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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. Human activity recognition based on Evolving Fuzzy Systems.

    PubMed

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

    2010-10-01

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

  4. 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.

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

    PubMed Central

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

    2016-01-01

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

  6. 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

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

    PubMed

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

    2015-04-08

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

  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

    Jalal, Ahmad; Kamal, Shaharyar; Kim, Daijin

    2014-07-02

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

  11. 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

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

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

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

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Pattern recognition and active vision in chickens.

    PubMed

    Dawkins, M S; Woodington, A

    2000-02-10

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

  18. 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.

  19. Combat Systems Department Employee Recognition System

    DTIC Science & Technology

    1996-08-01

    the individual’s view of positive reinforcement . Include them in discussions. Ask for their opinions. 4 NSWCDD/MP-96/137 SECTION 3 INSTRUCTIONS 3.1...PROVIDES POSITIVE REINFORCEMENT . THE EASIER IT IS TO DO, THE MORE LIKELY IT IS TO GET DONE. N-DEPARTMENT EMPLOYEE RECOGNITION SYSTEM PRI NCI PLES THERE ARE...INDIVIDUAL’S VIEW OF POSITIVE REINFORCEMENT . ASK THEM I Papa .18Iv 15 N-DEPARTMENT EMPLOYEE RECOGNITION SYSTEM * OUTLINE A. TASK FORCE MEMBERSHIP

  20. 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

  1. 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.

  2. Purposive recognition: an active and qualitative approach

    NASA Astrophysics Data System (ADS)

    Rivlin, Ehud; Aloimonos, Yiannis; Rosenfeld, Azriel

    1992-04-01

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

  3. Activity recognition with smartphone support.

    PubMed

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

    2014-06-01

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

  4. Pattern recognition systems and procedures

    NASA Technical Reports Server (NTRS)

    Nelson, G. D.; Serreyn, D. V.

    1972-01-01

    The objectives of the pattern recognition tasks are to develop (1) a man-machine interactive data processing system; and (2) procedures to determine effective features as a function of time for crops and soils. The signal analysis and dissemination equipment, SADE, is being developed as a man-machine interactive data processing system. SADE will provide imagery and multi-channel analog tape inputs for digitation and a color display of the data. SADE is an essential tool to aid in the investigation to determine useful features as a function of time for crops and soils. Four related studies are: (1) reliability of the multivariate Gaussian assumption; (2) usefulness of transforming features with regard to the classifier probability of error; (3) advantage of selecting quantizer parameters to minimize the classifier probability of error; and (4) advantage of using contextual data. The study of transformation of variables (features), especially those experimental studies which can be completed with the SADE system, will be done.

  5. Physical environment virtualization for human activities recognition

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

  6. 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.

  7. 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.

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

    PubMed

    Aktaruzzaman, Md; Scarabottolo, Nello; Sassi, Roberto

    2015-08-01

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

  9. 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

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. Toward the ultimate synthesis/recognition system.

    PubMed

    Furui, S

    1995-10-24

    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.

  15. 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

  16. 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

  17. 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.

  18. 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.

  19. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  1. Individual differences in involvement of the visual object recognition system during visual word recognition.

    PubMed

    Laszlo, Sarah; Sacchi, Elizabeth

    2015-01-01

    Individuals with dyslexia often evince reduced activation during reading in left hemisphere (LH) language regions. This can be observed along with increased activation in the right hemisphere (RH), especially in areas associated with object recognition - a pattern referred to as RH compensation. The mechanisms of RH compensation are relatively unclear. We hypothesize that RH compensation occurs when the RH object recognition system is called upon to supplement an underperforming LH visual word form recognition system. We tested this by collecting ERPs while participants with a range of reading abilities viewed words, objects, and word/object ambiguous items (e.g., "SMILE" shaped like a smile). Less experienced readers differentiate words, objects, and ambiguous items less strongly, especially over the RH. We suggest that this lack of differentiation may have negative consequences for dyslexic individuals demonstrating RH compensation.

  2. Selecting and implementing a voice recognition system.

    PubMed

    Wheeler, S; Cassimus, G C

    1999-01-01

    A single radiology department serves the three separate organizations that comprise Emory Healthcare in Atlanta--three separate hospitals, the Emory Clinic and the Emory University School of Medicine. In 1996, the chairman of Emory Healthcare issued a mandate to the radiology department to decrease its report turnaround time, provide better service and increase customer satisfaction. The area where the greatest effect could be made without involving the transcription area was the "exam complete to dictate" piece of the reporting process. A committee investigating voice recognition systems established an essential criteria for potential vendors--to be able to download patient scheduling and demographic information from the existing RIS to the new system. Second, the system had to be flexible and straightforward for doctors to learn. It must have a word processing package for easy report correction and editing, and a microphone that would rewind and correct dictation before recognition took place. To keep capital costs low for the pilot, the committee opted for server recognition rather than purchase the expensive workstations necessary for real-time recognition. A switch was made later to real-time recognition. PACS and voice recognition have proven to be highly complementary. Most importantly, the new system has had a tremendous impact on turnaround time in the "dictate to final" phase. Once in the 30-hour range, 65 percent of the reports are now turned around in less than 15 minutes, 80 percent in less than 30 minutes, and 90 percent in less than an hour.

  3. Recognition of human activities with wearable sensors

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  4. Practical vision based degraded text recognition system

    NASA Astrophysics Data System (ADS)

    Mohammad, Khader; Agaian, Sos; Saleh, Hani

    2011-02-01

    Rapid growth and progress in the medical, industrial, security and technology fields means more and more consideration for the use of camera based optical character recognition (OCR) Applying OCR to scanned documents is quite mature, and there are many commercial and research products available on this topic. These products achieve acceptable recognition accuracy and reasonable processing times especially with trained software, and constrained text characteristics. Even though the application space for OCR is huge, it is quite challenging to design a single system that is capable of performing automatic OCR for text embedded in an image irrespective of the application. Challenges for OCR systems include; images are taken under natural real world conditions, Surface curvature, text orientation, font, size, lighting conditions, and noise. These and many other conditions make it extremely difficult to achieve reasonable character recognition. Performance for conventional OCR systems drops dramatically as the degradation level of the text image quality increases. In this paper, a new recognition method is proposed to recognize solid or dotted line degraded characters. The degraded text string is localized and segmented using a new algorithm. The new method was implemented and tested using a development framework system that is capable of performing OCR on camera captured images. The framework allows parameter tuning of the image-processing algorithm based on a training set of camera-captured text images. Novel methods were used for enhancement, text localization and the segmentation algorithm which enables building a custom system that is capable of performing automatic OCR which can be used for different applications. The developed framework system includes: new image enhancement, filtering, and segmentation techniques which enabled higher recognition accuracies, faster processing time, and lower energy consumption, compared with the best state of the art published

  5. 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.

  6. Active Behavior Recognition in Beyond Visual Range Air Combat

    DTIC Science & Technology

    2015-05-01

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

  7. 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.

  8. 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.

  9. 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

  10. Multiview fusion for activity recognition using deep neural networks

    NASA Astrophysics Data System (ADS)

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

    2016-07-01

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

  11. 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

  12. 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

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

    NASA Astrophysics Data System (ADS)

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

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

  14. A Neural Network Object Recognition System

    DTIC Science & Technology

    1990-07-01

    useful for exploring different neural network configurations. There are three main computation phases of a model based object recognition system...segmentation, feature extraction, and object classification. This report focuses on the object classification stage. For segmentation, a neural network based...are available with the current system. Neural network based feature extraction may be added at a later date. The classification stage consists of a

  15. 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.

  16. 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

  17. A neural network based speech recognition system

    NASA Astrophysics Data System (ADS)

    Carroll, Edward J.; Coleman, Norman P., Jr.; Reddy, G. N.

    1990-02-01

    An overview is presented of the development of a neural network based speech recognition system. The two primary tasks involved were the development of a time invariant speech encoder and a pattern recognizer or detector. The speech encoder uses amplitude normalization and a Fast Fourier Transform to eliminate amplitude and frequency shifts of acoustic clues. The detector consists of a back-propagation network which accepts data from the encoder and identifies individual words. This use of neural networks offers two advantages over conventional algorithmic detectors: the detection time is no more than a few network time constants, and its recognition speed is independent of the number of the words in the vocabulary. The completed system has functioned as expected with high tolerance to input variation and with error rates comparable to a commercial system when used in a noisy environment.

  18. 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.

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

    PubMed Central

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

    2016-01-01

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

  20. 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.

  1. 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

  2. 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

  3. Cross domains Arabic named entity recognition system

    NASA Astrophysics Data System (ADS)

    Al-Ahmari, S. Saad; Abdullatif Al-Johar, B.

    2016-07-01

    Named Entity Recognition (NER) plays an important role in many Natural Language Processing (NLP) applications such as; Information Extraction (IE), Question Answering (QA), Text Clustering, Text Summarization and Word Sense Disambiguation. This paper presents the development and implementation of domain independent system to recognize three types of Arabic named entities. The system works based on a set of domain independent grammar-rules along with Arabic part of speech tagger in addition to gazetteers and lists of trigger words. The experimental results shown, that the system performed as good as other systems with better results in some cases of cross-domains corpora.

  4. Signal evolution in prey recognition systems.

    PubMed

    Pie, Marcio R

    2005-01-31

    In this paper a graphical model first developed in the context of kin recognition is adapted to the study of signalling in predator-prey systems. Antipredation strategies are envisioned as points along a signal-to-noise (S/N) axis, with concealing (low S/N) and conspicuous (high S/N) strategies being placed at opposite sides of this axis. Optimal prey recognition systems should find a trade-off between acceptance errors (going after a background cue as if it were a prey) and rejection errors (not going after a prey as if it were background noise). The model also predicts the types of cues the predator should use in opposite sides of the S/N axis.

  5. Dance recognition system using lower body movement.

    PubMed

    Simpson, Travis T; Wiesner, Susan L; Bennett, Bradford C

    2014-02-01

    The current means of locating specific movements in film necessitate hours of viewing, making the task of conducting research into movement characteristics and patterns tedious and difficult. This is particularly problematic for the research and analysis of complex movement systems such as sports and dance. While some systems have been developed to manually annotate film, to date no automated way of identifying complex, full body movement exists. With pattern recognition technology and knowledge of joint locations, automatically describing filmed movement using computer software is possible. This study used various forms of lower body kinematic analysis to identify codified dance movements. We created an algorithm that compares an unknown move with a specified start and stop against known dance moves. Our recognition method consists of classification and template correlation using a database of model moves. This system was optimized to include nearly 90 dance and Tai Chi Chuan movements, producing accurate name identification in over 97% of trials. In addition, the program had the capability to provide a kinematic description of either matched or unmatched moves obtained from classification recognition.

  6. Structural Target Analysis And Recognition System

    NASA Astrophysics Data System (ADS)

    Lee, Harry C.

    1984-06-01

    The structural target analysis and recognition system (STARS) is a pyramid and syntactical based vision system that uniquely classifies targets, using their viewable internal structure. Being a totally structural approach, STARS uses a resolution sequence to develop a hierarchical pyramid organized segmentation and formal language to perform the recognition function. Global structure of the target is derived by the segment connectivity of the inter-resolution levels, while local structure is based on the local relationship of segments at a single level. The relationships of both the global and local structures form a resolution syntax tree (RST). Two targets are said to be structurally similar if they have similar RSTs. The matching process of the RSTs proceeds from the root to the leaves of the tree. The depth to which the match progresses before failure or completion determines the degree of patch in a resolution sense. RSTs from various views of a target are grouped together to form a formal language. The underlying grammar is transformed into a stochastic grammar so as to accommodate segmentation and environmental variations. Recognition metrics are a function of the resolution structure and posterior probability at each resolution level. Because of the inherent resolution sequence, STARS can accommodate both candidate and reference targets from various resolutions.

  7. 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.

  8. Euro Banknote Recognition System for Blind People

    PubMed Central

    Dunai Dunai, Larisa; Chillarón Pérez, Mónica; Peris-Fajarnés, Guillermo; Lengua Lengua, Ismael

    2017-01-01

    This paper presents the development of a portable system with the aim of allowing blind people to detect and recognize Euro banknotes. The developed device is based on a Raspberry Pi electronic instrument and a Raspberry Pi camera, Pi NoIR (No Infrared filter) dotted with additional infrared light, which is embedded into a pair of sunglasses that permit blind and visually impaired people to independently handle Euro banknotes, especially when receiving their cash back when shopping. The banknote detection is based on the modified Viola and Jones algorithms, while the banknote value recognition relies on the Speed Up Robust Features (SURF) technique. The accuracies of banknote detection and banknote value recognition are 84% and 97.5%, respectively. PMID:28117703

  9. Euro Banknote Recognition System for Blind People.

    PubMed

    Dunai Dunai, Larisa; Chillarón Pérez, Mónica; Peris-Fajarnés, Guillermo; Lengua Lengua, Ismael

    2017-01-20

    This paper presents the development of a portable system with the aim of allowing blind people to detect and recognize Euro banknotes. The developed device is based on a Raspberry Pi electronic instrument and a Raspberry Pi camera, Pi NoIR (No Infrared filter) dotted with additional infrared light, which is embedded into a pair of sunglasses that permit blind and visually impaired people to independently handle Euro banknotes, especially when receiving their cash back when shopping. The banknote detection is based on the modified Viola and Jones algorithms, while the banknote value recognition relies on the Speed Up Robust Features (SURF) technique. The accuracies of banknote detection and banknote value recognition are 84% and 97.5%, respectively.

  10. 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.

  11. System and method for character recognition

    NASA Technical Reports Server (NTRS)

    Hong, J. P. (Inventor)

    1974-01-01

    A character recognition system is disclosed in which each character in a retina, defining a scanning raster, is scanned with random lines uniformly distributed over the retina. For each type of character to be recognized the system stores a probability density function (PDF) of the random line intersection lengths and/or a PDF of the random line number of intersections. As an unknown character is scanned, the random line intersection lengths and/or the random line number of intersections are accumulated and based on a comparison with the prestored PDFs a classification of the unknown character is performed.

  12. 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.

  13. 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.

  14. Study on Information Fusion Based Check Recognition System

    NASA Astrophysics Data System (ADS)

    Wang, Dong

    Automatic check recognition techniques play an important role in financial systems, especially in risk management. This paper presents a novel check recognition system based on multi-cue information fusion theory. For Chinese bank check, the amount can be independently determined by legal amount, courtesy amount, or E13B code. The check recognition algorithm consists of four steps: preprocessing, check layout analysis, segmentation and recognition, and information fusion. For layout analysis, an adaptive template matching algorithm is presented to locate the target recognition regions on the check. The hidden markov model is used to segment and recognize legal amount. Courtesy and E13B code are recognized by artificial neural network method, respectively. Finally, D-S evidence theory is then introduced to fuse above three recognition results for better recognition performance. Experimental results demonstrate that the system can robustly recognize checks and the information fusion based algorithm improves the recognition rate by 5~10 percent.

  15. 34 CFR 602.30 - Activities covered by recognition procedures.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

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

  16. Automatic TLI recognition system. Part 1: System description

    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 gives a general description of the ATR system.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Optical music recognition system which learns

    NASA Astrophysics Data System (ADS)

    Fujinaga, Ichiro

    1993-01-01

    This paper describes an optical music recognition system composed of a database and three interdependent processes: a recognizer, an editor, and a learner. Given a scanned image of a musical score, the recognizer locates, separates, and classifies symbols into musically meaningful categories. This classification is based on the k-nearest neighbor method using a subset of the database that contains features of symbols classified in previous recognition sessions. Output of the recognizer is corrected by a musically trained human operator using a music notation editor. The editor provides both visual and high-quality audio feedback of the output. Editorial corrections made by the operator are passed to the learner which then adds the newly acquired data to the database. The learner's main task, however, involves selecting a subset of the database and reweighing the importance of the features to improve accuracy and speed for subsequent sessions. Good preliminary results have been obtained with everything from professionally engraved scores to hand-written manuscripts.

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

    NASA Astrophysics Data System (ADS)

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

    2003-06-01

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

  5. 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…

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

    PubMed

    Rentschler, Ingo; Osman, Erol; Jüttner, Martin

    2009-01-01

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

  7. Voice recognition interface for a radiology information system

    NASA Astrophysics Data System (ADS)

    Hinson, William H.; Boehme, Johannes M.; Choplin, Robert H.; Santago, Peter, II

    1990-08-01

    We have implemented a voice recognition interface using a Dragon Systems VoiceScribe-1000 Speech Recognition system installed on an AT&T 6310 personal computer. The Dragon Systems DragonKey software allows the user to emulate keyboard functions using the speech recognition system and replaces the presently used bar code system. The software supports user voice training, grammar design and compilation, as well as speech recognition. We have successfully integrated this voice interface in the clinical report generation system for most standard mammography studies. We have found that the voice system provides a simple, user-friendly interface which is more widely accepted in a medical environment because of its similarities to tradition dictation. Although the system requires some initial time for voice training, it avoids potential delays in transcription and proofreading. This paper describes the design and implementation of this voice recognition interface in our department.

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

    PubMed

    Hui Huang; Xian Li; Ye Sun

    2016-08-01

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

  9. Recombinant Human Peptidoglycan Recognition Proteins Reveal Antichlamydial Activity.

    PubMed

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

    2016-07-01

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

  10. Recombinant Human Peptidoglycan Recognition Proteins Reveal Antichlamydial Activity

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2011-12-01

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

  12. 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.

  13. Fusion of Smartphone Motion Sensors for Physical Activity Recognition

    PubMed Central

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

    2014-01-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-10-25

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

  16. A Neural Network Based Speech Recognition System

    DTIC Science & Technology

    1990-02-01

    encoder and identifies individual words. This use of neural networks offers two advantages over conventional algorithmic detectors: the detection...environment. Keywords: Artificial intelligence; Neural networks : Back propagation; Speech recognition.

  17. 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

  18. 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.

  19. Beta-glucan recognition by the innate immune system.

    PubMed

    Goodridge, Helen S; Wolf, Andrea J; Underhill, David M

    2009-07-01

    Beta-glucans are recognized by the innate immune system. This recognition plays important roles in host defense and presents specific opportunities for clinical modulation of the host immune response. Neutrophils, macrophages, and dendritic cells among others express several receptors capable of recognizing beta-glucan in its various forms. This review explores what is currently known about beta-glucan recognition and how this recognition stimulates immune responses. Special emphasis is placed on Dectin-1, as we know the most about how this key beta-glucan receptor translates recognition into intracellular signaling, stimulates cellular responses, and participates in orchestrating the adaptive immune response.

  20. Application of Voice Recognition Input to Decision Support Systems

    DTIC Science & Technology

    1988-12-01

    Support System (GDSS) Talkwriter Human Computer Interface Voice Input Individual Decision Support System (IDSS) Voice Input/Output Man Machine Voice ... Interface Voice Processing Natural Language Voice Input Voice Recognition Natural Language Accessed Voice Recognizer Speech Entry Voice Vocabulary

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

    PubMed

    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.

  2. 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

  3. 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)

  4. 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.

  5. 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.

  6. Mid-level Features Improve Recognition of Interactive Activities

    DTIC Science & Technology

    2012-11-14

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

  7. 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

  8. 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.

  9. The Drosophila immune system detects bacteria through specific peptidoglycan recognition.

    PubMed

    Leulier, François; Parquet, Claudine; Pili-Floury, Sebastien; Ryu, Ji-Hwan; Caroff, Martine; Lee, Won-Jae; Mengin-Lecreulx, Dominique; Lemaitre, Bruno

    2003-05-01

    The Drosophila immune system discriminates between different classes of infectious microbes and responds with pathogen-specific defense reactions through selective activation of the Toll and the immune deficiency (Imd) signaling pathways. The Toll pathway mediates most defenses against Gram-positive bacteria and fungi, whereas the Imd pathway is required to resist infection by Gram-negative bacteria. The bacterial components recognized by these pathways remain to be defined. Here we report that Gram-negative diaminopimelic acid-type peptidoglycan is the most potent inducer of the Imd pathway and that the Toll pathway is predominantly activated by Gram-positive lysine-type peptidoglycan. Thus, the ability of Drosophila to discriminate between Gram-positive and Gram-negative bacteria relies on the recognition of specific forms of peptidoglycan.

  10. Improvements in the BYBLOS Continuous Speech Recognition System

    DTIC Science & Technology

    1990-11-01

    improve recognition accuracy, exploring new techniques for speaker-independent training, and developing speaker adaptation techniques that allow system...improve recognition accuracy, exploring new techniques for speaker-independent training, and developing speaker adaptation techniques that allow the system...4 Speaker AdaptationI I During the previous three-year eifort, we developed a technique for speaker adaptation in which we modified the HMM parameters

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

    NASA Astrophysics Data System (ADS)

    Wilson, Charles L.

    1993-04-01

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

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

    PubMed

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

    2013-09-15

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

  13. 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

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

    PubMed

    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; Letesson, Jean-Jacques; Tsolis, Renée M

    2013-06-01

    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, as a flagellin-deficient mutant of B. melitensis wasfound to cause histologically demonstrable injuries in the spleen of infected mice, we suggested that recognition of FliC plays a role in the immunological stand-off between Brucella and its host, which is characterized by a persistent infection with limited inflammatory pathology.

  15. 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.

  16. Speech recognition in dental software systems: features and functionality.

    PubMed

    Yuhaniak Irwin, Jeannie; Fernando, Shawn; Schleyer, Titus; Spallek, Heiko

    2007-01-01

    Speech recognition allows clinicians a hands-free option for interacting with computers, which is important for dentists who have difficulty using a keyboard and a mouse when working with patients. While roughly 13% of all general dentists with computers at chairside use speech recognition for data entry, 16% have tried and discontinued using this technology. In this study, researches explored the speech recognition features and functionality of four dental software applications. For each system, the documentation as well as the working program was evaluated to determine speech recognition capabilities. A comparison checklist was created to highlight each program's speech functionality. Next, after the development of charting scripts, feasibility user tests were conducted to determine if performance comparisons could be made across systems. While four systems were evaluated in the feature comparison, only two of the systems were reviewed during the feasibility user tests. Results show that current speech functionality, instead of being intuitive, is directly comparable to using a mouse. Further, systems require memorizing an enormous amount of specific terminology opposed to using natural language. User testing is a feasible way to measure the performance of speech recognition across systems and will be conducted in the near future. Overall, limited speech functionality reduces the ability of clinicians to interact directly with the computer during clinical care. This can hinder the benefits of electronic patient records and clinical decision support systems.

  17. 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

  18. 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.

  19. Neuropeptide S interacts with the basolateral amygdala noradrenergic system in facilitating object recognition memory consolidation.

    PubMed

    Han, Ren-Wen; Xu, Hong-Jiao; Zhang, Rui-San; Wang, Pei; Chang, Min; Peng, Ya-Li; Deng, Ke-Yu; Wang, Rui

    2014-01-01

    The noradrenergic activity in the basolateral amygdala (BLA) was reported to be involved in the regulation of object recognition memory. As the BLA expresses high density of receptors for Neuropeptide S (NPS), we investigated whether the BLA is involved in mediating NPS's effects on object recognition memory consolidation and whether such effects require noradrenergic activity. Intracerebroventricular infusion of NPS (1nmol) post training facilitated 24-h memory in a mouse novel object recognition task. The memory-enhancing effect of NPS could be blocked by the β-adrenoceptor antagonist propranolol. Furthermore, post-training intra-BLA infusions of NPS (0.5nmol/side) improved 24-h memory for objects, which was impaired by co-administration of propranolol (0.5μg/side). Taken together, these results indicate that NPS interacts with the BLA noradrenergic system in improving object recognition memory during consolidation.

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

    PubMed Central

    Long, Chengjiang; Hua, Gang; Kapoor, Ashish

    2015-01-01

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

  1. Active AU Based Patch Weighting for Facial Expression Recognition

    PubMed Central

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

    2017-01-01

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

  2. VOTAN V5000 speech recognition system performance test report

    NASA Astrophysics Data System (ADS)

    Fitzgerald, W. J.

    1984-08-01

    Evaluation of speech recognition equipment in both quiet office and noisy environments is necessary for such projects as the Low Data Rate Voice Terminal System (LDRVTS), which rely heavily on speech recognition technology for their implementation. To keep abreast of the current state of this changing technology, the VOTAN V5000 system is evaluated in this report. The selection of the VOTAN system for evaluation was influenced by a report in an article from the September/October 1982 issue of Speech Technology, a trade magazine, which described encouraging noise test results with the VOTAN unit in a NASA evaluation test.

  3. 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

  4. 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.

  5. The Recognition System for the Voluntary Wink with EMG

    NASA Astrophysics Data System (ADS)

    Mizutani, Kengi

    There are many reports about the system controlled by the eye movement in the medical instruments and human technology. In this study, we report a new way of recognition for the voluntary wink with EMG, which can use for the controller about some systems with free hand.

  6. 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.

  7. Developing A General Purpose Optical Character Recognition System

    NASA Astrophysics Data System (ADS)

    Marosi, I.; Kovacs, E.

    1989-07-01

    The most important points in the development of an OCR system are the font independence and the ability to read free layout text. The feature extraction algorithm based on contour tracing generates size invariant geometrical and topological features which make the recognition as font independent as possible. In our OCR system (Recognita) these features are arranged in a tree structure which enables fast classification to be done. The character and line finding algorithm is designed to meet the second requirement including the recognition of proportional spacing, ligatures, kerning and automatic separation of graphics and text.

  8. Adamantane in Drug Delivery Systems and Surface Recognition.

    PubMed

    Štimac, Adela; Šekutor, Marina; Mlinarić-Majerski, Kata; Frkanec, Leo; Frkanec, Ruža

    2017-02-16

    The adamantane moiety is widely applied in design and synthesis of new drug delivery systems and in surface recognition studies. This review focuses on liposomes, cyclodextrins, and dendrimers based on or incorporating adamantane derivatives. Our recent concept of adamantane as an anchor in the lipid bilayer of liposomes has promising applications in the field of targeted drug delivery and surface recognition. The results reported here encourage the development of novel adamantane-based structures and self-assembled supramolecular systems for basic chemical investigations as well as for biomedical application.

  9. Design of embedded intelligent monitoring system based on face recognition

    NASA Astrophysics Data System (ADS)

    Liang, Weidong; Ding, Yan; Zhao, Liangjin; Li, Jia; Hu, Xuemei

    2017-01-01

    In this paper, a new embedded intelligent monitoring system based on face recognition is proposed. The system uses Pi Raspberry as the central processor. A sensors group has been designed with Zigbee module in order to assist the system to work better and the two alarm modes have been proposed using the Internet and 3G modem. The experimental results show that the system can work under various light intensities to recognize human face and send alarm information in real time.

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

    PubMed

    Yang, Xiaodong; Tian, YingLi

    2017-05-01

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

  11. Activity Recognition and Semantic Description for Indoor Mobile Localization

    PubMed Central

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

    2017-01-01

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

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

    PubMed

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

    2013-07-01

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

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

    PubMed

    Knudsen, Daniel P; Gentner, Timothy Q

    2013-04-01

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

  14. Japanese document recognition and retrieval system using programmable SIMD processor

    NASA Astrophysics Data System (ADS)

    Miyahara, Sueharu; Suzuki, Akira; Tada, Shunkichi; Kawatani, Takahiko

    1991-02-01

    This paper describes a new efficient information-filing system for a large number of documents. The system is designed to recognize Japanese characters and make full-text searches across a document database. Key components of the system are a small fully-programmable parallel processor for both recognition and retrieval an image scanner for document input and a personal computer as the operator console. The processor is constructed by a bit-serial single instruction multiple data stream architecture (SIMD) and all components including the 256 processor elements and 11 MB of RAM are integrated on one board. The recognition process divides a document into text lines isolates each character extracts character pattern features and then identifies character categories. The entire process is performed by a single micro-program package down-loaded from the console. The recognition accuracy is more than 99. 0 for about 3 printed Japanese characters at a performance speed of more than 14 characters per second. The processor can also be made available for high speed information retrieval by changing the down-loaded microprogram package. The retrieval process can obtain sentences that include the same information as an inquiry text from the database previously created through character recognition. Retrieval performance is very fast with 20 million individual Japanese characters being examined each second when the database is stored in the processor''s IC memory. It was confirmed that a high performance but flexible and cost-effective document-information-processing system

  15. 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.

  16. The MIT Summit Speech Recognition System: A Progress Report

    DTIC Science & Technology

    1989-01-01

    understanding of the human communication process. Despite recent development of some speech recognition systems with high accuracy, the performance of such...over the past four decades on human communication , in the hope that such systems will one day have a performance approaching that of humans. We are...optimize its use. Third, the system must have a stochastic component to deal with the present state of ignorance in our understanding of the human

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2014-07-10

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

  19. 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.

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

    PubMed

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

    2008-01-01

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

  1. Pay for Performance. Implementation of the Performance Management and Recognition System.

    ERIC Educational Resources Information Center

    General Accounting Office, Washington, DC.

    This report describes the activities of five federal agencies as they made the transition from merit pay to the Performance Management and Recognition System (PMRS) during fiscal year 1985. It also discusses how PMRS addressed the problems identified with merit pay. In addition, the report presents information on the pay increases and performance…

  2. 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.

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

    PubMed

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

    2014-01-01

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

  4. 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.…

  5. 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.

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

    PubMed

    Shin, Young Hoon; Seo, Jiwon

    2016-10-29

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

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

    PubMed Central

    Shin, Young Hoon; Seo, Jiwon

    2016-01-01

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

  8. 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.

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

    NASA Astrophysics Data System (ADS)

    Kataoka, Hirokatsu; Hashimoto, Kiyoshi; Aoki, Yoshimitsu

    2015-02-01

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

  10. Self-amplified optical pattern recognition system

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang (Inventor)

    1994-01-01

    A self amplifying optical pattern recognizer includes a geometric system configuration similar to that of a Vander Lugt holographic matched filter configuration with a photorefractive crystal specifically oriented with respect to the input beams. An extraordinarily polarized, spherically converging object image beam is formed by laser illumination of an input object image and applied through a photorefractive crystal, such as a barium titanite (BaTiO.sub.3) crystal. A volume or thin-film dif ORIGIN OF THE INVENTION The invention described herein was made in the performance of work under a NASA contract, and is subject to the provisions of Public Law 96-517 (35 USC 202) in which the Contractor has elected to retain title.

  11. 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

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

    PubMed

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

    2015-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  14. Recognition of Streptococcus pneumoniae by the innate immune system.

    PubMed

    Koppe, Uwe; Suttorp, Norbert; Opitz, Bastian

    2012-04-01

    Streptococcus pneumoniae is both a frequent colonizer of the upper respiratory tract and a leading cause of life-threatening infections such as pneumonia, meningitis and sepsis. The innate immune system is critical for the control of colonization and for defence during invasive disease. Initially, pneumococci are recognized by different sensors of the innate immune system called pattern recognition receptors (PRRs), which control most subsequent host defence pathways. These PRRs include the transmembrane Toll-like receptors (TLRs) as well as the cytosolic NOD-like receptors (NLRs) and DNA sensors. Recognition of S. pneumoniae by members of these PRR families regulates the production of inflammatory mediators that orchestrate the following immune response of infected as well as neighbouring non-infected cells, stimulates the recruitment of immune cells such as neutrophils and macrophages, and shapes the adaptive immunity. This review summarizes the current knowledge of the function of different PRRs in S. pneumoniae infection.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  16. Challenges Associated with Providing Speech Recognition User Interfaces for Computer-Based Educational Systems.

    ERIC Educational Resources Information Center

    Bergeron, Bryan

    1991-01-01

    Discussion of speech recognition technology and its use in computer-assisted instruction focuses on prototype systems designed for medical education. Commercial speech recognition systems are described, hardware and software requirements are examined, and the use of a speech recognition system to streamline an existing user interface is discussed.…

  17. 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.

  18. Shape Recognition Using A CMAC Based Learning System

    NASA Astrophysics Data System (ADS)

    Glanz, F. H.; Miller, W. T.

    1988-02-01

    This paper discusses pattern recognition using a learning system which can learn an arbitrary function of the input and which has built-in generalization with the characteristic that similar inputs lead to similar outputs even for untrained inputs. The amount of similarity is controlled by a parameter of the program at compile time. Inputs and/or outputs may be vectors. The system is trained in a way similar to other pattern recognition systems using an LMS rule. Patterns in the input space are not separated by hyperplanes in the way they normally are using adaptive linear elements. As a result, linear separability is not the problem it is when using Perceptron or Adaline type elements. In fact, almost any shape category region is possible, and a region need not be simply connected nor convex. An example is given of geometric shape recognition using as features autoregressive model parameters representing the shape boundaries. These features are approximately independent of translation, rotation, and size of the shape. Results in the form of percent correct on test sets are given for eight different combinations of training and test sets derived from two groups of shapes.

  19. Toward Development of a Face Recognition System for Watchlist Surveillance.

    PubMed

    Kamgar-Parsi, Behrooz; Lawson, Wallace; Kamgar-Parsi, Behzad

    2011-10-01

    The interest in face recognition is moving toward real-world applications and uncontrolled sensing environments. An important application of interest is automated surveillance, where the objective is to recognize and track people who are on a watchlist. For this open world application, a large number of cameras that are increasingly being installed at many locations in shopping malls, metro systems, airports, etc., will be utilized. While a very large number of people will approach or pass by these surveillance cameras, only a small set of individuals must be recognized. That is, the system must reject every subject unless the subject happens to be on the watchlist. While humans routinely reject previously unseen faces as strangers, rejection of previously unseen faces has remained a difficult aspect of automated face recognition. In this paper, we propose an approach motivated by human perceptual ability of face recognition which can handle previously unseen faces. Our approach is based on identifying the decision region(s) in the face space which belong to the target person(s). This is done by generating two large sets of borderline images, projecting just inside and outside of the decision region. For each person on the watchlist, a dedicated classifier is trained. Results of extensive experiments support the effectiveness of our approach. In addition to extensive experiments using our algorithm and prerecorded images, we have conducted considerable live system experiments with people in realistic environments.

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

    PubMed

    Yusuf, Feridun; Maeder, Anthony; Basilakis, Jim

    2013-01-01

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

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

    PubMed

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

    2017-03-14

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

  2. 3D Multi-Spectrum Sensor System with Face Recognition

    PubMed Central

    Kim, Joongrock; Yu, Sunjin; Kim, Ig-Jae; Lee, Sangyoun

    2013-01-01

    This paper presents a novel three-dimensional (3D) multi-spectrum sensor system, which combines a 3D depth sensor and multiple optical sensors for different wavelengths. Various image sensors, such as visible, infrared (IR) and 3D sensors, have been introduced into the commercial market. Since each sensor has its own advantages under various environmental conditions, the performance of an application depends highly on selecting the correct sensor or combination of sensors. In this paper, a sensor system, which we will refer to as a 3D multi-spectrum sensor system, which comprises three types of sensors, visible, thermal-IR and time-of-flight (ToF), is proposed. Since the proposed system integrates information from each sensor into one calibrated framework, the optimal sensor combination for an application can be easily selected, taking into account all combinations of sensors information. To demonstrate the effectiveness of the proposed system, a face recognition system with light and pose variation is designed. With the proposed sensor system, the optimal sensor combination, which provides new effectively fused features for a face recognition system, is obtained. PMID:24072025

  3. 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.

  4. Emotion recognition system using short-term monitoring of physiological signals.

    PubMed

    Kim, K H; Bang, S W; Kim, S R

    2004-05-01

    A physiological signal-based emotion recognition system is reported. The system was developed to operate as a user-independent system, based on physiological signal databases obtained from multiple subjects. The input signals were electrocardiogram, skin temperature variation and electrodermal activity, all of which were acquired without much discomfort from the body surface, and can reflect the influence of emotion on the autonomic nervous system. The system consisted of preprocessing, feature extraction and pattern classification stages. Preprocessing and feature extraction methods were devised so that emotion-specific characteristics could be extracted from short-segment signals. Although the features were carefully extracted, their distribution formed a classification problem, with large overlap among clusters and large variance within clusters. A support vector machine was adopted as a pattern classifier to resolve this difficulty. Correct-classification ratios for 50 subjects were 78.4% and 61.8%, for the recognition of three and four categories, respectively.

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

    PubMed Central

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

    2016-01-01

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

  6. 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.

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

    PubMed

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

    2014-01-01

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

  8. 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

  9. 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

  10. 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.

  11. 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.

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

    PubMed

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

    2012-01-01

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

  13. 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.

  14. Increasing the information acquisition volume in iris recognition systems.

    PubMed

    Barwick, D Shane

    2008-09-10

    A significant hurdle for the widespread adoption of iris recognition in security applications is that the typically small imaging volume for eye placement results in systems that are not user friendly. Separable cubic phase plates at the lens pupil have been shown to ameliorate this disadvantage by increasing the depth of field. However, these phase masks have limitations on how efficiently they can capture the information-bearing spatial frequencies in iris images. The performance gains in information acquisition that can be achieved by more general, nonseparable phase masks is demonstrated. A detailed design method is presented, and simulations using representative designs allow for performance comparisons.

  15. Electronic system with memristive synapses for pattern recognition

    PubMed Central

    Park, Sangsu; Chu, Myonglae; Kim, Jongin; Noh, Jinwoo; Jeon, Moongu; Hun Lee, Byoung; Hwang, Hyunsang; Lee, Boreom; Lee, Byung-geun

    2015-01-01

    Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction. PMID:25941950

  16. Electronic system with memristive synapses for pattern recognition

    NASA Astrophysics Data System (ADS)

    Park, Sangsu; Chu, Myonglae; Kim, Jongin; Noh, Jinwoo; Jeon, Moongu; Hun Lee, Byoung; Hwang, Hyunsang; Lee, Boreom; Lee, Byung-Geun

    2015-05-01

    Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.

  17. Robust laser speckle recognition system for authenticity identification.

    PubMed

    Yeh, Chia-Hung; Sung, Po-Yi; Kuo, Chih-Hung; Yeh, Ruey-Nan

    2012-10-22

    This paper proposes a laser speckle recognition system for authenticity verification. Because of the unique imperfection surfaces of objects, laser speckle provides identifiable features for authentication. A Gabor filter, SIFT (Scale-Invariant Feature Transform), and projection were used to extract the features of laser speckle images. To accelerate the matching process, the extracted Gabor features were organized into an indexing structure using the K-means algorithm. Plastic cards were used as the target objects in the proposed system and the hardware of the speckle capturing system was built. The experimental results showed that the retrieval performance of the proposed method is accurate when the database contains 516 laser speckle images. The proposed system is robust and feasible for authenticity verification.

  18. Named entity recognition for bacterial Type IV secretion systems.

    PubMed

    Ananiadou, Sophia; Sullivan, Dan; Black, William; Levow, Gina-Anne; Gillespie, Joseph J; Mao, Chunhong; Pyysalo, Sampo; Kolluru, Balakrishna; Tsujii, Junichi; Sobral, Bruno

    2011-03-29

    Research on specialized biological systems is often hampered by a lack of consistent terminology, especially across species. In bacterial Type IV secretion systems genes within one set of orthologs may have over a dozen different names. Classifying research publications based on biological processes, cellular components, molecular functions, and microorganism species should improve the precision and recall of literature searches allowing researchers to keep up with the exponentially growing literature, through resources such as the Pathosystems Resource Integration Center (PATRIC, patricbrc.org). We developed named entity recognition (NER) tools for four entities related to Type IV secretion systems: 1) bacteria names, 2) biological processes, 3) molecular functions, and 4) cellular components. These four entities are important to pathogenesis and virulence research but have received less attention than other entities, e.g., genes and proteins. Based on an annotated corpus, large domain terminological resources, and machine learning techniques, we developed recognizers for these entities. High accuracy rates (>80%) are achieved for bacteria, biological processes, and molecular function. Contrastive experiments highlighted the effectiveness of alternate recognition strategies; results of term extraction on contrasting document sets demonstrated the utility of these classes for identifying T4SS-related documents.

  19. Named Entity Recognition for Bacterial Type IV Secretion Systems

    PubMed Central

    Black, William; Levow, Gina-Anne; Gillespie, Joseph J.; Mao, Chunhong; Pyysalo, Sampo; Kolluru, BalaKrishna; Tsujii, Junichi; Sobral, Bruno

    2011-01-01

    Research on specialized biological systems is often hampered by a lack of consistent terminology, especially across species. In bacterial Type IV secretion systems genes within one set of orthologs may have over a dozen different names. Classifying research publications based on biological processes, cellular components, molecular functions, and microorganism species should improve the precision and recall of literature searches allowing researchers to keep up with the exponentially growing literature, through resources such as the Pathosystems Resource Integration Center (PATRIC, patricbrc.org). We developed named entity recognition (NER) tools for four entities related to Type IV secretion systems: 1) bacteria names, 2) biological processes, 3) molecular functions, and 4) cellular components. These four entities are important to pathogenesis and virulence research but have received less attention than other entities, e.g., genes and proteins. Based on an annotated corpus, large domain terminological resources, and machine learning techniques, we developed recognizers for these entities. High accuracy rates (>80%) are achieved for bacteria, biological processes, and molecular function. Contrastive experiments highlighted the effectiveness of alternate recognition strategies; results of term extraction on contrasting document sets demonstrated the utility of these classes for identifying T4SS-related documents. PMID:21468321

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

    NASA Astrophysics Data System (ADS)

    Daniels, D. R.

    2008-09-01

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

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

    PubMed

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

    2010-11-01

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

  2. Development of the hidden Markov models based Lithuanian speech recognition system

    NASA Astrophysics Data System (ADS)

    Ringeliene, Z.; Lipeika, A.

    2010-09-01

    The paper presents a prototype of the speaker-independent Lithuanian isolated word recognition system. The system is based on the hidden Markov models, a powerful statistical method for modeling speech signals. The prototype system can be used for Lithuanian words recognition investigations and is a good starting point for the development of a more sophisticated recognition system. The system graphical user interface is easy to control. Visualization of the entire recognition process is useful for analyzing of the recognition results. Based on this recognizer, a system for Web browser control by voice was developed. The program, which implements control by voice commands, was integrated in the speech recognition system. The system performance was evaluated by using different sets of acoustic models and vocabularies.

  3. 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…

  4. 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.

  5. Real-time image restoration for iris recognition systems.

    PubMed

    Kang, Byung Jun; Park, Kang Ryoung

    2007-12-01

    In the field of biometrics, it has been reported that iris recognition techniques have shown high levels of accuracy because unique patterns of the human iris, which has very many degrees of freedom, are used. However, because conventional iris cameras have small depth-of-field (DOF) areas, input iris images can easily be blurred, which can lead to lower recognition performance, since iris patterns are transformed by the blurring caused by optical defocusing. To overcome these problems, an autofocusing camera can be used. However, this inevitably increases the cost, size, and complexity of the system. Therefore, we propose a new real-time iris image-restoration method, which can increase the camera's DOF without requiring any additional hardware. This paper presents five novelties as compared to previous works: 1) by excluding eyelash and eyelid regions, it is possible to obtain more accurate focus scores from input iris images; 2) the parameter of the point spread function (PSF) can be estimated in terms of camera optics and measured focus scores; therefore, parameter estimation is more accurate than it has been in previous research; 3) because the PSF parameter can be obtained by using a predetermined equation, iris image restoration can be done in real-time; 4) by using a constrained least square (CLS) restoration filter that considers noise, performance can be greatly enhanced; and 5) restoration accuracy can also be enhanced by estimating the weight value of the noise-regularization term of the CLS filter according to the amount of image blurring. Experimental results showed that iris recognition errors when using the proposed restoration method were greatly reduced as compared to those results achieved without restoration or those achieved using previous iris-restoration methods.

  6. New neural-networks-based 3D object recognition system

    NASA Astrophysics Data System (ADS)

    Abolmaesumi, Purang; Jahed, M.

    1997-09-01

    Three-dimensional object recognition has always been one of the challenging fields in computer vision. In recent years, Ulman and Basri (1991) have proposed that this task can be done by using a database of 2-D views of the objects. The main problem in their proposed system is that the correspondent points should be known to interpolate the views. On the other hand, their system should have a supervisor to decide which class does the represented view belong to. In this paper, we propose a new momentum-Fourier descriptor that is invariant to scale, translation, and rotation. This descriptor provides the input feature vectors to our proposed system. By using the Dystal network, we show that the objects can be classified with over 95% precision. We have used this system to classify the objects like cube, cone, sphere, torus, and cylinder. Because of the nature of the Dystal network, this system reaches to its stable point by a single representation of the view to the system. This system can also classify the similar views to a single class (e.g., for the cube, the system generated 9 different classes for 50 different input views), which can be used to select an optimum database of training views. The system is also very flexible to the noise and deformed views.

  7. 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.

  8. 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

  9. Point spread function engineering for iris recognition system design.

    PubMed

    Ashok, Amit; Neifeld, Mark A

    2010-04-01

    Undersampling in the detector array degrades the performance of iris-recognition imaging systems. We find that an undersampling of 8 x 8 reduces the iris-recognition performance by nearly a factor of 4 (on CASIA iris database), as measured by the false rejection ratio (FRR) metric. We employ optical point spread function (PSF) engineering via a Zernike phase mask in conjunction with multiple subpixel shifted image measurements (frames) to mitigate the effect of undersampling. A task-specific optimization framework is used to engineer the optical PSF and optimize the postprocessing parameters to minimize the FRR. The optimized Zernike phase enhanced lens (ZPEL) imager design with one frame yields an improvement of nearly 33% relative to a thin observation module by bounded optics (TOMBO) imager with one frame. With four frames the optimized ZPEL imager achieves a FRR equal to that of the conventional imager without undersampling. Further, the ZPEL imager design using 16 frames yields a FRR that is actually 15% lower than that obtained with the conventional imager without undersampling.

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

    ERIC Educational Resources Information Center

    Wallace, William P.; And Others

    1995-01-01

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

  11. 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...

  12. 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.

  13. 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

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

    PubMed

    Liu, Chung-Tse; Chan, Chia-Tai

    2016-08-19

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

  15. 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.

  16. Separate but interacting recognition memory systems for different senses: The role of the rat perirhinal cortex

    PubMed Central

    Albasser, Mathieu M.; Amin, Eman; Iordanova, Mihaela D.; Brown, Malcolm W.; Pearce, John M.; Aggleton, John P.

    2011-01-01

    Two different models (convergent and parallel) potentially describe how recognition memory, the ability to detect the re-occurrence of a stimulus, is organized across different senses. To contrast these two models, rats with or without perirhinal cortex lesions were compared across various conditions that controlled available information from specific sensory modalities. Intact rats not only showed visual, tactile, and olfactory recognition, but also overcame changes in the types of sensory information available between object sampling and subsequent object recognition, e.g., between sampling in the light and recognition in the dark, or vice versa. Perirhinal lesions severely impaired object recognition whenever visual cues were available, but spared olfactory recognition and tactile-based object recognition when tested in the dark. The perirhinal lesions also blocked the ability to recognize an object sampled in the light and then tested for recognition in the dark, or vice versa. The findings reveal parallel recognition systems for different senses reliant on distinct brain areas, e.g., perirhinal cortex for vision, but also show that: (1) recognition memory for multisensory stimuli involves competition between sensory systems and (2) perirhinal cortex lesions produce a bias to rely on vision, despite the presence of intact recognition memory systems serving other senses. PMID:21685150

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

    PubMed Central

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

    2013-01-01

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

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

    DOEpatents

    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.

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

    PubMed

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

    2015-06-01

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

  20. 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.

  1. 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.

  2. 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.

  3. Digital image pattern recognition system using normalized Fourier transform and normalized analytical Fourier-Mellin transform

    NASA Astrophysics Data System (ADS)

    Vélez-Rábago, Rodrigo; Solorza-Calderón, Selene; Jordan-Aramburo, Adina

    2016-12-01

    This work presents an image pattern recognition system invariant to translation, scale and rotation. The system uses the Fourier transform to achieve the invariance to translation and the analytical Forier-Mellin transform for the invariance to scale and rotation. According with the statistical theory of box-plots, the pattern recognition system has a confidence level at least of 95.4%.

  4. 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.

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

    NASA Astrophysics Data System (ADS)

    Albalooshi, Fatema A.; Asari, Vijayan K.

    2013-05-01

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

  6. 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.

  7. 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.

  8. Modularized reconfigurable system for target recognition with multi-DSP processing

    NASA Astrophysics Data System (ADS)

    Li, Yun; Li, Huili; Xie, Xiaoming

    2013-10-01

    A modularized reconfigurable system for target recognition with multi-DSP processing is designed to reconfigure the target recognition modules and update the distributed target feature libraries through the serial channel to adapt to the varied application. The system is separated into three independent modules and two work modes running at different time slides based on project switch. The modularized reconfiguration module is designed as a minimum security kernel separated from the target recognition module to decrease their coupling and interrelationship. This kind of multi-project design based on cyclic redundancy check presents a more independent and reliable target recognition system with modularized reconfiguration ability.

  9. Optical-digital-neural network system for aided target recognition

    NASA Astrophysics Data System (ADS)

    Farr, Keith B.; Hartman, Richard L.

    1995-07-01

    Many military systems have a critical need for aided target recognition, or cuing. This includes several systems with wide field-of-view search missions such as the UAV, EFOG-M, and Comanche. This report discusses one new approach: a multiple region of interest processor based on diffraction pattern sampling and digital neural network processing. In this concept an optical system segments the image into multiple, rectangular regions of interest and in parallel converts each ROI, be it visible, IR, or radar, to a spatial frequency power spectrum and samples that spectrum for 64 features. A neural network learns to correlate those features with target classes or identifications. A digital system uses the network weights to recognize unknown targets. The research discussed in this report using a single ROI processor showed a very high level of performance. Out of 1024 trials with models of five targets of F- 14, F-18, HIND, SCUD, and M1 tanks, there were 1023 correct classifications and 1 incorrect classification. Out of 1514 trials with those images plus 490 real clutter scenes, there were 1514 correct decisions between target or no-target. Of the 1024 target detections, there were 1023 correct classifications. Out of 60 trials with low resolution IR images of real scenes, there were 60 correct decisions between target and no-target. Of the 40 target detections, there were 40 correct classifications.

  10. 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).

  11. 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

  12. 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;…

  13. 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.

  14. 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

  15. 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.

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

    PubMed

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

    2016-12-03

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

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

    PubMed Central

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

    2016-01-01

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

  18. Application of CMOS image sensor OV9620 in number recognition system

    NASA Astrophysics Data System (ADS)

    Li, Yu-feng; Liang, Fei; Xue, Rong-kun

    2009-11-01

    An image acquisition system is introduced, which consists of a color CMOS image sensor (OV9620), SRAM (CY62148), CPLD (EPM7128AE) and DSP (TMS320VC5509A). The CPLD implements the logic and timing control to the system. SRAM stores the image data, and DSP controls the image acquisition system through the SCCB (Omni Vision Serial Camera Control Bus). The timing sequence of the CMOS image sensor OV9620 is analyzed. The imaging part and the high speed image data memory unit are designed. The system structure and its application of CMOS image sensor OV9620 in paper currency number recognition are also introduced. The hardware and software design of the image acquisition and recognition system is given. In this system, we use the template matching character recognition method to guarantee fast recognition speed and high correct recognition probability.

  19. 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.

  20. 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.

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

    PubMed

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

    2015-06-19

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

  2. Personal recognition using head-top image for health-monitoring system in the home.

    PubMed

    Nakajima, K; Sasaki, K

    2004-01-01

    Automatic health-monitoring systems for the smart house are being developed for the elderly. An automatic health-monitoring system needs a way of personal recognition when two or more aged persons live together. We propose a personal recognition method based on the space spectrum of the head-top image. We examined 33 head-top images from eleven subjects and achieved a personal recognition rate of 86.4 percent. When one subject with thinning hair was excluded, the personal recognition rate was 90.0 percent in 30 head-top images from ten subjects.

  3. 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…

  4. Understanding medial temporal activation in memory tasks: evidence from fMRI of encoding and recognition in a case of transient global amnesia.

    PubMed

    Westmacott, Robyn; Silver, Frank L; McAndrews, Mary Pat

    2008-01-01

    We used fMRI to examine the activation patterns of patient AE during encoding and recognition of visual scenes during an episode of transient global amnesia (TGA) and 3 months later. Controls (n = 5) showed bilateral (R > L) activation in parahippocampal and fusiform gyri during encoding and right-sided activation in the same regions associated with recognition of previously viewed scenes. AE showed a similar pattern at follow-up. During acute TGA, when performance was profoundly impaired, AE showed no medial temporal activation associated with encoding of new scenes or recognition of old scenes. In both contrasts, the percent signal change in relevant medial temporal regions was more than three standard deviations below the control sample mean. She did, however, show striking bilateral hippocampal activation for recognition attempts (old + new scenes > baseline) even though retrieval was unsuccessful (55% recognition accuracy). This finding was unique to AE on this occasion. This is the first study to document normalization of both encoding and recognition activation patterns in TGA. Furthermore, the strong hippocampal activation during unsuccessful retrieval highlights important issues in interpreting memory-related activations, particularly in dysfunctional systems.

  5. Individual recognition of social rank and social memory performance depends on a functional circadian system.

    PubMed

    Müller, L; Weinert, D

    2016-11-01

    In a natural environment, social abilities of an animal are important for its survival. Particularly, it must recognize its own social rank and the social rank of a conspecific and have a good social memory. While the role of the circadian system for object and spatial recognition and memory is well known, the impact of the social rank and circadian disruptions on social recognition and memory were not investigated so far. In the present study, individual recognition of social rank and social memory performance of Djungarian hamsters revealing different circadian phenotypes were investigated. Wild type (WT) animals show a clear and well-synchronized daily activity rhythm, whereas in arrhythmic (AR) hamsters, the suprachiasmatic nuclei (SCN) do not generate a circadian signal. The aim of the study was to investigate putative consequences of these deteriorations in the circadian system for animalś cognitive abilities. Hamsters were bred and kept under standardized housing conditions with food and water ad libitum and a 14l/10 D lighting regimen. Experimental animals were assigned to different groups (WT and AR) according to their activity pattern obtained by means of infrared motion sensors. Before the experiments, the animals were given to develop a dominant-subordinate relationship in a dyadic encounter. Experiment 1 dealt with individual recognition of social rank. Subordinate and dominant hamsters were tested in an open arena for their behavioral responses towards a familiar (known from the agonistic encounters) or an unfamiliar hamster (from another agonistic encounter) which had the same or an opposite social rank. The investigation time depended on the social rank of the WT subject hamster and its familiarity with the stimulus animal. Both subordinate and dominant WT hamsters preferred an unfamiliar subordinate stimulus animal. In contrast, neither subordinate nor dominant AR hamsters preferred any of the stimulus animals. Thus, disruptions in circadian

  6. 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.

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

    PubMed Central

    Munoz-Organero, Mario; Ruiz-Blazquez, Ramona

    2017-01-01

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

  8. 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.

  9. Eusocial evolution and the recognition systems in social insects.

    PubMed

    Krasnec, Michelle O; Breed, Michael D

    2012-01-01

    Eusocial species, animals which live in colonies with a reproductive division of labor, typically have closed societies, in which colony members are allowed entry and nonmembers, including animals of the same species, are excluded. This implies an ability to discriminate colony members ("self") from nonmembers ("nonself"). We draw analogies between this type of discrimination and MHC-mediated cellular recognition in vertebrates. Recognition of membership in eusocial colonies is typically mediated by differences in the surface chemistry between members and nonmembers and we review studies which support this hypothesis. In rare instances, visual signals mediate recognition. We highlight the need for better understanding of which surface compounds actually mediate recognition and for further work on how differences between colony members and nonmembers are perceived.

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. Fuzzy learning vector quantization neural network and its application for artificial odor recognition system

    NASA Astrophysics Data System (ADS)

    Kusumoputro, Benyamin; Budiarto, Hary; Jatmiko, Wisnu

    2000-03-01

    In this paper, a kind of fuzzy algorithm for learning vector quantization is developed and used as pattern classifiers with a supervised learning paradigm in artificial odor discrimination system. In this type of FLVQ, the neuron activation is derived through fuzziness of the input data, so that the neural system could deal with the statistical of the measurement error directly. During learning,the similarity between the training vector and the reference vectors are calculated, and the winning reference vector is updated in two ways. Firstly, by shifting the central position of the fuzzy reference vector toward or away from the input vector, and secondly, by modifying its fuzziness. Two types of fuzziness modifications are used, i.e., a constant modification factor and a variable modification factor. This type of FLVQ is different in nature with FALVQ, and in this paper, the performance of FNLVQ network is compared with that of FALVQ in artificial odor recognition system. Experimental results show that both FALVQ and FNLVQ provided high recognition probability in determining various learn-category of odors, however, the FNLVQ neural system has the ability to recognize the unlearn-category of odor that could not recognized by FALVQ neural system.

  15. 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, DEPARTMENT OF HEALTH AND HUMAN SERVICES GENERAL PROVISIONS SPECIAL PROGRAMS AND PROJECTS Recognition of...

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

    Faro, A; Giordano, D; Spampinato, C

    2006-01-01

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

  18. 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.

  19. A Noise-Robust Continuous Speech Recognition System Using Block-Based Dynamic Range Adjustment

    NASA Astrophysics Data System (ADS)

    Sun, Yiming; Miyanaga, Yoshikazu

    A new approach to speech feature estimation under noise circumstances is proposed in this paper. It is used in noise-robust continuous speech recognition (CSR). As the noise robust techniques in isolated word speech recognition, the running spectrum analysis (RSA), the running spectrum filtering (RSF) and the dynamic range adjustment (DRA) methods have been developed. Among them, only RSA has been applied to a CSR system. This paper proposes an extended DRA for a noise-robust CSR system. In the stage of speech recognition, a continuous speech waveform is automatically assigned to a block defined by a short time length. The extended DRA is applied to these estimated blocks. The average recognition rate of the proposed method has been improved under several different noise conditions. As a result, the recognition rates are improved up to 15% in various noises with 10 dB SNR.

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

    PubMed

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

    2016-03-01

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

  1. Rotation, scale and translation invariant pattern recognition system for color images

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

    This work presents a color image pattern recognition system invariant to rotation, scale and translation. The system works with three 1D signatures, one for each RGB color channel. The signatures are constructed based on Fourier transform, analytic Fourier-Mellin transform and Hilbert binary rings mask. According with the statistical theory of box-plots, the pattern recognition system has a confidence level at least of 95.4%.

  2. Research on gesture recognition of augmented reality maintenance guiding system based on improved SVM

    NASA Astrophysics Data System (ADS)

    Zhao, Shouwei; Zhang, Yong; Zhou, Bin; Ma, Dongxi

    2014-09-01

    Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system's image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.

  3. A presence-based context-aware chronic stress recognition system.

    PubMed

    Peternel, Klemen; Pogačnik, Matevž; Tavčar, Rudi; Kos, Andrej

    2012-11-16

    Stressors encountered in daily life may play an important role in personal well-being. Chronic stress can have a serious long-term impact on our physical as well as our psychological health, due to ongoing increased levels of the chemicals released in the ‘fight or flight’ response. The currently available stress assessment methods are usually not suitable for daily chronic stress measurement. The paper presents a context-aware chronic stress recognition system that addresses this problem. The proposed system obtains contextual data from various mobile sensors and other external sources in order to calculate the impact of ongoing stress. By identifying and visualizing ongoing stress situations of an individual user, he/she is able to modify his/her behavior in order to successfully avoid them. Clinical evaluation of the proposed methodology has been made in parallel by using electrodermal activity sensor. To the best of our knowledge, the system presented herein is the first one that enables recognition of chronic stress situations on the basis of user context.

  4. Design of miniature hybrid target recognition system with combination of FPGA+DSP

    NASA Astrophysics Data System (ADS)

    Luo, Shishang; Li, Xiujian; Jia, Hui; Hu, Wenhua; Nie, Yongming; Chang, Shengli

    2010-10-01

    With advantages of flexibility, high bandwidth, high spatial resolution and high-speed parallel operation, the opto-electronic hybrid target recognition system can be applied in many civil and military areas, such as video surveillance, intelligent navigation and robot vision. A miniature opto-electronic hybrid target recognition system based on FPGA+DSP is designed, which only employs single Fourier lens and with a focal length. With the precise timing control of the FPGA and images pretreatment of the DSP, the system performs both Fourier transform and inverse Fourier transform with all optical process, which can improve recognition speed and reduce the system volume remarkably. We analyzed the system performance, and a method to achieve scale invariant pattern recognition was proposed on the basis of lots of experiments.

  5. The Magnet view: pursuing ANCC Magnet recognition as a system or individual organization.

    PubMed

    Pinkerton, SueEllen

    2008-01-01

    Systems comprising more than one organization at some point think about whether or not to pursue Magnet recognition for each individual organization or as a system. There are several considerations when making this decision in each of the Model Components for the Magnet Recognition Program. Magnet recognition is not a checklist of achievements, but rather an enculturation of values, standards, vision, commitment, and pride. It is important to remember that each organization is different and is at a different place in their development at any one time. Making the decision to pursue system Magnet recognition should consider all important factors since if one organization in the system doesn't make the grade, the system is not Magnet recognized.

  6. 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.

  7. Korean Anaphora Recognition System to Develop Healthcare Dialogue-Type Agent

    PubMed Central

    Yang, Junggi

    2014-01-01

    Objectives Anaphora recognition is a process to identify exactly which noun has been used previously and relates to a pronoun that is included in a specific sentence later. Therefore, anaphora recognition is an essential element of a dialogue agent system. In the current study, all the merits of rule-based, machine learning-based, semantic-based anaphora recognition systems were combined to design and realize a new hybrid-type anaphora recognition system with an optimum capacity. Methods Anaphora recognition rules were encoded on the basis of the internal traits of referred expressions and adjacent contexts to realize a rule-based system and to serve as a baseline. A semantic database, related to predicate instances of sentences including referred expressions, was constructed to identify semantic co-relationships between the referent candidates (to which semantic tags were attached) and the semantic information of predicates. This approach would upgrade the anaphora recognition system by reducing the number of referent candidates. Additionally, to realize a machine learning-based system, an anaphora recognition model was developed on the basis of training data, which indicated referred expressions and referents. The three methods were further combined to develop a new single hybrid-based anaphora recognition system. Results The precision rate of the rule-based systems was 54.9%. However, the precision rate of the hybrid-based system was 63.7%, proving it to be the most efficient method. Conclusions The hybrid-based method, developed by the combination of rule-based and machine learning-based methods, represents a new system with enhanced functional capabilities as compared to other pre-existing individual methods. PMID:25405063

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2014-11-27

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

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

    PubMed Central

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

    2017-01-01

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

  11. 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.

  12. 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.

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

    ERIC Educational Resources Information Center

    Tarim, Kamuran

    2015-01-01

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

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

    DTIC Science & Technology

    2014-06-28

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

  15. 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…

  16. 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.

  17. 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.

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

    ERIC Educational Resources Information Center

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

    2008-01-01

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

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

    PubMed

    Zhang, Xujin; Samuel, Arthur G

    2015-01-01

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

  20. 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

  1. 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.

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

    PubMed

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

    2016-06-07

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

  3. 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.

  4. 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.

  5. 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.

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

    PubMed

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

    2014-01-01

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

  7. 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

  8. 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.

  9. Beta-band activity in auditory pathways reflects speech localization and recognition in bilateral cochlear implant users.

    PubMed

    Senkowski, Daniel; Pomper, Ulrich; Fitzner, Inga; Engel, Andreas Karl; Kral, Andrej

    2014-07-01

    In normal-hearing listeners, localization of auditory speech involves stimulus processing in the postero-dorsal pathway of the auditory system. In quiet environments, bilateral cochlear implant (CI) users show high speech recognition performance, but localization of auditory speech is poor, especially when discriminating stimuli from the same hemifield. Whether this difficulty relates to the inability of the auditory system to translate binaural electrical cues into neural signals, or to a functional reorganization of auditory cortical pathways following long periods of binaural deprivation is unknown. In this electroencephalography study, we examined the processing of auditory syllables in postlingually deaf adults with bilateral CIs and in normal-hearing adults. Participants were instructed to either recognize ("recognition" task) or localize ("localization" task) the syllables. The analysis focused on event-related potentials and oscillatory brain responses. N1 amplitudes in CI users were larger in the localization compared with recognition task, suggesting an enhanced stimulus processing effort in the localization task. Linear beamforming of oscillatory activity in CI users revealed stronger suppression of beta-band activity after 200 ms in the postero-dorsal auditory pathway for the localization compared with the recognition task. In normal-hearing adults, effects for longer latency event-related potentials were found, but no effects were observed for N1 amplitudes or beta-band responses. Our study suggests that difficulties in speech localization in bilateral CI users are not reflected in a functional reorganization of cortical auditory pathways. New signal processing strategies of cochlear devices preserving unambiguous binaural cues may improve auditory localization performance in bilateral CI users.

  10. 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

  11. 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.

  12. Human facial neural activities and gesture recognition for machine-interfacing applications

    PubMed Central

    Hamedi, M; Salleh, Sh-Hussain; Tan, TS; Ismail, K; Ali, J; Dee-Uam, C; Pavaganun, C; Yupapin, PP

    2011-01-01

    The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human–machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2–11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers. PMID:22267930

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

    PubMed

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

    2008-01-01

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

  14. Extended depth-of-field iris recognition system for a workstation environment

    NASA Astrophysics Data System (ADS)

    Narayanswamy, Ramkumar; Silveira, Paulo E. X.; Setty, Harsha; Pauca, V. P.; van der Gracht, Joseph

    2005-03-01

    Iris recognition imaging is attracting considerable interest as a viable alternative for personal identification and verification in many defense and security applications. However current iris recognition systems suffer from limited depth of field, which makes usage of these systems more difficult by an untrained user. Traditionally, the depth of field is increased by reducing the imaging system aperture, which adversely impacts the light capturing power and thus the system signal-to-noise ratio (SNR). In this paper we discuss a computational imaging system, referred to as Wavefront Coded(R) imaging, for increasing the depth of field without sacrificing the SNR or the resolution of the imaging system. This system employs a especially designed Wavefront Coded lens customized for iris recognition. We present experimental results that show the benefits of this technology for biometric identification.

  15. Recognition and dynamics of syntectonic sediment routing systems, southern Pyrenees

    NASA Astrophysics Data System (ADS)

    Allen, P. A.; Duller, R.; Fordyce, S.; Smithells, R.; Springett, J.; Whitchurch, A.; Whittaker, A.; Carter, A.; Fedele, J.-J.

    2009-04-01

    The erosional, transportational and depositional aspects of the biogeochemical cycles involving particulate sediment and solutes are integrated in sediment routing systems. The component parts of these tectonic-geomorphic systems communicate with each other, especially in response to changes in external forcing mechanisms such as tectonic perturbations and climate change; that is, sediment routing systems are characterized by important teleconnections. We are only just beginning to understand how these teleconnections work, and what it means for the spatial and temporal scales of system behaviour. One strategy for investigating the dynamics of sediment routing systems is to link information on the denudation of upstream source regions with downstream patterns of deposition. This is most likely to be fruitful where upstream catchments are tectonically active. Sediment is released into basins whose long-term subsidence is also controlled by tectonic activity. The spatial distribution of subsidence and the magnitude of the sediment discharge from the catchment are critical factors in the dispersal of sediment of different grain size and composition away from a mountain front. We investigate the coarse clastic sediment routing systems of mid-late Eocene age (40-34 Ma) that were deposited in basins located at the boundary of the Axial Zone and the thrust belt of the South-Central Unit on the southern flank of the Pyrenees, Spain. Most of the fan deposits of interest are found in the Pobla Basin, situated north of Tremp, which benefits from outstanding exposure conditions and rigorous previous work on biostratigraphy, magnetostratigraphy and sedimentology (Mellere 1993; Beamud et al. 2003). Distinct fan depositional systems can be identified and mapped on the basis of their sediment composition, detrital thermochronology, facies and architectures, which can be related to correspondingly distinct catchment properties (size, location, exhumational history, lithologies

  16. 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

  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. Low-Complexity Hand Gesture Recognition System for Continuous Streams of Digits and Letters.

    PubMed

    Poularakis, Stergios; Katsavounidis, Ioannis

    2016-09-01

    In this paper, we propose a complete gesture recognition framework based on maximum cosine similarity and fast nearest neighbor (NN) techniques, which offers high-recognition accuracy and great computational advantages for three fundamental problems of gesture recognition: 1) isolated recognition; 2) gesture verification; and 3) gesture spotting on continuous data streams. To support our arguments, we provide a thorough evaluation on three large publicly available databases, examining various scenarios, such as noisy environments, limited number of training examples, and time delay in system's response. Our experimental results suggest that this simple NN-based approach is quite accurate for trajectory classification of digits and letters and could become a promising approach for implementations on low-power embedded systems.

  19. Human multimedia display interface based on human activity recognition

    NASA Astrophysics Data System (ADS)

    Shang, Yiting; Lee, Eung-Joo

    2011-06-01

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

  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. An Integrated Wireless Wearable Sensor System for Posture Recognition and Indoor Localization

    PubMed Central

    Huang, Jian; Yu, Xiaoqiang; Wang, Yuan; Xiao, Xiling

    2016-01-01

    In order to provide better monitoring for the elderly or patients, we developed an integrated wireless wearable sensor system that can realize posture recognition and indoor localization in real time. Five designed sensor nodes which are respectively fixed on lower limbs and a standard Kalman filter are used to acquire basic attitude data. After the attitude angles of five body segments (two thighs, two shanks and the waist) are obtained, the pitch angles of the left thigh and waist are used to realize posture recognition. Based on all these attitude angles of body segments, we can also calculate the coordinates of six lower limb joints (two hip joints, two knee joints and two ankle joints). Then, a novel relative localization algorithm based on step length is proposed to realize the indoor localization of the user. Several sparsely distributed active Radio Frequency Identification (RFID) tags are used to correct the accumulative error in the relative localization algorithm and a set-membership filter is applied to realize the data fusion. The experimental results verify the effectiveness of the proposed algorithms. PMID:27809230

  2. An Integrated Wireless Wearable Sensor System for Posture Recognition and Indoor Localization.

    PubMed

    Huang, Jian; Yu, Xiaoqiang; Wang, Yuan; Xiao, Xiling

    2016-10-31

    In order to provide better monitoring for the elderly or patients, we developed an integrated wireless wearable sensor system that can realize posture recognition and indoor localization in real time. Five designed sensor nodes which are respectively fixed on lower limbs and a standard Kalman filter are used to acquire basic attitude data. After the attitude angles of five body segments (two thighs, two shanks and the waist) are obtained, the pitch angles of the left thigh and waist are used to realize posture recognition. Based on all these attitude angles of body segments, we can also calculate the coordinates of six lower limb joints (two hip joints, two knee joints and two ankle joints). Then, a novel relative localization algorithm based on step length is proposed to realize the indoor localization of the user. Several sparsely distributed active Radio Frequency Identification (RFID) tags are used to correct the accumulative error in the relative localization algorithm and a set-membership filter is applied to realize the data fusion. The experimental results verify the effectiveness of the proposed algorithms.

  3. 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.

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

    PubMed

    Jiang, Yuyu; Larson, Janet L

    2013-03-01

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

  5. 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.

  6. 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.

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

    PubMed

    Chernbumroong, Saisakul; Cang, Shuang; Yu, Hongnian

    2015-01-01

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

  8. Developing Speaker Recognition System: From Prototype to Practical Application

    NASA Astrophysics Data System (ADS)

    Fränti, Pasi; Saastamoinen, Juhani; Kärkkäinen, Ismo; Kinnunen, Tomi; Hautamäki, Ville; Sidoroff, Ilja

    In this paper, we summarize the main achievements made in the 4-year PUMS project during 2003-2007. The emphasis is on the practical implementations, how we have moved from Matlab and Praat scripting to C/C++ implemented applications in Windows, UNIX, Linux and Symbian environments, with the motivation to enhance technology transfer. We summarize how the baseline methods have been implemented in practice, how the results are utilized in forensic applications, and compare recognition results to the state-ofart and existing commercial products such as ASIS, FreeSpeech and VoiceNet.

  9. Effect of Task Duration on Voice Recognition System Performance.

    DTIC Science & Technology

    1981-09-01

    RENNTRACUCATONERS) . PRFORING ORIATIOCNMEANDTADDREN 1AG. PRORM ELEMETN RORMTS P MT GOV ACESSIN NO 3REA PIEN’ COA96TO NUMBER TITLE~~P 62721Nti S.TV---E II .~~~~6...Versus Arousal .................................... 41 iii LIST OF TABLES Page TABLE I, Mean T600 Recognition Error Rates.................... 20 TABLE Ii ...34 ~ __________ An Akai model 4000DS Mk II reel-to-reel tape recorder was connected to the Maico Audiometer and used to present stimuli to the subject. E

  10. Named Entity Recognition in a Hungarian NL Based QA System

    NASA Astrophysics Data System (ADS)

    Tikkl, Domonkos; Szidarovszky, P. Ferenc; Kardkovacs, Zsolt T.; Magyar, Gábor

    In WoW project our purpose is to create a complex search interface with the following features: search in the deep web content of contracted partners' databases, processing Hungarian natural language (NL) questions and transforming them to SQL queries for database access, image search supported by a visual thesaurus that describes in a structural form the visual content of images (also in Hungarian). This paper primarily focuses on a particular problem of question processing task: the entity recognition. Before going into details we give a short overview of the project's aims.

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

    PubMed

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

    2015-07-23

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

  12. Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

    PubMed Central

    Nguyen, Dat Tien; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

    2017-01-01

    The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body. PMID:28300783

  13. Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras.

    PubMed

    Nguyen, Dat Tien; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

    2017-03-16

    The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

  14. 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

  15. 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

  16. Extending the Capture Volume of an Iris Recognition System Using Wavefront Coding and Super-Resolution.

    PubMed

    Hsieh, Sheng-Hsun; Li, Yung-Hui; Tien, Chung-Hao; Chang, Chin-Chen

    2016-12-01

    Iris recognition has gained increasing popularity over the last few decades; however, the stand-off distance in a conventional iris recognition system is too short, which limits its application. In this paper, we propose a novel hardware-software hybrid method to increase the stand-off distance in an iris recognition system. When designing the system hardware, we use an optimized wavefront coding technique to extend the depth of field. To compensate for the blurring of the image caused by wavefront coding, on the software side, the proposed system uses a local patch-based super-resolution method to restore the blurred image to its clear version. The collaborative effect of the new hardware design and software post-processing showed great potential in our experiment. The experimental results showed that such improvement cannot be achieved by using a hardware-or software-only design. The proposed system can increase the capture volume of a conventional iris recognition system by three times and maintain the system's high recognition rate.

  17. AFRL/HECP Speaker Recognition Systems for the 2004 NIST Speaker Recognition Evaluation

    DTIC Science & Technology

    2007-11-02

    every 10 msec. For the work described here, the Entropic get-fO command was used to estimate FO and the probability of voicing. Next, one uses a peak...picker to determine the quasi-periodic instants of maximum excitation in the residual, which are assumed to correspond to glottal closures. The Entropic ...the probability of voicing were determined every 10 msec using the Entropic Signal Processing System (ESPS) get-fO command. Next, the first three

  18. 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.

  19. Duality of the carbohydrate-recognition system of Pseudomonas aeruginosa-II lectin (PA-IIL).

    PubMed

    Wu, Albert M; Gong, Yu-Ping; Li, Chia-Chen; Gilboa-Garber, Nechama

    2010-06-03

    The study of Pseudomonas aeruginosa-II lectin (PA-IIL) complexes with Man derivatives as a recognition factor has been neglected since its monomer is a very weak ligand. Here, the roles of Man oligomers and complexes in PA-IIL carbohydrate-recognition were studied by both enzyme-linked lectinosorbent and inhibition assays. From the results obtained, it is proposed that high density weak -OH conformation as seen in yeast mannan is also an important PA-IIL recognition factor. This finding provides a peculiar concept of the duality of PA-IIL recognition system for LFucalpha1--> and related complexes and for high density Manalpha1--> complexes present in polymannosylated target macromolecules.

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

    PubMed

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

    2010-06-01

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

  1. From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.

    PubMed

    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.

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. 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

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

    PubMed

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

    2015-06-29

    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.

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

    PubMed

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

    2015-10-18

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

  9. Automated alignment system for optical wireless communication systems using image recognition.

    PubMed

    Brandl, Paul; Weiss, Alexander; Zimmermann, Horst

    2014-07-01

    In this Letter, we describe the realization of a tracked line-of-sight optical wireless communication system for indoor data distribution. We built a laser-based transmitter with adaptive focus and ray steering by a microelectromechanical systems mirror. To execute the alignment procedure, we used a CMOS image sensor at the transmitter side and developed an algorithm for image recognition to localize the receiver's position. The receiver is based on a self-developed optoelectronic integrated chip with low requirements on the receiver optics to make the system economically attractive. With this system, we were able to set up the communication link automatically without any back channel and to perform error-free (bit error rate <10⁻⁹) data transmission over a distance of 3.5 m with a data rate of 3 Gbit/s.

  10. Patient State Recognition System for Healthcare Using Speech and Facial Expressions.

    PubMed

    Hossain, M Shamim

    2016-12-01

    Smart, interactive healthcare is necessary in the modern age. Several issues, such as accurate diagnosis, low-cost modeling, low-complexity design, seamless transmission, and sufficient storage, should be addressed while developing a complete healthcare framework. In this paper, we propose a patient state recognition system for the healthcare framework. We design the system in such a way that it provides good recognition accuracy, provides low-cost modeling, and is scalable. The system takes two main types of input, video and audio, which are captured in a multi-sensory environment. Speech and video input are processed separately during feature extraction and modeling; these two input modalities are merged at score level, where the scores are obtained from the models of different patients' states. For the experiments, 100 people were recruited to mimic a patient's states of normal, pain, and tensed. The experimental results show that the proposed system can achieve an average 98.2 % recognition accuracy.

  11. '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

  12. A multi-agent system simulating human splice site recognition.

    PubMed

    Vignal, L; Lisacek, F; Quinqueton, J; d'Aubenton-Carafa, Y; Thermes, C

    1999-06-15

    The present paper describes a method detecting splice sites automatically on the basis of sequence data and models of site/signal recognition supported by experimental evidences. The method is designed to simulate splicing and while doing so, track prediction failures, missing information and possibly test correcting hypotheses. Correlations between nucleotides in the splice site regions and the various elements of the acceptor region are evaluated and combined to assess compensating interactions between elements of the splicing machinery. A scanning model of the acceptor region and a model of interaction between the splicing complexes (exon definition model) are also incorporated in the detection process. Subsets of sites presenting deficiencies of several splice site elements could be identified. Further examination of these sites helps to determine lacking elements and refine models.

  13. Dual influences of early-life maternal deprivation on histone deacetylase activity and recognition memory in rats.

    PubMed

    Albuquerque Filho, Manoel Osório; de Freitas, Betânia Souza; Garcia, Rebeca Carvalho Lacerda; Crivelaro, Pedro Castilhos de Freitas; Schröder, Nadja; de Lima, Maria Noêmia Martins

    2017-03-06

    Exposure to stress early in life may negatively impact nervous system functioning, including increasing the proneness to learning and memory impairments later in life. Maternal deprivation, a model of early-life stress, hinders memory in adult rats and lessens brain-derived neurotrophic factor (BDNF) levels in the hippocampus in a very heterogeneous way among individuals. The main goal of the present study was to investigate the possible epigenetic modulation underlying recognition memory impairment and reduced BDNF levels in the hippocampus of adult maternally deprived rats. We also evaluated the potential ameliorating properties of the histone deacetylase (HDAC) inhibitor, sodium butyrate, on memory deficits and BDNF changes related to maternal deprivation. Maternally deprived animals were categorized as 'inferior learners' and 'superior learners' according to their performance in object recognition memory task in comparison to controls. Results indicated that HDAC activity was higher in individuals submitted to maternal deprivation with the worst cognitive performance (inferior learners). Acute administration of sodium butyrate increased histone H3 acetylation and BDNF levels, and restored recognition memory in maternally deprived animals with the worst cognitive performance. Moreover, we also showed that there is a positive correlation between BDNF levels and memory performance. Taken together, the results indicated that HDAC inhibitors could be considered as a possible therapeutic agent to improve cognitive performance in inferior learners. Further studies need to be conducted for a better comprehension of the mechanisms related to persistent alterations observed in adult life induced by early stressful circumstances and those leading to resilience.

  14. 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.

  15. A genotypic mutation system measuring mutations in restriction recognition sequences.

    PubMed Central

    Felley-Bosco, E; Pourzand, C; Zijlstra, J; Amstad, P; Cerutti, P

    1991-01-01

    The RFLP/PCR approach (restriction fragment length polymorphism/polymerase chain reaction) to genotypic mutation analysis described here measures mutations in restriction recognition sequences. Wild-type DNA is restricted before the resistant, mutated sequences are amplified by PCR and cloned. We tested the capacity of this experimental design to isolate a few copies of a mutated sequence of the human c-Ha-ras1 gene from a large excess of wild-type DNA. For this purpose we constructed a 272 bp fragment with 2 mutations in the PvuII recognition sequence 1727-1732 and studied the rescue by RFLP/PCR of a few copies of this 'PvuII mutant standard'. Following amplification with Taq-polymerase and cloning into lambda gt10, plaques containing wild-type sequence, PvuII mutant standard or Taq-polymerase induced bp changes were quantitated by hybridization with specific oligonucleotide probes. Our results indicate that 10 PvuII mutant standard copies can be rescued from 10(8) to 10(9) wild-type sequences. Taq polymerase errors originating from unrestricted, residual wild-type DNA were sequence dependent and consisted mostly of transversions originating at G.C bp. In contrast to a doubly mutated 'standard' the capacity to rescue single bp mutations by RFLP/PCR is limited by Taq-polymerase errors. Therefore, we assessed the capacity of our protocol to isolate a G to T transversion mutation at base pair 1698 of the MspI-site 1695-1698 of the c-Ha-ras1 gene from excess wild-type ras1 DNA. We found that 100 copies of the mutated ras1 fragment could be readily rescued from 10(8) copies of wild-type DNA. Images PMID:1676153

  16. An Event-Based Neurobiological Recognition System with Orientation Detector for Objects in Multiple Orientations

    PubMed Central

    Wang, Hanyu; Xu, Jiangtao; Gao, Zhiyuan; Lu, Chengye; Yao, Suying; Ma, Jianguo

    2016-01-01

    A new multiple orientation event-based neurobiological recognition system is proposed by integrating recognition and tracking function in this paper, which is used for asynchronous address-event representation (AER) image sensors. The characteristic of this system has been enriched to recognize the objects in multiple orientations with only training samples moving in a single orientation. The system extracts multi-scale and multi-orientation line features inspired by models of the primate visual cortex. An orientation detector based on modified Gaussian blob tracking algorithm is introduced for object tracking and orientation detection. The orientation detector and feature extraction block work in simultaneous mode, without any increase in categorization time. An addresses lookup table (addresses LUT) is also presented to adjust the feature maps by addresses mapping and reordering, and they are categorized in the trained spiking neural network. This recognition system is evaluated with the MNIST dataset which have played important roles in the development of computer vision, and the accuracy is increased owing to the use of both ON and OFF events. AER data acquired by a dynamic vision senses (DVS) are also tested on the system, such as moving digits, pokers, and vehicles. The experimental results show that the proposed system can realize event-based multi-orientation recognition. The work presented in this paper makes a number of contributions to the event-based vision processing system for multi-orientation object recognition. It develops a new tracking-recognition architecture to feedforward categorization system and an address reorder approach to classify multi-orientation objects using event-based data. It provides a new way to recognize multiple orientation objects with only samples in single orientation. PMID:27867346

  17. An Event-Based Neurobiological Recognition System with Orientation Detector for Objects in Multiple Orientations.

    PubMed

    Wang, Hanyu; Xu, Jiangtao; Gao, Zhiyuan; Lu, Chengye; Yao, Suying; Ma, Jianguo

    2016-01-01

    A new multiple orientation event-based neurobiological recognition system is proposed by integrating recognition and tracking function in this paper, which is used for asynchronous address-event representation (AER) image sensors. The characteristic of this system has been enriched to recognize the objects in multiple orientations with only training samples moving in a single orientation. The system extracts multi-scale and multi-orientation line features inspired by models of the primate visual cortex. An orientation detector based on modified Gaussian blob tracking algorithm is introduced for object tracking and orientation detection. The orientation detector and feature extraction block work in simultaneous mode, without any increase in categorization time. An addresses lookup table (addresses LUT) is also presented to adjust the feature maps by addresses mapping and reordering, and they are categorized in the trained spiking neural network. This recognition system is evaluated with the MNIST dataset which have played important roles in the development of computer vision, and the accuracy is increased owing to the use of both ON and OFF events. AER data acquired by a dynamic vision senses (DVS) are also tested on the system, such as moving digits, pokers, and vehicles. The experimental results show that the proposed system can realize event-based multi-orientation recognition. The work presented in this paper makes a number of contributions to the event-based vision processing system for multi-orientation object recognition. It develops a new tracking-recognition architecture to feedforward categorization system and an address reorder approach to classify multi-orientation objects using event-based data. It provides a new way to recognize multiple orientation objects with only samples in single orientation.

  18. 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.

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

    PubMed Central

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

    2010-01-01

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

  20. 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.

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

    PubMed

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

    2016-03-01

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

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

    PubMed Central

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

    2017-01-01

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

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

    PubMed Central

    Bourobou, Serge Thomas Mickala; Yoo, Younghwan

    2015-01-01

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

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

    PubMed

    Bourobou, Serge Thomas Mickala; Yoo, Younghwan

    2015-05-21

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

  5. 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.

  6. 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

  7. A Chemical Sensor Pattern Recognition System Using a Self-Training Neural Network Classifier With Automated Outlier Detection

    DTIC Science & Technology

    1998-04-17

    A device and method for a pattern recognition system using a self-training neural network classifier with automated outlier detection for use in...chemical sensor array systems. The pattern recognition system uses a Probabilistic Neural Network (PNN) training computer system to develop automated

  8. A computerized recognition system for the home-based physiotherapy exercises using an RGBD camera.

    PubMed

    Ar, Ilktan; Akgul, Yusuf Sinan

    2014-11-01

    Computerized recognition of the home based physiotherapy exercises has many benefits and it has attracted considerable interest among the computer vision community. However, most methods in the literature view this task as a special case of motion recognition. In contrast, we propose to employ the three main components of a physiotherapy exercise (the motion patterns, the stance knowledge, and the exercise object) as different recognition tasks and embed them separately into the recognition system. The low level information about each component is gathered using machine learning methods. Then, we use a generative Bayesian network to recognize the exercise types by combining the information from these sources at an abstract level, which takes the advantage of domain knowledge for a more robust system. Finally, a novel postprocessing step is employed to estimate the exercise repetitions counts. The performance evaluation of the system is conducted with a new dataset which contains RGB (red, green, and blue) and depth videos of home-based exercise sessions for commonly applied shoulder and knee exercises. The proposed system works without any body-part segmentation, bodypart tracking, joint detection, and temporal segmentation methods. In the end, favorable exercise recognition rates and encouraging results on the estimation of repetition counts are obtained.

  9. 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.

  10. Localization and recognition of traffic signs for automated vehicle control systems

    NASA Astrophysics Data System (ADS)

    Zadeh, Mahmoud M.; Kasvand, T.; Suen, Ching Y.

    1998-01-01

    We present a computer vision system for detection and recognition of traffic signs. Such systems are required to assist drivers and for guidance and control of autonomous vehicles on roads and city streets. For experiments we use sequences of digitized photographs and off-line analysis. The system contains four stages. First, region segmentation based on color pixel classification called SRSM. SRSM limits the search to regions of interest in the scene. Second, we use edge tracing to find parts of outer edges of signs which are circular or straight, corresponding to the geometrical shapes of traffic signs. The third step is geometrical analysis of the outer edge and preliminary recognition of each candidate region, which may be a potential traffic sign. The final step in recognition uses color combinations within each region and model matching. This system maybe used for recognition of other types of objects, provided that the geometrical shape and color content remain reasonably constant. The method is reliable, easy to implement, and fast, This differs form the road signs recognition method in the PROMETEUS. The overall structure of the approach is sketched.

  11. Object oriented image analysis based on multi-agent recognition system

    NASA Astrophysics Data System (ADS)

    Tabib Mahmoudi, Fatemeh; Samadzadegan, Farhad; Reinartz, Peter

    2013-04-01

    In this paper, the capabilities of multi-agent systems are used in order to solve object recognition difficulties in complex urban areas based on the characteristics of WorldView-2 satellite imagery and digital surface model (DSM). The proposed methodology has three main steps: pre-processing of dataset, object based image analysis and multi-agent object recognition. Classified regions obtained from object based image analysis are used as input datasets in the proposed multi-agent system in order to modify/improve results. In the first operational level of the proposed multi-agent system, various kinds of object recognition agents modify initial classified regions based on their spectral, textural and 3D structural knowledge. Then, in the second operational level, 2D structural knowledge and contextual relations are used by agents for reasoning and modification. Evaluation of the capabilities of the proposed object recognition methodology is performed on WorldView-2 imagery over Rio de Janeiro (Brazil) which has been collected in January 2010. According to the obtained results of the object based image analysis process, contextual relations and structural descriptors have high potential to modify general difficulties of object recognition. Using knowledge based reasoning and cooperative capabilities of agents in the proposed multi-agent system in this paper, most of the remaining difficulties are decreased and the accuracy of object based image analysis results is improved for about three percent.

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

    PubMed

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

    2016-12-12

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

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

    PubMed

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

    2017-03-15

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

  14. Surface imprinted thin polymer film systems with selective recognition for bovine serum albumin.

    PubMed

    Kryscio, David R; Peppas, Nicholas A

    2012-03-09

    Molecularly imprinted polymers are synthetic antibody mimics formed by the crosslinking of organic or inorganic polymers in the presence of an analyte which yields recognitive polymer networks with specific binding pockets for that biomolecule. Surface imprinted polymers were synthesized via a novel technique for the specific recognition of bovine serum albumin (BSA). Thin films of recognitive networks based on 2-(dimethylamino)ethyl methacrylate (DMAEMA) as the functional monomer and varying amounts of either N,N'-methylenebisacrylamide (MBA) or poly(ethylene glycol) (400) dimethacrylate (PEG400DMA) as the crosslinking agent were synthesized via UV free-radical polymerization and characterized. A clear and reproducible increase in recognition of the template BSA was demonstrated for these systems at 1.6-2.5 times more BSA recognized by the MIP sample relative to the control polymers. Additionally, these polymers exhibited selective recognition of the template relative to competing proteins with up to 2.9 times more BSA adsorbed than either glucose oxidase or bovine hemoglobin. These synthetic antibody mimics hold significant promise as the next generation of robust recognition elements in a wide range of bioassay and biosensor applications.

  15. Face recognition systems in monkey and human: are they the same thing?

    PubMed

    Yovel, Galit; Freiwald, Winrich A

    2013-01-01

    Primate societies are based on face recognition. Face recognition mechanisms have been studied most extensively in humans and macaque monkeys. In both species, multiple brain areas specialized for face processing have been found, and their functional properties are characterized with increasing detail, so we can now begin to address questions about similarities and differences of face-recognition systems across species with 25 million years of separate evolution. Both systems are organized into multiple face-selective cortical areas in spatial arrangements and with functional specializations, implying both hierarchical and parallel modes of information processing. Yet open questions about homologies remain. To address these, future studies employing similar techniques and experimental designs across multiple species are needed to identify a putative core primate face processing system and to understand its differentiations into the multiple branches of the primate order.

  16. Face recognition systems in monkey and human: are they the same thing?

    PubMed Central

    2013-01-01

    Primate societies are based on face recognition. Face recognition mechanisms have been studied most extensively in humans and macaque monkeys. In both species, multiple brain areas specialized for face processing have been found, and their functional properties are characterized with increasing detail, so we can now begin to address questions about similarities and differences of face-recognition systems across species with 25 million years of separate evolution. Both systems are organized into multiple face-selective cortical areas in spatial arrangements and with functional specializations, implying both hierarchical and parallel modes of information processing. Yet open questions about homologies remain. To address these, future studies employing similar techniques and experimental designs across multiple species are needed to identify a putative core primate face processing system and to understand its differentiations into the multiple branches of the primate order. PMID:23585928

  17. 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…

  18. Real-time unconstrained object recognition: a processing pipeline based on the mammalian visual system.

    PubMed

    Aguilar, Mario; Peot, Mark A; Zhou, Jiangying; Simons, Stephen; Liao, Yuwei; Metwalli, Nader; Anderson, Mark B

    2012-03-01

    The mammalian visual system is still the gold standard for recognition accuracy, flexibility, efficiency, and speed. Ongoing advances in our understanding of function and mechanisms in the visual system can now be leveraged to pursue the design of computer vision architectures that will revolutionize the state of the art in computer vision.

  19. 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…

  20. Recognition- and Reactivity-Based Fluorescent Probes for Studying Transition Metal Signaling in Living Systems

    PubMed Central

    2015-01-01

    Conspectus 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

  1. 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.

  2. An interactive VR system based on full-body tracking and gesture recognition

    NASA Astrophysics Data System (ADS)

    Zeng, Xia; Sang, Xinzhu; Chen, Duo; Wang, Peng; Guo, Nan; Yan, Binbin; Wang, Kuiru

    2016-10-01

    Most current virtual reality (VR) interactions are realized with the hand-held input device which leads to a low degree of presence. There is other solutions using sensors like Leap Motion to recognize the gestures of users in order to interact in a more natural way, but the navigation in these systems is still a problem, because they fail to map the actual walking to virtual walking only with a partial body of the user represented in the synthetic environment. Therefore, we propose a system in which users can walk around in the virtual environment as a humanoid model, selecting menu items and manipulating with the virtual objects using natural hand gestures. With a Kinect depth camera, the system tracks the joints of the user, mapping them to a full virtual body which follows the move of the tracked user. The movements of the feet can be detected to determine whether the user is in walking state, so that the walking of model in the virtual world can be activated and stopped by means of animation control in Unity engine. This method frees the hands of users comparing to traditional navigation way using hand-held device. We use the point cloud data getting from Kinect depth camera to recognize the gestures of users, such as swiping, pressing and manipulating virtual objects. Combining the full body tracking and gestures recognition using Kinect, we achieve our interactive VR system in Unity engine with a high degree of presence.

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

    PubMed

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

    2013-09-18

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

  4. A Gesture Recognition System for Detecting Behavioral Patterns of ADHD.

    PubMed

    Bautista, Miguel Ángel; Hernández-Vela, Antonio; Escalera, Sergio; Igual, Laura; Pujol, Oriol; Moya, Josep; Violant, Verónica; Anguera, María T

    2016-01-01

    We present an application of gesture recognition using an extension of dynamic time warping (DTW) to recognize behavioral patterns of attention deficit hyperactivity disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either Gaussian mixture models or an approximation of convex hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intraclass gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioral patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multimodal dataset (RGB plus depth) of ADHD children recordings with behavioral patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context.

  5. 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.

  6. 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

  7. 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).

  8. Contribution of pheromones processed by the main olfactory system to mate recognition in female mammals.

    PubMed

    Baum, Michael J

    2012-01-01

    Until recently it was widely believed that the ability of female mammals (with the likely exception of women) to identify and seek out a male breeding partner relied on the detection of non-volatile male pheromones by the female's vomeronasal organ (VNO) and their subsequent processing by a neural circuit that includes the accessory olfactory bulb (AOB), vomeronasal amygdala, and hypothalamus. Emperical data are reviewed in this paper that demonstrate the detection of volatile pheromones by the main olfactory epithelium (MOE) of female mice which, in turn, leads to the activation of a population of glomeruli and abutting mitral cells in the main olfactory bulb (MOB). Anatomical results along with functional neuroanatomical data demonstrate that some of these MOB mitral cells project to the vomeronasal amygdala. These particular MOB mitral cells were selectively activated (i.e., expressed Fos protein) by exposure to male as opposed to female urinary volatiles. A similar selectivity to opposite sex urinary volatiles was also seen in mitral cells of the AOB of female mice. Behavioral data from female mouse, ferret, and human are reviewed that implicate the main olfactory system, in some cases interacting with the accessory olfactory system, in mate recognition.

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

    PubMed

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

    2012-10-01

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

  10. A Dental Radiograph Recognition System Using Phase-Only Correlation for Human Identification

    NASA Astrophysics Data System (ADS)

    Ito, Koichi; Nikaido, Akira; Aoki, Takafumi; Kosuge, Eiko; Kawamata, Ryota; Kashima, Isamu

    In mass disasters such as earthquakes, fire disasters, tsunami, and terrorism, dental records have been used for identifying victims due to their processing time and accuracy. The greater the number of victims, the more time the identification tasks require, since a manual comparison between the dental radiograph records is done by forensic experts. Addressing this problem, this paper presents an efficient dental radiograph recognition system using Phase-Only Correlation (POC) for human identification. The use of phase components in 2D (two-dimensional) discrete Fourier transforms of dental radiograph images makes possible to achieve highly robust image registration and recognition. Experimental evaluation using a set of dental radiographs indicates that the proposed system exhibits efficient recognition performance for low-quality images.

  11. Performance of a neural-network-based 3-D object recognition system

    NASA Astrophysics Data System (ADS)

    Rak, Steven J.; Kolodzy, Paul J.

    1991-08-01

    Object recognition in laser radar sensor imagery is a challenging application of neural networks. The task involves recognition of objects at a variety of distances and aspects with significant levels of sensor noise. These variables are related to sensor parameters such as sensor signal strength and angular resolution, as well as object range and viewing aspect. The effect of these parameters on a fixed recognition system based on log-polar mapped features and an unsupervised neural network classifier are investigated. This work is an attempt to quantify the design parameters of a laser radar measurement system with respect to classifying and/or identifying objects by the shape of their silhouettes. Experiments with vehicle silhouettes rotated through 90 deg-of-view angle from broadside to head-on ('out-of-plane' rotation) have been used to quantify the performance of a log-polar map/neural-network based 3-D object recognition system. These experiments investigated several key issues such as category stability, category memory compression, image fidelity, and viewing aspect. Initial results indicate a compression from 720 possible categories (8 vehicles X 90 out-of-plane rotations) to a classifier memory with approximately 30 stable recognition categories. These results parallel the human experience of studying an object from several viewing angles yet recognizing it through a wide range of viewing angles. Results are presented illustrating category formation for an eight vehicle dataset as a function of several sensor parameters. These include: (1) sensor noise, as a function of carrier-to-noise ratio; (2) pixels on the vehicle, related to angular resolution and target range; and (3) viewing aspect, as related to sensor-to-platform depression angle. This work contributes to the formation of a three- dimensional object recognition system.

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

    PubMed

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

    2016-08-01

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

  13. A Smart Capsule System for Automated Detection of Intestinal Bleeding Using HSL Color Recognition

    PubMed Central

    Liu, Hongying; Yan, Xueping; Jia, Ziru; Pi, Xitian

    2016-01-01

    There are no ideal means for the diagnosis of intestinal bleeding diseases as of now, particularly in the small intestine. This study investigated an intelligent intestinal bleeding detection capsule system based on color recognition. After the capsule is swallowed, the bleeding detection module (containing a color-sensitive adsorptive film that changes color when absorbing intestinal juice,) is used to identify intestinal bleeding features. A hue-saturation-light color space method can be applied to detect bleeding according to the range of H and S values of the film color. Once bleeding features are recognized, a wireless transmission module is activated immediately to send an alarm signal to the outside; an in vitro module receives the signal and sends an alarm. The average power consumption of the entire capsule system is estimated to be about 2.1mW. Owing to its simplicity, reliability, and effectiveness, this system represents a new approach to the clinical diagnosis of intestinal bleeding diseases. PMID:27902728

  14. 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

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

    DTIC Science & Technology

    2013-05-01

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

  16. 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

  17. Speech recognition abilities of adults using cochlear implants with FM systems.

    PubMed

    Schafer, Erin C; Thibodeau, Linda M

    2004-01-01

    Speech recognition was evaluated for ten adults with normal hearing and eight adults with Nucleus cochlear implants (CIs) at several different signal-to-noise ratios (SNRs) and with three frequency modulated (FM) system arrangements: desktop, body worn, and miniature direct connect. Participants were asked to repeat Hearing in Noise Test (HINT) sentences presented with speech noise in a classroom setting and percent correct word repetition was determined. Performance was evaluated for both normal-hearing and CI participants with the desktop soundfield system. In addition, speech recognition for the CI participants was evaluated using two FM systems electrically coupled to their speech processors. When comparing the desktop sound field and the No-FM condition, only the listeners with normal hearing made significant improvements in speech recognition in noise. When comparing the performance across the three FM conditions for the CI listeners, the two electrically coupled FM systems resulted in significantly greater improvements in speech recognition in noise relative to the desktop soundfield system.

  18. Practical and cost-efficient design of fingerprint recognition system based on DSP

    NASA Astrophysics Data System (ADS)

    Ran, Chongjie; Xie, Mei

    2007-12-01

    In this paper, a practical and cost-efficient fingerprint recognition system model is proposed. It completes the functions of capturing fingerprint image, data transmission and fingerprint recognition. This system consists of six modules: Management Module (including TMS320VC5502 DSP and memories), Fingerprint Sensor Module (used to collect fingerprint image), Output Module (the interface to control electronic lock), Human-Machine Communication Module (seven-segment LED and keyboard), Debugger Interface Module (JTAG), Power Manager and Power Switchover Module. Unlike other fingerprint recognition systems, this system takes TI C5502 as core processor. It is a high-performance, low-power and fixed-point DSP and the whole system power can be supplied by batteries. The whole system can work more than 10000 times with batteries. In addition, a Power Switch Module, which can automatic switch the ways of power supply between wall adapter and batteries, is proposed in this paper. Furthermore, some software optimization makes this system practical. The design not only simplifies system's structure and reduces the cost of hardware, but also decreases the consumption of system power and resources. So, this hardware system can be used in practical applications, such as portable identification device, fingerprint lock etc. This system is mainly designed for fingerprint lock in this paper.

  19. NOD2 and Toll-Like Receptors Are Nonredundant Recognition Systems of Mycobacterium tuberculosis

    PubMed Central

    2005-01-01

    Infection with Mycobacterium tuberculosis is one of the leading causes of death worldwide. Recognition of M. tuberculosis by pattern recognition receptors is crucial for activation of both innate and adaptive immune responses. In the present study, we demonstrate that nucleotide-binding oligomerization domain 2 (NOD2) and Toll-like receptors (TLRs) are two nonredundant recognition mechanisms of M. tuberculosis. CHO cell lines transfected with human TLR2 or TLR4 were responsive to M. tuberculosis. TLR2 knock-out mice displayed more than 50% defective cytokine production after stimulation with mycobacteria, whereas TLR4-defective mice also released 30% less cytokines compared to controls. Similarly, HEK293T cells transfected with NOD2 responded to stimulation with M. tuberculosis. The important role of NOD2 for the recognition of M. tuberculosis was demonstrated in mononuclear cells of individuals homozygous for the 3020insC NOD2 mutation, who showed an 80% defective cytokine response after stimulation with M. tuberculosis. Finally, the mycobacterial TLR2 ligand 19-kDa lipoprotein and the NOD2 ligand muramyl dipeptide synergized for the induction of cytokines, and this synergism was lost in cells defective in either TLR2 or NOD2. Together, these results demonstrate that NOD2 and TLR pathways are nonredundant recognition mechanisms of M. tuberculosis that synergize for the induction of proinflammatory cytokines. PMID:16322770

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

    PubMed

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

    2013-02-05

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

  1. A dynamic gesture recognition system for the Korean sign language (KSL).

    PubMed

    Kim, J S; Jang, W; Bien, Z

    1996-01-01

    The sign language is a method of communication for the deaf-mute. Articulated gestures and postures of hands and fingers are commonly used for the sign language. This paper presents a system which recognizes the Korean sign language (KSL) and translates into a normal Korean text. A pair of data-gloves are used as the sensing device for detecting motions of hands and fingers. For efficient recognition of gestures and postures, a technique of efficient classification of motions is proposed and a fuzzy min-max neural network is adopted for on-line pattern recognition.

  2. Motorcycle Start-stop System based on Intelligent Biometric Voice Recognition

    NASA Astrophysics Data System (ADS)

    Winda, A.; Byan, W. R. E.; Sofyan; Armansyah; Zariantin, D. L.; Josep, B. G.

    2017-03-01

    Current mechanical key in the motorcycle is prone to bulgary, being stolen or misplaced. Intelligent biometric voice recognition as means to replace this mechanism is proposed as an alternative. The proposed system will decide whether the voice is belong to the user or not and the word utter by the user is ‘On’ or ‘Off’. The decision voice will be sent to Arduino in order to start or stop the engine. The recorded voice is processed in order to get some features which later be used as input to the proposed system. The Mel-Frequency Ceptral Coefficient (MFCC) is adopted as a feature extraction technique. The extracted feature is the used as input to the SVM-based identifier. Experimental results confirm the effectiveness of the proposed intelligent voice recognition and word recognition system. It show that the proposed method produces a good training and testing accuracy, 99.31% and 99.43%, respectively. Moreover, the proposed system shows the performance of false rejection rate (FRR) and false acceptance rate (FAR) accuracy of 0.18% and 17.58%, respectively. In the intelligent word recognition shows that the training and testing accuracy are 100% and 96.3%, respectively.

  3. Talking Back to Big Bird: Preschool Users and a Simple Speech Recognition System.

    ERIC Educational Resources Information Center

    Strommen, Erik F.; Frome, Francine S.

    1993-01-01

    Describes a study that examined the effectiveness of 1 configuration of automatic speech recognition software and hardware with 36 3 year olds and a comparison control group of 20 adults. The greater variability of children's speech is discussed; possible system modifications are considered; and future research is suggested. (18 references) (LRW)

  4. 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…

  5. 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.

  6. The Effect of Involuntary Motor Activity on Myoelectric Pattern Recognition: A Case Study with Chronic Stroke Patients

    PubMed Central

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

    2013-01-01

    This study investigates the effect of involuntary motor activity of paretic-spastic muscles on classification of surface electromyography (EMG) signals. Two data collection sessions were designed for 8 stroke subjects to voluntarily perform 11 functional movements using their affected forearm and hand at a relatively slow and fast speed. For each stroke subject, the degree of involuntary motor activity present in voluntary surface EMG recordings was qualitatively described from such slow and fast experimental protocols. Myoelectric pattern recognition analysis was performed using different combinations of voluntary surface EMG data recorded from slow and fast sessions. Across all tested stroke subjects, our results revealed that when involuntary surface EMG was absent or present in both training and testing datasets, high accuracies (> 96%, > 98%, respectively, averaged over all the subjects) can be achieved in classification of different movements using surface EMG signals from paretic muscles. When involuntary surface EMG was solely involved in either training or testing datasets, the classification accuracies were dramatically reduced (< 89%, < 85%, respectively). However, if both training and testing datasets contained EMG signals with presence and absence of involuntary EMG interference, high accuracies were still achieved (> 97%). The findings of this study can be used to guide appropriate design and implementation of myoelectric pattern recognition based systems or devices toward promoting robot-aided therapy for stroke rehabilitation. PMID:23860192

  7. 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.

  8. Active optical zoom system.

    PubMed

    Wang, Di; Wang, Qiong-Hua; Shen, Chuan; Zhou, Xin; Liu, Chun-Mei

    2014-11-01

    In this work, we propose an active optical zoom system. The zoom module of the system is formed by a liquid lens and a spatial light modulator (SLM). By controlling the focal lengths of the liquid lens and the encoded digital lens on the SLM panel, we can change the magnification of an image without mechanical moving parts and keep the output plane stationary. The magnification can change from 1/3 to 3/2 as the focal length of the encoded lens on the SLM changes from infinity to 24 cm. The proposed active zoom system is simple and flexible, and has widespread application in optical communications, imaging systems, and displays.

  9. 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.

  10. 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.

  11. Imaging nervous system activity.

    PubMed

    Fields, Douglas R; Shneider, Neil; Mentis, George Z; O'Donovan, Michael J

    2009-10-01

    This unit describes methods for loading ion- and voltage-sensitive dyes into neurons, with a particular focus on the spinal cord as a model system. In addition, we describe the use of these dyes to visualize neural activity. Although the protocols described here concern spinal networks in culture or an intact in vitro preparation, they can be, and have been, widely used in other parts of the nervous system.

  12. The impact of sensitive KIT D816V detection on recognition of indolent Systemic Mastocytosis.

    PubMed

    De Matteis, Giovanna; Zanotti, Roberta; Colarossi, Sabrina; De Benedittis, Caterina; Garcia-Montero, Andrès; Bonifacio, Massimiliano; Sartori, Marta; Aprili, Fiorenza; Caruso, Beatrice; Paviati, Elisa; Carli, Giuseppe; Perbellini, Omar; Zamò, Alberto; Bonadonna, Patrizia; Pizzolo, Giovanni; Guidi, Giancesare; Martinelli, Giovanni; Soverini, Simona

    2015-03-01

    Patients with Systemic Mastocytosis (SM) need a highly sensitive diagnostic test for D816V detection of the KIT receptor gene. Along with histology/cytology and flow cytometry evaluation, bone marrow (BM) from 110 consecutive adult patients referred with a suspicion of SM to Multidisciplinary Outpatient Clinic for Mastocytosis in Verona were tested both by Amplification Refractory Mutation System Reverse Transcriptase quantitative real time Polymerase Chain Reaction (ARMS-RT-qPCR) and RT-PCR+Restriction Fragment Length Polymorphism (RFLP) followed by Denaturing-High Performance Liquid Chromatography (D-HPLC) and Sanger sequencing. ARMS-RT-qPCR identified D816V mutation in 77 patients, corresponding to 100% of cases showing CD25(+) mast cells (MCs) whereas RT-PCR+RFLP/D-HPLC+sequencing revealed D816V mutations in 47 patients. According to the 2008 WHO criteria 75 SM, 1 Cutaneous Mastocytosis (CM), 1 monoclonal MC activation syndrome (MMAS), and 1 SM Associated with Haematologic Non-Mast Cell Disorder (SM-AHNMD) were diagnosed. Seventeen out 75 SM patients (23%) would have not satisfied sufficient WHO criteria on the basis of the sole RT-PCR+RFLP: these patients had significantly lower serum tryptase levels and amount of CD25(+) MCs. Therefore, ARMS-RT-qPCR might result particularly useful, in patients that do not fulfil major BM histological criterion, for the recognition of indolent SM with a very low MC burden.

  13. 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.

  14. 3-D Object Recognition Using Combined Overhead And Robot Eye-In-Hand Vision System

    NASA Astrophysics Data System (ADS)

    Luc, Ren C.; Lin, Min-Hsiung

    1987-10-01

    A new approach for recognizing 3-D objects using a combined overhead and eye-in-hand vision system is presented. A novel eye-in-hand vision system using a fiber-optic image array is described. The significance of this approach is the fast and accurate recognition of 3-D object information compared to traditional stereo image processing. For the recognition of 3-D objects, the over-head vision system will take 2-D top view image and the eye-in-hand vision system will take side view images orthogonal to the top view image plane. We have developed and demonstrated a unique approach to integrate this 2-D information into a 3-D representation based on a new approach called "3-D Volumetric Descrip-tion from 2-D Orthogonal Projections". The Unimate PUMA 560 and TRAPIX 5500 real-time image processor have been used to test the success of the entire system.

  15. A low-cost affinity purification system using β-1,3-glucan recognition protein and curdlan beads.

    PubMed

    Horiuchi, Masataka; Takahasi, Kiyohiro; Kobashigawa, Yoshihiro; Ochiai, Masanori; Inagaki, Fuyuhiko

    2012-08-01

    Silkworm β-1,3-glucan recognition protein (βGRP) tightly and specifically associates with β-1,3-glucan. We report here an affinity purification system named the 'GRP system', which uses the association between the β-1,3-glucan recognition domain of βGRP (GRP-tag), as an affinity tag, and curdlan beads. Curdlan is a water-insoluble β-1,3-glucan reagent, the low cost of which (about 100 JPY/g) allows the economical preparation of beads. Curdlan beads can be readily prepared by solubilization in an alkaline solution, followed by neutralization, sonication and centrifugation. We applied the GRP system to preparation of several proteins and revealed that the expression levels of the GRP-tagged proteins in soluble fractions were two or three times higher than those of the glutathione S-transferase (GST)-tagged proteins. The purity of the GRP-tagged proteins on the curdlan beads was comparable to that of the GST-tagged proteins on glutathione beads. The chemical stability of the GRP system was more robust than conventional affinity systems under various conditions, including low pH (4-6). Biochemical and structural analyses revealed that proteins produced using the GRP system were structurally and functionally active. Thus, the GRP system is suitable for both the large- and small-scale preparation of recombinant proteins for functional and structural analyses.

  16. 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

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

    PubMed Central

    2015-01-01

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

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

    PubMed

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

    2006-01-01

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

  19. 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

  20. [Creating language model of the forensic medicine domain for developing a autopsy recording system by automatic speech recognition].

    PubMed

    Niijima, H; Ito, N; Ogino, S; Takatori, T; Iwase, H; Kobayashi, M

    2000-11-01

    For the purpose of practical use of speech recognition technology for recording of forensic autopsy, a language model of the speech recording system, specialized for the forensic autopsy, was developed. The language model for the forensic autopsy by applying 3-gram model was created, and an acoustic model for Japanese speech recognition by Hidden Markov Model in addition to the above were utilized to customize the speech recognition engine for forensic autopsy. A forensic vocabulary set of over 10,000 words was compiled and some 300,000 sentence patterns were made to create the forensic language model, then properly mixing with a general language model to attain high exactitude. When tried by dictating autopsy findings, this speech recognition system was proved to be about 95% of recognition rate that seems to have reached to the practical usability in view of speech recognition software, though there remains rooms for improving its hardware and application-layer software.

  1. When does the visual system use viewpoint-invariant representations during recognition?

    PubMed

    Wilson, Kevin D; Farah, Martha J

    2003-05-01

    One popular model of object recognition claims that the visual system typically describes objects using view-specific representations, but that viewpoint-invariant representations are used when objects can be specified uniquely by the arrangement of parts along a single dimension. In a series of three naming experiments using novel, two-dimensional line drawings, we test this hypothesis against alternative accounts of when viewpoint-invariant representations are used during the recognition of upright and viewplane-rotated objects. Experiments 1 and 2 demonstrate that the number of dimensions along which featural information must be represented is the only stimulus feature that influences the type of representation used, consistent with the Tarr and Pinker model. Experiment 3, however, reveals that the use of viewpoint-invariant representations during recognition is not driven purely by stimulus features, and is at least partly under voluntary control. These data suggest that viewpoint-invariant representations are not automatically invoked by the visual system when the requisite stimulus features are present. Rather, our results suggest that top-down control processes, as well as bottom-up stimulus features, jointly determine the conditions under which the visual system uses viewpoint-invariant representations during visual recognition.

  2. 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.

  3. 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%.

  4. Active control system trends

    NASA Technical Reports Server (NTRS)

    Yore, E. E.; Gunderson, D. C.

    1976-01-01

    The active control concepts which achieve the benefit of improved mission performance and lower cost and generate system trends towards improved dynamic performance, more integration, and digital fly by wire mechanization are described. Analytical issues and implementation requirements and tools and approaches developed to address the analytical and implementation issues are briefly discussed.

  5. 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…

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

    PubMed

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

    2004-01-10

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

  8. 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

  9. Skin Cancer Recognition by Using a Neuro-Fuzzy System

    PubMed Central

    Salah, Bareqa; Alshraideh, Mohammad; Beidas, Rasha; Hayajneh, Ferial

    2011-01-01

    Skin cancer is the most prevalent cancer in the light-skinned population and it is generally caused by exposure to ultraviolet light. Early detection of skin cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose skin cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. To obviate these problems, image processing techniques, a neural network system (NN) and a fuzzy inference system were used in this study as promising modalities for detection of different types of skin cancer. The accuracy rate of the diagnosis of skin cancer by using the hierarchal neural network was 90.67% while using neuro-fuzzy system yielded a slightly higher rate of accuracy of 91.26% in diagnosis skin cancer type. The sensitivity of NN in diagnosing skin cancer was 95%, while the specificity was 88%. Skin cancer diagnosis by neuro-fuzzy system achieved sensitivity of 98% and a specificity of 89%. PMID:21340020

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

    PubMed

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

    2014-10-01

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

  11. 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.

  12. Crocins, the active constituents of Crocus sativus L., counteracted apomorphine-induced performance deficits in the novel object recognition task, but not novel object location task, in rats.

    PubMed

    Pitsikas, Nikolaos; Tarantilis, Petros A

    2017-02-17

    Schizophrenia is a chronic mental disease that affects nearly 1% of the population worldwide. Several lines of evidence suggest that the dopaminergic (DAergic) system might be compromised in schizophrenia. Specifically, the mixed dopamine (DA) D1/D2 receptor agonist apomorphine induces schizophrenia-like symptoms in rodents, including disruption of memory abilities. Crocins are among the active components of saffron (dried stigmas of Crocus sativus L. plant) and their implication in cognition is well documented. The present study investigated whether crocins counteract non-spatial and spatial recognition memory deficits induced by apomorphine in rats. For this purpose, the novel object recognition task (NORT) and the novel object location task (NOLT) were used. The effects of compounds on mobility in a locomotor activity chamber were also investigated in rats. Post-training peripheral administration of crocins (15 and 30mg/kg) counteracted apomorphine (1mg/kg)-induced performance deficits in the NORT. Conversely, crocins did not attenuate spatial recognition memory deficits produced by apomorphine in the NOLT. The present data show that crocins reversed non-spatial recognition memory impairments produced by dysfunction of the DAergic system and modulate different aspects of memory components (storage and/or retrieval). The effects of compounds on recognition memory cannot be attributed to changes in locomotor activity. Further, our findings illustrate a functional interaction between crocins and the DAergic system that may be of relevance for schizophrenia-like behavioral deficits. Therefore, the utilization of crocins as an adjunctive agent, for the treatment of cognitive deficits observed in schizophrenic patients should be further investigated.

  13. 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.

  14. Dynamically reconfigurable multiprocessor system for high-order-bidirectional-associative-memory-based image recognition

    NASA Astrophysics Data System (ADS)

    Wu, Chwan-Hwa; Roland, David A.

    1991-08-01

    In this paper a high-order bidirectional associative memory (HOBAM) based image recognition system and a dynamically reconfigurable multiprocessor system that achieves real- time response are reported. The HOBAM has been utilized to recognize corrupted images of human faces (with hats, glasses, masks, and slight translation and scaling effects). In addition, the HOBAM, incorporated with edge detection techniques, has been used to recognize isolated objects within multiple-object images. Successful recognition rates have been achieved. A dynamically reconfigurable multiprocessor system and parallel software have been developed to achieve real-time response for image recognition. The system consists of Inmos transputers and crossbar switches (IMS C004). The communication links can be dynamically connected by circuit switching. This is the first time and the transputers and crossbar switches are reported to form a low-cost multiprocessor system connected by a switching network. Moreover, the switching network simplifies the design of the communication in parallel software without handling the message routing. Although the HOBAM is a fully connected network, the algorithm minimizes the amount of information that needs to be exchanged between processors using a data compression technique. The detailed design of both hardware and software are discussed in the paper. Significant speedup through parallel processing is accomplished. The architecture of the experimental system is a cost-effective design for an embedded system for neural network applications on computer vision.

  15. 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

  16. Ornament Problem Suppression in Indonesian License Plate Recognition Systems

    NASA Astrophysics Data System (ADS)

    Mahatmaputra Tedjojuwono, Samuel

    2017-03-01

    Based on the original work of fast performance algorithm in detecting Indonesian license plate, the proposed work will solve the error found in the license plate localization process caused by plate like pattern within the image, which was called the ornament problem. Although not in all cases, this problem could exist when a car has banner, regular pattern, car’s front grill, that could miss understood by the system as license plate letters. The proposed work will implement filtering systems instead of machine learning approach. The filtering methods will follows three steps: detection filter based on the number of elements in the vector, based on the letter proportion of a license plate number, and based on the distance between detected letters. This approach will maintain the fast properties of the original algorithm and will increase the accuracy of localizing the license plate within the given image.

  17. 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.

  18. A robust technique for semantic annotation of group activities based on recognition of extracted features in video streams

    NASA Astrophysics Data System (ADS)

    Elangovan, Vinayak; Shirkhodaie, Amir

    2013-05-01

    Recognition and understanding of group activities can significantly improve situational awareness in Surveillance Systems. To maximize reliability and effectiveness of Persistent Surveillance Systems, annotations of sequential images gathered from video streams (i.e. imagery and acoustic features) must be fused together to generate semantic messages describing group activities (GA). To facilitate efficient fusion of extracted features from any physical sensors a common structure will suffice to ease integration of processed data into new comprehension. In this paper, we describe a framework for extraction and management of pertinent features/attributes vital for annotation of group activities reliably. A robust technique is proposed for fusion of generated events and entities' attributes from video streams. A modified Transducer Markup Language (TML) is introduced for semantic annotation of events and entities attributes. By aggregation of multi-attribute TML messages, we have demonstrated that salient group activities can be spatiotemporal can be reliable annotated. This paper discusses our experimental results; our analysis of a set of simulated group activities performed under different contexts and demonstrates the efficiency and effectiveness of the proposed modified TML data structure which facilitates seamless fusion of extracted information from video streams.

  19. 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.

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

    PubMed

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

    2015-01-01

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

  1. 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.

  2. A primitive-based 3D object recognition system

    NASA Technical Reports Server (NTRS)

    Dhawan, Atam P.

    1988-01-01

    An intermediate-level knowledge-based system for decomposing segmented data into three-dimensional primitives was developed to create an approximate three-dimensional description of the real world scene from a single two-dimensional perspective view. A knowledge-based approach was also developed for high-level primitive-based matching of three-dimensional objects. Both the intermediate-level decomposition and the high-level interpretation are based on the structural and relational matching; moreover, they are implemented in a frame-based environment.

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. Improving a HMM-based off-line handwriting recognition system using MME-PSO optimization

    NASA Astrophysics Data System (ADS)

    Hamdani, Mahdi; El Abed, Haikal; Hamdani, Tarek M.; Märgner, Volker; Alimi, Adel M.

    2011-01-01

    One of the trivial steps in the development of a classifier is the design of its architecture. This paper presents a new algorithm, Multi Models Evolvement (MME) using Particle Swarm Optimization (PSO). This algorithm is a modified version of the basic PSO, which is used to the unsupervised design of Hidden Markov Model (HMM) based architectures. For instance, the proposed algorithm is applied to an Arabic handwriting recognizer based on discrete probability HMMs. After the optimization of their architectures, HMMs are trained with the Baum- Welch algorithm. The validation of the system is based on the IfN/ENIT database. The performance of the developed approach is compared to the participating systems at the 2005 competition organized on Arabic handwriting recognition on the International Conference on Document Analysis and Recognition (ICDAR). The final system is a combination between an optimized HMM with 6 other HMMs obtained by a simple variation of the number of states. An absolute improvement of 6% of word recognition rate with about 81% is presented. This improvement is achieved comparing to the basic system (ARAB-IfN). The proposed recognizer outperforms also most of the known state-of-the-art systems.

  8. 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

  9. Optical sensing systems based on biomolecular recognition of recombinant proteins

    NASA Astrophysics Data System (ADS)

    Salins, Lyndon L.; Schauer-Vukasinovic, Vesna; Daunert, Sylvia

    1998-05-01

    SIte-directed mutagenesis and the associated site-specific fluorescent labeling of proteins can be used to rationally design reagentless fluorescent molecular senors. The phosphate binding protein (PBP) and calmodulin (CaM) bind to phosphate and calcium in a highly specific manner. These ions induce a hinge motion in the proteins, and the resultant conformational change constitutes the basis of the sensor development. By labeling each protein at a specific site with environment-sensitive fluorescent probes, these conformational changes can be monitored and related to the amount of analyte ion present. In this study, the polymerase chain reaction was used to construct mutants of PBP and CaM that have a single cysteine at positions 197 and 109, respectively. Each protein was site-specifically labeled through the sulfhydryl group of the introduced cysteine residue at a single location with an environment-sensitive fluorescent probe. Characterization of the steady-state fluorescence indicated an enhancement of signal intensity upon binding of the analyte ion. Highly sensitive and selective and selective sensing systems for phosphate and calcium were obtained by using this approach.

  10. Security and matching of partial fingerprint recognition systems

    NASA Astrophysics Data System (ADS)

    Jea, Tsai-Yang; Chavan, Viraj S.; Govindaraju, Venu; Schneider, John K.

    2004-08-01

    Despite advances in fingerprint identification techniques, matching incomplete or partial fingerprints still poses a difficult challenge. While the introduction of compact silicon chip-based sensors that capture only a part of the fingerprint area have made this problem important from a commercial perspective, there is also considerable interest on the topic for processing partial and latent fingerprints obtained at crime scenes. Attempts to match partial fingerprints using singular ridge structures-based alignment techniques fail when the partial print does not include such structures (e.g., core or delta). We present a multi-path fingerprint matching approach that utilizes localized secondary features derived using only the relative information of minutiae. Since the minutia-based fingerprint representation, is an ANSI-NIST standard, our approach has the advantage of being directly applicable to already existing databases. We also analyze the vulnerability of partial fingerprint identification systems to brute force attacks. The described matching approach has been tested on one of FVC2002"s DB1 database11. The experimental results show that our approach achieves an equal error rate of 1.25% and a total error rate of 1.8% (with FAR at 0.2% and FRR at 1.6%).

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

    PubMed

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

    2012-07-01

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

  12. Development of coffee maker service robot using speech and face recognition systems using POMDP

    NASA Astrophysics Data System (ADS)

    Budiharto, Widodo; Meiliana; Santoso Gunawan, Alexander Agung

    2016-07-01

    There are many development of intelligent service robot in order to interact with user naturally. This purpose can be done by embedding speech and face recognition ability on specific tasks to the robot. In this research, we would like to propose Intelligent Coffee Maker Robot which the speech recognition is based on Indonesian language and powered by statistical dialogue systems. This kind of robot can be used in the office, supermarket or restaurant. In our scenario, robot will recognize user's face and then accept commands from the user to do an action, specifically in making a coffee. Based on our previous work, the accuracy for speech recognition is about 86% and face recognition is about 93% in laboratory experiments. The main problem in here is to know the intention of user about how sweetness of the coffee. The intelligent coffee maker robot should conclude the user intention through conversation under unreliable automatic speech in noisy environment. In this paper, this spoken dialog problem is treated as a partially observable Markov decision process (POMDP). We describe how this formulation establish a promising framework by empirical results. The dialog simulations are presented which demonstrate significant quantitative outcome.

  13. 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)…

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

    DTIC Science & Technology

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

  15. A system for large-scale automatic traffic sign recognition and mapping

    NASA Astrophysics Data System (ADS)

    Chigorin, A.; Konushin, A.

    2013-10-01

    We present a system for the large-scale automatic traffic signs recognition and mapping and experimentally justify design choices made for different components of the system. Our system works with more than 140 different classes of traffic signs and does not require labor-intensive labelling of a large amount of training data due to the training on synthetically generated images. We evaluated our system on the large dataset of Russian traffic signs and made this dataset publically available to encourage future comparison.

  16. Reliable emotion recognition system based on dynamic adaptive fusion of forehead biopotentials and physiological signals.

    PubMed

    Khezri, Mahdi; Firoozabadi, Mohammad; Sharafat, Ahmad Reza

    2015-11-01

    In this study, we proposed a new adaptive method for fusing multiple emotional modalities to improve the performance of the emotion recognition system. Three-channel forehead biosignals along with peripheral physiological measurements (blood volume pressure, skin conductance, and interbeat intervals) were utilized as emotional modalities. Six basic emotions, i.e., anger, sadness, fear, disgust, happiness, and surprise were elicited by displaying preselected video clips for each of the 25 participants in the experiment; the physiological signals were collected simultaneously. In our multimodal emotion recognition system, recorded signals with the formation of several classification units identified the emotions independently. Then the results were fused using the adaptive weighted linear model to produce the final result. Each classification unit is assigned a weight that is determined dynamically by considering the performance of the units during the testing phase and the training phase results. This dynamic weighting scheme enables the emotion recognition system to adapt itself to each new user. The results showed that the suggested method outperformed conventional fusion of the features and classification units using the majority voting method. In addition, a considerable improvement, compared to the systems that used the static weighting schemes for fusing classification units, was also shown. Using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers, the overall classification accuracies of 84.7% and 80% were obtained in identifying the emotions, respectively. In addition, applying the forehead or physiological signals in the proposed scheme indicates that designing a reliable emotion recognition system is feasible without the need for additional emotional modalities.

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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

  2. 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

  3. The pros and cons of implementing PACS and speech recognition systems.

    PubMed

    Hayt, D B; Alexander, S

    2001-09-01

    The installation and implementation of a hospitalwide image management system and a speech recognition dictation system has had a dramatic and positive impact on radiology report turnaround times at Elmhurst Hospital Center, a 543-bed municipal teaching hospital located in New York City's Borough of Queens. The "lost film" problem has been eliminated. As a result, the percentage of unreported examinations has dropped from 25% to less than 1%. These performance improvements have significantly benefited the entire medical staff. With the successful implementation of a HL-7 standards-based radiology information system (RIS), a speech recognition dictation system, around-the-clock staffing of Board Certified radiologists, and a picture archiving and communication system (PACS), report turnaround time improved dramatically. Eighty-six percent of all examinations now are reported formally within a 12-hour period compared with a 3% average before implementation of the changes. However, with the use of the PACS and speech recognition technologies, new problems have arisen within the radiology department. These technologies, designed to enhance communications capabilities, also have significantly reduced the amount of clinician/radiologist dialogue. Easy and rapid access to patient images and reports has had a detrimental effect on that face-to-face consultations with clinicians, which were commonplace before PACS, and now have almost completely disappeared. The radiologist/clinician interchanges, which occurred frequently before a final report was dictated, often resulted in better understanding of the clinical problem and, hence, a more meaningful final report. Although a conferencing feature to facilitate communication exists within the PACS, it is not utilized by the clinicians. The dilemma is that as information about patients is made more available to the hospital staff, less information is provided about patients to the radiologists. Although the speech recognition

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

    PubMed Central

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

    2015-01-01

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

  5. 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.

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

    PubMed

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

    2014-09-03

    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. Using optical wavelet packet transform to improve the performance of an optoelectronic iris recognition system

    NASA Astrophysics Data System (ADS)

    Cai, De; Tan, Qiaofeng; Yan, Yingbai; Jin, Guofan; He, Qingsheng

    2005-01-01

    Iris, one important biometric feature, has unique advantages: it has complex texture and is almost unchanged for the lifespan. So iris recognition has been widely studied for intelligent personal identification. Most of researchers use wavelets as iris feature extractor. And their systems obtain high accuracy. But wavelet transform is time consuming, so the problem is to enhance the useful information but still keep high processing speed. This is the reason we propose an opto-electronic system for iris recognition because of high parallelism of optics. In this system, we use eigen-images generated corresponding to optimally chosen wavelet packets to compress the iris image bank. After optical correlation between eigen-images and input, the statistic features are extracted. Simulation shows that wavelet packets preprocessing of the input images results in higher identification rate. And this preprocessing can be fulfilled by optical wavelet packet transform (OWPT), a new optical transform introduced by us. To generate the approximations of 2-D wavelet packet basis functions for implementing OWPT, mother wavelet, which has scaling functions, is utilized. Using the cascade algorithm and 2-D separable wavelet transform scheme, an optical wavelet packet filter is constructed based on the selected best bases. Inserting this filter makes the recognition performance better.

  9. Syntax-directed content analysis of videotext: application to a map detection recognition system

    NASA Astrophysics Data System (ADS)

    Aradhye, Hrishikesh; Herson, James A.; Myers, Gregory

    2003-01-01

    Video is an increasingly important and ever-growing source of information to the intelligence and homeland defense analyst. A capability to automatically identify the contents of video imagery would enable the analyst to index relevant foreign and domestic news videos in a convenient and meaningful way. To this end, the proposed system aims to help determine the geographic focus of a news story directly from video imagery by detecting and geographically localizing political maps from news broadcasts, using the results of videotext recognition in lieu of a computationally expensive, scale-independent shape recognizer. Our novel method for the geographic localization of a map is based on the premise that the relative placement of text superimposed on a map roughly corresponds to the geographic coordinates of the locations the text represents. Our scheme extracts and recognizes videotext, and iteratively identifies the geographic area, while allowing for OCR errors and artistic freedom. The fast and reliable recognition of such maps by our system may provide valuable context and supporting evidence for other sources, such as speech recognition transcripts. The concepts of syntax-directed content analysis of videotext presented here can be extended to other content analysis systems.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

    Zhu, Xiangbin; Qiu, Huiling

    2016-01-01

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

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

    PubMed Central

    2016-01-01

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

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

    PubMed Central

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

    2017-01-01

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

  14. Imaging nervous system activity.

    PubMed

    Fields, R D; O'Donovan, M J

    2001-05-01

    Optical imaging methods rely upon visualization of three types of signals: (1) intrinsic optical signals, including light scattering and reflectance, birefringence, and spectroscopic changes of intrinsic molecules, such as NADH or oxyhemoglobin; (2) changes in fluorescence or absorbance of voltage-sensitive membrane dyes; and (3) changes in fluorescence or absorbance of calcium-sensitive indicator dyes. Of these, the most widely used approach is fluorescent microscopy of calcium-sensitive dyes. This unit describes protocols for the use of calcium-sensitive dyes and voltage-dependent dyes for studies of neuronal activity in culture, tissue slices, and en-bloc preparations of the central nervous system.

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

    PubMed

    Saez, Yago; Baldominos, Alejandro; Isasi, Pedro

    2016-12-30

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

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

    PubMed Central

    Saez, Yago; Baldominos, Alejandro; Isasi, Pedro

    2016-01-01

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

  18. 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.

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

    PubMed

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

    1975-07-25

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

  20. 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.

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

    PubMed

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

    2005-05-01

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

  2. 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.

  3. 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.

  4. 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.

  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.

  6. Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System

    PubMed Central

    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. PMID:22438704

  7. Automatic target recognition with image/video understanding systems based on network-symbolic models

    NASA Astrophysics Data System (ADS)

    Kuvich, Gary

    2004-09-01

    In past decades, the solution to ATR problem has been thought of as a solution to the Pattern Recognition problem. The reasons that Pattern Recognition problem has never been solved successfully and reliably for real-world images are more serious than lack of appropriate ideas. Vision is a part of a larger system that converts visual information into knowledge structures. These structures drive the vision process, resolving ambiguity and uncertainty via feedback, and provide image understanding, which is an interpretation of visual information in terms of these knowledge models. Vision mechanisms cannot be completely understood apart from the informational processes related to knowledge and intelligence. A reliable solution to the ATR problem is possible only within the solution of a more generic Image Understanding Problem. Biologically inspired Network-Symbolic representation, where both systematic structural/logical methods and neural/statistical methods are parts of a single mechanism, converts visual information into relational Network-Symbolic structures, avoiding precise computations of 3-D models. Logic of visual scenes can be captured in Network-Symbolic models and used for disambiguation of visual information. Network-Symbolic Transformations make possible invariant recognition of a real-world object as exemplar of a class. This allows for creating ATR systems, reliable in field conditions.

  8. 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.

  9. A commercial large-vocabulary discrete speech recognition system: DragonDictate.

    PubMed

    Mandel, M A

    1992-01-01

    DragonDictate is currently the only commercially available general-purpose, large-vocabulary speech recognition system. It uses discrete speech and is speaker-dependent, adapting to the speaker's voice and language model with every word. Its acoustic adaptability is based in a three-level phonology and a stochastic model of production. The phonological levels are phonemes, augmented triphones (phonemes-in-context or PICs), and steady-state spectral slices that are concatenated to approximate the spectra of these PICs (phonetic elements or PELs) and thus of words. Production is treated as a hidden Markov process, which the recognizer has to identify from its output, the spoken word. Findings of practical value to speech recognition are presented from research on six European languages.

  10. 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.

  11. 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.

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

    PubMed Central

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

    2010-01-01

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

  13. 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

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

    PubMed Central

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

    2012-01-01

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

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

    PubMed

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

    2007-11-01

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

  16. 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

  17. Synthesis of hybrid systems of pattern recognition on the basis of procedure of consecutive correction of decision functions

    NASA Astrophysics Data System (ADS)

    Lapko, V. A.; Lapko, A. V.; Yuronen, Yu P.

    2016-11-01

    Hybrid systems of pattern recognition in the conditions of large volumes of the training selections and not stationarity of classification objects are offered. Asymptotic properties of their decision function are investigated.

  18. A preliminary study on improving the recognition of esophageal speech using a hybrid system based on statistical voice conversion.

    PubMed

    Lachhab, Othman; Di Martino, Joseph; Elhaj, Elhassane Ibn; Hammouch, Ahmed

    2015-01-01

    In this paper, we propose a hybrid system based on a modified statistical GMM voice conversion algorithm for improving the recognition of esophageal speech. This hybrid system aims to compensate for the distorted information present in the esophageal acoustic features by using a voice conversion method. The esophageal speech is converted into a "target" laryngeal speech using an iterative statistical estimation of a transformation function. We did not apply a speech synthesizer for reconstructing the converted speech signal, given that the converted Mel cepstral vectors are used directly as input of our speech recognition system. Furthermore the feature vectors are linearly transformed by the HLDA (heteroscedastic linear discriminant analysis) method to reduce their size in a smaller space having good discriminative properties. The experimental results demonstrate that our proposed system provides an improvement of the phone recognition accuracy with an absolute increase of 3.40 % when compared with the phone recognition accuracy obtained with neither HLDA nor voice conversion.

  19. Real-time optical multiple object recognition and tracking system and method

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin (Inventor); Liu, Hua Kuang (Inventor)

    1987-01-01

    The invention relates to an apparatus and associated methods for the optical recognition and tracking of multiple objects in real time. Multiple point spatial filters are employed that pre-define the objects to be recognized at run-time. The system takes the basic technology of a Vander Lugt filter and adds a hololens. The technique replaces time, space and cost-intensive digital techniques. In place of multiple objects, the system can also recognize multiple orientations of a single object. This later capability has potential for space applications where space and weight are at a premium.

  20. Real-time optical multiple object recognition and tracking system and method

    NASA Astrophysics Data System (ADS)

    Chao, Tien-Hsin; Liu, Hua Kuang

    1987-12-01

    The invention relates to an apparatus and associated methods for the optical recognition and tracking of multiple objects in real time. Multiple point spatial filters are employed that pre-define the objects to be recognized at run-time. The system takes the basic technology of a Vander Lugt filter and adds a hololens. The technique replaces time, space and cost-intensive digital techniques. In place of multiple objects, the system can also recognize multiple orientations of a single object. This later capability has potential for space applications where space and weight are at a premium.

  1. A RFID authentication protocol based on infinite dimension pseudo random number generator for image recognition system

    NASA Astrophysics Data System (ADS)

    Tong, Qiaoling; Zou, Xuecheng; Tong, Hengqing

    2009-10-01

    Radio Frequency Identification (RFID) technology has been widely used in the image recognition system. However, the feature of the RFID system may bring out security threatens. In this paper, we analyze the existing RFID authentication protocols and state an infinite dimension pseudo random number generator to strengthen the protocol security. Then an authentication protocol based on infinite dimension pseudo random number generator is proposed. Compared to the traditional protocols, our method could resist various attack approaches, and protect the tag information and the location privacy of the tag holder efficiently.

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed Central

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

    2017-01-01

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

  4. 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

  5. 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

  6. 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.

  7. 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

  8. 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.

  9. Spatial Recognition of a Superconducting Quantum Interference Devices Nondestructive Evaluation System Using a Small Room-Temperature Probe

    NASA Astrophysics Data System (ADS)

    Chieh, Jen-Jie; Lin, I.-Sheng; Yang, Shieh-Yueh; Horng, Herng-Er; Hong, Chin-Yih; Yang, Hong-Chang

    2009-12-01

    A superconducting-qantum-interference-device (SQUID) nondestructive evaluation (NDE) system using a small room-temperature probe is developed for active scanning rather than for a massive movement occurring in a traditional SQUID NDE system. The small room-temperature probe is composed of a quadruple excitation coil and a double D-shaped pickup coil. It is connected to the input coil surrounding a high-Tc rf SQUID, immersed in liquid nitrogen, and shielded by a shielding can. Beyond the NDE function, the SQUID NDE scheme has spatial recognition functions, including the detection of the orientation and depth of a narrow line crack using different parameters, and the scanning of images of large objects with arbitrary shapes. Furthermore, the spatial sensitivity, limited by the size of the probe, reaches up to only 7 µm in the aspect of crack widths and 1 mm in the aspect of spatial intervals for precision NDE on a printed circuit board.

  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. How hippocampus and cortex contribute to recognition memory: revisiting the complementary learning systems model.

    PubMed

    Norman, Kenneth A

    2010-11-01

    We describe how the Complementary Learning Systems neural network model of recognition memory (Norman and O'Reilly (2003) Psychol Rev 104:611-646) can shed light on current debates regarding hippocampal and cortical contributions to recognition memory. We review simulation results illustrating three critical differences in how (according to the model) hippocampus and cortex contribute to recognition memory, all of which derive from the hippocampus' use of pattern separated representations. Pattern separation makes the hippocampus especially well-suited for discriminating between studied items and related lures; it makes the hippocampus especially poorly suited for computing global match; and it imbues the hippocampal ROC curve with a Y-intercept > 0. We also describe a key boundary condition on these differences: When the average level of similarity between items in an experiment is very high, hippocampal pattern separation can fail, at which point the hippocampal model will start to behave like the cortical model. We describe the implications of these simulation results for extant debates over how to describe hippocampal versus cortical contributions and how to measure these contributions.

  12. 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

  13. 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.

  14. Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

    PubMed Central

    Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

    2016-01-01

    Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596

  15. Handling real-world context awareness, uncertainty and vagueness in real-time human activity tracking and recognition with a fuzzy ontology-based hybrid method.

    PubMed

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

    2014-09-29

    Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and Sensors 2014, 14 18132 high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches.

  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. 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

  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. Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones

    PubMed Central

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

    2013-01-01

    Smartphone-based activity recognition (SP-AR) recognizes users' activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification) is performed on the device. Most of these online systems use either a high sampling rate (SR) or long data-window (DW) to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR) process, and an accurate AR-model in this case can be built using a low SR (20 Hz) and a small DW (3 s). The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW. PMID:24084108

  20. [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.

  1. RNA-guided complex from a bacterial immune system enhances target recognition through seed sequence interactions.

    PubMed

    Wiedenheft, Blake; van Duijn, Esther; Bultema, Jelle B; Bultema, Jelle; Waghmare, Sakharam P; Waghmare, Sakharam; Zhou, Kaihong; Barendregt, Arjan; Westphal, Wiebke; Heck, Albert J R; Heck, Albert; Boekema, Egbert J; Boekema, Egbert; Dickman, Mark J; Dickman, Mark; Doudna, Jennifer A

    2011-06-21

    Prokaryotes have evolved multiple versions of an RNA-guided adaptive immune system that targets foreign nucleic acids. In each case, transcripts derived from clustered regularly interspaced short palindromic repeats (CRISPRs) are thought to selectively target invading phage and plasmids in a sequence-specific process involving a variable cassette of CRISPR-associated (cas) genes. The CRISPR locus in Pseudomonas aeruginosa (PA14) includes four cas genes that are unique to and conserved in microorganisms harboring the Csy-type (CRISPR system yersinia) immune system. Here we show that the Csy proteins (Csy1-4) assemble into a 350 kDa ribonucleoprotein complex that facilitates target recognition by enhancing sequence-specific hybridization between the CRISPR RNA and complementary target sequences. Target recognition is enthalpically driven and localized to a "seed sequence" at the 5' end of the CRISPR RNA spacer. Structural analysis of the complex by small-angle X-ray scattering and single particle electron microscopy reveals a crescent-shaped particle that bears striking resemblance to the architecture of a large CRISPR-associated complex from Escherichia coli, termed Cascade. Although similarity between these two complexes is not evident at the sequence level, their unequal subunit stoichiometry and quaternary architecture reveal conserved structural features that may be common among diverse CRISPR-mediated defense systems.

  2. A split active site couples cap recognition by Dcp2 to activation

    PubMed Central

    Floor, Stephen N.; Jones, Brittnee N.; Hernandez, Gail A.; Gross, John D.

    2010-01-01

    Decapping by Dcp2 is an essential step in 5′-3′ mRNA decay. In yeast, decapping requires an open-to-closed transition in Dcp2, though the link between closure and catalysis remains elusive. Here we show using NMR that cap binds conserved residues on both the catalytic and regulatory domains of Dcp2. Lesions in the cap-binding site on the regulatory domain reduce the catalytic step two orders of magnitude and block formation of the closed state whereas Dcp1 enhances the catalytic step by a factor of ten and promotes closure. We conclude that closure occurs during the rate-limiting catalytic step of decapping, juxtaposing the cap-binding region of each domain to form a composite active site. This work suggests a model for regulation of decapping, where coactivators trigger decapping by stabilizing a labile composite active site. PMID:20711189

  3. Computerized literature reference system: use of an optical scanner and optical character recognition software.

    PubMed

    Lossef, S V; Schwartz, L H

    1990-09-01

    A computerized reference system for radiology journal articles was developed by using an IBM-compatible personal computer with a hand-held optical scanner and optical character recognition software. This allows direct entry of scanned text from printed material into word processing or data-base files. Additionally, line diagrams and photographs of radiographs can be incorporated into these files. A text search and retrieval software program enables rapid searching for keywords in scanned documents. The hand scanner and software programs are commercially available, relatively inexpensive, and easily used. This permits construction of a personalized radiology literature file of readily accessible text and images requiring minimal typing or keystroke entry.

  4. Recognition Of Partially Occluded Workpieces By A Knowledge-Based System

    NASA Astrophysics Data System (ADS)

    Serpico, S. B.; Vernazza, G.; Dellepiane, S.; Angela, P.

    1987-01-01

    A knowledge-based system is presented that is oriented toward partially occluded 2-D workpiece recognition in TV camera images. The generalized Hough transform is employed to extract elementary edge patterns. Intrinsic and relational information regarding elementary patterns is computed and then stored inside a net of frames. A similar net of frames is employed for workpiece model representation, for an easy matching with the previous net. A set of production rules provide the heuristics to find hints for locating focus-of-attention regions, while other production rules specify modalities for applying a hypothesis-generation-and-test process. Experimental results on a set of 20 workpieces are reported.

  5. Pattern recognition, attention, and information bottlenecks in the primate visual system

    NASA Astrophysics Data System (ADS)

    Van Essen, David; Olshausen, Bruno A.; Anderson, Clifford H.; Gallant, J. T.

    1991-07-01

    In its evolution, the primate visual system has developed impressive capabilities for recognizing complex patterns in natural images. This process involves many stages of analysis and a variety of information processing strategies. This paper concentrates on the importance of 'information bottlenecks,' which restrict the amount of information that can be handled at different stages of analysis. These steps are crucial for reducing the overwhelming computational complexity associated with recognizing countless objects from arbitrary viewing angles, distances, and perspectives. The process of directed visual attention is an especially important information bottleneck because of its flexibility in determining how information is routed to high-level pattern recognition centers.

  6. 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.

  7. 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.

  8. Pattern recognition system invariant to rotation and scale to identify color images

    NASA Astrophysics Data System (ADS)

    Coronel-Beltrán, Angel

    2014-10-01

    This work presents a pattern recognition digital system based on nonlinear correlations. The correlation peak values given by the system were analyzed by the peak-to-correlation energy (PCE) metric to determine the optimal value of the non-linear coefficient kin the k-law. The system was tested with 18 different color images of butterflies; each image was rotated from 0° to 180° with increments of 1° and scaled ±25% with increments of 1% and to take advantage of the color property of the images the RGB model was employed. The boxplot statistical analysis of the mean with ±2*EE (standard errors) for the PCE values set that the system invariant to rotation and scale has a confidence level at least of 95.4%.

  9. Technology Systems. Laboratory Activities.

    ERIC Educational Resources Information Center

    Brame, Ray; And Others

    This guide contains 43 modules of laboratory activities for technology education courses. Each module includes an instructor's resource sheet and the student laboratory activity. Instructor's resource sheets include some or all of the following elements: module number, course title, activity topic, estimated time, essential elements, objectives,…

  10. Toward design of an environment-aware adaptive locomotion-mode-recognition system.

    PubMed

    Du, Lin; Zhang, Fan; Liu, Ming; Huang, He

    2012-10-01

    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.

  11. A food recognition system for diabetic patients based on an optimized bag-of-features model.

    PubMed

    Anthimopoulos, Marios M; Gianola, Lauro; Scarnato, Luca; Diem, Peter; Mougiakakou, Stavroula G

    2014-07-01

    Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

  12. A Novel Model-Based Driving Behavior Recognition System Using Motion Sensors

    PubMed Central

    Wu, Minglin; Zhang, Sheng; Dong, Yuhan

    2016-01-01

    In this article, a novel driving behavior recognition system based on a specific physical model and motion sensory data is developed to promote traffic safety. Based on the theory of rigid body kinematics, we build a specific physical model to reveal the data change rule during the vehicle moving process. In this work, we adopt a nine-axis motion sensor including a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and apply a Kalman filter for noise elimination and an adaptive time window for data extraction. Based on the feature extraction guided by the built physical model, various classifiers are accomplished to recognize different driving behaviors. Leveraging the system, normal driving behaviors (such as accelerating, braking, lane changing and turning with caution) and aggressive driving behaviors (such as accelerating, braking, lane changing and turning with a sudden) can be classified with a high accuracy of 93.25%. Compared with traditional driving behavior recognition methods using machine learning only, the proposed system possesses a solid theoretical basis, performs better and has good prospects. PMID:27775625

  13. A Novel Model-Based Driving Behavior Recognition System Using Motion Sensors.

    PubMed

    Wu, Minglin; Zhang, Sheng; Dong, Yuhan

    2016-10-20

    In this article, a novel driving behavior recognition system based on a specific physical model and motion sensory data is developed to promote traffic safety. Based on the theory of rigid body kinematics, we build a specific physical model to reveal the data change rule during the vehicle moving process. In this work, we adopt a nine-axis motion sensor including a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and apply a Kalman filter for noise elimination and an adaptive time window for data extraction. Based on the feature extraction guided by the built physical model, various classifiers are accomplished to recognize different driving behaviors. Leveraging the system, normal driving behaviors (such as accelerating, braking, lane changing and turning with caution) and aggressive driving behaviors (such as accelerating, braking, lane changing and turning with a sudden) can be classified with a high accuracy of 93.25%. Compared with traditional driving behavior recognition methods using machine learning only, the proposed system possesses a solid theoretical basis, performs better and has good prospects.

  14. Dissociating Medial Temporal and Striatal Memory Systems With a Same/Different Matching Task: Evidence for Two Neural Systems in Human Recognition.

    PubMed

    Sinha, Neha; Glass, Arnold Lewis

    2017-01-01

    The medial temporal lobe and striatum have both been implicated as brain substrates of memory and learning. Here, we show dissociation between these two memory systems using a same/different matching task, in which subjects judged whether four-letter strings were the same or different. Different RT was determined by the left-to-right location of the first letter different between the study and test string, consistent with a left-to-right comparison of the study and test strings, terminating when a difference was found. This comparison process results in same responses being slower than different responses. Nevertheless, same responses were faster than different responses. Same responses were associated with hippocampus activation. Different responses were associated with both caudate and hippocampus activation. These findings are consistent with the dual-system hypothesis of mammalian memory and extend the model to human visual recognition.

  15. 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.

  16. Glycosylation as a target for recognition of influenza viruses by the innate immune system.

    PubMed

    Reading, Patrick C; Tate, Michelle D; Pickett, Danielle L; Brooks, Andrew G

    2007-01-01

    Glycosylation clearly plays an important role in the life cycle of influenza viruses and certain glycosylation sites are required for the structural integrity and stability of the HA and NA glycoproteins during biosynthesis and formation of intact virions. Furthermore, glycosylation has been shown to modulate the functions of influenza glycoproteins, in particular the recognition of host cell receptors and in shielding antigenic epitopes on the viral HA. The addition of oligosaccharide moieties to the globular head of the HA does, however, correlate with an increased sensitivity to the antiviral activities of SP-D and to recognition and destruction of virus via the MMR on murine macrophages. Consequently, the degree of glycosylation appears to be an important factor in determining sensitivity to lectin-mediated defences, and therefore in determining the ability of a particular virus strain to replicate in the respiratory tract of mice following intranasal infection. The mouse-adapted PR8 strain which lacks mannose-containing glycans from the head of its HA molecule was largely resistant to the antiviral activities of SP-D and the MMR in vitro and induced severed clinical disease following intranasal infection of mice. The finding that mannan treatment of BJx109-infected mice facilitated an early and dramatic enhancement of disease severity is also consistent with a major role for mannose-specific lectins in limiting influenza virus growth and spread in the respiratory tract.

  17. An important base triple anchors the substrate helix recognition surface within the Tetrahymena ribozyme active site.

    PubMed

    Szewczak, A A; Ortoleva-Donnelly, L; Zivarts, M V; Oyelere, A K; Kazantsev, A V; Strobel, S A

    1999-09-28

    Key to understanding the structural biology of catalytic RNA is determining the underlying networks of interactions that stabilize RNA folding, substrate binding, and catalysis. Here we demonstrate the existence and functional importance of a Hoogsteen base triple (U300.A97-U277), which anchors the substrate helix recognition surface within the Tetrahymena group I ribozyme active site. Nucleotide analog interference suppression analysis of the interacting functional groups shows that the U300.A97-U277 triple forms part of a network of hydrogen bonds that connect the P3 helix, the J8/7 strand, and the P1 substrate helix. Product binding and substrate cleavage kinetics experiments performed on mutant ribozymes that lack this base triple (C A-U, U G-C) or replace it with the isomorphous C(+).G-C triple show that the A97 Hoogsteen triple contributes to the stabilization of both substrate helix docking and the conformation of the ribozyme's active site. The U300. A97-U277 base triple is not formed in the recently reported crystallographic model of a portion of the group I intron, despite the presence of J8/7 and P3 in the RNA construct [Golden, B. L., Gooding, A. R., Podell, E. R. & Cech, T. R. (1998) Science 282, 259-264]. This, along with other biochemical evidence, suggests that the active site in the crystallized form of the ribozyme is not fully preorganized and that substantial rearrangement may be required for substrate helix docking and catalysis.

  18. [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