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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. PMID:20945515

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

    PubMed Central

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

    2013-01-01

    With the wide applications of vision based intelligent systems, image and video analysis technologies have attracted the attention of researchers in the computer vision field. In image and video analysis, human activity recognition is an important research direction. By interpreting and understanding human activities, we can recognize and predict the occurrence of crimes and help the police or other agencies react immediately. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation of the performance of human activity recognition. PMID:23353144

  5. 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

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

    PubMed

    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. Multiview human activity recognition system based on spatiotemporal template for video surveillance system

    NASA Astrophysics Data System (ADS)

    Kushwaha, Alok Kumar Singh; Srivastava, Rajeev

    2015-09-01

    An efficient view invariant framework for the recognition of human activities from an input video sequence is presented. The proposed framework is composed of three consecutive modules: (i) detect and locate people by background subtraction, (ii) view invariant spatiotemporal template creation for different activities, (iii) and finally, template matching is performed for view invariant activity recognition. The foreground objects present in a scene are extracted using change detection and background modeling. The view invariant templates are constructed using the motion history images and object shape information for different human activities in a video sequence. For matching the spatiotemporal templates for various activities, the moment invariants and Mahalanobis distance are used. The proposed approach is tested successfully on our own viewpoint dataset, KTH action recognition dataset, i3DPost multiview dataset, MSR viewpoint action dataset, VideoWeb multiview dataset, and WVU multiview human action recognition dataset. From the experimental results and analysis over the chosen datasets, it is observed that the proposed framework is robust, flexible, and efficient with respect to multiple views activity recognition, scale, and phase variations.

  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. Traffic Sign Recognition with Invariance to Lighting in Dual-Focal Active Camera System

    NASA Astrophysics Data System (ADS)

    Gu, Yanlei; Panahpour Tehrani, Mehrdad; Yendo, Tomohiro; Fujii, Toshiaki; Tanimoto, Masayuki

    In this paper, we present an automatic vision-based traffic sign recognition system, which can detect and classify traffic signs at long distance under different lighting conditions. To realize this purpose, the traffic sign recognition is developed in an originally proposed dual-focal active camera system. In this system, a telephoto camera is equipped as an assistant of a wide angle camera. The telephoto camera can capture a high accuracy image for an object of interest in the view field of the wide angle camera. The image from the telephoto camera provides enough information for recognition when the accuracy of traffic sign is low from the wide angle camera. In the proposed system, the traffic sign detection and classification are processed separately for different images from the wide angle camera and telephoto camera. Besides, in order to detect traffic sign from complex background in different lighting conditions, we propose a type of color transformation which is invariant to light changing. This color transformation is conducted to highlight the pattern of traffic signs by reducing the complexity of background. Based on the color transformation, a multi-resolution detector with cascade mode is trained and used to locate traffic signs at low resolution in the image from the wide angle camera. After detection, the system actively captures a high accuracy image of each detected traffic sign by controlling the direction and exposure time of the telephoto camera based on the information from the wide angle camera. Moreover, in classification, a hierarchical classifier is constructed and used to recognize the detected traffic signs in the high accuracy image from the telephoto camera. Finally, based on the proposed system, a set of experiments in the domain of traffic sign recognition is presented. The experimental results demonstrate that the proposed system can effectively recognize traffic signs at low resolution in different lighting conditions.

  10. Transfer Learning for Activity Recognition: A Survey

    PubMed Central

    Cook, Diane; Feuz, Kyle D.; Krishnan, Narayanan C.

    2013-01-01

    Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed. PMID:24039326

  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. Video Scene Recognition System

    NASA Astrophysics Data System (ADS)

    Wong, Robert Y.; Sallak, Rashid M.

    1983-03-01

    Microprocessors are used to show a possible implementation of a multiprocessoi system for video scene recognition operations. The system was designed in the multiple input stream and multiple data stream (MIMD) configuration. "Autonomous cooperation" among the working processors is supervised by a global operating system, the heart of which is the scheduler. The design of the scheduler and the overall operations of the system are discussed.

  13. Window size impact in human activity recognition.

    PubMed

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

    2014-01-01

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

  14. Window Size Impact in Human Activity Recognition

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2015-01-01

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

  16. Automatic speaker recognition system

    NASA Astrophysics Data System (ADS)

    Higgins, Alan; Naylor, Joe

    1984-07-01

    The Defense Communications Division of ITT (ITTDCD) has developed an automatic speaker recognition (ASR) system that meets the functional requirements defined in NRL's Statement of Work. This report is organized as follows. Chapter 2 is a short history of the development of the ASR system, both the algorithm and the implementation. Chapter 3 describes the methodology of system testing, and Chapter 4 summarizes test results. In Chapter 5, some additional testing performed using GFM test material is discussed. Conclusions derived from the contract work are given in Chapter 6.

  17. Kannada character recognition system using neural network

    NASA Astrophysics Data System (ADS)

    Kumar, Suresh D. S.; Kamalapuram, Srinivasa K.; Kumar, Ajay B. R.

    2013-03-01

    Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. As there is no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India. In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten Kannada character is resized into 20x30 Pixel. The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different Kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems.

  18. Pattern Recognition Protein Binds to Lipopolysaccharide and β-1,3-Glucan and Activates Shrimp Prophenoloxidase System*

    PubMed Central

    Amparyup, Piti; Sutthangkul, Jantiwan; Charoensapsri, Walaiporn; Tassanakajon, Anchalee

    2012-01-01

    The prophenoloxidase (proPO) system is activated upon recognition of pathogens by pattern recognition proteins (PRPs), including a lipopolysaccharide- and β-1,3-glucan-binding protein (LGBP). However, shrimp LGBPs that are involved in the proPO system have yet to be clarified. Here, we focus on characterizing the role of a Penaeus monodon LGBP (PmLGBP) in the proPO system. We found that PmLGBP transcripts are expressed primarily in the hemocytes and are increased at 24 h after pathogenic bacterium Vibrio harveyi challenge. The binding studies carried out using ELISA indicated that recombinant (r)PmLGBP binds to β-1,3-glucan and LPS with a dissociation constant of 6.86 × 10−7 m and 3.55 × 10−7 m, respectively. Furthermore, we found that rPmLGBP could enhance the phenoloxidase (PO) activity of hemocyte suspensions in the presence of LPS or β-1,3-glucan. Using dsRNA interference-mediated gene silencing assay, we further demonstrated that knockdown of PmLGBP in shrimp in vivo significantly decreased the PmLGBP transcript level but had no effect on the expression of the other immune genes tested, including shrimp antimicrobial peptides (AMPs). However, suppression of proPO expression down-regulated PmLGBP, proPO-activating enzyme (PmPPAE2), and AMPs (penaeidin and crustin). Such PmLGBP down-regulated shrimp showed significantly decreased total PO activity. We conclude that PmLGBP functions as a pattern recognition protein for LPS and β-1,3-glucan in the shrimp proPO activating system. PMID:22235126

  19. A face recognition embedded system

    NASA Astrophysics Data System (ADS)

    Pun, Kwok Ho; Moon, Yiu Sang; Tsang, Chi Chiu; Chow, Chun Tak; Chan, Siu Man

    2005-03-01

    This paper presents an experimental study of the implementation of a face recognition system in embedded systems. To investigate the feasibility and practicality of real time face recognition on such systems, a door access control system based on face recognition is built. Due to the limited computation power of embedded device, a semi-automatic scheme for face detection and eye location is proposed to solve these computationally hard problems. It is found that to achieve real time performance, optimization of the core face recognition module is needed. As a result, extensive profiling is done to pinpoint the execution hotspots in the system and optimization are carried out. After careful precision analysis, all slow floating point calculations are replaced with their fixed-point versions. Experimental results show that real time performance can be achieved without significant loss in recognition accuracy.

  20. 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.

  1. Cognitive object recognition system (CORS)

    NASA Astrophysics Data System (ADS)

    Raju, Chaitanya; Varadarajan, Karthik Mahesh; Krishnamurthi, Niyant; Xu, Shuli; Biederman, Irving; Kelley, Troy

    2010-04-01

    We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

  2. Ear recognition: a complete system

    NASA Astrophysics Data System (ADS)

    Abaza, Ayman; Harrison, Mary Ann F.

    2013-05-01

    Ear Recognition has recently received significant attention in the literature. Even though current ear recognition systems have reached a certain level of maturity, their success is still limited. This paper presents an efficient complete ear-based biometric system that can process five frames/sec; Hence it can be used for surveillance applications. The ear detection is achieved using Haar features arranged in a cascaded Adaboost classifier. The feature extraction is based on dividing the ear image into several blocks from which Local Binary Pattern feature distributions are extracted. These feature distributions are then fused at the feature level to represent the original ear texture in the classification stage. The contribution of this paper is three fold: (i) Applying a new technique for ear feature extraction, and studying various optimization parameters for that technique; (ii) Presenting a practical ear recognition system and a detailed analysis about error propagation in that system; (iii) Studying the occlusion effect of several ear parts. Detailed experiments show that the proposed ear recognition system achieved better performance (94:34%) compared to other shape-based systems as Scale-invariant feature transform (67:92%). The proposed approach can also handle efficiently hair occlusion. Experimental results show that the proposed system can achieve about (78%) rank-1 identification, even in presence of 60% occlusion.

  3. The Army word recognition system

    NASA Technical Reports Server (NTRS)

    Hadden, David R.; Haratz, David

    1977-01-01

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

  4. Divergent short- and long-term effects of acute stress in object recognition memory are mediated by endogenous opioid system activation.

    PubMed

    Nava-Mesa, Mauricio O; Lamprea, Marisol R; Múnera, Alejandro

    2013-11-01

    Acute stress induces short-term object recognition memory impairment and elicits endogenous opioid system activation. The aim of this study was thus to evaluate whether opiate system activation mediates the acute stress-induced object recognition memory changes. Adult male Wistar rats were trained in an object recognition task designed to test both short- and long-term memory. Subjects were randomly assigned to receive an intraperitoneal injection of saline, 1 mg/kg naltrexone or 3 mg/kg naltrexone, four and a half hours before the sample trial. Five minutes after the injection, half the subjects were submitted to movement restraint during four hours while the other half remained in their home cages. Non-stressed subjects receiving saline (control) performed adequately during the short-term memory test, while stressed subjects receiving saline displayed impaired performance. Naltrexone prevented such deleterious effect, in spite of the fact that it had no intrinsic effect on short-term object recognition memory. Stressed subjects receiving saline and non-stressed subjects receiving naltrexone performed adequately during the long-term memory test; however, control subjects as well as stressed subjects receiving a high dose of naltrexone performed poorly. Control subjects' dissociated performance during both memory tests suggests that the short-term memory test induced a retroactive interference effect mediated through light opioid system activation; such effect was prevented either by low dose naltrexone administration or by strongly activating the opioid system through acute stress. Both short-term memory retrieval impairment and long-term memory improvement observed in stressed subjects may have been mediated through strong opioid system activation, since they were prevented by high dose naltrexone administration. Therefore, the activation of the opioid system plays a dual modulating role in object recognition memory. PMID:24036398

  5. Design and test of a hybrid foot force sensing and GPS system for richer user mobility activity recognition.

    PubMed

    Zhang, Zelun; Poslad, Stefan

    2013-01-01

    Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during travelling) and transportation mode simultaneously, and a relatively high computational complexity. Here, a new GPS and Foot-Force (GPS + FF) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with GPS, e.g., in a mobile phone. User mobility activities that can be recognised include both daily user postures and common transportation modes: sitting, standing, walking, cycling, bus passenger, car passenger (including private cars and taxis) and car driver. The novelty of this work is that our approach provides a more comprehensive recognition capability in terms of reliably recognising both human posture and transportation mode simultaneously during travel. In addition, by comparing the new GPS + FF method with both an ACC method (62% accuracy) and a GPS + ACC based method (70% accuracy) as baseline methods, it obtains a higher accuracy (95%) with less computational complexity, when tested on a dataset obtained from ten individuals. PMID:24189333

  6. Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition

    PubMed 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

  7. 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.

  8. Incorporating Duration Information in Activity Recognition

    NASA Astrophysics Data System (ADS)

    Chaurasia, Priyanka; Scotney, Bryan; McClean, Sally; Zhang, Shuai; Nugent, Chris

    Activity recognition has become a key issue in smart home environments. The problem involves learning high level activities from low level sensor data. Activity recognition can depend on several variables; one such variable is duration of engagement with sensorised items or duration of intervals between sensor activations that can provide useful information about personal behaviour. In this paper a probabilistic learning algorithm is proposed that incorporates episode, time and duration information to determine inhabitant identity and the activity being undertaken from low level sensor data. Our results verify that incorporating duration information consistently improves the accuracy.

  9. 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.

  10. Purification of a beta-1,3-glucan recognition protein in the prophenoloxidase activating system from hemolymph of the silkworm, Bombyx mori.

    PubMed

    Ochiai, M; Ashida, M

    1988-08-25

    The plasma fraction (referred to as plasma-CPB) of silkworm hemolymph, from which a protein with affinity to beta-1,3-glucan was specifically removed according to Yoshida et al. (Yoshida, H., Ochiai, M., and Ashida, M. (1986), Biochem. Biophys. Res. Commun. 141, 1177-1184), was used to develop a method for quantitating the beta-1,3-glucan recognition protein of the prophenoloxidase activating system. In principle, a sample was judged to contain beta-1,3-glucan recognition protein when that sample could restore the ability of the system in plasma-CPB to be triggered by beta-1,3-glucan. Purification procedures for the recognition protein from silkworm hemolymph consisted of fractionation with ammonium sulfate, chromatography on DEAE-Toyopearl, Affi-Gel-heparin, and Mono Q and Superose 12 on the fast protein liquid chromatography system of Pharmacia LKB Biotechnology Inc. About 2.03 mg of beta-1,3-glucan recognition protein was obtained from 300 ml of hemolymph. The purified beta-1,3-glucan recognition protein was homogeneous as judged by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and isoelectric focusing-polyacrylamide gel electrophoresis. beta-1,3-Glucan recognition protein had a molecular mass of 62 kDa composed of a single polypeptide and an isoelectric point of pH 4.3. It bound to curdlan beads (composed of beta-1,3-glucan with average particle size of 80 micron) in the absence of divalent cation, whereas its binding to glucans with beta(1----4)- or alpha(1----6)-glycosidic linkages was not detected under the experimental conditions. Elution of the beta-1,3-glucan recognition protein bound to curdlan beads could be achieved under strongly denaturing conditions (after incubation of the beads with sodium dodecyl sulfate and beta-mercaptoethanol in boiling water for 5 min), but elution at room temperature was poor. Since beta-1,3-glucan recognition protein is the only protein in silkworm plasma with strong affinity to beta-1,3-glucan and endows the

  11. A Survey of Online Activity Recognition Using Mobile Phones

    PubMed Central

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

    2015-01-01

    Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research. PMID:25608213

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

    PubMed

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

    2015-01-01

    Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research. PMID:25608213

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

    PubMed

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

    2015-06-01

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

  14. 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

  15. Pattern activation/recognition theory of mind.

    PubMed

    du Castel, Bertrand

    2015-01-01

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

  16. Making Activity Recognition Robust against Deceptive Behavior

    PubMed Central

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

    2015-01-01

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

  17. Making Activity Recognition Robust against Deceptive Behavior.

    PubMed

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

    2015-01-01

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

  18. Activity Recognition on Streaming Sensor Data

    PubMed Central

    Krishnan, Narayanan C; Cook, Diane J

    2012-01-01

    Many real-world applications that focus on addressing needs of a human, require information about the activities being performed by the human in real-time. While advances in pervasive computing have lead to the development of wireless and non-intrusive sensors that can capture the necessary activity information, current activity recognition approaches have so far experimented on either a scripted or pre-segmented sequence of sensor events related to activities. In this paper we propose and evaluate a sliding window based approach to perform activity recognition in an on line or streaming fashion; recognizing activities as and when new sensor events are recorded. To account for the fact that different activities can be best characterized by different window lengths of sensor events, we incorporate the time decay and mutual information based weighting of sensor events within a window. Additional contextual information in the form of the previous activity and the activity of the previous window is also appended to the feature describing a sensor window. The experiments conducted to evaluate these techniques on real-world smart home datasets suggests that combining mutual information based weighting of sensor events and adding past contextual information into the feature leads to best performance for streaming activity recognition. PMID:24729780

  19. Activity Recognition on Streaming Sensor Data.

    PubMed

    Krishnan, Narayanan C; Cook, Diane J

    2014-02-01

    Many real-world applications that focus on addressing needs of a human, require information about the activities being performed by the human in real-time. While advances in pervasive computing have lead to the development of wireless and non-intrusive sensors that can capture the necessary activity information, current activity recognition approaches have so far experimented on either a scripted or pre-segmented sequence of sensor events related to activities. In this paper we propose and evaluate a sliding window based approach to perform activity recognition in an on line or streaming fashion; recognizing activities as and when new sensor events are recorded. To account for the fact that different activities can be best characterized by different window lengths of sensor events, we incorporate the time decay and mutual information based weighting of sensor events within a window. Additional contextual information in the form of the previous activity and the activity of the previous window is also appended to the feature describing a sensor window. The experiments conducted to evaluate these techniques on real-world smart home datasets suggests that combining mutual information based weighting of sensor events and adding past contextual information into the feature leads to best performance for streaming activity recognition. PMID:24729780

  20. 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.

  1. Epileptic activity recognition in EEG recording

    NASA Astrophysics Data System (ADS)

    Diambra, L.; de Figueiredo, J. C. Bastos; Malta, C. P.

    1999-12-01

    We apply Approximate Entropy (ApEn) algorithm in order to recognize epileptic activity in electroencephalogram recordings. ApEn is a recently developed statistical quantity for quantifying regularity and complexity. Our approach is illustrated regarding different types of epileptic activity. In all segments associated with epileptic activity analyzed here the complexity of the signal measured by ApEn drops abruptly. This fact can be useful for automatic recognition and detection of epileptic seizures.

  2. Active place recognition using image signatures

    NASA Astrophysics Data System (ADS)

    Engelson, Sean P.

    1992-11-01

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

  3. 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

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

    PubMed

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

    2016-01-01

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

  5. Human body contour data based activity recognition.

    PubMed

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

    2013-01-01

    This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate. PMID:24111015

  6. 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.

  7. 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.

  8. Improved MFCC algorithm in speaker recognition system

    NASA Astrophysics Data System (ADS)

    Shi, Yibo; Wang, Li

    2011-10-01

    In speaker recognition systems, one of the key feature parameters is MFCC, which can be used for speaker recognition. So, how to extract MFCC parameter in speech signals more exactly and efficiently, decides the performance of the system. Theoretically, MFCC parameters are used to describe the spectrum envelope of the vocal tract characteristics and often ignore the impacts of fundamental frequency. But in practice, MFCC can be influenced by fundamental frequency which can cause palpable performance reduction. So, smoothing MFCC (SMFCC), which based on smoothing short-term spectral amplitude envelope, has been proposed to improve MFCC algorithm. Experimental results show that improved MFCC parameters---SMFCC can degrade the bad influences of fundamental frequency effectively and upgrade the performances of speaker recognition system. Especially for female speakers, who have higher fundamental frequency, the recognition rate improves more significantly.

  9. Activity Discovery and Activity Recognition: A New Partnership

    PubMed Central

    Cook, Diane; Krishnan, Narayanan; Rashidi, Parisa

    2013-01-01

    Activity recognition has received increasing attention from the machine learning community. Of particular interest is the ability to recognize activities in real time from streaming data, but this presents a number of challenges not faced by traditional offline approaches. Among these challenges is handling the large amount of data that does not belong to a predefined class. In this paper, we describe a method by which activity discovery can be used to identify behavioral patterns in observational data. Discovering patterns in the data that does not belong to a predefined class aids in understanding this data and segmenting it into learnable classes. We demonstrate that activity discovery not only sheds light on behavioral patterns, but it can also boost the performance of recognition algorithms. We introduce this partnership between activity discovery and online activity recognition in the context of the CASAS smart home project and validate our approach using CASAS datasets. PMID:23033328

  10. Activity discovery and activity recognition: a new partnership.

    PubMed

    Cook, Diane J; Krishnan, Narayanan C; Rashidi, Parisa

    2013-06-01

    Activity recognition has received increasing attention from the machine learning community. Of particular interest is the ability to recognize activities in real time from streaming data, but this presents a number of challenges not faced by traditional offline approaches. Among these challenges is handling the large amount of data that does not belong to a predefined class. In this paper, we describe a method by which activity discovery can be used to identify behavioral patterns in observational data. Discovering patterns in the data that does not belong to a predefined class aids in understanding this data and segmenting it into learnable classes. We demonstrate that activity discovery not only sheds light on behavioral patterns, but it can also boost the performance of recognition algorithms. We introduce this partnership between activity discovery and online activity recognition in the context of the CASAS smart home project and validate our approach using CASAS data sets. PMID:23033328

  11. 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. PMID:25957504

  12. Recognition of metal cations by biological systems.

    PubMed

    Truter, M R

    1975-11-01

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

  13. 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. PMID:10558032

  14. 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

  15. Neuromagnetic activity during recognition of emotional pictures.

    PubMed

    Kissler, Johanna; Hauswald, Anne

    2008-06-01

    Recently studied 'old' stimuli lead to larger frontal and parietal ERP responses than 'new' stimuli. The present experiment investigated the neuromagnetic correlates (MEG) of this 'old-new' effect and its modulation by emotional stimulus content. Highly arousing pleasant, highly arousing unpleasant and un-arousing neutral photographs were presented to the participants with the instruction to memorize them. They were later re-presented together with new photographs in an old-new decision task. In line with previous ERP studies, a long-lasting old-new effect (350-700 ms) was found. Independently, an emotion effect also occurred, as reflected in a, particularly left temporal, activity increase for emotional pictures between 450 and 580 ms. Moreover, only for the pleasant pictures did the early part of the old-new effect, which is thought to reflect familiarity based recognition processes, interact with picture content: The old-new effect for pleasant pictures in frontal regions was larger than the one for neutral or unpleasant pictures between 350 and 450 ms. In parallel, subjects' responses were accelerated towards and biased in favour of classifying pleasant pictures as old. However, when false alarm rate was taken into account, there was no significant effect of emotional content on recognition accuracy. In sum, this MEG study demonstrates an effect of particularly pleasant emotional content on recognition memory which may be mediated by a familiarity based process. PMID:18335310

  16. 34 CFR 602.30 - Activities covered by recognition procedures.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 34 Education 3 2013-07-01 2013-07-01 false Activities covered by recognition procedures. 602.30... POSTSECONDARY EDUCATION, DEPARTMENT OF EDUCATION THE SECRETARY'S RECOGNITION OF ACCREDITING AGENCIES The Recognition Process Application and Review by Department Staff § 602.30 Activities covered by...

  17. 34 CFR 602.30 - Activities covered by recognition procedures.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 34 Education 3 2011-07-01 2011-07-01 false Activities covered by recognition procedures. 602.30... POSTSECONDARY EDUCATION, DEPARTMENT OF EDUCATION THE SECRETARY'S RECOGNITION OF ACCREDITING AGENCIES The Recognition Process Application and Review by Department Staff § 602.30 Activities covered by...

  18. 34 CFR 602.30 - Activities covered by recognition procedures.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 34 Education 3 2012-07-01 2012-07-01 false Activities covered by recognition procedures. 602.30... POSTSECONDARY EDUCATION, DEPARTMENT OF EDUCATION THE SECRETARY'S RECOGNITION OF ACCREDITING AGENCIES The Recognition Process Application and Review by Department Staff § 602.30 Activities covered by...

  19. 34 CFR 602.30 - Activities covered by recognition procedures.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 34 Education 3 2014-07-01 2014-07-01 false Activities covered by recognition procedures. 602.30... POSTSECONDARY EDUCATION, DEPARTMENT OF EDUCATION THE SECRETARY'S RECOGNITION OF ACCREDITING AGENCIES The Recognition Process Application and Review by Department Staff § 602.30 Activities covered by...

  20. 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... POSTSECONDARY EDUCATION, DEPARTMENT OF EDUCATION THE SECRETARY'S RECOGNITION OF ACCREDITING AGENCIES The Recognition Process Application and Review by Department Staff § 602.30 Activities covered by...

  1. 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.

  2. Recognition system rapid application prototyping tool

    NASA Astrophysics Data System (ADS)

    Mills, Stuart A.; Karins, James P.; Dydyk, Robert B.

    1997-03-01

    The recognition system rapid application prototyping tool (RSRAPT) was developed to evaluate various potential configurations of miniature ruggedized optical correlator (MROC) modules and to rapidly assess the feasibility of their use within systems such as missile seekers. RSRAPT is a simulation environment for rapidly prototyping, developing, and evaluating recognition systems that incorporate MROC technology. It is designed to interface to OLE compliant Windows applications using standard OLE interfaces. The system consists of nine key functional elements: sensor, detection, segmentation, pre-processor, filter selection, correlator, post-processor, identifier, and controller. The RSRAPT is a collection of object oriented server components, a client user interface and a recognitions system image and image sensor database. The server components are implemented to encapsulate processes that are typical to any optical-correlator based pattern recognition system. All the servers are implemented as Microsoft component object model objects. In addition to the system servers there are two key 'helper servers.' The first is the image server, which encapsulates all 'images'. This includes gray scale images and even complex images. The other supporting server is the filter generation server. This server trains the system on user data by calculating filters for user selected image types. The system hosts a library of standard image processing routines such as convolution, edge operators, clustering algorithms, median filtering, morphological operators such as erosion and dilation, connected components, region growing, and adaptive thresholding. In this paper we describe the simulator and show sample results from diverse applications.

  3. 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.

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

    SciTech Connect

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

    1987-03-01

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

  5. 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

  6. 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.

  7. 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

  8. 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

  9. 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.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

  12. 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

  13. Physical Human Activity Recognition Using Wearable Sensors.

    PubMed

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

    2015-01-01

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

  14. A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors

    PubMed Central

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

    2014-01-01

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

  15. 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

  16. 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.

  17. An online handwriting recognition system for Turkish

    NASA Astrophysics Data System (ADS)

    Vural, Esra; Erdogan, Hakan; Oflazer, Kemal; Yanikoglu, Berrin A.

    2005-01-01

    Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

  18. An online handwriting recognition system for Turkish

    NASA Astrophysics Data System (ADS)

    Vural, Esra; Erdogan, Hakan; Oflazer, Kemal; Yanikoglu, Berrin A.

    2004-12-01

    Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

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

    PubMed Central

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

    2015-01-01

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

  20. Improving robustness of speech recognition systems

    NASA Astrophysics Data System (ADS)

    Mitra, Vikramjit

    2010-11-01

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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

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

    PubMed

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

    2015-02-25

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

  12. 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.

  13. 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.

  14. 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…

  15. 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. PMID:27160295

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

    NASA Astrophysics Data System (ADS)

    Xiong, Bo; Izmirli, Ozgur

    2012-01-01

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

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

    PubMed Central

    Joongrock, Kim; Sunjin, Yu; Sangyoun, Lee

    2014-01-01

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

  18. Hybrid gesture recognition system for short-range use

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

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

  19. 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

  20. Active Shape Model-Based Gait Recognition Using Infrared Images

    NASA Astrophysics Data System (ADS)

    Kim, Daehee; Lee, Seungwon; Paik, Joonki

    We present a gait recognition system using infra-red (IR) images. Since an IR camera is not affected by the intensity of illumination, it is able to provide constant recognition performance regardless of the amount of illumination. Model-based object tracking algorithms enable robust tracking with partial occlusions or dynamic illumination. However, this algorithm often fails in tracking objects if strong edge exists near the object. Replacement of the input image by an IR image guarantees robust object region extraction because background edges do not affect the IR image. In conclusion, the proposed gait recognition algorithm improves accuracy in object extraction by using IR images and the improvements finally increase the recognition rate of gaits.

  1. Automated recognition system for power quality disturbances

    NASA Astrophysics Data System (ADS)

    Abdelgalil, Tarek

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

  2. Embedded palmprint recognition system using OMAP 3530.

    PubMed

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

    2012-01-01

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

  3. Embedded Palmprint Recognition System Using OMAP 3530

    PubMed Central

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

    2012-01-01

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

  4. 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

  5. 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

  6. 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)

  7. Sum Product Networks for Activity Recognition.

    PubMed

    Amer, Mohamed R; Todorovic, Sinisa

    2016-04-01

    This paper addresses detection and localization of human activities in videos. We focus on activities that may have variable spatiotemporal arrangements of parts, and numbers of actors. Such activities are represented by a sum-product network (SPN). A product node in SPN represents a particular arrangement of parts, and a sum node represents alternative arrangements. The sums and products are hierarchically organized, and grounded onto space-time windows covering the video. The windows provide evidence about the activity classes based on the Counting Grid (CG) model of visual words. This evidence is propagated bottom-up and top-down to parse the SPN graph for the explanation of the video. The node connectivity and model parameters of SPN and CG are jointly learned under two settings, weakly supervised, and supervised. For evaluation, we use our new Volleyball dataset, along with the benchmark datasets VIRAT, UT-Interactions, KTH, and TRECVID MED 2011. Our video classification and activity localization are superior to those of the state of the art on these datasets. PMID:26390445

  8. Effects of active navigation on object recognition in virtual environments.

    PubMed

    Hahm, Jinsun; Lee, Kanghee; Lim, Seung-Lark; Kim, Sei-Young; Kim, Hyun-Taek; Lee, Jang-Han

    2007-04-01

    We investigated the importance and efficiency of active and passive exploration on the recognition of objects in a variety of virtual environments (VEs). In this study, 54 participants were randomly allocated into one of active and passive navigation conditions. Active navigation was performed by allowing participants to self-pace and control their own navigation, but passive navigation was conducted by forced navigation. After navigating VEs, participants were asked to recognize the objects that had been in the VEs. Active navigation condition had a significantly higher percentage of hit responses (t (52) = 4.000, p < 0.01), and a significantly lower percentage of miss responses (t (52) = -3.763, p < 0.01) in object recognition than the passive condition. These results suggest that active navigation plays an important role in spatial cognition as well as providing an explanation for the efficiency of learning in a 3D-based program. PMID:17474852

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

    NASA Astrophysics Data System (ADS)

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

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

  10. Design and development of an ancient Chinese document recognition system

    NASA Astrophysics Data System (ADS)

    Peng, Liangrui; Xiu, Pingping; Ding, Xiaoqing

    2003-12-01

    The digitization of ancient Chinese documents presents new challenges to OCR (Optical Character Recognition) research field due to the large character set of ancient Chinese characters, variant font types, and versatile document layout styles, as these documents are historical reflections to the thousands of years of Chinese civilization. After analyzing the general characteristics of ancient Chinese documents, we present a solution for recognition of ancient Chinese documents with regular font-types and layout-styles. Based on the previous work on multilingual OCR in TH-OCR system, we focus on the design and development of two key technologies which include character recognition and page segmentation. Experimental results show that the developed character recognition kernel of 19,635 Chinese characters outperforms our original traditional Chinese recognition kernel; Benchmarked test on printed ancient Chinese books proves that the proposed system is effective for regular ancient Chinese documents.

  11. 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

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

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

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

  13. 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.

  14. Egocentric daily activity recognition via multitask clustering.

    PubMed

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

    2015-10-01

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

  15. Color recognition system for urine analyzer

    NASA Astrophysics Data System (ADS)

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

    2010-08-01

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

  16. Adaptive activity and environment recognition for mobile phones.

    PubMed

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

    2014-01-01

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

  17. Adaptive Activity and Environment Recognition for Mobile Phones

    PubMed Central

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

    2014-01-01

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

  18. 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.

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

    PubMed

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

    2015-06-01

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

  20. Robust Indoor Human Activity Recognition Using Wireless Signals.

    PubMed

    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

  1. 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

  2. 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. PMID:23911427

  3. Construction of DNA recognition sites active in Haemophilus transformation.

    PubMed Central

    Danner, D B; Smith, H O; Narang, S A

    1982-01-01

    Competent Haemophilus cells recognize and preferentially take up Haemophilus DNA during genetic transformation. This preferential uptake is correlated with the presence on incoming DNA of an 11-base-pair (bp) sequence, 5'-A-A-G-T-G-C-G-G-T-C-A-3'. To prove that this sequence is the recognition site that identifies Haemophilus DNA to the competent cell, we have now constructed a series of plasmids, each of which contains the 11-bp sequence. Using two different assay systems we have tested the ability of fragments from these plasmids to compete with cloned Haemophilus DNA fragments that naturally contain the 11-bp sequence. We find that the addition of the 11-bp sequence to a DNA fragment is necessary and sufficient for preferential uptake of that fragment. However, plasmid DNAs containing this sequence may vary as much as 48-fold in uptake activity, and this variation correlates with the A+T-richness of the DNA flanking the 11-mer. Images PMID:6285382

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

    NASA Astrophysics Data System (ADS)

    Oniga, Stefan; József, Sütő

    2015-12-01

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

  5. Ontology-based improvement to human activity recognition

    NASA Astrophysics Data System (ADS)

    Tahmoush, David; Bonial, Claire

    2016-05-01

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

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

    PubMed

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

    2009-06-01

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

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

    PubMed

    Katiyar, Gauri; Mehfuz, Shabana

    2016-01-01

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

  8. Optical Correlator for Face Recognition Using Collinear Holographic System

    NASA Astrophysics Data System (ADS)

    Watanabe, Eriko; Kodate, Kashiko

    2006-08-01

    We have constructed an optical correlator for fast face recognition. Recognition rate can be markedly improved, if reference images are optically recorded and can be accessed directly without converting them to digital signals. In addition, a large capacity of optical storage allows us to increase the size of the reference database. We propose a new optical correlator that integrates the optical correlation technology used in our face recognition system and collinear holography. From preliminary correlation experiments using the collinear optical set-up, we achieved excellent performance of high correlation peaks and low error rates. We expect an optical correlation of 10 μs/frame, i.e., 100,000 face/s when applied to face recognition. This system can also be applied to various image searches.

  9. 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

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

    PubMed

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

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Hirayama, Takatsugu; Iwai, Yoshio; Yachida, Masahiko

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

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

    NASA Technical Reports Server (NTRS)

    Huntsberger, Terry

    2011-01-01

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

  13. Molecular recognition in chemical and biological systems.

    PubMed

    Persch, Elke; Dumele, Oliver; Diederich, François

    2015-03-01

    Structure-based ligand design in medicinal chemistry and crop protection relies on the identification and quantification of weak noncovalent interactions and understanding the role of water. Small-molecule and protein structural database searches are important tools to retrieve existing knowledge. Thermodynamic profiling, combined with X-ray structural and computational studies, is the key to elucidate the energetics of the replacement of water by ligands. Biological receptor sites vary greatly in shape, conformational dynamics, and polarity, and require different ligand-design strategies, as shown for various case studies. Interactions between dipoles have become a central theme of molecular recognition. Orthogonal interactions, halogen bonding, and amide⋅⋅⋅π stacking provide new tools for innovative lead optimization. The combination of synthetic models and biological complexation studies is required to gather reliable information on weak noncovalent interactions and the role of water. PMID:25630692

  14. Human Activity Recognition from Environmental Background Sounds for Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Zhan, Yi; Nishimura, Jun; Kuroda, Tadahiro

    Sound feature extraction Mel Frequency Cepstral Coefficients (MFCC) and Vector Quantization (VQ) classification Linde-Buzo-Gray algorithm (LBG) algorithms are applied for recognizing the background sounds in the human daily activities. Applying these algorithms to twenty typical daily activity sounds, average recognition accuracy of 93.8% can be achieved. In these algorithms, how three parameters (i.e., Mel filters number, frame-to-frame overlap and LBG codebook cluster number) affect system's calculation burden and accuracy is also investigated. By adjusting these three parameters to an optimized combination, the multiplication and addition calculation burden can be reduced by 87.0% and 87.1% individually while maintaining the system's average accuracy rate at 92.5%. This is promising for future integration with other sensor (s) to fulfill daily activity recognition by using power aware Wireless Sensor Networks (WSN) systems.

  15. Activity recognition using correlated pattern mining for people with dementia.

    PubMed

    Sim, Kelvin; Phua, Clifton; Yap, Ghim-Eng; Biswas, Jit; Mokhtari, Mounir

    2011-01-01

    Due to the rapidly aging population around the world, senile dementia is growing into a prominent problem in many societies. To monitor the elderly dementia patients so as to assist them in carrying out their basic Activities of Daily Living (ADLs) independently, sensors are deployed in their homes. The sensors generate a stream of context information, i.e., snippets of the patient's current happenings, and pattern mining techniques can be applied to recognize the patient's activities based on these micro contexts. Most mining techniques aim to discover frequent patterns that correspond to certain activities. However, frequent patterns can be poor representations of activities. In this paper, instead of using frequent patterns, we propose using correlated patterns to represent activities. Using simulation data collected in a smart home testbed, our experimental results show that using correlated patterns rather than frequent ones improves the recognition performance by 35.5% on average. PMID:22256096

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

    PubMed

    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

  17. 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

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

    PubMed

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

    2016-06-01

    Smartphone based activity recognition has recently received remarkable attention in various applications of mobile health such as safety monitoring, fitness tracking, and disease prediction. To achieve more accurate and simplified medical monitoring, this paper proposes a self-learning scheme for patients' activity recognition, in which a patient only needs to carry an ordinary smartphone that contains common motion sensors. After the real-time data collection though this smartphone, we preprocess the data using coordinate system transformation to eliminate phone orientation influence. A set of robust and effective features are then extracted from the preprocessed data. Because a patient may inevitably perform various unpredictable activities that have no apriori knowledge in the training dataset, we propose a self-learning activity recognition scheme. The scheme determines whether there are apriori training samples and labeled categories in training pools that well match with unpredictable activity data. If not, it automatically assembles these unpredictable samples into different clusters and gives them new category labels. These clustered samples combined with the acquired new category labels are then merged into the training dataset to reinforce recognition ability of the self-learning model. In experiments, we evaluate our scheme using the data collected from two postoperative patient volunteers, including six labeled daily activities as the initial apriori categories in the training pool. Experimental results demonstrate that the proposed self-learning scheme for activity recognition works very well for most cases. When there exist several types of unseen activities without any apriori information, the accuracy reaches above 80 % after the self-learning process converges. PMID:27106584

  19. Neural Mechanisms and Information Processing in Recognition Systems

    PubMed Central

    Ozaki, Mamiko; Hefetz, Abraham

    2014-01-01

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

  20. Neural Mechanisms and Information Processing in Recognition Systems.

    PubMed

    Ozaki, Mamiko; Hefetz, Abraham

    2014-01-01

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

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

    PubMed

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

    2015-01-01

    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. PMID:26046317

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  3. A Dynamic Time Warping Approach to Real-Time Activity Recognition for Food Preparation

    NASA Astrophysics Data System (ADS)

    Pham, Cuong; Plötz, Thomas; Olivier, Patrick

    We present a dynamic time warping based activity recognition system for the analysis of low-level food preparation activities. Accelerometers embedded into kitchen utensils provide continuous sensor data streams while people are using them for cooking. The recognition framework analyzes frames of contiguous sensor readings in real-time with low latency. It thereby adapts to the idiosyncrasies of utensil use by automatically maintaining a template database. We demonstrate the effectiveness of the classification approach by a number of real-world practical experiments on a publically available dataset. The adaptive system shows superior performance compared to a static recognizer. Furthermore, we demonstrate the generalization capabilities of the system by gradually reducing the amount of training samples. The system achieves excellent classification results even if only a small number of training samples is available, which is especially relevant for real-world scenarios.

  4. Bacterial recognition pathways that lead to inflammasome activation

    PubMed Central

    Storek, Kelly M; Monack, Denise M

    2015-01-01

    Inflammasomes are multi-protein signaling platforms that upon activation trigger the maturation of the pro-inflammatory cytokines, interleukin-1β (IL-1β) and IL-18, and cell death. Inflammasome sensors detect microbial and host-derived molecules. Here, we review the mechanisms of inflammasome activation triggered by bacterial infection, primarily focusing on two model intracellular bacterial pathogens, Francisella novicida and Salmonella typhimurium. We discuss the complex relationship between bacterial recognition through direct and indirect detection by inflammasome sensors. We highlight regulation mechanisms that potentiate or limit inflammasome activation. We discuss the importance of caspase-1 and caspase-11 in host defense, and we examine the downstream consequences of inflammasome activation within the context of bacterial infections. PMID:25879288

  5. Demonstration of the ULTOR target recognition and tracking system

    NASA Astrophysics Data System (ADS)

    Hartman, Richard L.; Farr, Keith B.

    2003-08-01

    Advanced Optical Systems has developed the world's smallest and lowest cost, fully functional target recognition and tracking system. The heart of the ULTOR target recognition and tracking system is an optical correlator. The system includes real-time preprocessing, large filter stores, filter management logic, correlation detection and thresholding, correlation tracking, and data output. It is self contained, receiving operational commands as an Internet appliance. We will present a demonstration of some of the capabilities of the system using live video signals and real target models. The ULTOR system has wide application in both military and commercial settings. The Navy is considering use of the ULTOR system in several programs, including missile systems and unmanned aerial vehicles.

  6. Arousal recognition system based on heartbeat dynamics during auditory elicitation.

    PubMed

    Nardelli, Mimma; Valenza, Gaetano; Greco, Alberto; Lanata, Antonio; Scilingo, Enzo Pasquale

    2015-08-01

    This study reports on the recognition of different arousal levels, elicited by affective sounds, performed using estimates of autonomic nervous system dynamics. Specifically, as a part of the circumplex model of affect, arousal levels were recognized by properly combining information gathered from standard and nonlinear analysis of heartbeat dynamics, which was derived from the electrocardiogram (ECG). Affective sounds were gathered from the International Affective Digitized Sound System and grouped into four different levels of arousal. A group of 27 healthy volunteers underwent such elicitation while ECG signals were continuously recorded. Results showed that a quadratic discriminant classifier, as applied implementing a leave-one-subject-out procedure, achieved a recognition accuracy of 84.26%. Moreover, this study confirms the crucial role of heartbeat nonlinear dynamics for emotion recognition, hereby estimated through lagged Poincare plots. PMID:26737686

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

    NASA Astrophysics Data System (ADS)

    Dux, David Lee

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

  8. Development of a sonar-based object recognition system

    NASA Astrophysics Data System (ADS)

    Ecemis, Mustafa Ihsan

    2001-02-01

    Sonars are used extensively in mobile robotics for obstacle detection, ranging and avoidance. However, these range-finding applications do not exploit the full range of information carried in sonar echoes. In addition, mobile robots need robust object recognition systems. Therefore, a simple and robust object recognition system using ultrasonic sensors may have a wide range of applications in robotics. This dissertation develops and analyzes an object recognition system that uses ultrasonic sensors of the type commonly found on mobile robots. Three principal experiments are used to test the sonar recognition system: object recognition at various distances, object recognition during unconstrained motion, and softness discrimination. The hardware setup, consisting of an inexpensive Polaroid sonar and a data acquisition board, is described first. The software for ultrasound signal generation, echo detection, data collection, and data processing is then presented. Next, the dissertation describes two methods to extract information from the echoes, one in the frequency domain and the other in the time domain. The system uses the fuzzy ARTMAP neural network to recognize objects on the basis of the information content of their echoes. In order to demonstrate that the performance of the system does not depend on the specific classification method being used, the K- Nearest Neighbors (KNN) Algorithm is also implemented. KNN yields a test accuracy similar to fuzzy ARTMAP in all experiments. Finally, the dissertation describes a method for extracting features from the envelope function in order to reduce the dimension of the input vector used by the classifiers. Decreasing the size of the input vectors reduces the memory requirements of the system and makes it run faster. It is shown that this method does not affect the performance of the system dramatically and is more appropriate for some tasks. The results of these experiments demonstrate that sonar can be used to develop

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

    PubMed Central

    Knudsen, Daniel P.

    2013-01-01

    Sensory systems are dynamic. They must process a wide range of natural signals that facilitate adaptive behaviors in a manner that depends on an organism's constantly changing goals. A full understanding of the sensory physiology that underlies adaptive natural behaviors must therefore account for the activity of sensory systems in light of these behavioral goals. Here we present a novel technique that combines in vivo electrophysiological recording from awake, freely moving songbirds with operant conditioning techniques that allow control over birds' recognition of conspecific song, a widespread natural behavior in songbirds. We show that engaging in a vocal recognition task alters the response properties of neurons in the caudal mesopallium (CM), an avian analog of mammalian auditory cortex, in European starlings. Compared with awake, passive listening, active engagement of subjects in an auditory recognition task results in neurons responding to fewer song stimuli and a decrease in the trial-to-trial variability in their driven firing rates. Mean firing rates also change during active recognition, but not uniformly. Relative to nonengaged listening, active recognition causes increases in the driven firing rates in some neurons, decreases in other neurons, and stimulus-specific changes in other neurons. These changes lead to both an increase in stimulus selectivity and an increase in the information conveyed by the neurons about the animals' behavioral task. This study demonstrates the behavioral dependence of neural responses in the avian auditory forebrain and introduces the starling as a model for real-time monitoring of task-related neural processing of complex auditory objects. PMID:23303858

  10. 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

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

    PubMed

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

    2014-01-01

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

  12. A Compact Prototype of an Optical Pattern Recognition System

    NASA Technical Reports Server (NTRS)

    Jin, Y.; Liu, H. K.; Marzwell, N. I.

    1996-01-01

    In the Technology 2006 Case Studies/Success Stories presentation, we will describe and demonstrate a prototype of a compact optical pattern recognition system as an example of a successful technology transfer and continuuing development of state-of-the-art know-how by the close collaboration among government, academia, and small business via the NASA SBIR program. The prototype consists of a complete set of optical pattern recognition hardware with multi-channel storage and retrieval capability that is compactly configured inside a portable 1'X 2'X 3' aluminum case.

  13. New taste sensor system combined with chaotic recognition

    NASA Astrophysics Data System (ADS)

    Hu, Jie; Wang, Ping; Li, Rong

    2001-09-01

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

  14. 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. PMID:25571347

  15. 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.

  16. 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.

  17. 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.…

  18. 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.

  19. Extraction and Classification of Multichannel Electromyographic Activation Trajectories for Hand Movement Recognition.

    PubMed

    AbdelMaseeh, Meena; Chen, Tsu-Wei; Stashuk, Daniel W

    2016-06-01

    This paper proposes a system for hand movement recognition using multichannel electromyographic (EMG) signals obtained from the forearm surface. This system can be used to control prostheses or to provide inputs for a wide range of human computer interface systems. In this work, the hand movement recognition problem is formulated as a multi-class distance based classification of multi-dimensional sequences. More specifically, the extraction of multi-channel EMG activation trajectories underlying hand movements, and classifying the extracted trajectories using a metric based on multi-dimensional dynamic time warping are investigated. The developed methods are evaluated using the publicly available NINAPro database comprised of 40 different hand movements performed by 40 subjects. The average movement error rate obtained across the 40 subjects is 0.09±0.047. The low error rate demonstrates the efficacy of the proposed trajectory extraction method and the discriminability of the utilized distance metric. PMID:26099148

  20. 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.

  1. Object recognition in Williams syndrome: Uneven ventral stream activation

    PubMed Central

    O’Hearn, Kirsten; Roth, Jennifer K.; Courtney, Susan M.; Luna, Beatriz; Street, Whitney; Terwillinger, Robert; Landau, Barbara

    2010-01-01

    Williams syndrome (WS) is a genetic disorder associated with severe visuospatial deficits, relatively strong language skills, heightened social interest, and increased attention to faces. On the basis of the visuospatial impairments, this disorder has been characterized primarily as a deficit of the dorsal stream, the occipitoparietal brain regions that subserve visuospatial processing. However, some evidence indicates that this disorder may also affect the development of the ventral stream, the occipitotemporal cortical regions that subserve face and object recognition. The present studies examined ventral stream function in WS, with the hypothesis that faces would produce a relatively more mature pattern of ventral occipitotemporal cortical activation, relative to other objects that are also represented across these visual areas. We compared functional magnetic resonance imaging activation patterns during viewing of human faces, cat faces, houses and shoes in individuals with WS (age 14–27), typically developing 6–9 year olds (matched approximately on mental age), and typically developing 14–26 year olds (matched on chronological age). Typically developing individuals exhibited changes in the pattern of activation over age, consistent with previous reports. The ventral stream topography of the WS individuals differed from both control groups, however, reflecting the same level of activation to face stimuli as chronological age matches, but less activation to house stimuli than either mental age or chronological age matches. We discuss the possible causes of this unusual topography and implications for understanding the behavioral profile of people with WS. PMID:21477194

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

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Blasch, Erik

    2014-06-01

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

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

    NASA Astrophysics Data System (ADS)

    Miller, Richard L.

    1991-03-01

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

  4. Robot Command Interface Using an Audio-Visual Speech Recognition System

    NASA Astrophysics Data System (ADS)

    Ceballos, Alexánder; Gómez, Juan; Prieto, Flavio; Redarce, Tanneguy

    In recent years audio-visual speech recognition has emerged as an active field of research thanks to advances in pattern recognition, signal processing and machine vision. Its ultimate goal is to allow human-computer communication using voice, taking into account the visual information contained in the audio-visual speech signal. This document presents a command's automatic recognition system using audio-visual information. The system is expected to control the laparoscopic robot da Vinci. The audio signal is treated using the Mel Frequency Cepstral Coefficients parametrization method. Besides, features based on the points that define the mouth's outer contour according to the MPEG-4 standard are used in order to extract the visual speech information.

  5. 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.

  6. A pattern recognition system for JPEG steganography detection

    NASA Astrophysics Data System (ADS)

    Chen, C. L. Philip; Chen, Mei-Ching; Agaian, Sos; Zhou, Yicong; Roy, Anuradha; Rodriguez, Benjamin M.

    2012-10-01

    This paper builds up a pattern recognition system to detect anomalies in JPEG images, especially steganographic content. The system consists of feature generation, feature ranking and selection, feature extraction, and pattern classification. These processes tend to capture image characteristics, reduce the problem dimensionality, eliminate the noise inferences between features, and further improve classification accuracies on clean and steganography JPEG images. Based on the discussion and analysis of six popular JPEG steganography methods, the entire recognition system results in higher classification accuracies between clean and steganography classes compared to merely using individual feature subset for JPEG steganography detection. The strength of feature combination and preprocessing has been integrated even when a small amount of information is embedded. The work demonstrated in this paper is extensible and can be improved by integrating various new and current techniques.

  7. 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

  8. Speech recognition control system and method

    SciTech Connect

    Lemelson, J.H.

    1986-08-12

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

  9. Camera characterization for face recognition under active near-infrared illumination

    NASA Astrophysics Data System (ADS)

    Gernoth, Thorsten; Grigat, Rolf-Rainer

    2010-01-01

    Active near-infrared illumination may be used in a face recognition system to achieve invariance to changes of the visible illumination. Another benefit of active near-infrared illumination is the bright pupil effect which can be used to assist eye detection. But long time exposure to near-infrared radiation is hazardous to the eyes. The level of illumination is therefore limited by potentially harmful effects to the eyes. Image sensors for face recognition under active near-infrared illumination have therefore to be carefully selected to provide optimal image quality in the desired field of application. A model of the active illumination source is introduced. Safety issues with regard to near-infrared illumination are addressed using this model and a radiometric analysis. From the illumination model requirements on suitable imaging sensors are formulated. Standard image quality metrics are used to assess the imaging device performance under application typical conditions. The characterization of image quality is based on measurements of the Opto-Electronic Conversion Function, Modulation Transfer Function and noise. A methodology to select an image sensor for the desired field of application is given. Two cameras with low-cost image sensors are characterized using the key parameters that influence the image quality for face recognition.

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

    NASA Astrophysics Data System (ADS)

    Batchellor, M. P.

    1981-03-01

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

  11. 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. PMID:23823307

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

    PubMed

    Wang, Lukun

    2016-01-01

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

  13. 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.

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

    PubMed

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

    2014-05-01

    Activity recognition based on mobile embedded accelerometer is very important for developing human-centric pervasive applications such as healthcare, personalized recommendation and so on. However, the distribution of accelerometer data is heavily affected by varying users. The performance will degrade when the model trained on one person is used to others. To solve this problem, we propose a fast and accurate cross-person activity recognition model, known as TransRKELM (Transfer learning Reduced Kernel Extreme Learning Machine) which uses RKELM (Reduced Kernel Extreme Learning Machine) to realize initial activity recognition model. In the online phase OS-RKELM (Online Sequential Reduced Kernel Extreme Learning Machine) is applied to update the initial model and adapt the recognition model to new device users based on recognition results with high confidence level efficiently. Experimental results show that, the proposed model can adapt the classifier to new device users quickly and obtain good recognition performance. PMID:24513850

  15. 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

  16. 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

  17. 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

  18. 3D multi-spectrum sensor system with face recognition.

    PubMed

    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

  19. A Highly Accurate Face Recognition System Using Filtering Correlation

    NASA Astrophysics Data System (ADS)

    Watanabe, Eriko; Ishikawa, Sayuri; Kodate, Kashiko

    2007-09-01

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

  20. 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. PMID:25016308

  1. A Spiking Neural Network System for Robust Sequence Recognition.

    PubMed

    Yu, Qiang; Yan, Rui; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

    2016-03-01

    This paper proposes a biologically plausible network architecture with spiking neurons for sequence recognition. This architecture is a unified and consistent system with functional parts of sensory encoding, learning, and decoding. This is the first systematic model attempting to reveal the neural mechanisms considering both the upstream and the downstream neurons together. The whole system is a consistent temporal framework, where the precise timing of spikes is employed for information processing and cognitive computing. Experimental results show that the system is competent to perform the sequence recognition, being robust to noisy sensory inputs and invariant to changes in the intervals between input stimuli within a certain range. The classification ability of the temporal learning rule used in the system is investigated through two benchmark tasks that outperform the other two widely used learning rules for classification. The results also demonstrate the computational power of spiking neurons over perceptrons for processing spatiotemporal patterns. In summary, the system provides a general way with spiking neurons to encode external stimuli into spatiotemporal spikes, to learn the encoded spike patterns with temporal learning rules, and to decode the sequence order with downstream neurons. The system structure would be beneficial for developments in both hardware and software. PMID:25879976

  2. 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.

  3. 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.

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

    PubMed Central

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

    2008-01-01

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

  5. Detection of prosodics by using a speech recognition system

    NASA Astrophysics Data System (ADS)

    Hupp, N. A.

    1991-07-01

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

  6. 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.

  7. 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

  8. 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.

  9. Electronic system with memristive synapses for pattern recognition.

    PubMed

    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

  10. 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

    2011-01-01

    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

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

    ERIC Educational Resources Information Center

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

    2007-01-01

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

  12. A speech recognition system based on hybrid wavelet network including a fuzzy decision support system

    NASA Astrophysics Data System (ADS)

    Jemai, Olfa; Ejbali, Ridha; Zaied, Mourad; Ben Amar, Chokri

    2015-02-01

    This paper aims at developing a novel approach for speech recognition based on wavelet network learnt by fast wavelet transform (FWN) including a fuzzy decision support system (FDSS). Our contributions reside in, first, proposing a novel learning algorithm for speech recognition based on the fast wavelet transform (FWT) which has many advantages compared to other algorithms and in which major problems of the previous works to compute connection weights were solved. They were determined by a direct solution which requires computing matrix inversion, which may be intensive. However, the new algorithm was realized by the iterative application of FWT to compute connection weights. Second, proposing a new classification way for this speech recognition system. It operated a human reasoning mode employing a FDSS to compute similarity degrees between test and training signals. Extensive empirical experiments were conducted to compare the proposed approach with other approaches. Obtained results show that the new speech recognition system has a better performance than previously established ones.

  13. 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.

  14. 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.

  15. 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...

  16. Phonological Activation in Multi-Syllabic Sord Recognition

    ERIC Educational Resources Information Center

    Lee, Chang H.

    2007-01-01

    Three experiments were conducted to test the phonological recoding hypothesis in visual word recognition. Most studies on this issue have been conducted using mono-syllabic words, eventually constructing various models of phonological processing. Yet in many languages including English, the majority of words are multi-syllabic words. English…

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

    PubMed

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

    2015-01-01

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

  18. 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

  19. 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. PMID:25148563

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

    PubMed

    Liu, Chung-Tse; Chan, Chia-Tai

    2016-01-01

    Sufficient physical activity can reduce many adverse conditions and contribute to a healthy life. Nevertheless, inactivity is prevalent on an international scale. Improving physical activity is an essential concern for public health. Reminders that help people change their health behaviors are widely applied in health care services. However, timed-based reminders deliver periodic prompts suffer from flexibility and dependency issues which may decrease prompt effectiveness. We propose a fuzzy logic prompting mechanism, Accumulated Activity Effective Index Reminder (AAEIReminder), based on pattern recognition and activity effective analysis to manage physical activity. AAEIReminder recognizes activity levels using a smartphone-embedded sensor for pattern recognition and analyzing the amount of physical activity in activity effective analysis. AAEIReminder can infer activity situations such as the amount of physical activity and days spent exercising through fuzzy logic, and decides whether a prompt should be delivered to a user. This prompting system was implemented in smartphones and was used in a short-term real-world trial by seventeenth participants for validation. The results demonstrated that the AAEIReminder is feasible. The fuzzy logic prompting mechanism can deliver prompts automatically based on pattern recognition and activity effective analysis. AAEIReminder provides flexibility which may increase the prompts' efficiency. PMID:27548184

  1. 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

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

    PubMed

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

    2016-08-01

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

  3. 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. PMID:25868676

  4. 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.

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

    SciTech Connect

    Zheng, Yufeng

    2014-12-23

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

  6. Posture and Activity Recognition and Energy Expenditure Prediction in a Wearable Platform

    PubMed Central

    Sazonov, Edward; Hegde, Nagaraj; Browning, Raymond C.; Melanson, Edward L.; Sazonova, Nadezhda A.

    2015-01-01

    The use of wearable sensors coupled with the processing power of mobile phones may be an attractive way to provide real-time feedback about physical activity and energy expenditure (EE). Here we describe the use of a shoe-based wearable sensor system (SmartShoe) with a mobile phone for real-time recognition of various postures/physical activities and the resulting EE. To deal with processing power and memory limitations of the phone, we compare use of Support Vector Machines (SVM), Multinomial Logistic Discrimination (MLD), and Multi-Layer Perceptrons (MLP) for posture and activity classification followed by activity-branched EE estimation. The algorithms were validated using data from 15 subjects who performed up to 15 different activities of daily living during a four-hour stay in a room calorimeter. MLD and MLP demonstrated activity classification accuracy virtually identical to SVM (~95%), while reducing the running time and the memory requirements by a factor of >103. Comparison of perminute EE estimation using activity-branched models resulted in accurate EE prediction (RMSE=0.78 kcal/min for SVM and MLD activity classification, 0.77 kcal/min for MLP, vs. RMSE of 0.75 kcal/min for manual annotation). These results suggest that low-power computational algorithms can be successfully used for real-time physical activity monitoring and EE prediction on a wearable platform. PMID:26011870

  7. 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.

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

    PubMed

    Ren, Bo; Dong, Hai; Ramström, Olof

    2014-05-01

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

  9. A Primitive-Based 3D Object Recognition System

    NASA Astrophysics Data System (ADS)

    Dhawan, Atam P.

    1988-08-01

    A knowledge-based 3D object recognition system has been developed. The system uses the hierarchical structural, geometrical and relational knowledge in matching the 3D object models to the image data through pre-defined primitives. The primitives, we have selected, to begin with, are 3D boxes, cylinders, and spheres. These primitives as viewed from different angles covering complete 3D rotation range are stored in a "Primitive-Viewing Knowledge-Base" in form of hierarchical structural and relational graphs. The knowledge-based system then hypothesizes about the viewing angle and decomposes the segmented image data into valid primitives. A rough 3D structural and relational description is made on the basis of recognized 3D primitives. This description is now used in the detailed high-level frame-based structural and relational matching. The system has several expert and knowledge-based systems working in both stand-alone and cooperative modes to provide multi-level processing. This multi-level processing utilizes both bottom-up (data-driven) and top-down (model-driven) approaches in order to acquire sufficient knowledge to accept or reject any hypothesis for matching or recognizing the objects in the given image.

  10. 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.

  11. Study on pattern recognition method based on fiber optic perimeter system

    NASA Astrophysics Data System (ADS)

    Xu, Haiyan; Zhang, Xuewu; Zhang, Zhuo; Li, Min

    2015-10-01

    All-fiber interferometer sensor system is a new type of system, which could be used in long-distance, strong-EMI condition for monitoring and inspection. A fiber optic perimeter detection system based on all-fiber interferometric sensor is proposed, through the back-end analysis, processing and intelligent identification, which can distinguish effects of different intrusion activities. In this paper, the universal steps in triggering pattern recognition is introduced, which includes signal characteristics extracting by accurate endpoint detecting, templates establishing by training, and pattern matching. By training the samples acquired in the laboratory, this paper uses the wavelet transformation to decompose the detection signals of the intrusion activities into sub-signals in different frequency bands with multi-resolution analysis. Then extracts the features of the above mentioned intrusions signals by frequency band energy and wavelet information entropy and the system could recognize the intrusion activities occurred along the perimeter sensors. Experiment results show that the proposed method for the perimeter is able to differentiate intrusion signals from ambient noises such as windy and walk effectively. What's more, the recognition rate of the system is improved while deduced the false alarm rate, the approach is proved by large practical experiment and project.

  12. 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.

  13. 42 CFR 403.322 - Termination of agreements for Medicare recognition of State systems.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 42 Public Health 2 2012-10-01 2012-10-01 false Termination of agreements for Medicare recognition of State systems. 403.322 Section 403.322 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES... Reimbursement Control Systems § 403.322 Termination of agreements for Medicare recognition of State systems....

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

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 42 Public Health 2 2013-10-01 2013-10-01 false Termination of agreements for Medicare recognition of State systems. 403.322 Section 403.322 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES... Reimbursement Control Systems § 403.322 Termination of agreements for Medicare recognition of State systems....

  15. 42 CFR 403.322 - Termination of agreements for Medicare recognition of State systems.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 42 Public Health 2 2011-10-01 2011-10-01 false Termination of agreements for Medicare recognition of State systems. 403.322 Section 403.322 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES... Reimbursement Control Systems § 403.322 Termination of agreements for Medicare recognition of State systems....

  16. 42 CFR 403.322 - Termination of agreements for Medicare recognition of State systems.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

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

  17. 42 CFR 403.322 - Termination of agreements for Medicare recognition of State systems.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 42 Public Health 2 2014-10-01 2014-10-01 false Termination of agreements for Medicare recognition of State systems. 403.322 Section 403.322 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES... Reimbursement Control Systems § 403.322 Termination of agreements for Medicare recognition of State systems....

  18. 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.

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

    PubMed

    Dubois, Amandine; Charpillet, François

    2013-01-01

    Fall detection remains today an open issue for improving elderly people security. It is all the more pertinent today when more and more elderly people stay longer and longer at home. In this paper, we propose a method to detect fall using a system made up of RGB-Depth cameras. The major benefit of our approach is its low cost and the fact that the system is easy to distribute and install. In few words, the method is based on the detection in real time of the center of mass of any mobile object or person accurately determining its position in the 3D space and its velocity. We demonstrate in this paper that this information is adequate and robust enough for labeling the activity of a person among 8 possible situations. An evaluation has been conducted within a real smart environment with 26 subjects which were performing any of the eight activities (sitting, walking, going up, squatting, lying on a couch, falling, bending and lying down). Seven out of these eight activities were correctly detected among which falling which was detected without false positives. PMID:24110775

  20. Influence of time and length size feature selections for human activity sequences recognition.

    PubMed

    Fang, Hongqing; Chen, Long; Srinivasan, Raghavendiran

    2014-01-01

    In this paper, Viterbi algorithm based on a hidden Markov model is applied to recognize activity sequences from observed sensors events. Alternative features selections of time feature values of sensors events and activity length size feature values are tested, respectively, and then the results of activity sequences recognition performances of Viterbi algorithm are evaluated. The results show that the selection of larger time feature values of sensor events and/or smaller activity length size feature values will generate relatively better results on the activity sequences recognition performances. PMID:24075148

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

    PubMed Central

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

    2014-01-01

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

  2. 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. PMID:20577589

  3. 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).

  4. Walking intrusion signal recognition method for fiber fence system

    NASA Astrophysics Data System (ADS)

    Fang, Nian; Wang, Lutang; Jia, Dongjian; Shan, Chao; Huang, Zhaoming

    2009-11-01

    A recognition method based on the gait characteristic for walking intrusion signal is presented. The gait characteristic of a normal walker in the nature state is an average gait period of 1.2s, in which a step period is about 0.6s and a foot touchdown time is about 0.2s. When a person walks fast or runs, the step period is reduced to about 0.4s and the foot touchdown time still keeps about 0.2s. It is included in the vibration signal caused by a walking intruder inevitably. So the detection system output signal caused by a human intrusion is intermittent and periodical. If a sensing system output waveform has a period of 0.3-0.75s and a duration time of 0.15-0.25s, the disturbance source can be adjudged as a human intrusion, not as an animal or other random one. The effectiveness of the proposed method is verified by the experimental results with an in-line Sagnac interferometer fiber fence system and a φ-OTDR intrusion detection system, respectively.

  5. Unravelling glucan recognition systems by glycome microarrays using the designer approach and mass spectrometry.

    PubMed

    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

    2015-04-01

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

  6. 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

  7. Realtime recognition of complex human daily activities using human motion and location data.

    PubMed

    Zhu, Chun; Sheng, Weihua

    2012-09-01

    Daily activity recognition is very useful in robot-assisted living systems. In this paper, we proposed a method to recognize complex human daily activities which consist of simultaneous body activities and hand gestures in an indoor environment. A wireless power-aware motion sensor node is developed which consists of a commercial orientation sensor, a wireless communication module, and a power management unit. To recognize complex daily activities, three motion sensor nodes are attached to the right thigh, the waist, and the right hand of a human subject, while an optical motion capture system is used to obtain his/her location information. A three-level dynamic Bayesian network (DBN) is implemented to model the intratemporal and intertemporal constraints among the location, body activity, and hand gesture. The body activity and hand gesture are estimated using a Bayesian filter and a short-time Viterbi algorithm, which reduces the computational complexity and memory usage. We conducted experiments in a mock apartment environment and the obtained results showed the effectiveness and accuracy of our method. PMID:22434793

  8. 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.

  9. 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;…

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

    PubMed

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

    2015-01-01

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

  11. Pattern recognition receptors and central nervous system repair

    PubMed Central

    Kigerl, Kristina A.; de Rivero Vaccari, Juan Pablo; Dietrich, W. Dalton

    2016-01-01

    Pattern recognition receptors (PRRs) are part of the innate immune response and were originally discovered for their role in recognizing pathogens by ligating specific pathogen associated molecular patterns (PAMPs) expressed by microbes. Now the role of PRRs in sterile inflammation is also appreciated, responding to endogenous stimuli referred to as “damage associated molecular patterns” (DAMPs) instead of PAMPs. The main families of PRRs include Toll-like receptors (TLRs), Nod-like receptors (NLRs), RIG-like receptors (RLRs), AIM2-like receptors (ALRs), and C-type lectin receptors. Broad expression of these PRRs in the CNS and the release of DAMPs in and around sites of injury suggest an important role for these receptor families in mediating post-injury inflammation. Considerable data now show that PRRs are among the first responders to CNS injury and activation of these receptors on microglia, neurons, and astrocytes triggers an innate immune response in the brain and spinal cord. Here we discuss how the various PRR families are activated and can influence injury and repair processes following CNS injury. PMID:25017883

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

    NASA Astrophysics Data System (ADS)

    Hirasawa, Kazuki; Sawada, Shinya; Saitoh, Atsushi

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

  13. [Research on Anti-Camouflaged Target System Based on Spectral Detection and Image Recognition].

    PubMed

    Wang, Bo; Gao, Yu-bin; Lu, Xu-tao

    2015-05-01

    To be able to quickly and efficiently identify Enemy camouflaged maneuvering targets in the wild environment, target recognition system was designed based on spectral detection technology and video target recognition method. System was composed of the visible light image acquisition module and static interferometer module. The system used image recognition technology to obtain two dimensional video images of measurement region, and through spectrum detection technology to identify targets. Ultimately, measured target was rebuilt on the corresponding position in the image, so the visual target recognition was realized. After the theoretical derivation, identifiable target function formula of the system was obtained, and based on the functional relationship to complete the quantitative experiments for target recognition. In the experiments, maneuvering target in the battlefield environment was simulated by a car. At different distances, the background was respectively selected to detect a flat wasteland, bushes and abandoned buildings. Obvious target, coated camouflage target and covered disguises target was respectively spectrum detection. Experimental results show that spectrum detection technology can overcome the shortcomings of unrecognized the camouflaged target by traditional image target recognition method. Testing background had some influence on spectrum detection results, and the continuity of the background was conducive to target recognition. Covered disguises target was the hardest to identify in various camouflage mode. As the distance between the target and the system increases, signal to noise ratio of the system was reduced. In summary, the system can achieve effective recognition of camouflaged targets to meet the design requirements. PMID:26415476

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

    PubMed

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

    2016-09-01

    A smart home is a home environment enriched with sensing, actuation, communication and computation capabilities which permits to adapt it to inhabitants preferences and requirements. Establishing a proper strategy of actuation on the home environment can require complex computational tasks on the sensed data. This is the case of activity recognition, which consists in retrieving high-level knowledge about what occurs in the home environment and about the behaviour of the inhabitants. The inherent complexity of this application domain asks for tools able to properly support the design and implementation phases. This paper proposes a framework for the design and implementation of smart home applications focused on activity recognition in home environments. The framework mainly relies on the Cloud-assisted Agent-based Smart home Environment (CASE) architecture offering basic abstraction entities which easily allow to design and implement Smart Home applications. CASE is a three layered architecture which exploits the distributed multi-agent paradigm and the cloud technology for offering analytics services. Details about how to implement activity recognition onto the CASE architecture are supplied focusing on the low-level technological issues as well as the algorithms and the methodologies useful for the activity recognition. The effectiveness of the framework is shown through a case study consisting of a daily activity recognition of a person in a home environment. PMID:27468841

  15. A unified framework for activity recognition-based behavior analysis and action prediction in smart homes.

    PubMed

    Fatima, Iram; Fahim, Muhammad; Lee, Young-Koo; Lee, Sungyoung

    2013-01-01

    In recent years, activity recognition in smart homes is an active research area due to its applicability in many applications, such as assistive living and healthcare. Besides activity recognition, the information collected from smart homes has great potential for other application domains like lifestyle analysis, security and surveillance, and interaction monitoring. Therefore, discovery of users common behaviors and prediction of future actions from past behaviors become an important step towards allowing an environment to provide personalized service. In this paper, we develop a unified framework for activity recognition-based behavior analysis and action prediction. For this purpose, first we propose kernel fusion method for accurate activity recognition and then identify the significant sequential behaviors of inhabitants from recognized activities of their daily routines. Moreover, behaviors patterns are further utilized to predict the future actions from past activities. To evaluate the proposed framework, we performed experiments on two real datasets. The results show a remarkable improvement of 13.82% in the accuracy on average of recognized activities along with the extraction of significant behavioral patterns and precise activity predictions with 6.76% increase in F-measure. All this collectively help in understanding the users" actions to gain knowledge about their habits and preferences. PMID:23435057

  16. A Unified Framework for Activity Recognition-Based Behavior Analysis and Action Prediction in Smart Homes

    PubMed Central

    Fatima, Iram; Fahim, Muhammad; Lee, Young-Koo; Lee, Sungyoung

    2013-01-01

    In recent years, activity recognition in smart homes is an active research area due to its applicability in many applications, such as assistive living and healthcare. Besides activity recognition, the information collected from smart homes has great potential for other application domains like lifestyle analysis, security and surveillance, and interaction monitoring. Therefore, discovery of users common behaviors and prediction of future actions from past behaviors become an important step towards allowing an environment to provide personalized service. In this paper, we develop a unified framework for activity recognition-based behavior analysis and action prediction. For this purpose, first we propose kernel fusion method for accurate activity recognition and then identify the significant sequential behaviors of inhabitants from recognized activities of their daily routines. Moreover, behaviors patterns are further utilized to predict the future actions from past activities. To evaluate the proposed framework, we performed experiments on two real datasets. The results show a remarkable improvement of 13.82% in the accuracy on average of recognized activities along with the extraction of significant behavioral patterns and precise activity predictions with 6.76% increase in F-measure. All this collectively help in understanding the users” actions to gain knowledge about their habits and preferences. PMID:23435057

  17. 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.

  18. Real-time hardware continuous speech recognition system

    SciTech Connect

    Peckham, J.; Green, J.; Canning, J.; Stephens, P.

    1982-01-01

    Using both parallel and pipelined processing techniques, matching up to several hundred words from a previously stored vocabulary of whole word templates is possible in real time. An efficient single pass dynamic programming algorithm is used to find the sequence of templates that best represents the input. Continuous recognition is achieved using a traceback technique on partial recognition results. The acoustic analysis contains a number of features to improve performance. In particular a novel noise compensation algorithm is briefly described. 6 references.

  19. 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.

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

    PubMed

    Gelsema, E S; Landeweerd, G H

    1981-09-01

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

  1. Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn.

    PubMed

    Wang, Chunmei; Zou, Junzhong; Zhang, Jian; Wang, Min; Wang, Rubin

    2010-09-01

    This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman-Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG. PMID:21886676

  2. Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn

    PubMed Central

    Zou, Junzhong; Zhang, Jian; Wang, Min; Wang, Rubin

    2010-01-01

    This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman–Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG. PMID:21886676

  3. 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

  4. A neural network based artificial vision system for licence plate recognition.

    PubMed

    Draghici, S

    1997-02-01

    This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%. PMID:9228583

  5. Speech recognition based on pattern recognition techniques

    NASA Astrophysics Data System (ADS)

    Rabiner, Lawrence R.

    1990-05-01

    Algorithms for speech recognition can be characterized broadly as pattern recognition approaches and acoustic phonetic approaches. To date, the greatest degree of success in speech recognition has been obtained using pattern recognition paradigms. The use of pattern recognition techniques were applied to the problems of isolated word (or discrete utterance) recognition, connected word recognition, and continuous speech recognition. It is shown that understanding (and consequently the resulting recognizer performance) is best to the simplest recognition tasks and is considerably less well developed for large scale recognition systems.

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

    ERIC Educational Resources Information Center

    Paul, James E., Jr.

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

  7. Active-Metal Template Synthesis of a Halogen-Bonding Rotaxane for Anion Recognition.

    PubMed

    Langton, Matthew J; Xiong, Yaoyao; Beer, Paul D

    2015-12-21

    The synthesis of an all-halogen-bonding rotaxane for anion recognition is achieved by using active-metal templation. A flexible bis-iodotriazole-containing macrocycle is exploited for the metal-directed rotaxane synthesis. Endotopic binding of a Cu(I) template facilitates an active-metal CuAAC iodotriazole axle formation reaction that captures the interlocked rotaxane product. Following copper-template removal, exotopic coordination of a more sterically demanding rhenium(I) complex induces an inversion in the conformation of the macrocycle component, directing the iodotriazole halogen-bond donors into the rotaxane's interlocked binding cavity to facilitate anion recognition. PMID:26500150

  8. Multi-font printed Mongolian document recognition system

    NASA Astrophysics Data System (ADS)

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

    2009-01-01

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

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

    PubMed Central

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

    2011-01-01

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

  10. 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. PMID:26663662

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

    PubMed

    Schreiner, Thomas; Göldi, Maurice; Rasch, Björn

    2015-11-01

    Neural oscillations in the theta band have repeatedly been implicated in successful memory encoding and retrieval. Several recent studies have shown that memory retrieval can be facilitated by reactivating memories during their consolidation during sleep. However, it is still unknown whether reactivation during sleep also enhances subsequent retrieval-related neural oscillations. We have recently demonstrated that foreign vocabulary cues presented during sleep improve later recall of the associated translations. Here, we examined the effect of cueing foreign vocabulary during sleep on oscillatory activity during subsequent recognition testing after sleep. We show that those words that were replayed during sleep after learning (cued words) elicited stronger centroparietal theta activity during recognition as compared to noncued words. The reactivation-induced increase in theta oscillations during later recognition testing might reflect a strengthening of individual memory traces and the integration of the newly learned words into the mental lexicon by cueing during sleep. PMID:26235609

  12. 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.

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

    PubMed

    Tsutsui, Neil D

    2013-01-01

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

  14. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2005-12-01

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

  16. A Presence-Based Context-Aware Chronic Stress Recognition System

    PubMed Central

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

    2012-01-01

    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. PMID:23202238

  17. 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

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

    PubMed

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

    2014-01-01

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

  19. 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.

  20. 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.

  1. 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. PMID:23981534

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

    SciTech Connect

    Xia, Junyi Mart, Christopher; Bayouth, John

    2014-01-15

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

  3. 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

  4. 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…

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

    ERIC Educational Resources Information Center

    Paap, Kenneth R.; And Others

    1982-01-01

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

  6. 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.

  7. 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

  8. 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.

  9. Transparent Stretchable Self-Powered Patchable Sensor Platform with Ultrasensitive Recognition of Human Activities.

    PubMed

    Hwang, Byeong-Ung; Lee, Ju-Hyuck; Trung, Tran Quang; Roh, Eun; Kim, Do-Il; Kim, Sang-Woo; Lee, Nae-Eung

    2015-09-22

    Monitoring of human activities can provide clinically relevant information pertaining to disease diagnostics, preventive medicine, care for patients with chronic diseases, rehabilitation, and prosthetics. The recognition of strains on human skin, induced by subtle movements of muscles in the internal organs, such as the esophagus and trachea, and the motion of joints, was demonstrated using a self-powered patchable strain sensor platform, composed on multifunctional nanocomposites of low-density silver nanowires with a conductive elastomer of poly(3,4-ethylenedioxythiophene):polystyrenesulfonate/polyurethane, with high sensitivity, stretchability, and optical transparency. The ultra-low-power consumption of the sensor, integrated with both a supercapacitor and a triboelectric nanogenerator into a single transparent stretchable platform based on the same nanocomposites, results in a self-powered monitoring system for skin strain. The capability of the sensor to recognize a wide range of strain on skin has the potential for use in new areas of invisible stretchable electronics for human monitoring. A new type of transparent, stretchable, and ultrasensitive strain sensor based on a AgNW/PEDOT:PSS/PU nanocomposite was developed. The concept of a self-powered patchable sensor system integrated with a supercapacitor and a triboelectric nanogenerator that can be used universally as an autonomous invisible sensor system was used to detect the wide range of strain on human skin. PMID:26277994

  10. Object recognition based on spatial active basis template

    NASA Astrophysics Data System (ADS)

    Peng, Shaowu; Xu, Jingcheng

    2011-11-01

    This article presents a method for the object classification that combines a generative template and a discriminative classifier. The method is a variant of the support vector machine (SVM), which uses Multiple Kernel Learning (MKL). The features are extracted from a generative template so called Active Basis template. Before using them for object classification, we construct a visual vocabulary by clustering a set of training features according to their orientations. To keep the spatial information, a "spatial pyramid" is used. The strength of this approach is that it combines the rich information encoded in the generative template, the Active Basis, with the discriminative power of the SVM algorithm. We show promising results of experiments for images from the LHI dataset.

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

    NASA Astrophysics Data System (ADS)

    Hassanzadeh, Hamed; Keyvanpour, Mohammadreza

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

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

    ERIC Educational Resources Information Center

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

    2014-01-01

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

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

    PubMed

    Parketny, Joanna; Towler, John; Eimer, Martin

    2015-08-01

    Individuals with developmental prosopagnosia (DP) are strongly impaired in recognizing faces, but the causes of this deficit are not well understood. We employed event-related brain potentials (ERPs) to study the time-course of neural processes involved in the recognition of previously unfamiliar faces in DPs and in age-matched control participants with normal face recognition abilities. Faces of different individuals were presented sequentially in one of three possible views, and participants had to detect a specific Target Face ("Joe"). EEG was recorded during task performance to Target Faces, Nontarget Faces, or the participants' Own Face (which had to be ignored). The N250 component was measured as a marker of the match between a seen face and a stored representation in visual face memory. The subsequent P600f was measured as an index of attentional processes associated with the conscious awareness and recognition of a particular face. Target Faces elicited reliable N250 and P600f in the DP group, but both of these components emerged later in DPs than in control participants. This shows that the activation of visual face memory for previously unknown learned faces and the subsequent attentional processing and conscious recognition of these faces are delayed in DP. N250 and P600f components to Own Faces did not differ between the two groups, indicating that the processing of long-term familiar faces is less affected in DP. However, P600f components to Own Faces were absent in two participants with DP who failed to recognize their Own Face during the experiment. These results provide new evidence that face recognition deficits in DP may be linked to a delayed activation of visual face memory and explicit identity recognition mechanisms. PMID:26169316

  14. Human L-ficolin, a Recognition Molecule of the Lectin Activation Pathway of Complement, Activates Complement by Binding to Pneumolysin, the Major Toxin of Streptococcus pneumoniae

    PubMed Central

    Ali, Youssif M.; Kenawy, Hany I.; Muhammad, Adnan; Sim, Robert B.

    2013-01-01

    The complement system is an essential component of the immune response, providing a critical line of defense against different pathogens including S. pneumoniae. Complement is activated via three distinct pathways: the classical (CP), the alternative (AP) and the lectin pathway (LP). The role of Pneumolysin (PLY), a bacterial toxin released by S. pneumoniae, in triggering complement activation has been studied in vitro. Our results demonstrate that in both human and mouse sera complement was activated via the CP, initiated by direct binding of even non-specific IgM and IgG3 to PLY. Absence of CP activity in C1q−/− mouse serum completely abolished any C3 deposition. However, C1q depleted human serum strongly opsonized PLY through abundant deposition of C3 activation products, indicating that the LP may have a vital role in activating the human complement system on PLY. We identified that human L-ficolin is the critical LP recognition molecule that drives LP activation on PLY, while all of the murine LP recognition components fail to bind and activate complement on PLY. This work elucidates the detailed interactions between PLY and complement and shows for the first time a specific role of the LP in PLY-mediated complement activation in human serum. PMID:24349316

  15. 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. PMID:25879962

  16. 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.

  17. Sensor-Based Human Activity Recognition in a Multi-user Scenario

    NASA Astrophysics Data System (ADS)

    Wang, Liang; Gu, Tao; Tao, Xianping; Lu, Jian

    Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activities from sensor readings in a smart home environment. We develop a multimodal sensing platform and present a theoretical framework to recognize both single-user and multi-user activities. We conduct our trace collection done in a smart home, and evaluate our framework through experimental studies. Our experimental result shows that we achieve an average accuracy of 85.46% with CHMMs.

  18. 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. PMID:24123535

  19. 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

  20. The development of the speaker independent ARM continuous speech recognition system

    NASA Astrophysics Data System (ADS)

    Russell, M. J.

    1992-01-01

    The development of a speaker dependent continuous speech recognition system based on phoneme level hidden Markov models is described. The system is part of the Airborne Reconnaissance Mission (ARM) project, that aims at reaching the accurate recognition of continuously spoken airborne reconnaissance reports using a speech recognition system and Markov models. The system is configured to recognize continuously spoken airborne reconnaissance reports, a task which involves a vocabulary of approximately 500 words. On a test set of speech from 80 male subjects, the final system achieves a word accuracy of 74.1 percent with no explicit syntactic constraints. The evolution of the speaker independent ARM system, in terms of the performance of its various versions of the evaluation set, is summarized. Comparison of the final versions of the speaker dependent and speaker independent ARM systems shows that many of the empirically derived parameters are similar on both systems.

  1. Recognition of DNA insertion/deletion mismatches by an activity in Saccharomyces cerevisiae.

    PubMed

    Miret, J J; Parker, B O; Lahua, R S

    1996-02-15

    An activity in nuclear extracts of S.cerevisiae binds specifically to heteroduplexes containing four to nine extra bases in one strand. The specificity of this activity (IMR, for insertion mismatch recognition) in band shift assays was confirmed by competition experiments. IMR is biochemically and genetically distinct from the MSH2 dependent, single base mismatch binding activity. The two activities migrate differently during electrophoresis, they are differentially competable and their spectra of mispair binding are distinct. Furthermore, IMR activity is observed in extracts from an msh2- msh3- msh4- strain. IMR exhibits specificity for insertion mispairs in two different sequence contexts. Binding is influenced by the structure of the mismatch since an insertion with a hairpin configuration is not recognized by this activity. IMR does not result from single-strand binding because single-stranded probes to not yield IMR complex and single-stranded competitors are unable to displace insertion heteroduplexes from the complex. Similar results with intrinsically bent duplexes make it unlikely that recognition is conferred by a bend alone. Heteroduplexes bound by IMR do not contain any obvious damage. These findings are consistent with the idea that yeast contains a distinct recognition factor, IMR that is specific for insertion/deletion mismatches. PMID:8604316

  2. 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

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

    PubMed

    Blumenfeld, Henrike K; Marian, Viorica

    2013-01-01

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

  4. 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

  5. Activity Recognition Using Community Data to Complement Small Amounts of Labeled Instances.

    PubMed

    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

  6. 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

  7. Pathogen Recognition and Activation of the Innate Immune Response in Zebrafish

    PubMed Central

    van der Vaart, Michiel; Spaink, Herman P.; Meijer, Annemarie H.

    2012-01-01

    The zebrafish has proven itself as an excellent model to study vertebrate innate immunity. It presents us with possibilities for in vivo imaging of host-pathogen interactions which are unparalleled in mammalian model systems. In addition, its suitability for genetic approaches is providing new insights on the mechanisms underlying the innate immune response. Here, we review the pattern recognition receptors that identify invading microbes, as well as the innate immune effector mechanisms that they activate in zebrafish embryos. We compare the current knowledge about these processes in mammalian models and zebrafish and discuss recent studies using zebrafish infection models that have advanced our general understanding of the innate immune system. Furthermore, we use transcriptome analysis of zebrafish infected with E. tarda, S. typhimurium, and M. marinum to visualize the gene expression profiles resulting from these infections. Our data illustrate that the two acute disease-causing pathogens, E. tarda and S. typhimurium, elicit a highly similar proinflammatory gene induction profile, while the chronic disease-causing pathogen, M. marinum, induces a weaker and delayed innate immune response. PMID:22811714

  8. 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].

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

    PubMed

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-01

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

  10. 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

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

    NASA Astrophysics Data System (ADS)

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

    2011-06-01

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

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

    PubMed

    Deutsch, Markus; Burgsteiner, Harald

    2016-01-01

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

  13. 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

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

    PubMed

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

    2015-01-01

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

  15. Biomolecular recognition of antagonists by α7 nicotinic acetylcholine receptor: Antagonistic mechanism and structure-activity relationships studies.

    PubMed

    Peng, Wei; Ding, Fei

    2015-08-30

    As the key constituent of ligand-gated ion channels in the central nervous system, nicotinic acetylcholine receptors (nAChRs) and neurodegenerative diseases are strongly coupled in the human species. In recently years the developments of selective agonists by using nAChRs as the drug target have made a large progress, but the studies of selective antagonists are severely lacked. Currently these antagonists rest mainly on the extraction of partly natural products from some animals and plants; however, the production of these crude substances is quite restricted, and artificial synthesis of nAChR antagonists is still one of the completely new research fields. In the context of this manuscript, our primary objective was to comprehensively analyze the recognition patterns and the critical interaction descriptors between target α7 nAChR and a series of the novel compounds with potentially antagonistic activity by means of virtual screening, molecular docking and molecular dynamics simulation, and meanwhile these recognition reactions were also compared with the biointeraction of α7 nAChR with a commercially natural antagonist - methyllycaconitine. The results suggested clearly that there are relatively obvious differences of molecular structures between synthetic antagonists and methyllycaconitine, while the two systems have similar recognition modes on the whole. The interaction energy and the crucially noncovalent forces of the α7 nAChR-antagonists are ascertained according to the method of Molecular Mechanics/Generalized Born Surface Area. Several amino acid residues, such as B/Tyr-93, B/Lys-143, B/Trp-147, B/Tyr-188, B/Tyr-195, A/Trp-55 and A/Leu-118 played a major role in the α7 nAChR-antagonist recognition processes, in particular, residues B/Tyr-93, B/Trp-147 and B/Tyr-188 are the most important. These outcomes tally satisfactorily with the discussions of amino acid mutations. Based on the explorations of three-dimensional quantitative structure-activity

  16. Development of Biological Movement Recognition by Interaction between Active Basis Model and Fuzzy Optical Flow Division

    PubMed Central

    Loo, Chu Kiong

    2014-01-01

    Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003). Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human). Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach. PMID:24883361

  17. Development of biological movement recognition by interaction between active basis model and fuzzy optical flow division.

    PubMed

    Yousefi, Bardia; Loo, Chu Kiong

    2014-01-01

    Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003). Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human). Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach. PMID:24883361

  18. Voice activity detection for speaker verification systems

    NASA Astrophysics Data System (ADS)

    Borowski, Filip

    2008-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Najjar, Nayeff; Gupta, Shalabh

    2015-05-01

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

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

    PubMed

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

    2016-09-30

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

  1. 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.

  2. 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.

  3. Activity recognition of assembly tasks using body-worn microphones and accelerometers.

    PubMed

    Ward, Jamie A; Lukowicz, Paul; Tröster, Gerhard; Starner, Thad E

    2006-10-01

    In order to provide relevant information to mobile users, such as workers engaging in the manual tasks of maintenance and assembly, a wearable computer requires information about the user's specific activities. This work focuses on the recognition of activities that are characterized by a hand motion and an accompanying sound. Suitable activities can be found in assembly and maintenance work. Here, we provide an initial exploration into the problem domain of continuous activity recognition using on-body sensing. We use a mock "wood workshop" assembly task to ground our investigation. We describe a method for the continuous recognition of activities (sawing, hammering, filing, drilling, grinding, sanding, opening a drawer, tightening a vise, and turning a screwdriver) using microphones and three-axis accelerometers mounted at two positions on the user's arms. Potentially "interesting" activities are segmented from continuous streams of data using an analysis of the sound intensity detected at the two different locations. Activity classification is then performed on these detected segments using linear discriminant analysis (LDA) on the sound channel and hidden Markov models (HMMs) on the acceleration data. Four different methods at classifier fusion are compared for improving these classifications. Using user-dependent training, we obtain continuous average recall and precision rates (for positive activities) of 78 percent and 74 percent, respectively. Using user-independent training (leave-one-out across five users), we obtain recall rates of 66 percent and precision rates of 63 percent. In isolation, these activities were recognized with accuracies of 98 percent, 87 percent, and 95 percent for the user-dependent, user-independent, and user-adapted cases, respectively. PMID:16986539

  4. Concept Recognition in an Automatic Text-Processing System for the Life Sciences.

    ERIC Educational Resources Information Center

    Vleduts-Stokolov, Natasha

    1987-01-01

    Describes a system developed for the automatic recognition of biological concepts in titles of scientific articles; reports results of several pilot experiments which tested the system's performance; analyzes typical ambiguity problems encountered by the system; describes a disambiguation technique that was developed; and discusses future plans…

  5. Does viotin activate violin more than viocin? On the use of visual cues during visual-word recognition.

    PubMed

    Perea, Manuel; Panadero, Victoria

    2014-01-01

    The vast majority of neural and computational models of visual-word recognition assume that lexical access is achieved via the activation of abstract letter identities. Thus, a word's overall shape should play no role in this process. In the present lexical decision experiment, we compared word-like pseudowords like viotín (same shape as its base word: violín) vs. viocín (different shape) in mature (college-aged skilled readers), immature (normally reading children), and immature/impaired (young readers with developmental dyslexia) word-recognition systems. Results revealed similar response times (and error rates) to consistent-shape and inconsistent-shape pseudowords for both adult skilled readers and normally reading children - this is consistent with current models of visual-word recognition. In contrast, young readers with developmental dyslexia made significantly more errors to viotín-like pseudowords than to viocín-like pseudowords. Thus, unlike normally reading children, young readers with developmental dyslexia are sensitive to a word's visual cues, presumably because of poor letter representations. PMID:23948388

  6. 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

  7. 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

  8. Highly accurate recognition of human postures and activities through classification with rejection.

    PubMed

    Tang, Wenlong; Sazonov, Edward S

    2014-01-01

    Monitoring of postures and activities is used in many clinical and research applications, some of which may require highly reliable posture and activity recognition with desired accuracy well above 99% mark. This paper suggests a method for performing highly accurate recognition of postures and activities from data collected by a wearable shoe monitor (SmartShoe) through classification with rejection. Signals from pressure and acceleration sensors embedded in SmartShoe are used either as raw sensor data or after feature extraction. The Support vector machine (SVM) and multilayer perceptron (MLP) are used to implement classification with rejection. Unreliable observations are rejected by measuring the distance from the decision boundary and eliminating those observations that reside below rejection threshold. The results show a significant improvement (from 97.3% ± 2.3% to 99.8% ± 0.1%) in the classification accuracy after the rejection, using MLP with raw sensor data and rejecting 31.6% of observations. The results also demonstrate that MLP outperformed the SVM, and the classification accuracy based on raw sensor data was higher than the accuracy based on extracted features. The proposed approach will be especially beneficial in applications where high accuracy of recognition is desired while not all observations need to be assigned a class label. PMID:24403429

  9. 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

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

    PubMed

    Bourobou, Serge Thomas Mickala; Yoo, Younghwan

    2015-01-01

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

  11. 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

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

    PubMed

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

    2016-01-01

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

  13. 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.

  14. 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

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

    PubMed

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

    2013-11-01

    Earthworms Eisenia andrei, similarly to other invertebrates, rely on innate defense mechanisms based on the capability to recognize and respond to nonself. Here, we show a correlation between the expression of CCF, a crucial pattern-recognition receptor, and lysozyme, with enzyme activities in the gut of E. andrei earthworms following a microbial challenge. These data suggest that enzyme activities important for the release and recognition of molecular patterns by pattern-recognition molecules, as well as enzymes involved in effector pathways, are modulated during the microbial challenge. In particular, protease, laminarinase, and glucosaminidase activities were increased in parallel to up-regulated CCF and lysozyme expression. PMID:23999244

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

    PubMed

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

    2015-10-01

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

  17. '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

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

    PubMed

    Mak, Tak W

    2003-06-15

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

  19. Peptidoglycan Recognition Proteins kill bacteria by inducing suicide through protein-sensing two-component systems

    PubMed Central

    Kashyap, Des Raj; Wang, Minhui; Liu, Li-Hui; Boons, Geert-Jan; Gupta, Dipika; Dziarski, Roman

    2011-01-01

    Mammalian Peptidoglycan Recognition Proteins (PGRPs), similar to antimicrobial lectins, bind to bacterial cell wall and kill bacteria through an unknown mechanism. We show that PGRPs enter Gram-positive cell wall at the site of daughter cell separation during cell division. In Bacillus subtilis PGRPs activate the CssR-CssS two-component system that detects and disposes of misfolded proteins exported out of bacterial cells. This activation results in membrane depolarization, cessation of intracellular peptidoglycan, protein, RNA, and DNA synthesis, and production of hydroxyl radicals, which are responsible for bacterial death. PGRPs also bind to the outer membrane in Escherichia coli and activate functionally homologous CpxA-CpxR two-component system, which results in bacterial death. We excluded other potential bactericidal mechanisms (inhibition of extracellular peptidoglycan synthesis, hydrolysis of peptidoglycan, and membrane permeabilization). Thus we reveal a novel mechanism of bacterial killing by innate immunity proteins that bind to cell wall or outer membrane and exploit bacterial stress defense response to kill bacteria. PMID:21602801

  20. 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. PMID:24978803

  1. The role of asymmetric transfer in the evaluation of voice generation and recognition systems

    NASA Astrophysics Data System (ADS)

    Damos, D. L.

    1987-02-01

    The results of five experiments examining the effect of voice generation and recognition systems on dual task performance are presented. The extent to which asymmetric transfer biased the data in three of these experiments is determined by using statistical techniques and by comparing the data to the results of between subjects experiments. Generally, subjects performed task combinations better when stimuli for one of the tasks was presented auditorily using a voice generation system rather than visually on a display screen. In contrast, the use of a voice recognition system did not result in better dual task performance than the use of more conventional input devices.

  2. Designing a robust activity recognition framework for health and exergaming using wearable sensors.

    PubMed

    Alshurafa, Nabil; Xu, Wenyao; Liu, Jason J; Huang, Ming-Chun; Mortazavi, Bobak; Roberts, Christian K; Sarrafzadeh, Majid

    2014-09-01

    Detecting human activity independent of intensity is essential in many applications, primarily in calculating metabolic equivalent rates and extracting human context awareness. Many classifiers that train on an activity at a subset of intensity levels fail to recognize the same activity at other intensity levels. This demonstrates weakness in the underlying classification method. Training a classifier for an activity at every intensity level is also not practical. In this paper, we tackle a novel intensity-independent activity recognition problem where the class labels exhibit large variability, the data are of high dimensionality, and clustering algorithms are necessary. We propose a new robust stochastic approximation framework for enhanced classification of such data. Experiments are reported using two clustering techniques, K-Means and Gaussian Mixture Models. The stochastic approximation algorithm consistently outperforms other well-known classification schemes which validate the use of our proposed clustered data representation. We verify the motivation of our framework in two applications that benefit from intensity-independent activity recognition. The first application shows how our framework can be used to enhance energy expenditure calculations. The second application is a novel exergaming environment aimed at using games to reward physical activity performed throughout the day, to encourage a healthy lifestyle. PMID:24235280

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

    PubMed Central

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

    2011-01-01

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

  4. 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. PMID:24860037

  5. 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.

  6. Ventral Striatal Activity Correlates with Memory Confidence for Old- and New-Responses in a Difficult Recognition Test

    PubMed Central

    Schwarze, Ulrike; Bingel, Ulrike; Badre, David; Sommer, Tobias

    2013-01-01

    Activity in the ventral striatum has frequently been associated with retrieval success, i.e., it is higher for hits than correct rejections. Based on the prominent role of the ventral striatum in the reward circuit, its activity has been interpreted to reflect the higher subjective value of hits compared to correct rejections in standard recognition tests. This hypothesis was supported by a recent study showing that ventral striatal activity is higher for correct rejections than hits when the value of rejections is increased by external incentives. These findings imply that the striatal response during recognition is context-sensitive and modulated by the adaptive significance of “oldness” or “newness” to the current goals. The present study is based on the idea that not only external incentives, but also other deviations from standard recognition tests which affect the subjective value of specific response types should modulate striatal activity. Therefore, we explored ventral striatal activity in an unusually difficult recognition test that was characterized by low levels of confidence and accuracy. Based on the human uncertainty aversion, in such a recognition context, the subjective value of all high confident decisions is expected to be higher than usual, i.e., also rejecting items with high certainty is deemed rewarding. In an accompanying behavioural experiment, participants rated the pleasantness of each recognition response. As hypothesized, ventral striatal activity correlated in the current unusually difficult recognition test not only with retrieval success, but also with confidence. Moreover, participants indicated that they were more satisfied by higher confidence in addition to perceived oldness of an item. Taken together, the results are in line with the hypothesis that ventral striatal activity during recognition codes the subjective value of different response types that is modulated by the context of the recognition test. PMID:23472064

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

    PubMed Central

    2013-01-01

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

  8. Bacterial Peptide Recognition and Immune Activation Facilitated by Human Peptide Transporter PEPT2

    PubMed Central

    Swaan, Peter W.; Bensman, Timothy; Bahadduri, Praveen M.; Hall, Mark W.; Sarkar, Anasuya; Bao, Shengying; Khantwal, Chandra M.; Ekins, Sean; Knoell, Daren L.

    2008-01-01

    Microbial detection requires the recognition of pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors (PRRs) that are distributed on the cell surface and within the cytosol. The nucleotide-binding oligomerization domain (NOD)-like receptor (NLR) family functions as an intracellular PRR that triggers the innate immune response. The mechanism by which PAMPs enter the cytosol to interact with NLRs, particularly muropeptides derived from the bacterial proteoglycan cell wall, is poorly understood. PEPT2 is a proton-dependent transporter that mediates the active translocation of di- and tripeptides across epithelial tissues, including the lung. Using computational tools, we initially established that bacterial dipeptides, particularly γ-D-glutamyl-meso-diaminopimelic acid (γ-iE-DAP), are suitable substrates for PEPT2. We then determined in primary cultures of human upper airway epithelia and transiently transfected CHO-PEPT2 cell lines that γ-iE-DAP uptake was mediated by PEPT2 with an affinity constant of approximately 193 μM, whereas muramyl dipeptide was not transported. Exposure to γ-iE-DAP at the apical surface of differentiated, polarized cultures resulted in activation of the innate immune response in an NOD1- and RIP2-dependent manner, resulting in release of IL-6 and IL-8. Based on these findings we report that PEPT2 plays a vital role in microbial recognition by NLR proteins, particularly with regard to airborne pathogens, thereby participating in host defense in the lung. PMID:18474668

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

    NASA Astrophysics Data System (ADS)

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

    2000-10-01

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

  10. Bioacoustic systems: insights for acoustical imaging and pattern recognition (Invited Paper)

    NASA Astrophysics Data System (ADS)

    Altes, Richard A.

    1987-09-01

    Standard performance measures and statistical tests must be altered for research on animal sonar. The narrowband range-Doppler ambiguity function must be redefined to analyze wideband signals. A new range, cross-range ambiguity function is needed to represent angle estimation and spatial resolution properties of animal sonar systems. Echoes are transformed into time-frequency (spectrogram-like) representations by the peripheral auditory system. Detection, estimation, and pattern recognition capabilities of animals should thus be analyzed in terms of operations on spectrograms. The methods developed for bioacoustic research yield new insights into the design of man-made imaging and pattern recognition systems. The range, cross-range ambiguity function can be used to improve imaging performance. Important features for echo pattern recognition are illustrated by time-frequency plots showing (i) principal components for spectrograms and (ii) templates for optimum discrimination between data classes.

  11. Preliminary results on the use of linear discriminant analysis in the ARM continuous speech recognition system

    NASA Astrophysics Data System (ADS)

    Peeling, S. M.; Ponting, K. M.

    1991-12-01

    Linear discriminant analysis is used to generate speech data transformations. This transformed data is then used within the Airborne Reconnaissance Mission (ARM) continuous speech recognition system. The aim of the ARM project is accurate recognition of continuously spoken airborne reconnaissance reports using a speech recognition system based on phoneme level hidden Markov models. A fuller description of a linear discriminant analysis, which is applied to speaker dependent data in the ARM system, is given. Preliminary results are presented from experiments using transformed data alone and also in conjunction with one, or both, of the word transition penalties and variable frame rate analysis. Speaker dependent results are reported which are significantly better then the best obtained previously.

  12. 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

  13. Sudden event recognition: a survey.

    PubMed

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

    2013-01-01

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

  14. 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

  15. 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…

  16. PATTERN RECOGNITION/EXPERT SYSTEM FOR IDENTIFICATION OF TOXIC COMPOUNDS FROM LOW RESOLUTION MASS SPECTRA

    EPA Science Inventory

    An empirical rule-based pattern recognition/expert system for classifying, estimating molecular weights and identifying low resolution mass spectra of toxic and other organic compounds has been developed and evaluated. he system was designed to accommodate low concentration spect...

  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. Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition.

    PubMed

    Tao, Dapeng; Jin, Lianwen; Yuan, Yuan; Xue, Yang

    2016-06-01

    With the rapid development of mobile devices and pervasive computing technologies, acceleration-based human activity recognition, a difficult yet essential problem in mobile apps, has received intensive attention recently. Different acceleration signals for representing different activities or even a same activity have different attributes, which causes troubles in normalizing the signals. We thus cannot directly compare these signals with each other, because they are embedded in a nonmetric space. Therefore, we present a nonmetric scheme that retains discriminative and robust frequency domain information by developing a novel ensemble manifold rank preserving (EMRP) algorithm. EMRP simultaneously considers three aspects: 1) it encodes the local geometry using the ranking order information of intraclass samples distributed on local patches; 2) it keeps the discriminative information by maximizing the margin between samples of different classes; and 3) it finds the optimal linear combination of the alignment matrices to approximate the intrinsic manifold lied in the data. Experiments are conducted on the South China University of Technology naturalistic 3-D acceleration-based activity dataset and the naturalistic mobile-devices based human activity dataset to demonstrate the robustness and effectiveness of the new nonmetric scheme for acceleration-based human activity recognition. PMID:25265635

  19. Flexibility and molecular recognition in the immune system

    PubMed Central

    Jimenez, Ralph; Salazar, Georgina; Baldridge, Kim K.; Romesberg, Floyd E.

    2003-01-01

    Photon echo spectroscopy has been used to measure the response of three antibody-binding sites to perturbation from electronic excitation of a bound antigen, fluorescein. The three antibodies show motions that range in time scale from tens of femtoseconds to nanoseconds. Relative to the others, one antibody, 4-4-20, possesses a rigid binding site that likely results from a short and inflexible heavy chain complementarity-determining region 3 (HCDR3) loop and a critical Tyr that acts as a “molecular splint,” rigidifying the antigen across its most flexible internal degree of freedom. The remaining two antibodies, 34F10 and 40G4, despite being generated against the same antigen, possess binding sites that are considerably more flexible. The more flexible combining sites likely result from longer HCDR3 loops and a deletion in the light chain complementarity-determining region 1 (LCDR1) that removes the critical Tyr residue. The binding site flexibilities may result in varying mechanisms of antigen recognition including lock-and-key, induced-fit, and conformational selection. PMID:12518056

  20. 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. PMID:26684256

  1. Impact of a voice recognition system on report cycle time and radiologist reading time

    NASA Astrophysics Data System (ADS)

    Melson, David L.; Brophy, Robert; Blaine, G. James; Jost, R. Gilbert; Brink, Gary S.

    1998-07-01

    Because of its exciting potential to improve clinical service, as well as reduce costs, a voice recognition system for radiological dictation was recently installed at our institution. This system will be clinically successful if it dramatically reduces radiology report turnaround time without substantially affecting radiologist dictation and editing time. This report summarizes an observer study currently under way in which radiologist reporting times using the traditional transcription system and the voice recognition system are compared. Four radiologists are observed interpreting portable intensive care unit (ICU) chest examinations at a workstation in the chest reading area. Data are recorded with the radiologists using the transcription system and using the voice recognition system. The measurements distinguish between time spent performing clerical tasks and time spent actually dictating the report. Editing time and the number of corrections made are recorded. Additionally, statistics are gathered to assess the voice recognition system's impact on the report cycle time -- the time from report dictation to availability of an edited and finalized report -- and the length of reports.

  2. 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. PMID:25122896

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

    PubMed Central

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

    2014-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1989-01-01

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

  5. 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).

  6. BioTagger-GM: A Gene/Protein Name Recognition System

    PubMed Central

    Torii, Manabu; Hu, Zhangzhi; Wu, Cathy H.; Liu, Hongfang

    2009-01-01

    Objectives Biomedical named entity recognition (BNER) is a critical component in automated systems that mine biomedical knowledge in free text. Among different types of entities in the domain, gene/protein would be the most studied one for BNER. Our goal is to develop a gene/protein name recognition system BioTagger-GM that exploits rich information in terminology sources using powerful machine learning frameworks and system combination. Design BioTagger-GM consists of four main components: (1) dictionary lookup—gene/protein names in BioThesaurus and biomedical terms in UMLS Metathesaurus are tagged in text, (2) machine learning—machine learning systems are trained using dictionary lookup results as one type of feature, (3) post-processing—heuristic rules are used to correct recognition errors, and (4) system combination—a voting scheme is used to combine recognition results from multiple systems. Measurements The BioCreAtIvE II Gene Mention (GM) corpus was used to evaluate the proposed method. To test its general applicability, the method was also evaluated on the JNLPBA corpus modified for gene/protein name recognition. The performance of the systems was evaluated through cross-validation tests and measured using precision, recall, and F-Measure. Results BioTagger-GM achieved an F-Measure of 0.8887 on the BioCreAtIvE II GM corpus, which is higher than that of the first-place system in the BioCreAtIvE II challenge. The applicability of the method was also confirmed on the modified JNLPBA corpus. Conclusion The results suggest that terminology sources, powerful machine learning frameworks, and system combination can be integrated to build an effective BNER system. PMID:19074302

  7. A recognition method in holographic data storage system by using structural similarity

    NASA Astrophysics Data System (ADS)

    Chen, Yu-Ta; Ou-Yang, Mang; Lee, Cheng-Chung

    2013-09-01

    The holographic data storage system (HDSS) is a page-oriented storage system with advantages of great capacity and high speed. The page-oriented recording breaks the tradition of the optical storage of one-point recording. As the signal image is retrieved from the storage material in the HDSS, various noises influences the image and then the data retrieve will be difficultly from the image by using the thresholding method. For progressing on the thresholding method, a recognition method, based on the structural similarity, is proposed to replace the thresholding method in the HDSS. The recognition method is implemented that the image comparison between the receive image and reference image is performed by the structural similarity method to find the most similar reference image to the received image. In the experiment, by using recognition method, the bit error rate (BER) results in 26% decrease less than using the thresholding method in the HDSS. Owing to some strong effects, such as non-uniform intensity and strong speckle, still influencing on the received image, the recognition method is seemed to be slightly better than thresholding method. In the future, the strong effects would be reduced to improve the quality of the receive image and then the result of using the recognition method may be vastly better than the thresholding method.

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

    NASA Astrophysics Data System (ADS)

    Dai, Jun-jian; Han, Wen-bo

    2013-08-01

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

  9. Semantic Event Fusion of Different Visual Modality Concepts for Activity Recognition.

    PubMed

    Crispim-Junior, Carlos F; Buso, Vincent; Avgerinakis, Konstantinos; Meditskos, Georgios; Briassouli, Alexia; Benois-Pineau, Jenny; Kompatsiaris, Ioannis Yiannis; Bremond, Francois

    2016-08-01

    Combining multimodal concept streams from heterogeneous sensors is a problem superficially explored for activity recognition. Most studies explore simple sensors in nearly perfect conditions, where temporal synchronization is guaranteed. Sophisticated fusion schemes adopt problem-specific graphical representations of events that are generally deeply linked with their training data and focused on a single sensor. This paper proposes a hybrid framework between knowledge-driven and probabilistic-driven methods for event representation and recognition. It separates semantic modeling from raw sensor data by using an intermediate semantic representation, namely concepts. It introduces an algorithm for sensor alignment that uses concept similarity as a surrogate for the inaccurate temporal information of real life scenarios. Finally, it proposes the combined use of an ontology language, to overcome the rigidity of previous approaches at model definition, and a probabilistic interpretation for ontological models, which equips the framework with a mechanism to handle noisy and ambiguous concept observations, an ability that most knowledge-driven methods lack. We evaluate our contributions in multimodal recordings of elderly people carrying out IADLs. Results demonstrated that the proposed framework outperforms baseline methods both in event recognition performance and in delimiting the temporal boundaries of event instances. PMID:26955015

  10. Molecular Understanding of USP7 Substrate Recognition and C-Terminal Activation.

    PubMed

    Rougé, Lionel; Bainbridge, Travis W; Kwok, Michael; Tong, Raymond; Di Lello, Paola; Wertz, Ingrid E; Maurer, Till; Ernst, James A; Murray, Jeremy

    2016-08-01

    The deubiquitinating enzyme USP7 has a pivotal role in regulating the stability of proteins involved in fundamental cellular processes of normal biology and disease. Despite the importance of USP7, the mechanisms underlying substrate recognition and catalytic activation are poorly understood. Here we present structural, biochemical, and biophysical analyses elucidating the molecular mechanism by which the C-terminal 19 amino acids of USP7 (residues 1084-1102) enhance the ubiquitin cleavage activity of the deubiquitinase (DUB) domain. Our data demonstrate that the C-terminal peptide binds the activation cleft in the catalytic domain and stabilizes the catalytically competent conformation of USP7. Additional structures of longer fragments of USP7, as well as solution studies, provide insight into full-length USP7, the role of the UBL domains, and demonstrate that both substrate recognition and deubiquitinase activity are highly regulated by the catalytic and noncatalytic domains of USP7, a feature that could be essential for the proper function of multi-domain DUBs. PMID:27452404

  11. Recognition and Activation Domains Contribute to Allele-Specific Responses of an Arabidopsis NLR Receptor to an Oomycete Effector Protein

    PubMed Central

    Steinbrenner, Adam D.; Goritschnig, Sandra; Staskawicz, Brian J.

    2015-01-01

    In plants, specific recognition of pathogen effector proteins by nucleotide-binding leucine-rich repeat (NLR) receptors leads to activation of immune responses. RPP1, an NLR from Arabidopsis thaliana, recognizes the effector ATR1, from the oomycete pathogen Hyaloperonospora arabidopsidis, by direct association via C-terminal leucine-rich repeats (LRRs). Two RPP1 alleles, RPP1-NdA and RPP1-WsB, have narrow and broad recognition spectra, respectively, with RPP1-NdA recognizing a subset of the ATR1 variants recognized by RPP1-WsB. In this work, we further characterized direct effector recognition through random mutagenesis of an unrecognized ATR1 allele, ATR1-Cala2, screening for gain-of-recognition phenotypes in a tobacco hypersensitive response assay. We identified ATR1 mutants that a) confirm surface-exposed residues contribute to recognition by RPP1, and b) are recognized by and activate the narrow-spectrum allele RPP1-NdA, but not RPP1-WsB, in co-immunoprecipitation and bacterial growth inhibition assays. Thus, RPP1 alleles have distinct recognition specificities, rather than simply different sensitivity to activation. Using chimeric RPP1 constructs, we showed that RPP1-NdA LRRs were sufficient for allele-specific recognition (association with ATR1), but insufficient for receptor activation in the form of HR. Additional inclusion of the RPP1-NdA ARC2 subdomain, from the central NB-ARC domain, was required for a full range of activation specificity. Thus, cooperation between recognition and activation domains seems to be essential for NLR function. PMID:25671309

  12. Voice recognition.

    PubMed

    Mehta, Amit; McLoud, Theresa C

    2003-07-01

    Voice recognition represents one of the new technologies that are changing the practice of radiology. Thirty percent of radiology practices are either currently or plan to have voice recognition (VR) systems. VR software encompasses 4 core processes: spoken recognition of human speech, synthesis of human readable characters into speech, speaker identification and verification, and comprehension. Many software packages are available offering VR. All these packages should contain an interface with the radiology information system. The benefits include decreased turnaround time and cost savings. Its advantages include the transfer of secretarial duties to the radiologist with a result in decreased productivity. PMID:12867815

  13. 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.

  14. Recognition of Sound Environment by a Bathroom Monitoring System

    NASA Astrophysics Data System (ADS)

    Komoguchi, Naoyuki; Yamane, Kenji; Tanaka, Shogo

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

  15. 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…

  16. LARGE SCALE EVALUATION OF A PATTERN RECOGNITION/EXPERT SYSTEM FOR MASS SPECTRAL MOLECULAR WEIGHT ESTIMATION

    EPA Science Inventory

    A fast, personal-computer based method of estimating molecular weights of organic compounds from low resolution mass I spectra has been thoroughly evaluated. he method is based on a rule-based pattern,recognition/expert system approach which uses empirical linear corrections whic...

  17. SIFT-based error compensation for ear feature matching and recognition system

    NASA Astrophysics Data System (ADS)

    Jiang, Jingying; Zhang, Qi; Ma, Congcong; Lu, Junsheng; Xu, Kexin

    2015-03-01

    The current ear feature matching and recognition system, based on Scale Invariant Feature Transform (SIFT) image matching algorithm, can realize the human ear feature matching and detect the displacement of the human ear so as to reproduce the human ear position and posture. However, due to the influence of image acquisition equipment performance and lighting conditions, too dark or too bright background could bring the locally underexposed or overexposed image. This could result in the loss of some image details so as to make it impossible to identity the image and the recognition rate would be reduced. In this talk, the application of image gray level normalization processing can reduce the sensitivity of imaging to light intensity. Accordingly, it will greatly improve the recognition rate of human ears. Furthermore, it has been found that even if the object is stationary, the image matching results still have certain fluctuation changes, which could be caused by the system error. In order to reduce the error, the Background-based Compensation Model (BCM) has been established based on the investigation of the system error brought by the working environment changes. The results show that, BCM can be used to compensate the system errors of ear recognition matching and further improve the matching accuracy of human ear.

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

    NASA Astrophysics Data System (ADS)

    Khalifa, Mahmoud; Bingru, Yang

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

  19. Multiple ANN Recognizers for Adaptive Recognition of the Speech of Dysarthric Patients in AAL Systems.

    PubMed

    Nagy, Gabriella; Kutor, Laszlo

    2015-01-01

    People suffering from neuromuscular disorders are one of the main target groups of speech-controlled Ambient Assisted Living systems. However, the speech of these patients is often distorted because of the dysarthric symptoms of the disease. The dysarthria is known to become worse as the disease progresses. We propose a framework for an adaptive speech recognition system that may be able to follow the slow deterioration of speech quality without risking the accuracy of the system from incorrect data. PMID:26294603

  20. Activation reduction in anterior temporal cortices during repeated recognition of faces of personal acquaintances.

    PubMed

    Sugiura, M; Kawashima, R; Nakamura, K; Sato, N; Nakamura, A; Kato, T; Hatano, K; Schormann, T; Zilles, K; Sato, K; Ito, K; Fukuda, H

    2001-05-01

    Repeated recognition of the face of a familiar individual is known to show semantic repetition priming effect. In this study, normal subjects were repeatedly presented faces of their colleagues, and the effect of repetition on the regional cerebral blood flow change was measured using positron emission tomography. They repeated a set of three tasks: the familiar-face detection (F) task, the facial direction discrimination (D) task, and the perceptual control (C) task. During five repetitions of the F task, familiar faces were presented six times from different views in a pseudorandom order. Activation reduction through the repetition of the F tasks was observed in the bilateral anterior (anterolateral to the polar region) temporal cortices which are suggested to be involved in the access to the long-term memory concerning people. The bilateral amygdala, the hypothalamus, and the medial frontal cortices, were constantly activated during the F tasks, and considered to be associated with the behavioral significance of the presented familiar faces. Constant activation was also observed in the bilateral occipitotemporal regions and fusiform gyri and the right medial temporal regions during perception of the faces, and in the left medial temporal regions during the facial familiarity detection task, which are consistent with the results of previous functional brain imaging studies. The results have provided further information about the functional segregation of the anterior temporal regions in face recognition and long-term memory. PMID:11304083

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

    PubMed

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

    2008-12-01

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

  2. 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

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

    PubMed

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

    2015-01-01

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

  4. 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

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

    PubMed

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

    2015-01-01

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

  6. 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.

  7. Neuro-parity pattern recognition system and method

    SciTech Connect

    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.

  8. 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.

  9. 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

  10. A heart disease recognition embedded system with fuzzy cluster algorithm.

    PubMed

    de Carvalho, Helton Hugo; Moreno, Robson Luiz; Pimenta, Tales Cleber; Crepaldi, Paulo C; Cintra, Evaldo

    2013-06-01

    This article presents the viability analysis and the development of heart disease identification embedded system. It offers a time reduction on electrocardiogram - ECG signal processing by reducing the amount of data samples, without any significant loss. The goal of the developed system is the analysis of heart signals. The ECG signals are applied into the system that performs an initial filtering, and then uses a Gustafson-Kessel fuzzy clustering algorithm for the signal classification and correlation. The classification indicated common heart diseases such as angina, myocardial infarction and coronary artery diseases. The system uses the European electrocardiogram ST-T Database (EDB) as a reference for tests and evaluation. The results prove the system can perform the heart disease detection on a data set reduced from 213 to just 20 samples, thus providing a reduction to just 9.4% of the original set, while maintaining the same effectiveness. This system is validated in a Xilinx Spartan(®)-3A FPGA. The field programmable gate array (FPGA) implemented a Xilinx Microblaze(®) Soft-Core Processor running at a 50MHz clock rate. PMID:23394802

  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. PMID:22695257

  12. 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%.

  13. 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.

  14. Solar cell activation system

    SciTech Connect

    Apelian, L.

    1983-07-05

    A system for activating solar cells involves the use of phosphorescent paint, the light from which is amplified by a thin magnifying lens and used to activate solar cells. In a typical system, a member painted with phosphorescent paint is mounted adjacent a thin magnifying lens which focuses the light on a predetermined array of sensitive cells such as selenium, cadmium or silicon, mounted on a plastic board. A one-sided mirror is mounted adjacent the cells to reflect the light back onto said cells for purposes of further intensification. The cells may be coupled to rechargeable batteries or used to directly power a small radio or watch.

  15. Enhancement of Prediction for Manufacturing System Using Bayesian Decision Recognition

    NASA Astrophysics Data System (ADS)

    Jianhua, Yang; Fujimoto, Yasutaka

    A decision model stemmed from Bayesian theorem is proposed to describe the process of decision making for job sequence in manufacturing system. The construction of feature vector is firstly discussed with respect to the manufacturing system’s characteristic. Then a non-parametric model is employed to deal with general distribution for decision acquisition, where a binary division methodology is developed to limit the size of non-parametric model, including elimination of irrelevant features. At last, a PCB manufacturing system is given to demonstrate the efficiency of the model.

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

    PubMed

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

    2013-08-01

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

  17. Fluctuations in Neuronal Synchronization in Brain Activity Correlate with the Subjective Experience of Visual Recognition

    PubMed Central

    Dominguez, Luis Garcia; Erra, Ramon Guevara

    2007-01-01

    The scientific study of subjective experience is a current major research area in the neurosciences. Coordination patterns of brain activity are being studied to address the question of how brain function relates to behaviour, and particularly methods to estimate neuronal synchronization can unravel the spatio-temporal dynamics of the transient formation of neuronal assemblies. We report here a biophysical correlate of subjective experience. Subjects visualised figures with different levels of noise, while their brain activity was recorded using magnetoencephalography (MEG), and reported the moment in time (corresponding to a noise level) of figure recognition, which varied between individuals, as well as the moment when they saw the figure more clearly, which was mostly common among the participants (thus less subjective). This latter moment is considered to represent psychophysical stochastic resonance (PSR). Fluctuations in neuronal synchronization, quantified using a diffusion coefficient, were lower in occipital cortex when subjects recognised the figure, for a certain noise level, but did not correlate with the moment of PSR. A different pattern was observed in frontal cortex, where lower values of the diffusion coefficient in neuronal synchronization was maintained from the moment of recognition to the moment of PSR. No specific pattern was found analysing signals from temporal or parietal cortical areas. These observations provide support for distinct synchronization patterns in different cortical areas, and represent another demonstration that the subjective, first-person perspective is accessible to scientific methods. PMID:19669552

  18. Recognition of forearm muscle activity by continuous classification of multi-site mechanomyogram signals.

    PubMed

    Alves, Natasha; Chau, Tom

    2010-01-01

    Recent studies on identifying multiple activation states from mechanomyogram (MMG) signals for the purpose of controlling switching interfaces have employed pattern recognition methods where MMG signal features from multiple muscle sites are extracted and classified. The purpose of this study is to determine if MMG signal features retain enough discriminatory information to allow reliable continuous classification, and to determine if there is a decline in classification accuracy over short time periods. MMG signals were recorded from two accelerometers attached to the flexor carpi radialis and extensor carpi radialis muscles of 12 able-bodied participants as participants performed three classes of forearm muscle activity. The data were collected over five recording sessions, with a ten-minute interval between each session. The data were spliced into 256 ms epochs, and a comprehensive set of signal features was extracted. A pattern classifier, trained with continuously acquired signal features from the first recording session, was tested with signals recorded from the other sessions. The average classification accuracy over the five sessions was 89 ± 2%. There was no obvious declining trend in classification accuracy with time. These results show that MMG signals recorded at the forearm retain enough discriminatory information to allow continuous recognition of hand motion across multiple (>90) repetitions, and the MMG-classifier does not show short-term degradation. These results indicate the potential of MMG as a multifunction control signal for muscle-machine interfaces. PMID:21097038

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

    PubMed Central

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

    2015-01-01

    Formamidopyrimidine-DNA glycosylase (Fpg) excises 8-oxoguanine (oxoG) from DNA but ignores normal guanine. We combined molecular dynamics simulation and stopped-flow kinetics with fluorescence detection to track the events in the recognition of oxoG by Fpg and its mutants with a key phenylalanine residue, which intercalates next to the damaged base, changed to either alanine (F110A) or fluorescent reporter tryptophan (F110W). Guanine was sampled by Fpg, as evident from the F110W stopped-flow traces, but less extensively than oxoG. The wedgeless F110A enzyme could bend DNA but failed to proceed further in oxoG recognition. Modeling of the base eversion with energy decomposition suggested that the wedge destabilizes the intrahelical base primarily through buckling both surrounding base pairs. Replacement of oxoG with abasic (AP) site rescued the activity, and calculations suggested that wedge insertion is not required for AP site destabilization and eversion. Our results suggest that Fpg, and possibly other DNA glycosylases, convert part of the binding energy into active destabilization of their substrates, using the energy differences between normal and damaged bases for fast substrate discrimination. PMID:25520195

  20. A Pattern Analysis of Using Self-Organizing-Maps in a Unspoken Vowel Recognition System Based on Surface Electromyogram

    NASA Astrophysics Data System (ADS)

    Fukumoto, Hisao; Noguchi, Yusuke; Ohchi, Masashi; Furukawa, Tatsuya

    In this paper, we present some results of analysis on surface electromyogram (SEMG) using Self-Organizing -Maps (SOM) algorithm, which is one of the neural network algorithm, for unspoken vowel recognition system. Three pairs of electrodes were placed on facial muscles and SEMG signals were recorded. We have examined the classification of three pairs of the values of activity for each muscle using SOM algorithm. The SOM algorithm is also able to translate the multi-dimensional vectors of RMS values of SEMG signal into the two-dimensional map.

  1. 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.

  2. The research on pattern recognition in distributed fiber vibrant sensor system

    NASA Astrophysics Data System (ADS)

    Wu, Hongyan; Zhao, Dong; Xu, Haiyan

    2011-09-01

    Distributed Fiber Vibrant Sensor System is a new type of system, which could be used in long-distance, strong-EMI condition for monitoring vibration and sound signals. Position determination analysis toward this system is popular in previous papers, but pattern recognition of the output signals of the sensor has been missed for a long time. This function turns to critical especially when it is used for real security project in which quick response to intrusion is a must. After pre-processing the output signal of the system, a MFCC-based approach is provided in this paper to extract features of the sensing signals, which could be used for pattern recognition in real project, and the approach is proved by large practical experiments and projects.

  3. Systematic Approach for Validation of X-Ray Automatic Defect Recognition Systems

    SciTech Connect

    Navalgund, Megha; Venkatachalam, Rajashekar; Asati, Mahesh; Venugopal, Manoharan

    2007-03-21

    With the advent of digital radiography, there has been a gradual shift from operators viewing images to find defects to totally automated defect recognition (ADR) systems. This has resulted in reduced operator subjectivity, reduced operator fatigue, and increased productivity. These automated defect recognition solutions are based on reference or non-reference based approaches or a combination of both. There exists some amount of uncertainty or reluctance to accept automated systems in view of no systematic quantified metrics available on performance of these ADR systems in comparison to human operators. This paper describes the metrics that one could follow to quantify the performance of ADR systems such as detectability for different defect types and sizes, accuracy, false call rate, robustness to various noise levels etc., As it might be difficult to have images with defects of various sizes, shapes, contrast and noise levels, a methodology to generate images with simulated defects with variability's in parameters such as size, shape, contrast to noise ratio etc., is demonstrated. This can be used to generate probability of detection estimates for different defect types and geometries. This will result in establishing confidence limits for ADR systems and can be used to judge if it would meet specific customer requirement. This would facilitate increase in the acceptability of ADR systems over current manual defect recognition systems for applications in various industries such as Castings, Oil and Gas, Aviation etc.

  4. 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.

  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. 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.

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

    PubMed

    Wyss, Thomas; Mäder, Urs

    2010-11-01

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

  9. Complement activation by ligand-driven juxtaposition of discrete pattern recognition complexes.

    PubMed

    Degn, Søren E; Kjaer, Troels R; Kidmose, Rune T; Jensen, Lisbeth; Hansen, Annette G; Tekin, Mustafa; Jensenius, Jens C; Andersen, Gregers R; Thiel, Steffen

    2014-09-16

    Defining mechanisms governing translation of molecular binding events into immune activation is central to understanding immune function. In the lectin pathway of complement, the pattern recognition molecules (PRMs) mannan-binding lectin (MBL) and ficolins complexed with the MBL-associated serine proteases (MASP)-1 and MASP-2 cleave C4 and C2 to generate C3 convertase. MASP-1 was recently found to be the exclusive activator of MASP-2 under physiological conditions, yet the predominant oligomeric forms of MBL carry only a single MASP homodimer. This prompted us to investigate whether activation of MASP-2 by MASP-1 occurs through PRM-driven juxtaposition on ligand surfaces. We demonstrate that intercomplex activation occurs between discrete PRM/MASP complexes. PRM ligand binding does not directly escort the transition of MASP from zymogen to active enzyme in the PRM/MASP complex; rather, clustering of PRM/MASP complexes directly causes activation. Our results support a clustering-based mechanism of activation, fundamentally different from the conformational model suggested for the classical pathway of complement. PMID:25197071

  10. Complement activation by ligand-driven juxtaposition of discrete pattern recognition complexes

    PubMed Central

    Degn, Søren E.; Kjaer, Troels R.; Kidmose, Rune T.; Jensen, Lisbeth; Hansen, Annette G.; Tekin, Mustafa; Jensenius, Jens C.; Andersen, Gregers R.; Thiel, Steffen

    2014-01-01

    Defining mechanisms governing translation of molecular binding events into immune activation is central to understanding immune function. In the lectin pathway of complement, the pattern recognition molecules (PRMs) mannan-binding lectin (MBL) and ficolins complexed with the MBL-associated serine proteases (MASP)-1 and MASP-2 cleave C4 and C2 to generate C3 convertase. MASP-1 was recently found to be the exclusive activator of MASP-2 under physiological conditions, yet the predominant oligomeric forms of MBL carry only a single MASP homodimer. This prompted us to investigate whether activation of MASP-2 by MASP-1 occurs through PRM-driven juxtaposition on ligand surfaces. We demonstrate that intercomplex activation occurs between discrete PRM/MASP complexes. PRM ligand binding does not directly escort the transition of MASP from zymogen to active enzyme in the PRM/MASP complex; rather, clustering of PRM/MASP complexes directly causes activation. Our results support a clustering-based mechanism of activation, fundamentally different from the conformational model suggested for the classical pathway of complement. PMID:25197071

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

    PubMed Central

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

    2011-01-01

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

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

    PubMed Central

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

    1992-01-01

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

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

    PubMed

    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

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

    PubMed Central

    Nishikawa, Saori

    2015-01-01

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

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

    PubMed

    Nishikawa, Saori; Toshima, Tamotsu; Kobayashi, Masao

    2015-01-01

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

  16. 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.

  17. 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

  18. RAPTOR: radar plus thermal observation and recognition system

    NASA Astrophysics Data System (ADS)

    Charlesworth, Peter D.

    1995-09-01

    Typical targets to be detected in ground surveillance scenarios have two common properties that are physically distinct, motion and radiation. This applies to pedestrians, vehicles, trucks and light aircraft. Infrared sensors can be used for the detection of radiating bodies through the application of image processing techniques and Doppler radar is an excellent detector of radial body motion. RAPTOR is a system whose aim is to produce a ground surveillance device with higher detection performance and lower false and nuisance alarm rates than possible with either or both sensor types operating alone. RAPTOR combines two complementary sensors, an infrared imager and a pulsed Doppler radar, which automatically detect targets using different physical phenomena and then uses data fusion techniques to enhance the automatic target detection performance. The fusion process includes alignment, correlation, association, target data processing and multisensor management. RAPTOR uses the two sensors in parallel, bore sighted on a single rotational platform. Both sensors concurrently scan the spatial volume and the individual target detection results, 'soft-targets,' are combined using data fusion algorithms to generate a confirmed set of targets, 'hard-targets.'

  19. Substrate Recognition and Activity Regulation of the Escherichia coli mRNA Endonuclease MazF.

    PubMed

    Zorzini, Valentina; Mernik, Andrej; Lah, Jurij; Sterckx, Yann G J; De Jonge, Natalie; Garcia-Pino, Abel; De Greve, Henri; Versées, Wim; Loris, Remy

    2016-05-20

    Escherichia coli MazF (EcMazF) is the archetype of a large family of ribonucleases involved in bacterial stress response. The crystal structure of EcMazF in complex with a 7-nucleotide substrate mimic explains the relaxed substrate specificity of the E. coli enzyme relative to its Bacillus subtilis counterpart and provides a framework for rationalizing specificity in this enzyme family. In contrast to a conserved mode of substrate recognition and a conserved active site, regulation of enzymatic activity by the antitoxin EcMazE diverges from its B. subtilis homolog. Central in this regulation is an EcMazE-induced double conformational change as follows: a rearrangement of a crucial active site loop and a relative rotation of the two monomers in the EcMazF dimer. Both are induced by the C-terminal residues Asp-78-Trp-82 of EcMazE, which are also responsible for strong negative cooperativity in EcMazE-EcMazF binding. This situation shows unexpected parallels to the regulation of the F-plasmid CcdB activity by CcdA and further supports a common ancestor despite the different activities of the MazF and CcdB toxins. In addition, we pinpoint the origin of the lack of activity of the E24A point mutant of EcMazF in its inability to support the substrate binding-competent conformation of EcMazF. PMID:27026704

  20. 2'Fluoro Modification Differentially Modulates the Ability of RNAs to Activate Pattern Recognition Receptors.

    PubMed

    Lee, Youngju; Urban, Johannes H; Xu, Li; Sullenger, Bruce A; Lee, Jaewoo

    2016-06-01

    Although the use of RNAs has enormous therapeutic potential, these RNA-based therapies can trigger unwanted inflammatory responses by the activation of pattern recognition receptors (PRRs) and cause harmful side effects. In contrast, the immune activation by therapeutic RNAs can be advantageous for treating cancers. Thus, the immunogenicity of therapeutic RNAs should be deliberately controlled depending on the therapeutic applications of RNAs. In this study, we demonstrated that RNAs containing 2'fluoro (2'F) pyrimidines differentially controlled the activation of PRRs. The activity of RNAs that stimulate toll-like receptors 3 and 7 was abrogated by the incorporation of 2'F pyrimidine. By contrast, incorporation of 2'F pyrimidines enhanced the activity of retinoic acid-inducible gene 1-stimulating RNAs. Furthermore, we found that transfection with RNAs containing 2'F pyrimidine and 5' triphosphate (5'ppp) increased cell death and interferon-β expression in human cancer cells compared with transfection with 2'hydroxyl 5'ppp RNAs, whereas RNAs containing 2'O-methyl pyrimidine and 5'ppp completely abolished the induction of cell death and cytokine expression in the cells. Our findings suggest that incorporation of 2'F and 2'O-methyl nucleosides is a facile approach to differentially control the ability of therapeutic RNAs to activate or limit immune and inflammatory responses depending on therapeutic applications. PMID:26789413

  1. 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.

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

    PubMed Central

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

    2015-01-01

    Ferric uptake regulator (Fur) plays a key role in the iron homeostasis of prokaryotes, such as bacterial pathogens, but the molecular mechanisms and structural basis of Fur–DNA binding remain incompletely understood. Here, we report high-resolution structures of Magnetospirillum gryphiswaldense MSR-1 Fur in four different states: apo-Fur, holo-Fur, the Fur–feoAB1 operator complex and the Fur–Pseudomonas aeruginosa Fur box complex. Apo-Fur is a transition metal ion-independent dimer whose binding induces profound conformational changes and confers DNA-binding ability. Structural characterization, mutagenesis, biochemistry and in vivo data reveal that Fur recognizes DNA by using a combination of base readout through direct contacts in the major groove and shape readout through recognition of the minor-groove electrostatic potential by lysine. The resulting conformational plasticity enables Fur binding to diverse substrates. Our results provide insights into metal ion activation and substrate recognition by Fur that suggest pathways to engineer magnetotactic bacteria and antipathogenic drugs. PMID:26134419

  3. 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

  4. 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)…

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

    NASA Astrophysics Data System (ADS)

    Ricquebourg, Yann; Coüasnon, Bertrand; Guichard, Laurent

    2013-01-01

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

  6. 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

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

    PubMed

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

    2016-09-12

    Forgetting is a universal feature for most types of memories. The best-defined and extensively characterized behaviors that depict forgetting are natural memory decay and interference-based forgetting [1, 2]. Molecular mechanisms underlying the active forgetting remain to be determined for memories in vertebrates. Recent progress has begun to unravel such mechanisms underlying the active forgetting [3-11] that is induced through the behavior-dependent activation of intracellular signaling pathways. In Drosophila, training-induced activation of the small G protein Rac1 mediates natural memory decay and interference-based forgetting of aversive conditioning memory [3]. In mice, the activation of photoactivable-Rac1 in recently potentiated spines in a motor learning task erases the motor memory [12]. These lines of evidence prompted us to investigate a role for Rac1 in time-based natural memory decay and interference-based forgetting in mice. The inhibition of Rac1 activity in hippocampal neurons through targeted expression of a dominant-negative Rac1 form extended object recognition memory from less than 72 hr to over 72 hr, whereas Rac1 activation accelerated memory decay within 24 hr. Interference-induced forgetting of this memory was correlated with Rac1 activation and was completely blocked by inhibition of Rac1 activity. Electrophysiological recordings of long-term potentiation provided independent evidence that further supported a role for Rac1 activation in forgetting. Thus, Rac1-dependent forgetting is evolutionarily conserved from invertebrates to vertebrates. PMID:27593377

  8. 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.

  9. 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. PMID:26253158

  10. Surface acoustic wave sensor array system for trace organic vapor detection using pattern recognition analysis

    NASA Astrophysics Data System (ADS)

    Rose-Pehrsson, Susan L.; Grate, Jay W.; Klusty, Mark

    1993-03-01

    A sensor system using surface acoustic wave (SAW) vapor sensors has been fabricated and tested against hazardous organic vapors, simulants of these vapors, and potential background vapors. The vapor tests included two- and three-component mixtures, and covered a wide relative humidity range. The sensor system was compared of four SAW devices coated with different sorbent materials with different vapor selectivities. Preconcentrators were included to improve sensitivity. The vapor experiments were organized into a large data set analyzed using pattern recognition techniques. Pattern recognition algorithms were developed to identify two different classes of hazards. The algorithms were verified against a second data set not included in the training. Excellent sensitivity was achieved by the sensor coatings, and the pattern recognition analysis, and was also examined by the preconcentrators. The system can detect hazardous vapors of interest in the ppb range even in varying relative humidity and in the presence of background vapors. The system does not false alarm to a variety of other vapors including gasoline, jet fuel, diesel fuel and cigarette smoke.

  11. 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.

  12. 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

  13. 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

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

    PubMed

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

    2016-07-17

    Tumor suppressor p53 binds to the target in a genome and regulates the expression of downstream genes. p53 searches for the target by combining three-dimensional diffusion and one-dimensional sliding along the DNA. To examine the regulation mechanism of the target binding, we constructed the pseudo-wild type (pseudo-WT), activated (S392E), and inactive (R248Q) mutants of p53 and observed their target binding in long DNA using single-molecule fluorescence imaging. The pseudo-WT sliding along the DNA showed many pass events over the target and possessed target recognition probability (TRP) of 7±2%. The TRP increased to 18±2% for the activated mutant but decreased to 0% for the inactive mutant. Furthermore, the fraction of the target binding by the one-dimensional sliding among the total binding events increased from 63±9% for the pseudo-WT to 87±2% for the activated mutant. Control of TRP upon activation, as demonstrated here for p53, might be a general activation mechanism of transcription factors. PMID:27291286

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

    PubMed

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

    2016-05-01

    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 and germ-free (GF) mice had reduced amounts of antigen-specific IgG, smaller recall-stimulated cytokine responses, impaired follicular helper T (TFH) cell responses and reduced numbers of plasma cells. Recognition of symbiotic bacteria via the nucleotide-binding oligomerization domain containing 2 (Nod2) sensor in cells that express the integrin CD11c (encoded by Itgax) was required for the adjuvanticity of CT. Reconstitution of GF mice with a Nod2 agonist or monocolonization with Staphylococcus sciuri, which has 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 dendritic cells via intracellular cyclic AMP. These results show a role for the microbiota and the intracellular receptor Nod2 in promoting the mucosal adjuvant activity of CT. PMID:27064448

  16. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  18. 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.

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

    PubMed

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

    2015-06-01

    The development of efficient stroke-detection methods is of significant importance in today's society due to the effects and impact of stroke on health and economy worldwide. This study focuses on Human Activity Recognition (HAR), which is a key component in developing an early stroke-diagnosis tool. An overview of the proposed global approach able to discriminate normal resting from stroke-related paralysis is detailed. The main contributions include an extension of the Genetic Fuzzy Finite State Machine (GFFSM) method and a new hybrid feature selection (FS) algorithm involving Principal Component Analysis (PCA) and a voting scheme putting the cross-validation results together. Experimental results show that the proposed approach is a well-performing HAR tool that can be successfully embedded in devices. PMID:25684369

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

    PubMed Central

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

    2014-01-01

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

  1. Human action recognition using meta-cognitive neuro-fuzzy inference system.

    PubMed

    Subramanian, K; Suresh, S

    2012-12-01

    We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort. PMID:23186277

  2. Content-addressable holographic data storage system for invariant pattern recognition of gray-scale images.

    PubMed

    Joseph, Joby; Bhagatji, Alpana; Singh, Kehar

    2010-01-20

    Conventionally a holographic data storage system uses binary digital data as the input pages. We propose and demonstrate the use of a holographic data storage system for the purpose of invariant pattern recognition of gray-scale images. To improve the correlation accuracy for gray-scale images, we present a coding technique, phase Fourier transform (phase-FT) coding, to code a gray-scale image into a random and balanced digital binary image. In addition to the fact that a digital data page is obtained for incorporation into a holographic data storage system, this phase-FT coded image produces dc-free homogenized Fourier spectrum. This coded image can also be treated as an image for further processing, such as synthesis of distortion-invariant filters for invariant pattern recognition. A space-domain synthetic discriminant function (SDF) filter has been synthesized using these phase-FT coded images for rotation-invariant pattern recognition. Both simulation and experimental results are presented. The results show good correlation accuracy in comparison to correlation results obtained for SDF filter synthesized using the original gray-scale images themselves. PMID:20090813

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

    NASA Astrophysics Data System (ADS)

    Saponara, Sergio

    2013-02-01

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

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

    PubMed

    Busk, Peter Kamp; Lange, Lene

    2013-06-01

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

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

    PubMed

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

    2011-06-01

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

  6. 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.

  7. Evaluating a voice recognition system: finding the right product for your department.

    PubMed

    Freeh, M; Dewey, M; Brigham, L

    2001-06-01

    The Department of Radiology at the University of Utah Health Sciences Center has been in the process of transitioning from the traditional film-based department to a digital imaging department for the past 2 years. The department is now transitioning from the traditional method of dictating reports (dictation by radiologist to transcription to review and signing by radiologist) to a voice recognition system. The transition to digital operations will not be complete until we have the ability to directly interface the dictation process with the image review process. Voice recognition technology has advanced to the level where it can and should be an integral part of the new way of working in radiology and is an integral part of an efficient digital imaging department. The transition to voice recognition requires the task of identifying the product and the company that will best meet a department's needs. This report introduces the methods we used to evaluate the vendors and the products available as we made our purchasing decision. We discuss our evaluation method and provide a checklist that can be used by other departments to assist with their evaluation process. The criteria used in the evaluation process fall into the following major categories: user operations, technical infrastructure, medical dictionary, system interfaces, service support, cost, and company strength. Conclusions drawn from our evaluation process will be detailed, with the intention being to shorten the process for others as they embark on a similar venture. As more and more organizations investigate the many products and services that are now being offered to enhance the operations of a radiology department, it becomes increasingly important that solid methods are used to most effectively evaluate the new products. This report should help others complete the task of evaluating a voice recognition system and may be adaptable to other products as well. PMID:11442123

  8. 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

  9. 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

    2015-01-01

    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, our

  10. 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.

  11. 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

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

    NASA Astrophysics Data System (ADS)

    Hui, Zeng; Qiang, Li; Yu, Gu

    2016-02-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-02-01

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

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

    PubMed

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

    2012-01-01

    This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance. PMID:22438704

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

    PubMed

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

    2014-01-01

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

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

    PubMed Central

    2014-01-01

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

  17. 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)

  18. 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.

  19. 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. PMID:10840803

  20. 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.

  1. Development of a written music-recognition system using Java and open source technologies

    NASA Astrophysics Data System (ADS)

    Loibner, Gernot; Schwarzl, Andreas; Kovač, Matthias; Paulus, Dietmar; Pölzleitner, Wolfgang

    2005-10-01

    We report on the development of a software system to recognize and interpret printed music. The overall goal is to scan printed music sheets, analyze and recognize the notes, timing, and written text, and derive the all necessary information to use the computers MIDI sound system to play the music. This function is primarily useful for musicians who want to digitize printed music for editing purposes. There exist a number of commercial systems that offer such a functionality. However, on testing these systems, we were astonished on how weak they behave in their pattern recognition parts. Although we submitted very clear and rather flawless scanning input, none of these systems was able to e.g. recognize all notes, staff lines, and systems. They all require a high degree of interaction, post-processing, and editing to get a decent digital version of the hard copy material. In this paper we focus on the pattern recognition area. In a first approach we tested more or less standard methods of adaptive thresholding, blob detection, line detection, and corner detection to find the notes, staff lines, and candidate objects subject to OCR. Many of the objects on this type of material can be learned in a training phase. None of the commercial systems we saw offers the option to train special characters or unusual signatures. A second goal in this project is to use a modern software engineering platform. We were interested in how well Java and open source technologies are suitable for pattern recognition and machine vision. The scanning of music served as a case-study.

  2. A bio-inspired system for spatio-temporal recognition in static and video imagery

    NASA Astrophysics Data System (ADS)

    Khosla, Deepak; Moore, Christopher K.; Chelian, Suhas

    2007-04-01

    This paper presents a bio-inspired method for spatio-temporal recognition in static and video imagery. It builds upon and extends our previous work on a bio-inspired Visual Attention and object Recognition System (VARS). The VARS approach locates and recognizes objects in a single frame. This work presents two extensions of VARS. The first extension is a Scene Recognition Engine (SCE) that learns to recognize spatial relationships between objects that compose a particular scene category in static imagery. This could be used for recognizing the category of a scene, e.g., office vs. kitchen scene. The second extension is the Event Recognition Engine (ERE) that recognizes spatio-temporal sequences or events in sequences. This extension uses a working memory model to recognize events and behaviors in video imagery by maintaining and recognizing ordered spatio-temporal sequences. The working memory model is based on an ARTSTORE1 neural network that combines an ART-based neural network with a cascade of sustained temporal order recurrent (STORE)1 neural networks. A series of Default ARTMAP classifiers ascribes event labels to these sequences. Our preliminary studies have shown that this extension is robust to variations in an object's motion profile. We evaluated the performance of the SCE and ERE on real datasets. The SCE module was tested on a visual scene classification task using the LabelMe2 dataset. The ERE was tested on real world video footage of vehicles and pedestrians in a street scene. Our system is able to recognize the events in this footage involving vehicles and pedestrians.

  3. 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

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

    PubMed

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

    2015-01-01

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

  5. Integrin activation state determines selectivity for novel recognition sites in fibrillar collagens.

    PubMed

    Siljander, Pia R-M; Hamaia, Samir; Peachey, Anthony R; Slatter, David A; Smethurst, Peter A; Ouwehand, Willem H; Knight, C Graham; Farndale, Richard W

    2004-11-12

    Only three recognition motifs, GFOGER, GLOGER, and GASGER, all present in type I collagen, have been identified to date for collagen-binding integrins, such as alpha(2)beta(1). Sequence alignment was used to investigate the occurrence of related motifs in other human fibrillar collagens, and located a conserved array of novel GER motifs within their triple helical domains. We compared the integrin binding properties of synthetic triple helical peptides containing examples of such sequences (GLSGER, GMOGER, GAOGER, and GQRGER) or the previously identified motifs. Recombinant inserted (I) domains of integrin subunits alpha(1), alpha(2) and alpha(11) all bound poorly to all motifs other than GFOGER and GLOGER. Similarly, alpha(2)beta(1) -containing resting platelets adhered well only to GFOGER and GLOGER, while ADP-activated platelets, HT1080 cells and two active alpha(2)I domain mutants (E318W, locked open) bound all motifs well, indicating that affinity modulation determines the sequence selectivity of integrins. GxO/SGER peptides inhibited platelet adhesion to collagen monomers with order of potency F >/= L >/= M > A. These results establish GFOGER as a high affinity sequence, which can interact with the alpha(2)I domain in the absence of activation and suggest that integrin reactivity of collagens may be predicted from their GER content. PMID:15345717

  6. Self-recognition of one's own fall recruits the genuine bodily crisis-related brain activity.

    PubMed

    Atomi, Tomoaki; Noriuchi, Madoka; Oba, Kentaro; Atomi, Yoriko; Kikuchi, Yoshiaki

    2014-01-01

    While bipedalism is a fundamental evolutionary adaptation thought to be essential for the development of the human brain, the erect body is always an inch or two away from falling. Although the neural mechanism for automatically detecting one's own body instability is an important consideration, there have thus far been few functional neuroimaging studies because of the restrictions placed on participants' movements. Here, we used functional magnetic resonance imaging to investigate the neural substrate underlying whole body instability, based on the self-recognition paradigm that uses video stimuli consisting of one's own and others' whole bodies depicted in stable and unstable states. Analyses revealed significant activity in the regions which would be activated during genuine unstable bodily states: The right parieto-insular vestibular cortex, inferior frontal junction, posterior insula and parabrachial nucleus. We argue that these right-lateralized cortical and brainstem regions mediate vestibular information processing for detection of vestibular anomalies, defensive motor responding in which the necessary motor responses are automatically prepared/simulated to protect one's own body, and sympathetic activity as a form of alarm response during whole body instability. PMID:25525808

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

    PubMed

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

    2015-07-01

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

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

    PubMed Central

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

    2015-01-01

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

  9. 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.

  10. European Neutron Activation System.

    2013-01-11

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

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

    ERIC Educational Resources Information Center

    Hodges, Diane

    2004-01-01

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

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

    PubMed

    Nam, Yunyoung; Park, Jung Wook

    2013-03-01

    This paper presents a child activity recognition approach using a single 3-axis accelerometer and a barometric pressure sensor worn on a waist of the body to prevent child accidents such as unintentional injuries at home. Labeled accelerometer data are collected from children of both sexes up to the age of 16 to 29 months. To recognize daily activities, mean, standard deviation, and slope of time-domain features are calculated over sliding windows. In addition, the FFT analysis is adopted to extract frequency-domain features of the aggregated data, and then energy and correlation of acceleration data are calculated. Child activities are classified into 11 daily activities which are wiggling, rolling, standing still, standing up, sitting down, walking, toddling, crawling, climbing up, climbing down, and stopping. The overall accuracy of activity recognition was 98.43% using only a single- wearable triaxial accelerometer sensor and a barometric pressure sensor with a support vector machine. PMID:24235114

  13. Structural basis of RNA recognition and activation by innate immune receptor RIG-I

    SciTech Connect

    Jiang, Fuguo; Ramanathan, Anand; Miller, Matthew T.; Tang, Guo-Qing; Gale, Jr., Michael; Patel, Smita S.; Marcotrigiano, Joseph

    2012-05-29

    Retinoic-acid-inducible gene-I (RIG-I; also known as DDX58) is a cytoplasmic pathogen recognition receptor that recognizes pathogen-associated molecular pattern (PAMP) motifs to differentiate between viral and cellular RNAs. RIG-I is activated by blunt-ended double-stranded (ds)RNA with or without a 5'-triphosphate (ppp), by single-stranded RNA marked by a 5'-ppp and by polyuridine sequences. Upon binding to such PAMP motifs, RIG-I initiates a signalling cascade that induces innate immune defences and inflammatory cytokines to establish an antiviral state. The RIG-I pathway is highly regulated and aberrant signalling leads to apoptosis, altered cell differentiation, inflammation, autoimmune diseases and cancer. The helicase and repressor domains (RD) of RIG-I recognize dsRNA and 5'-ppp RNA to activate the two amino-terminal caspase recruitment domains (CARDs) for signalling. Here, to understand the synergy between the helicase and the RD for RNA binding, and the contribution of ATP hydrolysis to RIG-I activation, we determined the structure of human RIG-I helicase-RD in complex with dsRNA and an ATP analogue. The helicase-RD organizes into a ring around dsRNA, capping one end, while contacting both strands using previously uncharacterized motifs to recognize dsRNA. Small-angle X-ray scattering, limited proteolysis and differential scanning fluorimetry indicate that RIG-I is in an extended and flexible conformation that compacts upon binding RNA. These results provide a detailed view of the role of helicase in dsRNA recognition, the synergy between the RD and the helicase for RNA binding and the organization of full-length RIG-I bound to dsRNA, and provide evidence of a conformational change upon RNA binding. The RIG-I helicase-RD structure is consistent with dsRNA translocation without unwinding and cooperative binding to RNA. The structure yields unprecedented insight into innate immunity and has a broader impact on other areas of biology, including RNA

  14. 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.

  15. Toward fast feature adaptation and localization for real-time face recognition systems

    NASA Astrophysics Data System (ADS)

    Zuo, Fei; de With, Peter H.

    2003-06-01

    In a home environment, video surveillance employing face detection and recognition is attractive for new applications. Facial feature (e.g. eyes and mouth) localization in the face is an essential task for face recognition because it constitutes an indispensable step for face geometry normalization. This paper presents a new and efficient feature localization approach for real-time personal surveillance applications with low-quality images. The proposed approach consists of three major steps: (1) self-adaptive iris tracing, which is preceded by a trace-point selection process with multiple initializations to overcome the local convergence problem, (2) eye structure verification using an eye template with limited deformation freedom, and (3) eye-pair selection based on a combination of metrics. We have tested our facial feature localization method on about 100 randomly selected face images from the AR database and 30 face images downloaded from the Internet. The results show that our approach achieves a correct detection rate of 96%. Since our eye-selection technique does not involve time-consuming deformation processes, it yields relatively fast processing. The proposed algorithm has been successfully applied to a real-time home video surveillance system and proven to be an effective and computationally efficient face normalization method preceding the face recognition.

  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

    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 Sensors 2014, 14 18132 high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches. PMID:25268914

  17. 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

  18. Familiarity-Based Responding in Item Recognition: Evidence for the Role of Spreading Activation.

    ERIC Educational Resources Information Center

    Macht, Michael L.; O'Brien, Edward J.

    1980-01-01

    Results of three experiments indicated that latency of correct recognition was sensitive to the influence of a priming treatment. The magnitude of the priming effect depended on both the taxonomic frequency of the probe items, and the length of the interval between the prime and the recognition test. (Author/RD)

  19. Lipid activation of the signal recognition particle receptor provides spatial coordination of protein targeting

    PubMed Central

    Lam, Vinh Q.; Akopian, David; Rome, Michael; Henningsen, Doug

    2010-01-01

    The signal recognition particle (SRP) and SRP receptor comprise the major cellular machinery that mediates the cotranslational targeting of proteins to cellular membranes. It remains unclear how the delivery of cargos to the target membrane is spatially coordinated. We show here that phospholipid binding drives important conformational rearrangements that activate the bacterial SRP receptor FtsY and the SRP–FtsY complex. This leads to accelerated SRP–FtsY complex assembly, and allows the SRP–FtsY complex to more efficiently unload cargo proteins. Likewise, formation of an active SRP–FtsY GTPase complex exposes FtsY’s lipid-binding helix and enables stable membrane association of the targeting complex. Thus, membrane binding, complex assembly with SRP, and cargo unloading are inextricably linked to each other via conformational changes in FtsY. These allosteric communications allow the membrane delivery of cargo proteins to be efficiently coupled to their subsequent unloading and translocation, thus providing spatial coordination during protein targeting. PMID:20733058

  20. Platelet receptor recognition and cross-talk in collagen-induced activation of platelets.

    PubMed

    Farndale, R W; Slatter, D A; Siljander, P R-M; Jarvis, G E

    2007-07-01

    Comprehensive mapping of protein-binding sites within human collagen III has allowed the recognition motifs for integrin alpha(2)beta(1) and VWF A3 domain to be identified. Glycoprotein VI-binding sites are understood, although less well defined. This information, together with recent developments in understanding collagen fiber architecture, and crystal structures of the receptor collagen-binding domains, allows a coherent model for the interaction of collagen with the platelet surface to be developed. This complements our understanding of the orchestration of receptor presentation by membrane microdomains, such that the polyvalent collagen surface may stabilize signaling complexes within the heterogeneous receptor composition of the lipid raft. The ensuing interactions lead to the convergence of signals from each of the adhesive receptors, mediated by FcR gamma-chain and/or FcgammaRIIa, leading to concerted and co-operative platelet activation. Each receptor has a shear-dependent role, VWF/GpIb essential at high shear, and alpha(2)beta(1) at low and intermediate shear, whilst GpVI provides core signals that contribute to enhanced integrin affinity, tighter binding to collagen and consequent platelet activation. PMID:17635730

  1. The MPN domain of Caenorhabditis elegans UfSP modulates both substrate recognition and deufmylation activity.

    PubMed

    Ha, Byung Hak; Kim, Kyung Hee; Yoo, Hee Min; Lee, Weontae; EunKyeong Kim, Eunice

    2016-08-01

    Ubiquitin-fold modifier 1 (Ufm1) specific protease (UfSP) is a novel cysteine protease that activates Ufm1 from its precursor by processing the C-terminus to expose the conserved Gly necessary for substrate conjugation and de-conjugates Ufm1 from the substrate. There are two forms: UfSP1 and UfSP2, the later with an additional domain at the N-terminus. Ufm1 and both the conjugating and deconjugating enzymes are highly conserved. However, in Caenorhabditis elegans there is one UfSP which has extra 136 residues at the N terminus compared to UfSP2. The crystal structure of cUfSP reveals that these additional residues display a MPN fold while the rest of the structure mimics that of UfSP2. The MPN domain does not have the metalloprotease activity found in some MPN-domain containing protein, rather it is required for the recognition and deufmylation of the substrate of cUfSP, UfBP1. In addition, the MPN domain is also required for localization to the endoplasmic reticulum. PMID:27240952

  2. Multiresolution stroke sketch adaptive representation and neural network processing system for gray-level image recognition

    NASA Astrophysics Data System (ADS)

    Meystel, Alexander M.; Rybak, Ilya A.; Bhasin, Sanjay

    1992-11-01

    This paper describes a method for multiresolutional representation of gray-level images as hierarchial sets of strokes characterizing forms of objects with different degrees of generalization depending on the context of the image. This method transforms the original image into a hierarchical graph which allows for efficient coding in order to store, retrieve, and recognize the image. The method which is described is based upon finding the resolution levels for each image which minimizes the computations required. This becomes possible because of the use of a special image representation technique called Multiresolutional Attentional Representation for Recognition, based upon a feature which the authors call a stroke. This feature turns out to be efficient in the process of finding the appropriate system of resolutions and construction of the relational graph. Multiresolutional Attentional Representation for Recognition (MARR) is formed by a multi-layer neural network with recurrent inhibitory connections between neurons, the receptive fields of which are selectively tuned to detect the orientation of local contrasts in parts of the image with appropriate degree of generalization. This method simulates the 'coarse-to-fine' algorithm which an artist usually uses, making at attentional sketch of real images. The method, algorithms, and neural network architecture in this system can be used in many machine-vision systems with AI properties; in particular, robotic vision. We expect that systems with MARR can become a component of intelligent control systems for autonomous robots. Their architectures are mostly multiresolutional and match well with the multiple resolutions of the MARR structure.

  3. Feature-specific imaging: Extensions to adaptive object recognition and active illumination based scene reconstruction

    NASA Astrophysics Data System (ADS)

    Baheti, Pawan K.

    Computational imaging (CI) systems are hybrid imagers in which the optical and post-processing sub-systems are jointly optimized to maximize the task-specific performance. In this dissertation we consider a form of CI system that measures the linear projections (i.e., features) of the scene optically, and it is commonly referred to as feature-specific imaging (FSI). Most of the previous work on FSI has been concerned with image reconstruction. Previous FSI techniques have also been non-adaptive and restricted to the use of ambient illumination. We consider two novel extensions of the FSI system in this work. We first present an adaptive feature-specific imaging (AFSI) system and consider its application to a face-recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. We present both statistical and information-theoretic adaptation mechanisms for the AFSI system. The sequential hypothesis testing framework is used to determine the number of measurements required for achieving a specified misclassification probability. We demonstrate that AFSI system requires significantly fewer measurements than static-FSI (SFSI) and conventional imaging at low signal-to-noise ratio (SNR). We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage. Experimental results validating the AFSI system are presented. Next we present a FSI system based on the use of structured light. Feature measurements are obtained by projecting spatially structured illumination onto an object and collecting all of the reflected light onto a single photodetector. We refer to this system as feature-specific structured imaging (FSSI). Principal component features are used to define the illumination patterns. The optimal LMMSE operator is used to generate object estimates from the measurements. We demonstrate that this new imaging approach reduces imager complexity and provides improved image

  4. Reliability and Validity of Bilateral Ankle Accelerometer Algorithms for Activity Recognition and Walking Speed After Stroke

    PubMed Central

    Dobkin, Bruce H.; Xu, Xiaoyu; Batalin, Maxim; Thomas, Seth; Kaiser, William

    2015-01-01

    Background and Purpose Outcome measures of mobility for large stroke trials are limited to timed walks for short distances in a laboratory, step counters and ordinal scales of disability and quality of life. Continuous monitoring and outcome measurements of the type and quantity of activity in the community would provide direct data about daily performance, including compliance with exercise and skills practice during routine care and clinical trials. Methods Twelve adults with impaired ambulation from hemiparetic stroke and 6 healthy controls wore triaxial accelerometers on their ankles. Walking speed for repeated outdoor walks was determined by machine-learning algorithms and compared to a stopwatch calculation of speed for distances not known to the algorithm. The reliability of recognizing walking, exercise, and cycling by the algorithms was compared to activity logs. Results A high correlation was found between stopwatch-measured outdoor walking speed and algorithm-calculated speed (Pearson coefficient, 0.98; P=0.001) and for repeated measures of algorithm-derived walking speed (P=0.01). Bouts of walking >5 steps, variations in walking speed, cycling, stair climbing, and leg exercises were correctly identified during a day in the community. Compared to healthy subjects, those with stroke were, as expected, more sedentary and slower, and their gait revealed high paretic-to-unaffected leg swing ratios. Conclusions Test–retest reliability and concurrent and construct validity are high for activity pattern-recognition Bayesian algorithms developed from inertial sensors. This ratio scale data can provide real-world monitoring and outcome measurements of lower extremity activities and walking speed for stroke and rehabilitation studies. PMID:21636815

  5. A short-type peptidoglycan recognition protein from the silkworm: expression, characterization and involvement in the prophenoloxidase activation pathway.

    PubMed

    Chen, Kangkang; Liu, Chen; He, Yan; Jiang, Haobo; Lu, Zhiqiang

    2014-07-01

    Recognition of invading microbes as non-self is the first step of immune responses. In insects, peptidoglycan recognition proteins (PGRPs) detect peptidoglycans (PGs) of bacterial cell wall, leading to the activation of defense responses. Twelve PGRPs have been identified in the silkworm, Bombyx mori, through bioinformatics analysis. However, their biochemical functions are mostly uncharacterized. In this study, we found PGRP-S5 transcript levels were up-regulated in fat body and midgut after bacterial infection. Using recombinant protein isolated from Escherichia coli, we showed that PGRP-S5 binds to PGs from certain bacterial strains and induces bacteria agglutination. Enzyme activity assay confirmed PGRP-S5 is an amidase; we also showed it is an antibacterial protein effective against both Gram-positive and -negative bacteria. Additionally, we demonstrated that specific recognition of PGs by PGRP-S5 is involved in the prophenoloxidase activation pathway. Together, these data suggest the silkworm PGRP-S5 functions as a pattern recognition receptor for the prophenoloxidase pathway initiation and as an effecter to inhibit bacterial growth as well. We finally discussed possible roles of PGRP-S5 as a receptor for antimicrobial peptide gene induction and as an immune modulator in the midgut. PMID:24508981

  6. 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.

  7. CGP 36,742, an orally active GABAB receptor antagonist, facilitates memory in a social recognition test in rats.

    PubMed

    Mondadori, C; Moebius, H J; Zingg, M

    1996-05-01

    CGP 36,742, an orally active GABAB receptor antagonist, improves the retention performance of rats in a social recognition test. This effect is detectable over a very wide range of doses (0.03 to 300 mg/kg, p.o.). Considering its binding (32 mumol affinity for the GABAB site) the surprisingly potent activity of CGP 36,742 makes it appear quite possible that the effect is mediated by an as yet unknown receptor subtype. PMID:8762176

  8. 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.

  9. 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.

  10. γ-Cyclodextrin capped silver nanoparticles for molecular recognition and enhancement of antibacterial activity of chloramphenicol.

    PubMed

    Gannimani, Ramesh; Ramesh, Muthusamy; Mtambo, Sphamandla; Pillay, Karen; Soliman, Mahmoud E; Govender, Patrick

    2016-04-01

    Computational studies were conducted to identify the favourable formation of the inclusion complex of chloramphenicol with cyclodextrins. The results of molecular docking and molecular dynamics predicted the strongest interaction of chloramphenicol with γ-cyclodextrin. Further, the inclusion complex of chloramphenicol with γ-cyclodextrin was experimentally prepared and a phenomenon of inclusion was verified by using different characterization techniques such as thermogravimetric analysis, differential scanning calorimetry, (1)H nuclear magnetic resonance (NMR) and two dimensional nuclear overhauser effect spectroscopy (NOESY) experiments. From these results it was concluded that γ-cyclodextrins could be an appropriate cyclodextrin polymer which can be used to functionalize chloramphenicol on the surface of silver nanoparticles. In addition, γ-cyclodextrin capped silver nanoparticles were synthesized and characterized using UV-visible spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray analysis (EDX), Fourier transform infrared spectroscopy (FTIR) and zeta potential analysis. Molecular recognition of chloramphenicol by these cyclodextrin capped silver nanoparticles was confirmed by surface enhanced raman spectroscopy (SERS) experiments. Synergistic antibacterial effect of chloramphenicol with γ-cyclodextrin capped silver nanoparticles was evaluated against Pseudomonas aeruginosa (ATCC 27853), Enterococcus faecalis (ATCC 5129), Klebsiella pneumoniae (ATCC 700603) and Staphylococcus aureus (ATCC 43300). The results from the antibacterial experiment were favourable thus allowing us to conclude that the approach of modifying organic drug molecules with cyclodextrin capped inorganic silver nanoparticles could help to enhance the antibacterial activity of them. PMID:26824520

  11. 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.

  12. 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. PMID:25014934

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

    PubMed Central

    Du, Lin; Zhang, Fan; Liu, Ming

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Yew, Chuu Tian; Suandi, Shahrel Azmin

    2012-04-01

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

  15. 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. PMID:22996721

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

    PubMed

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

    2016-03-01

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

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

    PubMed

    Wang, Wen-Jie; Cheng, Wang; Luo, Ming; Yan, Qingyu; Yu, Hong-Mei; Li, Qiong; Cao, Dong-Dong; Huang, Shengfeng; Xu, Anlong; Mariuzza, Roy A; Chen, Yuxing; Zhou, Cong-Zhao

    2015-01-01

    Peptidoglycan recognition proteins (PGRPs), which have been identified in most animals, are pattern recognition molecules that involve antimicrobial defense. Resulting from extraordinary expansion of innate immune genes, the amphioxus encodes many PGRPs of diverse functions. For instance, three isoforms of PGRP encoded by Branchiostoma belcheri tsingtauense, termed BbtPGRP1~3, are fused with a chitin binding domain (CBD) at the N-terminus. Here we report the 2.7 Å crystal structure of BbtPGRP3, revealing an overall structure of an N-terminal hevein-like CBD followed by a catalytic PGRP domain. Activity assays combined with site-directed mutagenesis indicated that the individual PGRP domain exhibits amidase activity towards both DAP-type and Lys-type peptidoglycans (PGNs), the former of which is favored. The N-terminal CBD not only has the chitin-binding activity, but also enables BbtPGRP3 to gain a five-fold increase of amidase activity towards the Lys-type PGNs, leading to a significantly broadened substrate spectrum. Together, we propose that modular evolution via domain shuffling combined with gene horizontal transfer makes BbtPGRP1~3 novel PGRPs of augmented catalytic activity and broad recognition spectrum. PMID:26479246

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

    PubMed Central

    Wang, Wen-Jie; Cheng, Wang; Luo, Ming; Yan, Qingyu; Yu, Hong-Mei; Li, Qiong; Cao, Dong-Dong; Huang, Shengfeng; Xu, Anlong; Mariuzza, Roy A.; Chen, Yuxing; Zhou, Cong-Zhao

    2015-01-01

    Peptidoglycan recognition proteins (PGRPs), which have been identified in most animals, are pattern recognition molecules that involve antimicrobial defense. Resulting from extraordinary expansion of innate immune genes, the amphioxus encodes many PGRPs of diverse functions. For instance, three isoforms of PGRP encoded by Branchiostoma belcheri tsingtauense, termed BbtPGRP1~3, are fused with a chitin binding domain (CBD) at the N-terminus. Here we report the 2.7 Å crystal structure of BbtPGRP3, revealing an overall structure of an N-terminal hevein-like CBD followed by a catalytic PGRP domain. Activity assays combined with site-directed mutagenesis indicated that the individual PGRP domain exhibits amidase activity towards both DAP-type and Lys-type peptidoglycans (PGNs), the former of which is favored. The N-terminal CBD not only has the chitin-binding activity, but also enables BbtPGRP3 to gain a five-fold increase of amidase activity towards the Lys-type PGNs, leading to a significantly broadened substrate spectrum. Together, we propose that modular evolution via domain shuffling combined with gene horizontal transfer makes BbtPGRP1~3 novel PGRPs of augmented catalytic activity and broad recognition spectrum. PMID:26479246

  19. 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,…

  20. Selective recognition of acetylated histones by bromodomains in transcriptional co-activators

    PubMed Central

    Hassan, Ahmed H.; Awad, Salma; Al-Natour, Zeina; Othman, Samah; Mustafa, Farah; Rizvi, Tahir A.

    2006-01-01

    Bromodomains are present in many chromatin-associated proteins such as the SWI/SNF and RSC chromatin remodelling and the SAGA HAT (histone acetyltransferase) complexes, and can bind to acetylated lysine residues in the N-terminal tails of the histones. Lysine acetylation is a histone modification that forms a stable epigenetic mark on chromatin for bromodomain-containing proteins to dock and in turn regulate gene expression. In order to better understand how bromodomains read the ‘histone code’ and interact with acetylated histones, we have tested the interactions of several bromodomains within transcriptional co-activators with differentially acetylated histone tail peptides and HAT-acetylated histones. Using GST (glutathione S-transferase) pull-down assays, we show specificity of binding of some bromodomains to differentially acetylated H3 and H4 peptides as well as HAT-acetylated histones. Our results reveal that the Swi2/Snf2 bromodomain interacts with various acetylated H3 and H4 peptides, whereas the Gcn5 bromodomain interacts only with acetylated H3 peptides and tetra-acetylated H4 peptides. Additionally we show that the Spt7 bromodomain interacts with acetylated H3 peptides weakly, but not with acetylated H4 peptides. Some bromodomains such as the Bdf1-2 do not interact with most of the acetylated peptides tested. Results of the peptide experiments are confirmed with tests of interactions between these bromodomains and HAT-acetylated histones. Furthermore, we demonstrate that the Swi2/Snf2 bromodomain is important for the binding and the remodelling activity of the SWI/SNF complex on hyperacetylated nucleosomes. The selective recognition of the bromodomains observed in the present study accounts for the broad effects of bromodomain-containing proteins observed on binding to histones. PMID:17049045

  1. Automatic intraductal breast carcinoma classification using a neural network-based recognition system.

    PubMed

    Reigosa, A; Hernández, L; Torrealba, V; Barrios, V; Montilla, G; Bosnjak, A; Araez, M; Turiaf, M; Leon, A

    1998-07-01

    A contour-based automatic recognition system was applied to classify intraductal breast carcinoma into high nuclear grade and low nuclear grade in a digitized histologic image. The image discriminating characteristics were selected by their invariability condition to rotation and translation. They were acquired from cellular contours information. The totally interconnected multilayer perceptron network architecture was selected, and it was trained with the error back propagation algorithm. Forty cases were analyzed by the system and the diagnoses were compared with that of pathologist consensus, obtaining agreement in 97.5% (p < .00001 of cases). The system may become a very useful tool for the pathologist in the definitive classification of intraductal carcinoma. PMID:21223442

  2. A Single Accelerometer based Wireless Embedded System for Predefined Dynamic Gesture Recognition

    NASA Astrophysics Data System (ADS)

    Parsani, Rahul; Singh, Karandeep

    The use of hand gestures provides an attractive alternative to cumbersome interface devices for human-computer interaction. A complete embedded system which facilitates the data acquisition, analysis, recognition, and the transmission wirelessly, of human dynamic gestures to a computer, is described. An intuitive algorithm for processing the accelerometer data was implemented and tested. This method permits all the analysis to be done by the embedded system processor. The system is capable of recognizing gestures involving a combination of straight line motions in three dimensions. These gestures are then used to control a host computer which executes tasks based on the gesture received. A sample application showing how the gestures can be mapped to the English alphabet is also shown.

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

    USGS Publications Warehouse

    Maddox, Brian G.; Swadley, Casey L.

    2002-01-01

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

  4. Material recognition with the Medipix photon counting colour X-ray system

    NASA Astrophysics Data System (ADS)

    Norlin, B.; Manuilskiy, A.; Nilsson, H.-E.; Fröjdh, C.

    2004-09-01

    An energy sensitive imaging system like Medipix1 has proved to be promising in distinguishing different materials in an X-ray image of an object. We propose a general method utilising X-ray energy information for material recognition. For objects where the thickness of the materials is unknown, a convenient material parameter to identify is K=α1/α2, which is the ratio of the logarithms of the measured transmissions ln(t1)/ln(t2). If a database of the parameter K for different materials and energies is created, this method can be used for material recognition independent of the thickness of the materials.Series of images of an object consisting of aluminium and silicon were taken with different energy thresholds. The X-ray absorption for silicon and aluminium is very similar for the range 40-60keV and only differs for lower energies. The results show that it is possible to distinguish between aluminium and silicon on images achieved by Medipix1 using a standard dental source. By decreasing the spatial resolution a better contrast between the materials was achieved. The resolution of contrasts shown by the histograms was close to the limit of the system due to the statistical noise of the signal.

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

    NASA Astrophysics Data System (ADS)

    Dhawan, Atam P.

    1988-02-01

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

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

    NASA Technical Reports Server (NTRS)

    Dhawan, Atam P.

    1988-01-01

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

  7. 77 FR 31043 - National Technical Systems, Inc.: Expiration of Recognition as a Nationally Recognized Testing...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-05-24

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

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

    PubMed

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

    2016-08-16

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-07-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2008-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1998-03-01

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

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

    PubMed

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

    2015-06-18

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed Central

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

    2016-01-01

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

  15. Monitoring Activation of the Antiviral Pattern Recognition Receptors RIG-I And PKR By Limited Protease Digestion and Native PAGE

    PubMed Central

    Weber, Michaela; Weber, Friedemann

    2014-01-01

    Host defenses to virus infection are dependent on a rapid detection by pattern recognition receptors (PRRs) of the innate immune system. In the cytoplasm, the PRRs RIG-I and PKR bind to specific viral RNA ligands. This first mediates conformational switching and oligomerization, and then enables activation of an antiviral interferon response. While methods to measure antiviral host gene expression are well established, methods to directly monitor the activation states of RIG-I and PKR are only partially and less well established. Here, we describe two methods to monitor RIG-I and PKR stimulation upon infection with an established interferon inducer, the Rift Valley fever virus mutant clone 13 (Cl 13). Limited trypsin digestion allows to analyze alterations in protease sensitivity, indicating conformational changes of the PRRs. Trypsin digestion of lysates from mock infected cells results in a rapid degradation of RIG-I and PKR, whereas Cl 13 infection leads to the emergence of a protease-resistant RIG-I fragment. Also PKR shows a virus-induced partial resistance to trypsin digestion, which coincides with its hallmark phosphorylation at Thr 446. The formation of RIG-I and PKR oligomers was validated by native polyacrylamide gel electrophoresis (PAGE). Upon infection, there is a strong accumulation of RIG-I and PKR oligomeric complexes, whereas these proteins remained as monomers in mock infected samples. Limited protease digestion and native PAGE, both coupled to western blot analysis, allow a sensitive and direct measurement of two diverse steps of RIG-I and PKR activation. These techniques are relatively easy and quick to perform and do not require expensive equipment. PMID:25146252

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

    PubMed

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

    2014-01-01

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

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

    SciTech Connect

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

    1980-09-01

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

  18. Brain activity evidence for recognition without recollection after early hippocampal damage

    PubMed Central

    Düzel, E.; Vargha-Khadem, F.; Heinze, H. J.; Mishkin, M.

    2001-01-01

    Amnesic patients with early and seemingly isolated hippocampal injury show relatively normal recognition memory scores. The cognitive profile of these patients raises the possibility that this recognition performance is maintained mainly by stimulus familiarity in the absence of recollection of contextual information. Here we report electrophysiological data on the status of recognition memory in one of the patients, Jon. Jon's recognition of studied words lacks the event-related potential (ERP) index of recollection, viz., an increase in the late positive component (500–700 ms), under conditions that elicit it reliably in normal subjects. On the other hand, a decrease of the ERP amplitude between 300 and 500 ms, also reliably found in normal subjects, is well preserved. This so-called N400 effect has been linked to stimulus familiarity in previous ERP studies of recognition memory. In Jon, this link is supported by the finding that his recognized and unrecognized studied words evoked topographically distinct ERP effects in the N400 time window. These data suggest that recollection is more dependent on the hippocampal formation than is familiarity, consistent with the view that the hippocampal formation plays a special role in episodic memory, for which recollection is so critical. PMID:11438748

  19. Speech recognition and understanding

    SciTech Connect

    Vintsyuk, T.K.

    1983-05-01

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

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

    PubMed

    Smitha, K G; Vinod, A P

    2015-11-01

    Children with autism spectrum disorder have difficulty in understanding the emotional and mental states from the facial expressions of the people they interact. The inability to understand other people's emotions will hinder their interpersonal communication. Though many facial emotion recognition algorithms have been proposed in the literature, they are mainly intended for processing by a personal computer, which limits their usability in on-the-move applications where portability is desired. The portability of the system will ensure ease of use and real-time emotion recognition and that will aid for immediate feedback while communicating with caretakers. Principal component analysis (PCA) has been identified as the least complex feature extraction algorithm to be implemented in hardware. In this paper, we present a detailed study of the implementation of serial and parallel implementation of PCA in order to identify the most feasible method for realization of a portable emotion detector for autistic children. The proposed emotion recognizer architectures are implemented on Virtex 7 XC7VX330T FFG1761-3 FPGA. We achieved 82.3% detection accuracy for a word length of 8 bits. PMID:26239162