sedentary behavior with more accuracy without considering devices' position and orientation. PMID:25571109

  14. 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

  15. [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

  16. 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.

  17. The application of EMD in activity recognition based on a single triaxial accelerometer.

    PubMed

    Liao, Mengjia; Guo, Yi; Qin, Yajie; Wang, Yuanyuan

    2015-01-01

    Activities recognition using a wearable device is a very popular research field. Among all wearable sensors, the accelerometer is one of the most common sensors due to its versatility and relative ease of use. This paper proposes a novel method for activity recognition based on a single accelerometer. To process the activity information from accelerometer data, two kinds of signal features are extracted. Firstly, five features including the mean, the standard deviation, the entropy, the energy and the correlation are calculated. Then a method called empirical mode decomposition (EMD) is used for the feature extraction since accelerometer data are non-linear and non-stationary. Several time series named intrinsic mode functions (IMFs) can be obtained after the EMD. Additional features will be added by computing the mean and standard deviation of first three IMFs. A classifier called Adaboost is adopted for the final activities recognition. In the experiments, a single sensor is separately positioned in the waist, left thigh, right ankle and right arm. Results show that the classification accuracy is 94.69%, 86.53%, 91.84% and 92.65%, respectively. These relatively high performances demonstrate that activities can be detected irrespective of the position by reducing problems such as the movement constrain and discomfort.

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

    PubMed

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-18

    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.

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

    PubMed

    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

  20. 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

  1. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

    PubMed

    Capela, Nicole A; Lemaire, Edward D; Baddour, Natalie

    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.

  2. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

    PubMed

    Capela, Nicole A; Lemaire, Edward D; Baddour, Natalie

    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

  3. 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.

  4. Facial recognition of happiness among older adults with active and remitted major depression.

    PubMed

    Shiroma, Paulo R; Thuras, Paul; Johns, Brian; Lim, Kelvin O

    2016-09-30

    Biased emotion processing in depression might be a trait characteristic independent of mood improvement and a vulnerable factor to develop further depressive episodes. This phenomenon of among older adults with depression has not been adequately examined. In a 2-year cross-sectional study, 59 older patients with either active or remitted major depression, or never-depressed, completed a facial emotion recognition task (FERT) to probe perceptual bias of happiness. The results showed that depressed patients, compared with never depressed subjects, had a significant lower sensitivity to identify happiness particularly at moderate intensity of facial stimuli. Patients in remission from a previous major depressive episode but with none or minimal symptoms had similar sensitivity rate to identify happy facial expressions as compared to patients with an active depressive episode. Further studies would be necessary to confirm whether recognition of happy expression reflects a persistent perceptual bias of major depression in older adults. PMID:27428081

  5. [An Extraction and Recognition Method of the Distributed Optical Fiber Vibration Signal Based on EMD-AWPP and HOSA-SVM Algorithm].

    PubMed

    Zhang, Yanjun; Liu, Wen-zhe; Fu, Xing-hu; Bi, Wei-hong

    2016-02-01

    Given that the traditional signal processing methods can not effectively distinguish the different vibration intrusion signal, a feature extraction and recognition method of the vibration information is proposed based on EMD-AWPP and HOSA-SVM, using for high precision signal recognition of distributed fiber optic intrusion detection system. When dealing with different types of vibration, the method firstly utilizes the adaptive wavelet processing algorithm based on empirical mode decomposition effect to reduce the abnormal value influence of sensing signal and improve the accuracy of signal feature extraction. Not only the low frequency part of the signal is decomposed, but also the high frequency part the details of the signal disposed better by time-frequency localization process. Secondly, it uses the bispectrum and bicoherence spectrum to accurately extract the feature vector which contains different types of intrusion vibration. Finally, based on the BPNN reference model, the recognition parameters of SVM after the implementation of the particle swarm optimization can distinguish signals of different intrusion vibration, which endows the identification model stronger adaptive and self-learning ability. It overcomes the shortcomings, such as easy to fall into local optimum. The simulation experiment results showed that this new method can effectively extract the feature vector of sensing information, eliminate the influence of random noise and reduce the effects of outliers for different types of invasion source. The predicted category identifies with the output category and the accurate rate of vibration identification can reach above 95%. So it is better than BPNN recognition algorithm and improves the accuracy of the information analysis effectively. PMID:27209772

  6. The effects of negative emotion on encoding-related neural activity predicting item and source recognition.

    PubMed

    Yick, Yee Ying; Buratto, Luciano Grüdtner; Schaefer, Alexandre

    2015-07-01

    We report here a study that obtained reliable effects of emotional modulation of a well-known index of memory encoding--the electrophysiological "Dm" effect--using a recognition memory paradigm followed by a source memory task. In this study, participants performed an old-new recognition test of emotionally negative and neutral pictures encoded 1 day before the test, and a source memory task involving the retrieval of the temporal context in which pictures had been encoded. Our results showed that Dm activity was enhanced for all emotional items on a late positivity starting at ~400 ms post-stimulus onset, although Dm activity for high arousal items was also enhanced at an earlier stage (200-400 ms). Our results also showed that emotion enhanced Dm activity for items that were both recognised with or without correct source information. Further, when only high arousal items were considered, larger Dm amplitudes were observed if source memory was accurate. Three main conclusions are drawn from these findings. First, negative emotion can enhance encoding processes predicting the subsequent recognition of central item information. Second, if emotion reaches high levels of arousal, the encoding of contextual details can also be enhanced over and above the effects of emotion on central item encoding. Third, the morphology of our ERPs is consistent with a hybrid model of the role of attention in emotion-enhanced memory (Pottage and Schaefer, 2012).

  7. 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. PMID:27460152

  8. 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.

  9. The effects of negative emotion on encoding-related neural activity predicting item and source recognition.

    PubMed

    Yick, Yee Ying; Buratto, Luciano Grüdtner; Schaefer, Alexandre

    2015-07-01

    We report here a study that obtained reliable effects of emotional modulation of a well-known index of memory encoding--the electrophysiological "Dm" effect--using a recognition memory paradigm followed by a source memory task. In this study, participants performed an old-new recognition test of emotionally negative and neutral pictures encoded 1 day before the test, and a source memory task involving the retrieval of the temporal context in which pictures had been encoded. Our results showed that Dm activity was enhanced for all emotional items on a late positivity starting at ~400 ms post-stimulus onset, although Dm activity for high arousal items was also enhanced at an earlier stage (200-400 ms). Our results also showed that emotion enhanced Dm activity for items that were both recognised with or without correct source information. Further, when only high arousal items were considered, larger Dm amplitudes were observed if source memory was accurate. Three main conclusions are drawn from these findings. First, negative emotion can enhance encoding processes predicting the subsequent recognition of central item information. Second, if emotion reaches high levels of arousal, the encoding of contextual details can also be enhanced over and above the effects of emotion on central item encoding. Third, the morphology of our ERPs is consistent with a hybrid model of the role of attention in emotion-enhanced memory (Pottage and Schaefer, 2012). PMID:25936685

  10. 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.

  11. 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

    2016-01-01

    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

  12. 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

  13. Correlation between the activity of digestive enzymes and nonself recognition in the gut of Eisenia andrei earthworms.

    PubMed

    Procházková, Petra; Šustr, Vladimír; Dvořák, Jiří; Roubalová, Radka; Škanta, František; Pižl, Václav; Bilej, Martin

    2013-11-01

    Earthworms Eisenia andrei, similarly to other invertebrates, rely on innate defense mechanisms based on the capability to recognize and respond to nonself. Here, we show a correlation between the expression of CCF, a crucial pattern-recognition receptor, and lysozyme, with enzyme activities in the gut of E. andrei earthworms following a microbial challenge. These data suggest that enzyme activities important for the release and recognition of molecular patterns by pattern-recognition molecules, as well as enzymes involved in effector pathways, are modulated during the microbial challenge. In particular, protease, laminarinase, and glucosaminidase activities were increased in parallel to up-regulated CCF and lysozyme expression.

  14. Recognition of Physical Activities in Overweight Hispanic Youth Using KNOWME Networks

    PubMed Central

    Emken, BA; Li, M; Thatte, G; Lee, S; Annavaram, M; Mitra, U; Narayanan, S; Spruijt-Metz, D

    2011-01-01

    Background KNOWME Networks is a wireless body area network with two tri-axial accelerometers, a heart rate monitor, and mobile phone that acts as the data collection hub. One function of KNOWME Networks is to detect physical activity (PA) in overweight Hispanic youth. The purpose of this study was to evaluate the in-lab recognition accuracy of KNOWME. Methods Twenty overweight Hispanic participants (10 males; age 14.6±1.8 years), underwent four data collection sessions consisting of nine activities/session: lying down, sitting, sitting fidgeting, standing, standing fidgeting, standing playing an active video game, slow walking, brisk walking, and running. Data was used to train activity recognition models. The accuracy of personalized and generalized models is reported. Results Overall accuracy for personalized models was 84%. The most accurately detected activity was running (96%). The models had difficulty distinguishing between the static and fidgeting categories of sitting and standing. When static and fidgeting activity categories were collapsed, the overall accuracy improved to 94%. Personalized models demonstrated higher accuracy than generalized models. Conclusions KNOWME Networks can accurately detect a range of activities. KNOWME has the ability to collect and process data in real-time, building the foundation for tailored, real-time interventions to increase PA or decrease sedentary time. PMID:21934162

  15. 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.

  16. 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...

  17. Activity recognition using a single accelerometer placed at the wrist or ankle

    PubMed Central

    Mannini, Andrea; Intille, Stephen S.; Rosenberger, Mary; Sabatini, Angelo M.; Haskell, William

    2013-01-01

    PURPOSE Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based activity monitors that collect raw data. The goal is to increase wear time by asking subjects to wear the monitors on the wrist instead of the hip, and then to use information in the raw signal to improve activity type and intensity estimation. The purpose of this work is obtaining an algorithm to process wrist and ankle raw data and classify behavior into four broad activity classes: ambulation, cycling, sedentary and other. METHODS Participants (N = 33) wearing accelerometers on the wrist and ankle performed 26 daily activities. The accelerometer data were collected, cleaned, and preprocessed to extract features that characterize 2 s, 4 s, and 12.8 s data windows. Feature vectors encoding information about frequency and intensity of motion extracted from analysis of the raw signal were used with a support vector machine classifier to identify a subject’s activity. Results were compared with categories classified by a human observer. Algorithms were validated using a leave-one-subject-out strategy. The computational complexity of each processing step was also evaluated. RESULTS With 12.8 s windows, the proposed strategy showed high classification accuracies for ankle data (95.0%) that decreased to 84.7% for wrist data. Shorter (4 s) windows only minimally decreased performances of the algorithm on the wrist to 84.2%. CONCLUSIONS A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original dataset. The algorithm is computationally-efficient and could be implemented in real-time on mobile devices with only 4 s latency. PMID:23604069

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

    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.

  19. 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.

  20. 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.

  1. ssDNA-Functionalized Nanoceria: A Redox-Active Aptaswitch for Biomolecular Recognition.

    PubMed

    Bülbül, Gonca; Hayat, Akhtar; Andreescu, Silvana

    2016-04-01

    Quantification of biomolecular binding events is a critical step for the development of biorecognition assays for diagnostics and therapeutic applications. This paper reports the design of redox-active switches based on aptamer conjugated nanoceria for detection and quantification of biomolecular recognition. It is shown that the conformational transition state of the aptamer on nanoceria, combined with the redox properties of these particles can be used to create surface based structure switchable aptasensing platforms. Changes in the redox properties at the nanoceria surface upon binding of the ssDNA and its target analyte enables rapid and highly sensitive measurement of biomolecular interactions. This concept is demonstrated as a general applicable method to the colorimetric detection of DNA binding events. An example of a nanoceria aptaswitch for the colorimetric sensing of Ochratoxin A (OTA) and applicability to other targets is provided. The system can sensitively and selectivity detect as low as 0.15 × 10(-9) m OTA. This novel assay is simple in design and does not involve oligonucleotide labeling or elaborate nanoparticle modification steps. The proposed mechanism discovered here opens up a new way of designing optical sensing methods based on aptamer recognition. This approach can be broadly applicable to many bimolecular recognition processes and related applications. PMID:26844813

  2. Culture but not gender modulates amygdala activation during explicit emotion recognition

    PubMed Central

    2012-01-01

    Background Mounting evidence indicates that humans have significant difficulties in understanding emotional expressions from individuals of different ethnic backgrounds, leading to reduced recognition accuracy and stronger amygdala activation. However, the impact of gender on the behavioral and neural reactions during the initial phase of cultural assimilation has not been addressed. Therefore, we investigated 24 Asians students (12 females) and 24 age-matched European students (12 females) during an explicit emotion recognition task, using Caucasian facial expressions only, on a high-field MRI scanner. Results Analysis of functional data revealed bilateral amygdala activation to emotional expressions in Asian and European subjects. However, in the Asian sample, a stronger response of the amygdala emerged and was paralleled by reduced recognition accuracy, particularly for angry male faces. Moreover, no significant gender difference emerged. We also observed a significant inverse correlation between duration of stay and amygdala activation. Conclusion In this study we investigated the “alien-effect” as an initial problem during cultural assimilation and examined this effect on a behavioral and neural level. This study has revealed bilateral amygdala activation to emotional expressions in Asian and European females and males. In the Asian sample, a stronger response of the amygdala bilaterally was observed and this was paralleled by reduced performance, especially for anger and disgust depicted by male expressions. However, no gender difference occurred. Taken together, while gender exerts only a subtle effect, culture and duration of stay as well as gender of poser are shown to be relevant factors for emotion processing, influencing not only behavioral but also neural responses in female and male immigrants. PMID:22642400

  3. Impact of lead sub-chronic toxicity on recognition memory and motor activity of Wistar rat.

    PubMed

    Azzaoui, F Z; Ahami, A O T; Khadmaoui, A

    2009-01-15

    The aim of this research was to investigate the impact of lead nitrate administered in drinking water during 90 days (sub-chronic toxicity), on body weight gain, motor activity, brain lead accumulation and especially on recognition memory of Wistar rats. Two groups of young female Wistar rats were used. Treated rats received 20 mg L(-1) of lead nitrate diluted in drinking water, while control rats received drinking water only, for 3 months. An evolution of body weight, motor activity, object recognition memory and measure of brain lead levels has been evaluated. The body weight was taken weekly, whereas the memory abilities and the motor activity are measured once every fortnight alternatively, by submitting rats to the Open Field (OF) test and to the Novel Object Recognizing (NOR) memory test. The results have shown a non significant effect in gain of body weight. However, a high significance was shown for horizontal activity (p<0.01), long memory term (p<0.01), at the end of testing period and for brain lead levels (p<0.05) between studied groups.

  4. Activation of wingless targets requires bipartite recognition of DNA by TCF.

    PubMed

    Chang, Mikyung V; Chang, Jinhee L; Gangopadhyay, Anu; Shearer, Andrew; Cadigan, Ken M

    2008-12-01

    Specific recognition of DNA by transcription factors is essential for precise gene regulation. In Wingless (Wg) signaling in Drosophila, target gene regulation is controlled by T cell factor (TCF), which binds to specific DNA sequences through a high mobility group (HMG) domain. However, there is considerable variability in TCF binding sites, raising the possibility that they are not sufficient for target location. Some isoforms of human TCF contain a domain, termed the C-clamp, that mediates binding to an extended sequence in vitro. However, the significance of this extended sequence for the function of Wnt response elements (WREs) is unclear. In this report, we identify a cis-regulatory element that, to our knowledge, was previously unpublished. The element, named the TCF Helper site (Helper site), is essential for the activation of several WREs. This motif greatly augments the ability of TCF binding sites to respond to Wg signaling. Drosophila TCF contains a C-clamp that enhances in vitro binding to TCF-Helper site pairs and is required for transcriptional activation of WREs containing Helper sites. A genome-wide search for clusters of TCF and Helper sites identified two new WREs. Our data suggest that DNA recognition by fly TCF occurs through a bipartite mechanism, involving both the HMG domain and the C-clamp, which enables TCF to locate and activate WREs in the nucleus. PMID:19062282

  5. Active Control of Automotive Intake Noise under Rapid Acceleration using the Co-FXLMS Algorithm

    NASA Astrophysics Data System (ADS)

    Lee, Hae-Jin; Lee, Gyeong-Tae; Oh, Jae-Eung

    The method of reducing automotive intake noise can be classified by passive and active control techniques. However, passive control has a limited effect of noise reduction at low frequency range (below 500 Hz) and is limited by the space of the engine room. However, active control can overcome these passive control limitations. The active control technique mostly uses the Least-Mean-Square (LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time, particularly when the Filtered-X LMS (FXLMS) algorithm is applied to an active noise control (ANC) system. However, the convergence performance of the LMS algorithm decreases significantly when the FXLMS algorithm is applied to the active control of intake noise under rapidly accelerating driving conditions. Therefore, in this study, the Co-FXLMS algorithm was proposed to improve the control performance of the FXLMS algorithm during rapid acceleration. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. The performance of the Co-FXLMS algorithm is presented in comparison with that of the FXLMS algorithm. Experimental results show that active noise control using Co-FXLMS is effective in reducing automotive intake noise during rapid acceleration.

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

    PubMed

    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

  7. 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

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

    PubMed

    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.

  9. 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. PMID:22695257

  10. Visual pattern recognition based on spatio-temporal patterns of retinal ganglion cells’ activities

    PubMed Central

    Jing, Wei; Liu, Wen-Zhong; Gong, Xin-Wei; Gong, Hai-Qing

    2010-01-01

    Neural information is processed based on integrated activities of relevant neurons. Concerted population activity is one of the important ways for retinal ganglion cells to efficiently organize and process visual information. In the present study, the spike activities of bullfrog retinal ganglion cells in response to three different visual patterns (checker-board, vertical gratings and horizontal gratings) were recorded using multi-electrode arrays. A measurement of subsequence distribution discrepancy (MSDD) was applied to identify the spatio-temporal patterns of retinal ganglion cells’ activities in response to different stimulation patterns. The results show that the population activity patterns were different in response to different stimulation patterns, such difference in activity pattern was consistently detectable even when visual adaptation occurred during repeated experimental trials. Therefore, the stimulus pattern can be reliably discriminated according to the spatio-temporal pattern of the neuronal activities calculated using the MSDD algorithm. PMID:21886670

  11. 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.

  12. Comparison of optimization-algorithm based feature extraction from time data or time-frequency data for target recognition purposes

    NASA Astrophysics Data System (ADS)

    Strifors, H. C.; Abrahamson, S.; Andersson, T.; Gaunaurd, G. C.

    2006-05-01

    Ultra-wideband ground penetrating radar (GPR) systems have proved useful for extracting and displaying information for target recognition purposes. Target signatures whether in the time, frequency, or joint time-frequency domains, will substantially depend on the target's burial conditions such as the type of soil, burial depth, and the soil's moisture content. That dependence can be utilized for target recognition purposes as we have demonstrated previously. The signature template of each target was computed in the time-frequency domain from the returned echo when the target was buried at a known depth in the soil with a known moisture content. Then, for any returned echo the relative difference between the similarly computed target signature and a selected signature template was computed. A global optimization method together with our (approximate) target translation method (TTM) that signature difference, chosen as object function, was minimized by adjusting the depth and moisture content, now taken to be unknown parameters. The template that gave the smallest value of the minimized object function for the returned echo was taken as target classification and the corresponding values of the depth and moisture parameters as estimates of the target's burial conditions. This optimization technique can also be applied to time-series data, avoiding the need for time-frequency analysis. It is then of interest to evaluate the relative merits of time data and time-frequency data for target recognition. Such a comparison is here preformed using signals returned from dummy mines buried underground. The results of the analysis serve to assess the intrinsic worth of data in the time domain and in the time-frequency domain for identifying subsurface targets using a GPR. The targets are buried in a test field at the Swedish Explosive Ordnance Disposal and Demining Center (SWEDEC) at Eksjo, Sweden.

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

    PubMed Central

    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. PMID:25520195

  14. The activation of segmental and tonal information in visual word recognition.

    PubMed

    Li, Chuchu; Lin, Candise Y; Wang, Min; Jiang, Nan

    2013-08-01

    Mandarin Chinese has a logographic script in which graphemes map onto syllables and morphemes. It is not clear whether Chinese readers activate phonological information during lexical access, although phonological information is not explicitly represented in Chinese orthography. In the present study, we examined the activation of phonological information, including segmental and tonal information in Chinese visual word recognition, using the Stroop paradigm. Native Mandarin speakers named the presentation color of Chinese characters in Mandarin. The visual stimuli were divided into five types: color characters (e.g., , hong2, "red"), homophones of the color characters (S+T+; e.g., , hong2, "flood"), different-tone homophones (S+T-; e.g., , hong1, "boom"), characters that shared the same tone but differed in segments with the color characters (S-T+; e.g., , ping2, "bottle"), and neutral characters (S-T-; e.g., , qian1, "leading through"). Classic Stroop facilitation was shown in all color-congruent trials, and interference was shown in the incongruent trials. Furthermore, the Stroop effect was stronger for S+T- than for S-T+ trials, and was similar between S+T+ and S+T- trials. These findings suggested that both tonal and segmental forms of information play roles in lexical constraints; however, segmental information has more weight than tonal information. We proposed a revised visual word recognition model in which the functions of both segmental and suprasegmental types of information and their relative weights are taken into account. PMID:23400856

  15. 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

  16. An improved version of the table look-up algorithm for pattern recognition. [for MSS data processing

    NASA Technical Reports Server (NTRS)

    Eppler, W. G.

    1974-01-01

    The table look-up approach to pattern recognition has been used for 3 years at several research centers in a variety of applications. A new version has been developed which is faster, requires significantly less core memory, and retains full precision of the input data. The new version can be used on low-cost minicomputers having 32K words (16 bits each) of core memory and fixed-point arithmetic; no special-purpose hardware is required. An initial FORTRAN version of this system can classify an ERTS computer-compatible tape into 24 classes in less than 15 minutes.

  17. 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

  18. 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.

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

    PubMed

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

    2010-09-01

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

  20. 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

  1. 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.

  2. 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.

  3. Modeling activity-dependent plasticity in BCM spiking neural networks with application to human behavior recognition.

    PubMed

    Meng, Yan; Jin, Yaochu; Yin, Jun

    2011-12-01

    Spiking neural networks (SNNs) are considered to be computationally more powerful than conventional NNs. However, the capability of SNNs in solving complex real-world problems remains to be demonstrated. In this paper, we propose a substantial extension of the Bienenstock, Cooper, and Munro (BCM) SNN model, in which the plasticity parameters are regulated by a gene regulatory network (GRN). Meanwhile, the dynamics of the GRN is dependent on the activation levels of the BCM neurons. We term the whole model "GRN-BCM." To demonstrate its computational power, we first compare the GRN-BCM with a standard BCM, a hidden Markov model, and a reservoir computing model on a complex time series classification problem. Simulation results indicate that the GRN-BCM significantly outperforms the compared models. The GRN-BCM is then applied to two widely used datasets for human behavior recognition. Comparative results on the two datasets suggest that the GRN-BCM is very promising for human behavior recognition, although the current experiments are still limited to the scenarios in which only one object is moving in the considered video sequences.

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

    PubMed Central

    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

    2015-01-01

    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. PMID:26134419

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

    PubMed

    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

  6. 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

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

    PubMed

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

    2015-09-01

    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

  9. 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

  10. 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.

  11. 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.

  12. 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. PMID:27593377

  13. Activation of p53 Facilitates the Target Search in DNA by Enhancing the Target Recognition Probability.

    PubMed

    Itoh, Yuji; Murata, Agato; Sakamoto, Seiji; Nanatani, Kei; Wada, Takehiko; Takahashi, Satoshi; Kamagata, Kiyoto

    2016-07-17

    Tumor suppressor p53 binds to the target in a genome and regulates the expression of downstream genes. p53 searches for the target by combining three-dimensional diffusion and one-dimensional sliding along the DNA. To examine the regulation mechanism of the target binding, we constructed the pseudo-wild type (pseudo-WT), activated (S392E), and inactive (R248Q) mutants of p53 and observed their target binding in long DNA using single-molecule fluorescence imaging. The pseudo-WT sliding along the DNA showed many pass events over the target and possessed target recognition probability (TRP) of 7±2%. The TRP increased to 18±2% for the activated mutant but decreased to 0% for the inactive mutant. Furthermore, the fraction of the target binding by the one-dimensional sliding among the total binding events increased from 63±9% for the pseudo-WT to 87±2% for the activated mutant. Control of TRP upon activation, as demonstrated here for p53, might be a general activation mechanism of transcription factors.

  14. Making memories without trying: medial temporal lobe activity associated with incidental memory formation during recognition.

    PubMed

    Stark, Craig E L; Okado, Yoko

    2003-07-30

    Structures in the medial portions of the human temporal lobes (MTL) play a vital role in the ability to learn new facts and events, whether such learning is intentional or incidental. We examined neural activity in the MTL both while participants studied pictures of novel scenes and while they attempted to recognize which scenes had been previously presented. In a second surprise test we assessed participants' memory for items that were presented only during the previous recognition memory test. We present a novel approach to cross-participant alignment of neuroimaging data that provides more precise localization and enhanced statistical power within regions such as the MTL. Using this technique, we observed that the amount of MTL activity predicted participants' ability to subsequently remember scenes not only during the intentional study task, but also during the first memory retrieval test when only incidental encoding occurred. This encoding-related activity during memory retrieval was in the same subregions of the MTL as encoding-related activity during intentional study and is hypothesized to be one of the primary reasons why retrieval-related activity is often difficult to observe with neuroimaging techniques. PMID:12890767

  15. 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).

  16. 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.

  17. Computationally efficient algorithm for high sampling-frequency operation of active noise control

    NASA Astrophysics Data System (ADS)

    Rout, Nirmal Kumar; Das, Debi Prasad; Panda, Ganapati

    2015-05-01

    In high sampling-frequency operation of active noise control (ANC) system the length of the secondary path estimate and the ANC filter are very long. This increases the computational complexity of the conventional filtered-x least mean square (FXLMS) algorithm. To reduce the computational complexity of long order ANC system using FXLMS algorithm, frequency domain block ANC algorithms have been proposed in past. These full block frequency domain ANC algorithms are associated with some disadvantages such as large block delay, quantization error due to computation of large size transforms and implementation difficulties in existing low-end DSP hardware. To overcome these shortcomings, the partitioned block ANC algorithm is newly proposed where the long length filters in ANC are divided into a number of equal partitions and suitably assembled to perform the FXLMS algorithm in the frequency domain. The complexity of this proposed frequency domain partitioned block FXLMS (FPBFXLMS) algorithm is quite reduced compared to the conventional FXLMS algorithm. It is further reduced by merging one fast Fourier transform (FFT)-inverse fast Fourier transform (IFFT) combination to derive the reduced structure FPBFXLMS (RFPBFXLMS) algorithm. Computational complexity analysis for different orders of filter and partition size are presented. Systematic computer simulations are carried out for both the proposed partitioned block ANC algorithms to show its accuracy compared to the time domain FXLMS algorithm.

  18. Recognition-Domain Focused (RDF) Chemosensors: Versatile and Efficient Reporters of Protein Kinase Activity

    PubMed Central

    Luković, Elvedin; González-Vera, Juan A.; Imperiali, Barbara

    2009-01-01

    Catalyzed by kinases, serine/threonine and tyrosine phosphorylation is a vital mechanism of intracellular regulation. Thus, assays that easily monitor kinase activity are critical in both academic and pharmaceutical settings. We previously developed sulfonamido-oxine (Sox)-based fluorescent peptides following a β-turn focused (BTF) design for the continuous assay of kinase activity in vitro and in cell lysates. Upon phosphorylation of the Sox-containing peptide, the chromophore binds Mg2+ and undergoes chelation-enhanced fluorescence (CHEF). While the design was applied successfully to the development of several kinase sensors, an intrinsic limitation was that only residues C- or N-terminal to the phosphorylated residue could be used to derive specificity for the target kinase. To address this limitation, a new, recognition-domain focused (RDF) strategy has been developed that also relies on CHEF. In this approach, the requirement for the constrained β-turn motif is obviated by alkylation of a cysteine residue with a Sox-based derivative to afford an amino acid termed C-Sox. The RDF design allows inclusion of extended binding determinants to maximize recognition by the cognate kinase, which has now permitted the construction of chemosensors for a variety of representative Ser/Thr (PKCα, PKCβI, PKCδ, Pim2, Akt1, MK2 and PKA), as well as receptor (IRK) and non-receptor (Src, Abl) Tyr kinases with greatly enhanced selectivity. The new sensors have up to 28-fold improved catalytic efficiency and up to 66-fold lower KM when compared to the corresponding BTF probes. The improved generality of the strategy is exemplified with the synthesis and analysis of Sox-based probes for PKCβI and PKCδ, which were previously unattainable using the BTF approach. PMID:18759402

  19. Molecular Recognition of Agonist and Antagonist for Peroxisome Proliferator-Activated Receptor-α Studied by Molecular Dynamics Simulations

    PubMed Central

    Liu, Mengyuan; Wang, Lushan; Zhao, Xian; Sun, Xun

    2014-01-01

    Peroxisome proliferator activated receptor-α (PPAR-α) is a ligand-activated transcription factor which plays important roles in lipid and glucose metabolism. The aim of this work is to find residues which selectively recognize PPAR-α agonists and antagonists. To achieve this aim, PPAR-α/13M and PPAR-α/471 complexes were subjected to perform molecular dynamics simulations. This research suggests that several key residues only participate in agonist recognition, while some other key residues only contribute to antagonist recognition. It is hoped that such work is useful for medicinal chemists to design novel PPAR-α agonists and antagonists. PMID:24837836

  20. Speech recognition in noise with active and passive hearing protectors: a comparative study.

    PubMed

    Bockstael, Annelies; De Coensel, Bert; Botteldooren, Dick; D'Haenens, Wendy; Keppler, Hannah; Maes, Leen; Philips, Birgit; Swinnen, Freya; Bart, Vinck

    2011-06-01

    The perceived negative influence of standard hearing protectors on communication is a common argument for not wearing them. Thus, "augmented" protectors have been developed to improve speech intelligibility. Nevertheless, their actual benefit remains a point of concern. In this paper, speech perception with active earplugs is compared to standard passive custom-made earplugs. The two types of active protectors included amplify the incoming sound with a fixed level or to a user selected fraction of the maximum safe level. For the latter type, minimal and maximal amplification are selected. To compare speech intelligibility, 20 different speech-in-noise fragments are presented to 60 normal-hearing subjects and speech recognition is scored. The background noise is selected from realistic industrial noise samples with different intensity, frequency, and temporal characteristics. Statistical analyses suggest that the protectors' performance strongly depends on the noise condition. The active protectors with minimal amplification outclass the others for the most difficult and the easiest situations, but they also limit binaural listening. In other conditions, the passive protectors clearly surpass their active counterparts. Subsequently, test fragments are analyzed acoustically to clarify the results. This provides useful information for developing prototypes, but also indicates that tests with human subjects remain essential. PMID:21682395

  1. Structures of Down syndrome kinases, DYRKs, reveal mechanisms of kinase activation and substrate recognition.

    PubMed

    Soundararajan, Meera; Roos, Annette K; Savitsky, Pavel; Filippakopoulos, Panagis; Kettenbach, Arminja N; Olsen, Jesper V; Gerber, Scott A; Eswaran, Jeyanthy; Knapp, Stefan; Elkins, Jonathan M

    2013-06-01

    Dual-specificity tyrosine-(Y)-phosphorylation-regulated kinases (DYRKs) play key roles in brain development, regulation of splicing, and apoptosis, and are potential drug targets for neurodegenerative diseases and cancer. We present crystal structures of one representative member of each DYRK subfamily: DYRK1A with an ATP-mimetic inhibitor and consensus peptide, and DYRK2 including NAPA and DH (DYRK homology) box regions. The current activation model suggests that DYRKs are Ser/Thr kinases that only autophosphorylate the second tyrosine of the activation loop YxY motif during protein translation. The structures explain the roles of this tyrosine and of the DH box in DYRK activation and provide a structural model for DYRK substrate recognition. Phosphorylation of a library of naturally occurring peptides identified substrate motifs that lack proline in the P+1 position, suggesting that DYRK1A is not a strictly proline-directed kinase. Our data also show that DYRK1A wild-type and Y321F mutant retain tyrosine autophosphorylation activity. PMID:23665168

  2. Structures of Down Syndrome Kinases, DYRKs, Reveal Mechanisms of Kinase Activation and Substrate Recognition

    PubMed Central

    Soundararajan, Meera; Roos, Annette K.; Savitsky, Pavel; Filippakopoulos, Panagis; Kettenbach, Arminja N.; Olsen, Jesper V.; Gerber, Scott A.; Eswaran, Jeyanthy; Knapp, Stefan; Elkins, Jonathan M.

    2013-01-01

    Summary Dual-specificity tyrosine-(Y)-phosphorylation-regulated kinases (DYRKs) play key roles in brain development, regulation of splicing, and apoptosis, and are potential drug targets for neurodegenerative diseases and cancer. We present crystal structures of one representative member of each DYRK subfamily: DYRK1A with an ATP-mimetic inhibitor and consensus peptide, and DYRK2 including NAPA and DH (DYRK homology) box regions. The current activation model suggests that DYRKs are Ser/Thr kinases that only autophosphorylate the second tyrosine of the activation loop YxY motif during protein translation. The structures explain the roles of this tyrosine and of the DH box in DYRK activation and provide a structural model for DYRK substrate recognition. Phosphorylation of a library of naturally occurring peptides identified substrate motifs that lack proline in the P+1 position, suggesting that DYRK1A is not a strictly proline-directed kinase. Our data also show that DYRK1A wild-type and Y321F mutant retain tyrosine autophosphorylation activity. PMID:23665168

  3. 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.

  4. 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.

  5. 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.

  6. 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

    2016-01-01

    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. PMID:27548184

  7. 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.

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

    PubMed Central

    Liu, Chung-Tse; Chan, Chia-Tai

    2016-01-01

    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. PMID:27548184

  9. 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.

  10. 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.

  11. Building Group Recognition.

    ERIC Educational Resources Information Center

    Chartier, George

    1994-01-01

    Discusses the value of name recognition for theater companies. Describes steps toward identity and recognition, analyzing the group, the mission statement, symbolic logic, designing and identity, developing a communications plan, and meaningful activities. (SR)

  12. 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

  13. 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.

  14. 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. PMID:25752767

  15. 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

  16. 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.

  17. Looking Forward to Monday Morning: Ideas for Recognition and Appreciation Activities and Fun Things to Do at Work for Educators

    ERIC Educational Resources Information Center

    Hodges, Diane

    2004-01-01

    In this book, a former human resources director and school administrator, shares numerous staff appreciation and recognition activities that can be implemented to promote a positive environment and inspire staff members to look forward to the beginning of each new week. This insightful text presents low-cost, fun ideas that will help staff…

  18. 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

  19. 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

    2016-10-01

    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.

  20. 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

    2016-10-01

    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. PMID:27424557

  1. Child activity recognition based on cooperative fusion model of a triaxial accelerometer and a barometric pressure sensor.

    PubMed

    Nam, Yunyoung; Park, Jung Wook

    2013-03-01

    This paper presents a child activity recognition approach using a single 3-axis accelerometer and a barometric pressure sensor worn on a waist of the body to prevent child accidents such as unintentional injuries at home. Labeled accelerometer data are collected from children of both sexes up to the age of 16 to 29 months. To recognize daily activities, mean, standard deviation, and slope of time-domain features are calculated over sliding windows. In addition, the FFT analysis is adopted to extract frequency-domain features of the aggregated data, and then energy and correlation of acceleration data are calculated. Child activities are classified into 11 daily activities which are wiggling, rolling, standing still, standing up, sitting down, walking, toddling, crawling, climbing up, climbing down, and stopping. The overall accuracy of activity recognition was 98.43% using only a single- wearable triaxial accelerometer sensor and a barometric pressure sensor with a support vector machine.

  2. 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. PMID:23369568

  3. 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.

  4. [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.

  5. 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.

  6. 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.

  7. From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics.

    PubMed

    Wolfers, Thomas; Buitelaar, Jan K; Beckmann, Christian F; Franke, Barbara; Marquand, Andre F

    2015-10-01

    Psychiatric disorders are increasingly being recognised as having a biological basis, but their diagnosis is made exclusively behaviourally. A promising approach for 'biomarker' discovery has been based on pattern recognition methods applied to neuroimaging data, which could yield clinical utility in future. In this review we survey the literature on pattern recognition for making diagnostic predictions in psychiatric disorders, and evaluate progress made in translating such findings towards clinical application. We evaluate studies on many criteria, including data modalities used, the types of features extracted and algorithm applied. We identify problems common to many studies, such as a relatively small sample size and a primary focus on estimating generalisability within a single study. Furthermore, we highlight challenges that are not widely acknowledged in the field including the importance of accommodating disease prevalence, the necessity of more extensive validation using large carefully acquired samples, the need for methodological innovations to improve accuracy and to discriminate between multiple disorders simultaneously. Finally, we identify specific clinical contexts in which pattern recognition can add value in the short to medium term.

  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. 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.

  10. 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. PMID:26277994

  11. 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

  12. Reconstitution of CPSF active in polyadenylation: recognition of the polyadenylation signal by WDR33.

    PubMed

    Schönemann, Lars; Kühn, Uwe; Martin, Georges; Schäfer, Peter; Gruber, Andreas R; Keller, Walter; Zavolan, Mihaela; Wahle, Elmar

    2014-11-01

    Cleavage and polyadenylation specificity factor (CPSF) is the central component of the 3' processing machinery for polyadenylated mRNAs in metazoans: CPSF recognizes the polyadenylation signal AAUAAA, providing sequence specificity in both pre-mRNA cleavage and polyadenylation, and catalyzes pre-mRNA cleavage. Here we show that of the seven polypeptides that have been proposed to constitute CPSF, only four (CPSF160, CPSF30, hFip1, and WDR33) are necessary and sufficient to reconstitute a CPSF subcomplex active in AAUAAA-dependent polyadenylation, whereas CPSF100, CPSF73, and symplekin are dispensable. WDR33 is required for binding of reconstituted CPSF to AAUAAA-containing RNA and can be specifically UV cross-linked to such RNAs, as can CPSF30. Transcriptome-wide identification of WDR33 targets by photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) showed that WDR33 binds in and very close to the AAUAAA signal in vivo with high specificity. Thus, our data indicate that the large CPSF subunit participating in recognition of the polyadenylation signal is WDR33 and not CPSF160, as suggested by previous studies.

  13. 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.

  14. Real-time observation of signal recognition particle binding to actively translating ribosomes.

    PubMed

    Noriega, Thomas R; Chen, Jin; Walter, Peter; Puglisi, Joseph D

    2014-01-01

    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.

  15. Does Kaniso activate CASINO?: input coding schemes and phonology in visual-word recognition.

    PubMed

    Acha, Joana; Perea, Manuel

    2010-01-01

    Most recent input coding schemes in visual-word recognition assume that letter position coding is orthographic rather than phonological in nature (e.g., SOLAR, open-bigram, SERIOL, and overlap). This assumption has been drawn - in part - by the fact that the transposed-letter effect (e.g., caniso activates CASINO) seems to be (mostly) insensitive to phonological manipulations (e.g., Perea & Carreiras, 2006, 2008; Perea & Pérez, 2009). However, one could argue that the lack of a phonological effect in prior research was due to the fact that the manipulation always occurred in internal letter positions - note that phonological effects tend to be stronger for the initial syllable (Carreiras, Ferrand, Grainger, & Perea, 2005). To reexamine this issue, we conducted a masked priming lexical decision experiment in which we compared the priming effect for transposed-letter pairs (e.g., caniso-CASINO vs. caviro-CASINO) and for pseudohomophone transposed-letter pairs (kaniso-CASINO vs. kaviro-CASINO). Results showed a transposed-letter priming effect for the correctly spelled pairs, but not for the pseudohomophone pairs. This is consistent with the view that letter position coding is (primarily) orthographic in nature.

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. Activity Augmentation of Amphioxus Peptidoglycan Recognition Protein BbtPGRP3 via Fusion with a Chitin Binding Domain.

    PubMed

    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.

  1. 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

  2. Activity Augmentation of Amphioxus Peptidoglycan Recognition Protein BbtPGRP3 via Fusion with a Chitin Binding Domain.

    PubMed

    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

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. A novel algorithm for QSAR (quantitative structure-activity relationships)

    SciTech Connect

    Carter, S. ); Nikolic, S.; Trinajstic, N. )

    1989-01-01

    A novel approach to quantitative structure-activity relationships (QSAR) is proposed. It is based on the molecular descriptor named the stereo-identification (SID) number. The applicability of this approach to QSAR studies is tested on aquatic toxicities of phenols against fathead minnows (Phimephales promelas). Our approach reproduced successfully the bioactivities of phenols and is superior to the Hall-Kier model based on Randic's connectivity index.

  8. Experimental evaluation of leaky least-mean-square algorithms for active noise reduction in communication headsets

    NASA Astrophysics Data System (ADS)

    Cartes, David A.; Ray, Laura R.; Collier, Robert D.

    2002-04-01

    An adaptive leaky normalized least-mean-square (NLMS) algorithm has been developed to optimize stability and performance of active noise cancellation systems. The research addresses LMS filter performance issues related to insufficient excitation, nonstationary noise fields, and time-varying signal-to-noise ratio. The adaptive leaky NLMS algorithm is based on a Lyapunov tuning approach in which three candidate algorithms, each of which is a function of the instantaneous measured reference input, measurement noise variance, and filter length, are shown to provide varying degrees of tradeoff between stability and noise reduction performance. Each algorithm is evaluated experimentally for reduction of low frequency noise in communication headsets, and stability and noise reduction performance are compared with that of traditional NLMS and fixed-leakage NLMS algorithms. Acoustic measurements are made in a specially designed acoustic test cell which is based on the original work of Ryan et al. [``Enclosure for low frequency assessment of active noise reducing circumaural headsets and hearing protection,'' Can. Acoust. 21, 19-20 (1993)] and which provides a highly controlled and uniform acoustic environment. The stability and performance of the active noise reduction system, including a prototype communication headset, are investigated for a variety of noise sources ranging from stationary tonal noise to highly nonstationary measured F-16 aircraft noise over a 20 dB dynamic range. Results demonstrate significant improvements in stability of Lyapunov-tuned LMS algorithms over traditional leaky or nonleaky normalized algorithms, while providing noise reduction performance equivalent to that of the NLMS algorithm for idealized noise fields.

  9. Recognition of TLR2 N-glycans: critical role in ArtinM immunomodulatory activity.

    PubMed

    Mariano, Vania Sammartino; Zorzetto-Fernandes, Andre Luiz; da Silva, Thiago Aparecido; Ruas, Luciana Pereira; Nohara, Lilian L; Almeida, Igor Correia de; Roque-Barreira, Maria Cristina

    2014-01-01

    TLR2 plays a critical role in the protection against Paracoccidioides brasiliensis conferred by ArtinM administration. ArtinM, a D-mannose-binding lectin from Artocarpus heterophyllus, induces IL-12 production in macrophages and dendritic cells, which accounts for the T helper1 immunity that results from ArtinM administration. We examined the direct interaction of ArtinM with TLR2using HEK293A cells transfected with TLR2, alone or in combination with TLR1 or TLR6, together with accessory proteins. Stimulation with ArtinM induced NF-κB activation and interleukin (IL)-8 production in cells transfected with TLR2, TLR2/1, or TLR2/6. Murine macrophages that were stimulated with ArtinM had augmented TLR2 mRNA expression. Furthermore, pre-incubation of unstimulated macrophages with an anti-TLR2 antibody reduced the cell labeling with ArtinM. In addition, a microplate assay revealed that ArtinM bound to TLR2 molecules that had been captured by specific antibodies from a macrophages lysate. Notably,ArtinM binding to TLR2 was selectively inhibited when the lectin was pre-incubated with mannotriose. The biological relevance of the direct interaction of ArtinM with TLR2 glycans was assessed using macrophages from TLR2-KOmice, which produced significantly lower levels of IL-12 and IL-10 in response to ArtinM than macrophages from wild-type mice. Pre-treatment of murine macrophages with pharmacological inhibitors of signaling molecules demonstrated the involvement of p38 MAPK and JNK in the IL-12 production induced by ArtinM and the involvement ofPI3K in IL-10 production. Thus, ArtinM interacts directly with TLR2 or TLR2 heterodimers in a carbohydrate recognition-dependent manner and functions as a TLR2 agonist with immunomodulatory properties. PMID:24892697

  10. Evidence for a lectin activity for human interleukin 3 and modeling of its carbohydrate recognition domain.

    PubMed

    Zanetta, Jean-Pierre; Bindeus, Roland; Normand, Guy; Durier, Viviane; Lagant, Philippe; Maes, Emmanuel; Vergoten, Gérard

    2002-10-11

    We demonstrate that human interleukin 3 (IL-3) is a lectin recognizing specifically the glycosaminoglycan part of a chondroitin sulfate proteoglycan (PGS3; Normand, G., Kuchler, S., Meyer, A., Vincendon, G., and Zanetta, J. P. (1988) J. Neurochem. 51, 665-676) isolated from the adult rat brain. The specificity of the interaction of this particular proteoglycan with IL-3 is due to the abundance of GlcA(2S)beta 1,3GalNAc(4S)beta 1 disaccharide units as suggested by (1)H NMR. Computational docking experiments of the lower energy conformers of the different disaccharides from chondroitin sulfates reveal a privileged binding site for GlcA(2S)beta 1,3GalNAc(4S)beta 1 (involving His-26, Arg-29, Asn-70, and Trp-104) localized in an area of IL-3 different from the receptor-binding domain previously identified by others (Bagley, C. J., Phillips, J., Cambareri, B., Vadas, M. A., and Lopez, A. F. (1996) J. Biol. Chem. 271, 31922-31928). Molecular modeling of the mutation P33G, described as increasing the biological activity of IL-3 without affecting its receptor binding (Lokker, N. A., Movva, N. R., Strittmatter, U., Fagg, B., and Zenke, G. (1991) J. Biol. Chem. 266, 10624-10631) provokes a change of the three-dimensional structure of IL-3, especially in the area of the putative carbohydrate recognition domain defined above. Computational docking experiments of the different disaccharides of chondroitin sulfates indicate a loss of affinity for the previous ligand but a higher affinity for the classic disaccharide of chondroitin-4-sulfate. This change from a rare and specific ligand to a more abundant constituent of proteoglycans could induce an increased quantitative association between the IL-3 receptors and its ligands and, consequently, an increased signaling.

  11. Recognition of TLR2 N-glycans: critical role in ArtinM immunomodulatory activity.

    PubMed

    Mariano, Vania Sammartino; Zorzetto-Fernandes, Andre Luiz; da Silva, Thiago Aparecido; Ruas, Luciana Pereira; Nohara, Lilian L; Almeida, Igor Correia de; Roque-Barreira, Maria Cristina

    2014-01-01

    TLR2 plays a critical role in the protection against Paracoccidioides brasiliensis conferred by ArtinM administration. ArtinM, a D-mannose-binding lectin from Artocarpus heterophyllus, induces IL-12 production in macrophages and dendritic cells, which accounts for the T helper1 immunity that results from ArtinM administration. We examined the direct interaction of ArtinM with TLR2using HEK293A cells transfected with TLR2, alone or in combination with TLR1 or TLR6, together with accessory proteins. Stimulation with ArtinM induced NF-κB activation and interleukin (IL)-8 production in cells transfected with TLR2, TLR2/1, or TLR2/6. Murine macrophages that were stimulated with ArtinM had augmented TLR2 mRNA expression. Furthermore, pre-incubation of unstimulated macrophages with an anti-TLR2 antibody reduced the cell labeling with ArtinM. In addition, a microplate assay revealed that ArtinM bound to TLR2 molecules that had been captured by specific antibodies from a macrophages lysate. Notably,ArtinM binding to TLR2 was selectively inhibited when the lectin was pre-incubated with mannotriose. The biological relevance of the direct interaction of ArtinM with TLR2 glycans was assessed using macrophages from TLR2-KOmice, which produced significantly lower levels of IL-12 and IL-10 in response to ArtinM than macrophages from wild-type mice. Pre-treatment of murine macrophages with pharmacological inhibitors of signaling molecules demonstrated the involvement of p38 MAPK and JNK in the IL-12 production induced by ArtinM and the involvement ofPI3K in IL-10 production. Thus, ArtinM interacts directly with TLR2 or TLR2 heterodimers in a carbohydrate recognition-dependent manner and functions as a TLR2 agonist with immunomodulatory properties.

  12. 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.

  13. 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

  14. When Passive Feels Active - Delusion-Proneness Alters Self-Recognition in the Moving Rubber Hand Illusion

    PubMed Central

    Louzolo, Anaïs; Kalckert, Andreas; Petrovic, Predrag

    2015-01-01

    Psychotic patients have problems with bodily self-recognition such as the experience of self-produced actions (sense of agency) and the perception of the body as their own (sense of ownership). While it has been shown that such impairments in psychotic patients can be explained by hypersalient processing of external sensory input it has also been suggested that they lack normal efference copy in voluntary action. However, it is not known how problems with motor predictions like efference copy contribute to impaired sense of agency and ownership in psychosis or psychosis-related states. We used a rubber hand illusion based on finger movements and measured sense of agency and ownership to compute a bodily self-recognition score in delusion-proneness (indexed by Peters’ Delusion Inventory - PDI). A group of healthy subjects (n=71) experienced active movements (involving motor predictions) or passive movements (lacking motor predictions). We observed a highly significant correlation between delusion-proneness and self-recognition in the passive conditions, while no such effect was observed in the active conditions. This was seen for both ownership and agency scores. The result suggests that delusion-proneness is associated with hypersalient external input in passive conditions, resulting in an abnormal experience of the illusion. We hypothesize that this effect is not present in the active condition because deficient motor predictions counteract hypersalience in psychosis proneness. PMID:26090797

  15. Ciliary motility activity measurement using a dense optical flow algorithm.

    PubMed

    Parrilla, Eduardo; Armengot, Miguel; Mata, Manuel; Cortijo, Julio; Riera, Jaime; Hueso, José L; Moratal, David

    2013-01-01

    Persistent respiratory syncytial virus (RSV) infections have been associated with the exacerbation of chronic inflammatory diseases, including chronic obstructive pulmonary disease (COPD). This virus infects the respiratory epithelium, leading to chronic inflammation, and induces the release of mucins and the loss of cilia activity, two factors that determine mucus clearance and the increase in sputum volume. In this study, an automatic method has been established to determine the ciliary motility activity from cell cultures by means of optical flow computation, and has been applied to 136 control cultures and to 144 RSV-infected cultures. The control group presented an average of cell surface with cilia motility per field of 41 ± 15 % (mean ± standard deviation), while the infected group presented a 11 ± 5 %, t-Student p<0.001. The cutoff value to classify a infected specimen was <17.89 % (sensitivity 0.94, specificity 0.93). This methodology has proved to be a robust technique to evaluate cilia motility in cell cultures. PMID:24110720

  16. The Timing and Strength of Regional Brain Activation Associated with Word Recognition in Children with Reading Difficulties

    PubMed Central

    Rezaie, Roozbeh; Simos, Panagiotis G.; Fletcher, Jack M.; Juranek, Jenifer; Cirino, Paul T.; Li, Zhimin; Passaro, Antony D.; Papanicolaou, Andrew C.

    2011-01-01

    The study investigates the relative degree and timing of cortical activation across parietal, temporal, and frontal regions during performance of a continuous visual-word recognition task in children who experience reading difficulties (N = 44, RD) and typical readers (N = 40, NI). Minimum norm estimates of regional neurophysiological activity were obtained from magnetoencephalographic recordings. Children with RD showed bilaterally reduced neurophysiological activity in the superior and middle temporal gyri, and increased activity in rostral middle frontal and ventral occipitotemporal cortices, bilaterally. The temporal profile of activity in the RD group, featured near-simultaneous activity peaks in temporal, inferior parietal, and prefrontal regions, in contrast to a clear temporal progression of activity among these areas in the NI group. These results replicate and extend previous MEG and fMRI results demonstrating atypical, latency-dependent attributes of the brain circuit involved in word reading in children with reading difficulties. PMID:21647211

  17. Assessing Activity Pattern Similarity with Multidimensional Sequence Alignment based on a Multiobjective Optimization Evolutionary Algorithm

    PubMed Central

    Kwan, Mei-Po; Xiao, Ningchuan; Ding, Guoxiang

    2015-01-01

    Due to the complexity and multidimensional characteristics of human activities, assessing the similarity of human activity patterns and classifying individuals with similar patterns remains highly challenging. This paper presents a new and unique methodology for evaluating the similarity among individual activity patterns. It conceptualizes multidimensional sequence alignment (MDSA) as a multiobjective optimization problem, and solves this problem with an evolutionary algorithm. The study utilizes sequence alignment to code multiple facets of human activities into multidimensional sequences, and to treat similarity assessment as a multiobjective optimization problem that aims to minimize the alignment cost for all dimensions simultaneously. A multiobjective optimization evolutionary algorithm (MOEA) is used to generate a diverse set of optimal or near-optimal alignment solutions. Evolutionary operators are specifically designed for this problem, and a local search method also is incorporated to improve the search ability of the algorithm. We demonstrate the effectiveness of our method by comparing it with a popular existing method called ClustalG using a set of 50 sequences. The results indicate that our method outperforms the existing method for most of our selected cases. The multiobjective evolutionary algorithm presented in this paper provides an effective approach for assessing activity pattern similarity, and a foundation for identifying distinctive groups of individuals with similar activity patterns. PMID:26190858

  18. Geomagnetic Activity Forecasting Using Self-Learning Algorithms: Application in Space Weather Studies

    NASA Astrophysics Data System (ADS)

    Khalil, A. F.; Barakat, A. R.; McKee, M.

    2005-05-01

    The ability to forecast the geomagnetic activities is becoming more important as human activity in space becomes more prevalent. For example, early warning of geomagnetic storms could help mitigate their harmful effects on space electronics and on electrical power lines. Moreover, recently developed space weather algorithms that utilize physics-based models require future values of Kp as an input in order to forecast the ionospheric behavior. Computational learning theory and data-driven modeling techniques are new and rapidly expanding areas of research that aim at developing efficient learning algorithms. Here we compare self-learning algorithms regarding their abilities to forecast the level of geomagnetic activities, as represented by Kp. In particular, we consider the following algorithms: artificial neural networks, locally weighted projection regression, support vector machines, and relevance vector machines. Different parameters are considered such as: (1) length of forecasting time, (2) type and size of input data, and (3) training set size. These learning machines are compared regarding their generalization capabilities and structure reliabilities. The relative strengths and limitations of these algorithms will be presented.

  19. 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

  20. 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

  1. 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

  2. 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.

  3. 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. PMID:23948388

  4. Many neighbors are not silent. fMRI evidence for global lexical activity in visual word recognition.

    PubMed

    Braun, Mario; Jacobs, Arthur M; Richlan, Fabio; Hawelka, Stefan; Hutzler, Florian; Kronbichler, Martin

    2015-01-01

    Many neurocognitive studies investigated the neural correlates of visual word recognition, some of which manipulated the orthographic neighborhood density of words and nonwords believed to influence the activation of orthographically similar representations in a hypothetical mental lexicon. Previous neuroimaging research failed to find evidence for such global lexical activity associated with neighborhood density. Rather, effects were interpreted to reflect semantic or domain general processing. The present fMRI study revealed effects of lexicality, orthographic neighborhood density and a lexicality by orthographic neighborhood density interaction in a silent reading task. For the first time we found greater activity for words and nonwords with a high number of neighbors. We propose that this activity in the dorsomedial prefrontal cortex reflects activation of orthographically similar codes in verbal working memory thus providing evidence for global lexical activity as the basis of the neighborhood density effect. The interaction of lexicality by neighborhood density in the ventromedial prefrontal cortex showed lower activity in response to words with a high number compared to nonwords with a high number of neighbors. In the light of these results the facilitatory effect for words and inhibitory effect for nonwords with many neighbors observed in previous studies can be understood as being due to the operation of a fast-guess mechanism for words and a temporal deadline mechanism for nonwords as predicted by models of visual word recognition. Furthermore, we propose that the lexicality effect with higher activity for words compared to nonwords in inferior parietal and middle temporal cortex reflects the operation of an identification mechanism based on local lexico-semantic activity. PMID:26257634

  5. Many neighbors are not silent. fMRI evidence for global lexical activity in visual word recognition.

    PubMed

    Braun, Mario; Jacobs, Arthur M; Richlan, Fabio; Hawelka, Stefan; Hutzler, Florian; Kronbichler, Martin

    2015-01-01

    Many neurocognitive studies investigated the neural correlates of visual word recognition, some of which manipulated the orthographic neighborhood density of words and nonwords believed to influence the activation of orthographically similar representations in a hypothetical mental lexicon. Previous neuroimaging research failed to find evidence for such global lexical activity associated with neighborhood density. Rather, effects were interpreted to reflect semantic or domain general processing. The present fMRI study revealed effects of lexicality, orthographic neighborhood density and a lexicality by orthographic neighborhood density interaction in a silent reading task. For the first time we found greater activity for words and nonwords with a high number of neighbors. We propose that this activity in the dorsomedial prefrontal cortex reflects activation of orthographically similar codes in verbal working memory thus providing evidence for global lexical activity as the basis of the neighborhood density effect. The interaction of lexicality by neighborhood density in the ventromedial prefrontal cortex showed lower activity in response to words with a high number compared to nonwords with a high number of neighbors. In the light of these results the facilitatory effect for words and inhibitory effect for nonwords with many neighbors observed in previous studies can be understood as being due to the operation of a fast-guess mechanism for words and a temporal deadline mechanism for nonwords as predicted by models of visual word recognition. Furthermore, we propose that the lexicality effect with higher activity for words compared to nonwords in inferior parietal and middle temporal cortex reflects the operation of an identification mechanism based on local lexico-semantic activity.

  6. Human L-ficolin, a recognition molecule of the lectin activation pathway of complement, activates complement by binding to pneumolysin, the major toxin of Streptococcus pneumoniae.

    PubMed

    Ali, Youssif M; Kenawy, Hany I; Muhammad, Adnan; Sim, Robert B; Andrew, Peter W; Schwaeble, Wilhelm J

    2013-01-01

    The complement system is an essential component of the immune response, providing a critical line of defense against different pathogens including S. pneumoniae. Complement is activated via three distinct pathways: the classical (CP), the alternative (AP) and the lectin pathway (LP). The role of Pneumolysin (PLY), a bacterial toxin released by S. pneumoniae, in triggering complement activation has been studied in vitro. Our results demonstrate that in both human and mouse sera complement was activated via the CP, initiated by direct binding of even non-specific IgM and IgG3 to PLY. Absence of CP activity in C1q(-/-) mouse serum completely abolished any C3 deposition. However, C1q depleted human serum strongly opsonized PLY through abundant deposition of C3 activation products, indicating that the LP may have a vital role in activating the human complement system on PLY. We identified that human L-ficolin is the critical LP recognition molecule that drives LP activation on PLY, while all of the murine LP recognition components fail to bind and activate complement on PLY. This work elucidates the detailed interactions between PLY and complement and shows for the first time a specific role of the LP in PLY-mediated complement activation in human serum.

  7. Recognition of Handwriting from Electromyography

    PubMed Central

    Linderman, Michael; Lebedev, Mikhail A.; Erlichman, Joseph S.

    2009-01-01

    Handwriting – one of the most important developments in human culture – is also a methodological tool in several scientific disciplines, most importantly handwriting recognition methods, graphology and medical diagnostics. Previous studies have relied largely on the analyses of handwritten traces or kinematic analysis of handwriting; whereas electromyographic (EMG) signals associated with handwriting have received little attention. Here we show for the first time, a method in which EMG signals generated by hand and forearm muscles during handwriting activity are reliably translated into both algorithm-generated handwriting traces and font characters using decoding algorithms. Our results demonstrate the feasibility of recreating handwriting solely from EMG signals – the finding that can be utilized in computer peripherals and myoelectric prosthetic devices. Moreover, this approach may provide a rapid and sensitive method for diagnosing a variety of neurogenerative diseases before other symptoms become clear. PMID:19707562

  8. Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU

    PubMed Central

    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. PMID:24723812

  9. Algorithm for quantifying advanced carotid artery atherosclerosis in humans using MRI and active contours

    NASA Astrophysics Data System (ADS)

    Adams, Gareth; Vick, G. W., III; Bordelon, Cassius; Insull, William; Morrisett, Joel

    2002-05-01

    A new algorithm for measuring carotid artery volumes and estimating atherosclerotic plaque volumes from MRI images has been developed and validated using pressure-perfusion-fixed cadaveric carotid arteries. Our method uses an active contour algorithm with the generalized gradient vector field force as the external force to localize the boundaries of the artery on each MRI cross-section. Plaque volume is estimated by an automated algorithm based on estimating the normal wall thickness for each branch of the carotid. Triplicate volume measurements were performed by a single observer on thirty-eight pairs of cadaveric carotid arteries. The coefficient of variance (COV) was used to quantify measurement reproducibility. Aggregate volumes were computed for nine contiguous slices bounding the carotid bifurcation. The median (mean +/- SD) COV for the 76 aggregate arterial volumes was 0.93% (1.47% +/- 1.52%) for the lumen volume, 0.95% (1.06% +/- 0.67%) for the total artery volume, and 4.69% (5.39% +/- 3.97%) for the plaque volume. These results indicate that our algorithm provides repeatable measures of arterial volumes and a repeatable estimate of plaque volume of cadaveric carotid specimens through analysis of MRI images. The algorithm also significantly decreases the amount of time necessary to generate these measurements.

  10. 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.

  11. Handwritten digits recognition based on immune network

    NASA Astrophysics Data System (ADS)

    Li, Yangyang; Wu, Yunhui; Jiao, Lc; Wu, Jianshe

    2011-11-01

    With the development of society, handwritten digits recognition technique has been widely applied to production and daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN method for classification. Its characteristic is the novel network with parallel commutating and learning. The performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.

  12. 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.

  13. 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.

  14. Microprocessor for speech recognition

    SciTech Connect

    Ishizuka, H.; Watari, M.; Sakoe, H.; Chiba, S.; Iwata, T.; Matsuki, T.; Kawakami, Y.

    1983-01-01

    A new single-chip microprocessor for speech recognition has been developed utilizing multi-processor architecture and pipelined structure. By DP-matching algorithm, the processor recognizes up to 340 isolated words or 40 connected words in realtime. 6 references.

  15. 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

  16. Effects of Active and Passive Hearing Protection Devices on Sound Source Localization, Speech Recognition, and Tone Detection.

    PubMed

    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

  17. Novel Biotinylated Lipid Prodrugs of Acyclovir for the Treatment of Herpetic Keratitis (HK): Transporter Recognition, Tissue Stability and Antiviral Activity

    PubMed Central

    Vadlapudi, Aswani Dutt; Vadlapatla, Ramya Krishna; Earla, Ravinder; Sirimulla, Suman; Bailey, Jake Brain; Pal, Dhananjay; Mitra, Ashim K.

    2013-01-01

    Purpose Biotinylated lipid prodrugs of acyclovir (ACV) were designed to target the sodium dependent multivitamin transporter (SMVT) on the cornea to facilitate enhanced cellular absorption of ACV. Methods All the prodrugs were screened for in vitro cellular uptake, interaction with SMVT, docking analysis, cytotoxicity, enzymatic stability and antiviral activity. Results Uptake of biotinylated lipid prodrugs of ACV (B-R-ACV and B-12HS-ACV) was significantly higher than biotinylated prodrug (B-ACV), lipid prodrugs (R-ACV and 12HS-ACV) and ACV in corneal cells. Transepithelial transport across rabbit corneas indicated the recognition of the prodrugs by SMVT. Average Vina scores obtained from docking studies further confirmed that biotinylated lipid prodrugs possess enhanced affinity towards SMVT. All the prodrugs studied did not cause any cytotoxicity and were found to be safe and non-toxic. B-R-ACV and B-12HS-ACV were found to be relatively more stable in ocular tissue homogenates and exhibited excellent antiviral activity. Conclusions Biotinylated lipid prodrugs demonstrated synergistic improvement in cellular uptake due to recognition of the prodrugs by SMVT on the cornea and lipid mediated transcellular diffusion. These biotinylated lipid prodrugs appear to be promising drug candidates for the treatment of herpetic keratitis (HK) and may lower ACV resistance in patients with poor clinical response. PMID:23657675

  18. 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.

  19. NKG2D is a Key Receptor for Recognition of Bladder Cancer Cells by IL-2-Activated NK Cells and BCG Promotes NK Cell Activation

    PubMed Central

    García-Cuesta, Eva María; López-Cobo, Sheila; Álvarez-Maestro, Mario; Esteso, Gloria; Romera-Cárdenas, Gema; Rey, Mercedes; Cassady-Cain, Robin L.; Linares, Ana; Valés-Gómez, Alejandro; Reyburn, Hugh Thomson; Martínez-Piñeiro, Luis; Valés-Gómez, Mar

    2015-01-01

    Intravesical instillation of bacillus Calmette–Guérin (BCG) is used to treat superficial bladder cancer, either papillary tumors (after transurethral resection) or high-grade flat carcinomas (carcinoma in situ), reducing recurrence in about 70% of patients. Initially, BCG was proposed to work through an inflammatory response, mediated by phagocytic uptake of mycobacterial antigens and cytokine release. More recently, other immune effectors such as monocytes, natural killer (NK), and NKT cells have been suggested to play a role in this immune response. Here, we provide a comprehensive study of multiple bladder cancer cell lines as putative targets for immune cells and evaluated their recognition by NK cells in the presence and absence of BCG. We describe that different bladder cancer cells can express multiple activating and inhibitory ligands for NK cells. Recognition of bladder cancer cells depended mainly on NKG2D, with a contribution from NKp46. Surprisingly, exposure to BCG did not affect the immune phenotype of bladder cells nor increased NK cell recognition of purified IL-2-activated cell lines. However, NK cells were activated efficiently when BCG was included in mixed lymphocyte cultures, suggesting that NK activation after mycobacteria treatment requires the collaboration of various immune cells. We also analyzed the percentage of NK cells in peripheral blood of a cohort of bladder cancer patients treated with BCG. The total numbers of NK cells did not vary during treatment, indicating that a more detailed study of NK cell activation in the tumor site will be required to evaluate the response in each patient. PMID:26106390

  20. A comparison of two adaptive algorithms for the control of active engine mounts

    NASA Astrophysics Data System (ADS)

    Hillis, A. J.; Harrison, A. J. L.; Stoten, D. P.

    2005-08-01

    This paper describes work conducted in order to control automotive active engine mounts, consisting of a conventional passive mount and an internal electromagnetic actuator. Active engine mounts seek to cancel the oscillatory forces generated by the rotation of out-of-balance masses within the engine. The actuator generates a force dependent on a control signal from an algorithm implemented with a real-time DSP. The filtered-x least-mean-square (FXLMS) adaptive filter is used as a benchmark for comparison with a new implementation of the error-driven minimal controller synthesis (Er-MCSI) adaptive controller. Both algorithms are applied to an active mount fitted to a saloon car equipped with a four-cylinder turbo-diesel engine, and have no a priori knowledge of the system dynamics. The steady-state and transient performance of the two algorithms are compared and the relative merits of the two approaches are discussed. The Er-MCSI strategy offers significant computational advantages as it requires no cancellation path modelling. The Er-MCSI controller is found to perform in a fashion similar to the FXLMS filter—typically reducing chassis vibration by 50-90% under normal driving conditions.

  1. Probabilistic Open Set Recognition

    NASA Astrophysics Data System (ADS)

    Jain, Lalit Prithviraj

    Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary

  2. Soluble Collectin-12 (CL-12) Is a Pattern Recognition Molecule Initiating Complement Activation via the Alternative Pathway.

    PubMed

    Ma, Ying Jie; Hein, Estrid; Munthe-Fog, Lea; Skjoedt, Mikkel-Ole; Bayarri-Olmos, Rafael; Romani, Luigina; Garred, Peter

    2015-10-01

    Soluble defense collagens including the collectins play important roles in innate immunity. Recently, a new member of the collectin family named collectin-12 (CL-12 or CL-P1) has been identified. CL-12 is highly expressed in umbilical cord vascular endothelial cells as a transmembrane receptor and may recognize certain bacteria and fungi, leading to opsonophagocytosis. However, based on its structural and functional similarities with soluble collectins, we hypothesized the existence of a fluid-phase analog of CL-12 released from cells, which may function as a soluble pattern-recognition molecule. Using recombinant CL-12 full length or CL-12 extracellular domain, we determined the occurrence of soluble CL-12 shed from in vitro cultured cells. Western blot showed that soluble recombinant CL-12 migrated with a band corresponding to ∼ 120 kDa under reducing conditions, whereas under nonreducing conditions it presented multimeric assembly forms. Immunoprecipitation and Western blot analysis of human umbilical cord plasma enabled identification of a natural soluble form of CL-12 having an electrophoretic mobility pattern close to that of shed soluble recombinant CL-12. Soluble CL-12 could recognize Aspergillus fumigatus partially through the carbohydrate-recognition domain in a Ca(2+)-independent manner. This led to activation of the alternative pathway of complement exclusively via association with properdin on A. fumigatus as validated by detection of C3b deposition and formation of the terminal complement complex. These results demonstrate the existence of CL-12 in a soluble form and indicate a novel mechanism by which the alternative pathway of complement may be triggered directly by a soluble pattern-recognition molecule.

  3. 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

  4. 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

  5. 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

  6. Targeted reengineering of protein geranylgeranyltransferase type I selectivity functionally implicates active-site residues in protein-substrate recognition.

    PubMed

    Gangopadhyay, Soumyashree A; Losito, Erica L; Hougland, James L

    2014-01-21

    Posttranslational modifications are vital for the function of many proteins. Prenylation is one such modification, wherein protein geranylgeranyltransferase type I (GGTase-I) or protein farnesyltransferase (FTase) modify proteins by attaching a 20- or 15-carbon isoprenoid group, respectively, to a cysteine residue near the C-terminus of a target protein. These enzymes require a C-terminal Ca1a2X sequence on their substrates, with the a1, a2, and X residues serving as substrate-recognition elements for FTase and/or GGTase-I. While crystallographic structures of rat GGTase-I show a tightly packed and hydrophobic a2 residue binding pocket, consistent with a preference for moderately sized a2 residues in GGTase-I substrates, the functional impact of enzyme-substrate contacts within this active site remains to be determined. Using site-directed mutagenesis and peptide substrate structure-activity studies, we have identified specific active-site residues within rat GGTase-I involved in substrate recognition and developed novel GGTase-I variants with expanded/altered substrate selectivity. The ability to drastically alter GGTase-I selectivity mirrors similar behavior observed in FTase but employs mutation of a distinct set of structurally homologous active-site residues. Our work demonstrates that tunable selectivity may be a general phenomenon among multispecific enzymes involved in posttranslational modification and raises the possibility of variable substrate selectivity among GGTase-I orthologues from different organisms. Furthermore, the GGTase-I variants developed herein can serve as tools for studying GGTase-I substrate selectivity and the effects of prenylation pathway modifications on specific proteins. PMID:24344934

  7. Classification of EEG for Affect Recognition: An Adaptive Approach

    NASA Astrophysics Data System (ADS)

    Alzoubi, Omar; Calvo, Rafael A.; Stevens, Ronald H.

    Research on affective computing is growing rapidly and new applications are being developed more frequently. They use information about the affective/mental states of users to adapt their interfaces or add new functionalities. Face activity, voice, text physiology and other information about the user are used as input to affect recognition modules, which are built as classification algorithms. Brain EEG signals have rarely been used to build such classifiers due to the lack of a clear theoretical framework. We present here an evaluation of three different classification techniques and their adaptive variations of a 10-class emotion recognition experiment. Our results show that affect recognition from EEG signals might be possible and an adaptive algorithm improves the performance of the classification task.

  8. 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.

  9. On the use of sensor fusion to reduce the impact of rotational and additive noise in human activity recognition.

    PubMed

    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.

  10. 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

  11. Two Redundant Receptor-Like Cytoplasmic Kinases Function Downstream of Pattern Recognition Receptors to Regulate Activation of SA Biosynthesis.

    PubMed

    Kong, Qing; Sun, Tongjun; Qu, Na; Ma, Junling; Li, Meng; Cheng, Yu-Ti; Zhang, Qian; Wu, Di; Zhang, Zhibin; Zhang, Yuelin

    2016-06-01

    Salicylic acid (SA) serves as a critical signaling molecule in plant defense. Two transcription factors, SARD1 and CBP60g, control SA biosynthesis through regulating pathogen-induced expression of Isochorismate Synthase1, which encodes a key enzyme for SA biosynthesis. Here, we report that Pattern-Triggered Immunity Compromised Receptor-like Cytoplasmic Kinase1 (PCRK1) and PCRK2 function as key regulators of SA biosynthesis. In the pcrk1 pcrk2 double mutant, pathogen-induced expression of SARD1, CBP60g, and ICS1 is greatly reduced. The pcrk1 pcrk2 double mutant, but neither of the single mutants, exhibits reduced accumulation of SA and enhanced disease susceptibility to bacterial pathogens. Both PCRK1 and PCRK2 interact with the pattern recognition receptor FLS2, and treatment with pathogen-associated molecular patterns leads to rapid phosphorylation of PCRK2. Our data suggest that PCRK1 and PCRK2 function downstream of pattern recognition receptor in a signal relay leading to the activation of SA biosynthesis. PMID:27208222

  12. 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.

  13. 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.

  14. 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.

  15. 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. PMID:22436438

  16. 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. PMID:26691781

  17. 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.

  18. Brain dynamics in young infants' recognition of faces: EEG oscillatory activity in response to mother and stranger.

    PubMed

    Mash, Clay; Bornstein, Marc H; Arterberry, Martha E

    2013-05-01

    The brain electrical responses of 3-month-old infants were compared between images of familiar and unfamiliar faces. Infants were shown images of their mothers and of appearance-matched female strangers for 500 ms per trial while their electroencephalography was recorded. Electroencephalographic signals were segmented from stimulus onset through 1200 ms, and segments were analyzed in the time-frequency domain with a continuous wavelet transform. Differentiated responses were apparent in three time windows: 370-480, 610-690, and 830-960 ms. Across response windows, event-related synchronization or desynchronization was observed in beta or gamma frequency bands at the left frontal, midline central, bilateral temporal, and right parietal sites. In conclusion, these findings provide the first evidence of organized brain activity underlying familiar face recognition in very young infants and are discussed in relation to comparable patterns that have been observed in adults.

  19. 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.

  20. Variable Maternal Stress in Rats Alters Locomotor Activity, Social Behavior, and Recognition Memory in the Adult Offspring

    PubMed Central

    Wilson, Christina A.; Terry, Alvin V.

    2013-01-01

    Rats repeatedly exposed to variable prenatal stress (PNS) exhibit behavioral signs that are similar to those manifested in several neuropsychiatric disorders such as deficits in attention and inhibitory control, and impairments in memory-related task performance. The purpose of the study described here was to conduct a comprehensive battery of tests to further characterize the behavioral phenotype of PNS rats as well as to evaluate the sensitivity of the model to therapeutic interventions (i.e., to compounds previously shown to have therapeutic potential in neuropsychiatric disorders). The results of this study indicated that PNS in rats is associated with: 1) increased locomotor activity and stereotypic behaviors, 2) elevated sensitivity to the psychostimulant amphetamine, 3) increased aggressive behaviors toward both adult and juvenile rats and 4) delay-dependent deficits in recognition memory. There was no evidence that PNS rats exhibited deficits in other areas of motor function/learning, sensorimotor gating, spatial learning and memory, social withdrawal, or anhedonia. In addition, the results revealed that the second generation antipsychotic risperidone attenuated amphetamine-related increases in locomotor activity in PNS rats; however, the effect was not sustained over time. Furthermore, deficits in recognition memory in PNS rats were attenuated by the norepinephrine reuptake inhibitor, atomoxetine, but not by the α7 nicotinic acetylcholine receptor partial agonist, GTS-21. This study supports the supposition that important phenomenological similarities exist between rats exposed to PNS and patients afflicted with neuropsychiatric disorders thus further establishing the face validity of the model for evaluating potential therapeutic interventions. PMID:23287801

  1. 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. PMID:26868046

  2. 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.

  3. 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…

  4. A new method of NIR face recognition using kernel projection DCV and neural networks

    NASA Astrophysics Data System (ADS)

    Qiao, Ya; Lu, Yuan; Feng, Yun-song; Li, Feng; Ling, Yongshun

    2013-09-01

    A new face recognition system was proposed, which used active near infrared imaging system (ANIRIS) as face images acquisition equipment, used kernel discriminative common vector (KDCV) as the feature extraction algorithm and used neural network as the recognition method. The ANIRIS was established by 40 NIR LEDs which used as active light source and a HWB800-IR-80 near infrared filter which used together with CCD camera to serve as the imaging detector. Its function of reducing the influence of varying illuminations to recognition rate was discussed. The KDCV feature extraction and neural network recognition parts were realized by Matlab programming. The experiments on HITSZ Lab2 face database and self-built face database show that the average recognition rate reached more than 95%, proving the effectiveness of proposed system.

  5. C1q, the recognition subcomponent of the classical pathway of complement, drives microglial activation.

    PubMed

    Färber, Katrin; Cheung, Giselle; Mitchell, Daniel; Wallis, Russell; Weihe, Eberhard; Schwaeble, Wilhelm; Kettenmann, Helmut

    2009-02-15

    Microglia, central nervous system (CNS) resident phagocytic cells, persistently police the integrity of CNS tissue and respond to any kind of damage or pathophysiological changes. These cells sense and rapidly respond to danger and inflammatory signals by changing their cell morphology; by release of cytokines, chemokines, or nitric oxide; and by changing their MHC expression profile. We have shown previously that microglial biosynthesis of the complement subcomponent C1q may serve as a reliable marker of microglial activation ranging from undetectable levels of C1q biosynthesis in resting microglia to abundant C1q expression in activated, nonramified microglia. In this study, we demonstrate that cultured microglial cells respond to extrinsic C1q with a marked intracellular Ca(2+) increase. A shift toward proinflammatory microglial activation is indicated by the release of interleukin-6, tumor necrosis factor-alpha, and nitric oxide and the oxidative burst in rat primary microglial cells, an activation and differentiation process similar to the proinflammatory response of microglia to exposure to lipopolysaccharide. Our findings indicate 1) that extrinsic plasma C1q is involved in the initiation of microglial activation in the course of CNS diseases with blood-brain barrier impairment and 2) that C1q synthesized and released by activated microglia is likely to contribute in an autocrine/paracrine way to maintain and balance microglial activation in the diseased CNS tissue. PMID:18831010

  6. Deriving rules from activity diary data: A learning algorithm and results of computer experiments

    NASA Astrophysics Data System (ADS)

    Arentze, Theo A.; Hofman, Frank; Timmermans, Harry J. P.

    Activity-based models consider travel as a derived demand from the activities households need to conduct in space and time. Over the last 15 years, computational or rule-based models of activity scheduling have gained increasing interest in time-geography and transportation research. This paper argues that a lack of techniques for deriving rules from empirical data hinders the further development of rule-based systems in this area. To overcome this problem, this paper develops and tests an algorithm for inductively deriving rules from activity-diary data. The decision table formalism is used to exhaustively represent the theoretically possible decision rules that individuals may use in sequencing a given set of activities. Actual activity patterns of individuals are supplied to the system as examples. In an incremental learning process, the system progressively improves on the selection of rules used for reproducing the examples. Computer experiments based on simulated data are performed to fine-tune rule selection and rule value update functions. The results suggest that the system is effective and fairly robust for parameter settings. It is concluded, therefore, that the proposed approach opens up possibilities to derive empirically tested rule-based models of activity scheduling. Follow-up research will be concerned with testing the system on empirical data.

  7. Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition

    PubMed Central

    Wang, Kun-Ching

    2015-01-01

    The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII). The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD) algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC) and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech. PMID:25594590

  8. Effects of Varying Epoch Lengths, Wear Time Algorithms, and Activity Cut-Points on Estimates of Child Sedentary Behavior and Physical Activity from Accelerometer Data

    PubMed Central

    Banda, Jorge A.; Haydel, K. Farish; Davila, Tania; Desai, Manisha; Haskell, William L.; Matheson, Donna; Robinson, Thomas N.

    2016-01-01

    Objective To examine the effects of accelerometer epoch lengths, wear time (WT) algorithms, and activity cut-points on estimates of WT, sedentary behavior (SB), and physical activity (PA). Methods 268 7–11 year-olds with BMI ≥ 85th percentile for age and sex wore accelerometers on their right hips for 4–7 days. Data were processed and analyzed at epoch lengths of 1-, 5-, 10-, 15-, 30-, and 60-seconds. For each epoch length, WT minutes/day was determined using three common WT algorithms, and minutes/day and percent time spent in SB, light (LPA), moderate (MPA), and vigorous (VPA) PA were determined using five common activity cut-points. ANOVA tested differences in WT, SB, LPA, MPA, VPA, and MVPA when using the different epoch lengths, WT algorithms, and activity cut-points. Results WT minutes/day varied significantly by epoch length when using the NHANES WT algorithm (p < .0001), but did not vary significantly by epoch length when using the ≥ 20 minute consecutive zero or Choi WT algorithms. Minutes/day and percent time spent in SB, LPA, MPA, VPA, and MVPA varied significantly by epoch length for all sets of activity cut-points tested with all three WT algorithms (all p < .0001). Across all epoch lengths, minutes/day and percent time spent in SB, LPA, MPA, VPA, and MVPA also varied significantly across all sets of activity cut-points with all three WT algorithms (all p < .0001). Conclusions The common practice of converting WT algorithms and activity cut-point definitions to match different epoch lengths may introduce significant errors. Estimates of SB and PA from studies that process and analyze data using different epoch lengths, WT algorithms, and/or activity cut-points are not comparable, potentially leading to very different results, interpretations, and conclusions, misleading research and public policy. PMID:26938240

  9. Thermal-to-visible face recognition using partial least squares.

    PubMed

    Hu, Shuowen; Choi, Jonghyun; Chan, Alex L; Schwartz, William Robson

    2015-03-01

    Although visible face recognition has been an active area of research for several decades, cross-modal face recognition has only been explored by the biometrics community relatively recently. Thermal-to-visible face recognition is one of the most difficult cross-modal face recognition challenges, because of the difference in phenomenology between the thermal and visible imaging modalities. We address the cross-modal recognition problem using a partial least squares (PLS) regression-based approach consisting of preprocessing, feature extraction, and PLS model building. The preprocessing and feature extraction stages are designed to reduce the modality gap between the thermal and visible facial signatures, and facilitate the subsequent one-vs-all PLS-based model building. We incorporate multi-modal information into the PLS model building stage to enhance cross-modal recognition. The performance of the proposed recognition algorithm is evaluated on three challenging datasets containing visible and thermal imagery acquired under different experimental scenarios: time-lapse, physical tasks, mental tasks, and subject-to-camera range. These scenarios represent difficult challenges relevant to real-world applications. We demonstrate that the proposed method performs robustly for the examined scenarios. PMID:26366654

  10. Thermal-to-visible face recognition using partial least squares.

    PubMed

    Hu, Shuowen; Choi, Jonghyun; Chan, Alex L; Schwartz, William Robson

    2015-03-01

    Although visible face recognition has been an active area of research for several decades, cross-modal face recognition has only been explored by the biometrics community relatively recently. Thermal-to-visible face recognition is one of the most difficult cross-modal face recognition challenges, because of the difference in phenomenology between the thermal and visible imaging modalities. We address the cross-modal recognition problem using a partial least squares (PLS) regression-based approach consisting of preprocessing, feature extraction, and PLS model building. The preprocessing and feature extraction stages are designed to reduce the modality gap between the thermal and visible facial signatures, and facilitate the subsequent one-vs-all PLS-based model building. We incorporate multi-modal information into the PLS model building stage to enhance cross-modal recognition. The performance of the proposed recognition algorithm is evaluated on three challenging datasets containing visible and thermal imagery acquired under different experimental scenarios: time-lapse, physical tasks, mental tasks, and subject-to-camera range. These scenarios represent difficult challenges relevant to real-world applications. We demonstrate that the proposed method performs robustly for the examined scenarios.

  11. 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.

  12. Structural basis for chemokine recognition and activation of a viral G protein–coupled receptor

    PubMed Central

    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

    2015-01-01

    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. PMID:25745166

  13. Structural basis for the activation of innate immune pattern-recognition receptor RIG-I by viral RNA.

    PubMed

    Kowalinski, Eva; Lunardi, Thomas; McCarthy, Andrew A; Louber, Jade; Brunel, Joanna; Grigorov, Boyan; Gerlier, Denis; Cusack, Stephen

    2011-10-14

    RIG-I is a key innate immune pattern-recognition receptor that triggers interferon expression upon detection of intracellular 5'triphosphate double-stranded RNA (5'ppp-dsRNA) of viral origin. RIG-I comprises N-terminal caspase activation and recruitment domains (CARDs), a DECH helicase, and a C-terminal domain (CTD). We present crystal structures of the ligand-free, autorepressed, and RNA-bound, activated states of RIG-I. Inactive RIG-I has an open conformation with the CARDs sequestered by a helical domain inserted between the two helicase moieties. ATP and dsRNA binding induce a major rearrangement to a closed conformation in which the helicase and CTD bind the blunt end 5'ppp-dsRNA with perfect complementarity but incompatibly with continued CARD binding. We propose that after initial binding of 5'ppp-dsRNA to the flexibly linked CTD, co-operative tight binding of ATP and RNA to the helicase domain liberates the CARDs for downstream signaling. These findings significantly advance our molecular understanding of the activation of innate immune signaling helicases.

  14. 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

  15. 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

    2016-01-01

    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

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. A Novel Wearable Device for Food Intake and Physical Activity Recognition.

    PubMed

    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

  1. 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.

  2. 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.

  3. 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

  4. Phage P4 alpha protein is multifunctional with origin recognition, helicase and primase activities.

    PubMed Central

    Ziegelin, G; Scherzinger, E; Lurz, R; Lanka, E

    1993-01-01

    alpha Protein of satellite phage P4 of Escherichia coli is multifunctional in P4 replication with three activities. First, the protein (subunit M(r) = 84,900) complexes specifically the P4 origin and the cis replication region required for replication. alpha Protein interacts with all six type I repeats (TGTTCACC) present in the origin. Second, associated with the alpha protein is a DNA helicase activity that is fueled by hydrolysis of a nucleoside 5' triphosphate. All common NTPs except UTP and dTTP can serve as cofactors. Strand separation of partial duplexes containing tailed ends that resemble a replication fork is preferred, although a preformed fork is not absolutely required for the enzyme to invade and unwind duplex DNA. alpha Protein catalyzes unwinding in the 3'-5' direction with respect to the strand it has bound. Finally, the primase activity already demonstrated for alpha protein is due to synthesis of RNA primers. In vitro, alpha protein generates di- to pentaribonucleotides on single-stranded phage fd DNA. The predominant product is the dimer pppApG, on which most of the longer oligoribonucleotides are based. Using DNA oligonucleotides of defined sequence as templates, synthesis of pppApG was also detectable. To date, among prokaryotic and eukaryotic replication systems, gp alpha is the only protein known that combines three activities on one single polypeptide chain. Images PMID:8253092

  5. 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

    2014-01-01

    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 Sensors 2014, 14 19844 messages in video tracking paves the way for user interaction, correction and preparation of situation awareness reports. PMID:25340453

  6. 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.

  7. DNA Recognition by a σ54 Transcriptional Activator from Aquifex aeolicus

    DOE PAGES

    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 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

  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. 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…

  10. Roles of the N domain of the AAA+ Lon protease in substrate recognition, allosteric regulation and chaperone activity.

    PubMed

    Wohlever, Matthew L; Baker, Tania A; Sauer, Robert T

    2014-01-01

    Degron binding regulates the activities of the AAA+ Lon protease in addition to targeting proteins for degradation. The sul20 degron from the cell-division inhibitor SulA is shown here to bind to the N domain of Escherichia coli Lon, and the recognition site is identified by cross-linking and scanning for mutations that prevent sul20-peptide binding. These N-domain mutations limit the rates of proteolysis of model sul20-tagged substrates and ATP hydrolysis by an allosteric mechanism. Lon inactivation of SulA in vivo requires binding to the N domain and robust ATP hydrolysis but does not require degradation or translocation into the proteolytic chamber. Lon-mediated relief of proteotoxic stress and protein aggregation in vivo can also occur without degradation but is not dependent on robust ATP hydrolysis. In combination, these results demonstrate that Lon can function as a protease or a chaperone and reveal that some of its ATP-dependent biological activities do not require translocation.

  11. Fibrinogen-related protein from amphioxus Branchiostoma belcheri is a multivalent pattern recognition receptor with a bacteriolytic activity.

    PubMed

    Fan, Chunxin; Zhang, Shicui; Li, Lei; Chao, Yeqing

    2008-07-01

    Fibrinogen-related proteins (FREPs) containing fibrinogen-like (FBG) domain have been shown to be involved in immune responses in both invertebrates and vertebrates, but the underlying mechanisms remain ill-defined. In this study we isolated a cDNA encoding amphioxus (Branchiostoma belcheri) FREP homolog, BbFREP. BbFREP encoded a protein of 286 amino acids, which included a C-terminal FBG domain and clustered together with human fibrinogen beta and gamma chains. Quantitative real time PCR revealed that the expression of BbFREP was significantly up-regulated following challenge with lipopolysaccharides (LPS) or lipoteichoic acid (LTA). The recombinant BbFREP expressed in Pichia pastoris was able to specifically recognize the pathogen-associated molecular patterns (PAMPs) on the bacterial surfaces including LPS, peptidoglycan (PGN) and LTA, and displayed strong bacteriolytic activities against both Gram-negative bacterium Escherichia coli and Gram-positive bacterium Staphylococcus aureus. BbFREP was also able to bind to both E. coli and S. aureus. In situ hybridization indicated that BbFREP was mainly expressed in the hepatic caecum and hind-gut, agreeing basically with the primary expression of vertebrate FREP genes in the liver. All these suggest that BbFREP can function as a pattern recognition receptor with a bacteriolytic activity via interaction with LPS, LTA and PGN. It also bolsters the notion that the hepatic caecum of amphioxus is equivalent to the vertebrate liver, acting as a major tissue in acute phase response. PMID:18533266

  12. Prebiotic oligosaccharides reduce proinflammatory cytokines in intestinal Caco-2 cells via activation of PPARγ and peptidoglycan recognition protein 3.

    PubMed

    Zenhom, Marwa; Hyder, Ayman; de Vrese, Michael; Heller, Knut J; Roeder, Thomas; Schrezenmeir, Jürgen

    2011-05-01

    Prebiotic oligosaccharides modulate the intestinal microbiota and beneficially affect the human body by reducing intestinal inflammation. This immunomodulatory effect was assumed to be bacterial in origin. However, some observations suggest that oligosaccharides may exert an antiinflammatory effect per se. We hypothesized that oligosaccharides affect the intestinal immunity via activation of peptidoglycan recognition protein 3 (PGlyRP3), which reduces the expression of proinflammatory cytokines. Caco-2 cells were treated with the oligosaccharides, α3-sialyllactose, or fructooligosaccharides (Raftilose p95), and the effects of these treatments on PGlyRP3 and PPARγ expression, the release and expression of some proinflammatory cytokines, and NF-κB translocation were tested. Both oligosaccharides had antiinflammatory activity; they significantly reduced IL-12 secretion in Caco-2 cells and gene expression of IL-12p35, IL-8, and TNFα. They also reduced the gene expression and nuclear translocation of NF-κB. Both oligosaccharides dose and time dependently induced the production of PGlyRP3, the silencing of which by transfection of Caco-2 cells with specific small interfering RNA targeting PGlyRP3 abolished the antiinflammatory role of both oligosaccharides. Incubation of Caco-2 cells with both oligosaccharides induced PPARγ. Antagonizing PPARγ by culturing the cells with GW9662 for 24 h inhibited the oligosaccharide-induced PGlyRP3 production and the antiinflammatory effect of the oligosaccharides. We conclude that oligosaccharides may exert an antiinflammatory effect by inducing the nuclear receptor PPARγ, which regulates the antiinflammatory PGlyRP3.

  13. Face recognition based tensor structure

    NASA Astrophysics Data System (ADS)

    Yang, De-qiang; Ye, Zhi-xia; Zhao, Yang; Liu, Li-mei

    2012-01-01

    Face recognition has broad applications, and it is a difficult problem since face image can change with photographic conditions, such as different illumination conditions, pose changes and camera angles. How to obtain some invariable features for a face image is the key issue for a face recognition algorithm. In this paper, a novel tensor structure of face image is proposed to represent image features with eight directions for a pixel value. The invariable feature of the face image is then obtained from gradient decomposition to make up the tensor structure. Then the singular value decomposition (SVD) and principal component analysis (PCA) of this tensor structure are used for face recognition. The experimental results from this study show that many difficultly recognized samples can correctly be recognized, and the recognition rate is increased by 9%-11% in comparison with same type of algorithms.

  14. 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.

  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. Video face recognition against a watch list

    NASA Astrophysics Data System (ADS)

    Abbas, Jehanzeb; Dagli, Charlie K.; Huang, Thomas S.

    2007-10-01

    Due to a large increase in the video surveillance data recently in an effort to maintain high security at public places, we need more robust systems to analyze this data and make tasks like face recognition a realistic possibility in challenging environments. In this paper we explore a watch-list scenario where we use an appearance based model to classify query faces from low resolution videos into either a watch-list or a non-watch-list face. We then use our simple yet a powerful face recognition system to recognize the faces classified as watch-list faces. Where the watch-list includes those people that we are interested in recognizing. Our system uses simple feature machine algorithms from our previous work to match video faces against still images. To test our approach, we match video faces against a large database of still images obtained from a previous work in the field from Yahoo News over a period of time. We do this matching in an efficient manner to come up with a faster and nearly real-time system. This system can be incorporated into a larger surveillance system equipped with advanced algorithms involving anomalous event detection and activity recognition. This is a step towards more secure and robust surveillance systems and efficient video data analysis.

  17. 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

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. MEG measurement of event-related brain activity evoked by emotional prosody recognition.

    PubMed

    Yagura, H; Tonoike, M; Yamaguchi, M; Nakagawa, S; Sutani, K; Ogino, S

    2004-11-30

    Cortical areas involved in processing of emotional prosody (EP) in spoken language, such as joy or sadness, have been found in functional magnetic resonance imaging (fMRI) studies bilaterally or dominantly in the right frontal or temporal lobes. In this study, we investigated spatiotemporal patterns of cortical activity related to EP processing using magnetoencephalography (MEG). In this experiment, a joyful face (JF) or a sad face (SF) was displayed after voices which had emotional features of joy (joy prosody: JP) or sadness (sad prosody: SP) were presented. Subjects were requested to judge whether emotional features of the voice and the face were identical or not. MEG signals evoked by emotional voices were measured and significant differences of cortical activities associated with processing of emotional feature were observed between the right and left hemisphere during the latency of 100-150 ms that includes the N1m component. Our study suggests that MEG is a useful method, in addition to fMRI and event-related scalp potentials (ERP) for studying non-invasively EP processing in the human brain.

  3. 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.

  4. Recognition of enhancer element-specific histone methylation by TIP60 in transcriptional activation.

    PubMed

    Jeong, Kwang Won; Kim, Kyunghwan; Situ, Alan Jialun; Ulmer, Tobias S; An, Woojin; Stallcup, Michael R

    2011-12-01

    Many co-regulator proteins are recruited by DNA-bound transcription factors to remodel chromatin and activate transcription. However, mechanisms for coordinating actions of multiple co-regulator proteins are poorly understood. We demonstrate that multiple protein-protein interactions by the protein acetyltransferase TIP60 are required for estrogen-induced transcription of a subset of estrogen receptor alpha (ERα) 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

  5. Density-matrix renormalization group algorithm with multi-level active space.

    PubMed

    Ma, Yingjin; Wen, Jing; Ma, Haibo

    2015-07-21

    The density-matrix renormalization group (DMRG) method, which can deal with a large active space composed of tens of orbitals, is nowadays widely used as an efficient addition to traditional complete active space (CAS)-based approaches. In this paper, we present the DMRG algorithm with a multi-level (ML) control of the active space based on chemical intuition-based hierarchical orbital ordering, which is called as ML-DMRG with its self-consistent field (SCF) variant ML-DMRG-SCF. Ground and excited state calculations of H2O, N2, indole, and Cr2 with comparisons to DMRG references using fixed number of kept states (M) illustrate that ML-type DMRG calculations can obtain noticeable efficiency gains. It is also shown that the orbital re-ordering based on hierarchical multiple active subspaces may be beneficial for reducing computational time for not only ML-DMRG calculations but also DMRG ones with fixed M values. PMID:26203012

  6. 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

  7. 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. PMID:27607254

  8. Intracellular Shigella remodels its LPS to dampen the innate immune recognition and evade inflammasome activation.

    PubMed

    Paciello, Ida; Silipo, Alba; Lembo-Fazio, Luigi; Curcurù, Laura; Zumsteg, Anna; Noël, Gaëlle; Ciancarella, Valeria; Sturiale, Luisa; Molinaro, Antonio; Bernardini, Maria Lina

    2013-11-12

    LPS is a potent bacterial effector triggering the activation of the innate immune system following binding with the complex CD14, myeloid differentiation protein 2, and Toll-like receptor 4. The LPS of the enteropathogen Shigella flexneri is a hexa-acylated isoform possessing an optimal inflammatory activity. Symptoms of shigellosis are produced by severe inflammation caused by the invasion process of Shigella in colonic and rectal mucosa. Here we addressed the question of the role played by the Shigella LPS in eliciting a dysregulated inflammatory response of the host. We unveil that (i) Shigella is able to modify the LPS composition, e.g., the lipid A and core domains, during proliferation within epithelial cells; (ii) the LPS of intracellular bacteria (iLPS) and that of bacteria grown in laboratory medium differ in the number of acyl chains in lipid A, with iLPS being the hypoacylated; (iii) the immunopotential of iLPS is dramatically lower than that of bacteria grown in laboratory medium; (iv) both LPS forms mainly signal through the Toll-like receptor 4/myeloid differentiation primary response gene 88 pathway; (v) iLPS down-regulates the inflammasome-mediated release of IL-1β in Shigella-infected macrophages; and (vi) iLPS exhibits a reduced capacity to prime polymorfonuclear cells for an oxidative burst. We propose a working model whereby the two forms of LPS might govern different steps of the invasive process of Shigella. In the first phases, the bacteria, decorated with hypoacylated LPS, are able to lower the immune system surveillance, whereas, in the late phases, shigellae harboring immunopotent LPS are fully recognized by the immune system, which can then successfully resolve the infection.

  9. Intracellular Shigella remodels its LPS to dampen the innate immune recognition and evade inflammasome activation

    PubMed Central

    Paciello, Ida; Silipo, Alba; Lembo-Fazio, Luigi; Curcurù, Laura; Zumsteg, Anna; Noël, Gaëlle; Ciancarella, Valeria; Sturiale, Luisa; Molinaro, Antonio; Bernardini, Maria Lina

    2013-01-01

    LPS is a potent bacterial effector triggering the activation of the innate immune system following binding with the complex CD14, myeloid differentiation protein 2, and Toll-like receptor 4. The LPS of the enteropathogen Shigella flexneri is a hexa-acylated isoform possessing an optimal inflammatory activity. Symptoms of shigellosis are produced by severe inflammation caused by the invasion process of Shigella in colonic and rectal mucosa. Here we addressed the question of the role played by the Shigella LPS in eliciting a dysregulated inflammatory response of the host. We unveil that (i) Shigella is able to modify the LPS composition, e.g., the lipid A and core domains, during proliferation within epithelial cells; (ii) the LPS of intracellular bacteria (iLPS) and that of bacteria grown in laboratory medium differ in the number of acyl chains in lipid A, with iLPS being the hypoacylated; (iii) the immunopotential of iLPS is dramatically lower than that of bacteria grown in laboratory medium; (iv) both LPS forms mainly signal through the Toll-like receptor 4/myeloid differentiation primary response gene 88 pathway; (v) iLPS down-regulates the inflammasome-mediated release of IL-1β in Shigella-infected macrophages; and (vi) iLPS exhibits a reduced capacity to prime polymorfonuclear cells for an oxidative burst. We propose a working model whereby the two forms of LPS might govern different steps of the invasive process of Shigella. In the first phases, the bacteria, decorated with hypoacylated LPS, are able to lower the immune system surveillance, whereas, in the late phases, shigellae harboring immunopotent LPS are fully recognized by the immune system, which can then successfully resolve the infection. PMID:24167293

  10. 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.

  11. Face recognition performance with superresolution.

    PubMed

    Hu, Shuowen; Maschal, Robert; Young, S Susan; Hong, Tsai Hong; Phillips, P Jonathon

    2012-06-20

    With the prevalence of surveillance systems, face recognition is crucial to aiding the law enforcement community and homeland security in identifying suspects and suspicious individuals on watch lists. However, face recognition performance is severely affected by the low face resolution of individuals in typical surveillance footage, oftentimes due to the distance of individuals from the cameras as well as the small pixel count of low-cost surveillance systems. Superresolution image reconstruction has the potential to improve face recognition performance by using a sequence of low-resolution images of an individual's face in the same pose to reconstruct a more detailed high-resolution facial image. This work conducts an extensive performance evaluation of superresolution for a face recognition algorithm using a methodology and experimental setup consistent with real world settings at multiple subject-to-camera distances. Results show that superresolution image reconstruction improves face recognition performance considerably at the examined midrange and close range. PMID:22722306

  12. Face recognition performance with superresolution.

    PubMed

    Hu, Shuowen; Maschal, Robert; Young, S Susan; Hong, Tsai Hong; Phillips, P Jonathon

    2012-06-20

    With the prevalence of surveillance systems, face recognition is crucial to aiding the law enforcement community and homeland security in identifying suspects and suspicious individuals on watch lists. However, face recognition performance is severely affected by the low face resolution of individuals in typical surveillance footage, oftentimes due to the distance of individuals from the cameras as well as the small pixel count of low-cost surveillance systems. Superresolution image reconstruction has the potential to improve face recognition performance by using a sequence of low-resolution images of an individual's face in the same pose to reconstruct a more detailed high-resolution facial image. This work conducts an extensive performance evaluation of superresolution for a face recognition algorithm using a methodology and experimental setup consistent with real world settings at multiple subject-to-camera distances. Results show that superresolution image reconstruction improves face recognition performance considerably at the examined midrange and close range.

  13. Enhanced surface activity of SnO2 thin film verified by LM algorithm

    NASA Astrophysics Data System (ADS)

    Choudhury, Sandip Paul; Kumari, Navnita; Bhattacharjee, Ayon

    2016-04-01

    Impedance studies were conducted on spray deposited Cu doped SnO2 thin films. Rietveld analysis provided evidence of non-existence of any other phase due to doping. Controlled injection of ethanol vapor was done to study the surface activity of these films at different temperatures. The cole-cole plots of ethanol absorbed films to that of unexposed thin films were constructed at different temperatures and compared. The studies reveal that the electron scattering process was homogeneous in nature and the film had a narrow relaxation time. Levenberg-Marquardt algorithm with unweighted function was used for theoretical fitting of the cole-cole plots that revealed the weakening of the Fermi pinning level.

  14. Optimisation of halogenase enzyme activity by application of a genetic algorithm.

    PubMed

    Muffler, Kai; Retzlaff, Marco; van Pée, Karl-Heinz; Ulber, Roland

    2007-01-10

    A genetic algorithm (GA) was applied for the optimisation of an enzyme assay composition respectively the enzyme activity of a recombinantly produced FADH(2)-dependent halogenating enzyme. The examined enzyme belongs to the class of halogenases and is capable to halogenate tryptophan regioselective in position 5. Therefore, the expressed trp-5-halogenase can be an interesting tool in the manufacturing of serotonin precursors. The application of stochastic search strategies (e.g. GAs) is well suited for fast determination of the global optimum in multidimensional search spaces, where statistical approaches or even the popular classical one-factor-at-a-time method often failures by misleading to local optima. The concentrations of six different medium components were optimised and the maximum yield of the halogenated tryptophan could be increased from 3.5 up to 65%.

  15. Adaptive RSOV filter using the FELMS algorithm for nonlinear active noise control systems

    NASA Astrophysics Data System (ADS)

    Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou; Li, Tianrui

    2013-01-01

    This paper presents a recursive second-order Volterra (RSOV) filter to solve the problems of signal saturation and other nonlinear distortions that occur in nonlinear active noise control systems (NANC) used for actual applications. Since this nonlinear filter based on an infinite impulse response (IIR) filter structure can model higher than second-order and third-order nonlinearities for systems where the nonlinearities are harmonically related, the RSOV filter is more effective in NANC systems with either a linear secondary path (LSP) or a nonlinear secondary path (NSP). Simulation results clearly show that the RSOV adaptive filter using the multichannel structure filtered-error least mean square (FELMS) algorithm can further greatly reduce the computational burdens and is more suitable to eliminate nonlinear distortions in NANC systems than a SOV filter, a bilinear filter and a third-order Volterra (TOV) filter.

  16. 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.

  17. A new activity of doublecortin in recognition of the phospho-FIGQY tyrosine in the cytoplasmic domain of neurofascin.

    PubMed

    Kizhatil, Krishnakumar; Wu, Yi-Xin; Sen, Anindita; Bennett, Vann

    2002-09-15

    Doublecortin is a cytoplasmic protein mutated in the neuronal migration disorder X-linked lissencephaly. This study describes a novel activity of doublecortin in recognition of the FIGQY-phosphotyrosine motif present in the cytoplasmic domain of the L1 cell adhesion molecule neurofascin. Phospho-FIGQY-neurofascin (186 kDa) coimmunoprecipitated with doublecortin from detergent extracts of embryonic brain membranes, and this doublecortin-phospho-FIGQY neurofascin complex was disassociated by a synthetic phospho-FIGQY neurofascin peptide but not by a dephospho-FIGQY peptide. Doublecortin specifically recognized the phospho-FIGQY tyrosine in the context of a synthetic phospho-FIGQY neurofascin peptide and in phospho-FIGQY neurofascin isolated from cells treated with pervanadate. Mutations of doublecortin causing lissencephaly (R59H, D62N, and G253D) abolished binding to the phospho-FIGQY peptide and to phospho-FIGQY neurofascin. Finally, phospho-FIGQY neurofascin and doublecortin colocalize in developing axon tracts and in zones enriched in migrating neurons in the embryonic cerebral cortex. In the adult rostral migratory stream, doublecortin colocalizes in migrating neurons with a phospho-FIGQY bearing L1 CAM different from neurofascin. The finding that doublecortin associates with FIGQY-phosphorylated neurofascin provides the first connection of doublecortin with the plasma membrane and could be important for a function of doublecortin in directing neuronal migration.

  18. 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. PMID:26215485

  19. 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; Holton, Nicolas; Nekrasov, Vladimir; Ruan, Deling; Canlas, Patrick E.; Daudi, Arsalan; Petzold, Christopher J.; Singan, Vasanth R.; et al

    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

  20. 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

  1. I see/hear what you mean: semantic activation in visual word recognition depends on perceptual attention.

    PubMed

    Connell, Louise; Lynott, Dermot

    2014-04-01

    How does the meaning of a word affect how quickly we can recognize it? Accounts of visual word recognition allow semantic information to facilitate performance but have neglected the role of modality-specific perceptual attention in activating meaning. We predicted that modality-specific semantic information would differentially facilitate lexical decision and reading aloud, depending on how perceptual attention is implicitly directed by each task. Large-scale regression analyses showed the perceptual modalities involved in representing a word's referent concept influence how easily that word is recognized. Both lexical decision and reading-aloud tasks direct attention toward vision, and are faster and more accurate for strongly visual words. Reading aloud additionally directs attention toward audition and is faster and more accurate for strongly auditory words. Furthermore, the overall semantic effects are as large for reading aloud as lexical decision and are separable from age-of-acquisition effects. These findings suggest that implicitly directing perceptual attention toward a particular modality facilitates representing modality-specific perceptual information in the meaning of a word, which in turn contributes to the lexical decision or reading-aloud response.

  2. Structural Basis for Receptor Activity-Modifying Protein-Dependent Selective Peptide Recognition by a G Protein-Coupled Receptor

    PubMed Central

    Booe, Jason M.; Walker, Christopher S.; Barwell, James; Kuteyi, Gabriel; Simms, John; Jamaluddin, Muhammad A.; Warner, Margaret L.; Bill, Roslyn M.; Harris, Paul W.; Brimble, Margaret A.; Poyner, David R.; Hay, Debbie L.; Pioszak, Augen A.

    2015-01-01

    Summary Association of receptor activity-modifying proteins (RAMP1-3) with the G protein-coupled receptor (GPCR) calcitonin receptor-like receptor (CLR) enables selective recognition of the peptides calcitonin gene-related peptide (CGRP) and adrenomedullin (AM) that have diverse functions in the cardiovascular and lymphatic systems. How peptides selectively bind GPCR:RAMP complexes is unknown. We report crystal structures of CGRP analog-bound CLR:RAMP1 and AM-bound CLR:RAMP2 extracellular domain heterodimers at 2.5 and 1.8 Å resolutions, respectively. The peptides similarly occupy a shared binding site on CLR with conformations characterized by a β-turn structure near their C termini rather than the α-helical structure common to peptides that bind related GPCRs. The RAMPs augment the binding site with distinct contacts to the variable C-terminal peptide residues and elicit subtly different CLR conformations. The structures and accompanying pharmacology data reveal how a class of accessory membrane proteins modulate ligand binding of a GPCR and may inform drug development targeting CLR:RAMP complexes. PMID:25982113

  3. 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.

  4. Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines.

    PubMed

    Kher, Rahul; Pawar, Tanmay; Thakar, Vishvjit; Shah, Hitesh

    2015-02-01

    The use of wearable recorders for long-term monitoring of physiological parameters has increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five healthy subjects with four body movements or physical activities (PA)-left arm up down, right arm up down, waist twisting and walking-have been recorded using a wearable ECG recorder. The classification of these four PAs has been performed using neuro-fuzzy classifier (NFC) and support vector machines (SVM). The PA classification is based on the distinct, time-frequency features of the extracted motion artifacts contained in recorded A-ECG signals. The motion artifacts in A-ECG signals have been separated first by the discrete wavelet transform (DWT) and the time-frequency features of these motion artifacts have then been extracted using the Gabor transform. The Gabor energy feature vectors have been fed to the NFC and SVM classifiers. Both the classifiers have achieved a PA classification accuracy of over 95% for all subjects. PMID:25641014

  5. 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. PMID:19744941

  6. NLRP3 inflammasome activation downstream of cytoplasmic LPS recognition by both caspase-4 and caspase-5.

    PubMed

    Baker, Paul J; Boucher, Dave; Bierschenk, Damien; Tebartz, Christina; Whitney, Paul G; D'Silva, Damian B; Tanzer, Maria C; Monteleone, Mercedes; Robertson, Avril A B; Cooper, Matthew A; Alvarez-Diaz, Silvia; Herold, Marco J; Bedoui, Sammy; Schroder, Kate; Masters, Seth L

    2015-10-01

    Humans encode two inflammatory caspases that detect cytoplasmic LPS, caspase-4 and caspase-5. When activated, these trigger pyroptotic cell death and caspase-1-dependent IL-1β production; however the mechanism underlying this process is not yet confirmed. We now show that a specific NLRP3 inhibitor, MCC950, prevents caspase-4/5-dependent IL-1β production elicited by transfected LPS. Given that both caspase-4 and caspase-5 can detect cytoplasmic LPS, it is possible that these proteins exhibit some degree of redundancy. Therefore, we generated human monocytic cell lines in which caspase-4 and caspase-5 were genetically deleted either individually or together. We found that the deletion of caspase-4 suppressed cell death and IL-1β production following transfection of LPS into the cytoplasm, or in response to infection with Salmonella typhimurium. Although deletion of caspase-5 did not confer protection against transfected LPS, cell death and IL-1β production were reduced after infection with Salmonella. Furthermore, double deletion of caspase-4 and caspase-5 had a synergistic effect in the context of Salmonella infection. Our results identify the NLRP3 inflammasome as the specific platform for IL-1β maturation, downstream of cytoplasmic LPS detection by caspase-4/5. We also show that both caspase-4 and caspase-5 are functionally important for appropriate responses to intracellular Gram-negative bacteria.

  7. Active Site Sharing and Subterminal Hairpin Recognition in a New Class of DNA Transposases

    SciTech Connect

    Ronning, Donald R.; Guynet, Catherine; Ton-Hoang, Bao; Perez, Zhanita N.; Ghirlando, Rodolfo; Chandler, Michael; Dyda, Fred

    2010-07-20

    Many bacteria harbor simple transposable elements termed insertion sequences (IS). In Helicobacter pylori, the chimeric IS605 family elements are particularly interesting due to their proximity to genes encoding gastric epithelial invasion factors. Protein sequences of IS605 transposases do not bear the hallmarks of other well-characterized transposases. We have solved the crystal structure of full-length transposase (TnpA) of a representative member, ISHp608. Structurally, TnpA does not resemble any characterized transposase; rather, it is related to rolling circle replication (RCR) proteins. Consistent with RCR, Mg{sup 2+} and a conserved tyrosine, Tyr127, are essential for DNA nicking and the formation of a covalent intermediate between TnpA and DNA. TnpA is dimeric, contains two shared active sites, and binds two DNA stem loops representing the conserved inverted repeats near each end of ISHp608. The cocrystal structure with stem-loop DNA illustrates how this family of transposases specifically recognizes and pairs ends, necessary steps during transposition.

  8. 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.

  9. A single-CRD C-type lectin from oyster Crassostrea gigas mediates immune recognition and pathogen elimination with a potential role in the activation of complement system.

    PubMed

    Li, Hui; Zhang, Huan; Jiang, Shuai; Wang, Weilin; Xin, Lusheng; Wang, Hao; Wang, Lingling; Song, Linsheng

    2015-06-01

    C-type lectins (CTLs), serving as pattern recognition receptors (PRRs), are a superfamily of Ca(2+)-dependent carbohydrate-recognition proteins that participate in nonself-recognition and pathogen elimination. In the present study, a single carbohydrate-recognition domain (CRD) CTL was identified from oyster Crassostrea gigas (designated as CgCLec-2). There was only one CRD within the deduced amino acid sequence of CgCLec-2 consisting of 129 amino acid residues. A conserved EPN (Glu246-Pro247-Asn248) motif was found in Ca(2+)-binding site 2 of CgCLec-2. The CgCLec-2 mRNA could be detected in all the examined tissues at different expression levels in oysters. The mRNA expression of CgCLec-2 in hemocytes was up-regulated significantly at 6 h post Vibrio splendidus challenge. The recombinant CgCLec-2 (rCgCLec-2) could bind various Pathogen-Associated Molecular Patterns (PAMPs), including lipopolysaccharide, mannan and peptidoglycan, and displayed strong binding abilities to Vibrio anguillarum, V. splendidus and Yarrowiali polytica and week binding ability to Staphylococcus aureus. It could also enhance the phagocytic activity of oyster hemocytes to V. splendidus and exhibited growth suppression activity against gram-positive bacteria S. aureus but no effect on gram-negative bacteria V. splendidus. Furthermore, the interaction between rCgCLec-2 and rCgMASPL-1 was confirmed by GST Pull down. The results suggested that CgCLec-2 served as not only a PRR in immune recognition but also a regulatory factor in pathogen elimination, and played a potential role in the activation of complement system. PMID:25800112

  10. A single-CRD C-type lectin from oyster Crassostrea gigas mediates immune recognition and pathogen elimination with a potential role in the activation of complement system.

    PubMed

    Li, Hui; Zhang, Huan; Jiang, Shuai; Wang, Weilin; Xin, Lusheng; Wang, Hao; Wang, Lingling; Song, Linsheng

    2015-06-01

    C-type lectins (CTLs), serving as pattern recognition receptors (PRRs), are a superfamily of Ca(2+)-dependent carbohydrate-recognition proteins that participate in nonself-recognition and pathogen elimination. In the present study, a single carbohydrate-recognition domain (CRD) CTL was identified from oyster Crassostrea gigas (designated as CgCLec-2). There was only one CRD within the deduced amino acid sequence of CgCLec-2 consisting of 129 amino acid residues. A conserved EPN (Glu246-Pro247-Asn248) motif was found in Ca(2+)-binding site 2 of CgCLec-2. The CgCLec-2 mRNA could be detected in all the examined tissues at different expression levels in oysters. The mRNA expression of CgCLec-2 in hemocytes was up-regulated significantly at 6 h post Vibrio splendidus challenge. The recombinant CgCLec-2 (rCgCLec-2) could bind various Pathogen-Associated Molecular Patterns (PAMPs), including lipopolysaccharide, mannan and peptidoglycan, and displayed strong binding abilities to Vibrio anguillarum, V. splendidus and Yarrowiali polytica and week binding ability to Staphylococcus aureus. It could also enhance the phagocytic activity of oyster hemocytes to V. splendidus and exhibited growth suppression activity against gram-positive bacteria S. aureus but no effect on gram-negative bacteria V. splendidus. Furthermore, the interaction between rCgCLec-2 and rCgMASPL-1 was confirmed by GST Pull down. The results suggested that CgCLec-2 served as not only a PRR in immune recognition but also a regulatory factor in pathogen elimination, and played a potential role in the activation of complement system.