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Sample records for activity recognition system

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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

  8. 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

  9. 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.

  10. 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

  11. 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.

  12. 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

  13. 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

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. Speech recognition system for an automotive vehicle

    SciTech Connect

    Noso, K.; Futami, T.

    1987-01-13

    A speech recognition system is described for an automotive vehicle for activating vehicle actuators in response to predetermined spoken instructions supplied to the system via a microphone, which comprises: (a) a manually controlled record switch for deriving a record signal when activated; (b) a manually controlled recognition switch for deriving a recognition signal when activated; (c) a speech recognizer for sequentially recording reference spoken instructions whenever one reference spoken instruction is supplied to the system through the microphone while the record switch is activated, a memory having a storage area for each spoken instruction, and means for shifting access to each storage area for each spoken instruction has been recorded in the storage area provided therefore. A means is included for activating vehicle actuators sequentially whenever one recognition spoken instruction is supplied to the system via the microphone while the recognition switch is activated and when the spoken instruction to be recognized is similar to the reference spoken instruction; and (d) means for deriving skip instruction signal and for coupling the skip instruction signal to the speech recognizer to shift access from a currently accessed storage area for recording a current reference spoken instruction to a succeeding storage area for recording a succeeding reference spoken instruction even when the current reference spoken instruction is not supplied to the system through the microphone.

  19. 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.

  20. 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.

  1. An audiovisual emotion recognition system

    NASA Astrophysics Data System (ADS)

    Han, Yi; Wang, Guoyin; Yang, Yong; He, Kun

    2007-12-01

    Human emotions could be expressed by many bio-symbols. Speech and facial expression are two of them. They are both regarded as emotional information which is playing an important role in human-computer interaction. Based on our previous studies on emotion recognition, an audiovisual emotion recognition system is developed and represented in this paper. The system is designed for real-time practice, and is guaranteed by some integrated modules. These modules include speech enhancement for eliminating noises, rapid face detection for locating face from background image, example based shape learning for facial feature alignment, and optical flow based tracking algorithm for facial feature tracking. It is known that irrelevant features and high dimensionality of the data can hurt the performance of classifier. Rough set-based feature selection is a good method for dimension reduction. So 13 speech features out of 37 ones and 10 facial features out of 33 ones are selected to represent emotional information, and 52 audiovisual features are selected due to the synchronization when speech and video fused together. The experiment results have demonstrated that this system performs well in real-time practice and has high recognition rate. Our results also show that the work in multimodules fused recognition will become the trend of emotion recognition in the future.

  2. The Army word recognition system

    NASA Technical Reports Server (NTRS)

    Hadden, David R.; Haratz, David

    1977-01-01

    The application of speech recognition technology in the Army command and control area is presented. The problems associated with this program are described as well as as its relevance in terms of the man/machine interactions, voice inflexions, and the amount of training needed to interact with and utilize the automated system.

  3. 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.

  4. Humoral pattern recognition and the complement system.

    PubMed

    Degn, S E; Thiel, S

    2013-08-01

    In the context of immunity, pattern recognition is the art of discriminating friend from foe and innocuous from noxious. The basis of discrimination is the existence of evolutionarily conserved patterns on microorganisms, which are intrinsic to these microorganisms and necessary for their function and existence. Such immutable or slowly evolving patterns are ideal handles for recognition and have been targeted by early cellular immune defence mechanisms such as Toll-like receptors, NOD-like receptors, RIG-I-like receptors, C-type lectin receptors and by humoral defence mechanisms such as the complement system. Complement is a proteolytic cascade system comprising around 35 different soluble and membrane-bound proteins. It constitutes a central part of the innate immune system, mediating several major innate effector functions and modulating adaptive immune responses. The complement cascade proceeds via controlled, limited proteolysis and conformational changes of constituent proteins through three activation pathways: the classical pathway, the alternative pathway and the lectin pathway, which converge in common effector functions. Here, we review the nature of the pattern recognition molecules involved in complement activation, as well as their close relatives with no or unknown capacity for activating complement. We proceed to examine the composition of the pattern recognition complexes involved in complement activation, focusing on those of the lectin pathway, and arrive at a new model for their mechanism of operation, supported by recently emerging evidence.

  5. 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

  6. 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.

  7. 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.

  8. 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

  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.

  10. 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.

  11. 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

  12. 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.

  13. 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

  14. 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

  15. 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

  16. 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

  17. Practical automatic Arabic license plate recognition system

    NASA Astrophysics Data System (ADS)

    Mohammad, Khader; Agaian, Sos; Saleh, Hani

    2011-02-01

    Since 1970's, the need of an automatic license plate recognition system, sometimes referred as Automatic License Plate Recognition system, has been increasing. A license plate recognition system is an automatic system that is able to recognize a license plate number, extracted from image sensors. In specific, Automatic License Plate Recognition systems are being used in conjunction with various transportation systems in application areas such as law enforcement (e.g. speed limit enforcement) and commercial usages such as parking enforcement and automatic toll payment private and public entrances, border control, theft and vandalism control. Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. [License plate detection using cluster run length smoothing algorithm ].Generally, an automatic license plate localization and recognition system is made up of three modules; license plate localization, character segmentation and optical character recognition modules. This paper presents an Arabic license plate recognition system that is insensitive to character size, font, shape and orientation with extremely high accuracy rate. The proposed system is based on a combination of enhancement, license plate localization, morphological processing, and feature vector extraction using the Haar transform. The performance of the system is fast due to classification of alphabet and numerals based on the license plate organization. Experimental results for license plates of two different Arab countries show an average of 99 % successful license plate localization and recognition in a total of more than 20 different images captured from a complex outdoor environment. The results run times takes less time compared to conventional and many states of art methods.

  18. Toward the ultimate synthesis/recognition system.

    PubMed Central

    Furui, S

    1995-01-01

    This paper predicts speech synthesis, speech recognition, and speaker recognition technology for the year 2001, and it describes the most important research problems to be solved in order to arrive at these ultimate synthesis and recognition systems. The problems for speech synthesis include natural and intelligible voice production, prosody control based on meaning, capability of controlling synthesized voice quality and choosing individual speaking style, multilingual and multidialectal synthesis, choice of application-oriented speaking styles, capability of adding emotion, and synthesis from concepts. The problems for speech recognition include robust recognition against speech variations, adaptation/normalization to variations due to environmental conditions and speakers, automatic knowledge acquisition for acoustic and linguistic modeling, spontaneous speech recognition, naturalness and ease of human-machine interaction, and recognition of emotion. The problems for speaker recognition are similar to those for speech recognition. The research topics related to all these techniques include the use of articulatory and perceptual constraints and evaluation methods for measuring the quality of technology and systems. Images Fig. 3 PMID:7479723

  19. 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.

  20. 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

  1. Bipartite recognition of target RNAs activates DNA cleavage by the Type III-B CRISPR–Cas system

    PubMed Central

    Elmore, Joshua R.; Sheppard, Nolan F.; Ramia, Nancy; Deighan, Trace; Li, Hong; Terns, Rebecca M.; Terns, Michael P.

    2016-01-01

    CRISPR–Cas systems eliminate nucleic acid invaders in bacteria and archaea. The effector complex of the Type III-B Cmr system cleaves invader RNAs recognized by the CRISPR RNA (crRNA ) of the complex. Here we show that invader RNAs also activate the Cmr complex to cleave DNA. As has been observed for other Type III systems, Cmr eliminates plasmid invaders in Pyrococcus furiosus by a mechanism that depends on transcription of the crRNA target sequence within the plasmid. Notably, we found that the target RNA per se induces DNA cleavage by the Cmr complex in vitro. DNA cleavage activity does not depend on cleavage of the target RNA but notably does require the presence of a short sequence adjacent to the target sequence within the activating target RNA (rPAM [RNA protospacer-adjacent motif]). The activated complex does not require a target sequence (or a PAM) in the DNA substrate. Plasmid elimination by the P. furiosus Cmr system also does not require the Csx1 (CRISPR-associated Rossman fold [CARF] superfamily) protein. Plasmid silencing depends on the HD nuclease and Palm domains of the Cmr2 (Cas10 superfamily) protein. The results establish the Cmr complex as a novel DNA nuclease activated by invader RNAs containing a crRNA target sequence and a rPAM. PMID:26848045

  2. 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

  3. 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

  4. 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

  5. Human body contour data based activity recognition.

    PubMed

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

    2013-01-01

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

  6. Automatic TLI recognition system, general description

    SciTech Connect

    Lassahn, G.D.

    1997-02-01

    This report is a general description of an automatic target recognition system developed at the Idaho National Engineering Laboratory for the Department of Energy. A user`s manual is a separate volume, Automatic TLI Recognition System, User`s Guide, and a programmer`s manual is Automatic TLI Recognition System, Programmer`s Guide. This system was designed as an automatic target recognition system for fast screening of large amounts of multi-sensor image data, based on low-cost parallel processors. This system naturally incorporates image data fusion, and it gives uncertainty estimates. It is relatively low cost, compact, and transportable. The software is easily enhanced to expand the system`s capabilities, and the hardware is easily expandable to increase the system`s speed. In addition to its primary function as a trainable target recognition system, this is also a versatile, general-purpose tool for image manipulation and analysis, which can be either keyboard-driven or script-driven. This report includes descriptions of three variants of the computer hardware, a description of the mathematical basis if the training process, and a description with examples of the system capabilities.

  7. 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.

  8. Recognition of metal cations by biological systems.

    PubMed

    Truter, M R

    1975-11-01

    Recognition of metal cations by biological systems can be compared with the geochemical criteria for isomorphous replacement. Biological systems are more highly selective and much more rapid. Methods of maintaining an optimum concentration, including storage and transfer for the essential trace elements, copper and iron, used in some organisms are in part reproducible by coordination chemists while other features have not been reporduced in models. Poisoning can result from a foreign metal taking part in a reaction irreversibly so that the recognition site or molecule is not released. For major nutrients, sodium, potassium, magnesium and calcium, there are similarities to the trace metals in selective uptake but differences qualitatively and quantitatively in biological activity. Compounds selective for potassium replace all the solvation sphere with a symmetrical arrangement of oxygen atoms; those selective for sodium give an asymmetrical environment with retention of a solvent molecule. Experiments with naturally occurring antibiotics and synthetic model compounds have shown that flexibility is an important feature of selectivity and that for transfer or carrier properties there is an optimum (as opposed to a maximum) metal-ligand stability constant. Thallium is taken up instead of potassium and will activate some enzymes; it is suggested that the poisonous characteristics arise because the thallium ion may bind more strongly than potassium to part of a site and then fail to bind additional atoms as required for the biological activity. Criteria for the design of selective complexing agents are given with indications of those which might transfer more than one metal at once. PMID:1815

  9. 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.

  10. Automatic TLI recognition system beta prototype testing

    SciTech Connect

    Lassahn, G.D.

    1996-06-01

    This report describes the beta prototype automatic target recognition system ATR3, and some performance tests done with this system. This is a fully operational system, with a high computational speed. It is useful for findings any kind of target in digitized image data, and as a general purpose image analysis tool.

  11. The ubiquitin-editing protein A20 prevents dendritic cell activation, recognition of apoptotic cells, and systemic autoimmunity.

    PubMed

    Kool, Mirjam; van Loo, Geert; Waelput, Wim; De Prijck, Sofie; Muskens, Femke; Sze, Mozes; van Praet, Jens; Branco-Madeira, Filipe; Janssens, Sophie; Reizis, Boris; Elewaut, Dirk; Beyaert, Rudi; Hammad, Hamida; Lambrecht, Bart N

    2011-07-22

    Dendritic cells (DCs) regulate both immunity and tolerance. Here we have shown that the ubiquitin editing enzyme A20 (Tnfaip3) determines the activation threshold of DCs, via control of canonical NF-κB activation. Tnfaip3(fl/fl)Cd11c-cre(+) mice lacking A20 in DCs demonstrated spontaneous proliferation of conventional and double-negative T cells, their conversion to interferon-γ (IFN-γ)-producing effector cells, and expansion of plasma cells. They developed ds-DNA antibodies, nephritis, the antiphospholipid syndrome, and lymphosplenomegaly-features of systemic lupus erythematosus-and extramedullary hematopoiesis. A20-deficient DCs were resistant to apoptosis, caused by increased sensitivity to CD40L and RANKL prosurvival signals and upregulation of antiapoptotic proteins Bcl-2 and Bcl-x. They captured injected apoptotic cells more efficiently, resisted the inhibitory effects of apoptotic cells, and induced self-reactive effector lymphocytes. Because genetic polymorphisms in TNFAIP3 are associated with human autoimmune disorders, these findings identify A20-mediated control of DC activation as a crucial checkpoint in the development of systemic autoimmunity.

  12. License Plate Recognition System for Indian Vehicles

    NASA Astrophysics Data System (ADS)

    Sanap, P. R.; Narote, S. P.

    2010-11-01

    We consider the task of recognition of Indian vehicle number plates (also called license plates or registration plates in other countries). A system for Indian number plate recognition must cope with wide variations in the appearance of the plates. Each state uses its own range of designs with font variations between the designs. Also, vehicle owners may place the plates inside glass covered frames or use plates made of nonstandard materials. These issues compound the complexity of automatic number plate recognition, making existing approaches inadequate. We have developed a system that incorporates a novel combination of image processing and artificial neural network technologies to successfully locate and read Indian vehicle number plates in digital images. Commercial application of the system is envisaged.

  13. A Survey on Automatic Speaker Recognition Systems

    NASA Astrophysics Data System (ADS)

    Saquib, Zia; Salam, Nirmala; Nair, Rekha P.; Pandey, Nipun; Joshi, Akanksha

    Human listeners are capable of identifying a speaker, over the telephone or an entryway out of sight, by listening to the voice of the speaker. Achieving this intrinsic human specific capability is a major challenge for Voice Biometrics. Like human listeners, voice biometrics uses the features of a person's voice to ascertain the speaker's identity. The best-known commercialized forms of voice Biometrics is Speaker Recognition System (SRS). Speaker recognition is the computing task of validating a user's claimed identity using characteristics extracted from their voices. This literature survey paper gives brief introduction on SRS, and then discusses general architecture of SRS, biometric standards relevant to voice/speech, typical applications of SRS, and current research in Speaker Recognition Systems. We have also surveyed various approaches for SRS.

  14. Automatic stereoscopic system for person recognition

    NASA Astrophysics Data System (ADS)

    Murynin, Alexander B.; Matveev, Ivan A.; Kuznetsov, Victor D.

    1999-06-01

    A biometric access control system based on identification of human face is presented. The system developed performs remote measurements of the necessary face features. Two different scenarios of the system behavior are implemented. The first one assumes the verification of personal data entered by visitor from console using keyboard or card reader. The system functions as an automatic checkpoint, that strictly controls access of different visitors. The other scenario makes it possible to identify visitors without any person identifier or pass. Only person biometrics are used to identify the visitor. The recognition system automatically finds necessary identification information preliminary stored in the database. Two laboratory models of recognition system were developed. The models are designed to use different information types and sources. In addition to stereoscopic images inputted to computer from cameras the models can use voice data and some person physical characteristics such as person's height, measured by imaging system.

  15. Image enhancement method for fingerprint recognition system.

    PubMed

    Li, Shunshan; Wei, Min; Tang, Haiying; Zhuang, Tiange; Buonocore, Michael

    2005-01-01

    Image enhancement plays an important role in Fingerprint Recognition System. In this paper fingerprint image enhancement method, a refined Gabor filter, is presented. This enhancement method can connect the ridge breaks, ensures the maximal gray values located at the ridge center and has the ability to compensate for the nonlinear deformations. The result shows it can improve the performance of image enhancement.

  16. Learning and plan refinement in a knowledge-based system for automatic speech recognition

    SciTech Connect

    De Mori, R.; Lam, L.; Gilloux, M.

    1987-03-01

    This paper shows how a semiautomatic design of a speech recognition system can be done as a planning activity. Recognition performances are used for deciding plan refinement. Inductive learning is performed for setting action preconditions. Experimental results in the recognition of connected letters spoken by 100 speakers are presented.

  17. Laptop Computer - Based Facial Recognition System Assessment

    SciTech Connect

    R. A. Cain; G. B. Singleton

    2001-03-01

    The objective of this project was to assess the performance of the leading commercial-off-the-shelf (COTS) facial recognition software package when used as a laptop application. We performed the assessment to determine the system's usefulness for enrolling facial images in a database from remote locations and conducting real-time searches against a database of previously enrolled images. The assessment involved creating a database of 40 images and conducting 2 series of tests to determine the product's ability to recognize and match subject faces under varying conditions. This report describes the test results and includes a description of the factors affecting the results. After an extensive market survey, we selected Visionics' FaceIt{reg_sign} software package for evaluation and a review of the Facial Recognition Vendor Test 2000 (FRVT 2000). This test was co-sponsored by the US Department of Defense (DOD) Counterdrug Technology Development Program Office, the National Institute of Justice, and the Defense Advanced Research Projects Agency (DARPA). Administered in May-June 2000, the FRVT 2000 assessed the capabilities of facial recognition systems that were currently available for purchase on the US market. Our selection of this Visionics product does not indicate that it is the ''best'' facial recognition software package for all uses. It was the most appropriate package based on the specific applications and requirements for this specific application. In this assessment, the system configuration was evaluated for effectiveness in identifying individuals by searching for facial images captured from video displays against those stored in a facial image database. An additional criterion was that the system be capable of operating discretely. For this application, an operational facial recognition system would consist of one central computer hosting the master image database with multiple standalone systems configured with duplicates of the master operating in

  18. A neuromorphic system for video object recognition.

    PubMed

    Khosla, Deepak; Chen, Yang; Kim, Kyungnam

    2014-01-01

    Automated video object recognition is a topic of emerging importance in both defense and civilian applications. This work describes an accurate and low-power neuromorphic architecture and system for real-time automated video object recognition. Our system, Neuormorphic Visual Understanding of Scenes (NEOVUS), is inspired by computational neuroscience models of feed-forward object detection and classification pipelines for processing visual data. The NEOVUS architecture is inspired by the ventral (what) and dorsal (where) streams of the mammalian visual pathway and integrates retinal processing, object detection based on form and motion modeling, and object classification based on convolutional neural networks. The object recognition performance and energy use of the NEOVUS was evaluated by the Defense Advanced Research Projects Agency (DARPA) under the Neovision2 program using three urban area video datasets collected from a mix of stationary and moving platforms. These datasets are challenging and include a large number of objects of different types in cluttered scenes, with varying illumination and occlusion conditions. In a systematic evaluation of five different teams by DARPA on these datasets, the NEOVUS demonstrated the best performance with high object recognition accuracy and the lowest energy consumption. Its energy use was three orders of magnitude lower than two independent state of the art baseline computer vision systems. The dynamic power requirement for the complete system mapped to commercial off-the-shelf (COTS) hardware that includes a 5.6 Megapixel color camera processed by object detection and classification algorithms at 30 frames per second was measured at 21.7 Watts (W), for an effective energy consumption of 5.45 nanoJoules (nJ) per bit of incoming video. These unprecedented results show that the NEOVUS has the potential to revolutionize automated video object recognition toward enabling practical low-power and mobile video processing

  19. A neuromorphic system for video object recognition

    PubMed Central

    Khosla, Deepak; Chen, Yang; Kim, Kyungnam

    2014-01-01

    Automated video object recognition is a topic of emerging importance in both defense and civilian applications. This work describes an accurate and low-power neuromorphic architecture and system for real-time automated video object recognition. Our system, Neuormorphic Visual Understanding of Scenes (NEOVUS), is inspired by computational neuroscience models of feed-forward object detection and classification pipelines for processing visual data. The NEOVUS architecture is inspired by the ventral (what) and dorsal (where) streams of the mammalian visual pathway and integrates retinal processing, object detection based on form and motion modeling, and object classification based on convolutional neural networks. The object recognition performance and energy use of the NEOVUS was evaluated by the Defense Advanced Research Projects Agency (DARPA) under the Neovision2 program using three urban area video datasets collected from a mix of stationary and moving platforms. These datasets are challenging and include a large number of objects of different types in cluttered scenes, with varying illumination and occlusion conditions. In a systematic evaluation of five different teams by DARPA on these datasets, the NEOVUS demonstrated the best performance with high object recognition accuracy and the lowest energy consumption. Its energy use was three orders of magnitude lower than two independent state of the art baseline computer vision systems. The dynamic power requirement for the complete system mapped to commercial off-the-shelf (COTS) hardware that includes a 5.6 Megapixel color camera processed by object detection and classification algorithms at 30 frames per second was measured at 21.7 Watts (W), for an effective energy consumption of 5.45 nanoJoules (nJ) per bit of incoming video. These unprecedented results show that the NEOVUS has the potential to revolutionize automated video object recognition toward enabling practical low-power and mobile video processing

  20. A neuromorphic system for video object recognition.

    PubMed

    Khosla, Deepak; Chen, Yang; Kim, Kyungnam

    2014-01-01

    Automated video object recognition is a topic of emerging importance in both defense and civilian applications. This work describes an accurate and low-power neuromorphic architecture and system for real-time automated video object recognition. Our system, Neuormorphic Visual Understanding of Scenes (NEOVUS), is inspired by computational neuroscience models of feed-forward object detection and classification pipelines for processing visual data. The NEOVUS architecture is inspired by the ventral (what) and dorsal (where) streams of the mammalian visual pathway and integrates retinal processing, object detection based on form and motion modeling, and object classification based on convolutional neural networks. The object recognition performance and energy use of the NEOVUS was evaluated by the Defense Advanced Research Projects Agency (DARPA) under the Neovision2 program using three urban area video datasets collected from a mix of stationary and moving platforms. These datasets are challenging and include a large number of objects of different types in cluttered scenes, with varying illumination and occlusion conditions. In a systematic evaluation of five different teams by DARPA on these datasets, the NEOVUS demonstrated the best performance with high object recognition accuracy and the lowest energy consumption. Its energy use was three orders of magnitude lower than two independent state of the art baseline computer vision systems. The dynamic power requirement for the complete system mapped to commercial off-the-shelf (COTS) hardware that includes a 5.6 Megapixel color camera processed by object detection and classification algorithms at 30 frames per second was measured at 21.7 Watts (W), for an effective energy consumption of 5.45 nanoJoules (nJ) per bit of incoming video. These unprecedented results show that the NEOVUS has the potential to revolutionize automated video object recognition toward enabling practical low-power and mobile video processing

  1. Securing iris recognition systems against masquerade attacks

    NASA Astrophysics Data System (ADS)

    Galbally, Javier; Gomez-Barrero, Marta; Ross, Arun; Fierrez, Julian; Ortega-Garcia, Javier

    2013-05-01

    A novel two-stage protection scheme for automatic iris recognition systems against masquerade attacks carried out with synthetically reconstructed iris images is presented. The method uses different characteristics of real iris images to differentiate them from the synthetic ones, thereby addressing important security flaws detected in state-of-the-art commercial systems. Experiments are carried out on the publicly available Biosecure Database and demonstrate the efficacy of the proposed security enhancing approach.

  2. Recognition of bacterial plant pathogens: local, systemic and transgenerational immunity.

    PubMed

    Henry, Elizabeth; Yadeta, Koste A; Coaker, Gitta

    2013-09-01

    Bacterial pathogens can cause multiple plant diseases and plants rely on their innate immune system to recognize and actively respond to these microbes. The plant innate immune system comprises extracellular pattern recognition receptors that recognize conserved microbial patterns and intracellular nucleotide binding leucine-rich repeat (NLR) proteins that recognize specific bacterial effectors delivered into host cells. Plants lack the adaptive immune branch present in animals, but still afford flexibility to pathogen attack through systemic and transgenerational resistance. Here, we focus on current research in plant immune responses against bacterial pathogens. Recent studies shed light onto the activation and inactivation of pattern recognition receptors and systemic acquired resistance. New research has also uncovered additional layers of complexity surrounding NLR immune receptor activation, cooperation and sub-cellular localizations. Taken together, these recent advances bring us closer to understanding the web of molecular interactions responsible for coordinating defense responses and ultimately resistance.

  3. Phoneme fuzzy characterization in speech recognition systems

    NASA Astrophysics Data System (ADS)

    Beritelli, Francesco; Borrometi, Luca; Cuce, Antonino

    1997-10-01

    The acoustic approach to speech recognition has an important advantage compared with pattern recognition approach: it presents a lower complexity because it doesn't require explicit structures such as the hidden Markov model. In this work, we show how to characterize some phonetic classes of the Italian language in order to obtain a speaker and vocabulary independent speech recognition system. A phonetic data base is carried out with 200 continuous speech sentences of 12 speakers, 6 females and 6 males. The sentences are sampled at 8000 Hz and manual labelled with Asystem Sound Impression Software to obtain about 1600 units. We analyzed several speech parameters such as formants, LPC and reflection coefficients, energy, normal/differential zero crossing rate, cepstral and autocorrelation coefficients. The aim is the achievement of a phonetic recognizer to facilitate the so- called lexical access problem, that is to decode phonetic units into complete sense word strings. The knowledge is supplied to the recognizer in terms of fuzzy systems. The utilized software is called adaptive fuzzy modeler and it belongs to the rule generator family. A procedure has been implemented to integrate in the fuzzy system an 'expert' knowledge in order to obtain significant improvements in the recognition accuracy. Up to this point the tests show a recognition rate of 92% for the vocal class, 89% for the fricatives class and 94% for the nasal class, utilizing 1000 phonemes in phase of learning and 600 phonemes in phase of testing. Our intention is to complete the fuzzy recognizer extending this work to the other phonetic classes.

  4. Intelligent recognitive systems in nanomedicine.

    PubMed

    Culver, Heidi; Daily, Adam; Khademhosseini, Ali; Peppas, Nicholas

    2014-05-01

    There is a bright future in the development and utilization of nanoscale systems based on intelligent materials that can respond to external input providing a beneficial function. Specific functional groups can be incorporated into polymers to make them responsive to environmental stimuli such as pH, temperature, or varying concentrations of biomolecules. The fusion of such "intelligent" biomaterials with nanotechnology has led to the development of powerful therapeutic and diagnostic platforms. For example, targeted release of proteins and chemotherapeutic drugs has been achieved using pH-responsive nanocarriers while biosensors with ultra-trace detection limits are being made using nanoscale, molecularly imprinted polymers. The efficacy of therapeutics and the sensitivity of diagnostic platforms will continue to progress as unique combinations of responsive polymers and nanomaterials emerge.

  5. Intelligent recognitive systems in nanomedicine

    PubMed Central

    Culver, Heidi; Daily, Adam; Khademhosseini, Ali

    2014-01-01

    There is a bright future in the development and utilization of nanoscale systems based on intelligent materials that can respond to external input providing a beneficial function. Specific functional groups can be incorporated into polymers to make them responsive to environmental stimuli such as pH, temperature, or varying concentrations of biomolecules. The fusion of such “intelligent” biomaterials with nanotechnology has led to the development of powerful therapeutic and diagnostic platforms. For example, targeted release of proteins and chemotherapeutic drugs has been achieved using pH-responsive nanocarriers while biosensors with ultra-trace detection limits are being made using nanoscale, molecularly imprinted polymers. The efficacy of therapeutics and the sensitivity of diagnostic platforms will continue to progress as unique combinations of responsive polymers and nanomaterials emerge. PMID:24860724

  6. 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.

  7. 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

  8. 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

  9. Automatic TLI recognition system, user`s guide

    SciTech Connect

    Lassahn, G.D.

    1997-02-01

    This report describes how to use an automatic target recognition system (version 14). In separate volumes are a general description of the ATR system, Automatic TLI Recognition System, General Description, and a programmer`s manual, Automatic TLI Recognition System, Programmer`s Guide.

  10. 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

  11. 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

  12. 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

  13. Improving robustness of speech recognition systems

    NASA Astrophysics Data System (ADS)

    Mitra, Vikramjit

    2010-11-01

    Current Automatic Speech Recognition (ASR) systems fail to perform nearly as good as human speech recognition performance due to their lack of robustness against speech variability and noise contamination. The goal of this dissertation is to investigate these critical robustness issues, put forth different ways to address them and finally present an ASR architecture based upon these robustness criteria. Acoustic variations adversely affect the performance of current phone-based ASR systems, in which speech is modeled as 'beads-on-a-string', where the beads are the individual phone units. While phone units are distinctive in cognitive domain, they are varying in the physical domain and their variation occurs due to a combination of factors including speech style, speaking rate etc.; a phenomenon commonly known as 'coarticulation'. Traditional ASR systems address such coarticulatory variations by using contextualized phone-units such as triphones. Articulatory phonology accounts for coarticulatory variations by modeling speech as a constellation of constricting actions known as articulatory gestures. In such a framework, speech variations such as coarticulation and lenition are accounted for by gestural overlap in time and gestural reduction in space. To realize a gesture-based ASR system, articulatory gestures have to be inferred from the acoustic signal. At the initial stage of this research an initial study was performed using synthetically generated speech to obtain a proof-of-concept that articulatory gestures can indeed be recognized from the speech signal. It was observed that having vocal tract constriction trajectories (TVs) as intermediate representation facilitated the gesture recognition task from the speech signal. Presently no natural speech database contains articulatory gesture annotation; hence an automated iterative time-warping architecture is proposed that can annotate any natural speech database with articulatory gestures and TVs. Two natural

  14. Privacy protection schemes for fingerprint recognition systems

    NASA Astrophysics Data System (ADS)

    Marasco, Emanuela; Cukic, Bojan

    2015-05-01

    The deployment of fingerprint recognition systems has always raised concerns related to personal privacy. A fingerprint is permanently associated with an individual and, generally, it cannot be reset if compromised in one application. Given that fingerprints are not a secret, potential misuses besides personal recognition represent privacy threats and may lead to public distrust. Privacy mechanisms control access to personal information and limit the likelihood of intrusions. In this paper, image- and feature-level schemes for privacy protection in fingerprint recognition systems are reviewed. Storing only key features of a biometric signature can reduce the likelihood of biometric data being used for unintended purposes. In biometric cryptosystems and biometric-based key release, the biometric component verifies the identity of the user, while the cryptographic key protects the communication channel. Transformation-based approaches only a transformed version of the original biometric signature is stored. Different applications can use different transforms. Matching is performed in the transformed domain which enable the preservation of low error rates. Since such templates do not reveal information about individuals, they are referred to as cancelable templates. A compromised template can be re-issued using a different transform. At image-level, de-identification schemes can remove identifiers disclosed for objectives unrelated to the original purpose, while permitting other authorized uses of personal information. Fingerprint images can be de-identified by, for example, mixing fingerprints or removing gender signature. In both cases, degradation of matching performance is minimized.

  15. 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

  16. 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

  17. 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.

  18. Intelligent Facial Recognition Systems: Technology advancements for security applications

    SciTech Connect

    Beer, C.L.

    1993-07-01

    Insider problems such as theft and sabotage can occur within the security and surveillance realm of operations when unauthorized people obtain access to sensitive areas. A possible solution to these problems is a means to identify individuals (not just credentials or badges) in a given sensitive area and provide full time personnel accountability. One approach desirable at Department of Energy facilities for access control and/or personnel identification is an Intelligent Facial Recognition System (IFRS) that is non-invasive to personnel. Automatic facial recognition does not require the active participation of the enrolled subjects, unlike most other biological measurement (biometric) systems (e.g., fingerprint, hand geometry, or eye retinal scan systems). It is this feature that makes an IFRS attractive for applications other than access control such as emergency evacuation verification, screening, and personnel tracking. This paper discusses current technology that shows promising results for DOE and other security applications. A survey of research and development in facial recognition identified several companies and universities that were interested and/or involved in the area. A few advanced prototype systems were also identified. Sandia National Laboratories is currently evaluating facial recognition systems that are in the advanced prototype stage. The initial application for the evaluation is access control in a controlled environment with a constant background and with cooperative subjects. Further evaluations will be conducted in a less controlled environment, which may include a cluttered background and subjects that are not looking towards the camera. The outcome of the evaluations will help identify areas of facial recognition systems that need further development and will help to determine the effectiveness of the current systems for security applications.

  19. 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.

  20. Preliminary design of a terrain recognition system.

    PubMed

    Zhang, Fan; Fang, Zheng; Liu, Ming; Huang, He

    2011-01-01

    This paper aims to design a wearable terrain recognition system, which might assist the control of powered artificial prosthetic legs. A laser distance sensor and inertial measurement unit (TMU) sensors were mounted on human body. These sensors were used to identify the movement state of the user, reconstruct the geometry of the terrain in front of the user while walking, and recognize the type of terrain before the user stepped on it. Different sensor configurations were investigated and compared. The designed system was evaluated on one healthy human subject when walking on an obstacle course in the laboratory environment. The results showed that the reconstructed terrain height demonstrated clearer pattern difference among studied terrains when the laser was placed on the waist than that when the laser was mounted on the shank. The designed system with the laser on the waist accurately recognized 157 out of 160 tested terrain transitions, 300 ms-2870 ms before the user switched the negotiated terrains. These promising results demonstrated the potential application of the designed terrain recognition system to further improve the control of powered artificial legs.

  1. Edge detection techniques for iris recognition system

    NASA Astrophysics Data System (ADS)

    Tania, U. T.; Motakabber, S. M. A.; Ibrahimy, M. I.

    2013-12-01

    Nowadays security and authentication are the major parts of our daily life. Iris is one of the most reliable organ or part of human body which can be used for identification and authentication purpose. To develop an iris authentication algorithm for personal identification, this paper examines two edge detection techniques for iris recognition system. Between the Sobel and the Canny edge detection techniques, the experimental result shows that the Canny's technique has better ability to detect points in a digital image where image gray level changes even at slow rate.

  2. 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

  3. 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.

  4. Method and System for Object Recognition Search

    NASA Technical Reports Server (NTRS)

    Duong, Tuan A. (Inventor); Duong, Vu A. (Inventor); Stubberud, Allen R. (Inventor)

    2012-01-01

    A method for object recognition using shape and color features of the object to be recognized. An adaptive architecture is used to recognize and adapt the shape and color features for moving objects to enable object recognition.

  5. Recognition of error symptoms in large systems

    NASA Technical Reports Server (NTRS)

    Iyer, Ravishankar K.; Sridhar, V.

    1987-01-01

    A methodology for automatically detecting symptoms of frequently occurring errors in large computer systems is developed. The proposed symptom recognition methodology and its validation are based on probabilistic techniques. The technique is shown to work on real failure data from two CYBER systems at the University of Illinois. The methodology allows for the resolution between independent and dependent causes and, also quantifies a measure of the strength of relationship among errors. Comparison made with failure/repair information obtained from field maintenance engineers shows that in 85% of the cases, the error symptoms recognized by our approach correspond to real system problems. Further, the remaining 15% although not directly supported by field data, were confirmed as valid problems. Some of these were shown to be persistent problems which otherwise would have been considered as minor transients and hence ignored.

  6. Evaluation of a voice recognition system for the MOTAS pseudo pilot station function

    NASA Technical Reports Server (NTRS)

    Houck, J. A.

    1982-01-01

    The Langley Research Center has undertaken a technology development activity to provide a capability, the mission oriented terminal area simulation (MOTAS), wherein terminal area and aircraft systems studies can be performed. An experiment was conducted to evaluate state-of-the-art voice recognition technology and specifically, the Threshold 600 voice recognition system to serve as an aircraft control input device for the MOTAS pseudo pilot station function. The results of the experiment using ten subjects showed a recognition error of 3.67 percent for a 48-word vocabulary tested against a programmed vocabulary of 103 words. After the ten subjects retrained the Threshold 600 system for the words which were misrecognized or rejected, the recognition error decreased to 1.96 percent. The rejection rates for both cases were less than 0.70 percent. Based on the results of the experiment, voice recognition technology and specifically the Threshold 600 voice recognition system were chosen to fulfill this MOTAS function.

  7. Eardrum-inspired active sensors for self-powered cardiovascular system characterization and throat-attached anti-interference voice recognition.

    PubMed

    Yang, Jin; Chen, Jun; Su, Yuanjie; Jing, Qingshen; Li, Zhaoling; Yi, Fang; Wen, Xiaonan; Wang, Zhaona; Wang, Zhong Lin

    2015-02-25

    The first bionic membrane sensor based on triboelectrification is reported for self-powered physiological and behavioral measurements such as local internal body pressures for non-invasive human health assessment. The sensor can also be for self-powered anti-interference throat voice recording and recognition, as well as high-accuracy multimodal biometric authentication, thus potentially expanding the scope of applications in self-powered wearable medical/health monitoring, interactive input/control devices as well as accurate, reliable, and less intrusive biometric authentication systems. PMID:25640534

  8. Eardrum-inspired active sensors for self-powered cardiovascular system characterization and throat-attached anti-interference voice recognition.

    PubMed

    Yang, Jin; Chen, Jun; Su, Yuanjie; Jing, Qingshen; Li, Zhaoling; Yi, Fang; Wen, Xiaonan; Wang, Zhaona; Wang, Zhong Lin

    2015-02-25

    The first bionic membrane sensor based on triboelectrification is reported for self-powered physiological and behavioral measurements such as local internal body pressures for non-invasive human health assessment. The sensor can also be for self-powered anti-interference throat voice recording and recognition, as well as high-accuracy multimodal biometric authentication, thus potentially expanding the scope of applications in self-powered wearable medical/health monitoring, interactive input/control devices as well as accurate, reliable, and less intrusive biometric authentication systems.

  9. Automatic TLI recognition system, programmer`s guide

    SciTech Connect

    Lassahn, G.D.

    1997-02-01

    This report describes the software of an automatic target recognition system (version 14), from a programmer`s point of view. The intent is to provide information that will help people who wish to modify the software. In separate volumes are a general description of the ATR system, Automatic TLI Recognition System, General Description, and a user`s manual, Automatic TLI Recognition System, User`s Guide. 2 refs.

  10. Recognition of Endogenous Nucleic Acids by the Innate Immune System.

    PubMed

    Roers, Axel; Hiller, Björn; Hornung, Veit

    2016-04-19

    Recognition of DNA and RNA by endosomal and cytosolic sensors constitutes a central element in the detection of microbial invaders by the innate immune system. However, the capacity of these sensors to discriminate between microbial and endogenous nucleic acids is limited. Over the past few years, evidence has accumulated to suggest that endogenous DNA or RNA species can engage nucleic-acid-sensing pattern-recognition receptors that can trigger or sustain detrimental pathology. Here, we review principles of how the activation of innate sensors by host nucleic acids is prevented in the steady state and discuss four important determinants of whether a nucleic-acid-driven innate response is mounted. These include structural features of the ligand being sensed, the subcellular location and quantity of pathogen-derived or endogenous nucleic acids, and the regulation of sensor-activation thresholds. Furthermore, we emphasize disease mechanisms initiated by failure to discriminate self from non-self in nucleic acid detection.

  11. The species recognition system: a new corollary for the human fetoembryonic defense system hypothesis.

    PubMed

    Clark, G F; Dell, A; Morris, H R; Patankar, M S; Easton, R L

    2001-01-01

    We have previously suggested that the human fetus is protected during human development by a system of both soluble and cell surface associated glycoconjugates that utilize their carbohydrate sequences as functional groups to enable them to evoke tolerance. The proposed model has been referred to as the human fetoembryonic defense system hypothesis (hu-FEDS). In this paradigm, it has previously been proposed that similar oligosaccharides are used to mediate crucial recognition events required during both human sperm-egg binding and immune-inflammatory cell interactions. This vertical integration suggested to us that the sperm-egg binding itself is related to universal recognition events that occur between immune and inflammatory cells, except that in this case recognition of 'species' rather than recognition of 'self' is being manifested. In this paper, we have designated this component of hu-FEDS as the species recognition system (SRS). We propose that the SRS is an integral component of the hu-FEDS used to enable sperm-egg recognition and protection of the gametes from potential immune responses. Recent structural data indicates that the glycan sequences implicated in mediating murine gamete recognition are also expressed on CD45 in activated murine T lymphocytes and cytotoxic T lymphocytes. This overlap supports our contention that there is an overlap between the immune and gamete recognition systems. Therefore the hu-FEDS paradigm may be a subset of a larger model that also applies to other placental mammals. We therefore propose that the hu-FEDS model for protection should in the future be referred to as the eutherian fetoembryonic defense system hypothesis (eu-FEDS) to account for this extension. The possibility exists that the SRS component of eu-FEDS could predate eutherians and extend to all sexually reproducing organisms. Future investigation of the interactions between the immune and gamete recognition system will be required to determine the degree of

  12. 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.

  13. Developing a Credit Recognition System for Chinese Higher Education Institutions

    ERIC Educational Resources Information Center

    Li, Fuhui

    2015-01-01

    In recent years, a credit recognition system has been developing in Chinese higher education institutions. Much research has been done on this development, but it has been concentrated on system building, barriers/issues and international practices. The relationship between credit recognition system reforms and democratisation of higher education…

  14. Face Recognition System with Holographic Memory and Stereovision Technology

    NASA Astrophysics Data System (ADS)

    Honma, Satoshi; Yagisawa, Yasuaki; Momose, Hidetomo; Sekiguchi, Toru

    2011-09-01

    We have proposed a face recognition system with holographic memory and stereovision technology (FARSHAS). In this system, facial three-dimensional data is captured by stereovision technology and then the facial images at a position in front of the virtual camera is reconstructed automatically. Using the corrected facial images, we estimated theoretically the error rate of the facial recognition system.

  15. A real-time optical automatic target recognition system

    NASA Astrophysics Data System (ADS)

    Chen, Huaixin; Nan, Jianshe; Li, Xiaosun; Wei, Honggang

    2004-04-01

    Automatic target recognition (ATR) technique has been applied in both civil and military. In this paper, we present a new optical pattern recognition system for target recognition. This system includes synthetic discriminate function (SDF) based practical optimized filters for the 3-D targets, the Reference Filter Libs for high correlation SNR, the mapping between the input (object regions) and the output (correlation peaks), and neural networks (ANN) for final decision making. The Real-time optical target recognition is realized by temporal multiplexing technique with electronic addressing spatial light modulator. The experiment results show that the proposed OPR system is efficient and reliable.

  16. 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.

  17. A road sign detection and recognition system for mobile devices

    NASA Astrophysics Data System (ADS)

    Xiong, Bo; Izmirli, Ozgur

    2012-01-01

    We present an automatic road sign detection and recognition service system for mobile devices. The system is based on a client-server architecture which allows mobile users to take pictures of road signs and request detection and recognition service from a centralized server for processing. The preprocessing, detection and recognition take place at the server end and consequently, the result is sent back to the mobile device. For road sign detection, we use particular color features calculated from the input image. Recognition is implemented using a neural network based on normalized color histogram features. We report on the effects of various parameters on recognition accuracy. Our results demonstrate that the system can provide an efficient framework for locale-dependent road sign recognition with multilingual support.

  18. Random-profiles-based 3D face recognition system.

    PubMed

    Kim, Joongrock; Yu, Sunjin; Lee, Sangyoun

    2014-01-01

    In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation.

  19. Random-Profiles-Based 3D Face Recognition System

    PubMed Central

    Joongrock, Kim; Sunjin, Yu; Sangyoun, Lee

    2014-01-01

    In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation. PMID:24691101

  20. Hybrid gesture recognition system for short-range use

    NASA Astrophysics Data System (ADS)

    Minagawa, Akihiro; Fan, Wei; Katsuyama, Yutaka; Takebe, Hiroaki; Ozawa, Noriaki; Hotta, Yoshinobu; Sun, Jun

    2012-03-01

    In recent years, various gesture recognition systems have been studied for use in television and video games[1]. In such systems, motion areas ranging from 1 to 3 meters deep have been evaluated[2]. However, with the burgeoning popularity of small mobile displays, gesture recognition systems capable of operating at much shorter ranges have become necessary. The problems related to such systems are exacerbated by the fact that the camera's field of view is unknown to the user during operation, which imposes several restrictions on his/her actions. To overcome the restrictions generated from such mobile camera devices, and to create a more flexible gesture recognition interface, we propose a hybrid hand gesture system, in which two types of gesture recognition modules are prepared and with which the most appropriate recognition module is selected by a dedicated switching module. The two recognition modules of this system are shape analysis using a boosting approach (detection-based approach)[3] and motion analysis using image frame differences (motion-based approach)(for example, see[4]). We evaluated this system using sample users and classified the resulting errors into three categories: errors that depend on the recognition module, errors caused by incorrect module identification, and errors resulting from user actions. In this paper, we show the results of our investigations and explain the problems related to short-range gesture recognition systems.

  1. Automated recognition system for power quality disturbances

    NASA Astrophysics Data System (ADS)

    Abdelgalil, Tarek

    The application of deregulation policies in electric power systems has resulted in the necessity to quantify the quality of electric power. This fact highlights the need for a new monitoring strategy which is capable of tracking, detecting, classifying power quality disturbances, and then identifying the source of the disturbance. The objective of this work is to design an efficient and reliable power quality monitoring strategy that uses the advances in signal processing and pattern recognition to overcome the deficiencies that exist in power quality monitoring devices. The purposed monitoring strategy has two stages. The first stage is to detect, track, and classify any power quality violation by the use of on-line measurements. In the second stage, the source of the classified power quality disturbance must be identified. In the first stage, an adaptive linear combiner is used to detect power quality disturbances. Then, the Teager Energy Operator and Hilbert Transform are utilized for power quality event tracking. After the Fourier, Wavelet, and Walsh Transforms are employed for the feature extraction, two approaches are then exploited to classify the different power quality disturbances. The first approach depends on comparing the disturbance to be classified with a stored set of signatures for different power quality disturbances. The comparison is developed by using Hidden Markov Models and Dynamic Time Warping. The second approach depends on employing an inductive inference to generate the classification rules directly from the data. In the second stage of the new monitoring strategy, only the problem of identifying the location of the switched capacitor which initiates the transients is investigated. The Total Least Square-Estimation of Signal Parameters via Rotational Invariance Technique is adopted to estimate the amplitudes and frequencies of the various modes contained in the voltage signal measured at the facility entrance. After extracting the

  2. 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.

  3. Fingerprint recognition system by use of graph matching

    NASA Astrophysics Data System (ADS)

    Shen, Wei; Shen, Jun; Zheng, Huicheng

    2001-09-01

    Fingerprint recognition is an important subject in biometrics to identify or verify persons by physiological characteristics, and has found wide applications in different domains. In the present paper, we present a finger recognition system that combines singular points and structures. The principal steps of processing in our system are: preprocessing and ridge segmentation, singular point extraction and selection, graph representation, and finger recognition by graphs matching. Our fingerprint recognition system is implemented and tested for many fingerprint images and the experimental result are satisfactory. Different techniques are used in our system, such as fast calculation of orientation field, local fuzzy dynamical thresholding, algebraic analysis of connections and fingerprints representation and matching by graphs. Wed find that for fingerprint database that is not very large, the recognition rate is very high even without using a prior coarse category classification. This system works well for both one-to-few and one-to-many problems.

  4. Embedded Palmprint Recognition System Using OMAP 3530

    PubMed Central

    Shen, Linlin; Wu, Shipei; Zheng, Songhao; Ji, Zhen

    2012-01-01

    We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the ccentral pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance. PMID:22438721

  5. Human-inspired sound environment recognition system for assistive vehicles

    NASA Astrophysics Data System (ADS)

    González Vidal, Eduardo; Fredes Zarricueta, Ernesto; Auat Cheein, Fernando

    2015-02-01

    Objective. The human auditory system acquires environmental information under sound stimuli faster than visual or touch systems, which in turn, allows for faster human responses to such stimuli. It also complements senses such as sight, where direct line-of-view is necessary to identify objects, in the environment recognition process. This work focuses on implementing human reaction to sound stimuli and environment recognition on assistive robotic devices, such as robotic wheelchairs or robotized cars. These vehicles need environment information to ensure safe navigation. Approach. In the field of environment recognition, range sensors (such as LiDAR and ultrasonic systems) and artificial vision devices are widely used; however, these sensors depend on environment constraints (such as lighting variability or color of objects), and sound can provide important information for the characterization of an environment. In this work, we propose a sound-based approach to enhance the environment recognition process, mainly for cases that compromise human integrity, according to the International Classification of Functioning (ICF). Our proposal is based on a neural network implementation that is able to classify up to 15 different environments, each selected according to the ICF considerations on environment factors in the community-based physical activities of people with disabilities. Main results. The accuracy rates in environment classification ranges from 84% to 93%. This classification is later used to constrain assistive vehicle navigation in order to protect the user during daily activities. This work also includes real-time outdoor experimentation (performed on an assistive vehicle) by seven volunteers with different disabilities (but without cognitive impairment and experienced in the use of wheelchairs), statistical validation, comparison with previously published work, and a discussion section where the pros and cons of our system are evaluated. Significance

  6. 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.

  7. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

    PubMed Central

    Partila, Pavol; Voznak, Miroslav; Tovarek, Jaromir

    2015-01-01

    The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency. PMID:26346654

  8. An Evaluation of PC-Based Optical Character Recognition Systems.

    ERIC Educational Resources Information Center

    Schreier, E. M.; Uslan, M. M.

    1991-01-01

    The review examines six personal computer-based optical character recognition (OCR) systems designed for use by blind and visually impaired people. Considered are OCR components and terms, documentation, scanning and reading, command structure, conversion, unique features, accuracy of recognition, scanning time, speed, and cost. (DB)

  9. 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.

  10. Voice-Recognition System Records Inspection Data

    NASA Technical Reports Server (NTRS)

    Rochester, Larry L.

    1993-01-01

    Main Injector Voice Activated Record (MIVAR) system acts on vocal commands and processes spoken inspection data into electronic and printed inspection reports. Devised to improve acquisition and recording of data from borescope inspections of interiors of liquid-oxygen-injecting tubes on main engine of Space Shuttle. With modifications, system used in other situations to relieve inspectors of manual recording of data. Enhances flow of work and quality of data acquired by enabling inspector to remain visually focused on workpiece.

  11. Cellular Phone Face Recognition System Based on Optical Phase Correlation

    NASA Astrophysics Data System (ADS)

    Watanabe, Eriko; Ishikawa, Sayuri; Ohta, Maiko; Kodate, Kashiko

    We propose a high security facial recognition system using a cellular phone on the mobile network. This system is composed of a face recognition engine based on optical phase correlation which uses phase information with emphasis on a Fourier domain, a control sever and the cellular phone with a compact camera for taking pictures, as a portable terminal. Compared with various correlation methods, our face recognition engine revealed the most accurate EER of less than 1%. By using the JAVA interface on this system, we implemented the stable system taking pictures, providing functions to prevent spoofing while transferring images. This recognition system was tested on 300 women students and the results proved this system effective.

  12. Remote weapon station for automatic target recognition system demand analysis

    NASA Astrophysics Data System (ADS)

    Lei, Zhang; Li, Sheng-cai; Shi, Cai

    2015-08-01

    Introduces a remote weapon station basic composition and the main advantage, analysis of target based on image automatic recognition system for remote weapon station of practical significance, the system elaborated the image based automatic target recognition system in the photoelectric stabilized technology, multi-sensor image fusion technology, integrated control target image enhancement, target behavior risk analysis technology, intelligent based on the character of the image automatic target recognition algorithm research, micro sensor technology as the key technology of the development in the field of demand.

  13. 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.

  14. 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

  15. 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

  16. Color recognition system for urine analyzer

    NASA Astrophysics Data System (ADS)

    Zhu, Lianqing; Wang, Zicai; Lin, Qian; Dong, Mingli

    2010-08-01

    In order to increase the speed of photoelectric conversion, a linear CCD is applied as the photoelectric converter instead of the traditional photodiode. A white LED is used as the light source of the system. The color information of the urine test strip is transferred into the CCD through a reflecting optical system. It is then converted to digital signals by an A/D converter. The test results of urine analysis are obtained by a data processing system. An ARM microprocessor is selected as the CPU of the system and a CPLD is employed to provide a driving timing for the CCD drive and the A/D converter. Active HDL7.2 and Verilog HDL are used to simulate the driving timing of the CPLD. Experimental results show that the correctness rate of the test results is better than 90%. The system satisfies the requirements of the color information collection of urine analyzer.

  17. Automatic TLI recognition system. Part 2: User`s guide

    SciTech Connect

    Partin, J.K.; Lassahn, G.D.; Davidson, J.R.

    1994-05-01

    This report describes an automatic target recognition system for fast screening of large amounts of multi-sensor image data, based on low-cost parallel processors. This system uses image data fusion and gives uncertainty estimates. It is relatively low cost, compact, and transportable. The software is easily enhanced to expand the system`s capabilities, and the hardware is easily expandable to increase the system`s speed. This volume is a user`s manual for an Automatic Target Recognition (ATR) system. This guide is intended to provide enough information and instruction to allow individuals to the system for their own applications.

  18. 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

  19. 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.

  20. Social context predicts recognition systems in ant queens.

    PubMed

    Dreier, S; D'Ettorre, P

    2009-03-01

    Recognition of group-members is a key feature of sociality. Ants use chemical communication to discriminate nestmates from intruders, enhancing kin cooperation and preventing parasitism. The recognition code is embedded in their cuticular chemical profile, which typically varies between colonies. We predicted that ants might be capable of accurate recognition in unusual situations when few individuals interact repeatedly, as new colonies started by two to three queens. Individual recognition would be favoured by selection when queens establish dominance hierarchies, because repeated fights for dominance are costly; but it would not evolve in absence of hierarchies. We previously showed that Pachycondyla co-founding queens, which form dominance hierarchies, have accurate individual recognition based on chemical cues. Here, we used the ant Lasius niger to test the null hypothesis that individual recognition does not occur when co-founding queens do not establish dominance hierarchies. Indeed, L. niger queens show a similar level of aggression towards both co-foundresses and intruders, indicating that they are unable of individual recognition, contrary to Pachycondyla. Additionally, the variation in chemical profiles of Lasius and Pachycondyla queens is comparable, thus informational constraints are unlikely to apply. We conclude that selection pressure from the social context is of crucial significance for the sophistication of recognition systems.

  1. Covert recognition and the neural system for face processing.

    PubMed

    Schweinberger, Stefan R; Burton, A Mike

    2003-02-01

    In this viewpoint, we discuss the new evidence on covert face recognition in prosopagnosia presented by Bobes et al. (2003, this issue) and by Sperber and Spinnler (2003, this issue). Contrary to earlier hypotheses, both papers agree that covert and overt face recognition are based on the same mechanism. In line with this suggestion, an analysis of reported cases with prosopagnosia indicates that a degree of successful encoding of facial representations is a prerequisite for covert recognition to occur. While we agree with this general conclusion as far as Bobes et al.'s and Sperber and Spinnler's data are concerned, we also discuss evidence for a dissociation between different measures of covert recognition. Specifically, studies in patients with Capgras delusion and patients with prosopagnosia suggest that skin conductance and behavioural indexes of covert face recognition are mediated by partially different mechanisms. We also discuss implications of the new data for models of normal face recognition that have been successful in simulating covert recognition phenomena (e.g., Young and Burton, 1999, and O'Reilly et al., 1999). Finally, in reviewing recent neurophysiological and brain imaging evidence concerning the neural system for face processing, we argue that the relationship between ERP components (specifically, N170, N250r, and N400) and different cognitive processes in face recognition is beginning to emerge. PMID:12627750

  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. 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

  4. 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.

  5. 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.

  6. A hybrid recognition system for off-line handwritten characters.

    PubMed

    Katiyar, Gauri; Mehfuz, Shabana

    2016-01-01

    Computer based pattern recognition is a process that involves several sub-processes, including pre-processing, feature extraction, feature selection, and classification. Feature extraction is the estimation of certain attributes of the target patterns. Selection of the right set of features is the most crucial and complex part of building a pattern recognition system. In this work we have combined multiple features extracted using seven different approaches. The novelty of this approach is to achieve better accuracy and reduced computational time for recognition of handwritten characters using Genetic Algorithm which optimizes the number of features along with a simple and adaptive Multi Layer Perceptron classifier. Experiments have been performed using standard database of CEDAR (Centre of Excellence for Document Analysis and Recognition) for English alphabet. The experimental results obtained on this database demonstrate the effectiveness of this system. PMID:27066370

  7. Innate immune recognition of flagellin limits systemic persistence of Brucella

    PubMed Central

    Terwagne, Matthieu; Ferooz, Jonathan; Rolán, Hortensia G.; Sun, Yao-Hui; Atluri, Vidya; Xavier, Mariana N.; Franchi, Luigi; Núñez, Gabriel; Legrand, Thomas; Flavell, Richard A.; De Bolle, Xavier

    2014-01-01

    Brucella are facultative intracellular bacteria that cause chronic infections by limiting innate immune recognition. It is currently unknown whether Brucella FliC flagellin, the monomeric subunit of flagellar filament, is sensed by the host during infection. Here, we used two mutants of Brucella melitensis, either lacking or overexpressing flagellin to show that FliC hinders bacterial replication in vivo. The use of cells and mice genetically deficient for different components of inflammasomes suggested that FliC was a target of the cytosolic innate immune receptor NLRC4 in vivo but not in macrophages in vitro where the response to FliC was nevertheless dependent on the cytosolic adaptor ASC, therefore suggesting a new pathway of cytosolic flagellin sensing. However, our work also suggested that the lack of TLR5 activity of Brucella flagellin and the regulation of its synthesis and/or delivery into host cells are both part of the stealthy strategy of Brucella towards the innate immune system. Nevertheless, since a flagellin-deficient mutant of B. melitensis was found to cause histologically demonstrable injuries in the spleen of infected mice, we suggested that recognition of FliC plays a role in the immunologic standoff between Brucella and its host, which is characterized by a persistent infection with limited inflammatory pathology. PMID:23227931

  8. 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

  9. 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.

  10. 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

  11. FACELOCK-Lock Control Security System Using Face Recognition-

    NASA Astrophysics Data System (ADS)

    Hirayama, Takatsugu; Iwai, Yoshio; Yachida, Masahiko

    A security system using biometric person authentication technologies is suited to various high-security situations. The technology based on face recognition has advantages such as lower user’s resistance and lower stress. However, facial appearances change according to facial pose, expression, lighting, and age. We have developed the FACELOCK security system based on our face recognition methods. Our methods are robust for various facial appearances except facial pose. Our system consists of clients and a server. The client communicates with the server through our protocol over a LAN. Users of our system do not need to be careful about their facial appearance.

  12. Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Huntsberger, Terry

    2011-01-01

    The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

  13. Neural Mechanisms and Information Processing in Recognition Systems

    PubMed Central

    Ozaki, Mamiko; Hefetz, Abraham

    2014-01-01

    Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold. PMID:26462936

  14. 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.

  15. 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

  16. 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.

  17. 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

  18. 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.

  19. Design of speaker recognition system based on artificial neural network

    NASA Astrophysics Data System (ADS)

    Chen, Yanhong; Wang, Li; Lin, Han; Li, Jinlong

    2012-10-01

    Speaker recognition is to recognize speaker's identity from its voice which contains physiological and behavioral characteristics unique to each individual. In this paper, the artificial neural network model, which has very good capacity of non-linear division in characteristic space, is used for pattern matching. The speaker's sample characteristic domain is built for his mixed voice characteristic signals based on Kmeanlbg algorithm. Then the dimension of the inputting eigenvector is reduced, and the redundant information is got rid of. On this basis, BP neural network is used to divide capacity area for characteristic space nonlinearly, and the BP neural network acts as a classifier for the speaker. Finally, a speaker recognition system based on the neural network is realized and the experiment results validate the recognition performance and robustness of the system.

  20. A speech recognition system for data collection in precision agriculture

    NASA Astrophysics Data System (ADS)

    Dux, David Lee

    Agricultural producers have shown interest in collecting detailed, accurate, and meaningful field data through field scouting, but scouting is labor intensive. They use yield monitor attachments to collect weed and other field data while driving equipment. However, distractions from using a keyboard or buttons while driving can lead to driving errors or missed data points. At Purdue University, researchers have developed an ASR system to allow equipment operators to collect georeferenced data while keeping hands and eyes on the machine during harvesting and to ease georeferencing of data collected during scouting. A notebook computer retrieved locations from a GPS unit and displayed and stored data in Excel. A headset microphone with a single earphone collected spoken input while allowing the operator to hear outside sounds. One-, two-, or three-word commands activated appropriate VBA macros. Four speech recognition products were chosen based on hardware requirements and ability to add new terms. After training, speech recognition accuracy was 100% for Kurzweil VoicePlus and Verbex Listen for the 132 vocabulary words tested, during tests walking outdoors or driving an ATV. Scouting tests were performed by carrying the system in a backpack while walking in soybean fields. The system recorded a point or a series of points with each utterance. Boundaries of points showed problem areas in the field and single points marked rocks and field corners. Data were displayed as an Excel chart to show a real-time map as data were collected. The information was later displayed in a GIS over remote sensed field images. Field corners and areas of poor stand matched, with voice data explaining anomalies in the image. The system was tested during soybean harvest by using voice to locate weed patches. A harvester operator with little computer experience marked points by voice when the harvester entered and exited weed patches or areas with poor crop stand. The operator found the

  1. 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

  2. New taste sensor system combined with chaotic recognition

    NASA Astrophysics Data System (ADS)

    Hu, Jie; Wang, Ping; Li, Rong

    2001-09-01

    Taste sensor as a new kind of chemical sensor has been studied by many researchers. We have developed several types of taste sensor system and some new recognition methods for taste substance. Kiyoshi Toko et al proposed a new kind of chaos taste sensor that is based on sensor chaos dynamics. In this paper, we improve the taste sensor based on chaos dynamics and proposed a new method for the pattern recognition of tastes. We use three kinds of tastes, i.e., sweetness, salty taste, and sourness. They cause the membrane oscillate in different form, and the complexity is not the same. We can detect taste based on the new method.

  3. Design and implementation of face recognition system based on Windows

    NASA Astrophysics Data System (ADS)

    Zhang, Min; Liu, Ting; Li, Ailan

    2015-07-01

    In view of the basic Windows login password input way lacking of safety and convenient operation, we will introduce the biometrics technology, face recognition, into the computer to login system. Not only can it encrypt the computer system, also according to the level to identify administrators at all levels. With the enhancement of the system security, user input can neither be a cumbersome nor worry about being stolen password confidential.

  4. 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.

  5. An Overview of Hand Gestures Recognition System Techniques

    NASA Astrophysics Data System (ADS)

    Farhana Mod Ma'asum, Farah; Sulaiman, Suhana; Saparon, Azilah

    2015-11-01

    Hand gesture recognition system has evolved tremendously in the recent few years because of its ability to interact with machine efficiently. Mankind tries to incorporate human gestures into modern technology by searching and finding a replacement of multi touch technology which does not require any touching movement on screen. This paper presents an overview on several methods to realize hand gesture recognition by using three main modules: camera and segmentation module, detection module and feature extraction module. There are many methods which can be used to get the respective results depending on its advantages. Summary of previous research and results of hand gesture methods as well as comparison between gesture recognition are also given in this paper.

  6. Automatic Speech Acquisition and Recognition for Spacesuit Audio Systems

    NASA Technical Reports Server (NTRS)

    Ye, Sherry

    2015-01-01

    NASA has a widely recognized but unmet need for novel human-machine interface technologies that can facilitate communication during astronaut extravehicular activities (EVAs), when loud noises and strong reverberations inside spacesuits make communication challenging. WeVoice, Inc., has developed a multichannel signal-processing method for speech acquisition in noisy and reverberant environments that enables automatic speech recognition (ASR) technology inside spacesuits. The technology reduces noise by exploiting differences between the statistical nature of signals (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, ASR accuracy can be improved to the level at which crewmembers will find the speech interface useful. System components and features include beam forming/multichannel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, and ASR decoding. Arithmetic complexity models were developed and will help designers of real-time ASR systems select proper tasks when confronted with constraints in computational resources. In Phase I of the project, WeVoice validated the technology. The company further refined the technology in Phase II and developed a prototype for testing and use by suited astronauts.

  7. 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.…

  8. 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.

  9. 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.

  10. System integration of pattern recognition, adaptive aided, upper limb prostheses

    NASA Technical Reports Server (NTRS)

    Lyman, J.; Freedy, A.; Solomonow, M.

    1975-01-01

    The requirements for successful integration of a computer aided control system for multi degree of freedom artificial arms are discussed. Specifications are established for a system which shares control between a human amputee and an automatic control subsystem. The approach integrates the following subsystems: (1) myoelectric pattern recognition, (2) adaptive computer aiding; (3) local reflex control; (4) prosthetic sensory feedback; and (5) externally energized arm with the functions of prehension, wrist rotation, elbow extension and flexion and humeral rotation.

  11. Training methodologies for dependent Speech Recognition (SR) systems

    NASA Astrophysics Data System (ADS)

    Miller, Richard L.

    1991-03-01

    An experiment was conducted to determine whether a dependent (SR) system would perform with different accuracies given different ways in which it was trained. The experiment used an SR system (Voice Navigator) which is based on Dragon Systems, Inc. (proprietary) technology. Fifteen subjects trained three different voice patterns each and conducted four tests to compile statistics about the recognition accuracy for each pattern. The experiment was successful and demonstrated that the training method used can have significant impact on the performance of a dependent SR system. This thesis discusses the research methodology, reviews and analyzes the data collected, and states conclusions drawn about the particular dependent SR system used in the experiment.

  12. The advantages of stereo vision in a face recognition system

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Blasch, Erik

    2014-06-01

    Humans can recognize a face with binocular vision, while computers typically use a single face image. It is known that the performance of face recognition (by a computer) can be improved using the score fusion of multimodal images and multiple algorithms. A question is: Can we apply stereo vision to a face recognition system? We know that human binocular vision has many advantages such as stereopsis (3D vision), binocular summation, and singleness of vision including fusion of binocular images (cyclopean image). For face recognition, a 3D face or 3D facial features are typically computed from a pair of stereo images. In human visual processes, the binocular summation and singleness of vision are similar as image fusion processes. In this paper, we propose an advanced face recognition system with stereo imaging capability, which is comprised of two 2-in-1 multispectral (visible and thermal) cameras and three recognition algorithms (circular Gaussian filter, face pattern byte, and linear discriminant analysis [LDA]). Specifically, we present and compare stereo fusion at three levels (images, features, and scores) by using stereo images (from left camera and right camera). Image fusion is achieved with three methods (Laplacian pyramid, wavelet transform, average); feature fusion is done with three logical operations (AND, OR, XOR); and score fusion is implemented with four classifiers (LDA, k-nearest neighbor, support vector machine, binomial logical regression). The system performance is measured by probability of correct classification (PCC) rate (reported as accuracy rate in this paper) and false accept rate (FAR). The proposed approaches were validated with a multispectral stereo face dataset from 105 subjects. Experimental results show that any type of stereo fusion can improve the PCC, meanwhile reduce the FAR. It seems that stereo image/feature fusion is superior to stereo score fusion in terms of recognition performance. Further score fusion after image

  13. Speech recognition control system and method

    SciTech Connect

    Lemelson, J.H.

    1986-08-12

    This invention relates to a system and method for weighing articles and quantities of material wherein computing functions are performed to effect calculations and the control of a visual presentation means such as a display or printer or the generation of signals for use in recording a transaction. In particular, the invention relates to such a weighing and computing apparatus and method which operates or varies in response to speech signals generated by selected words of speech spoken into a microphone by an operator of the apparatus. It is known in the art to electronically detect the weight of articles and containers of material and to generate electrical signals which are indicative of such weight. It is also known to effect a computation with respect to such signals and additional signals generated by manually operating selected keys of a keyboard wherein the additional signals represent one or more additional variables which must be divided into or multiplied by the numerical representation of the weights of articles weighed by such apparatus.

  14. Time and timing in the acoustic recognition system of crickets

    PubMed Central

    Hennig, R. Matthias; Heller, Klaus-Gerhard; Clemens, Jan

    2014-01-01

    The songs of many insects exhibit precise timing as the result of repetitive and stereotyped subunits on several time scales. As these signals encode the identity of a species, time and timing are important for the recognition system that analyzes these signals. Crickets are a prominent example as their songs are built from sound pulses that are broadcast in a long trill or as a chirped song. This pattern appears to be analyzed on two timescales, short and long. Recent evidence suggests that song recognition in crickets relies on two computations with respect to time; a short linear-nonlinear (LN) model that operates as a filter for pulse rate and a longer integration time window for monitoring song energy over time. Therefore, there is a twofold role for timing. A filter for pulse rate shows differentiating properties for which the specific timing of excitation and inhibition is important. For an integrator, however, the duration of the time window is more important than the precise timing of events. Here, we first review evidence for the role of LN-models and integration time windows for song recognition in crickets. We then parameterize the filter part by Gabor functions and explore the effects of duration, frequency, phase, and offset as these will correspond to differently timed patterns of excitation and inhibition. These filter properties were compared with known preference functions of crickets and katydids. In a comparative approach, the power for song discrimination by LN-models was tested with the songs of over 100 cricket species. It is demonstrated how the acoustic signals of crickets occupy a simple 2-dimensional space for song recognition that arises from timing, described by a Gabor function, and time, the integration window. Finally, we discuss the evolution of recognition systems in insects based on simple sensory computations. PMID:25161622

  15. Investigation of parameters affecting voice recognition systems in C3 Systems

    NASA Astrophysics Data System (ADS)

    Batchellor, M. P.

    1981-03-01

    This research investigates the use of a voice recognition system by military operators officer, enlisted, male and female. The application intended is the use of a discrete utterance voice recognition system in a command center environment. The system would be used by members of a watch team to execute ad hoc queries against an automated data base in support of their command center duties. The following factors were examined: the adaptability of a random sample of active duty military personnel to a voice input system; the accuracy of such a system; the effects of male versus female operators; and the effects of officer versus enlisted operators -- the advantages/disadvantages of using three, five or ten trained passes to train the voice system. Results showed no significant difference in error rates between the categories of officer and enlisted nor between male and female. Three training passes had a slightly higher error rate than five or ten passes but five and ten passes were the same.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  17. 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.

  18. Speech recognition systems on the Cell Broadband Engine

    SciTech Connect

    Liu, Y; Jones, H; Vaidya, S; Perrone, M; Tydlitat, B; Nanda, A

    2007-04-20

    In this paper we describe our design, implementation, and first results of a prototype connected-phoneme-based speech recognition system on the Cell Broadband Engine{trademark} (Cell/B.E.). Automatic speech recognition decodes speech samples into plain text (other representations are possible) and must process samples at real-time rates. Fortunately, the computational tasks involved in this pipeline are highly data-parallel and can receive significant hardware acceleration from vector-streaming architectures such as the Cell/B.E. Identifying and exploiting these parallelism opportunities is challenging, but also critical to improving system performance. We observed, from our initial performance timings, that a single Cell/B.E. processor can recognize speech from thousands of simultaneous voice channels in real time--a channel density that is orders-of-magnitude greater than the capacity of existing software speech recognizers based on CPUs (central processing units). This result emphasizes the potential for Cell/B.E.-based speech recognition and will likely lead to the future development of production speech systems using Cell/B.E. clusters.

  19. A Highly Accurate Face Recognition System Using Filtering Correlation

    NASA Astrophysics Data System (ADS)

    Watanabe, Eriko; Ishikawa, Sayuri; Kodate, Kashiko

    2007-09-01

    The authors previously constructed a highly accurate fast face recognition optical correlator (FARCO) [E. Watanabe and K. Kodate: Opt. Rev. 12 (2005) 460], and subsequently developed an improved, super high-speed FARCO (S-FARCO), which is able to process several hundred thousand frames per second. The principal advantage of our new system is its wide applicability to any correlation scheme. Three different configurations were proposed, each depending on correlation speed. This paper describes and evaluates a software correlation filter. The face recognition function proved highly accurate, seeing that a low-resolution facial image size (64 × 64 pixels) has been successfully implemented. An operation speed of less than 10 ms was achieved using a personal computer with a central processing unit (CPU) of 3 GHz and 2 GB memory. When we applied the software correlation filter to a high-security cellular phone face recognition system, experiments on 30 female students over a period of three months yielded low error rates: 0% false acceptance rate and 2% false rejection rate. Therefore, the filtering correlation works effectively when applied to low resolution images such as web-based images or faces captured by a monitoring camera.

  20. 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

  1. 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

  2. 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.

  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. PMID:24513850

  4. CNN: a speaker recognition system using a cascaded neural network.

    PubMed

    Zaki, M; Ghalwash, A; Elkouny, A A

    1996-05-01

    The main emphasis of this paper is to present an approach for combining supervised and unsupervised neural network models to the issue of speaker recognition. To enhance the overall operation and performance of recognition, the proposed strategy integrates the two techniques, forming one global model called the cascaded model. We first present a simple conventional technique based on the distance measured between a test vector and a reference vector for different speakers in the population. This particular distance metric has the property of weighting down the components in those directions along which the intraspeaker variance is large. The reason for presenting this method is to clarify the discrepancy in performance between the conventional and neural network approach. We then introduce the idea of using unsupervised learning technique, presented by the winner-take-all model, as a means of recognition. Due to several tests that have been conducted and in order to enhance the performance of this model, dealing with noisy patterns, we have preceded it with a supervised learning model--the pattern association model--which acts as a filtration stage. This work includes both the design and implementation of both conventional and neural network approaches to recognize the speakers templates--which are introduced to the system via a voice master card and preprocessed before extracting the features used in the recognition. The conclusion indicates that the system performance in case of neural network is better than that of the conventional one, achieving a smooth degradation in respect of noisy patterns, and higher performance in respect of noise-free patterns.

  5. 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.

  6. A survey on acoustic signature recognition and classification techniques for persistent surveillance systems

    NASA Astrophysics Data System (ADS)

    Shirkhodaie, Amir; Alkilani, Amjad

    2012-06-01

    Application of acoustic sensors in Persistent Surveillance Systems (PSS) has received considerable attention over the last two decades because they can be rapidly deployed and have low cost. Conventional utilization of acoustic sensors in PSS spans a wide range of applications including: vehicle classification, target tracking, activity understanding, speech recognition, shooter detection, etc. This paper presents a current survey of physics-based acoustic signature classification techniques for outdoor sounds recognition and understanding. Particularly, this paper focuses on taxonomy and ontology of acoustic signatures resulted from group activities. The taxonomy and supportive ontology considered include: humanvehicle, human-objects, and human-human interactions. This paper, in particular, exploits applicability of several spectral analysis techniques as a means to maximize likelihood of correct acoustic source detection, recognition, and discrimination. Spectral analysis techniques based on Fast Fourier Transform, Discrete Wavelet Transform, and Short Time Fourier Transform are considered for extraction of features from acoustic sources. In addition, comprehensive overviews of most current research activities related to scope of this work are presented with their applications. Furthermore, future potential direction of research in this area is discussed for improvement of acoustic signature recognition and classification technology suitable for PSS applications.

  7. Relaxing the constraints on image capture for iris recognition systems

    NASA Astrophysics Data System (ADS)

    Smith, Peter A.; Rickman, John M.; Hartsell, Jeremy W.

    2012-06-01

    Iris recognition is considered to be one of the most accurate biometrics, but user inconvenience during the image acquisition phase has limited its widespread use. Image capture is largely constrained to well-controlled situations, where subjects must remain relatively stationary or within a capture "volume" close to the camera. As a consequence, iris recognition systems have a reputation for being borderline intrusive, and less friendly for both subjects and operators. To support the development of a more natural and acceptable iris capture system, we have sought to develop a pre-processor driven imaging system that predicts a maximal opportunity window for iris capture for a subject engaged in natural motion based on predictive head and eye movement algorithms. This paper describes a first-generation prototype iris capture system that utilizes this approach. A wide field of view camera is used to track a person's face and provide head pose data as input to the predictive algorithm. The algorithm is then used to direct a second narrow field of view camera to capture the iris image more reliably. This system serves as a platform for further development of head movement prediction algorithms used to enhance the probability of iris capture in moving or uncooperative subjects.

  8. Embedded knowledge-based system for automatic target recognition

    NASA Astrophysics Data System (ADS)

    Aboutalib, A. O.

    1990-10-01

    The development of a reliable Automatic Target Recognition (ATE) system is considered a very critical and challenging problem. Existing ATE Systems have inherent limitations in terms of recognition performance and the ability to learn and adapt. Artificial Intelligence Techniques have the potential to improve the performance of ATh Systems. In this paper, we presented a novel Knowledge-Engineering tool, termed, the Automatic Reasoning Process (ARP) , that can be used to automatically develop and maintain a Knowledge-Base (K-B) for the ATR Systems. In its learning mode, the ARP utilizes Learning samples to automatically develop the ATR K-B, which consists of minimum size sets of necessary and sufficient conditions for each target class. In its operational mode, the ARP infers the target class from sensor data using the ATh K-B System. The ARP also has the capability to reason under uncertainty, and can support both statistical and model-based approaches for ATR development. The capabilities of the ARP are compared and contrasted to those of another Knowledge-Engineering tool, termed, the Automatic Rule Induction (ARI) which is based on maximizing the mutual information. The AR? has been implemented in LISP on a VAX-GPX workstation.

  9. Military personnel recognition system using texture, colour, and SURF features

    NASA Astrophysics Data System (ADS)

    Irhebhude, Martins E.; Edirisinghe, Eran A.

    2014-06-01

    This paper presents an automatic, machine vision based, military personnel identification and classification system. Classification is done using a Support Vector Machine (SVM) on sets of Army, Air Force and Navy camouflage uniform personnel datasets. In the proposed system, the arm of service of personnel is recognised by the camouflage of a persons uniform, type of cap and the type of badge/logo. The detailed analysis done include; camouflage cap and plain cap differentiation using gray level co-occurrence matrix (GLCM) texture feature; classification on Army, Air Force and Navy camouflaged uniforms using GLCM texture and colour histogram bin features; plain cap badge classification into Army, Air Force and Navy using Speed Up Robust Feature (SURF). The proposed method recognised camouflage personnel arm of service on sets of data retrieved from google images and selected military websites. Correlation-based Feature Selection (CFS) was used to improve recognition and reduce dimensionality, thereby speeding the classification process. With this method success rates recorded during the analysis include 93.8% for camouflage appearance category, 100%, 90% and 100% rates of plain cap and camouflage cap categories for Army, Air Force and Navy categories, respectively. Accurate recognition was recorded using SURF for the plain cap badge category. Substantial analysis has been carried out and results prove that the proposed method can correctly classify military personnel into various arms of service. We show that the proposed method can be integrated into a face recognition system, which will recognise personnel in addition to determining the arm of service which the personnel belong. Such a system can be used to enhance the security of a military base or facility.

  10. Detection of prosodics by using a speech recognition system

    NASA Astrophysics Data System (ADS)

    Hupp, N. A.

    1991-07-01

    The problem was to determine the ability of a speech recognizer to extract prosodic speech features, such as pitch and stress, and to examine these features for application to future voice recognition systems. The Speech Systems Incorporated (SSI) speech recognizer demonstrated that it could detect prosodic features and that these features do indicate the word and/or syllable that is stressed by the speaker. The research examined the effect of prosodics, such as pitch, amplitude, and duration, on word and syllable stress by using the SSI. Subjects read phases and sentences, using a given intonation and stress. The three sections of the experiment compared questions and answers, words stressed within a sentence, and noun/verb pairs, such as object and subject. The results were analyzed both on the syllable level and the word level. In all cases, there was a significant increase in pitch, amplitude, and duration when comparing stressed words and syllables to unstressed words and syllables. When comparing unstressed words only, it was also noted that the first word in a sentence has an increase in pitch, amplitude, and duration. The threshold could be set in recognition systems for each of these parameters. Current speech recognizers do not use acoustic data above the word level. This research shows that we have the capability of developing better speech systems by incorporating prosodics with new linguistic software.

  11. Formal Implementation of a Performance Evaluation Model for the Face Recognition System

    PubMed Central

    Shin, Yong-Nyuo; Kim, Jason; Lee, Yong-Jun; Shin, Woochang; Choi, Jin-Young

    2008-01-01

    Due to usability features, practical applications, and its lack of intrusiveness, face recognition technology, based on information, derived from individuals' facial features, has been attracting considerable attention recently. Reported recognition rates of commercialized face recognition systems cannot be admitted as official recognition rates, as they are based on assumptions that are beneficial to the specific system and face database. Therefore, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of any face recognition system. In this paper, we propose and formalize a performance evaluation model for the biometric recognition system, implementing an evaluation tool for face recognition systems based on the proposed model. Furthermore, we performed evaluations objectively by providing guidelines for the design and implementation of a performance evaluation system, formalizing the performance test process. PMID:18317524

  12. 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.

  13. 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…

  14. 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.

  15. 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

  16. Collocation and Pattern Recognition Effects on System Failure Remediation

    NASA Technical Reports Server (NTRS)

    Trujillo, Anna C.; Press, Hayes N.

    2007-01-01

    Previous research found that operators prefer to have status, alerts, and controls located on the same screen. Unfortunately, that research was done with displays that were not designed specifically for collocation. In this experiment, twelve subjects evaluated two displays specifically designed for collocating system information against a baseline that consisted of dial status displays, a separate alert area, and a controls panel. These displays differed in the amount of collocation, pattern matching, and parameter movement compared to display size. During the data runs, subjects kept a randomly moving target centered on a display using a left-handed joystick and they scanned system displays to find a problem in order to correct it using the provided checklist. Results indicate that large parameter movement aided detection and then pattern recognition is needed for diagnosis but the collocated displays centralized all the information subjects needed, which reduced workload. Therefore, the collocated display with large parameter movement may be an acceptable display after familiarization because of the possible pattern recognition developed with training and its use.

  17. Memristor-MOS analog correlator for pattern recognition system.

    PubMed

    Han, Ca-Ram; Lee, Sang-Jin; Oh, Kwang-Seok; Cho, Kyoungrok

    2013-05-01

    Emergence of new materials having significant improved properties continues to influence the formulation of novel architectures and as such new developments pave the way for innovative circuits and systems such as those required in visual imaging and recognition systems. In this paper we introduce a novel approach for the design of an analog comparator suitable for pattern matching using two Memristors as part of both the stored image data as well as that of the input signal. Our proposed comparator based on Memristor-CMOS fabrication process generates a signal indicating similarity/dissimilarity between two pattern data derived from image sensor and the corresponding Memristor-based template memory. For convenience, we also present an overview of a simplified Memristor model and hence provide simulation results for comparison with that of a conventional analog CMOS comparator. PMID:23858860

  18. Business model for sensor-based fall recognition systems.

    PubMed

    Fachinger, Uwe; Schöpke, Birte

    2014-01-01

    AAL systems require, in addition to sophisticated and reliable technology, adequate business models for their launch and sustainable establishment. This paper presents the basic features of alternative business models for a sensor-based fall recognition system which was developed within the context of the "Lower Saxony Research Network Design of Environments for Ageing" (GAL). The models were developed parallel to the R&D process with successive adaptation and concretization. An overview of the basic features (i.e. nine partial models) of the business model is given and the mutual exclusive alternatives for each partial model are presented. The partial models are interconnected and the combinations of compatible alternatives lead to consistent alternative business models. However, in the current state, only initial concepts of alternative business models can be deduced. The next step will be to gather additional information to work out more detailed models.

  19. Memristor-MOS analog correlator for pattern recognition system.

    PubMed

    Han, Ca-Ram; Lee, Sang-Jin; Oh, Kwang-Seok; Cho, Kyoungrok

    2013-05-01

    Emergence of new materials having significant improved properties continues to influence the formulation of novel architectures and as such new developments pave the way for innovative circuits and systems such as those required in visual imaging and recognition systems. In this paper we introduce a novel approach for the design of an analog comparator suitable for pattern matching using two Memristors as part of both the stored image data as well as that of the input signal. Our proposed comparator based on Memristor-CMOS fabrication process generates a signal indicating similarity/dissimilarity between two pattern data derived from image sensor and the corresponding Memristor-based template memory. For convenience, we also present an overview of a simplified Memristor model and hence provide simulation results for comparison with that of a conventional analog CMOS comparator.

  20. Pattern-Recognition System for Approaching a Known Target

    NASA Technical Reports Server (NTRS)

    Huntsberger, Terrance; Cheng, Yang

    2008-01-01

    A closed-loop pattern-recognition system is designed to provide guidance for maneuvering a small exploratory robotic vehicle (rover) on Mars to return to a landed spacecraft to deliver soil and rock samples that the spacecraft would subsequently bring back to Earth. The system could be adapted to terrestrial use in guiding mobile robots to approach known structures that humans could not approach safely, for such purposes as reconnaissance in military or law-enforcement applications, terrestrial scientific exploration, and removal of explosive or other hazardous items. The system has been demonstrated in experiments in which the Field Integrated Design and Operations (FIDO) rover (a prototype Mars rover equipped with a video camera for guidance) is made to return to a mockup of Mars-lander spacecraft. The FIDO rover camera autonomously acquires an image of the lander from a distance of 125 m in an outdoor environment. Then under guidance by an algorithm that performs fusion of multiple line and texture features in digitized images acquired by the camera, the rover traverses the intervening terrain, using features derived from images of the lander truss structure. Then by use of precise pattern matching for determining the position and orientation of the rover relative to the lander, the rover aligns itself with the bottom of ramps extending from the lander, in preparation for climbing the ramps to deliver samples to the lander. The most innovative aspect of the system is a set of pattern-recognition algorithms that govern a three-phase visual-guidance sequence for approaching the lander. During the first phase, a multifeature fusion algorithm integrates the outputs of a horizontal-line-detection algorithm and a wavelet-transform-based visual-area-of-interest algorithm for detecting the lander from a significant distance. The horizontal-line-detection algorithm is used to determine candidate lander locations based on detection of a horizontal deck that is part of the

  1. 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...

  2. 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

  3. 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

  4. 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

  5. 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.

  6. 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

  7. 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.

  8. 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

  9. Face recognition system and method using face pattern words and face pattern bytes

    SciTech Connect

    Zheng, Yufeng

    2014-12-23

    The present invention provides a novel system and method for identifying individuals and for face recognition utilizing facial features for face identification. The system and method of the invention comprise creating facial features or face patterns called face pattern words and face pattern bytes for face identification. The invention also provides for pattern recognitions for identification other than face recognition. The invention further provides a means for identifying individuals based on visible and/or thermal images of those individuals by utilizing computer software implemented by instructions on a computer or computer system and a computer readable medium containing instructions on a computer system for face recognition and identification.

  10. An overview of the SPHINX speech recognition system

    NASA Astrophysics Data System (ADS)

    Lee, Kai-Fu; Hon, Hsiao-Wuen; Reddy, Raj

    1990-01-01

    A description is given of SPHINX, a system that demonstrates the feasibility of accurate, large-vocabulary, speaker-independent, continuous speech recognition. SPHINX is based on discrete hidden Markov models (HMMs) with linear-predictive-coding derived parameters. To provide speaker independence, knowledge was added to these HMMs in several ways: multiple codebooks of fixed-width parameters, and an enhanced recognizer with carefully designed models and word-duration modeling. To deal with coarticulation in continuous speech, yet still adequately represent a large vocabulary, two new subword speech units are introduced: function-word-dependent phone models and generalized triphone models. With grammars of perplexity 997, 60, and 20, SPHINX attained word accuracies of 71, 94, and 96 percent, respectively, on a 997-word task.

  11. A carbohydrate-anion recognition system in aprotic solvents.

    PubMed

    Ren, Bo; Dong, Hai; Ramström, Olof

    2014-05-01

    A carbohydrate-anion recognition system in nonpolar solvents is reported, in which complexes form at the B-faces of β-D-pyranosides with H1-, H3-, and H5-cis patterns similar to carbohydrate-π interactions. The complexation effect was evaluated for a range of carbohydrate structures; it resulted in either 1:1 carbohydrate-anion complexes, or 1:2 complex formation depending on the protection pattern of the carbohydrate. The interaction was also evaluated with different anions and solvents. In both cases it resulted in significant binding differences. The results indicate that complexation originates from van der Waals interactions or weak CH⋅⋅⋅A(-) hydrogen bonds between the binding partners and is related to electron-withdrawing groups of the carbohydrates as well as increased hydrogen-bond-accepting capability of the anions. PMID:24616327

  12. FaceID: A face detection and recognition system

    SciTech Connect

    Shah, M.B.; Rao, N.S.V.; Olman, V.; Uberbacher, E.C.; Mann, R.C.

    1996-12-31

    A face detection system that automatically locates faces in gray-level images is described. Also described is a system which matches a given face image with faces in a database. Face detection in an Image is performed by template matching using templates derived from a selected set of normalized faces. Instead of using original gray level images, vertical gradient images were calculated and used to make the system more robust against variations in lighting conditions and skin color. Faces of different sizes are detected by processing the image at several scales. Further, a coarse-to-fine strategy is used to speed up the processing, and a combination of whole face and face component templates are used to ensure low false detection rates. The input to the face recognition system is a normalized vertical gradient image of a face, which is compared against a database using a set of pretrained feedforward neural networks with a winner-take-all fuser. The training is performed by using an adaptation of the backpropagation algorithm. This system has been developed and tested using images from the FERET database and a set of images obtained from Rowley, et al and Sung and Poggio.

  13. 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.

  14. 42 CFR 403.322 - Termination of agreements for Medicare recognition of State systems.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 42 Public Health 2 2010-10-01 2010-10-01 false Termination of agreements for Medicare recognition of State systems. 403.322 Section 403.322 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES... Reimbursement Control Systems § 403.322 Termination of agreements for Medicare recognition of State systems....

  15. An automatic speech recognition system with speaker-independent identification support

    NASA Astrophysics Data System (ADS)

    Caranica, Alexandru; Burileanu, Corneliu

    2015-02-01

    The novelty of this work relies on the application of an open source research software toolkit (CMU Sphinx) to train, build and evaluate a speech recognition system, with speaker-independent support, for voice-controlled hardware applications. Moreover, we propose to use the trained acoustic model to successfully decode offline voice commands on embedded hardware, such as an ARMv6 low-cost SoC, Raspberry PI. This type of single-board computer, mainly used for educational and research activities, can serve as a proof-of-concept software and hardware stack for low cost voice automation systems.

  16. 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.

  17. High-emulation mask recognition with high-resolution hyperspectral video capture system

    NASA Astrophysics Data System (ADS)

    Feng, Jiao; Fang, Xiaojing; Li, Shoufeng; Wang, Yongjin

    2014-11-01

    We present a method for distinguishing human face from high-emulation mask, which is increasingly used by criminals for activities such as stealing card numbers and passwords on ATM. Traditional facial recognition technique is difficult to detect such camouflaged criminals. In this paper, we use the high-resolution hyperspectral video capture system to detect high-emulation mask. A RGB camera is used for traditional facial recognition. A prism and a gray scale camera are used to capture spectral information of the observed face. Experiments show that mask made of silica gel has different spectral reflectance compared with the human skin. As multispectral image offers additional spectral information about physical characteristics, high-emulation mask can be easily recognized.

  18. 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.

  19. 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.

  20. Poka Yoke system based on image analysis and object recognition

    NASA Astrophysics Data System (ADS)

    Belu, N.; Ionescu, L. M.; Misztal, A.; Mazăre, A.

    2015-11-01

    Poka Yoke is a method of quality management which is related to prevent faults from arising during production processes. It deals with “fail-sating” or “mistake-proofing”. The Poka-yoke concept was generated and developed by Shigeo Shingo for the Toyota Production System. Poka Yoke is used in many fields, especially in monitoring production processes. In many cases, identifying faults in a production process involves a higher cost than necessary cost of disposal. Usually, poke yoke solutions are based on multiple sensors that identify some nonconformities. This means the presence of different equipment (mechanical, electronic) on production line. As a consequence, coupled with the fact that the method itself is an invasive, affecting the production process, would increase its price diagnostics. The bulky machines are the means by which a Poka Yoke system can be implemented become more sophisticated. In this paper we propose a solution for the Poka Yoke system based on image analysis and identification of faults. The solution consists of a module for image acquisition, mid-level processing and an object recognition module using associative memory (Hopfield network type). All are integrated into an embedded system with AD (Analog to Digital) converter and Zync 7000 (22 nm technology).

  1. Mapping parahippocampal systems for recognition and recency memory in the absence of the rat hippocampus

    PubMed Central

    Kinnavane, L; Amin, E; Horne, M; Aggleton, J P

    2014-01-01

    The present study examined immediate-early gene expression in the perirhinal cortex of rats with hippocampal lesions. The goal was to test those models of recognition memory which assume that the perirhinal cortex can function independently of the hippocampus. The c-fos gene was targeted, as its expression in the perirhinal cortex is strongly associated with recognition memory. Four groups of rats were examined. Rats with hippocampal lesions and their surgical controls were given either a recognition memory task (novel vs. familiar objects) or a relative recency task (objects with differing degrees of familiarity). Perirhinal Fos expression in the hippocampal-lesioned groups correlated with both recognition and recency performance. The hippocampal lesions, however, had no apparent effect on overall levels of perirhinal or entorhinal cortex c-fos expression in response to novel objects, with only restricted effects being seen in the recency condition. Network analyses showed that whereas the patterns of parahippocampal interactions were differentially affected by novel or familiar objects, these correlated networks were not altered by hippocampal lesions. Additional analyses in control rats revealed two modes of correlated medial temporal activation. Novel stimuli recruited the pathway from the lateral entorhinal cortex (cortical layer II or III) to hippocampal field CA3, and thence to CA1. Familiar stimuli recruited the direct pathway from the lateral entorhinal cortex (principally layer III) to CA1. The present findings not only reveal the independence from the hippocampus of some perirhinal systems associated with recognition memory, but also show how novel stimuli engage hippocampal subfields in qualitatively different ways from familiar stimuli. PMID:25264133

  2. 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.

  3. 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

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

    PubMed

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

    2010-01-01

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

  5. Unravelling Glucan Recognition Systems by Glycome Microarrays Using the Designer Approach and Mass Spectrometry*

    PubMed Central

    Palma, Angelina S.; Liu, Yan; Zhang, Hongtao; Zhang, Yibing; McCleary, Barry V.; Yu, Guangli; Huang, Qilin; Guidolin, Leticia S.; Ciocchini, Andres E.; Torosantucci, Antonella; Wang, Denong; Carvalho, Ana Luísa; Fontes, Carlos M. G. A.; Mulloy, Barbara; Childs, Robert A.; Feizi, Ten; Chai, Wengang

    2015-01-01

    Glucans are polymers of d-glucose with differing linkages in linear or branched sequences. They are constituents of microbial and plant cell-walls and involved in important bio-recognition processes, including immunomodulation, anticancer activities, pathogen virulence, and plant cell-wall biodegradation. Translational possibilities for these activities in medicine and biotechnology are considerable. High-throughput micro-methods are needed to screen proteins for recognition of specific glucan sequences as a lead to structure–function studies and their exploitation. We describe construction of a “glucome” microarray, the first sequence-defined glycome-scale microarray, using a “designer” approach from targeted ligand-bearing glucans in conjunction with a novel high-sensitivity mass spectrometric sequencing method, as a screening tool to assign glucan recognition motifs. The glucome microarray comprises 153 oligosaccharide probes with high purity, representing major sequences in glucans. Negative-ion electrospray tandem mass spectrometry with collision-induced dissociation was used for complete linkage analysis of gluco-oligosaccharides in linear “homo” and “hetero” and branched sequences. The system is validated using antibodies and carbohydrate-binding modules known to target α- or β-glucans in different biological contexts, extending knowledge on their specificities, and applied to reveal new information on glucan recognition by two signaling molecules of the immune system against pathogens: Dectin-1 and DC-SIGN. The sequencing of the glucan oligosaccharides by the MS method and their interrogation on the microarrays provides detailed information on linkage, sequence and chain length requirements of glucan-recognizing proteins, and are a sensitive means of revealing unsuspected sequences in the polysaccharides. PMID:25670804

  6. Vermont STep Ahead Recognition System: QRS Profile. The Child Care Quality Rating System (QRS) Assessment

    ERIC Educational Resources Information Center

    Child Trends, 2010

    2010-01-01

    This paper presents a profile of Vermont's STep Ahead Recognition System (STARS) prepared as part of the Child Care Quality Rating System (QRS) Assessment Study. The profile consists of several sections and their corresponding descriptions including: (1) Program Information; (2) Rating Details; (3) Quality Indicators for All Child Care Programs;…

  7. On Assisting a Visual-Facial Affect Recognition System with Keyboard-Stroke Pattern Information

    NASA Astrophysics Data System (ADS)

    Stathopoulou, I.-O.; Alepis, E.; Tsihrintzis, G. A.; Virvou, M.

    Towards realizing a multimodal affect recognition system, we are considering the advantages of assisting a visual-facial expression recognition system with keyboard-stroke pattern information. Our work is based on the assumption that the visual-facial and keyboard modalities are complementary to each other and that their combination can significantly improve the accuracy in affective user models. Specifically, we present and discuss the development and evaluation process of two corresponding affect recognition subsystems, with emphasis on the recognition of 6 basic emotional states, namely happiness, sadness, surprise, anger and disgust as well as the emotion-less state which we refer to as neutral. We find that emotion recognition by the visual-facial modality can be aided greatly by keyboard-stroke pattern information and the combination of the two modalities can lead to better results towards building a multimodal affect recognition system.

  8. 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.

  9. Recognition of Specified RNA Modifications by the Innate Immune System.

    PubMed

    Eigenbrod, Tatjana; Keller, Patrick; Kaiser, Steffen; Rimbach, Katharina; Dalpke, Alexander H; Helm, Mark

    2015-01-01

    Microbial nucleic acids have been described as important activators of human innate immune responses by triggering so-called pattern recognition receptors (PRRs) that are expressed on innate immune cells, including plasmacytoid dendritic cells and monocytes. Although host and microbial nucleic acids share pronounced chemical and structural similarities, they significantly differ in their posttranscriptional modification profile, allowing the host to discriminate between self and nonself. In this regard, ribose 2'-O-methylation has been discovered as suppressor of RNA-induced PRR activation. Although 2'-O-methylation occurs with higher frequencies in eukaryotic than in prokaryotic RNA, the immunosuppressive properties of 2'-O-methylated nucleotides may be misused by certain bacteria as immune evasion mechanism. In the course of identifying inhibitory RNA modifications, our groups have synthesized and comparatively analyzed a series of differentially modified RNAs, so-called modivariants, for their immune stimulatory capacities. In this chapter, we will detail the protocols for the design and synthesis of RNA modivariants by molecular cut-and-paste techniques (referred to as molecular surgery) and describe testing of their immune stimulatory properties upon transfection into peripheral blood mononuclear cells. PMID:26253966

  10. Pattern recognition receptors and central nervous system repair.

    PubMed

    Kigerl, Kristina A; de Rivero Vaccari, Juan Pablo; Dietrich, W Dalton; Popovich, Phillip G; Keane, Robert W

    2014-08-01

    Pattern recognition receptors (PRRs) are part of the innate immune response and were originally discovered for their role in recognizing pathogens by ligating specific pathogen associated molecular patterns (PAMPs) expressed by microbes. Now the role of PRRs in sterile inflammation is also appreciated, responding to endogenous stimuli referred to as "damage associated molecular patterns" (DAMPs) instead of PAMPs. The main families of PRRs include Toll-like receptors (TLRs), Nod-like receptors (NLRs), RIG-like receptors (RLRs), AIM2-like receptors (ALRs), and C-type lectin receptors. Broad expression of these PRRs in the CNS and the release of DAMPs in and around sites of injury suggest an important role for these receptor families in mediating post-injury inflammation. Considerable data now show that PRRs are among the first responders to CNS injury and activation of these receptors on microglia, neurons, and astrocytes triggers an innate immune response in the brain and spinal cord. Here we discuss how the various PRR families are activated and can influence injury and repair processes following CNS injury.

  11. 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.

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

    NASA Astrophysics Data System (ADS)

    Hirasawa, Kazuki; Sawada, Shinya; Saitoh, Atsushi

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

  13. Influence of human factors on performance of Chinese speech recognition systems.

    PubMed

    Chen, S; Jiang, Q; Yu, T

    1996-08-01

    Human factors affecting the performance of the speaker dependent isolated Chinese speech recognition system (SRS) were studied. The results showed that the training mode and dialect factors had significant influence upon recognition accuracy; speaker's psychological factor, cooperativeness and educational background had certain influence, while factors such as sex, age, and profession of the speaker had less influence on recognition performance. With the results taken into consideration, ways for improving performance of the speech recognition systems were explored from the point of view of man-machine integration.

  14. Comparison of rule-building expert systems with pattern recognition for the classification of analytical data

    SciTech Connect

    Derde, M.P.; Buydens, L.; Guns, C.; Massart, D.L.; Hopke, P.K.

    1987-07-15

    Two expert systems of the rule-building type, TIMM and EX-TRAN, are compared with pattern recognition methods for the classification of olive oils of different origins. Both expert systems are more user-friendly than the pattern recognition programs and TIMM yields slightly better results than nearest neighbors classifiers and linear discriminant analysis.

  15. A Single-System Account of the Relationship between Priming, Recognition, and Fluency

    ERIC Educational Resources Information Center

    Berry, Christopher J.; Shanks, David R.; Henson, Richard N. A.

    2008-01-01

    A single-system computational model of priming and recognition was applied to studies that have looked at the relationship between priming, recognition, and fluency in continuous identification paradigms. The model was applied to 3 findings that have been interpreted as evidence for a multiple-systems account: (a) priming can occur for items not…

  16. Natural language understanding and speech recognition for industrial vision systems

    NASA Astrophysics Data System (ADS)

    Batchelor, Bruce G.

    1992-11-01

    The accepted method of programming machine vision systems for a new application is to incorporate sub-routines from a standard library into code, written specially for the given task. Typical programming languages that might be used here are Pascal, C, and assembly code, although other `conventional' (i.e., imperative) languages are often used instead. The representation of an algorithm to recognize a certain object, in the form of, say, a C language program is clumsy and unnatural, compared to the alternative process of describing the object itself and leaving the software to search for it. The latter method, known as declarative programming, is used extensively both when programming in Prolog and when people talk to one another in English, or other natural languages. Programs to understand a limited sub-set of a natural language can also be written conveniently in Prolog. The article considers the prospects for talking to an image processing system, using only slightly constrained English. Moderately priced speech recognition devices, which interface to a standard desk-top computer and provide a limited repertoire (200 words) as well as the ability to identify isolated words, are already available commercially. At the moment, the goal of talking in English to a computer is incompletely fulfilled. Yet, sufficient progress has been made to encourage greater effort in this direction.

  17. 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?

  18. 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

  19. 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.

  20. Implementation of age and gender recognition system for intelligent digital signage

    NASA Astrophysics Data System (ADS)

    Lee, Sang-Heon; Sohn, Myoung-Kyu; Kim, Hyunduk

    2015-12-01

    Intelligent digital signage systems transmit customized advertising and information by analyzing users and customers, unlike existing system that presented advertising in the form of broadcast without regard to type of customers. Currently, development of intelligent digital signage system has been pushed forward vigorously. In this study, we designed a system capable of analyzing gender and age of customers based on image obtained from camera, although there are many different methods for analyzing customers. We conducted age and gender recognition experiments using public database. The age/gender recognition experiments were performed through histogram matching method by extracting Local binary patterns (LBP) features after facial area on input image was normalized. The results of experiment showed that gender recognition rate was as high as approximately 97% on average. Age recognition was conducted based on categorization into 5 age classes. Age recognition rates for women and men were about 67% and 68%, respectively when that conducted separately for different gender.

  1. Visual object recognition for mobile tourist information systems

    NASA Astrophysics Data System (ADS)

    Paletta, Lucas; Fritz, Gerald; Seifert, Christin; Luley, Patrick; Almer, Alexander

    2005-03-01

    We describe a mobile vision system that is capable of automated object identification using images captured from a PDA or a camera phone. We present a solution for the enabling technology of outdoors vision based object recognition that will extend state-of-the-art location and context aware services towards object based awareness in urban environments. In the proposed application scenario, tourist pedestrians are equipped with GPS, W-LAN and a camera attached to a PDA or a camera phone. They are interested whether their field of view contains tourist sights that would point to more detailed information. Multimedia type data about related history, the architecture, or other related cultural context of historic or artistic relevance might be explored by a mobile user who is intending to learn within the urban environment. Learning from ambient cues is in this way achieved by pointing the device towards the urban sight, capturing an image, and consequently getting information about the object on site and within the focus of attention, i.e., the users current field of view.

  2. Temporal pattern recognition based on instantaneous spike rate coding in a simple auditory system.

    PubMed

    Nabatiyan, A; Poulet, J F A; de Polavieja, G G; Hedwig, B

    2003-10-01

    Auditory pattern recognition by the CNS is a fundamental process in acoustic communication. Because crickets communicate with stereotyped patterns of constant frequency syllables, they are established models to investigate the neuronal mechanisms of auditory pattern recognition. Here we provide evidence that for the neural processing of amplitude-modulated sounds, the instantaneous spike rate rather than the time-averaged neural activity is the appropriate coding principle by comparing both coding parameters in a thoracic interneuron (Omega neuron ON1) of the cricket (Gryllus bimaculatus) auditory system. When stimulated with different temporal sound patterns, the analysis of the instantaneous spike rate demonstrates that the neuron acts as a low-pass filter for syllable patterns. The instantaneous spike rate is low at high syllable rates, but prominent peaks in the instantaneous spike rate are generated as the syllable rate resembles that of the species-specific pattern. The occurrence and repetition rate of these peaks in the neuronal discharge are sufficient to explain temporal filtering in the cricket auditory pathway as they closely match the tuning of phonotactic behavior to different sound patterns. Thus temporal filtering or "pattern recognition" occurs at an early stage in the auditory pathway.

  3. 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.

  4. Multi-Stage System for Automatic Target Recognition

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin; Lu, Thomas T.; Ye, David; Edens, Weston; Johnson, Oliver

    2010-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an

  5. The use of ISPAHAN: interactive system for statistical pattern recognition and analysis.

    PubMed

    Gelsema, E S; Landeweerd, G H

    1981-09-01

    ISPAHAN, the interactive system for statistical pattern recognition and analysis, was developed at the Department of Medical Information at the Free University of Amsterdam. It has been used in many pattern recognition problems, such as white blood cell recognition, typification of wave forms in ECG analysis, segmentation of ECG signals and resonance detection in high-energy particle physics. The structure and capabilities of ISPAHAN are presented along with an example of its use in the field of white blood cell recognition. PMID:7294538

  6. A Limited-Vocabulary, Multi-Speaker Automatic Isolated Word Recognition System.

    ERIC Educational Resources Information Center

    Paul, James E., Jr.

    Techniques for automatic recognition of isolated words are investigated, and a computer simulation of a word recognition system is effected. Considered in detail are data acquisition and digitizing, word detection, amplitude and time normalization, short-time spectral estimation including spectral windowing, spectral envelope approximation,…

  7. Multi-font printed Mongolian document recognition system

    NASA Astrophysics Data System (ADS)

    Peng, Liangrui; Liu, Changsong; Ding, Xiaoqing; Wang, Hua; Jin, Jianming

    2009-01-01

    Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of projection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.

  8. Recognition as a challenging label-free optical sensing system

    NASA Astrophysics Data System (ADS)

    Gauglitz, Günter

    2013-05-01

    Optical biosensors are increasingly used in application areas of environmental analysis, healthcare and food safety. The quality of the biosensor's results depends on the interaction layer, the detection principles, and evaluation strategies, not only on the biopolymer layer but also especially on recognition elements. Using label-free optical sensing, non-specific interaction between sample and transducer has to be reduced, and the selectivity of recognition elements has to be improved. For this reason, strategies to avoid non-specific interaction even in blood and milk are discussed, a variety of upcoming recognition is given. Based on the classification of direct optical detection methods, some examples for the above mentioned applications are reviewed. Trends as well as advantages of parallel multisport detection for kinetic evaluation are also part of the lecture.

  9. 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

  10. Dissecting ant recognition systems in the age of genomics.

    PubMed

    Tsutsui, Neil D

    2013-01-01

    Hamilton is probably best known for his seminal work demonstrating the role of kin selection in social evolution. His work made it clear that, for individuals to direct their altruistic behaviours towards appropriate recipients (kin), mechanisms must exist for kin recognition. In the social insects, colonies are typically comprised of kin, and colony recognition cues are used as proxies for kinship cues. Recent years have brought rapid advances in our understanding of the genetic and molecular mechanisms that are used for this process. Here, I review some of the most notable advances, particularly the contributions from recent ant genome sequences and molecular biology. PMID:24132093

  11. 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.

  12. 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.

  13. A computer aided treatment event recognition system in radiation therapy

    SciTech Connect

    Xia, Junyi Mart, Christopher; Bayouth, John

    2014-01-15

    Purpose: To develop an automated system to safeguard radiation therapy treatments by analyzing electronic treatment records and reporting treatment events. Methods: CATERS (Computer Aided Treatment Event Recognition System) was developed to detect treatment events by retrieving and analyzing electronic treatment records. CATERS is designed to make the treatment monitoring process more efficient by automating the search of the electronic record for possible deviations from physician's intention, such as logical inconsistencies as well as aberrant treatment parameters (e.g., beam energy, dose, table position, prescription change, treatment overrides, etc). Over a 5 month period (July 2012–November 2012), physicists were assisted by the CATERS software in conducting normal weekly chart checks with the aims of (a) determining the relative frequency of particular events in the authors’ clinic and (b) incorporating these checks into the CATERS. During this study period, 491 patients were treated at the University of Iowa Hospitals and Clinics for a total of 7692 fractions. Results: All treatment records from the 5 month analysis period were evaluated using all the checks incorporated into CATERS after the training period. About 553 events were detected as being exceptions, although none of them had significant dosimetric impact on patient treatments. These events included every known event type that was discovered during the trial period. A frequency analysis of the events showed that the top three types of detected events were couch position override (3.2%), extra cone beam imaging (1.85%), and significant couch position deviation (1.31%). The significant couch deviation is defined as the number of treatments where couch vertical exceeded two times standard deviation of all couch verticals, or couch lateral/longitudinal exceeded three times standard deviation of all couch laterals and longitudinals. On average, the application takes about 1 s per patient when

  14. 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

  15. Effects of emotional and perceptual-motor stress on a voice recognition system's accuracy: An applied investigation

    NASA Astrophysics Data System (ADS)

    Poock, G. K.; Martin, B. J.

    1984-02-01

    This was an applied investigation examining the ability of a speech recognition system to recognize speakers' inputs when the speakers were under different stress levels. Subjects were asked to speak to a voice recognition system under three conditions: (1) normal office environment, (2) emotional stress, and (3) perceptual-motor stress. Results indicate a definite relationship between voice recognition system performance and the type of low stress reference patterns used to achieve recognition.

  16. 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.

  17. 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.

  18. Improving the recognition of fingerprint biometric system using enhanced image fusion

    NASA Astrophysics Data System (ADS)

    Alsharif, Salim; El-Saba, Aed; Stripathi, Reshma

    2010-04-01

    Fingerprints recognition systems have been widely used by financial institutions, law enforcement, border control, visa issuing, just to mention few. Biometric identifiers can be counterfeited, but considered more reliable and secure compared to traditional ID cards or personal passwords methods. Fingerprint pattern fusion improves the performance of a fingerprint recognition system in terms of accuracy and security. This paper presents digital enhancement and fusion approaches that improve the biometric of the fingerprint recognition system. It is a two-step approach. In the first step raw fingerprint images are enhanced using high-frequency-emphasis filtering (HFEF). The second step is a simple linear fusion process between the raw images and the HFEF ones. It is shown that the proposed approach increases the verification and identification of the fingerprint biometric recognition system, where any improvement is justified using the correlation performance metrics of the matching algorithm.

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

    ERIC Educational Resources Information Center

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

    2010-01-01

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

  20. 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…

  1. 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

  2. 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

  3. 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.

  4. A new accurate pill recognition system using imprint information

    NASA Astrophysics Data System (ADS)

    Chen, Zhiyuan; Kamata, Sei-ichiro

    2013-12-01

    Great achievements in modern medicine benefit human beings. Also, it has brought about an explosive growth of pharmaceuticals that current in the market. In daily life, pharmaceuticals sometimes confuse people when they are found unlabeled. In this paper, we propose an automatic pill recognition technique to solve this problem. It functions mainly based on the imprint feature of the pills, which is extracted by proposed MSWT (modified stroke width transform) and described by WSC (weighted shape context). Experiments show that our proposed pill recognition method can reach an accurate rate up to 92.03% within top 5 ranks when trying to classify more than 10 thousand query pill images into around 2000 categories.

  5. Development of an Environment-Aware Locomotion Mode Recognition System for Powered Lower Limb Prostheses.

    PubMed

    Liu, Ming; Wang, Ding; Helen Huang, He

    2016-04-01

    This paper aimed to develop and evaluate an environment-aware locomotion mode recognition system for volitional control of powered artificial legs. A portable terrain recognition (TR) module, consisting of an inertia measurement unit and a laser distance meter, was built to identify the type of terrain in front of the wearer while walking. A decision tree was used to classify the terrain types and provide either coarse or refined information about the walking environment. Then, the obtained environmental information was modeled as a priori probability and was integrated with a neuromuscular-mechanical-fusion-based locomotion mode (LM) recognition system. The designed TR module and environmental-aware LM recognition system was evaluated separately on able-bodied subjects and a transfemoral amputee online. The results showed that the TR module provided high quality environmental information: TR accuracy is above 98% and terrain transitions are detected over 500 ms before the time required to switch the prosthesis control mode. This enabled smooth locomotion mode transitions for the wearers. The obtained environmental information further improved the performance of LM recognition system, regardless of whether coarse or refined information was used. In addition, the environment-aware LM recognition system produced reliable online performance when the TR output was relatively noisy, which indicated the potential of this system to operate in unconstructed environment. This paper demonstrated that environmental information should be considered for operating wearable lower limb robotic devices, such as prosthetics and orthotics.

  6. 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…

  7. 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.

  8. 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

  9. 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.

  10. 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

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

    PubMed

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

    2014-01-01

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

  12. 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.

  13. 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.

  14. 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.

  15. A Yersinia secreted effector protein promotes virulence by preventing inflammasome recognition of the type III secretion system

    PubMed Central

    Brodsky, Igor E.; Palm, Noah W.; Sadanand, Saheli; Ryndak, Michelle B.; Sutterwala, Fayyaz S.; Flavell, Richard A.; Bliska, James B.; Medzhitov, Ruslan

    2010-01-01

    Pattern recognition receptors (PRRs) detect conserved microbial structures generally absent from eukaryotes. Bacterial pathogens commonly utilize pore-forming toxins or specialized secretion systems to deliver virulence factors that promote bacterial replication by modulating host cell physiology. Detection of these secretion systems or toxins by nucleotide-binding oligomerization domain leucine-rich-repeat proteins (NLRs) triggers the assembly of multiprotein complexes, termed inflammasomes, necessary for caspase-1 activation. Here we demonstrate that caspase-1 activation in response to the Yersinia type III secretion system (T3SS) requires the adapter ASC, and involves both NLRP3 and NLRC4 inflammasomes. We further identify a Yersinia type III secreted effector protein, YopK, which prevents inflammasome activation by preventing cellular recognition of the T3SS. Inflammasome-mediated sensing of the T3SS promotes bacterial clearance from infected tissues in vivo. These data demonstrate that a class of bacterial proteins interferes with cellular recognition of bacterial secretion systems, which contributes to bacterial survival within host tissues. PMID:20478539

  16. Recognition- and reactivity-based fluorescent probes for studying transition metal signaling in living systems.

    PubMed

    Aron, Allegra T; Ramos-Torres, Karla M; Cotruvo, Joseph A; Chang, Christopher J

    2015-08-18

    Metals are essential for life, playing critical roles in all aspects of the central dogma of biology (e.g., the transcription and translation of nucleic acids and synthesis of proteins). Redox-inactive alkali, alkaline earth, and transition metals such as sodium, potassium, calcium, and zinc are widely recognized as dynamic signals, whereas redox-active transition metals such as copper and iron are traditionally thought of as sequestered by protein ligands, including as static enzyme cofactors, in part because of their potential to trigger oxidative stress and damage via Fenton chemistry. Metals in biology can be broadly categorized into two pools: static and labile. In the former, proteins and other macromolecules tightly bind metals; in the latter, metals are bound relatively weakly to cellular ligands, including proteins and low molecular weight ligands. Fluorescent probes can be useful tools for studying the roles of transition metals in their labile forms. Probes for imaging transition metal dynamics in living systems must meet several stringent criteria. In addition to exhibiting desirable photophysical properties and biocompatibility, they must be selective and show a fluorescence turn-on response to the metal of interest. To meet this challenge, we have pursued two general strategies for metal detection, termed "recognition" and "reactivity". Our design of transition metal probes makes use of a recognition-based approach for copper and nickel and a reactivity-based approach for cobalt and iron. This Account summarizes progress in our laboratory on both the development and application of fluorescent probes to identify and study the signaling roles of transition metals in biology. In conjunction with complementary methods for direct metal detection and genetic and/or pharmacological manipulations, fluorescent probes for transition metals have helped reveal a number of principles underlying transition metal dynamics. In this Account, we give three recent

  17. 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

  18. 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

  19. 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

  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, security, and processing time comparisons of biometric fingerprint recognition system using digital and optical enhancements

    NASA Astrophysics Data System (ADS)

    Alsharif, Salim; El-Saba, Aed; Jagapathi, Rajendarreddy

    2011-06-01

    Fingerprint recognition is one of the most commonly used forms of biometrics and has been widely used in daily life due to its feasibility, distinctiveness, permanence, accuracy, reliability, and acceptability. Besides cost, issues related to accuracy, security, and processing time in practical biometric recognition systems represent the most critical factors that makes these systems widely acceptable. Accurate and secure biometric systems often require sophisticated enhancement and encoding techniques that burdens the overall processing time of the system. In this paper we present a comparison between common digital and optical enhancementencoding techniques with respect to their accuracy, security and processing time, when applied to biometric fingerprint systems.

  2. 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.

  3. High-Precise and Robust Face-Recognition System Based on Optical Parallel Correlator

    NASA Astrophysics Data System (ADS)

    Kodate, Kashiko

    2005-10-01

    Facial recognition is applied in a wide range of security systems, and has been studied since the 1970s, with extensive research into and development of digital processing. However, there is only available a 1:1 verification system combined with ID card identification, or an ID-less system with a small number of images in the database. The number of images that can be stored is limited, and recognition has to be improved to account for photos taken at different angles. Commercially available facial recognition systems for the most part utilize digital computers performing electronic pattern recognition. In contrast, optical analog operations can process two-dimensional images instantaneously in parallel using a lens-based Fourier transform function. In the 1960s two methods were proposed, the Vanderlugt correlator and the joint transform correlator (JTC). We present a new scheme using a multi-channel parallel JTC to make better use of spatial parallelism, through the use of a diffraction-type multi-level zone-plate array to extend a single-channel JTC. Our project's objectives were: (i) to design a matched filter which equips the system with high recognition capability at a faster calculation speed by analyzing the spatial frequency of facial image elements, and (ii) to create a four-channel Vanderlugt correlator with super-high-speed (1000 frame/s) optical parallel facial recognition system, robust enough for 1:N identification, for a large database with 4000 images. Automation was also achieved for the entire process via a practical controlling system. The achieved super-high-speed facial recognition system based on optical parallelism is faster in its processing time than the JTC optical correlator.

  4. 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.

  5. 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.

  6. 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

  7. 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

  8. Automatic micropropagation of plants--the vision-system: graph rewriting as pattern recognition

    NASA Astrophysics Data System (ADS)

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

    1993-03-01

    The automation of plant-micropropagation is necessary to produce high amounts of biomass. Plants have to be dissected on particular cutting-points. A vision-system is needed for the recognition of the cutting-points on the plants. With this background, this contribution is directed to the underlying formalism to determine cutting-points on abstract-plant models. We show the usefulness of pattern recognition by graph-rewriting along with some examples in this context.

  9. A Smartwatch-Based Assistance System for the Elderly Performing Fall Detection, Unusual Inactivity Recognition and Medication Reminding.

    PubMed

    Deutsch, Markus; Burgsteiner, Harald

    2016-01-01

    The growing number of elderly people in our society makes it increasingly important to help them live an independent and self-determined life up until a high age. A smartwatch-based assistance system should be implemented that is capable of automatically detecting emergencies and helping elderly people to adhere to their medical therapy. Using the acceleration data of a widely available smartwatch, we implemented fall detection and inactivity recognition based on a smartphone connected via Bluetooth. The resulting system is capable of performing fall detection, inactivity recognition, issuing medication reminders and alerting relatives upon manual activation. Though some challenges, like the dependence on a smartphone remain, the resulting system is a promising approach to help elderly people as well as their relatives to live independently and with a feeling of safety. PMID:27139412

  10. 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

  11. 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

  12. 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.

  13. Active optical zoom system

    DOEpatents

    Wick, David V.

    2005-12-20

    An active optical zoom system changes the magnification (or effective focal length) of an optical imaging system by utilizing two or more active optics in a conventional optical system. The system can create relatively large changes in system magnification with very small changes in the focal lengths of individual active elements by leveraging the optical power of the conventional optical elements (e.g., passive lenses and mirrors) surrounding the active optics. The active optics serve primarily as variable focal-length lenses or mirrors, although adding other aberrations enables increased utility. The active optics can either be LC SLMs, used in a transmissive optical zoom system, or DMs, used in a reflective optical zoom system. By appropriately designing the optical system, the variable focal-length lenses or mirrors can provide the flexibility necessary to change the overall system focal length (i.e., effective focal length), and therefore magnification, that is normally accomplished with mechanical motion in conventional zoom lenses. The active optics can provide additional flexibility by allowing magnification to occur anywhere within the FOV of the system, not just on-axis as in a conventional system.

  14. Object detection by optical correlator and intelligence recognition surveillance systems

    NASA Astrophysics Data System (ADS)

    Sheng, Yunlong

    2013-09-01

    We report a recent work on robust object detection in high-resolution aerial imagery in urban environment for Intelligence, Surveillance and Recognition (ISR) missions. Our approaches used the simple linear iterative clustering (SLIC) algorithm, which combines regional and edge information to form the superpixels. The irregularity in size and shape of the superpixels measured with the Hausdorff distance served to determine the salient regions in the very large aerial images. Then, the car detection was performed with both the component-based approach and the featurebased approaches. We merged the superpixels with the statistical region merging (SRM) algorithm. The regions were described by the radiometric, geometrical moments and shape features, and classified using the Support Vector Machine (SVM). The cast shadow were detected and removed by a radiometry based tricolor attenuation model (TAM). Detection of object parts is less sensitive to occlusion, rotation, and changes in scale, view angle and illumination than detection of the object as whole. The object parts were combined to the object according to their unique spatial relations. On the other hand, we used the invariant scale invariant feature transform (SIFT) features to describe superpixels and classed them by the SVM as belong or not to the object. All along our recent work we still trace the brilliant ideas in early days by H. John Caulfield and other pioneers of optical pattern recognition, for improving the discrimination of the matched spatial filter with linear combinations of cross-correlations, which have been inherited transformed and reinvented to achieve tremendous progress.

  15. Voice activity detection for speaker verification systems

    NASA Astrophysics Data System (ADS)

    Borowski, Filip

    2008-01-01

    Complex algorithm for speech activity detection was presented in this article. It is based on speech enhancement, features extraction and final detection algorithm. The first one was published in ETSI standard as a module of "Advanced front-end feature extraction algorithm" in distributed speech recognition system. It consists of two main parts, noise estimatiom and Wiener filtering. For the final detection modified linear prediction coefficients and spectral entropy features are extracted form denoised signal.

  16. 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.

  17. Vision-based object detection and recognition system for intelligent vehicles

    NASA Astrophysics Data System (ADS)

    Ran, Bin; Liu, Henry X.; Martono, Wilfung

    1999-01-01

    Recently, a proactive crash mitigation system is proposed to enhance the crash avoidance and survivability of the Intelligent Vehicles. Accurate object detection and recognition system is a prerequisite for a proactive crash mitigation system, as system component deployment algorithms rely on accurate hazard detection, recognition, and tracking information. In this paper, we present a vision-based approach to detect and recognize vehicles and traffic signs, obtain their information, and track multiple objects by using a sequence of color images taken from a moving vehicle. The entire system consist of two sub-systems, the vehicle detection and recognition sub-system and traffic sign detection and recognition sub-system. Both of the sub- systems consist of four models: object detection model, object recognition model, object information model, and object tracking model. In order to detect potential objects on the road, several features of the objects are investigated, which include symmetrical shape and aspect ratio of a vehicle and color and shape information of the signs. A two-layer neural network is trained to recognize different types of vehicles and a parameterized traffic sign model is established in the process of recognizing a sign. Tracking is accomplished by combining the analysis of single image frame with the analysis of consecutive image frames. The analysis of the single image frame is performed every ten full-size images. The information model will obtain the information related to the object, such as time to collision for the object vehicle and relative distance from the traffic sings. Experimental results demonstrated a robust and accurate system in real time object detection and recognition over thousands of image frames.

  18. 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.

  19. 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

  20. Toward a unified model of face and object recognition in the human visual system

    PubMed Central

    Wallis, Guy

    2013-01-01

    Our understanding of the mechanisms and neural substrates underlying visual recognition has made considerable progress over the past 30 years. During this period, accumulating evidence has led many scientists to conclude that objects and faces are recognised in fundamentally distinct ways, and in fundamentally distinct cortical areas. In the psychological literature, in particular, this dissociation has led to a palpable disconnect between theories of how we process and represent the two classes of object. This paper follows a trend in part of the recognition literature to try to reconcile what we know about these two forms of recognition by considering the effects of learning. Taking a widely accepted, self-organizing model of object recognition, this paper explains how such a system is affected by repeated exposure to specific stimulus classes. In so doing, it explains how many aspects of recognition generally regarded as unusual to faces (holistic processing, configural processing, sensitivity to inversion, the other-race effect, the prototype effect, etc.) are emergent properties of category-specific learning within such a system. Overall, the paper describes how a single model of recognition learning can and does produce the seemingly very different types of representation associated with faces and objects. PMID:23966963

  1. From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems

    PubMed Central

    Yildiz, Izzet B.; von Kriegstein, Katharina; Kiebel, Stefan J.

    2013-01-01

    Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents—an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments. PMID:24068902

  2. Toward a unified model of face and object recognition in the human visual system.

    PubMed

    Wallis, Guy

    2013-01-01

    Our understanding of the mechanisms and neural substrates underlying visual recognition has made considerable progress over the past 30 years. During this period, accumulating evidence has led many scientists to conclude that objects and faces are recognised in fundamentally distinct ways, and in fundamentally distinct cortical areas. In the psychological literature, in particular, this dissociation has led to a palpable disconnect between theories of how we process and represent the two classes of object. This paper follows a trend in part of the recognition literature to try to reconcile what we know about these two forms of recognition by considering the effects of learning. Taking a widely accepted, self-organizing model of object recognition, this paper explains how such a system is affected by repeated exposure to specific stimulus classes. In so doing, it explains how many aspects of recognition generally regarded as unusual to faces (holistic processing, configural processing, sensitivity to inversion, the other-race effect, the prototype effect, etc.) are emergent properties of category-specific learning within such a system. Overall, the paper describes how a single model of recognition learning can and does produce the seemingly very different types of representation associated with faces and objects. PMID:23966963

  3. Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems

    NASA Technical Reports Server (NTRS)

    Huang, Yiteng; Chen, Jingdong; Chen, Shaoyan

    2010-01-01

    A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources.

  4. 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.

  5. Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction

    PubMed Central

    Sun, Qian; Feng, Hao; Yan, Xueying; Zeng, Zhoumo

    2015-01-01

    This paper proposes a novel feature extraction method for intrusion event recognition within a phase-sensitive optical time-domain reflectometer (Φ-OTDR) sensing system. Feature extraction of time domain signals in these systems is time-consuming and may lead to inaccuracies due to noise disturbances. The recognition accuracy and speed of current systems cannot meet the requirements of Φ-OTDR online vibration monitoring systems. In the method proposed in this paper, the time-space domain signal is used for feature extraction instead of the time domain signal. Feature vectors are obtained from morphologic features of time-space domain signals. A scatter matrix is calculated for the feature selection. Experiments show that the feature extraction method proposed in this paper can greatly improve recognition accuracies, with a lower computation time than traditional methods, i.e., a recognition accuracy of 97.8% can be achieved with a recognition time of below 1 s, making it is very suitable for Φ-OTDR system online vibration monitoring. PMID:26131671

  6. 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.

  7. 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

  8. [Research on Barrier-free Home Environment System Based on Speech Recognition].

    PubMed

    Zhu, Husheng; Yu, Hongliu; Shi, Ping; Fang, Youfang; Jian, Zhuo

    2015-10-01

    The number of people with physical disabilities is increasing year by year, and the trend of population aging is more and more serious. In order to improve the quality of the life, a control system of accessible home environment for the patients with serious disabilities was developed to control the home electrical devices with the voice of the patients. The control system includes a central control platform, a speech recognition module, a terminal operation module, etc. The system combines the speech recognition control technology and wireless information transmission technology with the embedded mobile computing technology, and interconnects the lamp, electronic locks, alarms, TV and other electrical devices in the home environment as a whole system through a wireless network node. The experimental results showed that speech recognition success rate was more than 84% in the home environment.

  9. [Research on Barrier-free Home Environment System Based on Speech Recognition].

    PubMed

    Zhu, Husheng; Yu, Hongliu; Shi, Ping; Fang, Youfang; Jian, Zhuo

    2015-10-01

    The number of people with physical disabilities is increasing year by year, and the trend of population aging is more and more serious. In order to improve the quality of the life, a control system of accessible home environment for the patients with serious disabilities was developed to control the home electrical devices with the voice of the patients. The control system includes a central control platform, a speech recognition module, a terminal operation module, etc. The system combines the speech recognition control technology and wireless information transmission technology with the embedded mobile computing technology, and interconnects the lamp, electronic locks, alarms, TV and other electrical devices in the home environment as a whole system through a wireless network node. The experimental results showed that speech recognition success rate was more than 84% in the home environment. PMID:26964305

  10. 'Order from disorder sprung': recognition and regulation in the immune system.

    PubMed

    Mak, Tak W

    2003-06-15

    Milton's epic poem Paradise lost supplies a colourful metaphor for the immune system and its responses to pathogens. With the role of Satan played by pathogens seeking to destroy the paradise of human health, GOD intervenes and imposes order out of chaos. In this context, GOD means 'generation of diversity': the capacity of the innate and specific immune responses to recognize and eliminate a universe of pathogens. Thus, the immune system can be thought of as an entity that self-assembles the elements required to combat bodily invasion and injury. In so doing, it brings to bear the power of specific recognition: the ability to distinguish self from non-self, and the threatening from the benign. This ability to define and protect self is evolutionarily very old. Self-recognition and biochemical and barrier defences can be detected in primitive organisms, and elements of these mechanisms are built upon in an orderly way to establish the mammalian immune system. Innate immune responses depend on the use of a limited number of germline-encoded receptors to recognize conserved molecular patterns that occur on the surfaces of a broad range of pathogens. The B and T lymphocytes of the specific immune response use complex gene-rearrangement machinery to generate a diversity of antigen receptors capable of recognizing any pathogen in the universe. Binding to receptors on both innate and specific immune-system cells triggers intricate intracellular signalling pathways that lead to new gene transcription and effector-cell activation. And yet, regulation is imposed on these responses so that Paradise is not lost to the turning of the immune system onto self-tissues, the spectre of autoimmunity. Lymphocyte activation requires multiple signals and intercellular interactions. Mechanisms exist to establish tolerance to self by the selection and elimination of cells recognizing self-antigens. Immune system cell populations are reduced by programmed cell death once the pathogen

  11. 'Order from disorder sprung': recognition and regulation in the immune system.

    PubMed

    Mak, Tak W

    2003-06-15

    Milton's epic poem Paradise lost supplies a colourful metaphor for the immune system and its responses to pathogens. With the role of Satan played by pathogens seeking to destroy the paradise of human health, GOD intervenes and imposes order out of chaos. In this context, GOD means 'generation of diversity': the capacity of the innate and specific immune responses to recognize and eliminate a universe of pathogens. Thus, the immune system can be thought of as an entity that self-assembles the elements required to combat bodily invasion and injury. In so doing, it brings to bear the power of specific recognition: the ability to distinguish self from non-self, and the threatening from the benign. This ability to define and protect self is evolutionarily very old. Self-recognition and biochemical and barrier defences can be detected in primitive organisms, and elements of these mechanisms are built upon in an orderly way to establish the mammalian immune system. Innate immune responses depend on the use of a limited number of germline-encoded receptors to recognize conserved molecular patterns that occur on the surfaces of a broad range of pathogens. The B and T lymphocytes of the specific immune response use complex gene-rearrangement machinery to generate a diversity of antigen receptors capable of recognizing any pathogen in the universe. Binding to receptors on both innate and specific immune-system cells triggers intricate intracellular signalling pathways that lead to new gene transcription and effector-cell activation. And yet, regulation is imposed on these responses so that Paradise is not lost to the turning of the immune system onto self-tissues, the spectre of autoimmunity. Lymphocyte activation requires multiple signals and intercellular interactions. Mechanisms exist to establish tolerance to self by the selection and elimination of cells recognizing self-antigens. Immune system cell populations are reduced by programmed cell death once the pathogen

  12. 'Order from disorder sprung': recognition and regulation in the immune system

    NASA Astrophysics Data System (ADS)

    Mak, Tak W.

    2003-06-01

    Milton's epic poem Paradise lost supplies a colourful metaphor for the immune system and its responses to pathogens. With the role of Satan played by pathogens seeking to destroy the paradise of human health, GOD intervenes and imposes order out of chaos. In this context, GOD means 'generation of diversity': the capacity of the innate and specific immune responses to recognize and eliminate a universe of pathogens. Thus, the immune system can be thought of as an entity that self-assembles the elements required to combat bodily invasion and injury. In so doing, it brings to bear the power of specific recognition: the ability to distinguish self from non-self, and the threatening from the benign. This ability to define and protect self is evolutionarily very old. Self-recognition and biochemical and barrier defences can be detected in primitive organisms, and elements of these mechanisms are built upon in an orderly way to establish the mammalian immune system. Innate immune responses depend on the use of a limited number of germline-encoded receptors to recognize conserved molecular patterns that occur on the surfaces of a broad range of pathogens. The B and T lymphocytes of the specific immune response use complex gene-rearrangement machinery to generate a diversity of antigen receptors capable of recognizing any pathogen in the universe. Binding to receptors on both innate and specific immune-system cells triggers intricate intracellular signalling pathways that lead to new gene transcription and effector-cell activation. And yet, regulation is imposed on these responses so that Paradise is not lost to the turning of the immune system onto self-tissues, the spectre of autoimmunity. Lymphocyte activation requires multiple signals and intercellular interactions. Mechanisms exist to establish tolerance to self by the selection and elimination of cells recognizing self-antigens. Immune system cell populations are reduced by programmed cell death once the pathogen

  13. 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).

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. 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

  19. 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

  20. 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

  1. 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.

  2. Face Recognition for Access Control Systems Combining Image-Difference Features Based on a Probabilistic Model

    NASA Astrophysics Data System (ADS)

    Miwa, Shotaro; Kage, Hiroshi; Hirai, Takashi; Sumi, Kazuhiko

    We propose a probabilistic face recognition algorithm for Access Control System(ACS)s. Comparing with existing ACSs using low cost IC-cards, face recognition has advantages in usability and security that it doesn't require people to hold cards over scanners and doesn't accept imposters with authorized cards. Therefore face recognition attracts more interests in security markets than IC-cards. But in security markets where low cost ACSs exist, price competition is important, and there is a limitation on the quality of available cameras and image control. Therefore ACSs using face recognition are required to handle much lower quality images, such as defocused and poor gain-controlled images than high security systems, such as immigration control. To tackle with such image quality problems we developed a face recognition algorithm based on a probabilistic model which combines a variety of image-difference features trained by Real AdaBoost with their prior probability distributions. It enables to evaluate and utilize only reliable features among trained ones during each authentication, and achieve high recognition performance rates. The field evaluation using a pseudo Access Control System installed in our office shows that the proposed system achieves a constant high recognition performance rate independent on face image qualities, that is about four times lower EER (Equal Error Rate) under a variety of image conditions than one without any prior probability distributions. On the other hand using image difference features without any prior probabilities are sensitive to image qualities. We also evaluated PCA, and it has worse, but constant performance rates because of its general optimization on overall data. Comparing with PCA, Real AdaBoost without any prior distribution performs twice better under good image conditions, but degrades to a performance as good as PCA under poor image conditions.

  3. 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.

  4. Drosophila GRAIL: An intelligent system for gene recognition in Drosophila DNA sequences

    SciTech Connect

    Xu, Ying; Einstein, J.R.; Uberbacher, E.C.; Helt, G.; Rubin, G.

    1995-06-01

    An AI-based system for gene recognition in Drosophila DNA sequences was designed and implemented. The system consists of two main modules, one for coding exon recognition and one for single gene model construction. The exon recognition module finds a coding exon by recognition of its splice junctions (or translation start) and coding potential. The core of this module is a set of neural networks which evaluate an exon candidate for the possibility of being a true coding exon using the ``recognized`` splice junction (or translation start) and coding signals. The recognition process consists of four steps: generation of an exon candidate pool, elimination of improbable candidates using heuristic rules, candidate evaluation by trained neural networks, and candidate cluster resolution and final exon prediction. The gene model construction module takes as input the clustered exon candidates and builds a ``best`` possible single gene model using an efficient dynamic programming algorithm. 129 Drosophila sequences consisting of 441 coding exons including 216358 coding bases were extructed from GenBank and used to build statistical matrices and to train the neural networks. On this training set the system recognized 97% of the coding messages and predicted only 5% false messages. Among the ``correctly`` predicted exons, 68% match the actual exon exactly and 96% have at least one edge predicted correctly. On an independent test set consisting of 30 Drosophila sequences, the system recognized 96% of the coding messages and predicted 7% false messages.

  5. 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

  6. Crozier’s paradox revisited: maintenance of genetic recognition systems by disassortative mating

    PubMed Central

    2013-01-01

    Background Organisms are predicted to behave more favourably towards relatives, and kin-biased cooperation has been found in all domains of life from bacteria to vertebrates. Cooperation based on genetic recognition cues is paradoxical because it disproportionately benefits individuals with common phenotypes, which should erode the required cue polymorphism. Theoretical models suggest that many recognition loci likely have some secondary function that is subject to diversifying selection, keeping them variable. Results Here, we use individual-based simulations to investigate the hypothesis that the dual use of recognition cues to facilitate social behaviour and disassortative mating (e.g. for inbreeding avoidance) can maintain cue diversity over evolutionary time. Our model shows that when organisms mate disassortatively with respect to their recognition cues, cooperation and recognition locus diversity can persist at high values, especially when outcrossed matings produce more surviving offspring. Mating system affects cue diversity via at least four distinct mechanisms, and its effects interact with other parameters such as population structure. Also, the attrition of cue diversity is less rapid when cooperation does not require an exact cue match. Using a literature review, we show that there is abundant empirical evidence that heritable recognition cues are simultaneously used in social and sexual behaviour. Conclusions Our models show that mate choice is one possible resolution of the paradox of genetic kin recognition, and the literature review suggests that genetic recognition cues simultaneously inform assortative cooperation and disassortative mating in a large range of taxa. However, direct evidence is scant and there is substantial scope for future work. PMID:24070498

  7. Low-cost speech recognition system for small vocabulary and independent speaker

    NASA Astrophysics Data System (ADS)

    Teh, Chih Chiang; Jong, Ching C.; Siek, Liter

    2000-10-01

    In this paper an ASIC implementation of a low cost speech recognition system for small vocabulary, 15 isolated word, speaker independent is presented. The IC is a digital block that receives a 12 bit sample with a sampling rate of 11.025 kHz as its input. The IC is running at 10 MHz system clock and targeted at 0.35 micrometers CMOS process. The whole chip, which includes the speech recognition system core, RAM and ROM contains about 61000 gates. The die size is 1.5 mm by 3 mm. The current design had been coded in VHDL for hardware implementation and its functionality is identical with the Matlab simulation. The average speech recognition rate for this IC is 89 percent for 15 isolated words.

  8. A Computer-Based Gaming System for Assessing Recognition Performance (RECOG).

    ERIC Educational Resources Information Center

    Little, Glenn A.; And Others

    This report documents a computer-based gaming system for assessing recognition performance (RECOG). The game management system is programmed in a modular manner to: instruct the student on how to play the game, retrieve and display individual images, keep track of how well individuals play and provide them feedback, and link these components by…

  9. Introduction and Overview of the Vicens-Reddy Speech Recognition System.

    ERIC Educational Resources Information Center

    Kameny, Iris; Ritea, H.

    The Vicens-Reddy System is unique in the sense that it approaches the problem of speech recognition as a whole, rather than treating particular aspects of the problems as in previous attempts. For example, where earlier systems treated only segmentation of speech into phoneme groups, or detected phonemes in a given context, the Vicens-Reddy System…

  10. 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.

  11. An improved cortex-like neuromorphic system for target recognitions

    NASA Astrophysics Data System (ADS)

    Tsitiridis, Aristeidis; Yuen, Peter; Hong, Kan; Chen, Tong; Ibrahim, Izzati; Jackman, James; James, David; Richardson, Mark

    2010-10-01

    This paper reports on the enhancement of biologically-inspired machine vision through a rotation invariance mechanism. Research over the years has suggested that rotation invariance is one of the fundamental generic elements of object constancy, a known generic visual ability of the human brain. Cortex-like vision unlike conventional pixel based machine vision is achieved by mimicking neuromorphic mechanisms of the primates' brain. In this preliminary study, rotation invariance is implemented through histograms from Gabor features of an object. The performance of rotation invariance in the neuromorphic algorithm is assessed by the classification accuracies of a test data set which consists of image objects in five different orientations. It is found that a much more consistent classification result over these five different oriented data sets has been achieved by the integrated rotation invariance neuromorphic algorithm compared to the one without. In addition, the issue of varying aspect ratios of input images to these models is also addressed, in an attempt to create a robust algorithm against a wider variability of input data. The extension of the present achievement is to improve the recognition accuracies while incorporating it to a series of different real-world scenarios which would challenge the approach accordingly.

  12. Body posture recognition and turning recording system for the care of bed bound patients.

    PubMed

    Hsiao, Rong-Shue; Mi, Zhenqiang; Yang, Bo-Ru; Kau, Lih-Jen; Bitew, Mekuanint Agegnehu; Li, Tzu-Yu

    2015-01-01

    This paper proposes body posture recognition and turning recording system for assisting the care of bed bound patients in nursing homes. The system continuously detects the patient's body posture and records the length of time for each body posture. If the patient remains in the same body posture long enough to develop pressure ulcers, the system notifies caregivers to change the patient's body posture. The objective of recording is to provide the log of body turning for querying of patients' family members. In order to accurately detect patient's body posture, we developed a novel pressure sensing pad which contains force sensing resistor sensors. Based on the proposed pressure sensing pad, we developed a bed posture recognition module which includes a bed posture recognition algorithm. The algorithm is based on fuzzy theory. The body posture recognition algorithm can detect the patient's bed posture whether it is right lateral decubitus, left lateral decubitus, or supine. The detected information of patient's body posture can be then transmitted to the server of healthcare center by the communication module to perform the functions of recording and notification. Experimental results showed that the average posture recognition accuracy for our proposed module is 92%. PMID:26444814

  13. A Single-System Model Predicts Recognition Memory and Repetition Priming in Amnesia

    PubMed Central

    Kessels, Roy P.C.; Wester, Arie J.; Shanks, David R.

    2014-01-01

    We challenge the claim that there are distinct neural systems for explicit and implicit memory by demonstrating that a formal single-system model predicts the pattern of recognition memory (explicit) and repetition priming (implicit) in amnesia. In the current investigation, human participants with amnesia categorized pictures of objects at study and then, at test, identified fragmented versions of studied (old) and nonstudied (new) objects (providing a measure of priming), and made a recognition memory judgment (old vs new) for each object. Numerous results in the amnesic patients were predicted in advance by the single-system model, as follows: (1) deficits in recognition memory and priming were evident relative to a control group; (2) items judged as old were identified at greater levels of fragmentation than items judged new, regardless of whether the items were actually old or new; and (3) the magnitude of the priming effect (the identification advantage for old vs new items) overall was greater than that of items judged new. Model evidence measures also favored the single-system model over two formal multiple-systems models. The findings support the single-system model, which explains the pattern of recognition and priming in amnesia primarily as a reduction in the strength of a single dimension of memory strength, rather than a selective explicit memory system deficit. PMID:25122896

  14. A single-system model predicts recognition memory and repetition priming in amnesia.

    PubMed

    Berry, Christopher J; Kessels, Roy P C; Wester, Arie J; Shanks, David R

    2014-08-13

    We challenge the claim that there are distinct neural systems for explicit and implicit memory by demonstrating that a formal single-system model predicts the pattern of recognition memory (explicit) and repetition priming (implicit) in amnesia. In the current investigation, human participants with amnesia categorized pictures of objects at study and then, at test, identified fragmented versions of studied (old) and nonstudied (new) objects (providing a measure of priming), and made a recognition memory judgment (old vs new) for each object. Numerous results in the amnesic patients were predicted in advance by the single-system model, as follows: (1) deficits in recognition memory and priming were evident relative to a control group; (2) items judged as old were identified at greater levels of fragmentation than items judged new, regardless of whether the items were actually old or new; and (3) the magnitude of the priming effect (the identification advantage for old vs new items) overall was greater than that of items judged new. Model evidence measures also favored the single-system model over two formal multiple-systems models. The findings support the single-system model, which explains the pattern of recognition and priming in amnesia primarily as a reduction in the strength of a single dimension of memory strength, rather than a selective explicit memory system deficit.

  15. The design and implementation of effective face detection and recognition system

    NASA Astrophysics Data System (ADS)

    Sun, Yigui

    2011-06-01

    In the paper, a face detection and recognition system (FDRS) based on video sequences and still image is proposed. It uses the AdaBoost algorithm to detect human face in the image or frame, adopts Discrete Cosine Transforms (DCT) for feature extraction and recognition in face image. The related technologies are firstly outlined. Then, the system requirements and UML use case diagram are described. In addition, the paper mainly introduces the design solution and key procedures. The FDRS's source-code is built in VC++, Standard Template Library (STL) and Intel Open Source Computer Vision Library (OpenCV).

  16. A smart pattern recognition system for the automatic identification of aerospace acoustic sources

    NASA Technical Reports Server (NTRS)

    Cabell, R. H.; Fuller, C. R.

    1989-01-01

    An intelligent air-noise recognition system is described that uses pattern recognition techniques to distinguish noise signatures of five different types of acoustic sources, including jet planes, propeller planes, a helicopter, train, and wind turbine. Information for classification is calculated using the power spectral density and autocorrelation taken from the output of a single microphone. Using this system, as many as 90 percent of test recordings were correctly identified, indicating that the linear discriminant functions developed can be used for aerospace source identification.

  17. 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

  18. Design of no blind area perimeter intrusion recognition system based on fisheye lens

    NASA Astrophysics Data System (ADS)

    Dai, Jun-jian; Han, Wen-bo

    2013-08-01

    The Perimeter intrusion recognition technology has slowly become an indispensable function in the intelligent video surveillance system. The existed always use the multiple video acquisition nodes to respectively control a monitoring area and each node alarm independently. However, the existed solutions are difficult to avoid the existence of monitoring blind area, and can't suitable for the perimeter environment with irregular outline, and at the same time, because of the too many nodes, it inevitably decreased the overall accuracy of intrusion recognition system and increased the cost of system. To avoid the above defects, this paper mainly talks about the following three aspects. Firstly, we used the fisheye lens as the optical system of video acquisition node, and it evidently enhances each node's information acquisition ability. And in this way, we just need to decorate a small amount of video acquisition node to get no blind area environmental information of the perimeter when against a larger monitoring situation. Secondly, due to the inexistence of blind area, the system will have enough video image information to generate the 360 degree panoramic image for monitoring environment, and finally the system server collected the wide-angle image information to splice into the panoramic video image. Finally, the system will use the panoramic image to complete the intrusion behavior recognition, thus we can effectively avoid the parallel computation in many nodes independently invasion of recognition, and this can greatly reduces the dependence for the multiple CPU operation platform and enhances the reliability of the system. The field test results show that, with the help of this paper's solution, the perimeter of the invasion of recognition system can effectively avoids the recognition of blind area. In the same recognition algorithm and same level delay premise, it greatly reduces the monitoring system server configuration requirements, especially for the

  19. 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.

  20. 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

  1. Construction of a large scale integrated map of macrophage pathogen recognition and effector systems

    PubMed Central

    2010-01-01

    Background In an effort to better understand the molecular networks that underpin macrophage activation we have been assembling a map of relevant pathways. Manual curation of the published literature was carried out in order to define the components of these pathways and the interactions between them. This information has been assembled into a large integrated directional network and represented graphically using the modified Edinburgh Pathway Notation (mEPN) scheme. Results The diagram includes detailed views of the toll-like receptor (TLR) pathways, other pathogen recognition systems, NF-kappa-B, apoptosis, interferon signalling, MAP-kinase cascades, MHC antigen presentation and proteasome assembly, as well as selected views of the transcriptional networks they regulate. The integrated pathway includes a total of 496 unique proteins, the complexes formed between them and the processes in which they are involved. This produces a network of 2,170 nodes connected by 2,553 edges. Conclusions The pathway diagram is a navigable visual aid for displaying a consensus view of the pathway information available for these systems. It is also a valuable resource for computational modelling and aid in the interpretation of functional genomics data. We envisage that this work will be of value to those interested in macrophage biology and also contribute to the ongoing Systems Biology community effort to develop a standard notation scheme for the graphical representation of biological pathways. PMID:20470404

  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. Recognition of Sound Environment by a Bathroom Monitoring System

    NASA Astrophysics Data System (ADS)

    Komoguchi, Naoyuki; Yamane, Kenji; Tanaka, Shogo

    Developing a monitoring system for a bathroom is important to prevent aged persons from accidents. The authors previously developed a bathroom monitoring system using an acoustic sensor which measured the water level of a bathtub and the temperature and also recognized the sound environment. The sound environment was however occasionally mis-recognized with the system. The present paper proposes a new method which recognizes the sound environment in the bathroom more accurately. Experiments demonstrate the effectiveness of the method.

  4. 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.

  5. Evaluating Automatic Speech Recognition-Based Language Learning Systems: A Case Study

    ERIC Educational Resources Information Center

    van Doremalen, Joost; Boves, Lou; Colpaert, Jozef; Cucchiarini, Catia; Strik, Helmer

    2016-01-01

    The purpose of this research was to evaluate a prototype of an automatic speech recognition (ASR)-based language learning system that provides feedback on different aspects of speaking performance (pronunciation, morphology and syntax) to students of Dutch as a second language. We carried out usability reviews, expert reviews and user tests to…

  6. The effect of involuntary motor activity on myoelectric pattern recognition: a case study with chronic stroke patients

    NASA Astrophysics Data System (ADS)

    Zhang, Xu; Li, Yun; Chen, Xiang; Li, Guanglin; Zev Rymer, William; Zhou, Ping

    2013-08-01

    Objective. This study investigates the effect of the involuntary motor activity of paretic-spastic muscles on the classification of surface electromyography (EMG) signals. Approach. Two data collection sessions were designed for 8 stroke subjects to voluntarily perform 11 functional movements using their affected forearm and hand at relatively slow and fast speeds. For each stroke subject, the degree of involuntary motor activity present in the voluntary surface EMG recordings was qualitatively described from such slow and fast experimental protocols. Myoelectric pattern recognition analysis was performed using different combinations of voluntary surface EMG data recorded from the slow and fast sessions. Main results. Across all tested stroke subjects, our results revealed that when involuntary surface EMG is absent or present in both the training and testing datasets, high accuracies (>96%, >98%, respectively, averaged over all the subjects) can be achieved in the classification of different movements using surface EMG signals from paretic muscles. When involuntary surface EMG was solely involved in either the training or testing datasets, the classification accuracies were dramatically reduced (<89%, <85%, respectively). However, if both the training and testing datasets contained EMG signals with the presence and absence of involuntary EMG interference, high accuracies were still achieved (>97%). Significance. The findings of this study can be used to guide the appropriate design and implementation of myoelectric pattern recognition based systems or devices toward promoting robot-aided therapy for stroke rehabilitation.

  7. A Novel Word Based Arabic Handwritten Recognition System Using SVM Classifier

    NASA Astrophysics Data System (ADS)

    Khalifa, Mahmoud; Bingru, Yang

    Every language script has its structure, characteristic, and feature. Character based word recognition depends on the feature available to be extracted from character. Word based script recognition overcome the problem of character segmenting and can be applied for several languages (Arabic, Urdu, Farsi... est.). In this paper Arabic handwritten is classified as word based system. Firstly, words segmented and normalized in size to fit the DCT input. Then extract feature characteristic by computing the Euclidean distance between pairs of objects in n-by-m data matrix X. Based on the point's operator of extrema, feature was extracted. Then apply one to one-Class Support Vector Machines (SVMs) as a discriminative framework in order to address feature classification. The approach was tested with several public databases and we get high efficiency rate recognition.

  8. Summary of the transfer of optical processing to systems: optical pattern recognition program

    NASA Astrophysics Data System (ADS)

    Lindell, Scott D.

    1995-06-01

    Martin Marietta has successfully completed a TOPS optical pattern recognition program. The program culminated in August 1994 with an automatic target recognition flight demonstration inwhich an M60A2 tank was acquired, identified, and tracked with a visible seeker from a UH-1 helicopter flying a fiber optic guided missile (FOG-M) mission profile. The flight demonstration was conducted by the US Army Missile Command (MICOM) and supported by Martin Marietta. The pattern recognition system performance for acquiring and identifying the M60A2 tank, which was positioned among an array with five other vehicle types, was 90% probability of correct identification and a 4% false identification for over 40,000 frames of imagery processed. Imagery was processed at a 15 Hz input rate with a 1 ft3, 76 W, 4 GFLOP processor performing up to 800 correlations per second.

  9. Data management in pattern recognition and image processing systems

    NASA Technical Reports Server (NTRS)

    Zobrist, A. L.; Bryant, N. A.

    1976-01-01

    Data management considerations are important to any system which handles large volumes of data or where the manipulation of data is technically sophisticated. A particular problem is the introduction of image-formatted files into the mainstream of data processing application. This report describes a comprehensive system for the manipulation of image, tabular, and graphical data sets which involve conversions between the various data types. A key characteristic is the use of image processing technology to accomplish data management tasks. Because of this, the term 'image-based information system' has been adopted.

  10. Neuro-parity pattern recognition system and method

    DOEpatents

    Gross, Kenneth C.; Singer, Ralph M.; Van Alstine, Rollin G.; Wegerich, Stephan W.; Yue, Yong

    2000-01-01

    A method and system for monitoring a process and determining its condition. Initial data is sensed, a first set of virtual data is produced by applying a system state analyzation to the initial data, a second set of virtual data is produced by applying a neural network analyzation to the initial data and a parity space analyzation is applied to the first and second set of virtual data and also to the initial data to provide a parity space decision about the condition of the process. A logic test can further be applied to produce a further system decision about the state of the process.

  11. Dynamic combinatorial libraries: from exploring molecular recognition to systems chemistry.

    PubMed

    Li, Jianwei; Nowak, Piotr; Otto, Sijbren

    2013-06-26

    Dynamic combinatorial chemistry (DCC) is a subset of combinatorial chemistry where the library members interconvert continuously by exchanging building blocks with each other. Dynamic combinatorial libraries (DCLs) are powerful tools for discovering the unexpected and have given rise to many fascinating molecules, ranging from interlocked structures to self-replicators. Furthermore, dynamic combinatorial molecular networks can produce emergent properties at systems level, which provide exciting new opportunities in systems chemistry. In this perspective we will highlight some new methodologies in this field and analyze selected examples of DCLs that are under thermodynamic control, leading to synthetic receptors, catalytic systems, and complex self-assembled supramolecular architectures. Also reviewed are extensions of the principles of DCC to systems that are not at equilibrium and may therefore harbor richer functional behavior. Examples include self-replication and molecular machines.

  12. Model-based vision system for automatic recognition of structures in dental radiographs

    NASA Astrophysics Data System (ADS)

    Acharya, Raj S.; Samarabandu, Jagath K.; Hausmann, E.; Allen, K. A.

    1991-07-01

    X-ray diagnosis of destructive periodontal disease requires assessing serial radiographs by an expert to determine the change in the distance between cemento-enamel junction (CEJ) and the bone crest. To achieve this without the subjectivity of a human expert, a knowledge based system is proposed to automatically locate the two landmarks which are the CEJ and the level of alveolar crest at its junction with the periodontal ligament space. This work is a part of an ongoing project to automatically measure the distance between CEJ and the bone crest along a line parallel to the axis of the tooth. The approach presented in this paper is based on identifying a prominent feature such as the tooth boundary using local edge detection and edge thresholding to establish a reference and then using model knowledge to process sub-regions in locating the landmarks. Segmentation techniques invoked around these regions consists of a neural-network like hierarchical refinement scheme together with local gradient extraction, multilevel thresholding and ridge tracking. Recognition accuracy is further improved by first locating the easily identifiable parts of the bone surface and the interface between the enamel and the dentine and then extending these boundaries towards the periodontal ligament space and the tooth boundary respectively. The system is realized as a collection of tools (or knowledge sources) for pre-processing, segmentation, primary and secondary feature detection and a control structure based on the blackboard model to coordinate the activities of these tools.

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. 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

  18. Macromolecular recognition: Structural aspects of the origin of the genetic system

    NASA Technical Reports Server (NTRS)

    Rein, Robert; Barak, Dov; Luo, Ning; Zielinski, Theresa Julia; Shibata, Masayuki

    1991-01-01

    Theoretical simulation of prebiotic chemical processes is an invaluable tool for probing the phenomenon of evolution of life. Using computational and modeling techniques and guided by analogies from present day systems we, seek to understand the emergence of genetic apparatus, enzymatic catalysis and protein synthesis under prebiotic conditions. In one possible scenario, the RNA enzymatic reaction plays a key role in the emergence of the self-replicating and offers a clue to the onset of enzymatic catalysis prior to the existence of the protein biosynthetic machinery. Our ultimate goal is to propose a simple RNA segment which contains the specificity and catalytic activity of the contemporary RNA enzyme and which could emerge in a primordial chemical environment. To understand the mechanism of ribozyme catalyzed reactions, ab initio and semi-empirical (ZINDO) programs were used to investigate the reaction path of transphosphorylation. A special emphasis was placed on the possible catalytic and structural roles played by the coordinated magnesium cation. Both the inline and adjacent mechanisms of transphosphorylation have been studied. Another important aspect of this reaction is the identity of the functional groups which are essential for the acid base catalysis. The structural characteristics of the target helices, particularly a possible role of G center dot T pair, is under examination by molecular dynamics (MD) simulation technique. Modeling of the ancestral aminoacyl-tRNA synthetases (aRS) may provide important clues to the emergence of the genetic code and the protein synthetic machinery. Assuming that the catalytic function evolved before the elements of specific recognition of a particular amino acid, we are exploring the minimal structural requirements for the catalysis of tRNA aminoacylation. The molecular modeling system SYBYL was used for this study based on the high resolution crystallographic structures of the present day tyrosyl-adenylate:tyrRS and

  19. Semi-automatic ground truth generation for license plate recognition system

    NASA Astrophysics Data System (ADS)

    Wang, Shen-Zheng; Zhao, San-Lung; Chen, Yi-Yuan; Lan, Kung-Ming

    2011-09-01

    License plate recognition (LPR) system is to help alert relevant personnel of any passing vehicle in the surveillance area. In order to test algorithms for license plate recognition, it is necessary to have input frames in which the ground truth is determined. The purpose of ground truth data is here to provide an absolute reference for performance evaluation or training purposes. However, annotating ground truth data for real-life inputs is very disturbing task because of timeconsuming manual. In this paper, we proposed a method of semi-automatic ground truth generation for license plate recognition in video sequences. The method started from region of interesting detection to rapidly extract characters lines followed by a license plate recognition system to verify the license plate regions and recognized the numbers. On the top of the LPR system, we incorporated a tracking-validation mechanism to detect the time interval of passing vehicles in input sequences. The tracking mechanism is initialized by a single license plate region in one frame. Moreover, in order to tolerate the variation of the license plate appearances in the input sequences, the validator would be updated by capturing positive and negatives samples during tracking. Experimental results show that the proposed method can achieve promising results.

  20. Vision-based obstacle recognition system for automated lawn mower robot development

    NASA Astrophysics Data System (ADS)

    Mohd Zin, Zalhan; Ibrahim, Ratnawati

    2011-06-01

    Digital image processing techniques (DIP) have been widely used in various types of application recently. Classification and recognition of a specific object using vision system require some challenging tasks in the field of image processing and artificial intelligence. The ability and efficiency of vision system to capture and process the images is very important for any intelligent system such as autonomous robot. This paper gives attention to the development of a vision system that could contribute to the development of an automated vision based lawn mower robot. The works involve on the implementation of DIP techniques to detect and recognize three different types of obstacles that usually exist on a football field. The focus was given on the study on different types and sizes of obstacles, the development of vision based obstacle recognition system and the evaluation of the system's performance. Image processing techniques such as image filtering, segmentation, enhancement and edge detection have been applied in the system. The results have shown that the developed system is able to detect and recognize various types of obstacles on a football field with recognition rate of more 80%.

  1. 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

  2. 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

  3. 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.

  4. A region finding method to remove the noise from the images of the human hand gesture recognition system

    NASA Astrophysics Data System (ADS)

    Khan, Muhammad Jibran; Mahmood, Waqas

    2015-12-01

    The performance of the human hand gesture recognition systems depends on the quality of the images presented to the system. Since these systems work in real time environment the images may be corrupted by some environmental noise. By removing the noise the performance of the system can be enhanced. So far different noise removal methods have been presented in many researches to eliminate the noise but all have its own limitations. We have presented a region finding method to deal with the environmental noise that gives better results and enhances the performance of the human hand gesture recognition systems so that the recognition rate of the system can be improved.

  5. 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

  6. Communication Systems. Laboratory Activities.

    ERIC Educational Resources Information Center

    Sutherland, Barbara, Ed.

    This communication systems guide provides teachers with learning activities for secondary students. Introductory materials include an instructional planning outline and worksheet, an outline of essential elements, a list of objectives, a course description, and a content outline. The guide contains 32 modules on the following topics: story…

  7. Production Systems. Laboratory Activities.

    ERIC Educational Resources Information Center

    Gallaway, Ann, Ed.

    This production systems guide provides teachers with learning activities for secondary students. Introductory materials include an instructional planning outline and worksheet, an outline of essential elements, domains and objectives, a course description, and a content outline. The guide contains 30 modules on the following topics: production…

  8. Performance Evaluation of Speech Recognition Systems as a Next-Generation Pilot-Vehicle Interface Technology

    NASA Technical Reports Server (NTRS)

    Arthur, Jarvis J., III; Shelton, Kevin J.; Prinzel, Lawrence J., III; Bailey, Randall E.

    2016-01-01

    During the flight trials known as Gulfstream-V Synthetic Vision Systems Integrated Technology Evaluation (GV-SITE), a Speech Recognition System (SRS) was used by the evaluation pilots. The SRS system was intended to be an intuitive interface for display control (rather than knobs, buttons, etc.). This paper describes the performance of the current "state of the art" Speech Recognition System (SRS). The commercially available technology was evaluated as an application for possible inclusion in commercial aircraft flight decks as a crew-to-vehicle interface. Specifically, the technology is to be used as an interface from aircrew to the onboard displays, controls, and flight management tasks. A flight test of a SRS as well as a laboratory test was conducted.

  9. Molecular Recognition of Paired Receptors in the Immune System

    PubMed Central

    Kuroki, Kimiko; Furukawa, Atsushi; Maenaka, Katsumi

    2012-01-01

    Cell surface receptors are responsible for regulating cellular function on the front line, the cell membrane. Interestingly, accumulating evidence clearly reveals that the members of cell surface receptor families have very similar extracellular ligand-binding regions but opposite signaling systems, either inhibitory or stimulatory. These receptors are designated as paired receptors. Paired receptors often recognize not only physiological ligands but also non-self ligands, such as viral and bacterial products, to fight infections. In this review, we introduce several representative examples of paired receptors, focusing on two major structural superfamilies, the immunoglobulin-like and the C-type lectin-like receptors, and explain how these receptors distinguish self and non-self ligands to maintain homeostasis in the immune system. We further discuss the evolutionary aspects of these receptors as well as the potential drug targets for regulating diseases. PMID:23293633

  10. 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

  11. 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

  12. Foundations for a syntatic pattern recognition system for genomic DNA sequences

    SciTech Connect

    Searles, D.B.

    1993-03-01

    The goal of the proposed work is the creation of a software system that will perform sophisticated pattern recognition and related functions at a level of abstraction and with expressive power beyond current general-purpose pattern-matching systems for biological sequences; and with a more uniform language, environment, and graphical user interface, and with greater flexibility, extensibility, embeddability, and ability to incorporate other algorithms, than current special-purpose analytic software.

  13. Place recognition and heading retrieval are mediated by dissociable cognitive systems in mice.

    PubMed

    Julian, Joshua B; Keinath, Alexander T; Muzzio, Isabel A; Epstein, Russell A

    2015-05-19

    A lost navigator must identify its current location and recover its facing direction to restore its bearings. We tested the idea that these two tasks--place recognition and heading retrieval--might be mediated by distinct cognitive systems in mice. Previous work has shown that numerous species, including young children and rodents, use the geometric shape of local space to regain their sense of direction after disorientation, often ignoring nongeometric cues even when they are informative. Notably, these experiments have almost always been performed in single-chamber environments in which there is no ambiguity about place identity. We examined the navigational behavior of mice in a two-chamber paradigm in which animals had to both recognize the chamber in which they were located (place recognition) and recover their facing direction within that chamber (heading retrieval). In two experiments, we found that mice used nongeometric features for place recognition, but simultaneously failed to use these same features for heading retrieval, instead relying exclusively on spatial geometry. These results suggest the existence of separate systems for place recognition and heading retrieval in mice that are differentially sensitive to geometric and nongeometric cues. We speculate that a similar cognitive architecture may underlie human navigational behavior.

  14. Place recognition and heading retrieval are mediated by dissociable cognitive systems in mice

    PubMed Central

    Julian, Joshua B.; Keinath, Alexander T.; Muzzio, Isabel A.; Epstein, Russell A.

    2015-01-01

    A lost navigator must identify its current location and recover its facing direction to restore its bearings. We tested the idea that these two tasks—place recognition and heading retrieval—might be mediated by distinct cognitive systems in mice. Previous work has shown that numerous species, including young children and rodents, use the geometric shape of local space to regain their sense of direction after disorientation, often ignoring nongeometric cues even when they are informative. Notably, these experiments have almost always been performed in single-chamber environments in which there is no ambiguity about place identity. We examined the navigational behavior of mice in a two-chamber paradigm in which animals had to both recognize the chamber in which they were located (place recognition) and recover their facing direction within that chamber (heading retrieval). In two experiments, we found that mice used nongeometric features for place recognition, but simultaneously failed to use these same features for heading retrieval, instead relying exclusively on spatial geometry. These results suggest the existence of separate systems for place recognition and heading retrieval in mice that are differentially sensitive to geometric and nongeometric cues. We speculate that a similar cognitive architecture may underlie human navigational behavior. PMID:25941390

  15. Evaluation of a radio based ADL interaction recognition system in a day hospital for old age psychiatry with healthy probands.

    PubMed

    Neuhaeuser, J; Diehl-Schmid, J; Lueth, T C

    2011-01-01

    In this contribution the evaluation of a system called "Eventlogger" is presented, which is installed in a day hospital for old age psychiatry. The Eventlogger is a radio based module with an adjustable communication range, able to recognize interaction of the user with objects or with other people. It is intended to function as a monitoring tool for the users' activities. Due to the demographic change monitoring systems for elderly people become more important. In this paper the "simple activities of daily living" (sADL) is introduced as well as the evaluation for the recognition of sADL in a day hospital for old age psychiatry with healthy probands is presented. Together with the first approaches of post processing for better results it is shown that the system is now ready to be used with patients of the day hospital for old age psychiatry.

  16. 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.

  17. Automated inspection of micro-defect recognition system for color filter

    NASA Astrophysics Data System (ADS)

    Jeffrey Kuo, Chung-Feng; Peng, Kai-Ching; Wu, Han-Cheng; Wang, Ching-Chin

    2015-07-01

    This study focused on micro-defect recognition and classification in color filters. First, six types of defects were examined, namely grain, black matrix hole (BMH), indium tin oxide (ITO) defect, missing edge and shape (MES), highlights, and particle. Orthogonal projection was applied to locate each pixel in a test image. Then, an image comparison was performed to mark similar blocks on the test image. The block that best resembled the template was chosen as the new template (or matching adaptive template). Afterwards, image subtraction was applied to subtract the pixels at the same location in each block of the test image from the matching adaptive template. The control limit law employed logic operation to separate the defect from the background region. The complete defect structure was obtained by the morphology method. Next, feature values, including defect gray value, red, green, and blue (RGB) color components, and aspect ratio were obtained as the classifier input. The experimental results showed that defect recognition could be completed as fast as 0.154 s using the proposed recognition system and software. In micro-defect classification, back-propagation neural network (BPNN) and minimum distance classifier (MDC) served as the defect classification decision theories for the five acquired feature values. To validate the proposed system, this study used 41 defects as training samples, and treated the feature values of 307 test samples as the BPNN classifier inputs. The total recognition rate was 93.7%. When an MDC was used, the total recognition rate was 96.8%, indicating that the MDC method is feasible in applying automatic optical inspection technology to classify micro-defects of color filters. The proposed system is proven to successfully improve the production yield and lower costs.

  18. A history-taking system that uses continuous speech recognition.

    PubMed Central

    Johnson, K.; Poon, A.; Shiffman, S.; Lin, R.; Fagan, L.

    1992-01-01

    Q-MED is an automated history-taking system that uses speaker-independent continuous speech as its main interface modality. Q-MED is designed to allow a patient to enter her basic symptoms by engaging in a dialog with the program. Error-recovery mechanisms help to eliminate findings resulting from misrecognitions or incorrect parses. An evaluation of the natural language parser that Q-MED uses to map user utterances to findings showed an overall semantic accuracy of 87 percent; Q-MED asks more specific questions to capture findings that were not volunteered, or that were unable to be parsed in their initial, open-ended form. PMID:1482973

  19. Pattern-recognition system application to EBR-II plant-life extension

    SciTech Connect

    King, R.W.; Radtke, W.H.; Mott, J.E.

    1988-01-01

    A computer-based pattern-recognition system, the System State Analyzer (SSA), is being used as part of the EBR-II plant-life extension program for detection of degradation and other abnormalities in plant systems. The SSA is used for surveillance of the EBR-II primary system instrumentation, primary sodium pumps, and plant heat balances. Early results of this surveillance indicate that the SSA can detect instrumentation degradation and system performance degradation over varying time intervals, and can provide derived signal values to replace signals from failed critical sensors. These results are being used in planning for extended-life operation of EBR-II.

  20. Matricryptic sites control tissue injury responses in the cardiovascular system: relationships to pattern recognition receptor regulated events.

    PubMed

    Davis, George E

    2010-03-01

    This review addresses new concepts related to the importance of how cells within the cardiovascular system respond to matricryptic sites generated from the extracellular matrix (ECM) following tissue injury. A model is presented whereby matricryptic sites exposed from the ECM result in activation of multiple cell surface receptors including integrins, scavenger receptors, and toll-like receptors which together are hypothesized to coactivate downstream signaling pathways which alter cell behaviors following tissue injury. Of great interest are the relationships between matricryptic fragments of ECM called matricryptins and other stimuli that activate cells during injury states such as released components from cells (DNA, RNA, cytoskeletal components such as actin) or products from infectious agents in innate immunity responses. These types of cell activating molecules, which are composed of repeating molecular elements, are known to interact with pattern recognition receptors that (i) are expressed from cell surfaces, (ii) are released from cells following tissue injury, or (iii) circulate as components of plasma. Thus, cell recognition of matricryptic sites from the ECM appears to be an important component of a broad cell and tissue sensory system to detect and respond to environmental cues generated following varied types of tissue injury.

  1. 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.

  2. 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

  3. 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

  4. A Tumor-specific MicroRNA Recognition System Facilitates the Accurate Targeting to Tumor Cells by Magnetic Nanoparticles

    PubMed Central

    Yu, Yingting; Yao, Yi; Yan, Hao; Wang, Rui; Zhang, Zhenming; Sun, Xiaodan; Zhao, Lingyun; Ao, Xiang; Xie, Zhen; Wu, Qiong

    2016-01-01

    Targeted therapy for cancer is a research area of great interest, and magnetic nanoparticles (MNPs) show great potential as targeted carriers for therapeutics. One important class of cancer biomarkers is microRNAs (miRNAs), which play a significant role in tumor initiation and progression. In this study, a cascade recognition system containing multiple plasmids, including a Tet activator, a lacI repressor gene driven by the TetOn promoter, and a reporter gene repressed by the lacI repressor and influenced by multiple endogenous miRNAs, was used to recognize cells that display miRNA signals that are characteristic of cancer. For this purpose, three types of signal miRNAs with high proliferation and metastasis abilities were chosen (miR-21, miR-145, and miR-9). The response of this system to the human breast cancer MCF-7 cell line was 3.2-fold higher than that to the human breast epithelial HBL100 cell line and almost 7.5-fold higher than that to human embryonic kidney HEK293T cells. In combination with polyethyleneimine-modified MNPs, this recognition system targeted the tumor location in situ in an animal model, and an ~42% repression of tumor growth was achieved. Our study provides a new combination of magnetic nanocarrier and gene therapy based on miRNAs that are active in vivo, which has potential for use in future cancer therapies. PMID:27138178

  5. Perceived Parenting Mediates Serotonin Transporter Gene (5-HTTLPR) and Neural System Function during Facial Recognition: A Pilot Study

    PubMed Central

    Nishikawa, Saori

    2015-01-01

    This study examined changes in prefrontal oxy-Hb levels measured by NIRS (Near-Infrared Spectroscopy) during a facial-emotion recognition task in healthy adults, testing a mediational/moderational model of these variables. Fifty-three healthy adults (male = 35, female = 18) aged between 22 to 37 years old (mean age = 24.05 years old) provided saliva samples, completed a EMBU questionnaire (Swedish acronym for Egna Minnen Beträffande Uppfostran [My memories of upbringing]), and participated in a facial-emotion recognition task during NIRS recording. There was a main effect of maternal rejection on RoxH (right frontal activation during an ambiguous task), and a gene × environment (G×E) interaction on RoxH, suggesting that individuals who carry the SL or LL genotype and who endorse greater perceived maternal rejection show less right frontal activation than SL/LL carriers with lower perceived maternal rejection. Finally, perceived parenting style played a mediating role in right frontal activation via the 5-HTTLPR genotype. Early-perceived parenting might influence neural activity in an uncertain situation i.e. rating ambiguous faces among individuals with certain genotypes. This preliminary study makes a small contribution to the mapping of an influence of gene and behaviour on the neural system. More such attempts should be made in order to clarify the links. PMID:26418317

  6. 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.

  7. Development of a Mandarin-English Bilingual Speech Recognition System for Real World Music Retrieval

    NASA Astrophysics Data System (ADS)

    Zhang, Qingqing; Pan, Jielin; Lin, Yang; Shao, Jian; Yan, Yonghong

    In recent decades, there has been a great deal of research into the problem of bilingual speech recognition-to develop a recognizer that can handle inter- and intra-sentential language switching between two languages. This paper presents our recent work on the development of a grammar-constrained, Mandarin-English bilingual Speech Recognition System (MESRS) for real world music retrieval. Two of the main difficult issues in handling the bilingual speech recognition systems for real world applications are tackled in this paper. One is to balance the performance and the complexity of the bilingual speech recognition system; the other is to effectively deal with the matrix language accents in embedded language**. In order to process the intra-sentential language switching and reduce the amount of data required to robustly estimate statistical models, a compact single set of bilingual acoustic models derived by phone set merging and clustering is developed instead of using two separate monolingual models for each language. In our study, a novel Two-pass phone clustering method based on Confusion Matrix (TCM) is presented and compared with the log-likelihood measure method. Experiments testify that TCM can achieve better performance. Since potential system users' native language is Mandarin which is regarded as a matrix language in our application, their pronunciations of English as the embedded language usually contain Mandarin accents. In order to deal with the matrix language accents in embedded language, different non-native adaptation approaches are investigated. Experiments show that model retraining method outperforms the other common adaptation methods such as Maximum A Posteriori (MAP). With the effective incorporation of approaches on phone clustering and non-native adaptation, the Phrase Error Rate (PER) of MESRS for English utterances was reduced by 24.47% relatively compared to the baseline monolingual English system while the PER on Mandarin utterances was

  8. ADASY (Active Daylighting System)

    NASA Astrophysics Data System (ADS)

    Vázquez-Moliní, Daniel; González-Montes, Mario; Fernández-Balbuena, Antonio Á.; Bernabéu, Eusebio; García-Botella, Ángel; García-Rodríguez, Lucas; Pohl, Wilfried

    2009-08-01

    The main objective of ADASY (Active Daylighting System) work is to design a façade static daylighting system oriented to office applications, mainly. The goal of the project is to save energy by guiding daylight into a building for lighting purpose. With this approach we can reduce the electrical load for artificial lighting, completing it with sustainable energy. The collector of the system is integrated on a vertical façade and its distribution guide is always horizontal inside of the false ceiling. ADASY is designed with a specific patent pending caption system, a modular light-guide and light extractor luminaire system. Special care has been put on the final cost of the system and its building integration purpose. The current ADASY configuration is able to illuminate 40 m2 area with a 300lx-400lx level in the mid time work hours; furthermore it has a good enough spatial uniformity distribution and a controlled glare. The data presented in this study are the result of simulation models and have been confirmed by a physical scaled prototype. ADASY's main advantages over regular illumination systems are: -Low maintenance; it has not mobile pieces and therefore it lasts for a long time and require little attention once installed. - No energy consumption; solar light continue working even if there has been a power outage. - High quality of light: the colour rendering of light is very high - Psychological benefits: People working with daylight get less stress and more comfort, increasing productivity. - Health benefits

  9. Statistical assessment of speech system performance

    NASA Technical Reports Server (NTRS)

    Moshier, Stephen L.

    1977-01-01

    Methods for the normalization of performance tests results of speech recognition systems are presented. Technological accomplishments in speech recognition systems, as well as planned research activities are described.

  10. Kanji Recognition by Second Language Learners: Exploring Effects of First Language Writing Systems and Second Language Exposure

    ERIC Educational Resources Information Center

    Matsumoto, Kazumi

    2013-01-01

    This study investigated whether learners of Japanese with different first language (L1) writing systems use different recognition strategies and whether second language (L2) exposure affects L2 kanji recognition. The study used a computerized lexical judgment task with 3 types of kanji characters to investigate these questions: (a)…

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

    NASA Astrophysics Data System (ADS)

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

    2015-07-01

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

  12. 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.

  13. 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

  14. 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

  15. 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

  16. Development of Portable Automatic Number Plate Recognition System on Android Mobile Phone

    NASA Astrophysics Data System (ADS)

    Mutholib, Abdul; Gunawan, Teddy S.; Chebil, Jalel; Kartiwi, Mira

    2013-12-01

    The Automatic Number Plate Recognition (ANPR) System has performed as the main role in various access control and security, such as: tracking of stolen vehicles, traffic violations (speed trap) and parking management system. In this paper, the portable ANPR implemented on android mobile phone is presented. The main challenges in mobile application are including higher coding efficiency, reduced computational complexity, and improved flexibility. Significance efforts are being explored to find suitable and adaptive algorithm for implementation of ANPR on mobile phone. ANPR system for mobile phone need to be optimize due to its limited CPU and memory resources, its ability for geo-tagging image captured using GPS coordinates and its ability to access online database to store the vehicle's information. In this paper, the design of portable ANPR on android mobile phone will be described as follows. First, the graphical user interface (GUI) for capturing image using built-in camera was developed to acquire vehicle plate number in Malaysia. Second, the preprocessing of raw image was done using contrast enhancement. Next, character segmentation using fixed pitch and an optical character recognition (OCR) using neural network were utilized to extract texts and numbers. Both character segmentation and OCR were using Tesseract library from Google Inc. The proposed portable ANPR algorithm was implemented and simulated using Android SDK on a computer. Based on the experimental results, the proposed system can effectively recognize the license plate number at 90.86%. The required processing time to recognize a license plate is only 2 seconds on average. The result is consider good in comparison with the results obtained from previous system that was processed in a desktop PC with the range of result from 91.59% to 98% recognition rate and 0.284 second to 1.5 seconds recognition time.

  17. A Kinect based sign language recognition system using spatio-temporal features

    NASA Astrophysics Data System (ADS)

    Memiş, Abbas; Albayrak, Songül

    2013-12-01

    This paper presents a sign language recognition system that uses spatio-temporal features on RGB video images and depth maps for dynamic gestures of Turkish Sign Language. Proposed system uses motion differences and accumulation approach for temporal gesture analysis. Motion accumulation method, which is an effective method for temporal domain analysis of gestures, produces an accumulated motion image by combining differences of successive video frames. Then, 2D Discrete Cosine Transform (DCT) is applied to accumulated motion images and temporal domain features transformed into spatial domain. These processes are performed on both RGB images and depth maps separately. DCT coefficients that represent sign gestures are picked up via zigzag scanning and feature vectors are generated. In order to recognize sign gestures, K-Nearest Neighbor classifier with Manhattan distance is performed. Performance of the proposed sign language recognition system is evaluated on a sign database that contains 1002 isolated dynamic signs belongs to 111 words of Turkish Sign Language (TSL) in three different categories. Proposed sign language recognition system has promising success rates.

  18. Evaluation of lexicon size variations on a verification and rejection system based on SVM, for accurate and robust recognition of handwritten words

    NASA Astrophysics Data System (ADS)

    Ricquebourg, Yann; Coüasnon, Bertrand; Guichard, Laurent

    2013-01-01

    The transcription of handwritten words remains a still challenging and difficult task. When processing full pages, approaches are limited by the trade-off between automatic recognition errors and the tedious aspect of human user verification. In this article, we present our investigations to improve the capabilities of an automatic recognizer, so as to be able to reject unknown words (not to take wrong decisions) while correctly rejecting (i.e. to recognize as much as possible from the lexicon of known words). This is the active research topic of developing a verification system that optimize the trade-off between performance and reliability. To minimize the recognition errors, a verification system is usually used to accept or reject the hypotheses produced by an existing recognition system. Thus, we re-use our novel verification architecture1 here: the recognition hypotheses are re-scored by a set of support vector machines, and validated by a verification mechanism based on multiple rejection thresholds. In order to tune these (class-dependent) rejection thresholds, an algorithm based on dynamic programming has been proposed which focus on maximizing the recognition rate for a given error rate. Experiments have been carried out on the RIMES database in three steps. The first two showed that this approach results in a performance superior or equal to other state-of-the-art rejection methods. We focus here on the third one showing that this verification system also greatly improves results of keywords extraction in a set of handwritten words, with a strong robustness to lexicon size variations (21 lexicons have been tested from 167 entries up to 5,600 entries) which is particularly relevant to our application context cooperating with humans, and only made possible thanks to the rejection ability of this proposed system. The proposed verification system, compared to a HMM with simple rejection, improves on average the recognition rate by 57% (resp. 33% and 21%) for

  19. Face recognition system for set-top box-based intelligent TV.

    PubMed

    Lee, Won Oh; Kim, Yeong Gon; Hong, Hyung Gil; Park, Kang Ryoung

    2014-01-01

    Despite the prevalence of smart TVs, many consumers continue to use conventional TVs with supplementary set-top boxes (STBs) because of the high cost of smart TVs. However, because the processing power of a STB is quite low, the smart TV functionalities that can be implemented in a STB are very limited. Because of this, negligible research has been conducted regarding face recognition for conventional TVs with supplementary STBs, even though many such studies have been conducted with smart TVs. In terms of camera sensors, previous face recognition systems have used high-resolution cameras, cameras with high magnification zoom lenses, or camera systems with panning and tilting devices that can be used for face recognition from various positions. However, these cameras and devices cannot be used in intelligent TV environments because of limitations related to size and cost, and only small, low cost web-cameras can be used. The resulting face recognition performance is degraded because of the limited resolution and quality levels of the images. Therefore, we propose a new face recognition system for intelligent TVs in order to overcome the limitations associated with low resource set-top box and low cost web-cameras. We implement the face recognition system using a software algorithm that does not require special devices or cameras. Our research has the following four novelties: first, the candidate regions in a viewer's face are detected in an image captured by a camera connected to the STB via low processing background subtraction and face color filtering; second, the detected candidate regions of face are transmitted to a server that has high processing power in order to detect face regions accurately; third, in-plane rotations of the face regions are compensated based on similarities between the left and right half sub-regions of the face regions; fourth, various poses of the viewer's face region are identified using five templates obtained during the initial user

  20. Face Recognition System for Set-Top Box-Based Intelligent TV

    PubMed Central

    Lee, Won Oh; Kim, Yeong Gon; Hong, Hyung Gil; Park, Kang Ryoung

    2014-01-01

    Despite the prevalence of smart TVs, many consumers continue to use conventional TVs with supplementary set-top boxes (STBs) because of the high cost of smart TVs. However, because the processing power of a STB is quite low, the smart TV functionalities that can be implemented in a STB are very limited. Because of this, negligible research has been conducted regarding face recognition for conventional TVs with supplementary STBs, even though many such studies have been conducted with smart TVs. In terms of camera sensors, previous face recognition systems have used high-resolution cameras, cameras with high magnification zoom lenses, or camera systems with panning and tilting devices that can be used for face recognition from various positions. However, these cameras and devices cannot be used in intelligent TV environments because of limitations related to size and cost, and only small, low cost web-cameras can be used. The resulting face recognition performance is degraded because of the limited resolution and quality levels of the images. Therefore, we propose a new face recognition system for intelligent TVs in order to overcome the limitations associated with low resource set-top box and low cost web-cameras. We implement the face recognition system using a software algorithm that does not require special devices or cameras. Our research has the following four novelties: first, the candidate regions in a viewer's face are detected in an image captured by a camera connected to the STB via low processing background subtraction and face color filtering; second, the detected candidate regions of face are transmitted to a server that has high processing power in order to detect face regions accurately; third, in-plane rotations of the face regions are compensated based on similarities between the left and right half sub-regions of the face regions; fourth, various poses of the viewer's face region are identified using five templates obtained during the initial user

  1. 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

  2. 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.

  3. 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

  4. Classification of fragments of objects by the Fourier masks pattern recognition system

    NASA Astrophysics Data System (ADS)

    Barajas-García, Carolina; Solorza-Calderón, Selene; Álvarez-Borrego, Josué

    2016-05-01

    The automation process of the pattern recognition for fragments of objects is a challenge to humanity. For humans it is relatively easy to classify the fragment of some object even if it is isolated and perhaps this identification could be more complicated if it is partially overlapped by other object. However, the emulation of the functions of the human eye and brain by a computer is not a trivial issue. This paper presents a pattern recognition digital system based on Fourier binary rings masks in order to classify fragments of objects. The system is invariant to position, scale and rotation, and it is robust in the classification of images that have noise. Moreover, it classifies images that present an occlusion or elimination of approximately 50% of the area of the object.

  5. Non-intrusive gesture recognition system combining with face detection based on Hidden Markov Model

    NASA Astrophysics Data System (ADS)

    Jin, Jing; Wang, Yuanqing; Xu, Liujing; Cao, Liqun; Han, Lei; Zhou, Biye; Li, Minggao

    2014-11-01

    A non-intrusive gesture recognition human-machine interaction system is proposed in this paper. In order to solve the hand positioning problem which is a difficulty in current algorithms, face detection is used for the pre-processing to narrow the search area and find user's hand quickly and accurately. Hidden Markov Model (HMM) is used for gesture recognition. A certain number of basic gesture units are trained as HMM models. At the same time, an improved 8-direction feature vector is proposed and used to quantify characteristics in order to improve the detection accuracy. The proposed system can be applied in interaction equipments without special training for users, such as household interactive television

  6. All-organic microelectromechanical systems integrating specific molecular recognition--a new generation of chemical sensors.

    PubMed

    Ayela, Cédric; Dubourg, Georges; Pellet, Claude; Haupt, Karsten

    2014-09-01

    Cantilever-type all-organic microelectromechanical systems based on molecularly imprinted polymers for specific analyte recognition are used as chemical sensors. They are produced by a simple spray-coating-shadow-masking process. Analyte binding to the cantilever generates a measurable change in its resonance frequency. This allows label-free detection by direct mass sensing of low-molecular-weight analytes at nanomolar concentrations.

  7. Automatic recognition of fundamental tissues on histology images of the human cardiovascular system.

    PubMed

    Mazo, Claudia; Trujillo, Maria; Alegre, Enrique; Salazar, Liliana

    2016-10-01

    Cardiovascular disease is the leading cause of death worldwide. Therefore, techniques for improving diagnosis and treatment in this field have become key areas for research. In particular, approaches for tissue image processing may support education system and medical practice. In this paper, an approach to automatic recognition and classification of fundamental tissues, using morphological information is presented. Taking a 40× or 10× histological image as input, three clusters are created with the k-means algorithm using a structural tensor and the red and the green channels. Loose connective tissue, light regions and cell nuclei are recognised on 40× images. Then, the cell nuclei's features - shape and spatial projection - and light regions are used to recognise and classify epithelial cells and tissue into flat, cubic and cylindrical. In a similar way, light regions, loose connective and muscle tissues are recognised on 10× images. Finally, the tissue's function and composition are used to refine muscle tissue recognition. Experimental validation is then carried out by histologist following expert criteria, along with manually annotated images that are used as a ground-truth. The results revealed that the proposed approach classified the fundamental tissues in a similar way to the conventional method employed by histologists. The proposed automatic recognition approach provides for epithelial tissues a sensitivity of 0.79 for cubic, 0.85 for cylindrical and 0.91 for flat. Furthermore, the experts gave our method an average score of 4.85 out of 5 in the recognition of loose connective tissue and 4.82 out of 5 for muscle tissue recognition.

  8. 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.

  9. 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

  10. Pilot study on the feasibility of a computerized speech recognition charting system.

    PubMed

    Feldman, C A; Stevens, D

    1990-08-01

    The objective of this study was to determine the feasibility of developing and using a voice recognition computerized charting system to record dental clinical examination data. More specifically, the study was designed to analyze the time and error differential between the traditional examiner/recorder method (ASSISTANT) and computerized voice recognition method (VOICE). DMFS examinations were performed twice on 20 patients using the traditional ASSISTANT and the VOICE charting system. A statistically significant difference was found when comparing the mean ASSISTANT time of 2.69 min to the VOICE time of 3.72 min (P less than 0.001). No statistically significant difference was found when comparing the mean ASSISTANT recording errors of 0.1 to VOICE recording errors of 0.6 (P = 0.059). 90% of the patients indicated they felt comfortable with the dentist talking to a computer and only 5% of the sample indicated they opposed VOICE. Results from this pilot study indicate that a charting system utilizing voice recognition technology could be considered a viable alternative to traditional examiner/recorder methods of clinical charting.

  11. Face recognition system using multiple face model of hybrid Fourier feature under uncontrolled illumination variation.

    PubMed

    Hwang, Wonjun; Wang, Haitao; Kim, Hyunwoo; Kee, Seok-Cheol; Kim, Junmo

    2011-04-01

    The authors present a robust face recognition system for large-scale data sets taken under uncontrolled illumination variations. The proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a hybrid Fourier-based facial feature extraction, and a score fusion scheme. First, in the preprocessing stage, a face image is transformed into an illumination-insensitive image, called an "integral normalized gradient image," by normalizing and integrating the smoothed gradients of a facial image. Then, for feature extraction of complementary classifiers, multiple face models based upon hybrid Fourier features are applied. The hybrid Fourier features are extracted from different Fourier domains in different frequency bandwidths, and then each feature is individually classified by linear discriminant analysis. In addition, multiple face models are generated by plural normalized face images that have different eye distances. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system using the face recognition grand challenge (FRGC) experimental protocols is evaluated; FRGC is a large available data set. Experimental results on the FRGC version 2.0 data sets have shown that the proposed method shows an average of 81.49% verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses. PMID:20923738

  12. Real-time color/shape-based traffic signs acquisition and recognition system

    NASA Astrophysics Data System (ADS)

    Saponara, Sergio

    2013-02-01

    A real-time system is proposed to acquire from an automotive fish-eye CMOS camera the traffic signs, and provide their automatic recognition on the vehicle network. Differently from the state-of-the-art, in this work color-detection is addressed exploiting the HSI color space which is robust to lighting changes. Hence the first stage of the processing system implements fish-eye correction and RGB to HSI transformation. After color-based detection a noise deletion step is implemented and then, for the classification, a template-based correlation method is adopted to identify potential traffic signs, of different shapes, from acquired images. Starting from a segmented-image a matching with templates of the searched signs is carried out using a distance transform. These templates are organized hierarchically to reduce the number of operations and hence easing real-time processing for several types of traffic signs. Finally, for the recognition of the specific traffic sign, a technique based on extraction of signs characteristics and thresholding is adopted. Implemented on DSP platform the system recognizes traffic signs in less than 150 ms at a distance of about 15 meters from 640x480-pixel acquired images. Tests carried out with hundreds of images show a detection and recognition rate of about 93%.

  13. 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.

  14. 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.

  15. Neutron activation analysis system

    DOEpatents

    Taylor, M.C.; Rhodes, J.R.

    1973-12-25

    A neutron activation analysis system for monitoring a generally fluid media, such as slurries, solutions, and fluidized powders, including two separate conduit loops for circulating fluid samples within the range of radiation sources and detectors is described. Associated with the first loop is a neutron source that emits s high flux of slow and thermal neutrons. The second loop employs a fast neutron source, the flux from which is substantially free of thermal neutrons. Adjacent to both loops are gamma counters for spectrographic determination of the fluid constituents. Other gsmma sources and detectors are arranged across a portion of each loop for deterMining the fluid density. (Official Gazette)

  16. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    NASA Astrophysics Data System (ADS)

    Vargas, Lorena P.; Barba, Leiner; Torres, C. O.; Mattos, L.

    2011-01-01

    This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

  17. 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

  18. 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

  19. 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

  20. 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.

  1. 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

  2. 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

  3. 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

  4. 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.

  5. Dual chiral recognition system involving cyclodextrin derivatives in capillary electrophoresis II. Enhancement of enantioselectivity.

    PubMed

    Jakubetz, H; Juza, M; Schurig, V

    1998-05-01

    The enantiomer separation of hexobarbital was investigated by open tubular electrochromatography (OTEC) using the chiral stationary phase (CSP) CHIRASIL-DEX (a permethylated beta-cyclodextrin covalently linked to a dimethylpolysiloxane) and by cyclodextrin-electrokinetic chromatogaphy (CD-EKC) using anionic beta-cyclodextrin-sulfo-n-propyl ether (SPE-beta-CD) and cationic beta-cyclodextrin-2-hydroxy-3-trimethylammoniumpropyl ether chloride (HTAP-beta-CD) added to the running buffer. By employing two chiral selectors, the enantiomer separation of hexobarbital was then studied simultaneously by OTEC with CHIRASIL-DEX and by CD-EKC with either SPE-beta-CD or HTAP-beta-CD in the dual chiral recognition mode. In conjunction with CHIRASIL-DEX, anionic SPE-beta-CD decreased the chiral separation factor alpha due to compensation of enantioselectivity whereas the cationic additive HTAP-beta-CD increased the chiral separation factor alpha due to enhancement of enantioselectivity. It is concluded that CHIRASIL-DEX imparts an opposite enantioselectivity to the enantiomers of hexobarbital as compared to the charged CDs SPE-beta-CD and HTAP-beta-CD. Unusual peak broadening phenomena are observed in the dual chiral recognition system comprised of CHIRASIL-DEX and HTAP-beta-CD. The possible consequences of accidental dual chiral recognition systems caused by wall stacking effects of the mobile phase additives onto the inner surface of the capillary column are discussed. PMID:9629908

  6. Complete vision-based traffic sign recognition supported by an I2V communication system.

    PubMed

    García-Garrido, Miguel A; Ocaña, Manuel; Llorca, David F; Arroyo, Estefanía; Pozuelo, Jorge; Gavilán, Miguel

    2012-01-01

    This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance.

  7. Parallel System Architecture (PSA): An efficient approach for automatic recognition of volcano-seismic events

    NASA Astrophysics Data System (ADS)

    Cortés, Guillermo; García, Luz; Álvarez, Isaac; Benítez, Carmen; de la Torre, Ángel; Ibáñez, Jesús

    2014-02-01

    Automatic recognition of volcano-seismic events is becoming one of the most demanded features in the early warning area at continuous monitoring facilities. While human-driven cataloguing is time-consuming and often an unreliable task, an appropriate machine framework allows expert technicians to focus only on result analysis and decision-making. This work presents an alternative to serial architectures used in classic recognition systems introducing a parallel implementation of the whole process: configuration, feature extraction, feature selection and classification stages are independently carried out for each type of events in order to exploit the intrinsic properties of each signal class. The system uses Gaussian Mixture Models (GMMs) to classify the database recorded at Deception Volcano Island (Antarctica) obtaining a baseline recognition rate of 84% with a cepstral-based waveform parameterization in the serial architecture. The parallel approach increases the results to close to 92% using mixture-based parameterization vectors or up to 91% when the vector size is reduced by 19% via the Discriminative Feature Selection (DFS) algorithm. Besides the result improvement, the parallel architecture represents a major step in terms of flexibility and reliability thanks to the class-focused analysis, providing an efficient tool for monitoring observatories which require real-time solutions.

  8. Efficient live face detection to counter spoof attack in face recognition systems

    NASA Astrophysics Data System (ADS)

    Biswas, Bikram Kumar; Alam, Mohammad S.

    2015-03-01

    Face recognition is a critical tool used in almost all major biometrics based security systems. But recognition, authentication and liveness detection of the face of an actual user is a major challenge because an imposter or a non-live face of the actual user can be used to spoof the security system. In this research, a robust technique is proposed which detects liveness of faces in order to counter spoof attacks. The proposed technique uses a three-dimensional (3D) fast Fourier transform to compare spectral energies of a live face and a fake face in a mathematically selective manner. The mathematical model involves evaluation of energies of selective high frequency bands of average power spectra of both live and non-live faces. It also carries out proper recognition and authentication of the face of the actual user using the fringe-adjusted joint transform correlation technique, which has been found to yield the highest correlation output for a match. Experimental tests show that the proposed technique yields excellent results for identifying live faces.

  9. Novel approaches to improve iris recognition system performance based on local quality evaluation and feature fusion.

    PubMed

    Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; Chen, Huiling; He, Fei; Pang, Yutong

    2014-01-01

    For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.

  10. Novel approaches to improve iris recognition system performance based on local quality evaluation and feature fusion.

    PubMed

    Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; Chen, Huiling; He, Fei; Pang, Yutong

    2014-01-01

    For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system. PMID:24693243

  11. Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion

    PubMed Central

    2014-01-01

    For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system. PMID:24693243

  12. New pattern recognition system in the e-nose for Chinese spirit identification

    NASA Astrophysics Data System (ADS)

    Hui, Zeng; Qiang, Li; Yu, Gu

    2016-02-01

    This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance (QCM) principle, and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an 8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value (A), root-mean-square value (RMS), shape factor value (Sf), crest factor value (Cf), impulse factor value (If), clearance factor value (CLf), kurtosis factor value (Kv) are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis (PCA) method. Finally the back propagation (BP) neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively. Project supported by the National High Technology Research and Development Program of China (Grant No. 2013AA030901) and the Fundamental Research Funds for the Central Universities, China (Grant No. FRF-TP-14-120A2).

  13. Spectral pattern recognition of controlled substances in street samples using artificial neural network system

    NASA Astrophysics Data System (ADS)

    Poryvkina, Larisa; Aleksejev, Valeri; Babichenko, Sergey M.; Ivkina, Tatjana

    2011-04-01

    The NarTest fluorescent technique is aimed at the detection of analyte of interest in street samples by recognition of its specific spectral patterns in 3-dimentional Spectral Fluorescent Signatures (SFS) measured with NTX2000 analyzer without chromatographic or other separation of controlled substances from a mixture with cutting agents. The illicit drugs have their own characteristic SFS features which can be used for detection and identification of narcotics, however typical street sample consists of a mixture with cutting agents: adulterants and diluents. Many of them interfere the spectral shape of SFS. The expert system based on Artificial Neural Networks (ANNs) has been developed and applied for such pattern recognition in SFS of street samples of illicit drugs.

  14. Using Speech Recognition to Enhance the Tongue Drive System Functionality in Computer Access

    PubMed Central

    Huo, Xueliang; Ghovanloo, Maysam

    2013-01-01

    Tongue Drive System (TDS) is a wireless tongue operated assistive technology (AT), which can enable people with severe physical disabilities to access computers and drive powered wheelchairs using their volitional tongue movements. TDS offers six discrete commands, simultaneously available to the users, for pointing and typing as a substitute for mouse and keyboard in computer access, respectively. To enhance the TDS performance in typing, we have added a microphone, an audio codec, and a wireless audio link to its readily available 3-axial magnetic sensor array, and combined it with a commercially available speech recognition software, the Dragon Naturally Speaking, which is regarded as one of the most efficient ways for text entry. Our preliminary evaluations indicate that the combined TDS and speech recognition technologies can provide end users with significantly higher performance than using each technology alone, particularly in completing tasks that require both pointing and text entry, such as web surfing. PMID:22255801

  15. Social hackers: integration in the host chemical recognition system by a paper wasp social parasite.

    PubMed

    Turillazzi, S; Sledge, M F; Dani, F R; Cervo, R; Massolo, A; Fondelli, L

    2000-04-01

    Obligate social parasites in the social insects have lost the worker caste and the ability to establish nests. As a result, parasites must usurp a host nest, overcome the host recognition system, and depend on the host workers to rear their offspring. We analysed cuticular hydrocarbon profiles of live parasite females of the paper wasp social parasite Polistes sulcifer before and after usurpation of host nests, using the non-destructive technique of solid-phase micro-extraction. Our results reveal that hydrocarbon profiles of parasites change after usurpation of host nests to match the cuticular profile of the host species. Chemical evidence further shows that the parasite queen changes the odour of the nest by the addition of a parasite-specific hydrocarbon. We discuss the possible role of this in the recognition and acceptance of the parasite and its offspring in the host colony.

  16. Social Hackers: Integration in the Host Chemical Recognition System by a Paper Wasp Social Parasite

    NASA Astrophysics Data System (ADS)

    Turillazzi, S.; Sledge, M. F.; Dani, F. R.; Cervo, R.; Massolo, A.; Fondelli, L.

    Obligate social parasites in the social insects have lost the worker caste and the ability to establish nests. As a result, parasites must usurp a host nest, overcome the host recognition system, and depend on the host workers to rear their offspring. We analysed cuticular hydrocarbon profiles of live parasite females of the paper wasp social parasite Polistes sulcifer before and after usurpation of host nests, using the non-destructive technique of solid-phase micro-extraction. Our results reveal that hydrocarbon profiles of parasites change after usurpation of host nests to match the cuticular profile of the host species. Chemical evidence further shows that the parasite queen changes the odour of the nest by the addition of a parasite-specific hydrocarbon. We discuss the possible role of this in the recognition and acceptance of the parasite and its offspring in the host colony.

  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. Object Recognition System-on-Chip Using the Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Reyna-Rojas, Roberto; Houzet, Dominique; Dragomirescu, Daniela; Carlier, Florent; Ouadjaout, Salim

    2005-12-01

    The first aim of this work is to propose the design of a system-on-chip (SoC) platform dedicated to digital image and signal processing, which is tuned to implement efficiently multiply-and-accumulate (MAC) vector/matrix operations. The second aim of this work is to implement a recent promising neural network method, namely, the support vector machine (SVM) used for real-time object recognition, in order to build a vision machine. With such a reconfigurable and programmable SoC platform, it is possible to implement any SVM function dedicated to any object recognition problem. The final aim is to obtain an automatic reconfiguration of the SoC platform, based on the results of the learning phase on an objects' database, which makes it possible to recognize practically any object without manual programming. Recognition can be of any kind that is from image to signal data. Such a system is a general-purpose automatic classifier. Many applications can be considered as a classification problem, but are usually treated specifically in order to optimize the cost of the implemented solution. The cost of our approach is more important than a dedicated one, but in a near future, hundreds of millions of gates will be common and affordable compared to the design cost. What we are proposing here is a general-purpose classification neural network implemented on a reconfigurable SoC platform. The first version presented here is limited in size and thus in object recognition performances, but can be easily upgraded according to technology improvements.

  19. European Neutron Activation System.

    2013-01-11

    Version 03 EASY-2010 (European Activation System) consists of a wide range of codes, data and documentation all aimed at satisfying the objective of calculating the response of materials irradiated in a neutron flux. The main difference from the previous version is the upper energy limit, which has increased from 20 to 60 MeV. It is designed to investigate both fusion devices and accelerator based materials test facilities that will act as intense sources of high-energymore » neutrons causing significant activation of the surrounding materials. The very general nature of the calculational method and the data libraries means that it is applicable (with some reservations) to all situations (e.g. fission reactors or neutron sources) where materials are exposed to neutrons below 60 MeV. EASY can be divided into two parts: data and code development tools and user tools and data. The former are required to develop the latter, but EASY users only need to be able to use the inventory code FISPACT and be aware of the contents of the EAF library (the data source). The complete EASY package contains the FISPACT-2007 inventory code, the EAF-2003, EAF-2005, EAF-2007 and EAF-2010 libraries, and the EASY User Interface for the Window version. The activation package EASY-2010 is the result of significant development to extend the upper energy range from 20 to 60 MeV so that it is capable of being used for IFMIF calculations. The EAF-2010 library contains 66,256 reactions, almost five times more than in EAF-2003 (12,617). Deuteron-induced and proton-induced cross section libraries are also included, and can be used with EASY to enable calculations of the activation due to deuterons and proton [2].« less

  20. Implementation theory of distortion-invariant pattern recognition for optical and digital signal processing systems

    NASA Astrophysics Data System (ADS)

    Lhamon, Michael Earl

    A pattern recognition system which uses complex correlation filter banks requires proportionally more computational effort than single-real valued filters. This introduces increased computation burden but also introduces a higher level of parallelism, that common computing platforms fail to identify. As a result, we consider algorithm mapping to both optical and digital processors. For digital implementation, we develop computationally efficient pattern recognition algorithms, referred to as, vector inner product operators that require less computational effort than traditional fast Fourier methods. These algorithms do not need correlation and they map readily onto parallel digital architectures, which imply new architectures for optical processors. These filters exploit circulant-symmetric matrix structures of the training set data representing a variety of distortions. By using the same mathematical basis as with the vector inner product operations, we are able to extend the capabilities of more traditional correlation filtering to what we refer to as "Super Images". These "Super Images" are used to morphologically transform a complicated input scene into a predetermined dot pattern. The orientation of the dot pattern is related to the rotational distortion of the object of interest. The optical implementation of "Super Images" yields feature reduction necessary for using other techniques, such as artificial neural networks. We propose a parallel digital signal processor architecture based on specific pattern recognition algorithms but general enough to be applicable to other similar problems. Such an architecture is classified as a data flow architecture. Instead of mapping an algorithm to an architecture, we propose mapping the DSP architecture to a class of pattern recognition algorithms. Today's optical processing systems have difficulties implementing full complex filter structures. Typically, optical systems (like the 4f correlators) are limited to phase

  1. 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)

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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.

  7. 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.

  8. 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

  9. GRAIL: An integrated artificial intelligence system for gene recognition and interpretation

    SciTech Connect

    Guan, X.; Mann, R.C.; Einstein, J.R.; Mural, R.J.; Uberbacher, E.C.

    1991-01-01

    This paper describes the development of an integrated Artificial Intelligence system GRAIL (Gene Recognition and Analysis Internet Link). This system uses a combination of multiple sensor neural network, expert system, and parallel search tools to recognize and interpret genes in DNA sequences. A simple electronic mail (E-mail) interface makes the system accessible through Internet. The strength of our system in recognizing and interpreting genes in DNA sequences and the simple E-mail interface have already attracted more than 30 users. The successful integration of neural network, expert system and highly parallel search tools to solve such a complicated problem as gene interpretation indicates that the technology itself is likely to have many applications in solving other problems. 6 refs.

  10. Design of a hand-shape acquisition and recognition system based on DSP

    NASA Astrophysics Data System (ADS)

    Li, Wenwen; Liu, Fu; Gao, Lei

    2013-10-01

    In this paper, we design a hand-shape image acquisition and processing system based on DSP for solving the problem of hand-shape recognition. Acquisition configuration was designed, and the key parts (encoder, decoder, memory chip etc.) are selected. And a new method for hand-shape recognition based on wavelet moment is presented to overcome some shortage in present method for hand shape recognition. Firstly, image processing including binary processing and segment of hand silhouette are used, and then translation and scale normalization algorithms is implemented on the palms and fingers image. After that the wavelet moment characteristics of image are extracted. At last, support vector is achieved by training 100 images data in images database, 10 testing images were selected randomly to verify validity and feasibility of algorithms. Experimental results indicate that the 10 testing images are all classified correctly. The new method of extracting hand shape wavelet moment as characteristic matrix is easy to realize with characteristic of high utility and accuracy, and solve the problem of translation, rotation and scaling during the image acquisition process without positioning aids.

  11. Performance of Language-Coordinated Collective Systems: A Study of Wine Recognition and Description

    PubMed Central

    Zubek, Julian; Denkiewicz, Michał; Dębska, Agnieszka; Radkowska, Alicja; Komorowska-Mach, Joanna; Litwin, Piotr; Stępień, Magdalena; Kucińska, Adrianna; Sitarska, Ewa; Komorowska, Krystyna; Fusaroli, Riccardo; Tylén, Kristian; Rączaszek-Leonardi, Joanna

    2016-01-01

    Most of our perceptions of and engagements with the world are shaped by our immersion in social interactions, cultural traditions, tools and linguistic categories. In this study we experimentally investigate the impact of two types of language-based coordination on the recognition and description of complex sensory stimuli: that of red wine. Participants were asked to taste, remember and successively recognize samples of wines within a larger set in a two-by-two experimental design: (1) either individually or in pairs, and (2) with or without the support of a sommelier card—a cultural linguistic tool designed for wine description. Both effectiveness of recognition and the kinds of errors in the four conditions were analyzed. While our experimental manipulations did not impact recognition accuracy, bias-variance decomposition of error revealed non-trivial differences in how participants solved the task. Pairs generally displayed reduced bias and increased variance compared to individuals, however the variance dropped significantly when they used the sommelier card. The effect of sommelier card reducing the variance was observed only in pairs, individuals did not seem to benefit from the cultural linguistic tool. Analysis of descriptions generated with the aid of sommelier cards shows that pairs were more coherent and discriminative than individuals. The findings are discussed in terms of global properties and dynamics of collective systems when constrained by different types of cultural practices. PMID:27729875

  12. 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…

  13. 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

  14. 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.

  15. Prediction of Period-Doubling Bifurcation Based on Dynamic Recognition and Its Application to Power Systems

    NASA Astrophysics Data System (ADS)

    Chen, Danfeng; Wang, Cong

    In this paper, a bifurcation prediction approach is proposed based on dynamic recognition and further applied to predict the period-doubling bifurcation (PDB) of power systems. Firstly, modeling of the internal dynamics of nonlinear systems is obtained through deterministic learning (DL), and the modeling results are applied for constructing the dynamic training pattern database. Specifically, training patterns are chosen according to the hierarchical structured knowledge representation based on the qualitative property of dynamical systems, which is capable of arranging the dynamical models into a specific order in the pattern database. Then, a dynamic recognition-based bifurcation prediction approach is suggested. As a result, perturbations implying PDB on the testing patterns can be predicted through the minimum dynamic error between the training patterns and testing patterns by recalling the knowledge restored in the pattern database. Finally, the second-order single-machine to infinite bus power system model is introduced to check the effectiveness of this prediction approach, which implies PDB under small periodic parameter perturbations. The key point that determines the prediction effect mainly lies in two methods: (1) accurate approximation of the unknown system dynamics through DL guarantees the feasibility of the prediction process; (2) the qualitative property of PDB and the generalization ability of DL algorithm ensure the validity of the selected training patterns. Simulations are included to illustrate the effectiveness of the proposed approach.

  16. Handling Real-World Context Awareness, Uncertainty and Vagueness in Real-Time Human Activity Tracking and Recognition with a Fuzzy Ontology-Based Hybrid Method

    PubMed Central

    Díaz-Rodríguez, Natalia; Cadahía, Olmo León; Cuéllar, Manuel Pegalajar; Lilius, Johan; Calvo-Flores, Miguel Delgado

    2014-01-01

    Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches. PMID:25268914

  17. [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.

  18. Measurement of reach envelopes with a four-camera Selective Spot Recognition (SELSPOT) system

    NASA Technical Reports Server (NTRS)

    Stramler, J. H., Jr.; Woolford, B. J.

    1983-01-01

    The basic Selective Spot Recognition (SELSPOT) system is essentially a system which uses infrared LEDs and a 'camera' with an infrared-sensitive photodetector, a focusing lens, and some A/D electronics to produce a digital output representing an X and Y coordinate for each LED for each camera. When the data are synthesized across all cameras with appropriate calibrations, an XYZ set of coordinates is obtained for each LED at a given point in time. Attention is given to the operating modes, a system checkout, and reach envelopes and software. The Video Recording Adapter (VRA) represents the main addition to the basic SELSPOT system. The VRA contains a microprocessor and other electronics which permit user selection of several options and some interaction with the system.

  19. Immunopathological roles of cytokines, chemokines, signaling molecules, and pattern-recognition receptors in systemic lupus erythematosus.

    PubMed

    Yu, Shui-Lian; Kuan, Woon-Pang; Wong, Chun-Kwok; Li, Edmund K; Tam, Lai-Shan

    2012-01-01

    Systemic lupus erythematosus (SLE) is an autoimmune disease with unknown etiology affecting more than one million individuals each year. It is characterized by B- and T-cell hyperactivity and by defects in the clearance of apoptotic cells and immune complexes. Understanding the complex process involved and the interaction between various cytokines, chemokines, signaling molecules, and pattern-recognition receptors (PRRs) in the immune pathways will provide valuable information on the development of novel therapeutic targets for treating SLE. In this paper, we review the immunopathological roles of novel cytokines, chemokines, signaling molecules, PRRs, and their interactions in immunoregulatory networks and suggest how their disturbances may implicate pathological conditions in SLE.

  20. Proposal of Self-Learning and Recognition System of Facial Expression

    NASA Astrophysics Data System (ADS)

    Ogawa, Yukihiro; Kato, Kunihito; Yamamoto, Kazuhiko

    We describe realization of more complicated function by using the information acquired from some equipped unripe functions. The self-learning and recognition system of the human facial expression, which achieved under the natural relation between human and robot, are proposed. The robot with this system can understand human facial expressions and behave according to their facial expressions after the completion of learning process. The system modelled after the process that a baby learns his/her parents’ facial expressions. Equipping the robot with a camera the system can get face images and equipping the CdS sensors on the robot’s head the robot can get the information of human action. Using the information of these sensors, the robot can get feature of each facial expression. After self-learning is completed, when a person changed his facial expression in front of the robot, the robot operates actions under the relevant facial expression.

  1. Illumination analysis of the digital pattern recognition system by Bessel masks and one-dimensional signatures

    NASA Astrophysics Data System (ADS)

    Solorza, S.; Álvarez-Borrego, J.

    2013-11-01

    The effects of illumination variations in digital images are a trend topic of the pattern recognition field. The luminance information of the objects help to classify them, however the environment illumination could cause a lot of problem if the system is not illumination invariant. Some applications of this topic include image and video quality, biometrics classification, etc. In this work an illumination analysis for a digital system invariant to position and rotation based on Fourier transform, Bessel masks, one-dimensional signatures and linear correlations are presented. The digital system was tested using a reference database of 21 fossil diatoms images of gray-scale and 307 x 307 pixels. The digital system has shown an excellent performance in the classification of 60,480 problem images which have different non-homogeneous illumination.

  2. Design and implementation of a real time and train less eye state recognition system

    NASA Astrophysics Data System (ADS)

    Dehnavi, Mohammad; Eshghi, Mohammad

    2012-12-01

    Eye state recognition is one of the main stages of many image processing systems such as driver drowsiness detection system and closed-eye photo correction. Driver drowsiness is one of the main causes in the road accidents around the world. In these circumstances, a fast and accurate driver drowsiness detection system can prevent these accidents. In this article, we proposed a fast algorithm for determining the state of an eye, based on the difference between iris/pupil color and white area of the eye. In the proposed method, vertical projection is used to determine the eye state. This method is suitable for hardware implementation to be used in a fast and online drowsiness detection system. The proposed method, along with other needed preprocessing stages, is implemented on Field Programmable Gate Array chips. The results show that the proposed low-complex algorithm has sufficient speed and accuracy, to be used in real-world conditions.

  3. Applied learning-based color tone mapping for face recognition in video surveillance system

    NASA Astrophysics Data System (ADS)

    Yew, Chuu Tian; Suandi, Shahrel Azmin

    2012-04-01

    In this paper, we present an applied learning-based color tone mapping technique for video surveillance system. This technique can be applied onto both color and grayscale surveillance images. The basic idea is to learn the color or intensity statistics from a training dataset of photorealistic images of the candidates appeared in the surveillance images, and remap the color or intensity of the input image so that the color or intensity statistics match those in the training dataset. It is well known that the difference in commercial surveillance cameras models, and signal processing chipsets used by different manufacturers will cause the color and intensity of the images to differ from one another, thus creating additional challenges for face recognition in video surveillance system. Using Multi-Class Support Vector Machines as the classifier on a publicly available video surveillance camera database, namely SCface database, this approach is validated and compared to the results of using holistic approach on grayscale images. The results show that this technique is suitable to improve the color or intensity quality of video surveillance system for face recognition.

  4. Toward Design of an Environment-Aware Adaptive Locomotion-Mode-Recognition System

    PubMed Central

    Du, Lin; Zhang, Fan; Liu, Ming

    2013-01-01

    In this study, we aimed to improve the performance of a locomotion-mode-recognition system based on neuromuscular-mechanical fusion by introducing additional information about the walking environment. Linear-discriminant-analysis-based classifiers were first designed to identify a lower limb prosthesis user’s locomotion mode based on electromyographic signals recorded from residual leg muscles and ground reaction forces measured from the prosthetic pylon. Nine transfemoral amputees who wore a passive hydraulic knee or powered prosthetic knee participated in this study. Information about the walking terrain was simulated and modeled as prior probability based on the principle of maximum entropy and integrated into the discriminant functions of the classifier. When the correct prior knowledge of walking terrain was simulated, the classification accuracy for each locomotion mode significantly increased and no task transitions were missed. In addition, simulated incorrect prior knowledge did not significantly reduce system performance, indicating that our design is robust against noisy and imperfect prior information. Furthermore, these observations were independent of the type of prosthesis applied. The promising results in this study may assist the further development of an environment-aware adaptive system for locomotion-mode recognition for powered lower limb prostheses or orthoses. PMID:22996721

  5. Proposed study to determine potential flight applications and human factors design guidelines of voice recognition/synthesis systems

    NASA Technical Reports Server (NTRS)

    Bergeron, H. P.

    1983-01-01

    An effort to evaluate the human factors aspects and potential of voice recognition/synthesis techniques and the application of present and near-future (5 years) voice recognition/synthesis systems as a pilot/aircraft cockpit interface capability in an operational environment is discussed. The analysis will emphasize applications for single pilot instrument flight rules operations but will also include applications for other categories of aircraft with various levels of complexity.

  6. [Research on Multi-Spectral Target Recognition System Based on the Magneto-Optical Modulation].

    PubMed

    Yan, Xiao-yan; Qin, Jian-min; Qiao, Ji-pin

    2016-03-01

    The technology of target recognition based on characteristic multi-spectrum has many advantages, such as strong detection capability and discriminating capability of target species. But there are some problems, it requires that you obtain the background spectrum as a priori knowledge, and it requires that the change of background spectrum is small with time. Thereby its application of real-time object recognition is limited in the new environment, or the complex environment. Based on magneto-optical modulation and characteristic multi-spectrum the method is designed, and the target is identified without prior access to the background spectrum. In order to achieve the function of the target information in the one acquisition time for tested, compared to conventional methods in terms of target detection, it's adaptability is better than before on the battlefield, and it is of more practical significance. Meanwhile, the magneto-optical modulator is used to suppress the interference of stray light background, thereby improving the probability of target recognition. Since the magneto-optical modulation provides incremental iterative target spectral information, therefore, even if the unknown background spectrum or background spectrum change is large, it can significantly improve the recognition accuracy of information through an iterative target spectrum. Different test targets back shimmering light intensity and background intensity values were analyzed during experiments, results showed that three targets for linearly polarized reflectance modulation is significantly stronger than the background. And it was of great influence to visible imaging target identification when measured target used camouflage color, but the system of polarization modulation type can still recognize target well. On this basis, the target range within 0.5 km x 2 km multi-wavelength characteristics of the target species were identified. When using three characteristic wavelengths, the

  7. [Research on Multi-Spectral Target Recognition System Based on the Magneto-Optical Modulation].

    PubMed

    Yan, Xiao-yan; Qin, Jian-min; Qiao, Ji-pin

    2016-03-01

    The technology of target recognition based on characteristic multi-spectrum has many advantages, such as strong detection capability and discriminating capability of target species. But there are some problems, it requires that you obtain the background spectrum as a priori knowledge, and it requires that the change of background spectrum is small with time. Thereby its application of real-time object recognition is limited in the new environment, or the complex environment. Based on magneto-optical modulation and characteristic multi-spectrum the method is designed, and the target is identified without prior access to the background spectrum. In order to achieve the function of the target information in the one acquisition time for tested, compared to conventional methods in terms of target detection, it's adaptability is better than before on the battlefield, and it is of more practical significance. Meanwhile, the magneto-optical modulator is used to suppress the interference of stray light background, thereby improving the probability of target recognition. Since the magneto-optical modulation provides incremental iterative target spectral information, therefore, even if the unknown background spectrum or background spectrum change is large, it can significantly improve the recognition accuracy of information through an iterative target spectrum. Different test targets back shimmering light intensity and background intensity values were analyzed during experiments, results showed that three targets for linearly polarized reflectance modulation is significantly stronger than the background. And it was of great influence to visible imaging target identification when measured target used camouflage color, but the system of polarization modulation type can still recognize target well. On this basis, the target range within 0.5 km x 2 km multi-wavelength characteristics of the target species were identified. When using three characteristic wavelengths, the

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. An Intelligent Systems Approach to Automated Object Recognition: A Preliminary Study

    USGS Publications Warehouse

    Maddox, Brian G.; Swadley, Casey L.

    2002-01-01

    Attempts at fully automated object recognition systems have met with varying levels of success over the years. However, none of the systems have achieved high enough accuracy rates to be run unattended. One of the reasons for this may be that they are designed from the computer's point of view and rely mainly on image-processing methods. A better solution to this problem may be to make use of modern advances in computational intelligence and distributed processing to try to mimic how the human brain is thought to recognize objects. As humans combine cognitive processes with detection techniques, such a system would combine traditional image-processing techniques with computer-based intelligence to determine the identity of various objects in a scene.

  14. Recognition and delivery of effector proteins into eukaryotic cells by bacterial secretion systems.

    PubMed

    Cambronne, Eric D; Roy, Craig R

    2006-08-01

    The direct transport of virulence proteins from bacterium to host has emerged as a common strategy employed by Gram-negative pathogens to establish infections. Specialized secretion systems function to facilitate this process. The delivery of 'effector' proteins by these secretion systems is currently confined to two functionally similar but mechanistically distinct pathways, termed type III and type IV secretion. The type III secretion pathway is ancestrally related to the multiprotein complexes that assemble flagella, whereas the type IV mechanism probably emerged from the protein complexes that support conjugal transfer of DNA. Although both pathways serve to transport proteins from the bacterium to host, the recognition of the effector protein substrates and the secretion information contained in these proteins appear highly distinct. Here, we review the mechanisms involved in the selection of substrates by each of these transport systems and secretion signal information required for substrate transport. PMID:16734660

  15. 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.

  16. 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

  17. 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

  18. An open and configurable embedded system for EMG pattern recognition implementation for artificial arms.

    PubMed

    Jun Liu; Fan Zhang; Huang, He Helen

    2014-01-01

    Pattern recognition (PR) based on electromyographic (EMG) signals has been developed for multifunctional artificial arms for decades. However, assessment of EMG PR control for daily prosthesis use is still limited. One of the major barriers is the lack of a portable and configurable embedded system to implement the EMG PR control. This paper aimed to design an open and configurable embedded system for EMG PR implementation so that researchers can easily modify and optimize the control algorithms upon our designed platform and test the EMG PR control outside of the lab environments. The open platform was built on an open source embedded Linux Operating System running a high-performance Gumstix board. Both the hardware and software system framework were openly designed. The system was highly flexible in terms of number of inputs/outputs and calibration interfaces used. Such flexibility enabled easy integration of our embedded system with different types of commercialized or prototypic artificial arms. Thus far, our system was portable for take-home use. Additionally, compared with previously reported embedded systems for EMG PR implementation, our system demonstrated improved processing efficiency and high system precision. Our long-term goals are (1) to develop a wearable and practical EMG PR-based control for multifunctional artificial arms, and (2) to quantify the benefits of EMG PR-based control over conventional myoelectric prosthesis control in a home setting.

  19. A knowledge-based object recognition system for applications in the space station

    NASA Technical Reports Server (NTRS)

    Dhawan, Atam P.

    1988-01-01

    A knowledge-based three-dimensional (3D) object recognition system is being developed. The system uses primitive-based hierarchical relational and structural matching for the recognition of 3D objects in the two-dimensional (2D) image for interpretation of the 3D scene. At present, the pre-processing, low-level preliminary segmentation, rule-based segmentation, and the feature extraction are completed. The data structure of the primitive viewing knowledge-base (PVKB) is also completed. Algorithms and programs based on attribute-trees matching for decomposing the segmented data into valid primitives were developed. The frame-based structural and relational descriptions of some objects were created and stored in a knowledge-base. This knowledge-base of the frame-based descriptions were developed on the MICROVAX-AI microcomputer in LISP environment. The simulated 3D scene of simple non-overlapping objects as well as real camera data of images of 3D objects of low-complexity have been successfully interpreted.

  20. 77 FR 31043 - National Technical Systems, Inc.: Expiration of Recognition as a Nationally Recognized Testing...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-05-24

    ... recognition effective on the date of the notice (63 FR 68306). On June 21, 2007, OSHA renewed the recognition of NTS as an NRTL (see 72 FR 34320), which extended the recognition for a period of five years, to... Health Act of 1970 (29 U.S.C. 657(g)(2)), ] Secretary of Labor's Order No. 1-2012 (77 FR 3912), and...

  1. FCaZm intelligent recognition system for locating areas prone to strong earthquakes in the Andean and Caucasian mountain belts

    NASA Astrophysics Data System (ADS)

    Gvishiani, A. D.; Dzeboev, B. A.; Agayan, S. M.

    2016-07-01

    The fuzzy clustering and zoning method (FCAZm) of systems analysis is suggested for recognizing the areas of the probable generation of the epicenters of significant, strong, and the strongest earthquakes. FCAZm is a modified version of the previous FCAZ algorithmic system, which is advanced by the creation of the blocks of artificial intelligence that develop the system-forming algorithms. FCAZm has been applied for recognizing areas where the epicenters of the strongest ( M ≥ 73/4) earthquakes within the Andes mountain belt in the South America and significant earthquakes ( M ≥ 5) in the Caucasus can emerge. The reliability of the obtained results was assessed by the seismic-history type control experiments. The recognized highly seismic zones were compared with the ones previously recognized by the EPA method and by the initial version of the FCAZ system. The modified FCAZm system enabled us to pass from simple pattern recognition in the problem of recognizing the locations of the probable emergence of strong earthquakes to systems analysis. In particular, using FCAZm we managed to uniquely recognize a subsystem of highly seismically active zones from the nonempty complement using the exact boundary.

  2. Interpretation of fault system in the Tana Sag, Kenya, using edge recognition techniques and Euler deconvolution

    NASA Astrophysics Data System (ADS)

    Chen, Qing; Dong, Yunpeng; Cheng, Shunyou; Han, Li; Xu, Hai-hong; Chen, Hao

    2014-10-01

    The recognition of fault structures in gravity anomaly data is the most important step in the interpretation of geological and geophysical data. Edge detection and edge enhancement are commonly used for researching the edge locations of geological bodies. The edge recognition techniques applied in this paper are the tilt angle (TA) and its horizontal derivative (TA-THDR), as well as the normalized vertical derivative of the total horizontal derivative (NVDR-THDR), in which higher order derivative was involved. The results of TA and its total horizontal derivative provide detailed information to reflect the deeper sources boundaries more accurately and effectively. The information outside the geological body edges obtained from the NVDR-THDR results is eliminated by using a threshold value greater than 0, which improves the horizontal resolution to detect the fault structures with smaller scale or deeper depth. However, tilt angle and its horizontal derivative are found to be sensitive to noise, as the higher order derivative is. The noise effect is reduced by using upward continuation during the processing of the measure gravity data. To better recognize the fault system, we have compared the results obtained from the derivative mode of Euler deconvolution technique with those from TA and its total horizontal derivative and NVDR-THDR. Application to the Tana Sag demonstrates that the results correspond well with those inferred from previous work. The faults within Tana Sag can be divided into large-scale NW (NWW) trending and small-scale NE (NEE) trending, with the latter generally cutting off the former. And we also have found that the comparative analysis of edge recognition techniques and the ED method can extract richer information on source body edges and identify more horizontal fault locations than previous methods. These three techniques have agreed closely in detecting the edge boundaries of the deeper sources with clear precision.

  3. Major neurotransmitter systems in dorsal hippocampus and basolateral amygdala control social recognition memory.

    PubMed

    Garrido Zinn, Carolina; Clairis, Nicolas; Silva Cavalcante, Lorena Evelyn; Furini, Cristiane Regina Guerino; de Carvalho Myskiw, Jociane; Izquierdo, Ivan

    2016-08-16

    Social recognition memory (SRM) is crucial for reproduction, forming social groups, and species survival. Despite its importance, SRM is still relatively little studied. Here we examine the participation of the CA1 region of the dorsal hippocampus (CA1) and the basolateral amygdala (BLA) and that of dopaminergic, noradrenergic, and histaminergic systems in both structures in the consolidation of SRM. Male Wistar rats received intra-CA1 or intra-BLA infusions of different drugs immediately after the sample phase of a social discrimination task and 24-h later were subjected to a 5-min retention test. Animals treated with the protein synthesis inhibitor, anisomycin, into either the CA1 or BLA were unable to recognize the previously exposed juvenile (familiar) during the retention test. When infused into the CA1, the β-adrenoreceptor agonist, isoproterenol, the D1/D5 dopaminergic receptor antagonist, SCH23390, and the H2 histaminergic receptor antagonist, ranitidine, also hindered the recognition of the familiar juvenile 24-h later. The latter drug effects were more intense in the CA1 than in the BLA. When infused into the BLA, the β-adrenoreceptor antagonist, timolol, the D1/D5 dopamine receptor agonist, SKF38393, and the H2 histaminergic receptor agonist, ranitidine, also hindered recognition of the familiar juvenile 24-h later. In all cases, the impairment to recognize the familiar juvenile was abolished by the coinfusion of agonist plus antagonist. Clearly, both the CA1 and BLA, probably in that order, play major roles in the consolidation of SRM, but these roles are different in each structure vis-à-vis the involvement of the β-noradrenergic, D1/D5-dopaminergic, and H2-histaminergic receptors therein. PMID:27482097

  4. Real-time imaging systems' combination of methods to achieve automatic target recognition

    NASA Astrophysics Data System (ADS)

    Maraviglia, Carlos G.; Williams, Elmer F.; Pezzulich, Alan Z.

    1998-03-01

    Using a combination of strategies real time imaging weapons systems are achieving their goals of detecting their intended targets. The demands of acquiring a target in a cluttered environment in a timely manner with a high degree of confidence demands compromise be made as to having a truly automatic system. A combination of techniques such as dedicated image processing hardware, real time operating systems, mixes of algorithmic methods, and multi-sensor detectors are a forbearance of the unleashed potential of future weapons system and their incorporation in truly autonomous target acquisition. Elements such as position information, sensor gain controls, way marks for mid course correction, and augmentation with different imaging spectrums as well as future capabilities such as neural net expert systems and decision processors over seeing a fusion matrix architecture may be considered tools for a weapon system's achievement of its ultimate goal. Currently, acquiring a target in a cluttered environment in a timely manner with a high degree of confidence demands compromises be made as to having a truly automatic system. It is now necessary to include a human in the track decision loop, a system feature that may be long lived. Automatic Track Recognition will still be the desired goal in future systems due to the variability of military missions and desirability of an expendable asset. Furthermore, with the increasing incorporation of multi-sensor information into the track decision the human element's real time contribution must be carefully engineered.

  5. Pose-variant facial expression recognition using an embedded image system

    NASA Astrophysics Data System (ADS)

    Song, Kai-Tai; Han, Meng-Ju; Chang, Shuo-Hung

    2008-12-01

    In recent years, one of the most attractive research areas in human-robot interaction is automated facial expression recognition. Through recognizing the facial expression, a pet robot can interact with human in a more natural manner. In this study, we focus on the facial pose-variant problem. A novel method is proposed in this paper to recognize pose-variant facial expressions. After locating the face position in an image frame, the active appearance model (AAM) is applied to track facial features. Fourteen feature points are extracted to represent the variation of facial expressions. The distance between feature points are defined as the feature values. These feature values are sent to a support vector machine (SVM) for facial expression determination. The pose-variant facial expression is classified into happiness, neutral, sadness, surprise or anger. Furthermore, in order to evaluate the performance for practical applications, this study also built a low resolution database (160x120 pixels) using a CMOS image sensor. Experimental results show that the recognition rate is 84% with the self-built database.

  6. Detection and location of multiple events by MARS. Final report. [Multiple Arrival Recognition System

    SciTech Connect

    Wang, J.; Masso, J.F.; Archambeau, C.B.; Savino, J.M.

    1980-09-01

    Seismic data from two explosions was processed using the Systems Science and Software MARS (Multiple Arrival Recognition System) seismic event detector in an effort to determine their relative spatial and temporal separation on the basis of seismic data alone. The explosions were less than 1.0 kilometer apart and were separated by less than 0.5 sec in origin times. The seismic data consisted of nine local accelerograms (r < 1.0 km) and four regional (240 through 400 km) seismograms. The MARS processing clearly indicates the presence of multiple explosions, but the restricted frequency range of the data inhibits accurate time picks and hence limits the precision of the event location.

  7. 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

  8. Recognition of Intensive Valence and Arousal Affective States via Facial Electromyographic Activity in Young and Senior Adults

    PubMed Central

    Li, Hang; Walter, Steffen; Hrabal, David; Rukavina, Stefanie; Limbrecht-Ecklundt, Kerstin; Hoffman, Holger; Traue, Harald C.

    2016-01-01

    Background Research suggests that interaction between humans and digital environments characterizes a form of companionship in addition to technical convenience. To this effect, humans have attempted to design computer systems able to demonstrably empathize with the human affective experience. Facial electromyography (EMG) is one such technique enabling machines to access to human affective states. Numerous studies have investigated the effects of valence emotions on facial EMG activity captured over the corrugator supercilii (frowning muscle) and zygomaticus major (smiling muscle). The arousal emotion, specifically, has not received much research attention, however. In the present study, we sought to identify intensive valence and arousal affective states via facial EMG activity. Methods Ten blocks of affective pictures were separated into five categories: neutral valence/low arousal (0VLA), positive valence/high arousal (PVHA), negative valence/high arousal (NVHA), positive valence/low arousal (PVLA), and negative valence/low arousal (NVLA), and the ability of each to elicit corresponding valence and arousal affective states was investigated at length. One hundred and thirteen participants were subjected to these stimuli and provided facial EMG. A set of 16 features based on the amplitude, frequency, predictability, and variability of signals was defined and classified using a support vector machine (SVM). Results We observed highly accurate classification rates based on the combined corrugator and zygomaticus EMG, ranging from 75.69% to 100.00% for the baseline and five affective states (0VLA, PVHA, PVLA, NVHA, and NVLA) in all individuals. There were significant differences in classification rate accuracy between senior and young adults, but there was no significant difference between female and male participants. Conclusion Our research provides robust evidences for recognition of intensive valence and arousal affective states in young and senior adults. These

  9. Template protection and its implementation in 3D face recognition systems

    NASA Astrophysics Data System (ADS)

    Zhou, Xuebing

    2007-04-01

    As biometric recognition systems are widely applied in various application areas, security and privacy risks have recently attracted the attention of the biometric community. Template protection techniques prevent stored reference data from revealing private biometric information and enhance the security of biometrics systems against attacks such as identity theft and cross matching. This paper concentrates on a template protection algorithm that merges methods from cryptography, error correction coding and biometrics. The key component of the algorithm is to convert biometric templates into binary vectors. It is shown that the binary vectors should be robust, uniformly distributed, statistically independent and collision-free so that authentication performance can be optimized and information leakage can be avoided. Depending on statistical character of the biometric template, different approaches for transforming biometric templates into compact binary vectors are presented. The proposed methods are integrated into a 3D face recognition system and tested on the 3D facial images of the FRGC database. It is shown that the resulting binary vectors provide an authentication performance that is similar to the original 3D face templates. A high security level is achieved with reasonable false acceptance and false rejection rates of the system, based on an efficient statistical analysis. The algorithm estimates the statistical character of biometric templates from a number of biometric samples in the enrollment database. For the FRGC 3D face database, the small distinction of robustness and discriminative power between the classification results under the assumption of uniquely distributed templates and the ones under the assumption of Gaussian distributed templates is shown in our tests.

  10. Dispatcher Recognition of Stroke Using the National Academy Medical Priority Dispatch System

    PubMed Central

    Buck, Brian H; Starkman, Sidney; Eckstein, Marc; Kidwell, Chelsea S; Haines, Jill; Huang, Rainy; Colby, Daniel; Saver, Jeffrey L

    2009-01-01

    Background Emergency Medical Dispatchers (EMDs) play an important role in optimizing stroke care if they are able to accurately identify calls regarding acute cerebrovascular disease. This study was undertaken to assess the diagnostic accuracy of the current national protocol guiding dispatcher questioning of 911 callers to identify stroke, QA Guide v 11.1 of the National Academy Medical Priority Dispatch System (MPDS). Methods We identified all Los Angeles Fire Department paramedic transports of patients to UCLA Medical Center during the 12 month period from January to December 2005 in a prospectively maintained database. Dispatcher-assigned MPDS codes for each of these patient transports were abstracted from the paramedic run sheets and compared to final hospital discharge diagnosis. Results Among 3474 transported patients, 96 (2.8%) had a final diagnosis of stroke or transient ischemic attack. Dispatchers assigned a code of potential stroke to 44.8% of patients with a final discharge diagnosis of stroke or TIA. Dispatcher identification of stroke showed a sensitivity of 0.41, specificity of 0.96, positive predictive value of 0.45, and negative predictive value of 0.95. Conclusions Dispatcher recognition of stroke calls using the widely employed MPDS algorithm is suboptimal, with failure to identify more than half of stroke patients as likely stroke. Revisions to the current national dispatcher structured interview and complaint identification algorithm for stroke may facilitate more accurate recognition of stroke by EMDs. PMID:19390065

  11. Single-Walled Carbon Nanotubes as Fluorescence Biosensors for Pathogen Recognition in Water Systems

    DOE PAGES

    Upadhyayula, Venkata K. K.; Ghoshroy, Soumitra; Nair, Vinod S.; Smith, Geoffrey B.; Mitchell, Martha C.; Deng, Shuguang

    2008-01-01

    Tmore » he possibility of using single-walled carbon nanotubes (SWCNTs) aggregates as fluorescence sensors for pathogen recognition in drinking water treatment applications has been studied. Batch adsorption study is conducted to adsorb large concentrations of Staphylococcus aureus aureus SH 1000 and Escherichia coli pKV-11 on single-walled carbon nanotubes. Subsequently the immobilized bacteria are detected with confocal microscopy by coating the nanotubes with fluorescence emitting antibodies.he Freundlich adsorption equilibrium constant ( k ) for S.aureus and E.coli determined from batch adsorption study was found to be 9 × 10 8 and 2 × 10 8  ml/g, respectively.he visualization of bacterial cells adsorbed on fluorescently modified carbon nanotubes is also clearly seen.he results indicate that hydrophobic single-walled carbon nanotubes have excellent bacterial adsorption capacity and fluorescent detection capability.his is an important advancement in designing fluorescence biosensors for pathogen recognition in water systems.« less

  12. Validity and reliability of a voice-recognition game analysis system for field sports.

    PubMed

    Schokman, P; Le Rossignol, P F; Sparrow, W A

    2002-12-01

    The purpose of this study was to assess the ability of observers to use voice-recognition analysis to accurately classify gait transitions and quantify gait durations typical of team games. Inter-rater and intra-rater reliability was also determined. Four males were filmed performing pre-determined gait protocols. each comprising different sequences of walking, jogging, running and sprinting. Two operators independently classified gait transitions and the time spent in each gait was determined by the voice recognition system. All gait modes as measured by trained observers demonstrated statistically significant correlations (p < 0.01) to pre-determined measurement criteria. The mean absolute error for all gait transitions was less than half a second (0.32-0.36 s) with the maximum percentage error being approximately 4% for the walk, jog and run gaits and 10% for sprinting. Gait classification error was low at 1.9%. The intra-rater and inter-rater reliability was consistently high ranging from r = 0.87 to 0.99. In conclusion, observers using voicerecognition software provided valid measures of time spent in each of the four gait categories with 90% or better accuracy achieved.

  13. Facial emotion recognition system for autistic children: a feasible study based on FPGA implementation.

    PubMed

    Smitha, K G; Vinod, A P

    2015-11-01

    Children with autism spectrum disorder have difficulty in understanding the emotional and mental states from the facial expressions of the people they interact. The inability to understand other people's emotions will hinder their interpersonal communication. Though many facial emotion recognition algorithms have been proposed in the literature, they are mainly intended for processing by a personal computer, which limits their usability in on-the-move applications where portability is desired. The portability of the system will ensure ease of use and real-time emotion recognition and that will aid for immediate feedback while communicating with caretakers. Principal component analysis (PCA) has been identified as the least complex feature extraction algorithm to be implemented in hardware. In this paper, we present a detailed study of the implementation of serial and parallel implementation of PCA in order to identify the most feasible method for realization of a portable emotion detector for autistic children. The proposed emotion recognizer architectures are implemented on Virtex 7 XC7VX330T FFG1761-3 FPGA. We achieved 82.3% detection accuracy for a word length of 8 bits.

  14. 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

  15. 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.

  16. 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.

  17. NLRP7 and related inflammasome activating pattern recognition receptors and their function in host defense and disease.

    PubMed

    Radian, Alexander D; de Almeida, Lucia; Dorfleutner, Andrea; Stehlik, Christian

    2013-01-01

    Host defense requires the maturation and release of the pro-inflammatory cytokines interleukin (IL)-1β and IL-18 and the induction of pyroptotic cell death, which depends on the activation of inflammatory Caspases within inflammasomes by innate immune cells. Several cytosolic pattern recognition receptors (PRRs) have been implicated in this process in response to infectious and sterile agonists. Here we summarize the current knowledge on inflammasome-organizing PRRs, emphasizing the recently described NLRP7, and their implications in human disease.

  18. Pattern-recognition system, designed on GPU, for discriminating between injured normal and pathological knee cartilage.

    PubMed

    Kostopoulos, Spiros; Sidiropoulos, Konstantinos; Glotsos, Dimitris; Athanasiadis, Emmanouil; Boutsikou, Konstantina; Lavdas, Eleftherios; Oikonomou, Georgia; Fezoulidis, Ioannis V; Vlychou, Marianna; Hantes, Michael; Cavouras, Dionisis

    2013-06-01

    The aim was to design a pattern-recognition (PR) system for discriminating between normal and pathological knee articular cartilage of the medial femoral (MFC) and tibial condyles (MTC). The data set comprised segmented regions of interest (ROIs) from coronal and sagittal 3-T magnetic resonance images of the MFC and MTC cartilage of young patients, 28 with abnormality-free knee and 16 with pathological findings. The PR system was designed employing the probabilistic neural network classifier, textural features from the segmented ROIs and the leave-one-out evaluation method, while the PR system's precision to "unseen" data was assessed by employing the external cross-validation method. Optimal system design was accomplished on a consumer graphics processing unit (GPU) using Compute Unified Device Architecture parallel programming. PR system design on the GPU required about 3.5 min against 15 h on a CPU-based system. Highest classification accuracies for the MFC and MTC cartilages were 93.2% and 95.5%, and accuracies to "unseen" data were 89% and 86%, respectively. The proposed PR system is housed in a PC, equipped with a consumer GPU, and it may be easily retrained when new verified data are incorporated in its repository and may be of value as a second-opinion tool in a clinical environment.

  19. Speech recognition and understanding

    SciTech Connect

    Vintsyuk, T.K.

    1983-05-01

    This article discusses the automatic processing of speech signals with the aim of finding a sequence of works (speech recognition) or a concept (speech understanding) being transmitted by the speech signal. The goal of the research is to develop an automatic typewriter that will automatically edit and type text under voice control. A dynamic programming method is proposed in which all possible class signals are stored, after which the presented signal is compared to all the stored signals during the recognition phase. Topics considered include element-by-element recognition of words of speech, learning speech recognition, phoneme-by-phoneme speech recognition, the recognition of connected speech, understanding connected speech, and prospects for designing speech recognition and understanding systems. An application of the composition dynamic programming method for the solution of basic problems in the recognition and understanding of speech is presented.

  20. 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

  1. [High-speed target recognition positioning system based on multi-spectral radiation characteristics].

    PubMed

    Li, Jian-Min; Wang, Gao

    2014-11-01

    In order to achieve quick recognition and positioning of the high-speed target, using multi-spectral radiation combined with acoustic positioning technology, in the passive state, the blast wave spectral characteristics and acoustic characteristics of the measured target were rapidly obtained, thus analysis was performed to determine the type, location and other important parameters. Multi-spectral radiation detection target recognition formula was deduced. The accuracy of the optical path length and the logical integration time was calculated by shock acoustic positioning method. Experiments used 5.56 mm NATO bullets, 7.62 mm 56-rifle bullets, 12.7 mm 54 type machine-gun bullets as a target identified projectile. Interference fringes were collected by the static Fourier transform interferometer system and ICX387AL type CCD, and the peak of sound pressure was collected using 2209 pulse sound pressure meter made by B & K Company from Denmark Experimental results show that for the 5.56 mm NATO bullets, the three characteristic wavelengths position amplitudes are close to each other, with the maximum amplitude at 966 nm; For the 7.62 mm 56-rifle bullets, 935 nm is the maximum amplitude position, while for 966 and 997 nm position the magnitudes are sunukar; For 12.7 mm 54 type machine-gun bullets, the three wavelengths show a ladder-like distribution. With the increase in the detection distance spectral radiation energy decreased. Meanwhile, with the decrease in the total radiation spectrum, the spectrum of target was affected strongly by background noise, and the SNR of system was decreased. But the spectral characteristics of different target still exist, the target species can be identified by the system with the ratio algorithm of characteristic peaks. Through spectral calibration and characteristic wavelengths extraction, the target can successfully identify the type of projectile and target position, and it meets the design requirements. PMID:25752076

  2. 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.

  3. Perceived Task-Difficulty Recognition from Log-File Information for the Use in Adaptive Intelligent Tutoring Systems

    ERIC Educational Resources Information Center

    Janning, Ruth; Schatten, Carlotta; Schmidt-Thieme, Lars

    2016-01-01

    Recognising students' emotion, affect or cognition is a relatively young field and still a challenging task in the area of intelligent tutoring systems. There are several ways to use the output of these recognition tasks within the system. The approach most often mentioned in the literature is using it for giving feedback to the students. The…

  4. Dynamic Data Driven Applications Systems (DDDAS) modeling for automatic target recognition

    NASA Astrophysics Data System (ADS)

    Blasch, Erik; Seetharaman, Guna; Darema, Frederica

    2013-05-01

    The Dynamic Data Driven Applications System (DDDAS) concept uses applications modeling, mathematical algorithms, and measurement systems to work with dynamic systems. A dynamic systems such as Automatic Target Recognition (ATR) is subject to sensor, target, and the environment variations over space and time. We use the DDDAS concept to develop an ATR methodology for multiscale-multimodal analysis that seeks to integrated sensing, processing, and exploitation. In the analysis, we use computer vision techniques to explore the capabilities and analogies that DDDAS has with information fusion. The key attribute of coordination is the use of sensor management as a data driven techniques to improve performance. In addition, DDDAS supports the need for modeling from which uncertainty and variations are used within the dynamic models for advanced performance. As an example, we use a Wide-Area Motion Imagery (WAMI) application to draw parallels and contrasts between ATR and DDDAS systems that warrants an integrated perspective. This elementary work is aimed at triggering a sequence of deeper insightful research towards exploiting sparsely sampled piecewise dense WAMI measurements - an application where the challenges of big-data with regards to mathematical fusion relationships and high-performance computations remain significant and will persist. Dynamic data-driven adaptive computations are required to effectively handle the challenges with exponentially increasing data volume for advanced information fusion systems solutions such as simultaneous target tracking and ATR.

  5. 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

  6. High-accuracy and robust face recognition system based on optical parallel correlator using a temporal image sequence

    NASA Astrophysics Data System (ADS)

    Watanabe, Eriko; Ishikawa, Mami; Ohta, Maiko; Kodate, Kashiko

    2005-09-01

    Face recognition is used in a wide range of security systems, such as monitoring credit card use, searching for individuals with street cameras via Internet and maintaining immigration control. There are still many technical subjects under study. For instance, the number of images that can be stored is limited under the current system, and the rate of recognition must be improved to account for photo shots taken at different angles under various conditions. We implemented a fully automatic Fast Face Recognition Optical Correlator (FARCO) system by using a 1000 frame/s optical parallel correlator designed and assembled by us. Operational speed for the 1: N (i.e. matching a pair of images among N, where N refers to the number of images in the database) identification experiment (4000 face images) amounts to less than 1.5 seconds, including the pre/post processing. From trial 1: N identification experiments using FARCO, we acquired low error rates of 2.6% False Reject Rate and 1.3% False Accept Rate. By making the most of the high-speed data-processing capability of this system, much more robustness can be achieved for various recognition conditions when large-category data are registered for a single person. We propose a face recognition algorithm for the FARCO while employing a temporal image sequence of moving images. Applying this algorithm to a natural posture, a two times higher recognition rate scored compared with our conventional system. The system has high potential for future use in a variety of purposes such as search for criminal suspects by use of street and airport video cameras, registration of babies at hospitals or handling of an immeasurable number of images in a database.

  7. Portable Electronic Nose System for Identification of Synthesized Gasoline Using Metal Oxide Gas Sensor and Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Kim, Young Wung; Park, Hong Bae; Lee, In Soo; Cho, Jung Hwan

    2011-09-01

    This paper describes a portable electronic nose (e-nose) system for use in the identification of synthesized gasoline, comprised of a single semiconductor type of gas sensor and pattern recognition neural networks. The designed e-nose system consists of a one-chip microcontroller, a pre-concentrator, and a gas sensor. Two different neural networks, a multilayer perceptron (MLP) neural network and a fuzzy ARTMAP neural network were applied to discriminate synthesized gasoline from normal gasoline. The results of the classification showed 100% and 85% recognition rates for the training data set and testing data set, respectively.

  8. Modeling of Nanoparticular Magnetoresistive Systems and the Impact on Molecular Recognition

    PubMed Central

    Teich, Lisa; Kappe, Daniel; Rempel, Thomas; Meyer, Judith; Schröder, Christian; Hütten, Andreas

    2015-01-01

    The formation of magnetic bead or nanoparticle superstructures due to magnetic dipole dipole interactions can be used as configurable matter in order to realize low-cost magnetoresistive sensors with very high GMR-effect amplitudes. Experimentally, this can be realized by immersing magnetic beads or nanoparticles in conductive liquid gels and rearranging them by applying suitable external magnetic fields. After gelatinization of the gel matrix the bead or nanoparticle positions are fixed and the resulting system can be used as a magnetoresistive sensor. In order to optimize such sensor structures we have developed a simulation tool chain that allows us not only to study the structuring process in the liquid state but also to rigorously calculate the magnetoresistive characteristic curves for arbitrary nanoparticle arrangements. As an application, we discuss the role of magnetoresistive sensors in finding answers to molecular recognition. PMID:25903554

  9. The inheritance of female preference functions in a mate recognition system.

    PubMed Central

    Ritchie, M G

    2000-01-01

    Mate recognition systems (MRSs) play a major role in sexual selection and speciation, yet few studies have analysed both male and female components in detail. Here, female preference functions have been characterized for the tettigoniid bushcricket Ephippiger ephippiger, and the inheritance of male song and female preference functions followed in crosses between subspecies. Songs are disproportionately determined by sex-linked genes. However, there is no evidence for a role of maternally derived sex-linked genes in female preference or of maternal effects. At the genetic level, there is a mismatch between peak preferences and male song, consistent with an evolutionary history of persistent directional preferences. Such a pattern of inheritance could contribute to the process of speciation via the evolution of new MRSs. PMID:10722212

  10. Controllable Drug Release System in Living Cells Triggered by Enzyme-Substrate Recognition.

    PubMed

    Liu, Pengchang; Wang, Xiaoliang; Hiltunen, Kalervo; Chen, Zhijun

    2015-12-01

    Vehicles can deliver the drug molecules into cells, yet immunoreaction of the commonly used capping agents and release triggers limit their biomedical use. This shortcoming might be circumvented through replacing these chemicals with certain biomolecules. Here, we show a new and facile way to encapsulate the drug delivery vehicles and release the cargos in a highly controllable manner via modulating supramolecular interactions between enzyme, substrate, and vehicle. The cargo release from the vehicles within cells can be achieved upon substrate treatment. Yeast cells were used, allowing for a fast and cost-effective way for imaging and morphological analysis. We believe this new platform can be readily extended to various carrier systems for different purposes based on shifting the recognition pattern of enzyme-substrate pairs. PMID:26562724

  11. Effect of chromosome arrangements on mate recognition system leading to behavioral isolation in Drosophila ananassae.

    PubMed

    Nanda, Punita; Singh, Bashisth N

    2011-02-01

    The mechanisms of speciation that appear in the early stages of reproductive isolation has been of recent interest to evolutionary biologists. Experiments were conducted to study behavioral isolation between karyotypically different homozygous strains derived from natural populations of Drosophila ananassae. Three mass cultures stocks established from flies collected from natural populations were employed and homozygous stocks (ST/ST and AL/AL) were made through selection for homozygosity. By employing male-choice technique, mating success was scored by direct observation in the Elens-Wattiaux mating chamber. There is preference for homogamic matings in all the three populations and the differences between homogamic and heterogamic matings are statistically significant in two populations (Lucknow and Varanasi). These findings provide evidence that there is incipient sexual isolation between karyotypically different strains of D. ananassae derived from natural populations which shows that chromosome arrangements may affect the mate recognition system in D. ananassae.

  12. Stereovision-based 3D field recognition for automatic guidance system of off-road vehicle

    NASA Astrophysics Data System (ADS)

    Zhang, Fangming; Ying, Yibin; Shen, Chuan; Jiang, Huanyu; Zhang, Qin

    2005-11-01

    A stereovision-based disparity evaluation algorithm was developed for rice crop field recognition. The gray level intensities and the correlation relation were integrated to produce the disparities of stereo-images. The surface of ground and rice were though as two rough planes, but their disparities waved in a narrow range. The cut/uncut edges of rice crops were first detected and track through the images. We used a step model to locate those edge positions. The points besides the edges were matched respectively to get disparity values using area correlation method. The 3D camera coordinates were computed based on those disparities. The vehicle coordinates were obtained by multiplying the 3D camera coordinates with a transform formula. It has been implemented on an agricultural robot and evaluated in rice crop field with straight rows. The results indicated that the developed stereovision navigation system is capable of reconstructing the field image.

  13. A distributed automatic target recognition system using multiple low resolution sensors

    NASA Astrophysics Data System (ADS)

    Yue, Zhanfeng; Lakshmi Narasimha, Pramod; Topiwala, Pankaj

    2008-04-01

    In this paper, we propose a multi-agent system which uses swarming techniques to perform high accuracy Automatic Target Recognition (ATR) in a distributed manner. The proposed system can co-operatively share the information from low-resolution images of different looks and use this information to perform high accuracy ATR. An advanced, multiple-agent Unmanned Aerial Vehicle (UAV) systems-based approach is proposed which integrates the processing capabilities, combines detection reporting with live video exchange, and swarm behavior modalities that dramatically surpass individual sensor system performance levels. We employ real-time block-based motion analysis and compensation scheme for efficient estimation and correction of camera jitter, global motion of the camera/scene and the effects of atmospheric turbulence. Our optimized Partition Weighted Sum (PWS) approach requires only bitshifts and additions, yet achieves a stunning 16X pixel resolution enhancement, which is moreover parallizable. We develop advanced, adaptive particle-filtering based algorithms to robustly track multiple mobile targets by adaptively changing the appearance model of the selected targets. The collaborative ATR system utilizes the homographies between the sensors induced by the ground plane to overlap the local observation with the received images from other UAVs. The motion of the UAVs distorts estimated homography frame to frame. A robust dynamic homography estimation algorithm is proposed to address this, by using the homography decomposition and the ground plane surface estimation.

  14. Scene interpretation module for an active vision system

    NASA Astrophysics Data System (ADS)

    Remagnino, P.; Matas, J.; Illingworth, John; Kittler, Josef

    1993-08-01

    In this paper an implementation of a high level symbolic scene interpreter for an active vision system is considered. The scene interpretation module uses low level image processing and feature extraction results to achieve object recognition and to build up a 3D environment map. The module is structured to exploit spatio-temporal context provided by existing partial world interpretations and has spatial reasoning to direct gaze control and thereby achieve efficient and robust processing using spatial focus of attention. The system builds and maintains an awareness of an environment which is far larger than a single camera view. Experiments on image sequences have shown that the system can: establish its position and orientation in a partially known environment, track simple moving objects such as cups and boxes, temporally integrate recognition results to establish or forget object presence, and utilize spatial focus of attention to achieve efficient and robust object recognition. The system has been extensively tested using images from a single steerable camera viewing a simple table top scene containing box and cylinder-like objects. Work is currently progressing to further develop its competences and interface it with the Surrey active stereo vision head, GETAFIX.

  15. 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