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Sample records for efficient signal recognition

  1. A two-step recognition of signal sequences determines the translocation efficiency of proteins.

    Science.gov (United States)

    Belin, D; Bost, S; Vassalli, J D; Strub, K

    1996-02-01

    The cytosolic and secreted, N-glycosylated, forms of plasminogen activator inhibitor-2 (PAI-2) are generated by facultative translocation. To study the molecular events that result in the bi-topological distribution of proteins, we determined in vitro the capacities of several signal sequences to bind the signal recognition particle (SRP) during targeting, and to promote vectorial transport of murine PAI-2 (mPAI-2). Interestingly, the six signal sequences we compared (mPAI-2 and three mutated derivatives thereof, ovalbumin and preprolactin) were found to have the differential activities in the two events. For example, the mPAI-2 signal sequence first binds SRP with moderate efficiency and secondly promotes the vectorial transport of only a fraction of the SRP-bound nascent chains. Our results provide evidence that the translocation efficiency of proteins can be controlled by the recognition of their signal sequences at two steps: during SRP-mediated targeting and during formation of a committed translocation complex. This second recognition may occur at several time points during the insertion/translocation step. In conclusion, signal sequences have a more complex structure than previously anticipated, allowing for multiple and independent interactions with the translocation machinery.

  2. A two-step recognition of signal sequences determines the translocation efficiency of proteins.

    OpenAIRE

    Belin, D; Bost, S; Vassalli, J D; Strub, K

    1996-01-01

    The cytosolic and secreted, N-glycosylated, forms of plasminogen activator inhibitor-2 (PAI-2) are generated by facultative translocation. To study the molecular events that result in the bi-topological distribution of proteins, we determined in vitro the capacities of several signal sequences to bind the signal recognition particle (SRP) during targeting, and to promote vectorial transport of murine PAI-2 (mPAI-2). Interestingly, the six signal sequences we compared (mPAI-2 and three mutated...

  3. Automatic recognition of smoke-plume signatures in lidar signal

    Science.gov (United States)

    Utkin, Andrei B.; Lavrov, Alexander; Vilar, Rui

    2008-10-01

    A simple and robust algorithm for lidar-signal classification based on the fast extraction of sufficiently pronounced peaks and their recognition with a perceptron, whose efficiency is enhanced by a fast nonlinear preprocessing that increases the signal dimension, is reported. The method allows smoke-plume recognition with an error rate as small as 0.31% (19 misdetections and 4 false alarms in analyzing a test set of 7409 peaks).

  4. Automatic modulation recognition of communication signals

    CERN Document Server

    Azzouz, Elsayed Elsayed

    1996-01-01

    Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process. Drawing on several years of research, the authors provide a cr...

  5. Partially Supervised Approach in Signal Recognition

    Directory of Open Access Journals (Sweden)

    Catalina COCIANU

    2009-01-01

    Full Text Available The paper focuses on the potential of principal directions based approaches in signal classification and recognition. In probabilistic models, the classes are represented in terms of multivariate density functions, and an object coming from a certain class is modeled as a random vector whose repartition has the density function corresponding to this class. In cases when there is no statistical information concerning the set of density functions corresponding to the classes involved in the recognition process, usually estimates based on the information extracted from available data are used instead. In the proposed methodology, the characteristics of a class are given by a set of eigen vectors of the sample covariance matrix. The overall dissimilarity of an object X with a given class C is computed as the disturbance of the structure of C, when X is allotted to C. A series of tests concerning the behavior of the proposed recognition algorithm are reported in the final section of the paper.

  6. Efficient Interaction Recognition through Positive Action Representation

    Directory of Open Access Journals (Sweden)

    Tao Hu

    2013-01-01

    Full Text Available This paper proposes a novel approach to decompose two-person interaction into a Positive Action and a Negative Action for more efficient behavior recognition. A Positive Action plays the decisive role in a two-person exchange. Thus, interaction recognition can be simplified to Positive Action-based recognition, focusing on an action representation of just one person. Recently, a new depth sensor has become widely available, the Microsoft Kinect camera, which provides RGB-D data with 3D spatial information for quantitative analysis. However, there are few publicly accessible test datasets using this camera, to assess two-person interaction recognition approaches. Therefore, we created a new dataset with six types of complex human interactions (i.e., named K3HI, including kicking, pointing, punching, pushing, exchanging an object, and shaking hands. Three types of features were extracted for each Positive Action: joint, plane, and velocity features. We used continuous Hidden Markov Models (HMMs to evaluate the Positive Action-based interaction recognition method and the traditional two-person interaction recognition approach with our test dataset. Experimental results showed that the proposed recognition technique is more accurate than the traditional method, shortens the sample training time, and therefore achieves comprehensive superiority.

  7. Robust Peak Recognition in Intracranial Pressure Signals

    Directory of Open Access Journals (Sweden)

    Bergsneider Marvin

    2010-10-01

    Full Text Available Abstract Background The waveform morphology of intracranial pressure pulses (ICP is an essential indicator for monitoring, and forecasting critical intracranial and cerebrovascular pathophysiological variations. While current ICP pulse analysis frameworks offer satisfying results on most of the pulses, we observed that the performance of several of them deteriorates significantly on abnormal, or simply more challenging pulses. Methods This paper provides two contributions to this problem. First, it introduces MOCAIP++, a generic ICP pulse processing framework that generalizes MOCAIP (Morphological Clustering and Analysis of ICP Pulse. Its strength is to integrate several peak recognition methods to describe ICP morphology, and to exploit different ICP features to improve peak recognition. Second, it investigates the effect of incorporating, automatically identified, challenging pulses into the training set of peak recognition models. Results Experiments on a large dataset of ICP signals, as well as on a representative collection of sampled challenging ICP pulses, demonstrate that both contributions are complementary and significantly improve peak recognition performance in clinical conditions. Conclusion The proposed framework allows to extract more reliable statistics about the ICP waveform morphology on challenging pulses to investigate the predictive power of these pulses on the condition of the patient.

  8. Physical signals for protein–DNA recognition

    International Nuclear Information System (INIS)

    Cao, Xiao-Qin; Zeng, Jia; Yan, Hong

    2009-01-01

    This paper discovers consensus physical signals around eukaryotic splice sites, transcription start sites, and replication origin start and end sites on a genome-wide scale based on their DNA flexibility profiles calculated by three different flexibility models. These salient physical signals are localized highly rigid and flexible DNAs, which may play important roles in protein–DNA recognition by the sliding search mechanism. The found physical signals lead us to a detailed hypothetical view of the search process in which a DNA-binding protein first finds a genomic region close to the target site from an arbitrary starting location by three-dimensional (3D) hopping and intersegment transfer mechanisms for long distances, and subsequently uses the one-dimensional (1D) sliding mechanism facilitated by the localized highly rigid DNAs to accurately locate the target flexible binding site within 30 bp (base pair) short distances. Guided by these physical signals, DNA-binding proteins rapidly search the entire genome to recognize a specific target site from the 3D to 1D pathway. Our findings also show that current promoter prediction programs (PPPs) based on DNA physical properties may suffer from lots of false positives because other functional sites such as splice sites and replication origins have similar physical signals as promoters do

  9. Efficient CEPSTRAL Normalization for Robust Speech Recognition

    National Research Council Canada - National Science Library

    Liu, Fu-Hua; Stern, Richard M; Huang, Xuedong; Acero, Alejandro

    1993-01-01

    In this paper we describe and compare the performance of a series of cepstrum-based procedures that enable the CMU SPHINX-II speech recognition system to maintain a high level of recognition accuracy...

  10. Description and recognition of patterns in stochastic signals. [Electroencephalograms

    Energy Technology Data Exchange (ETDEWEB)

    Flik, T [Technische Univ. Berlin (F.R. Germany). Informatik-Forschungsgruppe Rechnerorganisation und Schaltwerke

    1975-10-01

    A method is shown for the description and recognition of patterns in stochastic signals such as electroencephalograms. For pattern extraction the signal is segmented at times of minimum amplitudes. The describing features consist of geometric values of the so defined patterns. The classification algorithm is based on the regression analysis, which is well known in the field of character recognition. For an economic classification a method is proposed which reduces the number of features. The quality of this pattern recognition method is demonstrated by the detection of spike wave complexes in electroencephalograms. The pattern description and recognition are provided for processing on a digital computer. (DE)

  11. An Efficient Reconfigurable Architecture for Fingerprint Recognition

    Directory of Open Access Journals (Sweden)

    Satish S. Bhairannawar

    2016-01-01

    Full Text Available The fingerprint identification is an efficient biometric technique to authenticate human beings in real-time Big Data Analytics. In this paper, we propose an efficient Finite State Machine (FSM based reconfigurable architecture for fingerprint recognition. The fingerprint image is resized, and Compound Linear Binary Pattern (CLBP is applied on fingerprint, followed by histogram to obtain histogram CLBP features. Discrete Wavelet Transform (DWT Level 2 features are obtained by the same methodology. The novel matching score of CLBP is computed using histogram CLBP features of test image and fingerprint images in the database. Similarly, the DWT matching score is computed using DWT features of test image and fingerprint images in the database. Further, the matching scores of CLBP and DWT are fused with arithmetic equation using improvement factor. The performance parameters such as TSR (Total Success Rate, FAR (False Acceptance Rate, and FRR (False Rejection Rate are computed using fusion scores with correlation matching technique for FVC2004 DB3 Database. The proposed fusion based VLSI architecture is synthesized on Virtex xc5vlx30T-3 FPGA board using Finite State Machine resulting in optimized parameters.

  12. Efficient iris recognition by characterizing key local variations.

    Science.gov (United States)

    Ma, Li; Tan, Tieniu; Wang, Yunhong; Zhang, Dexin

    2004-06-01

    Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.

  13. Auditory signal design for automatic number plate recognition system

    NARCIS (Netherlands)

    Heydra, C.G.; Jansen, R.J.; Van Egmond, R.

    2014-01-01

    This paper focuses on the design of an auditory signal for the Automatic Number Plate Recognition system of Dutch national police. The auditory signal is designed to alert police officers of suspicious cars in their proximity, communicating priority level and location of the suspicious car and

  14. Wavelet-based ground vehicle recognition using acoustic signals

    Science.gov (United States)

    Choe, Howard C.; Karlsen, Robert E.; Gerhart, Grant R.; Meitzler, Thomas J.

    1996-03-01

    We present, in this paper, a wavelet-based acoustic signal analysis to remotely recognize military vehicles using their sound intercepted by acoustic sensors. Since expedited signal recognition is imperative in many military and industrial situations, we developed an algorithm that provides an automated, fast signal recognition once implemented in a real-time hardware system. This algorithm consists of wavelet preprocessing, feature extraction and compact signal representation, and a simple but effective statistical pattern matching. The current status of the algorithm does not require any training. The training is replaced by human selection of reference signals (e.g., squeak or engine exhaust sound) distinctive to each individual vehicle based on human perception. This allows a fast archiving of any new vehicle type in the database once the signal is collected. The wavelet preprocessing provides time-frequency multiresolution analysis using discrete wavelet transform (DWT). Within each resolution level, feature vectors are generated from statistical parameters and energy content of the wavelet coefficients. After applying our algorithm on the intercepted acoustic signals, the resultant feature vectors are compared with the reference vehicle feature vectors in the database using statistical pattern matching to determine the type of vehicle from where the signal originated. Certainly, statistical pattern matching can be replaced by an artificial neural network (ANN); however, the ANN would require training data sets and time to train the net. Unfortunately, this is not always possible for many real world situations, especially collecting data sets from unfriendly ground vehicles to train the ANN. Our methodology using wavelet preprocessing and statistical pattern matching provides robust acoustic signal recognition. We also present an example of vehicle recognition using acoustic signals collected from two different military ground vehicles. In this paper, we will

  15. Development of precursors recognition methods in vector signals

    Science.gov (United States)

    Kapralov, V. G.; Elagin, V. V.; Kaveeva, E. G.; Stankevich, L. A.; Dremin, M. M.; Krylov, S. V.; Borovov, A. E.; Harfush, H. A.; Sedov, K. S.

    2017-10-01

    Precursor recognition methods in vector signals of plasma diagnostics are presented. Their requirements and possible options for their development are considered. In particular, the variants of using symbolic regression for building a plasma disruption prediction system are discussed. The initial data preparation using correlation analysis and symbolic regression is discussed. Special attention is paid to the possibility of using algorithms in real time.

  16. Automatic Speech Recognition from Neural Signals: A Focused Review

    Directory of Open Access Journals (Sweden)

    Christian Herff

    2016-09-01

    Full Text Available Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e.~patients suffering from locked-in syndrome. For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people.This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography. As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the emph{Brain-to-text} system.

  17. Functional characterization of recombinant chloroplast signal recognition particle

    NARCIS (Netherlands)

    Groves, M R; Mant, A; Kuhn, A; Koch, J; Dübel, S; Robinson, C; Sinning, I

    2001-01-01

    The signal recognition particle (SRP) is a ubiquitous system for the targeting of membrane and secreted proteins. The chloroplast SRP (cpSRP) is unique among SRPs in that it possesses no RNA and is functional in post-translational as well as co-translational targeting. We have expressed and purified

  18. Archaea Signal Recognition Particle Shows the Way

    Directory of Open Access Journals (Sweden)

    Christian Zwieb

    2010-01-01

    Full Text Available Archaea SRP is composed of an SRP RNA molecule and two bound proteins named SRP19 and SRP54. Regulated by the binding and hydrolysis of guanosine triphosphates, the RNA-bound SRP54 protein transiently associates not only with the hydrophobic signal sequence as it emerges from the ribosomal exit tunnel, but also interacts with the membrane-associated SRP receptor (FtsY. Comparative analyses of the archaea genomes and their SRP component sequences, combined with structural and biochemical data, support a prominent role of the SRP RNA in the assembly and function of the archaea SRP. The 5e motif, which in eukaryotes binds a 72 kilodalton protein, is preserved in most archaea SRP RNAs despite the lack of an archaea SRP72 homolog. The primary function of the 5e region may be to serve as a hinge, strategically positioned between the small and large SRP domain, allowing the elongated SRP to bind simultaneously to distant ribosomal sites. SRP19, required in eukaryotes for initiating SRP assembly, appears to play a subordinate role in the archaea SRP or may be defunct. The N-terminal A region and a novel C-terminal R region of the archaea SRP receptor (FtsY are strikingly diverse or absent even among the members of a taxonomic subgroup.

  19. Increased Efficiency of Face Recognition System using Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Rajani Muraleedharan

    2006-02-01

    Full Text Available This research was inspired by the need of a flexible and cost effective biometric security system. The flexibility of the wireless sensor network makes it a natural choice for data transmission. Swarm intelligence (SI is used to optimize routing in distributed time varying network. In this paper, SI maintains the required bit error rate (BER for varied channel conditions while consuming minimal energy. A specific biometric, the face recognition system, is discussed as an example. Simulation shows that the wireless sensor network is efficient in energy consumption while keeping the transmission accuracy, and the wireless face recognition system is competitive to the traditional wired face recognition system in classification accuracy.

  20. Heartbeat Signal from Facial Video for Biometric Recognition

    DEFF Research Database (Denmark)

    Haque, Mohammad Ahsanul; Nasrollahi, Kamal; Moeslund, Thomas B.

    2015-01-01

    Different biometric traits such as face appearance and heartbeat signal from Electrocardiogram (ECG)/Phonocardiogram (PCG) are widely used in the human identity recognition. Recent advances in facial video based measurement of cardio-physiological parameters such as heartbeat rate, respiratory rate......, and blood volume pressure provide the possibility of extracting heartbeat signal from facial video instead of using obtrusive ECG or PCG sensors in the body. This paper proposes the Heartbeat Signal from Facial Video (HSFV) as a new biometric trait for human identity recognition, for the first time...... to the best of our knowledge. Feature extraction from the HSFV is accomplished by employing Radon transform on a waterfall model of the replicated HSFV. The pairwise Minkowski distances are obtained from the Radon image as the features. The authentication is accomplished by a decision tree based supervised...

  1. A Novel Energy-Efficient Approach for Human Activity Recognition.

    Science.gov (United States)

    Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Peng, Ao; Tang, Biyu; Lu, Hai; Shi, Haibin; Zheng, Huiru

    2017-09-08

    In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.

  2. Robust Indoor Human Activity Recognition Using Wireless Signals.

    Science.gov (United States)

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

    2015-07-15

    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.

  3. CNNs for sinusoidal signal recognition in hearing rehabilitation

    Science.gov (United States)

    Carnimeo, Leonarda; Giaquinto, Antonio

    2003-04-01

    In this paper, a contribution is given to provide a tool to the recognition of sinusoidal signals with a particular reference to the field of pediatric hearing rehabilitation. To this purpose, a synthesis technique previously developed by the authors' is used to design a Cellular Neural Network for an Associative Memory able to compare submitted discrete-time sinusoidal signals with memorized ones. A robustness analysis of the synthesized associative memory is also developed both for noisy inputs and for parameter variations. Simulation results are then reported to illustrate the performances of the designed network.

  4. An Online Full-Body Motion Recognition Method Using Sparse and Deficient Signal Sequences

    Directory of Open Access Journals (Sweden)

    Chengyu Guo

    2014-01-01

    Full Text Available This paper presents a method to recognize continuous full-body human motion online by using sparse, low-cost sensors. The only input signals needed are linear accelerations without any rotation information, which are provided by four Wiimote sensors attached to the four human limbs. Based on the fused hidden Markov model (FHMM and autoregressive process, a predictive fusion model (PFM is put forward, which considers the different influences of the upper and lower limbs, establishes HMM for each part, and fuses them using a probabilistic fusion model. Then an autoregressive process is introduced in HMM to predict the gesture, which enables the model to deal with incomplete signal data. In order to reduce the number of alternatives in the online recognition process, a graph model is built that rejects parts of motion types based on the graph structure and previous recognition results. Finally, an online signal segmentation method based on semantics information and PFM is presented to finish the efficient recognition task. The results indicate that the method is robust with a high recognition rate of sparse and deficient signals and can be used in various interactive applications.

  5. Robust Indoor Human Activity Recognition Using Wireless Signals

    Directory of Open Access Journals (Sweden)

    Yi Wang

    2015-07-01

    Full Text Available 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.

  6. Contribution to automatic speech recognition. Analysis of the direct acoustical signal. Recognition of isolated words and phoneme identification

    International Nuclear Information System (INIS)

    Dupeyrat, Benoit

    1981-01-01

    This report deals with the acoustical-phonetic step of the automatic recognition of the speech. The parameters used are the extrema of the acoustical signal (coded in amplitude and duration). This coding method, the properties of which are described, is simple and well adapted to a digital processing. The quality and the intelligibility of the coded signal after reconstruction are particularly satisfactory. An experiment for the automatic recognition of isolated words has been carried using this coding system. We have designed a filtering algorithm operating on the parameters of the coding. Thus the characteristics of the formants can be derived under certain conditions which are discussed. Using these characteristics the identification of a large part of the phonemes for a given speaker was achieved. Carrying on the studies has required the development of a particular methodology of real time processing which allowed immediate evaluation of the improvement of the programs. Such processing on temporal coding of the acoustical signal is extremely powerful and could represent, used in connection with other methods an efficient tool for the automatic processing of the speech.(author) [fr

  7. An Efficiency Analysis of Augmented Reality Marker Recognition Algorithm

    Directory of Open Access Journals (Sweden)

    Kurpytė Dovilė

    2014-05-01

    Full Text Available The article reports on the investigation of augmented reality system which is designed for identification and augmentation of 100 different square markers. Marker recognition efficiency was investigated by rotating markers along x and y axis directions in range from −90° to 90°. Virtual simulations of four environments were developed: a an intense source of light, b an intense source of light falling from the left side, c the non-intensive light source falling from the left side, d equally falling shadows. The graphics were created using the OpenGL graphics computer hardware interface; image processing was programmed in C++ language using OpenCV, while augmented reality was developed in Java programming language using NyARToolKit. The obtained results demonstrate that augmented reality marker recognition algorithm is accurate and reliable in the case of changing lighting conditions and rotational angles - only 4 % markers were unidentified. Assessment of marker recognition efficiency let to propose marker classification strategy in order to use it for grouping various markers into distinct markers’ groups possessing similar recognition properties.

  8. Quantative Evaluation of the Efficiency of Facial Bio-potential Signals Based on Forehead Three-Channel Electrode Placement For Facial Gesture Recognition Applicable in a Human-Machine Interface

    Directory of Open Access Journals (Sweden)

    Iman Mohammad Rezazadeh

    2010-06-01

    Full Text Available Introduction: Today, facial bio-potential signals are employed in many human-machine interface applications for enhancing and empowering the rehabilitation process. The main point to achieve that goal is to record appropriate bioelectric signals from the human face by placing and configuring electrodes over it in the right way. In this paper, heuristic geometrical position and configuration of the electrodes has been proposed for improving the quality of the acquired signals and consequently enhancing the performance of the facial gesture classifier. Materials and Methods: Investigation and evaluation of the electrodes' proper geometrical position and configuration can be performed using two methods: clinical and modeling. In the clinical method, the electrodes are placed in predefined positions and the elicited signals from them are then processed. The performance of the method is evaluated based on the results obtained. On the other hand, in the modeling approach, the quality of the recorded signals and their information content are evaluated only by modeling and simulation. In this paper, both methods have been utilized together. First, suitable electrode positions and configuration were proposed and evaluated by modeling and simulation. Then, the experiment was performed with a predefined protocol on 7 healthy subjects to validate the simulation results. Here, the recorded signals were passed through parallel butterworth filter banks to obtain facial EMG, EOG and EEG signals and the RMS features of each 256 msec time slot were extracted.  By using the power of Subtractive Fuzzy C-Mean (SFCM, 8 different facial gestures (including smiling, frowning, pulling up left and right lip corners, left/right/up and down movements of the eyes were discriminated. Results: According to the three-channel electrode configuration derived from modeling of the dipoles effects on the surface electrodes and by employing the SFCM classifier, an average 94

  9. Differential theory of learning for efficient neural network pattern recognition

    Science.gov (United States)

    Hampshire, John B., II; Vijaya Kumar, Bhagavatula

    1993-09-01

    We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

  10. Emotion Recognition of Speech Signals Based on Filter Methods

    Directory of Open Access Journals (Sweden)

    Narjes Yazdanian

    2016-10-01

    Full Text Available Speech is the basic mean of communication among human beings.With the increase of transaction between human and machine, necessity of automatic dialogue and removing human factor has been considered. The aim of this study was to determine a set of affective features the speech signal is based on emotions. In this study system was designs that include three mains sections, features extraction, features selection and classification. After extraction of useful features such as, mel frequency cepstral coefficient (MFCC, linear prediction cepstral coefficients (LPC, perceptive linear prediction coefficients (PLP, ferment frequency, zero crossing rate, cepstral coefficients and pitch frequency, Mean, Jitter, Shimmer, Energy, Minimum, Maximum, Amplitude, Standard Deviation, at a later stage with filter methods such as Pearson Correlation Coefficient, t-test, relief and information gain, we came up with a method to rank and select effective features in emotion recognition. Then Result, are given to the classification system as a subset of input. In this classification stage, multi support vector machine are used to classify seven type of emotion. According to the results, that method of relief, together with multi support vector machine, has the most classification accuracy with emotion recognition rate of 93.94%.

  11. Feature Fusion Algorithm for Multimodal Emotion Recognition from Speech and Facial Expression Signal

    Directory of Open Access Journals (Sweden)

    Han Zhiyan

    2016-01-01

    Full Text Available In order to overcome the limitation of single mode emotion recognition. This paper describes a novel multimodal emotion recognition algorithm, and takes speech signal and facial expression signal as the research subjects. First, fuse the speech signal feature and facial expression signal feature, get sample sets by putting back sampling, and then get classifiers by BP neural network (BPNN. Second, measure the difference between two classifiers by double error difference selection strategy. Finally, get the final recognition result by the majority voting rule. Experiments show the method improves the accuracy of emotion recognition by giving full play to the advantages of decision level fusion and feature level fusion, and makes the whole fusion process close to human emotion recognition more, with a recognition rate 90.4%.

  12. Fast and efficient indexing approach for object recognition

    Science.gov (United States)

    Hefnawy, Alaa; Mashali, Samia A.; Rashwan, Mohsen; Fikri, Magdi

    1999-08-01

    This paper introduces a fast and efficient indexing approach for both 2D and 3D model-based object recognition in the presence of rotation, translation, and scale variations of objects. The indexing entries are computed after preprocessing the data by Haar wavelet decomposition. The scheme is based on a unified image feature detection approach based on Zernike moments. A set of low level features, e.g. high precision edges, gray level corners, are estimated by a set of orthogonal Zernike moments, calculated locally around every image point. A high dimensional, highly descriptive indexing entries are then calculated based on the correlation of these local features and employed for fast access to the model database to generate hypotheses. A list of the most candidate models is then presented by evaluating the hypotheses. Experimental results are included to demonstrate the effectiveness of the proposed indexing approach.

  13. A sensor-based wrist pulse signal processing and lung cancer recognition.

    Science.gov (United States)

    Zhang, Zhichao; Zhang, Yuan; Yao, Lina; Song, Houbing; Kos, Anton

    2018-03-01

    Pulse diagnosis is an efficient method in traditional Chinese medicine for detecting the health status of a person in a non-invasive and convenient way. Jin's pulse diagnosis (JPD) is a very efficient recent development that is gradually recognized and well validated by the medical community in recent years. However, no acceptable results have been achieved for lung cancer recognition in the field of biomedical signal processing using JPD. More so, there is no standard JPD pulse feature defined with respect to pulse signals. Our work is designed mainly for care giving service conveniently at home to the people having lung cancer by proposing a novel wrist pulse signal processing method, having an insight from JPD. We developed an iterative slide window (ISW) algorithm to segment the de-noised signal into single periods. We analyzed the characteristics of the segmented pulse waveform and for the first time summarized 26 features to classify the pulse waveforms of healthy individuals and lung cancer patients using a cubic support vector machine (CSVM). The result achieved by the proposed method is found to be 78.13% accurate. Copyright © 2018 Elsevier Inc. All rights reserved.

  14. 2nd International Symposium on Signal Processing and Intelligent Recognition Systems

    CERN Document Server

    Bandyopadhyay, Sanghamitra; Krishnan, Sri; Li, Kuan-Ching; Mosin, Sergey; Ma, Maode

    2016-01-01

    This Edited Volume contains a selection of refereed and revised papers originally presented at the second International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS-2015), December 16-19, 2015, Trivandrum, India. The program committee received 175 submissions. Each paper was peer reviewed by at least three or more independent referees of the program committee and the 59 papers were finally selected. The papers offer stimulating insights into biometrics, digital watermarking, recognition systems, image and video processing, signal and speech processing, pattern recognition, machine learning and knowledge-based systems. The book is directed to the researchers and scientists engaged in various field of signal processing and related areas. .

  15. Health monitoring of 90° bolted joints using fuzzy pattern recognition of ultrasonic signals

    International Nuclear Information System (INIS)

    Jalalpour, M; El-Osery, A I; Austin, E M; Reda Taha, M M

    2014-01-01

    Bolted joints are important parts for aerospace structures. However, there is a significant risk associated with assembling bolted joints due to potential human error during the assembly process. Such errors are expensive to find and correct if exposed during environmental testing, yet checking the integrity of individual fasteners after assembly would be a time consuming task. Recent advances in structural health monitoring (SHM) can provide techniques to not only automate this process but also make it reliable. This integrity monitoring requires damage features to be related to physical conditions representing the structural integrity of bolted joints. In this paper an SHM technique using ultrasonic signals and fuzzy pattern recognition to monitor the integrity of 90° bolted joints in aerospace structures is described. The proposed technique is based on normalized fast Fourier transform (NFFT) of transmitted signals and fuzzy pattern recognition. Moreover, experimental observations of a case study on an aluminum 90° bolted joint are presented. We demonstrate the ability of the proposed method to efficiently monitor and indicate bolted joint integrity. (paper)

  16. Prompt recognition of brain states by their EEG signals

    DEFF Research Database (Denmark)

    Peters, B.O.; Pfurtscheller, G.; Flyvbjerg, H.

    1997-01-01

    Brain states corresponding to intention of movement of left and right index finger and right foot are classified by a ''committee'' of artificial neural networks processing individual channels of 56-electrode electroencephalograms (EEGs). Correct recognition is achieved in 83% of cases...

  17. Automatic recognition of the unconscious reactions from physiological signals

    NARCIS (Netherlands)

    Ivonin, L.; Chang, H.M.; Chen, W.; Rauterberg, G.W.M.; Holzinger, A.; Ziefle, M.; Hitz, M.; Debevc, M.

    2013-01-01

    While the research in affective computing has been exclusively dealing with the recognition of explicit affective and cognitive states, carefully designed psychological and neuroimaging studies indicated that a considerable part of human experiences is tied to a deeper level of a psyche and not

  18. Investigation of efficient features for image recognition by neural networks.

    Science.gov (United States)

    Goltsev, Alexander; Gritsenko, Vladimir

    2012-04-01

    In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better. Copyright © 2011 Elsevier Ltd. All rights reserved.

  19. [A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

    Science.gov (United States)

    Zhang, Y; Liu, A; Yu, K

    1999-06-01

    A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

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

    Science.gov (United States)

    Lhamon, Michael Earl

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

  1. EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals

    Directory of Open Access Journals (Sweden)

    Jiaduo Zhao

    2016-01-01

    Full Text Available In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector’s false alarms.

  2. EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals.

    Science.gov (United States)

    Zhao, Jiaduo; Gong, Weiguo; Tang, Yuzhen; Li, Weihong

    2016-01-20

    In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR) signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD)-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector's false alarms.

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

    Science.gov (United States)

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

    2014-06-01

    This study is intended for describing the application of the Photoplethysmography (PPG) signal and the time domain features acquired from its first and second derivatives for biometric identification. For this purpose, a sum of 40 features has been extracted and a feature-ranking algorithm is proposed. This proposed algorithm calculates the contribution of each feature to biometric recognition and collocates the features, the contribution of which is from great to small. While identifying the contribution of the features, the Euclidean distance and absolute distance formulas are used. The efficiency of the proposed algorithms is demonstrated by the results of the k-NN (k-nearest neighbor) classifier applications of the features. During application, each 15-period-PPG signal belonging to two different durations from each of the thirty healthy subjects were used with a PPG data acquisition card. The first PPG signals recorded from the subjects were evaluated as the 1st configuration; the PPG signals recorded later at a different time as the 2nd configuration and the combination of both were evaluated as the 3rd configuration. When the results were evaluated for the k-NN classifier model created along with the proposed algorithm, an identification of 90.44% for the 1st configuration, 94.44% for the 2nd configuration, and 87.22% for the 3rd configuration has successfully been attained. The obtained results showed that both the proposed algorithm and the biometric identification model based on this developed PPG signal are very promising for contactless recognizing the people with the proposed method. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Signal Detection with Criterion Noise: Applications to Recognition Memory

    Science.gov (United States)

    Benjamin, Aaron S.; Diaz, Michael; Wee, Serena

    2009-01-01

    A tacit but fundamental assumption of the theory of signal detection is that criterion placement is a noise-free process. This article challenges that assumption on theoretical and empirical grounds and presents the noisy decision theory of signal detection (ND-TSD). Generalized equations for the isosensitivity function and for measures of…

  5. An Efficient Solution for Hand Gesture Recognition from Video Sequence

    Directory of Open Access Journals (Sweden)

    PRODAN, R.-C.

    2012-08-01

    Full Text Available The paper describes a system of hand gesture recognition by image processing for human robot interaction. The recognition and interpretation of the hand postures acquired through a video camera allow the control of the robotic arm activity: motion - translation and rotation in 3D - and tightening/releasing the clamp. A gesture dictionary was defined and heuristic algorithms for recognition were developed and tested. The system can be used for academic and industrial purposes, especially for those activities where the movements of the robotic arm were not previously scheduled, for training the robot easier than using a remote control. Besides the gesture dictionary, the novelty of the paper consists in a new technique for detecting the relative positions of the fingers in order to recognize the various hand postures, and in the achievement of a robust system for controlling robots by postures of the hands.

  6. Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

    Directory of Open Access Journals (Sweden)

    Tomasz Ciszewski

    2006-01-01

    Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

  7. Automatic shape recognition of a fast transient signal

    International Nuclear Information System (INIS)

    Charles, Gilbert.

    1976-01-01

    A system was developed to recognize if the shape of a signal x(t) is similar (or identical) to the one of an element yi(t) of an ensemble S composed by N known signals, that are memorised. x(t) is a time limited T 2 ) give the similarity measure of two signals. To solve the problem of the digital recording of the signals x(t) two devices were realized: a digital-to-analog converter which permits the recording of fast transient signals (band pass>1GHz, sampling-frequency approximately 100GHz, resolution: 9 bits, 576 samples); an automatic attenuator which scales the signal x(t) before the digitalization (the band pass is 70MHz at -1dB). A theoretical analysis permits to determine what must be the resolution of the digital-to-analog converter as a fonction of the signal-caracteristics and of the wanted precision for the calculus of rho 2 [fr

  8. Fast and efficient local features detection for building recognition

    DEFF Research Database (Denmark)

    Nguyen, Phuong Giang; Andersen, Hans Jørgen

    2011-01-01

    The vast growth of image databases creates many challenges for computer vision applications, for instance image retrieval and object recognition. Large variation in imaging conditions such as illumination and geometrical properties (including scale, rotation, and viewpoint) gives rise to the need...

  9. Signal recognition and parameter estimation of BPSK-LFM combined modulation

    Science.gov (United States)

    Long, Chao; Zhang, Lin; Liu, Yu

    2015-07-01

    Intra-pulse analysis plays an important role in electronic warfare. Intra-pulse feature abstraction focuses on primary parameters such as instantaneous frequency, modulation, and symbol rate. In this paper, automatic modulation recognition and feature extraction for combined BPSK-LFM modulation signals based on decision theoretic approach is studied. The simulation results show good recognition effect and high estimation precision, and the system is easy to be realized.

  10. An Efficient Framework for Road Sign Detection and Recognition

    Directory of Open Access Journals (Sweden)

    Duanling Li

    2014-02-01

    Full Text Available Road sign detection and recognition is a significant and challenging issue not only for assisting drivers but also navigating mobile robots. In this paper, we propose a novel and fast approach for the automatic detection and recognition of road signs. First, we use Hue Saturation Intensity (HSI color space to segment the road signs color. And then we locate the road signs based on the geometry symmetry, as almost all the shapes of road sign shapes are symmetrical such circle, rectangle, triangle and octagon. The proposed shape feature is further applied to classify the shape initially. Finally, the road signs are exactly recognized by support vector machine (SVM classifiers. We test our proposed method on real road images and the experimental results show that it can detect and recognize road signs rapidly and accurately.

  11. Improved Techniques for Automatic Chord Recognition from Music Audio Signals

    Science.gov (United States)

    Cho, Taemin

    2014-01-01

    This thesis is concerned with the development of techniques that facilitate the effective implementation of capable automatic chord transcription from music audio signals. Since chord transcriptions can capture many important aspects of music, they are useful for a wide variety of music applications and also useful for people who learn and perform…

  12. Sub-Audible Speech Recognition Based upon Electromyographic Signals

    Science.gov (United States)

    Jorgensen, Charles C. (Inventor); Lee, Diana D. (Inventor); Agabon, Shane T. (Inventor)

    2012-01-01

    Method and system for processing and identifying a sub-audible signal formed by a source of sub-audible sounds. Sequences of samples of sub-audible sound patterns ("SASPs") for known words/phrases in a selected database are received for overlapping time intervals, and Signal Processing Transforms ("SPTs") are formed for each sample, as part of a matrix of entry values. The matrix is decomposed into contiguous, non-overlapping two-dimensional cells of entries, and neural net analysis is applied to estimate reference sets of weight coefficients that provide sums with optimal matches to reference sets of values. The reference sets of weight coefficients are used to determine a correspondence between a new (unknown) word/phrase and a word/phrase in the database.

  13. Signalling components of the house mouse mate recognition system

    Czech Academy of Sciences Publication Activity Database

    Bímová, Barbora; Albrecht, Tomáš; Macholán, Miloš; Piálek, Jaroslav

    2009-01-01

    Roč. 80, č. 1 (2009), s. 20-27 ISSN 0376-6357 R&D Projects: GA AV ČR IAA600930506 Institutional research plan: CEZ:AV0Z60930519; CEZ:AV0Z50450515 Keywords : Faeces * Olfactory communication * Salivary and rogen binding protein * Sexual preferences * Urinary signals Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 1.527, year: 2009

  14. Event recognition using signal spectrograms in long pulse experiments

    International Nuclear Information System (INIS)

    Gonzalez, J.; Ruiz, M.; Barrera, E.; Arcas, G.; Lopez, J. M.; Vega, J.

    2010-01-01

    As discharge duration increases, real-time complex analysis of the signal becomes more important. In this context, data acquisition and processing systems must provide models for designing experiments which use event oriented plasma control. One example of advanced data analysis is signal classification. The off-line statistical analysis of a large number of discharges provides information to develop algorithms for the determination of the plasma parameters from measurements of magnetohydrodinamic waves, for example, to detect density fluctuations induced by the Alfven cascades using morphological patterns. The need to apply different algorithms to the signals and to address different processing algorithms using the previous results necessitates the use of an event-based experiment. The Intelligent Test and Measurement System platform is an example of architecture designed to implement distributed data acquisition and real-time processing systems. The processing algorithm sequence is modeled using an event-based paradigm. The adaptive capacity of this model is based on the logic defined by the use of state machines in SCXML. The Intelligent Test and Measurement System platform mixes a local multiprocessing model with a distributed deployment of services based on Jini.

  15. Predominant membrane localization is an essential feature of the bacterial signal recognition particle receptor

    Directory of Open Access Journals (Sweden)

    Graumann Peter

    2009-11-01

    Full Text Available Abstract Background The signal recognition particle (SRP receptor plays a vital role in co-translational protein targeting, because it connects the soluble SRP-ribosome-nascent chain complex (SRP-RNCs to the membrane bound Sec translocon. The eukaryotic SRP receptor (SR is a heterodimeric protein complex, consisting of two unrelated GTPases. The SRβ subunit is an integral membrane protein, which tethers the SRP-interacting SRα subunit permanently to the endoplasmic reticulum membrane. The prokaryotic SR lacks the SRβ subunit and consists of only the SRα homologue FtsY. Strikingly, although FtsY requires membrane contact for functionality, cell fractionation studies have localized FtsY predominantly to the cytosolic fraction of Escherichia coli. So far, the exact function of the soluble SR in E. coli is unknown, but it has been suggested that, in contrast to eukaryotes, the prokaryotic SR might bind SRP-RNCs already in the cytosol and only then initiates membrane targeting. Results In the current study we have determined the contribution of soluble FtsY to co-translational targeting in vitro and have re-analysed the localization of FtsY in vivo by fluorescence microscopy. Our data show that FtsY can bind to SRP-ribosome nascent chains (RNCs in the absence of membranes. However, these soluble FtsY-SRP-RNC complexes are not efficiently targeted to the membrane. In contrast, we observed effective targeting of SRP-RNCs to membrane-bond FtsY. These data show that soluble FtsY does not contribute significantly to cotranslational targeting in E. coli. In agreement with this observation, our in vivo analyses of FtsY localization in bacterial cells by fluorescence microscopy revealed that the vast majority of FtsY was localized to the inner membrane and that soluble FtsY constituted only a negligible species in vivo. Conclusion The exact function of the SRP receptor (SR in bacteria has so far been enigmatic. Our data show that the bacterial SR is

  16. Efficient feature for classification of eye movements using electrooculography signals

    OpenAIRE

    Phukpattaranont Pornchai; Aungsakul Siriwadee; Phinyomark Angkoon; Limsakul Chusak

    2016-01-01

    Electrooculography (EOG) signal is widely and successfully used to detect activities of human eye. The advantages of the EOG-based interface over other conventional interfaces have been presented in the last two decades; however, due to a lot of information in EOG signals, the extraction of useful features should be done before the classification task. In this study, an efficient feature extracted from two directional EOG signals: vertical and horizontal si...

  17. Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

    Science.gov (United States)

    Zhang, Ming; Xie, Fei; Zhao, Jing; Sun, Rui; Zhang, Lei; Zhang, Yue

    2018-04-01

    The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

  18. Dual-Process Theory and Signal-Detection Theory of Recognition Memory

    Science.gov (United States)

    Wixted, John T.

    2007-01-01

    Two influential models of recognition memory, the unequal-variance signal-detection model and a dual-process threshold/detection model, accurately describe the receiver operating characteristic, but only the latter model can provide estimates of recollection and familiarity. Such estimates often accord with those provided by the remember-know…

  19. Investigating Strength and Frequency Effects in Recognition Memory Using Type-2 Signal Detection Theory

    Science.gov (United States)

    Higham, Philip A.; Perfect, Timothy J.; Bruno, Davide

    2009-01-01

    Criterion- versus distribution-shift accounts of frequency and strength effects in recognition memory were investigated with Type-2 signal detection receiver operating characteristic (ROC) analysis, which provides a measure of metacognitive monitoring. Experiment 1 demonstrated a frequency-based mirror effect, with a higher hit rate and lower…

  20. On the Measurement of Criterion Noise in Signal Detection Theory: The Case of Recognition Memory

    Science.gov (United States)

    Kellen, David; Klauer, Karl Christoph; Singmann, Henrik

    2012-01-01

    Traditional approaches within the framework of signal detection theory (SDT; Green & Swets, 1966), especially in the field of recognition memory, assume that the positioning of response criteria is not a noisy process. Recent work (Benjamin, Diaz, & Wee, 2009; Mueller & Weidemann, 2008) has challenged this assumption, arguing not only…

  1. Intracellular Zn(2+) signaling in the dentate gyrus is required for object recognition memory.

    Science.gov (United States)

    Takeda, Atsushi; Tamano, Haruna; Ogawa, Taisuke; Takada, Shunsuke; Nakamura, Masatoshi; Fujii, Hiroaki; Ando, Masaki

    2014-11-01

    The role of perforant pathway-dentate granule cell synapses in cognitive behavior was examined focusing on synaptic Zn(2+) signaling in the dentate gyrus. Object recognition memory was transiently impaired when extracellular Zn(2+) levels were decreased by injection of clioquinol and N,N,N',N'-tetrakis-(2-pyridylmethyl) ethylendediamine. To pursue the effect of the loss and/or blockade of Zn(2+) signaling in dentate granule cells, ZnAF-2DA (100 pmol, 0.1 mM/1 µl), an intracellular Zn(2+) chelator, was locally injected into the dentate molecular layer of rats. ZnAF-2DA injection, which was estimated to chelate intracellular Zn(2+) signaling only in the dentate gyrus, affected object recognition memory 1 h after training without affecting intracellular Ca(2+) signaling in the dentate molecular layer. In vivo dentate gyrus long-term potentiation (LTP) was affected under the local perfusion of the recording region (the dentate granule cell layer) with 0.1 mM ZnAF-2DA, but not with 1-10 mM CaEDTA, an extracellular Zn(2+) chelator, suggesting that the blockade of intracellular Zn(2+) signaling in dentate granule cells affects dentate gyrus LTP. The present study demonstrates that intracellular Zn(2+) signaling in the dentate gyrus is required for object recognition memory, probably via dentate gyrus LTP expression. Copyright © 2014 Wiley Periodicals, Inc.

  2. A simple and efficient optical character recognition system for basic ...

    Indian Academy of Sciences (India)

    are on the way for the development of efficient OCR systems for Indian languages, .... Each vowel has a vowel sign (modifier) and each consonant has a basic form (prim- itive). ..... as a single class of character in the first stage of classification.

  3. Assessment of Homomorphic Analysis for Human Activity Recognition from Acceleration Signals.

    Science.gov (United States)

    Vanrell, Sebastian Rodrigo; Milone, Diego Humberto; Rufiner, Hugo Leonardo

    2017-07-03

    Unobtrusive activity monitoring can provide valuable information for medical and sports applications. In recent years, human activity recognition has moved to wearable sensors to deal with unconstrained scenarios. Accelerometers are the preferred sensors due to their simplicity and availability. Previous studies have examined several \\azul{classic} techniques for extracting features from acceleration signals, including time-domain, time-frequency, frequency-domain, and other heuristic features. Spectral and temporal features are the preferred ones and they are generally computed from acceleration components, leaving the acceleration magnitude potential unexplored. In this study, based on homomorphic analysis, a new type of feature extraction stage is proposed in order to exploit discriminative activity information present in acceleration signals. Homomorphic analysis can isolate the information about whole body dynamics and translate it into a compact representation, called cepstral coefficients. Experiments have explored several configurations of the proposed features, including size of representation, signals to be used, and fusion with other features. Cepstral features computed from acceleration magnitude obtained one of the highest recognition rates. In addition, a beneficial contribution was found when time-domain and moving pace information was included in the feature vector. Overall, the proposed system achieved a recognition rate of 91.21% on the publicly available SCUT-NAA dataset. To the best of our knowledge, this is the highest recognition rate on this dataset.

  4. Quantifying the Energy Efficiency of Object Recognition and Optical Flow

    Science.gov (United States)

    2014-03-28

    other linear solvers, such as conjugate- gradient (CG), preconditioned conjugate-gradient (PCG), and red-black Gauss Seidel (RB). We have also... converge faster than others. We will consider the trade-off between per- iteration efficiency and convergence rate in the next section. 3.1.4 Analysis of...performing solver, in terms of quality per number of iterations , is the preconditioned conjugate gradient solver, followed by the red-black Gauss

  5. Intelligent Automatic Right-Left Sign Lamp Based on Brain Signal Recognition System

    Science.gov (United States)

    Winda, A.; Sofyan; Sthevany; Vincent, R. S.

    2017-12-01

    Comfort as a part of the human factor, plays important roles in nowadays advanced automotive technology. Many of the current technologies go in the direction of automotive driver assistance features. However, many of the driver assistance features still require physical movement by human to enable the features. In this work, the proposed method is used in order to make certain feature to be functioning without any physical movement, instead human just need to think about it in their mind. In this work, brain signal is recorded and processed in order to be used as input to the recognition system. Right-Left sign lamp based on the brain signal recognition system can potentially replace the button or switch of the specific device in order to make the lamp work. The system then will decide whether the signal is ‘Right’ or ‘Left’. The decision of the Right-Left side of brain signal recognition will be sent to a processing board in order to activate the automotive relay, which will be used to activate the sign lamp. Furthermore, the intelligent system approach is used to develop authorized model based on the brain signal. Particularly Support Vector Machines (SVMs)-based classification system is used in the proposed system to recognize the Left-Right of the brain signal. Experimental results confirm the effectiveness of the proposed intelligent Automatic brain signal-based Right-Left sign lamp access control system. The signal is processed by Linear Prediction Coefficient (LPC) and Support Vector Machines (SVMs), and the resulting experiment shows the training and testing accuracy of 100% and 80%, respectively.

  6. Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals.

    Science.gov (United States)

    Zhuang, Ning; Zeng, Ying; Yang, Kai; Zhang, Chi; Tong, Li; Yan, Bin

    2018-03-12

    Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods.

  7. Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals

    Science.gov (United States)

    Zeng, Ying; Yang, Kai; Tong, Li; Yan, Bin

    2018-01-01

    Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods. PMID:29534515

  8. Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

    Directory of Open Access Journals (Sweden)

    Zhaoqin Peng

    2013-01-01

    Full Text Available Algorithms based on the ground reflex pressure (GRF signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA and kernel principal component analysis (KPCA for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM, SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots.

  9. Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain.

    Science.gov (United States)

    Zhuang, Ning; Zeng, Ying; Tong, Li; Zhang, Chi; Zhang, Hanming; Yan, Bin

    2017-01-01

    This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference of time series, the first difference of phase, and the normalized energy. The performance of the proposed method is verified on a publicly available emotional database. The results show that the three features are effective for emotion recognition. The role of each IMF is inquired and we find that high frequency component IMF1 has significant effect on different emotional states detection. The informative electrodes based on EMD strategy are analyzed. In addition, the classification accuracy of the proposed method is compared with several classical techniques, including fractal dimension (FD), sample entropy, differential entropy, and discrete wavelet transform (DWT). Experiment results on DEAP datasets demonstrate that our method can improve emotion recognition performance.

  10. Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

    International Nuclear Information System (INIS)

    Jin Jing; Wei Biao; Feng Peng; Tang Yuelin; Zhou Mi

    2010-01-01

    Based on the interdependent relationship between fission neutrons ( 252 Cf) and fission chain ( 235 U system), the paper presents the time-frequency feature analysis and recognition in fission neutron signal based on support vector machine (SVM) through the analysis on signal characteristics and the measuring principle of the 252 Cf fission neutron signal. The time-frequency characteristics and energy features of the fission neutron signal are extracted by using wavelet decomposition and de-noising wavelet packet decomposition, and then applied to training and classification by means of support vector machine based on statistical learning theory. The results show that, it is effective to obtain features of nuclear signal via wavelet decomposition and de-noising wavelet packet decomposition, and the latter can reflect the internal characteristics of the fission neutron system better. With the training accomplished, the SVM classifier achieves an accuracy rate above 70%, overcoming the lack of training samples, and verifying the effectiveness of the algorithm. (authors)

  11. FPGA IMPLEMENTATION OF ADAPTIVE INTEGRATED SPIKING NEURAL NETWORK FOR EFFICIENT IMAGE RECOGNITION SYSTEM

    Directory of Open Access Journals (Sweden)

    T. Pasupathi

    2014-05-01

    Full Text Available Image recognition is a technology which can be used in various applications such as medical image recognition systems, security, defense video tracking, and factory automation. In this paper we present a novel pipelined architecture of an adaptive integrated Artificial Neural Network for image recognition. In our proposed work we have combined the feature of spiking neuron concept with ANN to achieve the efficient architecture for image recognition. The set of training images are trained by ANN and target output has been identified. Real time videos are captured and then converted into frames for testing purpose and the image were recognized. The machine can operate at up to 40 frames/sec using images acquired from the camera. The system has been implemented on XC3S400 SPARTAN-3 Field Programmable Gate Arrays.

  12. Molecular Pathways for Immune Recognition of Preproinsulin Signal Peptide in Type 1 Diabetes.

    Science.gov (United States)

    Kronenberg-Versteeg, Deborah; Eichmann, Martin; Russell, Mark A; de Ru, Arnoud; Hehn, Beate; Yusuf, Norkhairin; van Veelen, Peter A; Richardson, Sarah J; Morgan, Noel G; Lemberg, Marius K; Peakman, Mark

    2018-04-01

    The signal peptide region of preproinsulin (PPI) contains epitopes targeted by HLA-A-restricted (HLA-A0201, A2402) cytotoxic T cells as part of the pathogenesis of β-cell destruction in type 1 diabetes. We extended the discovery of the PPI epitope to disease-associated HLA-B*1801 and HLA-B*3906 (risk) and HLA-A*1101 and HLA-B*3801 (protective) alleles, revealing that four of six alleles present epitopes derived from the signal peptide region. During cotranslational translocation of PPI, its signal peptide is cleaved and retained within the endoplasmic reticulum (ER) membrane, implying it is processed for immune recognition outside of the canonical proteasome-directed pathway. Using in vitro translocation assays with specific inhibitors and gene knockout in PPI-expressing target cells, we show that PPI signal peptide antigen processing requires signal peptide peptidase (SPP). The intramembrane protease SPP generates cytoplasm-proximal epitopes, which are transporter associated with antigen processing (TAP), ER-luminal epitopes, which are TAP independent, each presented by different HLA class I molecules and N-terminal trimmed by ER aminopeptidase 1 for optimal presentation. In vivo, TAP expression is significantly upregulated and correlated with HLA class I hyperexpression in insulin-containing islets of patients with type 1 diabetes. Thus, PPI signal peptide epitopes are processed by SPP and loaded for HLA-guided immune recognition via pathways that are enhanced during disease pathogenesis. © 2018 by the American Diabetes Association.

  13. The time course of individual face recognition: A pattern analysis of ERP signals.

    Science.gov (United States)

    Nemrodov, Dan; Niemeier, Matthias; Mok, Jenkin Ngo Yin; Nestor, Adrian

    2016-05-15

    An extensive body of work documents the time course of neural face processing in the human visual cortex. However, the majority of this work has focused on specific temporal landmarks, such as N170 and N250 components, derived through univariate analyses of EEG data. Here, we take on a broader evaluation of ERP signals related to individual face recognition as we attempt to move beyond the leading theoretical and methodological framework through the application of pattern analysis to ERP data. Specifically, we investigate the spatiotemporal profile of identity recognition across variation in emotional expression. To this end, we apply pattern classification to ERP signals both in time, for any single electrode, and in space, across multiple electrodes. Our results confirm the significance of traditional ERP components in face processing. At the same time though, they support the idea that the temporal profile of face recognition is incompletely described by such components. First, we show that signals associated with different facial identities can be discriminated from each other outside the scope of these components, as early as 70ms following stimulus presentation. Next, electrodes associated with traditional ERP components as well as, critically, those not associated with such components are shown to contribute information to stimulus discriminability. And last, the levels of ERP-based pattern discrimination are found to correlate with recognition accuracy across subjects confirming the relevance of these methods for bridging brain and behavior data. Altogether, the current results shed new light on the fine-grained time course of neural face processing and showcase the value of novel methods for pattern analysis to investigating fundamental aspects of visual recognition. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. Emotion recognition techniques using physiological signals and video games -Systematic review-

    OpenAIRE

    Callejas-Cuervo, Mauro; Martínez-Tejada, Laura Alejandra; Alarcón-Aldana, Andrea Catherine

    2017-01-01

    Abstract Emotion recognition systems from physiological signals are innovative techniques that allow studying the behavior and reaction of an individual when exposed to information that may evoke emotional reactions through multimedia tools, for example, video games. This type of approach is used to identify the behavior of an individual in different fields, such as medicine, education, psychology, etc., in order to assess the effect that the content has on the individual that is interacting ...

  15. Communicative Signals Promote Object Recognition Memory and Modulate the Right Posterior STS.

    Science.gov (United States)

    Redcay, Elizabeth; Ludlum, Ruth S; Velnoskey, Kayla R; Kanwal, Simren

    2016-01-01

    Detection of communicative signals is thought to facilitate knowledge acquisition early in life, but less is known about the role these signals play in adult learning or about the brain systems supporting sensitivity to communicative intent. The current study examined how ostensive gaze cues and communicative actions affect adult recognition memory and modulate neural activity as measured by fMRI. For both the behavioral and fMRI experiments, participants viewed a series of videos of an actress acting on one of two objects in front of her. Communicative context in the videos was manipulated in a 2 × 2 design in which the actress either had direct gaze (Gaze) or wore a visor (NoGaze) and either pointed at (Point) or reached for (Reach) one of the objects (target) in front of her. Participants then completed a recognition memory task with old (target and nontarget) objects and novel objects. Recognition memory for target objects in the Gaze conditions was greater than NoGaze, but no effects of gesture type were seen. Similarly, the fMRI video-viewing task revealed a significant effect of Gaze within right posterior STS (pSTS), but no significant effects of Gesture. Furthermore, pSTS sensitivity to Gaze conditions was related to greater memory for objects viewed in Gaze, as compared with NoGaze, conditions. Taken together, these results demonstrate that the ostensive, communicative signal of direct gaze preceding an object-directed action enhances recognition memory for attended items and modulates the pSTS response to object-directed actions. Thus, establishment of a communicative context through ostensive signals remains an important component of learning and memory into adulthood, and the pSTS may play a role in facilitating this type of social learning.

  16. Between Efficiency, Capability and Recognition: Competing Epistemes in Global Governance Reforms

    Science.gov (United States)

    Chan, Jennifer

    2007-01-01

    This article examines global governance reforms as a site of contestation between three different "truths"/epistemes (the market, human rights principles, and cultural identity) in terms of the competing principles of efficiency, capability, and recognition. Nancy Fraser's conceptions of participation parity and a dialogical approach of…

  17. An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network

    Science.gov (United States)

    Su, Zhongqing; Ye, Lin

    2004-08-01

    The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform and artificial neural network algorithms was developed; this was actualized in a signal processing package (SPP). The ISPPR technique comprehensively functions as signal filtration, data compression, characteristic extraction, information mapping and pattern recognition, capable of extracting essential yet concise features from acquired raw wave signals and further assisting in structural health evaluation. For validation, the SPP was applied to the prediction of crack growth in an alloy structural beam and construction of a damage parameter database for defect identification in CF/EP composite structures. It was clearly apparent that the elastic wave propagation-based damage assessment could be dramatically streamlined by introduction of the ISPPR technique.

  18. Surface EMG signals based motion intent recognition using multi-layer ELM

    Science.gov (United States)

    Wang, Jianhui; Qi, Lin; Wang, Xiao

    2017-11-01

    The upper-limb rehabilitation robot is regard as a useful tool to help patients with hemiplegic to do repetitive exercise. The surface electromyography (sEMG) contains motion information as the electric signals are generated and related to nerve-muscle motion. These sEMG signals, representing human's intentions of active motions, are introduced into the rehabilitation robot system to recognize upper-limb movements. Traditionally, the feature extraction is an indispensable part of drawing significant information from original signals, which is a tedious task requiring rich and related experience. This paper employs a deep learning scheme to extract the internal features of the sEMG signals using an advanced Extreme Learning Machine based auto-encoder (ELMAE). The mathematical information contained in the multi-layer structure of the ELM-AE is used as the high-level representation of the internal features of the sEMG signals, and thus a simple ELM can post-process the extracted features, formulating the entire multi-layer ELM (ML-ELM) algorithm. The method is employed for the sEMG based neural intentions recognition afterwards. The case studies show the adopted deep learning algorithm (ELM-AE) is capable of yielding higher classification accuracy compared to the Principle Component Analysis (PCA) scheme in 5 different types of upper-limb motions. This indicates the effectiveness and the learning capability of the ML-ELM in such motion intent recognition applications.

  19. Gender differences in the recognition of emotional faces: are men less efficient?

    Directory of Open Access Journals (Sweden)

    Ana Ruiz-Ibáñez

    2017-06-01

    Full Text Available As research in recollection of stimuli with emotional valence indicates, emotions influence memory. Many studies in face and emotional facial expression recognition have focused on age (young and old people and gender-associated (men and women differences. Nevertheless, this kind of studies has produced contradictory results, because of that, it would be necessary to study gender involvement in depth. The main objective of our research consists of analyzing the differences in image recognition using faces with emotional facial expressions between two groups composed by university students aged 18-30. The first group is constituted by men and the second one by women. The results showed statistically significant differences in face corrected recognition (hit rate - false alarm rate: the women demonstrated a better recognition than the men. However, other analyzed variables as time or efficiency do not provide conclusive results. Furthermore, a significant negative correlation between the time used and the efficiency when doing the task was found in the male group. This information reinforces not only the hypothesis of gender difference in face recognition, in favor of women, but also these ones that suggest a different cognitive processing of facial stimuli in both sexes. Finally, we argue the necessity of a greater research related to variables as age or sociocultural level.

  20. Recognition

    DEFF Research Database (Denmark)

    Gimmler, Antje

    2017-01-01

    In this article, I shall examine the cognitive, heuristic and theoretical functions of the concept of recognition. To evaluate both the explanatory power and the limitations of a sociological concept, the theory construction must be analysed and its actual productivity for sociological theory mus...

  1. Analysis of physiological signals for recognition of boredom, pain, and surprise emotions.

    Science.gov (United States)

    Jang, Eun-Hye; Park, Byoung-Jun; Park, Mi-Sook; Kim, Sang-Hyeob; Sohn, Jin-Hun

    2015-06-18

    The aim of the study was to examine the differences of boredom, pain, and surprise. In addition to that, it was conducted to propose approaches for emotion recognition based on physiological signals. Three emotions, boredom, pain, and surprise, are induced through the presentation of emotional stimuli and electrocardiography (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmography (PPG) as physiological signals are measured to collect a dataset from 217 participants when experiencing the emotions. Twenty-seven physiological features are extracted from the signals to classify the three emotions. The discriminant function analysis (DFA) as a statistical method, and five machine learning algorithms (linear discriminant analysis (LDA), classification and regression trees (CART), self-organizing map (SOM), Naïve Bayes algorithm, and support vector machine (SVM)) are used for classifying the emotions. The result shows that the difference of physiological responses among emotions is significant in heart rate (HR), skin conductance level (SCL), skin conductance response (SCR), mean skin temperature (meanSKT), blood volume pulse (BVP), and pulse transit time (PTT), and the highest recognition accuracy of 84.7% is obtained by using DFA. This study demonstrates the differences of boredom, pain, and surprise and the best emotion recognizer for the classification of the three emotions by using physiological signals.

  2. The role of multimodal signals in species recognition between tree-killing bark beetles in a narrow sympatric zone.

    Science.gov (United States)

    Deepa S. Pureswaran; Richard W. Hofstetter; Brian Sullivan; Kristen A. Potter

    2016-01-01

    When related species coexist, selection pressure should favor evolution of species recognition mechanisms to prevent interspecific pairing and wasteful reproductive encounters. We investigated the potential role of pheromone and acoustic signals in species recognition between two species of tree-killing bark beetles, the southern pine beetle, Dendroctonus frontalis...

  3. Asymmetry in Signal Oscillations Contributes to Efficiency of Periodic Systems.

    Science.gov (United States)

    Bae, Seul-A; Acevedo, Alison; Androulakis, Ioannis P

    2016-01-01

    Oscillations are an important feature of cellular signaling that result from complex combinations of positive- and negative-feedback loops. The encoding and decoding mechanisms of oscillations based on amplitude and frequency have been extensively discussed in the literature in the context of intercellular and intracellular signaling. However, the fundamental questions of whether and how oscillatory signals offer any competitive advantages-and, if so, what-have not been fully answered. We investigated established oscillatory mechanisms and designed a study to analyze the oscillatory characteristics of signaling molecules and system output in an effort to answer these questions. Two classic oscillators, Goodwin and PER, were selected as the model systems, and corresponding no-feedback models were created for each oscillator to discover the advantage of oscillating signals. Through simulating the original oscillators and the matching no-feedback models, we show that oscillating systems have the capability to achieve better resource-to-output efficiency, and we identify oscillatory characteristics that lead to improved efficiency.

  4. Efficient feature for classification of eye movements using electrooculography signals

    Directory of Open Access Journals (Sweden)

    Phukpattaranont Pornchai

    2016-01-01

    Full Text Available Electrooculography (EOG signal is widely and successfully used to detect activities of human eye. The advantages of the EOG-based interface over other conventional interfaces have been presented in the last two decades; however, due to a lot of information in EOG signals, the extraction of useful features should be done before the classification task. In this study, an efficient feature extracted from two directional EOG signals: vertical and horizontal signals has been presented and evaluated. There are the maximum peak and valley amplitude values, the maximum peak and valley position values, and slope, which are derived from both vertical and horizontal signals. In the experiments, EOG signals obtained from five healthy subjects with ten directional eye movements were employed: up, down, right, left, up-right, up-left, down-right down-left clockwise and counterclockwise. The mean feature values and their standard deviations have been reported. The difference between the mean values of the proposed feature from different eye movements can be clearly seen. Using the scatter plot, the differences in features can be also clearly observed. Results show that classification accuracy can approach 100% with a simple distinction feature rule. The proposed features can be useful for various advanced human-computer interface applications in future researches.

  5. The tradeoff between signal detection and recognition rules auditory sensitivity under variable background noise conditions.

    Science.gov (United States)

    Lugli, Marco

    2015-12-07

    Animal acoustic communication commonly takes place under masked conditions. For instance, sound signals relevant for mating and survival are very often masked by background noise, which makes their detection and recognition by organisms difficult. Ambient noise (AN) varies in level and shape among different habitats, but also remarkable variations in time and space occurs within the same habitat. Variable AN conditions mask hearing thresholds of the receiver in complex and unpredictable ways, thereby causing distortions in sound perception. When communication takes place in a noisy environment, a highly sensitive system might confer no advantage to the receiver compared to a less sensitive one. The effects of noise masking on auditory thresholds and hearing-related functions are well known, and the potential role of AN in the evolution of the species' auditory sensitivity has been recognized by few authors. The mechanism of the underlying selection process has never been explored, however. Here I present a simple fitness model that seeks for the best sensitivity of a hearing system performing the detection and recognition of the sound under variable AN conditions. The model predicts higher sensitivity (i.e. lower hearing thresholds) as best strategy for species living in quiet habitats and lower sensitivity (i.e. higher hearing thresholds) as best strategy for those living in noisy habitats provided the cost of incorrect recognition is not low. The tradeoff between detection and recognition of acoustic signals appears to be a key factor determining the best level of hearing sensitivity of a species when acoustic communication is corrupted by noise. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. Application of an automatic pattern recognition for aleatory signals for the surveillance of nuclear reactor and rotating machinery

    International Nuclear Information System (INIS)

    Nascimento, J.A. do.

    1982-02-01

    An automatic pattern recognition program PSDREC, developed for the surveillance of nuclear reactor and rotating machinery is described and the relevant theory is outlined. Pattern recognition analysis of noise signals is a powerful technique for assessing 'system normality' in dynamic systems. This program, with applies 8 statistical tests to calculated power spectral density (PSD) distribution, was earlier installed in a PDP-11/45 computer at IPEN. To analyse recorded signals from three systems, namely an operational BWR power reactor (neutron signals), a water pump and a diesel engine (vibration signals) this technique was used. Results of the tests are considered satisfactory. (Author) [pt

  7. Pattern-recognition receptors: signaling pathways and dysregulation in canine chronic enteropathies-brief review.

    Science.gov (United States)

    Heilmann, Romy M; Allenspach, Karin

    2017-11-01

    Pattern-recognition receptors (PRRs) are expressed by innate immune cells and recognize pathogen-associated molecular patterns (PAMPs) as well as endogenous damage-associated molecular pattern (DAMP) molecules. With a large potential for synergism or convergence between their signaling pathways, PRRs orchestrate a complex interplay of cellular mediators and transcription factors, and thus play a central role in homeostasis and host defense. Aberrant activation of PRR signaling, mutations of the receptors and/or their downstream signaling molecules, and/or DAMP/PAMP complex-mediated receptor signaling can potentially lead to chronic auto-inflammatory diseases or development of cancer. PRR signaling pathways appear to also present an interesting new avenue for the modulation of inflammatory responses and to serve as potential novel therapeutic targets. Evidence for a dysregulation of the PRR toll-like receptor (TLR)2, TLR4, TLR5, and TLR9, nucleotide-binding oligomerization domain-containing protein (NOD)2, and the receptor of advanced glycation end products (RAGE) exists in dogs with chronic enteropathies. We describe the TLR, NOD2, and RAGE signaling pathways and evaluate the current veterinary literature-in comparison to human medicine-to determine the role of TLRs, NOD2, and RAGE in canine chronic enteropathies.

  8. Urtica dioica modulates hippocampal insulin signaling and recognition memory deficit in streptozotocin induced diabetic mice.

    Science.gov (United States)

    Patel, Sita Sharan; Gupta, Sahil; Udayabanu, Malairaman

    2016-06-01

    Diabetes mellitus has been associated with functional abnormalities in the hippocampus and performance of cognitive function. Urtica dioica (UD) has been used in the treatment of diabetes. In our previous report we observed that UD extract attenuate diabetes mediated associative and spatial memory dysfunction. The present study aimed to evaluate the effect of UD extract on mouse model of diabetes-induced recognition memory deficit and explore the possible mechanism behind it. Streptozotocin (STZ) (50 mg/kg, i.p. consecutively for 5 days) was used to induce diabetes followed by UD extract (50 mg/kg, oral) or rosiglitazone (ROSI) (5 mg/kg, oral) administration for 8 weeks. STZ induced diabetic mice showed significant decrease in hippocampal insulin signaling and translocation of glucose transporter type 4 (GLUT4) to neuronal membrane resulting in cognitive dysfunction and hypolocomotion. UD treatment effectively improved hippocampal insulin signaling, glucose tolerance and recognition memory performance in diabetic mice, which was comparable to ROSI. Further, diabetes mediated oxidative stress and inflammation was reversed by chronic UD or ROSI administration. UD leaves extract acts via insulin signaling pathway and might prove to be effective for the diabetes mediated central nervous system complications.

  9. Energy efficient smartphone-based activity recognition using fixed-point arithmetic

    OpenAIRE

    Anguita, Davide; Ghio, Alessandro; Oneto, Luca; Llanas Parra, Francesc Xavier; Reyes Ortiz, Jorge Luis

    2013-01-01

    In this paper we propose a novel energy efficient approach for the recognition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabled and the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better pre- serve the smartphone battery lifetime with respect to the conventional flo...

  10. ANOVA Based Approch for Efficient Customer Recognition: Dealing with Common Names

    OpenAIRE

    Saberi , Morteza; Saberi , Zahra

    2015-01-01

    Part 2: Artificial Intelligence for Knowledge Management; International audience; This study proposes an Analysis of Variance (ANOVA) technique that focuses on the efficient recognition of customers with common names. The continuous improvement of Information and communications technologies (ICT) has led customers to have new expectations and concerns from their related organization. These new expectations bring various difficulties for organizations’ help desk to meet their customers’ needs....

  11. Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems.

    Science.gov (United States)

    Fang, Fuming; Shinozaki, Takahiro

    2018-01-01

    Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data.

  12. Energy-Efficient Real-Time Human Activity Recognition on Smart Mobile Devices

    Directory of Open Access Journals (Sweden)

    Jin Lee

    2016-01-01

    Full Text Available Nowadays, human activity recognition (HAR plays an important role in wellness-care and context-aware systems. Human activities can be recognized in real-time by using sensory data collected from various sensors built in smart mobile devices. Recent studies have focused on HAR that is solely based on triaxial accelerometers, which is the most energy-efficient approach. However, such HAR approaches are still energy-inefficient because the accelerometer is required to run without stopping so that the physical activity of a user can be recognized in real-time. In this paper, we propose a novel approach for HAR process that controls the activity recognition duration for energy-efficient HAR. We investigated the impact of varying the acceleration-sampling frequency and window size for HAR by using the variable activity recognition duration (VARD strategy. We implemented our approach by using an Android platform and evaluated its performance in terms of energy efficiency and accuracy. The experimental results showed that our approach reduced energy consumption by a minimum of about 44.23% and maximum of about 78.85% compared to conventional HAR without sacrificing accuracy.

  13. A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG-Based Emotion Recognition

    Directory of Open Access Journals (Sweden)

    Xin Chai

    2017-05-01

    Full Text Available Electroencephalography (EEG-based emotion recognition is an important element in psychiatric health diagnosis for patients. However, the underlying EEG sensor signals are always non-stationary if they are sampled from different experimental sessions or subjects. This results in the deterioration of the classification performance. Domain adaptation methods offer an effective way to reduce the discrepancy of marginal distribution. However, for EEG sensor signals, both marginal and conditional distributions may be mismatched. In addition, the existing domain adaptation strategies always require a high level of additional computation. To address this problem, a novel strategy named adaptive subspace feature matching (ASFM is proposed in this paper in order to integrate both the marginal and conditional distributions within a unified framework (without any labeled samples from target subjects. Specifically, we develop a linear transformation function which matches the marginal distributions of the source and target subspaces without a regularization term. This significantly decreases the time complexity of our domain adaptation procedure. As a result, both marginal and conditional distribution discrepancies between the source domain and unlabeled target domain can be reduced, and logistic regression (LR can be applied to the new source domain in order to train a classifier for use in the target domain, since the aligned source domain follows a distribution which is similar to that of the target domain. We compare our ASFM method with six typical approaches using a public EEG dataset with three affective states: positive, neutral, and negative. Both offline and online evaluations were performed. The subject-to-subject offline experimental results demonstrate that our component achieves a mean accuracy and standard deviation of 80.46% and 6.84%, respectively, as compared with a state-of-the-art method, the subspace alignment auto-encoder (SAAE, which

  14. Investigating strength and frequency effects in recognition memory using type-2 signal detection theory.

    Science.gov (United States)

    Higham, Philip A; Perfect, Timothy J; Bruno, Davide

    2009-01-01

    Criterion- versus distribution-shift accounts of frequency and strength effects in recognition memory were investigated with Type-2 signal detection receiver operating characteristic (ROC) analysis, which provides a measure of metacognitive monitoring. Experiment 1 demonstrated a frequency-based mirror effect, with a higher hit rate and lower false alarm rate, for low frequency words compared with high frequency words. In Experiment 2, the authors manipulated item strength with repetition, which showed an increased hit rate but no effect on the false alarm rate. Whereas Type-1 indices were ambiguous as to whether these effects were based on a criterion- or distribution-shift model, the two models predict opposite effects on Type-2 distractor monitoring under some assumptions. Hence, Type-2 ROC analysis discriminated between potential models of recognition that could not be discriminated using Type-1 indices alone. In Experiment 3, the authors manipulated Type-1 response bias by varying the number of old versus new response categories to confirm the assumptions made in Experiments 1 and 2. The authors conclude that Type-2 analyses are a useful tool for investigating recognition memory when used in conjunction with more traditional Type-1 analyses.

  15. An Accurate and Efficient User Authentication Mechanism on Smart Glasses Based on Iris Recognition

    Directory of Open Access Journals (Sweden)

    Yung-Hui Li

    2017-01-01

    Full Text Available In modern society, mobile devices (such as smart phones and wearable devices have become indispensable to almost everyone, and people store personal data in devices. Therefore, how to implement user authentication mechanism for private data protection on mobile devices is a very important issue. In this paper, an intelligent iris recognition mechanism is designed to solve the problem of user authentication in wearable smart glasses. Our contributions include hardware and software. On the hardware side, we design a set of internal infrared camera modules, including well-designed infrared light source and lens module, which is able to take clear iris images within 2~5 cm. On the software side, we propose an innovative iris segmentation algorithm which is both efficient and accurate to be used on smart glasses device. Another improvement to the traditional iris recognition is that we propose an intelligent Hamming distance (HD threshold adaptation method which dynamically fine-tunes the HD threshold used for verification according to empirical data collected. Our final system can perform iris recognition with 66 frames per second on a smart glasses platform with 100% accuracy. As far as we know, this system is the world’s first application of iris recognition on smart glasses.

  16. A Robust and Efficient Algorithm for Tool Recognition and Localization for Space Station Robot

    Directory of Open Access Journals (Sweden)

    Lingbo Cheng

    2014-12-01

    Full Text Available This paper studies a robust target recognition and localization method for a maintenance robot in a space station, and its main goal is to solve the target affine transformation caused by microgravity and the strong reflection and refraction of sunlight and lamplight in the cabin, as well as the occlusion of other objects. In this method, an Affine Scale Invariant Feature Transform (Affine-SIFT algorithm is proposed to extract enough local feature points with a fully affine invariant, and the stable matching point is obtained from the above point for target recognition by the selected Random Sample Consensus (RANSAC algorithm. Then, in order to localize the target, the effective and appropriate 3D grasping scope of the target is defined, and we determine and evaluate the grasping precision with the estimated affine transformation parameters presented in this paper. Finally, the threshold of RANSAC is optimized to enhance the accuracy and efficiency of target recognition and localization, and the scopes of illumination, vision distance and viewpoint angle for robot are evaluated to obtain effective image data by Root-Mean-Square Error (RMSE. An experimental system to simulate the illumination environment in a space station is established. Enough experiments have been carried out, and the experimental results show both the validity of the proposed definition of the grasping scope and the feasibility of the proposed recognition and localization method.

  17. An application of viola jones method for face recognition for absence process efficiency

    Science.gov (United States)

    Rizki Damanik, Rudolfo; Sitanggang, Delima; Pasaribu, Hendra; Siagian, Hendrik; Gulo, Frisman

    2018-04-01

    Absence was a list of documents that the company used to record the attendance time of each employee. The most common problem in a fingerprint machine is the identification of a slow sensor or a sensor not recognizing a finger. The employees late to work because they get difficulties at fingerprint system, they need about 3 – 5 minutes to absence when the condition of finger is wet or not fit. To overcome this problem, this research tried to utilize facial recognition for attendance process. The method used for facial recognition was Viola Jones. Through the processing phase of the RGB face image was converted into a histogram equalization face image for the next stage of recognition. The result of this research was the absence process could be done less than 1 second with a maximum slope of ± 700 and a distance of 20-200 cm. After implement facial recognition the process of absence is more efficient, just take less 1 minute to absence.

  18. Signal Recognition Particle 54 kD Protein (SRP54 from the Marine Sponge Geodia cydonium

    Directory of Open Access Journals (Sweden)

    Sonja Durajlija-Žinić

    2002-01-01

    Full Text Available In the systematic search for phylogenetically conserved proteins in the simplest and most ancient extant metazoan phylum – Porifera, we have identified and analyzed a cDNA encoding the signal recognition particle 54 kD protein (SRP54 from the marine sponge Geodia cydonium (Demospongiae. The signal recognition particle (SRP is a universally conserved ribonucleoprotein complex of a very ancient origin, comprising SRP RNA and several proteins (six in mammals. The nucleotide sequence of the sponge cDNA predicts a protein of 499 amino acid residues with a calculated Mr of 55175. G. cydonium SRP54 displays unusually high overall similarity (90 % with human/mammalian SRP54 proteins, higher than with Drosophila melanogaster (88 %, or Caenorhabditis elegans (82 %. The same was found for the majority of known and phylogenetically conserved proteins from sponges, indicating that the molecular evolutionary rates in protein coding genes in Porifera as well as in highly developed mammals (vertebrates are slower, when compared with the rates in homologous genes from invertebrates (insects, nematodes. Therefore, genes/proteins from sponges might be the best candidates for the reconstruction of ancient structures of proteins and genome/proteome complexity in the ancestral organism, common to all multicellular animals.

  19. Entamoeba Clone-Recognition Experiments: Morphometrics, Aggregative Behavior, and Cell-Signaling Characterization.

    Science.gov (United States)

    Espinosa, Avelina; Paz-Y-Miño-C, Guillermo; Hackey, Meagan; Rutherford, Scott

    2016-05-01

    Studies on clone- and kin-discrimination in protists have proliferated during the past decade. We report clone-recognition experiments in seven Entamoeba lineages (E. invadens IP-1, E. invadens VK-1:NS, E. terrapinae, E. moshkovskii Laredo, E. moshkovskii Snake, E. histolytica HM-1:IMSS and E. dispar). First, we characterized morphometrically each clone (length, width, and cell-surface area) and documented how they differed statistically from one another (as per single-variable or canonical-discriminant analyses). Second, we demonstrated that amebas themselves could discriminate self (clone) from different (themselves vs. other clones). In mix-cell-line cultures between closely-related (E. invadens IP-1 vs. E. invadens VK-1:NS) or distant-phylogenetic clones (E. terrapinae vs. E. moshkovskii Laredo), amebas consistently aggregated with same-clone members. Third, we identified six putative cell-signals secreted by the amebas (RasGap/Ankyrin, coronin-WD40, actin, protein kinases, heat shock 70, and ubiquitin) and which known functions in Entamoeba spp. included: cell proliferation, cell adhesion, cell movement, and stress-induced encystation. To our knowledge, this is the first multi-clone characterization of Entamoeba spp. morphometrics, aggregative behavior, and cell-signaling secretion in the context of clone-recognition. Protists allow us to study cell-cell recognition from ecological and evolutionary perspectives. Modern protistan lineages can be central to studies about the origins and evolution of multicellularity. © 2016 The Author(s) Journal of Eukaryotic Microbiology © 2016 International Society of Protistologists.

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

    Directory of Open Access Journals (Sweden)

    Jahed Mehran

    2007-12-01

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

  1. Angle-of-arrival-based gesture recognition using ultrasonic multi-frequency signals

    KAUST Repository

    Chen, Hui

    2017-11-02

    Hand gestures are tools for conveying information, expressing emotion, interacting with electronic devices or even serving disabled people as a second language. A gesture can be recognized by capturing the movement of the hand, in real time, and classifying the collected data. Several commercial products such as Microsoft Kinect, Leap Motion Sensor, Synertial Gloves and HTC Vive have been released and new solutions have been proposed by researchers to handle this task. These systems are mainly based on optical measurements, inertial measurements, ultrasound signals and radio signals. This paper proposes an ultrasonic-based gesture recognition system using AOA (Angle of Arrival) information of ultrasonic signals emitted from a wearable ultrasound transducer. The 2-D angles of the moving hand are estimated using multi-frequency signals captured by a fixed receiver array. A simple redundant dictionary matching classifier is designed to recognize gestures representing the numbers from `0\\' to `9\\' and compared with a neural network classifier. Average classification accuracies of 95.5% and 94.4% are obtained, respectively, using the two classification methods.

  2. The rationale for energy efficiency policy: Assessing the recognition of the multiple benefits of energy efficiency retrofit policy

    International Nuclear Information System (INIS)

    Kerr, Niall; Gouldson, Andy; Barrett, John

    2017-01-01

    The rationale for energy efficiency policy can be framed in terms of a variety of different benefits. This paper considers how different benefits have been used within the overall rationale for energy efficient retrofit policy in different contexts. We posit that different rationales may be used for the same policy response, and that the form of rationale used may affect the design, delivery or the level of policy support, with different rationales making it easier to account for different results. Considering retrofit policy in the contexts of the UK, Germany, New Zealand and Ireland, we characterise policy rationale in each case, assessing what the key perceived benefits have been, and whether they have changed over time. The analysis identifies some marked differences between cases with the recognition of benefits and the ensuing policy rationale resulting from a complex mix of political, social and economic influences. We find that recognition of multiple benefits may not equate with multiplied policy support, and instead it is more likely that different rationales will have relevance at different times, for different audiences. The findings highlight that, alongside evidence for policy, it is important to also consider how the overall rationale for policy is eventually framed. - Highlights: • Energy efficiency as a policy issue with perceived multiple benefits. • Assessment of the influence of different benefits on rationale for energy efficient retrofit policy. • How does the rationale for retrofit policy differ in different national policy contexts. • To what extent are the perceived multiple benefits of policy recognised. • What influence does eventual rationale for policy have on the policy implemented.

  3. AN EFFICIENT SELF-UPDATING FACE RECOGNITION SYSTEM FOR PLASTIC SURGERY FACE

    Directory of Open Access Journals (Sweden)

    A. Devi

    2016-08-01

    Full Text Available Facial recognition system is fundamental a computer application for the automatic identification of a person through a digitized image or a video source. The major cause for the overall poor performance is related to the transformations in appearance of the user based on the aspects akin to ageing, beard growth, sun-tan etc. In order to overcome the above drawback, Self-update process has been developed in which, the system learns the biometric attributes of the user every time the user interacts with the system and the information gets updated automatically. The procedures of Plastic surgery yield a skilled and endurable means of enhancing the facial appearance by means of correcting the anomalies in the feature and then treating the facial skin with the aim of getting a youthful look. When plastic surgery is performed on an individual, the features of the face undergo reconstruction either locally or globally. But, the changes which are introduced new by plastic surgery remain hard to get modeled by the available face recognition systems and they deteriorate the performances of the face recognition algorithm. Hence the Facial plastic surgery produces changes in the facial features to larger extent and thereby creates a significant challenge to the face recognition system. This work introduces a fresh Multimodal Biometric approach making use of novel approaches to boost the rate of recognition and security. The proposed method consists of various processes like Face segmentation using Active Appearance Model (AAM, Face Normalization using Kernel Density Estimate/ Point Distribution Model (KDE-PDM, Feature extraction using Local Gabor XOR Patterns (LGXP and Classification using Independent Component Analysis (ICA. Efficient techniques have been used in each phase of the FRAS in order to obtain improved results.

  4. Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition

    Directory of Open Access Journals (Sweden)

    Vito Janko

    2017-12-01

    Full Text Available The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

  5. Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition.

    Science.gov (United States)

    Janko, Vito; Luštrek, Mitja

    2017-12-29

    The recognition of the user's context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system's energy expenditure and the system's accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

  6. Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition

    Science.gov (United States)

    Janko, Vito

    2017-01-01

    The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy. PMID:29286301

  7. Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Prima Dewi Purnamasari

    2017-05-01

    Full Text Available The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN, and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction

  8. Subject-independent emotion recognition based on physiological signals: a three-stage decision method.

    Science.gov (United States)

    Chen, Jing; Hu, Bin; Wang, Yue; Moore, Philip; Dai, Yongqiang; Feng, Lei; Ding, Zhijie

    2017-12-20

    Collaboration between humans and computers has become pervasive and ubiquitous, however current computer systems are limited in that they fail to address the emotional component. An accurate understanding of human emotions is necessary for these computers to trigger proper feedback. Among multiple emotional channels, physiological signals are synchronous with emotional responses; therefore, analyzing physiological changes is a recognized way to estimate human emotions. In this paper, a three-stage decision method is proposed to recognize four emotions based on physiological signals in the multi-subject context. Emotion detection is achieved by using a stage-divided strategy in which each stage deals with a fine-grained goal. The decision method consists of three stages. During the training process, the initial stage transforms mixed training subjects to separate groups, thus eliminating the effect of individual differences. The second stage categorizes four emotions into two emotion pools in order to reduce recognition complexity. The third stage trains a classifier based on emotions in each emotion pool. During the testing process, a test case or test trial will be initially classified to a group followed by classification into an emotion pool in the second stage. An emotion will be assigned to the test trial in the final stage. In this paper we consider two different ways of allocating four emotions into two emotion pools. A comparative analysis is also carried out between the proposal and other methods. An average recognition accuracy of 77.57% was achieved on the recognition of four emotions with the best accuracy of 86.67% to recognize the positive and excited emotion. Using differing ways of allocating four emotions into two emotion pools, we found there is a difference in the effectiveness of a classifier on learning each emotion. When compared to other methods, the proposed method demonstrates a significant improvement in recognizing four emotions in the

  9. Effects of cue modality and emotional category on recognition of nonverbal emotional signals in schizophrenia.

    Science.gov (United States)

    Vogel, Bastian D; Brück, Carolin; Jacob, Heike; Eberle, Mark; Wildgruber, Dirk

    2016-07-07

    Impaired interpretation of nonverbal emotional cues in patients with schizophrenia has been reported in several studies and a clinical relevance of these deficits for social functioning has been assumed. However, it is unclear to what extent the impairments depend on specific emotions or specific channels of nonverbal communication. Here, the effect of cue modality and emotional categories on accuracy of emotion recognition was evaluated in 21 patients with schizophrenia and compared to a healthy control group (n = 21). To this end, dynamic stimuli comprising speakers of both genders in three different sensory modalities (auditory, visual and audiovisual) and five emotional categories (happy, alluring, neutral, angry and disgusted) were used. Patients with schizophrenia were found to be impaired in emotion recognition in comparison to the control group across all stimuli. Considering specific emotions more severe deficits were revealed in the recognition of alluring stimuli and less severe deficits in the recognition of disgusted stimuli as compared to all other emotions. Regarding cue modality the extent of the impairment in emotional recognition did not significantly differ between auditory and visual cues across all emotional categories. However, patients with schizophrenia showed significantly more severe disturbances for vocal as compared to facial cues when sexual interest is expressed (alluring stimuli), whereas more severe disturbances for facial as compared to vocal cues were observed when happiness or anger is expressed. Our results confirmed that perceptual impairments can be observed for vocal as well as facial cues conveying various social and emotional connotations. The observed differences in severity of impairments with most severe deficits for alluring expressions might be related to specific difficulties in recognizing the complex social emotional information of interpersonal intentions as compared to "basic" emotional states. Therefore

  10. Orexin signaling during social defeat stress influences subsequent social interaction behaviour and recognition memory.

    Science.gov (United States)

    Eacret, Darrell; Grafe, Laura A; Dobkin, Jane; Gotter, Anthony L; Rengerb, John J; Winrow, Christopher J; Bhatnagar, Seema

    2018-06-11

    Orexins are neuropeptides synthesized in the lateral hypothalamus that influence arousal, feeding, reward pathways, and the response to stress. However, the role of orexins in repeated stress is not fully characterized. Here, we examined how orexins and their receptors contribute to the coping response during repeated social defeat and subsequent anxiety-like and memory-related behaviors. Specifically, we used Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) to stimulate orexins prior to each of five consecutive days of social defeat stress in adult male rats. Additionally, we determined the role of the orexin 2 receptor in these behaviors by using a selective orexin 2 receptor antagonist (MK-1064) administered prior to each social defeat. Following the 5 day social defeat conditioning period, rats were evaluated in social interaction and novel object recognition paradigms to assess anxiety-like behavior and recognition memory, respectively. Activation of orexin neurons by DREADDs prior to each social defeat decreased the average latency to become defeated across 5 days, indicative of a passive coping strategy that we have previously linked to a stress vulnerable phenotype. Moreover, stimulation of orexin signaling during defeat conditioning decreased subsequent social interaction and performance in the novel object recognition test indicating increased subsequent anxiety-like behavior and reduced working memory. Blocking the orexin 2 receptor during repeated defeat did not alter these effects. Together, our results suggest that orexin neuron activation produces a passive coping phenotype during social defeat leading to subsequent anxiety-like behaviors and memory deficits. Copyright © 2018. Published by Elsevier B.V.

  11. Study of the vocal signal in the amplitude-time representation. Speech segmentation and recognition algorithms

    International Nuclear Information System (INIS)

    Baudry, Marc

    1978-01-01

    This dissertation exposes an acoustical and phonetical study of vocal signal. The complex pattern of the signal is segmented into simple sub-patterns and each one of these sub-patterns may be segmented again into another more simplest patterns with lower level. Application of pattern recognition techniques facilitates on one hand this segmentation and on the other hand the definition of the structural relations between the sub-patterns. Particularly, we have developed syntactic techniques in which the rewriting rules, context-sensitive, are controlled by predicates using parameters evaluated on the sub-patterns themselves. This allow to generalize a pure syntactic analysis by adding a semantic information. The system we expose, realizes pre-classification and a partial identification of the phonemes as also the accurate detection of each pitch period. The voice signal is analysed directly using the amplitude-time representation. This system has been implemented on a mini-computer and it works in the real time. (author) [fr

  12. Pattern Recognition of Signals for the Fault-Slip Type of Rock Burst in Coal Mines

    Directory of Open Access Journals (Sweden)

    X. S. Liu

    2015-01-01

    Full Text Available The fault-slip type of rock burst is a major threat to the safety of coal mining, and effectively recognizing its signals patterns is the foundation for the early warning and prevention. At first, a mechanical model of the fault-slip was established and the mechanism of the rock burst induced by the fault-slip was revealed. Then, the patterns of the electromagnetic radiation, acoustic emission (AE, and microseismic signals in the fault-slip type of rock burst were proposed, in that before the rock burst occurs, the electromagnetic radiation intensity near the sliding surface increases rapidly, the AE energy rises exponentially, and the energy released by microseismic events experiences at least one peak and is close to the next peak. At last, in situ investigations were performed at number 1412 coal face in the Huafeng Mine, China. Results showed that the signals patterns proposed are in good agreement with the process of the fault-slip type of rock burst. The pattern recognition can provide a basis for the early warning and the implementation of relief measures of the fault-slip type of rock burst.

  13. Orientation Encoding and Viewpoint Invariance in Face Recognition: Inferring Neural Properties from Large-Scale Signals.

    Science.gov (United States)

    Ramírez, Fernando M

    2018-05-01

    Viewpoint-invariant face recognition is thought to be subserved by a distributed network of occipitotemporal face-selective areas that, except for the human anterior temporal lobe, have been shown to also contain face-orientation information. This review begins by highlighting the importance of bilateral symmetry for viewpoint-invariant recognition and face-orientation perception. Then, monkey electrophysiological evidence is surveyed describing key tuning properties of face-selective neurons-including neurons bimodally tuned to mirror-symmetric face-views-followed by studies combining functional magnetic resonance imaging (fMRI) and multivariate pattern analyses to probe the representation of face-orientation and identity information in humans. Altogether, neuroimaging studies suggest that face-identity is gradually disentangled from face-orientation information along the ventral visual processing stream. The evidence seems to diverge, however, regarding the prevalent form of tuning of neural populations in human face-selective areas. In this context, caveats possibly leading to erroneous inferences regarding mirror-symmetric coding are exposed, including the need to distinguish angular from Euclidean distances when interpreting multivariate pattern analyses. On this basis, this review argues that evidence from the fusiform face area is best explained by a view-sensitive code reflecting head angular disparity, consistent with a role of this area in face-orientation perception. Finally, the importance is stressed of explicit models relating neural properties to large-scale signals.

  14. Blockade of intracellular Zn2+ signaling in the dentate gyrus erases recognition memory via impairment of maintained LTP.

    Science.gov (United States)

    Tamano, Haruna; Minamino, Tatsuya; Fujii, Hiroaki; Takada, Shunsuke; Nakamura, Masatoshi; Ando, Masaki; Takeda, Atsushi

    2015-08-01

    There is no evidence on the precise role of synaptic Zn2+ signaling on the retention and recall of recognition memory. On the basis of the findings that intracellular Zn2+ signaling in the dentate gyrus is required for object recognition, short-term memory, the present study deals with the effect of spatiotemporally blocking Zn2+ signaling in the dentate gyrus after LTP induction and learning. Three-day-maintained LTP was impaired 1 day after injection of clioquinol into the dentate gyrus, which transiently reduced intracellular Zn2+ signaling in the dentate gyrus. The irreversible impairment was rescued not only by co-injection of ZnCl2 , which ameliorated the loss of Zn2+ signaling, but also by pre-injection of Jasplakinolide, a stabilizer of F-actin, prior to clioquinol injection. Simultaneously, 3-day-old space recognition memory was impaired 1 day after injection of clioquinol into the dentate gyrus, but not by pre-injection of Jasplakinolide. Jasplakinolide also rescued both impairments of 3-day-maintained LTP and 3-day-old memory after injection of ZnAF-2DA into the dentate gyrus, which blocked intracellular Zn2+ signaling in the dentate gyrus. The present paper indicates that the blockade and/or loss of intracellular Zn2+ signaling in the dentate gyrus coincidently impair maintained LTP and recognition memory. The mechanism maintaining LTP via intracellular Zn2+ signaling in dentate granule cells, which may be involved in the formation of F-actin, may retain space recognition memory. © 2015 Wiley Periodicals, Inc.

  15. Are there differential deficits in facial emotion recognition between paranoid and non-paranoid schizophrenia? A signal detection analysis.

    Science.gov (United States)

    Huang, Charles Lung-Cheng; Hsiao, Sigmund; Hwu, Hai-Gwo; Howng, Shen-Long

    2013-10-30

    This study assessed facial emotion recognition abilities in subjects with paranoid and non-paranoid schizophrenia (NPS) using signal detection theory. We explore the differential deficits in facial emotion recognition in 44 paranoid patients with schizophrenia (PS) and 30 non-paranoid patients with schizophrenia (NPS), compared to 80 healthy controls. We used morphed faces with different intensities of emotion and computed the sensitivity index (d') of each emotion. The results showed that performance differed between the schizophrenia and healthy controls groups in the recognition of both negative and positive affects. The PS group performed worse than the healthy controls group but better than the NPS group in overall performance. Performance differed between the NPS and healthy controls groups in the recognition of all basic emotions and neutral faces; between the PS and healthy controls groups in the recognition of angry faces; and between the PS and NPS groups in the recognition of happiness, anger, sadness, disgust, and neutral affects. The facial emotion recognition impairment in schizophrenia may reflect a generalized deficit rather than a negative-emotion specific deficit. The PS group performed worse than the control group, but better than the NPS group in facial expression recognition, with differential deficits between PS and NPS patients. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  16. Blockade of intracellular Zn2+ signaling in the basolateral amygdala affects object recognition memory via attenuation of dentate gyrus LTP.

    Science.gov (United States)

    Fujise, Yuki; Kubota, Mitsuyasu; Suzuki, Miki; Tamano, Haruna; Takeda, Atsushi

    2017-09-01

    Hippocampus-dependent memory is modulated by the amygdala. However, it is unknown whether intracellular Zn 2+ signaling in the amygdala is involved in hippocampus-dependent memory. On the basis of the evidence that intracellular Zn 2+ signaling in dentate granule cells (DGC) is necessary for object recognition memory via LTP at medial perforant pathway (PP)-DGC synapses, the present study examined whether intracellular Zn 2+ signaling in the amygdala influences object recognition memory via modulation of LTP at medial PP-DGC synapses. When ZnAF-2DA (100 μM, 2 μl) was injected into the basolateral amygdala (BLA), intracellular ZnAF-2 locally chelated intracellular Zn 2+ in the amygdala. Recognition memory was affected when training of object recognition test was performed 20 min after ZnAF-2DA injection into the BLA. Twenty minutes after injection of ZnAF-2DA into the BLA, LTP induction at medial PP-DGC synapses was attenuated, while LTP induction at PP-BLA synapses was potentiated and LTP induction at BLA-DGC synapses was attenuated. These results suggest that intracellular Zn 2+ signaling in the BLA is involved in BLA-associated LTP and modulates LTP at medial PP-DGC synapses, followed by modulation of object recognition memory. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. A Prosthetic Hand Body Area Controller Based on Efficient Pattern Recognition Control Strategies.

    Science.gov (United States)

    Benatti, Simone; Milosevic, Bojan; Farella, Elisabetta; Gruppioni, Emanuele; Benini, Luca

    2017-04-15

    Poliarticulated prosthetic hands represent a powerful tool to restore functionality and improve quality of life for upper limb amputees. Such devices offer, on the same wearable node, sensing and actuation capabilities, which are not equally supported by natural interaction and control strategies. The control in state-of-the-art solutions is still performed mainly through complex encoding of gestures in bursts of contractions of the residual forearm muscles, resulting in a non-intuitive Human-Machine Interface (HMI). Recent research efforts explore the use of myoelectric gesture recognition for innovative interaction solutions, however there persists a considerable gap between research evaluation and implementation into successful complete systems. In this paper, we present the design of a wearable prosthetic hand controller, based on intuitive gesture recognition and a custom control strategy. The wearable node directly actuates a poliarticulated hand and wirelessly interacts with a personal gateway (i.e., a smartphone) for the training and personalization of the recognition algorithm. Through the whole system development, we address the challenge of integrating an efficient embedded gesture classifier with a control strategy tailored for an intuitive interaction between the user and the prosthesis. We demonstrate that this combined approach outperforms systems based on mere pattern recognition, since they target the accuracy of a classification algorithm rather than the control of a gesture. The system was fully implemented, tested on healthy and amputee subjects and compared against benchmark repositories. The proposed approach achieves an error rate of 1.6% in the end-to-end real time control of commonly used hand gestures, while complying with the power and performance budget of a low-cost microcontroller.

  18. Efficient Mobility Management Signalling in Network Mobility Supported PMIPV6

    Directory of Open Access Journals (Sweden)

    Ananthi Jebaseeli Samuelraj

    2015-01-01

    Full Text Available Proxy Mobile IPV6 (PMIPV6 is a network based mobility management protocol which supports node’s mobility without the contribution from the respective mobile node. PMIPV6 is initially designed to support individual node mobility and it should be enhanced to support mobile network movement. NEMO-BSP is an existing protocol to support network mobility (NEMO in PMIPV6 network. Due to the underlying differences in basic protocols, NEMO-BSP cannot be directly applied to PMIPV6 network. Mobility management signaling and data structures used for individual node’s mobility should be modified to support group nodes’ mobility management efficiently. Though a lot of research work is in progress to implement mobile network movement in PMIPV6, it is not yet standardized and each suffers with different shortcomings. This research work proposes modifications in NEMO-BSP and PMIPV6 to achieve NEMO support in PMIPV6. It mainly concentrates on optimizing the number and size of mobility signaling exchanged while mobile network or mobile network node changes its access point.

  19. Efficient Mobility Management Signalling in Network Mobility Supported PMIPV6.

    Science.gov (United States)

    Samuelraj, Ananthi Jebaseeli; Jayapal, Sundararajan

    2015-01-01

    Proxy Mobile IPV6 (PMIPV6) is a network based mobility management protocol which supports node's mobility without the contribution from the respective mobile node. PMIPV6 is initially designed to support individual node mobility and it should be enhanced to support mobile network movement. NEMO-BSP is an existing protocol to support network mobility (NEMO) in PMIPV6 network. Due to the underlying differences in basic protocols, NEMO-BSP cannot be directly applied to PMIPV6 network. Mobility management signaling and data structures used for individual node's mobility should be modified to support group nodes' mobility management efficiently. Though a lot of research work is in progress to implement mobile network movement in PMIPV6, it is not yet standardized and each suffers with different shortcomings. This research work proposes modifications in NEMO-BSP and PMIPV6 to achieve NEMO support in PMIPV6. It mainly concentrates on optimizing the number and size of mobility signaling exchanged while mobile network or mobile network node changes its access point.

  20. Efficient Spatio-Temporal Local Binary Patterns for Spontaneous Facial Micro-Expression Recognition

    Science.gov (United States)

    Wang, Yandan; See, John; Phan, Raphael C.-W.; Oh, Yee-Hui

    2015-01-01

    Micro-expression recognition is still in the preliminary stage, owing much to the numerous difficulties faced in the development of datasets. Since micro-expression is an important affective clue for clinical diagnosis and deceit analysis, much effort has gone into the creation of these datasets for research purposes. There are currently two publicly available spontaneous micro-expression datasets—SMIC and CASME II, both with baseline results released using the widely used dynamic texture descriptor LBP-TOP for feature extraction. Although LBP-TOP is popular and widely used, it is still not compact enough. In this paper, we draw further inspiration from the concept of LBP-TOP that considers three orthogonal planes by proposing two efficient approaches for feature extraction. The compact robust form described by the proposed LBP-Six Intersection Points (SIP) and a super-compact LBP-Three Mean Orthogonal Planes (MOP) not only preserves the essential patterns, but also reduces the redundancy that affects the discriminality of the encoded features. Through a comprehensive set of experiments, we demonstrate the strengths of our approaches in terms of recognition accuracy and efficiency. PMID:25993498

  1. Recognition of secretory proteins in Escherichia coli requires signals in addition to the signal sequence and slow folding

    Directory of Open Access Journals (Sweden)

    Flower Ann M

    2002-11-01

    Full Text Available Abstract Background The Sec-dependent protein export apparatus of Escherichia coli is very efficient at correctly identifying proteins to be exported from the cytoplasm. Even bacterial strains that carry prl mutations, which allow export of signal sequence-defective precursors, accurately differentiate between cytoplasmic and mutant secretory proteins. It was proposed previously that the basis for this precise discrimination is the slow folding rate of secretory proteins, resulting in binding by the secretory chaperone, SecB, and subsequent targeting to translocase. Based on this proposal, we hypothesized that a cytoplasmic protein containing a mutation that slows its rate of folding would be recognized by SecB and therefore targeted to the Sec pathway. In a Prl suppressor strain the mutant protein would be exported to the periplasm due to loss of ability to reject non-secretory proteins from the pathway. Results In the current work, we tested this hypothesis using a mutant form of λ repressor that folds slowly. No export of the mutant protein was observed, even in a prl strain. We then examined binding of the mutant λ repressor to SecB. We did not observe interaction by either of two assays, indicating that slow folding is not sufficient for SecB binding and targeting to translocase. Conclusions These results strongly suggest that to be targeted to the export pathway, secretory proteins contain signals in addition to the canonical signal sequence and the rate of folding.

  2. A DESIGN FRAMEWORK FOR HUMAN EMOTION RECOGNITION USING ELECTROCARDIOGRAM AND SKIN CONDUCTANCE RESPONSE SIGNALS

    Directory of Open Access Journals (Sweden)

    KHAIRUN NISA’ MINHAD

    2017-11-01

    Full Text Available Identification of human emotional state while driving a vehicle can help in understanding the human behaviour. Based on this identification, a response system can be developed in order to mitigate the impact that may be resulted from the behavioural changes. However, the adaptation of emotions to the environment at most scenarios is subjective to an individual’s perspective. Many factors, mainly cultural and geography, gender, age, life style and history, level of education and professional status, can affect the detection of human emotional affective states. This work investigated sympathetic responses toward human emotions defined by using electrocardiography (ECG and skin conductance response (SCR signals recorded simultaneously. This work aimed to recognize ECG and SCR patterns of the investigated emotions measured using selected sensor. A pilot study was conducted to evaluate the proposed framework. Initial results demonstrated the importance of suitability of the stimuli used to evoke the emotions and high opportunity for the ECG and SCR signals to be used in the automotive real-time emotion recognition systems.

  3. Type I interferon and pattern recognition receptor signaling following particulate matter inhalation

    Directory of Open Access Journals (Sweden)

    Erdely Aaron

    2012-07-01

    Full Text Available Abstract Background Welding, a process that generates an aerosol containing gases and metal-rich particulates, induces adverse physiological effects including inflammation, immunosuppression and cardiovascular dysfunction. This study utilized microarray technology and subsequent pathway analysis as an exploratory search for markers/mechanisms of in vivo systemic effects following inhalation. Mice were exposed by inhalation to gas metal arc – stainless steel (GMA-SS welding fume at 40 mg/m3 for 3 hr/d for 10 d and sacrificed 4 hr, 14 d and 28 d post-exposure. Whole blood cells, aorta and lung were harvested for global gene expression analysis with subsequent Ingenuity Pathway Analysis and confirmatory qRT-PCR. Serum was collected for protein profiling. Results The novel finding was a dominant type I interferon signaling network with the transcription factor Irf7 as a central component maintained through 28 d. Remarkably, these effects showed consistency across all tissues indicating a systemic type I interferon response that was complemented by changes in serum proteins (decreased MMP-9, CRP and increased VCAM1, oncostatin M, IP-10. In addition, pulmonary expression of interferon α and β and Irf7 specific pattern recognition receptors (PRR and signaling molecules (Ddx58, Ifih1, Dhx58, ISGF3 were induced, an effect that showed specificity when compared to other inflammatory exposures. Also, a canonical pathway indicated a coordinated response of multiple PRR and associated signaling molecules (Tlr7, Tlr2, Clec7a, Nlrp3, Myd88 to inhalation of GMA-SS. Conclusion This methodological approach has the potential to identify consistent, prominent and/or novel pathways and provides insight into mechanisms that contribute to pulmonary and systemic effects following toxicant exposure.

  4. Estradiol-Induced Object Recognition Memory Consolidation Is Dependent on Activation of mTOR Signaling in the Dorsal Hippocampus

    Science.gov (United States)

    Fortress, Ashley M.; Fan, Lu; Orr, Patrick T.; Zhao, Zaorui; Frick, Karyn M.

    2013-01-01

    The mammalian target of rapamycin (mTOR) signaling pathway is an important regulator of protein synthesis and is essential for various forms of hippocampal memory. Here, we asked whether the enhancement of object recognition memory consolidation produced by dorsal hippocampal infusion of 17[Beta]-estradiol (E[subscript 2]) is dependent on mTOR…

  5. Influence of different envelope maskers on signal recognition and neuronal representation in the auditory system of a grasshopper.

    Directory of Open Access Journals (Sweden)

    Daniela Neuhofer

    Full Text Available BACKGROUND: Animals that communicate by sound face the problem that the signals arriving at the receiver often are degraded and masked by noise. Frequency filters in the receiver's auditory system may improve the signal-to-noise ratio (SNR by excluding parts of the spectrum which are not occupied by the species-specific signals. This solution, however, is hardly amenable to species that produce broad band signals or have ears with broad frequency tuning. In mammals auditory filters exist that work in the temporal domain of amplitude modulations (AM. Do insects also use this type of filtering? PRINCIPAL FINDINGS: Combining behavioural and neurophysiological experiments we investigated whether AM filters may improve the recognition of masked communication signals in grasshoppers. The AM pattern of the sound, its envelope, is crucial for signal recognition in these animals. We degraded the species-specific song by adding random fluctuations to its envelope. Six noise bands were used that differed in their overlap with the spectral content of the song envelope. If AM filters contribute to reduced masking, signal recognition should depend on the degree of overlap between the song envelope spectrum and the noise spectra. Contrary to this prediction, the resistance against signal degradation was the same for five of six masker bands. Most remarkably, the band with the strongest frequency overlap to the natural song envelope (0-100 Hz impaired acceptance of degraded signals the least. To assess the noise filter capacities of single auditory neurons, the changes of spike trains as a function of the masking level were assessed. Increasing levels of signal degradation in different frequency bands led to similar changes in the spike trains in most neurones. CONCLUSIONS: There is no indication that auditory neurones of grasshoppers are specialized to improve the SNR with respect to the pattern of amplitude modulations.

  6. An Efficient Multimodal 2D + 3D Feature-based Approach to Automatic Facial Expression Recognition

    KAUST Repository

    Li, Huibin

    2015-07-29

    We present a fully automatic multimodal 2D + 3D feature-based facial expression recognition approach and demonstrate its performance on the BU-3DFE database. Our approach combines multi-order gradient-based local texture and shape descriptors in order to achieve efficiency and robustness. First, a large set of fiducial facial landmarks of 2D face images along with their 3D face scans are localized using a novel algorithm namely incremental Parallel Cascade of Linear Regression (iPar-CLR). Then, a novel Histogram of Second Order Gradients (HSOG) based local image descriptor in conjunction with the widely used first-order gradient based SIFT descriptor are used to describe the local texture around each 2D landmark. Similarly, the local geometry around each 3D landmark is described by two novel local shape descriptors constructed using the first-order and the second-order surface differential geometry quantities, i.e., Histogram of mesh Gradients (meshHOG) and Histogram of mesh Shape index (curvature quantization, meshHOS). Finally, the Support Vector Machine (SVM) based recognition results of all 2D and 3D descriptors are fused at both feature-level and score-level to further improve the accuracy. Comprehensive experimental results demonstrate that there exist impressive complementary characteristics between the 2D and 3D descriptors. We use the BU-3DFE benchmark to compare our approach to the state-of-the-art ones. Our multimodal feature-based approach outperforms the others by achieving an average recognition accuracy of 86.32%. Moreover, a good generalization ability is shown on the Bosphorus database.

  7. An Efficient Multimodal 2D + 3D Feature-based Approach to Automatic Facial Expression Recognition

    KAUST Repository

    Li, Huibin; Ding, Huaxiong; Huang, Di; Wang, Yunhong; Zhao, Xi; Morvan, Jean-Marie; Chen, Liming

    2015-01-01

    We present a fully automatic multimodal 2D + 3D feature-based facial expression recognition approach and demonstrate its performance on the BU-3DFE database. Our approach combines multi-order gradient-based local texture and shape descriptors in order to achieve efficiency and robustness. First, a large set of fiducial facial landmarks of 2D face images along with their 3D face scans are localized using a novel algorithm namely incremental Parallel Cascade of Linear Regression (iPar-CLR). Then, a novel Histogram of Second Order Gradients (HSOG) based local image descriptor in conjunction with the widely used first-order gradient based SIFT descriptor are used to describe the local texture around each 2D landmark. Similarly, the local geometry around each 3D landmark is described by two novel local shape descriptors constructed using the first-order and the second-order surface differential geometry quantities, i.e., Histogram of mesh Gradients (meshHOG) and Histogram of mesh Shape index (curvature quantization, meshHOS). Finally, the Support Vector Machine (SVM) based recognition results of all 2D and 3D descriptors are fused at both feature-level and score-level to further improve the accuracy. Comprehensive experimental results demonstrate that there exist impressive complementary characteristics between the 2D and 3D descriptors. We use the BU-3DFE benchmark to compare our approach to the state-of-the-art ones. Our multimodal feature-based approach outperforms the others by achieving an average recognition accuracy of 86.32%. Moreover, a good generalization ability is shown on the Bosphorus database.

  8. Selecting Informative Features of the Helicopter and Aircraft Acoustic Signals in the Adaptive Autonomous Information Systems for Recognition

    Directory of Open Access Journals (Sweden)

    V. K. Hohlov

    2017-01-01

    Full Text Available The article forms the rationale for selecting the informative features of the helicopter and aircraft acoustic signals to solve a problem of their recognition and shows that the most informative ones are the counts of extrema in the energy spectra of the input signals, which represent non-centered random variables. An apparatus of the multiple initial regression coefficients was selected as a mathematical tool of research. The application of digital re-circulators with positive and negative feedbacks, which have the comb-like frequency characteristics, solves the problem of selecting informative features. A distinguishing feature of such an approach is easy agility of the comb frequency characteristics just through the agility of a delay value of digital signal in the feedback circuit. Adding an adaptation block to the selection block of the informative features enables us to ensure the invariance of used informative feature and counts of local extrema of the spectral power density to the airspeed of a helicopter. The paper gives reasons for the principle of adaptation and the structure of the adaptation block. To form the discriminator characteristics are used the cross-correlation statistical characteristics of the quadrature components of acoustic signal realizations, obtained by Hilbert transform. The paper proposes to provide signal recognition using a regression algorithm that allows handling the non-centered informative features and using a priori information about coefficients of initial regression of signal and noise.The simulation in Matlab Simulink has shown that selected informative features of signals in regressive processing of signal realizations of 0.5 s duration have good separability, and based on a set of 100 acoustic signal realizations in each class in full-scale conditions, has proved ensuring a correct recognition probability of 0.975, at least. The considered principles of informative features selection and adaptation can

  9. Direct ubiquitin independent recognition and degradation of a folded protein by the eukaryotic proteasomes-origin of intrinsic degradation signals.

    Directory of Open Access Journals (Sweden)

    Amit Kumar Singh Gautam

    Full Text Available Eukaryotic 26S proteasomes are structurally organized to recognize, unfold and degrade globular proteins. However, all existing model substrates of the 26S proteasome in addition to ubiquitin or adaptor proteins require unstructured regions in the form of fusion tags for efficient degradation. We report for the first time that purified 26S proteasome can directly recognize and degrade apomyoglobin, a globular protein, in the absence of ubiquitin, extrinsic degradation tags or adaptor proteins. Despite a high affinity interaction, absence of a ligand and presence of only helices/loops that follow the degradation signal, apomyoglobin is degraded slowly by the proteasome. A short floppy F-helix exposed upon ligand removal and in conformational equilibrium with a disordered structure is mandatory for recognition and initiation of degradation. Holomyoglobin, in which the helix is buried, is neither recognized nor degraded. Exposure of the floppy F-helix seems to sensitize the proteasome and primes the substrate for degradation. Using peptide panning and competition experiments we speculate that initial encounters through the floppy helix and additional strong interactions with N-terminal helices anchors apomyoglobin to the proteasome. Stabilizing helical structure in the floppy F-helix slows down degradation. Destabilization of adjacent helices accelerates degradation. Unfolding seems to follow the mechanism of helix unraveling rather than global unfolding. Our findings while confirming the requirement for unstructured regions in degradation offers the following new insights: a origin and identification of an intrinsic degradation signal in the substrate, b identification of sequences in the native substrate that are likely to be responsible for direct interactions with the proteasome, and c identification of critical rate limiting steps like exposure of the intrinsic degron and destabilization of an unfolding intermediate that are presumably

  10. Signal recognition particle assembly in relation to the function of amplified nucleoli of Xenopus oocytes.

    Science.gov (United States)

    Sommerville, John; Brumwell, Craig L; Politz, Joan C Ritland; Pederson, Thoru

    2005-03-15

    The signal recognition particle (SRP) is a ribonucleoprotein machine that controls the translation and intracellular sorting of membrane and secreted proteins. The SRP contains a core RNA subunit with which six proteins are assembled. Recent work in both yeast and mammalian cells has identified the nucleolus as a possible initial site of SRP assembly. In the present study, SRP RNA and protein components were identified in the extrachromosomal, amplified nucleoli of Xenopus laevis oocytes. Fluorescent SRP RNA microinjected into the oocyte nucleus became specifically localized in the nucleoli, and endogenous SRP RNA was also detected in oocyte nucleoli by RNA in situ hybridization. An initial step in the assembly of SRP involves the binding of the SRP19 protein to SRP RNA. When green fluorescent protein (GFP)-tagged SRP19 protein was injected into the oocyte cytoplasm it was imported into the nucleus and became concentrated in the amplified nucleoli. After visiting the amplified nucleoli, GFP-tagged SRP19 protein was detected in the cytoplasm in a ribonucleoprotein complex, having a sedimentation coefficient characteristic of the SRP. These results suggest that the amplified nucleoli of Xenopus oocytes produce maternal stores not only of ribosomes, the classical product of nucleoli, but also of SRP, presumably as a global developmental strategy for stockpiling translational machinery for early embryogenesis.

  11. A Simple Decision Rule for Recognition of Poly(A) Tail Signal Motifs in Human Genome

    KAUST Repository

    AbouEisha, Hassan M.

    2015-05-12

    Background is the numerous attempts were made to predict motifs in genomic sequences that correspond to poly (A) tail signals. Vast portion of this effort has been directed to a plethora of nonlinear classification methods. Even when such approaches yield good discriminant results, identifying dominant features of regulatory mechanisms nevertheless remains a challenge. In this work, we look at decision rules that may help identifying such features. Findings are we present a simple decision rule for classification of candidate poly (A) tail signal motifs in human genomic sequence obtained by evaluating features during the construction of gradient boosted trees. We found that values of a single feature based on the frequency of adenine in the genomic sequence surrounding candidate signal and the number of consecutive adenine molecules in a well-defined region immediately following the motif displays good discriminative potential in classification of poly (A) tail motifs for samples covered by the rule. Conclusions is the resulting simple rule can be used as an efficient filter in construction of more complex poly(A) tail motifs classification algorithms.

  12. Signal processing, sensor fusion, and target recognition; Proceedings of the Meeting, Orlando, FL, Apr. 20-22, 1992

    Science.gov (United States)

    Libby, Vibeke; Kadar, Ivan

    Consideration is given to a multiordered mapping technique for target prioritization, a neural network approach to multiple-target-tracking problems, a multisensor fusion algorithm for multitarget multibackground classification, deconvolutiom of multiple images of the same object, neural networks and genetic algorithms for combinatorial optimization of sensor data fusion, classification of atmospheric acoustic signals from fixed-wing aircraft, and an optics approach to sensor fusion for target recognition. Also treated are a zoom lens for automatic target recognition, a hybrid model for the analysis of radar sensors, an innovative test bed for developing and assessing air-to-air noncooperative target identification algorithms, SAR imagery scene segmentation using fractal processing, sonar feature-based bandwidth compression, laboratory experiments for a new sonar system, computational algorithms for discrete transform using fixed-size filter matrices, and pattern recognition for power systems.

  13. Anti-signal recognition particle autoantibody ELISA validation and clinical associations.

    Science.gov (United States)

    Aggarwal, Rohit; Oddis, Chester V; Goudeau, Danielle; Fertig, Noreen; Metes, Ilinca; Stephens, Chad; Qi, Zengbiao; Koontz, Diane; Levesque, Marc C

    2015-07-01

    The aim of this study was to develop and validate a quantitative anti-signal recognition particle (SRP) autoantibody serum ELISA in patients with myositis and longitudinal association with myositis disease activity. We developed a serum ELISA using recombinant purified full-length human SRP coated on ELISA plates and a secondary antibody that bound human IgG to detect anti-SRP binding. Protein immunoprecipitation was used as the gold standard for the presence of anti-SRP. Serum samples from three groups were analysed: SRP(+) myositis subjects by immunoprecipitation, SRP(-) myositis subjects by immunoprecipitation and non-myositis controls. The ELISA's sensitivity, specificity, positive predictive value and negative predictive value were evaluated. Percentage agreement and test-retest reliability were assessed. Serial samples from seven SRP immunoprecipitation-positive subjects were also tested, along with serum muscle enzymes and manual muscle testing. Using immunoprecipitation, we identified 26 SRP(+) myositis patients and 77 SRP(-) controls (including 38 patients with necrotizing myopathy). Non-myositis control patients included SLE (n = 4) and SSc (n = 7) patients. Anti-SRP positivity by ELISA showed strong agreement (97.1%) with immunoprecipitation (κ = 0.94). The sensitivity, specificity, positive predictive value, and negative predictive value of the anti-SRP ELISA were 88, 100, 100 and 96, respectively. The area under the curve was 0.94, and test-retest reliability was strong (r = 0.91, P < 0.001). Serial samples showed that anti-SRP levels paralleled changes in muscle enzymes and manual muscle testing. We developed a quantitative ELISA for detecting serum anti-SRP autoantibodies and validated the assay in myositis. Longitudinal assessment of SRP levels by ELISA may be a useful biomarker for disease activity. © The Author 2014. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions

  14. Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

    Directory of Open Access Journals (Sweden)

    Osowski Stanisław

    2017-03-01

    Full Text Available The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.

  15. Reconstruction efficiency and precision for the events detected by the BIS-2 installation using the Perun pattern recognition program

    International Nuclear Information System (INIS)

    Burilkov, D.T.; Genchev, V.I.; Markov, P.K.; Likhachev, M.F.; Takhtamyshev, G.G.; Todorov, P.T.; Trayanov, P.K.

    1982-01-01

    Results of studying the efficiency and accuracy of the track and event reconstruction with the Perun pattern recognition program used in the experiments carried out at the BIS-2 installation are presented. The Monte Carlo method is used for simulating the processes of neutron interactions with matter. The con-- clusion is made that the Perun program correctly, with good accuracy and high efficiency reconstructs complex multiparticle events [ru

  16. Electromyography (EMG) signal recognition using combined discrete wavelet transform based adaptive neuro-fuzzy inference systems (ANFIS)

    Science.gov (United States)

    Arozi, Moh; Putri, Farika T.; Ariyanto, Mochammad; Khusnul Ari, M.; Munadi, Setiawan, Joga D.

    2017-01-01

    People with disabilities are increasing from year to year either due to congenital factors, sickness, accident factors and war. One form of disability is the case of interruptions of hand function. The condition requires and encourages the search for solutions in the form of creating an artificial hand with the ability as a human hand. The development of science in the field of neuroscience currently allows the use of electromyography (EMG) to control the motion of artificial prosthetic hand into the necessary use of EMG as an input signal to control artificial prosthetic hand. This study is the beginning of a significant research planned in the development of artificial prosthetic hand with EMG signal input. This initial research focused on the study of EMG signal recognition. Preliminary results show that the EMG signal recognition using combined discrete wavelet transform and Adaptive Neuro-Fuzzy Inference System (ANFIS) produces accuracy 98.3 % for training and 98.51% for testing. Thus the results can be used as an input signal for Simulink block diagram of a prosthetic hand that will be developed on next study. The research will proceed with the construction of artificial prosthetic hand along with Simulink program controlling and integrating everything into one system.

  17. Application of pattern recognition technique on randon signals for automatic monitoring of dynamic systems with emphasis on nuclear reactors

    International Nuclear Information System (INIS)

    Nascimento, J.A. do.

    1981-01-01

    The time varying or noise component of dynamic system parameters contains information on the system state. Pattern recognition analysis of noise signals for such systems is a powerful technique for assessing 'system normality' or 'correct operation'. Data analysis with modern small computers enables the otherwise unmanageable volumes of data to be processed on line and the results presented in a meaningful form. These informations provide necessary data for maintaining the system at optimum operating conditions. An automatic pattern recognition program, PSDREC, developmed for the surveillance of nuclear reactor and rotating machinery is described, and the relevant theory is outlined. This program, which applies 8 statistical tests to calculated power spectral density (PSD) distributions, was earlier installed in a PDP-11/45 computer at IPEN. In this work it has been used to separately analyse recorded signals from three systems, namely an operational BWR power reactor (neutron signals), a water pump and a diesel motor (vibration signals). The latter two were, respectively, operated over a wide-range of flow and load conditions. The statistical tests were applied to frequency bands of (0,1-40) Hz, (0-1000) Hz and (0,20000) Hz. for the BWR, pump and diesel signal data, respectively. Operation and analysis conditions are given together with representative graphs of the analysed PSD distributions. Results of the tests - discussed in some detail - are considered to be satisfactory. (Author) [pt

  18. An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ahmadi Majid

    2003-01-01

    Full Text Available This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF neural network with a hybrid learning algorithm (HLA has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.

  19. Efficient ECG Signal Compression Using Adaptive Heart Model

    National Research Council Canada - National Science Library

    Szilagyi, S

    2001-01-01

    This paper presents an adaptive, heart-model-based electrocardiography (ECG) compression method. After conventional pre-filtering the waves from the signal are localized and the model's parameters are determined...

  20. Time and Memory Efficient Online Piecewise Linear Approximation of Sensor Signals.

    Science.gov (United States)

    Grützmacher, Florian; Beichler, Benjamin; Hein, Albert; Kirste, Thomas; Haubelt, Christian

    2018-05-23

    Piecewise linear approximation of sensor signals is a well-known technique in the fields of Data Mining and Activity Recognition. In this context, several algorithms have been developed, some of them with the purpose to be performed on resource constrained microcontroller architectures of wireless sensor nodes. While microcontrollers are usually constrained in computational power and memory resources, all state-of-the-art piecewise linear approximation techniques either need to buffer sensor data or have an execution time depending on the segment’s length. In the paper at hand, we propose a novel piecewise linear approximation algorithm, with a constant computational complexity as well as a constant memory complexity. Our proposed algorithm’s worst-case execution time is one to three orders of magnitude smaller and its average execution time is three to seventy times smaller compared to the state-of-the-art Piecewise Linear Approximation (PLA) algorithms in our experiments. In our evaluations, we show that our algorithm is time and memory efficient without sacrificing the approximation quality compared to other state-of-the-art piecewise linear approximation techniques, while providing a maximum error guarantee per segment, a small parameter space of only one parameter, and a maximum latency of one sample period plus its worst-case execution time.

  1. Deep features for efficient multi-biometric recognition with face and ear images

    Science.gov (United States)

    Omara, Ibrahim; Xiao, Gang; Amrani, Moussa; Yan, Zifei; Zuo, Wangmeng

    2017-07-01

    Recently, multimodal biometric systems have received considerable research interest in many applications especially in the fields of security. Multimodal systems can increase the resistance to spoof attacks, provide more details and flexibility, and lead to better performance and lower error rate. In this paper, we present a multimodal biometric system based on face and ear, and propose how to exploit the extracted deep features from Convolutional Neural Networks (CNNs) on the face and ear images to introduce more powerful discriminative features and robust representation ability for them. First, the deep features for face and ear images are extracted based on VGG-M Net. Second, the extracted deep features are fused by using a traditional concatenation and a Discriminant Correlation Analysis (DCA) algorithm. Third, multiclass support vector machine is adopted for matching and classification. The experimental results show that the proposed multimodal system based on deep features is efficient and achieves a promising recognition rate up to 100 % by using face and ear. In addition, the results indicate that the fusion based on DCA is superior to traditional fusion.

  2. HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data

    Directory of Open Access Journals (Sweden)

    Linlin Guo

    2018-01-01

    Full Text Available The joint of WiFi-based and vision-based human activity recognition has attracted increasing attention in the human-computer interaction, smart home, and security monitoring fields. We propose HuAc, the combination of WiFi-based and Kinect-based activity recognition system, to sense human activity in an indoor environment with occlusion, weak light, and different perspectives. We first construct a WiFi-based activity recognition dataset named WiAR to provide a benchmark for WiFi-based activity recognition. Then, we design a mechanism of subcarrier selection according to the sensitivity of subcarriers to human activities. Moreover, we optimize the spatial relationship of adjacent skeleton joints and draw out a corresponding relationship between CSI and skeleton-based activity recognition. Finally, we explore the fusion information of CSI and crowdsourced skeleton joints to achieve the robustness of human activity recognition. We implemented HuAc using commercial WiFi devices and evaluated it in three kinds of scenarios. Our results show that HuAc achieves an average accuracy of greater than 93% using WiAR dataset.

  3. Recognition Memory zROC Slopes for Items with Correct versus Incorrect Source Decisions Discriminate the Dual Process and Unequal Variance Signal Detection Models

    Science.gov (United States)

    Starns, Jeffrey J.; Rotello, Caren M.; Hautus, Michael J.

    2014-01-01

    We tested the dual process and unequal variance signal detection models by jointly modeling recognition and source confidence ratings. The 2 approaches make unique predictions for the slope of the recognition memory zROC function for items with correct versus incorrect source decisions. The standard bivariate Gaussian version of the unequal…

  4. UHF Signal Processing and Pattern Recognition of Partial Discharge in Gas-Insulated Switchgear Using Chromatic Methodology.

    Science.gov (United States)

    Wang, Xiaohua; Li, Xi; Rong, Mingzhe; Xie, Dingli; Ding, Dan; Wang, Zhixiang

    2017-01-18

    The ultra-high frequency (UHF) method is widely used in insulation condition assessment. However, UHF signal processing algorithms are complicated and the size of the result is large, which hinders extracting features and recognizing partial discharge (PD) patterns. This article investigated the chromatic methodology that is novel in PD detection. The principle of chromatic methodologies in color science are introduced. The chromatic processing represents UHF signals sparsely. The UHF signals obtained from PD experiments were processed using chromatic methodology and characterized by three parameters in chromatic space ( H , L , and S representing dominant wavelength, signal strength, and saturation, respectively). The features of the UHF signals were studied hierarchically. The results showed that the chromatic parameters were consistent with conventional frequency domain parameters. The global chromatic parameters can be used to distinguish UHF signals acquired by different sensors, and they reveal the propagation properties of the UHF signal in the L-shaped gas-insulated switchgear (GIS). Finally, typical PD defect patterns had been recognized by using novel chromatic parameters in an actual GIS tank and good performance of recognition was achieved.

  5. Autonomic nervous system dynamics for mood and emotional-state recognition significant advances in data acquisition, signal processing and classification

    CERN Document Server

    Valenza, Gaetano

    2014-01-01

    This monograph reports on advances in the measurement and study of autonomic nervous system (ANS) dynamics as a source of reliable and effective markers for mood state recognition and assessment of emotional responses. Its primary impact will be in affective computing and the application of emotion-recognition systems. Applicative studies of biosignals such as: electrocardiograms; electrodermal responses; respiration activity; gaze points; and pupil-size variation are covered in detail, and experimental results explain how to characterize the elicited affective levels and mood states pragmatically and accurately using the information thus extracted from the ANS. Nonlinear signal processing techniques play a crucial role in understanding the ANS physiology underlying superficially noticeable changes and provide important quantifiers of cardiovascular control dynamics. These have prognostic value in both healthy subjects and patients with mood disorders. Moreover, Autonomic Nervous System Dynamics for Mood and ...

  6. Omni-PolyA: a method and tool for accurate recognition of Poly(A) signals in human genomic DNA

    KAUST Repository

    Magana-Mora, Arturo

    2017-08-15

    BackgroundPolyadenylation is a critical stage of RNA processing during the formation of mature mRNA, and is present in most of the known eukaryote protein-coding transcripts and many long non-coding RNAs. The correct identification of poly(A) signals (PAS) not only helps to elucidate the 3′-end genomic boundaries of a transcribed DNA region and gene regulatory mechanisms but also gives insight into the multiple transcript isoforms resulting from alternative PAS. Although progress has been made in the in-silico prediction of genomic signals, the recognition of PAS in DNA genomic sequences remains a challenge.ResultsIn this study, we analyzed human genomic DNA sequences for the 12 most common PAS variants. Our analysis has identified a set of features that helps in the recognition of true PAS, which may be involved in the regulation of the polyadenylation process. The proposed features, in combination with a recognition model, resulted in a novel method and tool, Omni-PolyA. Omni-PolyA combines several machine learning techniques such as different classifiers in a tree-like decision structure and genetic algorithms for deriving a robust classification model. We performed a comparison between results obtained by state-of-the-art methods, deep neural networks, and Omni-PolyA. Results show that Omni-PolyA significantly reduced the average classification error rate by 35.37% in the prediction of the 12 considered PAS variants relative to the state-of-the-art results.ConclusionsThe results of our study demonstrate that Omni-PolyA is currently the most accurate model for the prediction of PAS in human and can serve as a useful complement to other PAS recognition methods. Omni-PolyA is publicly available as an online tool accessible at www.cbrc.kaust.edu.sa/omnipolya/.

  7. A Study on Efficient Robust Speech Recognition with Stochastic Dynamic Time Warping

    OpenAIRE

    孫, 喜浩

    2014-01-01

    In recent years, great progress has been made in automatic speech recognition (ASR) system. The hidden Markov model (HMM) and dynamic time warping (DTW) are the two main algorithms which have been widely applied to ASR system. Although, HMM technique achieves higher recognition accuracy in clear speech environment and noisy environment. It needs large-set of words and realizes the algorithm more complexly.Thus, more and more researchers have focused on DTW-based ASR system.Dynamic time warpin...

  8. The Picture Superiority Effect in Recognition Memory: A Developmental Study Using the Response Signal Procedure

    Science.gov (United States)

    Defeyter, Margaret Anne; Russo, Riccardo; McPartlin, Pamela Louise

    2009-01-01

    Items studied as pictures are better remembered than items studied as words even when test items are presented as words. The present study examined the development of this picture superiority effect in recognition memory. Four groups ranging in age from 7 to 20 years participated. They studied words and pictures, with test stimuli always presented…

  9. Time and nature of the signal for maternal recognition of pregnancy in the pig

    NARCIS (Netherlands)

    Meulen, van der J.

    1989-01-01

    A vital link in a complex of physiological processes occurring during early pregnancy is the so-called maternal recognition of pregnancy: the prolongation of ovarian luteal function for continuation of progesterone secretion by an anti-luteolytic action of the developing embryos.

  10. An Efficient Implementation of Generalized Delayed Signal Cancellation PLL

    DEFF Research Database (Denmark)

    Golestan, Saeed; Fernandez, Francisco Daniel Freijedo; Vidal, Ana

    2016-01-01

    voltage quantities, an efficient and low-cost implementation of the GDSCPLL is suggested in this paper. The proposed structure, which is called the enhanced GDSC-PLL (EGDSC-PLL), uses a nonadaptive GDSC operator as its prefiltering stage, and compensates the phase-shift and amplitude scaling caused...... stability under all circumstances; 2) adapting the GDSC to grid frequency variations increases the implementation complexity and computational effort, particularly when the interpolation techniques are used for this purpose. To avoid these problems while maintaining high accuracy in the extraction of grid...

  11. USE OF IMAGE ENHANCEMENT TECHNIQUES FOR IMPROVING REAL TIME FACE RECOGNITION EFFICIENCY ON WEARABLE GADGETS

    Directory of Open Access Journals (Sweden)

    MUHAMMAD EHSAN RANA

    2017-01-01

    Full Text Available The objective of this research is to study the effects of image enhancement techniques on face recognition performance of wearable gadgets with an emphasis on recognition rate.In this research, a number of image enhancement techniques are selected that include brightness normalization, contrast normalization, sharpening, smoothing, and various combinations of these. Subsequently test images are obtained from AT&T database and Yale Face Database B to investigate the effect of these image enhancement techniques under various conditions such as change of illumination and face orientation and expression.The evaluation of data, collected during this research, revealed that the effect of image pre-processing techniques on face recognition highly depends on the illumination condition under which these images are taken. It is revealed that the benefit of applying image enhancement techniques on face images is best seen when there is high variation of illumination among images. Results also indicate that highest recognition rate is achieved when images are taken under low light condition and image contrast is enhanced using histogram equalization technique and then image noise is reduced using median smoothing filter. Additionally combination of contrast normalization and mean smoothing filter shows good result in all scenarios. Results obtained from test cases illustrate up to 75% improvement in face recognition rate when image enhancement is applied to images in given scenarios.

  12. Get rich quick: the signal to respond procedure reveals the time course of semantic richness effects during visual word recognition.

    Science.gov (United States)

    Hargreaves, Ian S; Pexman, Penny M

    2014-05-01

    According to several current frameworks, semantic processing involves an early influence of language-based information followed by later influences of object-based information (e.g., situated simulations; Santos, Chaigneau, Simmons, & Barsalou, 2011). In the present study we examined whether these predictions extend to the influence of semantic variables in visual word recognition. We investigated the time course of semantic richness effects in visual word recognition using a signal-to-respond (STR) paradigm fitted to a lexical decision (LDT) and a semantic categorization (SCT) task. We used linear mixed effects to examine the relative contributions of language-based (number of senses, ARC) and object-based (imageability, number of features, body-object interaction ratings) descriptions of semantic richness at four STR durations (75, 100, 200, and 400ms). Results showed an early influence of number of senses and ARC in the SCT. In both LDT and SCT, object-based effects were the last to influence participants' decision latencies. We interpret our results within a framework in which semantic processes are available to influence word recognition as a function of their availability over time, and of their relevance to task-specific demands. Copyright © 2014 Elsevier B.V. All rights reserved.

  13. Binding of Signal Recognition Particle Gives Ribosome/Nascent Chain Complexes a Competitive Advantage in Endoplasmic Reticulum Membrane Interaction

    Science.gov (United States)

    Neuhof, Andrea; Rolls, Melissa M.; Jungnickel, Berit; Kalies, Kai-Uwe; Rapoport, Tom A.

    1998-01-01

    Most secretory and membrane proteins are sorted by signal sequences to the endoplasmic reticulum (ER) membrane early during their synthesis. Targeting of the ribosome-nascent chain complex (RNC) involves the binding of the signal sequence to the signal recognition particle (SRP), followed by an interaction of ribosome-bound SRP with the SRP receptor. However, ribosomes can also independently bind to the ER translocation channel formed by the Sec61p complex. To explain the specificity of membrane targeting, it has therefore been proposed that nascent polypeptide-associated complex functions as a cytosolic inhibitor of signal sequence- and SRP-independent ribosome binding to the ER membrane. We report here that SRP-independent binding of RNCs to the ER membrane can occur in the presence of all cytosolic factors, including nascent polypeptide-associated complex. Nontranslating ribosomes competitively inhibit SRP-independent membrane binding of RNCs but have no effect when SRP is bound to the RNCs. The protective effect of SRP against ribosome competition depends on a functional signal sequence in the nascent chain and is also observed with reconstituted proteoliposomes containing only the Sec61p complex and the SRP receptor. We conclude that cytosolic factors do not prevent the membrane binding of ribosomes. Instead, specific ribosome targeting to the Sec61p complex is provided by the binding of SRP to RNCs, followed by an interaction with the SRP receptor, which gives RNC–SRP complexes a selective advantage in membrane targeting over nontranslating ribosomes. PMID:9436994

  14. Fatigue Crack Growth Behavior of and Recognition of AE Signals from Composite Patch-Repaired Aluminum Panel

    International Nuclear Information System (INIS)

    Kim, Sung Jin; Kwon, Oh Yang; Jang, Yong Joon

    2007-01-01

    The fatigue crack growth behavior of a cracked and patch-repaired Ah2024-T3 panel has been monitored by acoustic emission(AE). The overall crack growth rate was reduced The crack propagation into the adjacent hole was also retarded by introducing the patch repair. AE signals due to crack growth after the patch repair and those due to debonding of the plate-patch interface were discriminated by using the principal component analysis. The former showed high center frequency and low amplitude, whereas the latter showed long rise tine, low frequency and high amplitude. This type of AE signal recognition method could be effective for the prediction of fatigue crack growth behavior in the patch-repaired structures with the aid of AE source location

  15. Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal.

    Science.gov (United States)

    Arjunan, Sridhar Poosapadi; Kumar, Dinesh Kant; Jayadeva J

    2016-02-01

    Identifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from amputee residual muscle is required for controlling the myoelectric prosthetic hand. In this study, we have computed the signal fractal dimension (FD) and maximum fractal length (MFL) during different grip patterns performed by healthy and transradial amputee subjects. The FD and MFL of the sEMG, referred to as the fractal features, were classified using twin support vector machines (TSVM) to recognize the handgrips. TSVM requires fewer support vectors, is suitable for data sets with unbalanced distributions, and can simultaneously be trained for improving both sensitivity and specificity. When compared with other methods, this technique resulted in improved grip recognition accuracy, sensitivity, and specificity, and this improvement was significant (κ=0.91).

  16. Dynamic Programming Algorithms in Speech Recognition

    Directory of Open Access Journals (Sweden)

    Titus Felix FURTUNA

    2008-01-01

    Full Text Available In a system of speech recognition containing words, the recognition requires the comparison between the entry signal of the word and the various words of the dictionary. The problem can be solved efficiently by a dynamic comparison algorithm whose goal is to put in optimal correspondence the temporal scales of the two words. An algorithm of this type is Dynamic Time Warping. This paper presents two alternatives for implementation of the algorithm designed for recognition of the isolated words.

  17. Energy Efficient Scheduling of Real Time Signal Processing Applications through Combined DVFS and DPM

    OpenAIRE

    Nogues , Erwan; Pelcat , Maxime; Menard , Daniel; Mercat , Alexandre

    2016-01-01

    International audience; This paper proposes a framework to design energy efficient signal processing systems. The energy efficiency is provided by combining Dynamic Frequency and Voltage Scaling (DVFS) and Dynamic Power Management (DPM). The framework is based on Synchronous Dataflow (SDF) modeling of signal processing applications. A transformation to a single rate form is performed to expose the application parallelism. An automated scheduling is then performed, minimizing the constraint of...

  18. An ultra-efficient nonlinear planar integrated platform for optical signal processing and generation

    DEFF Research Database (Denmark)

    Pu, Minhao; Ottaviano, Luisa; Semenova, Elizaveta

    2017-01-01

    This paper will discuss the recently developed integrated platform: AlGaAs-oninsulator and its broad range of nonlinear applications. Recent demonstrations of broadband optical signal processing and efficient frequency comb generations in this platform will be reviewed.......This paper will discuss the recently developed integrated platform: AlGaAs-oninsulator and its broad range of nonlinear applications. Recent demonstrations of broadband optical signal processing and efficient frequency comb generations in this platform will be reviewed....

  19. Audiovisual spoken word recognition as a clinical criterion for sensory aids efficiency in Persian-language children with hearing loss.

    Science.gov (United States)

    Oryadi-Zanjani, Mohammad Majid; Vahab, Maryam; Bazrafkan, Mozhdeh; Haghjoo, Asghar

    2015-12-01

    The aim of this study was to examine the role of audiovisual speech recognition as a clinical criterion of cochlear implant or hearing aid efficiency in Persian-language children with severe-to-profound hearing loss. This research was administered as a cross-sectional study. The sample size was 60 Persian 5-7 year old children. The assessment tool was one of subtests of Persian version of the Test of Language Development-Primary 3. The study included two experiments: auditory-only and audiovisual presentation conditions. The test was a closed-set including 30 words which were orally presented by a speech-language pathologist. The scores of audiovisual word perception were significantly higher than auditory-only condition in the children with normal hearing (Paudiovisual presentation conditions (P>0.05). The audiovisual spoken word recognition can be applied as a clinical criterion to assess the children with severe to profound hearing loss in order to find whether cochlear implant or hearing aid has been efficient for them or not; i.e. if a child with hearing impairment who using CI or HA can obtain higher scores in audiovisual spoken word recognition than auditory-only condition, his/her auditory skills have appropriately developed due to effective CI or HA as one of the main factors of auditory habilitation. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  20. Striatal intrinsic reinforcement signals during recognition memory: relationship to response bias and dysregulation in schizophrenia

    Directory of Open Access Journals (Sweden)

    Daniel H Wolf

    2011-12-01

    Full Text Available Ventral striatum (VS is a critical brain region for reinforcement learning and motivation, and VS hypofunction is implicated in psychiatric disorders including schizophrenia. Providing rewards or performance feedback has been shown to activate VS. Intrinisically motivated subjects performing challenging cognitive tasks are likely to engage reinforcement circuitry even in the absence of external feedback or incentives. However, such intrinsic reinforcement responses have received little attention, have not been examined in relation to behavioral performance, and have not been evaluated for impairment in neuropsychiatric disorders such as schizophrenia. Here we used fMRI to examine a challenging 'old' vs. 'new' visual recognition task in healthy subjects and patients with schizophrenia. Targets were unique fractal stimuli previously presented as salient distractors in a visual oddball task, producing incidental memory encoding. Based on the prediction error theory of reinforcement learning, we hypothesized that correct target recognition would activate VS in controls, and that this activation would be greater in subjects with lower expectation of responding correctly as indexed by a more conservative response bias. We also predicted these effects would be reduced in patients with schizophrenia. Consistent with these predictions, controls activated VS and other reinforcement processing regions during correct recognition, with greater VS activation in those with a more conservative response bias. Patients did not show either effect, with significant group differences suggesting hyporesponsivity in patients to internally-generated feedback. These findings highlight the importance of accounting for intrinsic motivation and reward when studying cognitive tasks, and add to growing evidence of reward circuit dysfunction in schizophrenia that may impact cognition and function.

  1. Electrophysiological signals associated with fluency of different levels of processing reveal multiple contributions to recognition memory.

    Science.gov (United States)

    Li, Bingbing; Taylor, Jason R; Wang, Wei; Gao, Chuanji; Guo, Chunyan

    2017-08-01

    Processing fluency appears to influence recognition memory judgements, and the manipulation of fluency, if misattributed to an effect of prior exposure, can result in illusory memory. Although it is well established that fluency induced by masked repetition priming leads to increased familiarity, manipulations of conceptual fluency have produced conflicting results, variously affecting familiarity or recollection. Some recent studies have found that masked conceptual priming increases correct recollection (Taylor & Henson, 2012), and the magnitude of this behavioural effect correlates with analogous fMRI BOLD priming effects in brain regions associated with recollection (Taylor, Buratto, & Henson, 2013). However, the neural correlates and time-courses of masked repetition and conceptual priming were not compared directly in previous studies. The present study used event-related potentials (ERPs) to identify and compare the electrophysiological correlates of masked repetition and conceptual priming and investigate how they contribute to recognition memory. Behavioural results were consistent with previous studies: Repetition primes increased familiarity, whereas conceptual primes increased correct recollection. Masked repetition and conceptual priming also decreased the latency of late parietal component (LPC). Masked repetition priming was associated with an early P200 effect and a later parietal maximum N400 effect, whereas masked conceptual priming was only associated with a central-parietal maximum N400 effect. In addition, the topographic distributions of the N400 repetition priming and conceptual priming effects were different. These results suggest that fluency at different levels of processing is associated with different ERP components, and contributes differentially to subjective recognition memory experiences. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Detection and recognition of mechanical, digging and vehicle signals in the optical fiber pre-warning system

    Science.gov (United States)

    Tian, Qing; Yang, Dan; Zhang, Yuan; Qu, Hongquan

    2018-04-01

    This paper presents detection and recognition method to locate and identify harmful intrusions in the optical fiber pre-warning system (OFPS). Inspired by visual attention architecture (VAA), the process flow is divided into two parts, i.e., data-driven process and task-driven process. At first, data-driven process takes all the measurements collected by the system as input signals, which is handled by detection method to locate the harmful intrusion in both spatial domain and time domain. Then, these detected intrusion signals are taken over by task-driven process. Specifically, we get pitch period (PP) and duty cycle (DC) of the intrusion signals to identify the mechanical and manual digging (MD) intrusions respectively. For the passing vehicle (PV) intrusions, their strong low frequency component can be used as good feature. In generally, since the harmful intrusion signals only account for a small part of whole measurements, the data-driven process reduces the amount of input data for subsequent task-driven process considerably. Furthermore, the task-driven process determines the harmful intrusions orderly according to their severity, which makes a priority mechanism for the system as well as targeted processing for different harmful intrusion. At last, real experiments are performed to validate the effectiveness of this method.

  3. New method for recognition of sterol signalling molecules: Methinium salts as receptors for sulphated steroids

    Czech Academy of Sciences Publication Activity Database

    Kejík, Z.; Bříza, T.; Králová, Jarmila; Mikula, I.; Poučková, P.; Martásek, P.; Král, V.

    2015-01-01

    Roč. 94, February 2015 (2015), s. 15-20 ISSN 1878-5867 R&D Projects: GA TA ČR(CZ) TE01020028; GA ČR(CZ) GAP303/11/1291; GA MŠk(CZ) LH14008; GA MŠk(CZ) CZ.1.07/2.300/30.0060; GA MŠk(CZ) ED1.1.00/02.0109 Institutional support: RVO:68378050 Keywords : Polymethinium salts * Sulphated sterols * Molecular recognition * Synthetic receptors Subject RIV: EB - Genetics ; Molecular Biology

  4. Neutron-gamma discrimination employing pattern recognition of the signal from liquid scintillator

    International Nuclear Information System (INIS)

    Kamada, Kohji; Enokido, Uhji; Ogawa, Seiji

    1999-01-01

    A pattern recognition method was applied to the neutron-gamma discrimination of the pulses from the liquid scintillator, NE-213. The circuit for the discrimination is composed of A/D converter, fast SCA, memory control circuit, two digital delay lines and two buffer memories. All components are packed on a small circuit board and are installed into a personal computer. Experiments using a weak 252 Cf n-γ source were undertaken to test the feasibility of the circuit. The circuit is of very easy adjustment and, at the same time, of very economical price when compared with usual discrimination circuits, such as the TAC system

  5. Neutron-gamma discrimination employing pattern recognition of the signal from liquid scintillator

    CERN Document Server

    Kamada, K; Ogawa, S

    1999-01-01

    A pattern recognition method was applied to the neutron-gamma discrimination of the pulses from the liquid scintillator, NE-213. The circuit for the discrimination is composed of A/D converter, fast SCA, memory control circuit, two digital delay lines and two buffer memories. All components are packed on a small circuit board and are installed into a personal computer. Experiments using a weak sup 2 sup 5 sup 2 Cf n-gamma source were undertaken to test the feasibility of the circuit. The circuit is of very easy adjustment and, at the same time, of very economical price when compared with usual discrimination circuits, such as the TAC system.

  6. Power-efficient method for IM-DD optical transmission of multiple OFDM signals.

    Science.gov (United States)

    Effenberger, Frank; Liu, Xiang

    2015-05-18

    We propose a power-efficient method for transmitting multiple frequency-division multiplexed (FDM) orthogonal frequency-division multiplexing (OFDM) signals in intensity-modulation direct-detection (IM-DD) optical systems. This method is based on quadratic soft clipping in combination with odd-only channel mapping. We show, both analytically and experimentally, that the proposed approach is capable of improving the power efficiency by about 3 dB as compared to conventional FDM OFDM signals under practical bias conditions, making it a viable solution in applications such as optical fiber-wireless integrated systems where both IM-DD optical transmission and OFDM signaling are important.

  7. Angle-of-arrival-based gesture recognition using ultrasonic multi-frequency signals

    KAUST Repository

    Chen, Hui; Ballal, Tarig; Saad, Mohamed; Al-Naffouri, Tareq Y.

    2017-01-01

    transducer. The 2-D angles of the moving hand are estimated using multi-frequency signals captured by a fixed receiver array. A simple redundant dictionary matching classifier is designed to recognize gestures representing the numbers from `0' to `9

  8. A Simple Decision Rule for Recognition of Poly(A) Tail Signal Motifs in Human Genome

    KAUST Repository

    AbouEisha, Hassan M.; Chikalov, Igor; Moshkov, Mikhail; Jankovic, Boris R.

    2015-01-01

    Background is the numerous attempts were made to predict motifs in genomic sequences that correspond to poly (A) tail signals. Vast portion of this effort has been directed to a plethora of nonlinear classification methods. Even when such approaches

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

    Science.gov (United States)

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

    2013-01-01

    Familiarity discrimination has a significant impact on the pattern of food intake across species. However, the mechanism by which the recognition memory controls feeding is unclear. Here, we show that the nematode Caenorhabditis elegans forms a memory of particular foods after experience and displays behavioral plasticity, increasing the feeding response when they subsequently recognize the familiar food. We found that recognition of familiar food activates the pair of ADF chemosensory neurons, which subsequently increase serotonin release. The released serotonin activates the feeding response mainly by acting humorally and directly activates SER-7, a type 7 serotonin receptor, in MC motor neurons in the feeding organ. Our data suggest that worms sense the taste and/or smell of novel bacteria, which overrides the stimulatory effect of familiar bacteria on feeding by suppressing the activity of ADF or its upstream neurons. Our study provides insight into the mechanism by which familiarity discrimination alters behavior. DOI: http://dx.doi.org/10.7554/eLife.00329.001 PMID:23390589

  10. Development of Adaptive AE Signal Pattern Recognition Program and Application to Classification of Defects in Metal Contact Regions of Rotating Component

    International Nuclear Information System (INIS)

    Lee, K. Y.; Lee, C. M.; Kim, J. S.

    1996-01-01

    In this study, the artificial defects in rotary compressor are classified using pattern recognition of acoustic emission signal. For this purpose the computer program is developed. The neural network classifier is compared with the statistical classifier such as the linear discriminant function classifier and empirical Bayesian classifier. It is concluded that the former is better. It is possible to acquire the recognition rate of above 99% by neural network classifier

  11. Recognition of NEMP and LEMP signals based on auto-regression model and artificial neutral network

    International Nuclear Information System (INIS)

    Li Peng; Song Lijun; Han Chao; Zheng Yi; Cao Baofeng; Li Xiaoqiang; Zhang Xueqin; Liang Rui

    2010-01-01

    Auto-regression (AR) model, one power spectrum estimation method of stationary random signals, and artificial neutral network were adopted to recognize nuclear and lightning electromagnetic pulses. Self-correlation function and Burg algorithms were used to acquire the AR model coefficients as eigenvalues, and BP artificial neural network was introduced as the classifier with different numbers of hidden layers and hidden layer nodes. The results show that AR model is effective in those signals, feature extraction, and the Burg algorithm is more effective than the self-correlation function algorithm. (authors)

  12. Speech Recognition

    Directory of Open Access Journals (Sweden)

    Adrian Morariu

    2009-01-01

    Full Text Available This paper presents a method of speech recognition by pattern recognition techniques. Learning consists in determining the unique characteristics of a word (cepstral coefficients by eliminating those characteristics that are different from one word to another. For learning and recognition, the system will build a dictionary of words by determining the characteristics of each word to be used in the recognition. Determining the characteristics of an audio signal consists in the following steps: noise removal, sampling it, applying Hamming window, switching to frequency domain through Fourier transform, calculating the magnitude spectrum, filtering data, determining cepstral coefficients.

  13. Hepatitis B virus polymerase blocks pattern recognition receptor signaling via interaction with DDX3: implications for immune evasion.

    Directory of Open Access Journals (Sweden)

    Haifeng Wang

    Full Text Available Viral infection leads to induction of pattern-recognition receptor signaling, which leads to interferon regulatory factor (IRF activation and ultimately interferon (IFN production. To establish infection, many viruses have strategies to evade the innate immunity. For the hepatitis B virus (HBV, which causes chronic infection in the liver, the evasion strategy remains uncertain. We now show that HBV polymerase (Pol blocks IRF signaling, indicating that HBV Pol is the viral molecule that effectively counteracts host innate immune response. In particular, HBV Pol inhibits TANK-binding kinase 1 (TBK1/IkappaB kinase-epsilon (IKKepsilon, the effector kinases of IRF signaling. Intriguingly, HBV Pol inhibits TBK1/IKKepsilon activity by disrupting the interaction between IKKepsilon and DDX3 DEAD box RNA helicase, which was recently shown to augment TBK1/IKKepsilon activity. This unexpected role of HBV Pol may explain how HBV evades innate immune response in the early phase of the infection. A therapeutic implication of this work is that a strategy to interfere with the HBV Pol-DDX3 interaction might lead to the resolution of life-long persistent infection.

  14. How to detect a cuckoo egg : A signal-detection theory model for recognition and learning

    NARCIS (Netherlands)

    Rodriguez-Girones, MA; Lotem, A

    This article presents a model of egg rejection in cases of brood parasitism. The model is developed in three stages in the framework of signal-detection theory. We first assume that the behavior of host females is adapted to the relevant parameters concerning the appearance of the eggs they lay. In

  15. Human Age Recognition by Electrocardiogram Signal Based on Artificial Neural Network

    Science.gov (United States)

    Dasgupta, Hirak

    2016-12-01

    The objective of this work is to make a neural network function approximation model to detect human age from the electrocardiogram (ECG) signal. The input vectors of the neural network are the Katz fractal dimension of the ECG signal, frequencies in the QRS complex, male or female (represented by numeric constant) and the average of successive R-R peak distance of a particular ECG signal. The QRS complex has been detected by short time Fourier transform algorithm. The successive R peak has been detected by, first cutting the signal into periods by auto-correlation method and then finding the absolute of the highest point in each period. The neural network used in this problem consists of two layers, with Sigmoid neuron in the input and linear neuron in the output layer. The result shows the mean of errors as -0.49, 1.03, 0.79 years and the standard deviation of errors as 1.81, 1.77, 2.70 years during training, cross validation and testing with unknown data sets, respectively.

  16. Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals.

    Science.gov (United States)

    Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

    2015-01-01

    In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.

  17. Visual detection and microplate assay for Staphylococcus aureus based on aptamer recognition coupled to tyramine signal amplification

    International Nuclear Information System (INIS)

    Yuan, Jinglei; Li, Can; Ma, Xiaoyuan; Xia, Yu; Chen, Jie; Wang, Zhouping; Yu, Ye

    2014-01-01

    We have developed a specific method for the visual detection of Staphylococcus aureus based on aptamer recognition coupled to tyramine signal amplification technology. A biotinylated aptamer specific for S. aureus was immobilized on the surface of the wells of a microplate via biotin-avidin binding. Then, the target bacteria (S. aureus), the biotinylated-aptamer-streptavidin-HRP conjugates, biotinylated tyramine, hydrogen peroxide and streptavidin-HRP were successively placed in the wells of the microplate. After adding TMB reagent and stop solution, the intensity of the yellow reaction product can be visually inspected or measured with a plate reader. Under optimized conditions, there is a linear relationship between absorbance at 450 nm and the concentration of S. aureus in the 10 to 107 cfu mL −1 concentration range (with an R 2 of 0.9976). The limit of detection is 8 cfu mL −1 . (author)

  18. Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals.

    Science.gov (United States)

    Elhaj, Fatin A; Salim, Naomie; Harris, Arief R; Swee, Tan Tian; Ahmed, Taqwa

    2016-04-01

    Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart abnormalities. Due to the presence of noise, the non-stationary nature of the ECG signal (i.e. the changing morphology of the ECG signal with respect to time) and the irregularity of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. The computer-aided analysis of ECG results assists physicians to detect cardiovascular diseases. The development of many existing arrhythmia systems has depended on the findings from linear experiments on ECG data which achieve high performance on noise-free data. However, nonlinear experiments characterize the ECG signal more effectively sense, extract hidden information in the ECG signal, and achieve good performance under noisy conditions. This paper investigates the representation ability of linear and nonlinear features and proposes a combination of such features in order to improve the classification of ECG data. In this study, five types of beat classes of arrhythmia as recommended by the Association for Advancement of Medical Instrumentation are analyzed: non-ectopic beats (N), supra-ventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F) and unclassifiable and paced beats (U). The characterization ability of nonlinear features such as high order statistics and cumulants and nonlinear feature reduction methods such as independent component analysis are combined with linear features, namely, the principal component analysis of discrete wavelet transform coefficients. The features are tested for their ability to differentiate different classes of data using different classifiers, namely, the support vector machine and neural network methods with tenfold cross-validation. Our proposed method is able to classify the N, S, V, F and U arrhythmia classes with high accuracy (98.91%) using a combined support

  19. Categorical perception of a natural, multivariate signal: Mating call recognition in túngara frogs

    OpenAIRE

    Baugh, A. T.; Akre, K. L.; Ryan, M. J.

    2008-01-01

    Categorical perception is common in humans, but it is not known whether animals perceive continuous variation in their own multidimensional social signals categorically. There are two components to categorical perception: labeling and discrimination. In the first, continuously variable stimuli on each side of a category boundary are labeled. In the second, there is strong discrimination between stimuli from opposite sides of the boundary, whereas stimuli on the same side of the boundary are n...

  20. Metallurgical flow recognition by random signal analysis of stress wave emissions

    International Nuclear Information System (INIS)

    Woodward, B.

    1973-01-01

    The present study involves detailed random signal analysis of individual 'bursts' of emission with objective of 'reading' their frequency spectra to identify specific metallurgical mechanisms. Mild steel unnotched testpieces were used in the early stages of development of this research. From a fracture mechanics point of view this research could lead to a powerful nondestructive testing device allowing identification of interior, instead of only surface, deformation mechanisms. (author)

  1. A novel human-machine interface based on recognition of multi-channel facial bioelectric signals

    International Nuclear Information System (INIS)

    Razazadeh, Iman Mohammad; Firoozabadi, S. Mohammad; Golpayegani, S.M.R.H.; Hu, H.

    2011-01-01

    Full text: This paper presents a novel human-machine interface for disabled people to interact with assistive systems for a better quality of life. It is based on multichannel forehead bioelectric signals acquired by placing three pairs of electrodes (physical channels) on the Fron-tails and Temporalis facial muscles. The acquired signals are passes through a parallel filter bank to explore three different sub-bands related to facial electromyogram, electrooculogram and electroencephalogram. The root mean features of the bioelectric signals analyzed within non-overlapping 256 ms windows were extracted. The subtractive fuzzy c-means clustering method (SFCM) was applied to segment the feature space and generate initial fuzzy based Takagi-Sugeno rules. Then, an adaptive neuro-fuzzy inference system is exploited to tune up the premises and consequence parameters of the extracted SFCMs. rules. The average classifier discriminating ratio for eight different facial gestures (smiling, frowning, pulling up left/right lips corner, eye movement to left/right/up/down is between 93.04% and 96.99% according to different combinations and fusions of logical features. Experimental results show that the proposed interface has a high degree of accuracy and robustness for discrimination of 8 fundamental facial gestures. Some potential and further capabilities of our approach in human-machine interfaces are also discussed. (author)

  2. A Similarity Analysis of Audio Signal to Develop a Human Activity Recognition Using Similarity Networks

    Directory of Open Access Journals (Sweden)

    Alejandra García-Hernández

    2017-11-01

    Full Text Available Human Activity Recognition (HAR is one of the main subjects of study in the areas of computer vision and machine learning due to the great benefits that can be achieved. Examples of the study areas are: health prevention, security and surveillance, automotive research, and many others. The proposed approaches are carried out using machine learning techniques and present good results. However, it is difficult to observe how the descriptors of human activities are grouped. In order to obtain a better understanding of the the behavior of descriptors, it is important to improve the abilities to recognize the human activities. This paper proposes a novel approach for the HAR based on acoustic data and similarity networks. In this approach, we were able to characterize the sound of the activities and identify those activities looking for similarity in the sound pattern. We evaluated the similarity of the sounds considering mainly two features: the sound location and the materials that were used. As a result, the materials are a good reference classifying the human activities compared with the location.

  3. Not sensitive, yet less biased: A signal detection theory perspective on mindfulness, attention, and recognition memory.

    Science.gov (United States)

    Rosenstreich, Eyal; Ruderman, Lital

    2016-07-01

    The practice of mindfulness has been argued to increase attention control and improve memory performance. However, it was recently suggested that the effect of mindfulness on memory may be due to a shift in response-bias, rather than to an increase in memory-sensitivity. The present study examined the mindfulness-attention-memory triad. Participants filled in the five-facets of mindfulness questionnaire, and completed two recognition blocks; in the first attention was full, whereas in the second attention was divided during the encoding of information. It was found that the facet of non-judging (NJ) moderated the impact of attention on memory, such that responses of high NJ participants were less biased and remained constant even when attention was divided. Facets of mindfulness were not associated with memory sensitivity. These findings suggest that mindfulness may affect memory through decision making processes, rather than through directing attentional resources to the encoding of information. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. [-25]A Similarity Analysis of Audio Signal to Develop a Human Activity Recognition Using Similarity Networks.

    Science.gov (United States)

    García-Hernández, Alejandra; Galván-Tejada, Carlos E; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Velasco-Elizondo, Perla; Cárdenas-Vargas, Rogelio

    2017-11-21

    Human Activity Recognition (HAR) is one of the main subjects of study in the areas of computer vision and machine learning due to the great benefits that can be achieved. Examples of the study areas are: health prevention, security and surveillance, automotive research, and many others. The proposed approaches are carried out using machine learning techniques and present good results. However, it is difficult to observe how the descriptors of human activities are grouped. In order to obtain a better understanding of the the behavior of descriptors, it is important to improve the abilities to recognize the human activities. This paper proposes a novel approach for the HAR based on acoustic data and similarity networks. In this approach, we were able to characterize the sound of the activities and identify those activities looking for similarity in the sound pattern. We evaluated the similarity of the sounds considering mainly two features: the sound location and the materials that were used. As a result, the materials are a good reference classifying the human activities compared with the location.

  5. Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Aleš Procházka

    2018-05-01

    Full Text Available Multimodal signal analysis based on sophisticated sensors, efficient communicationsystems and fast parallel processing methods has a rapidly increasing range of multidisciplinaryapplications. The present paper is devoted to pattern recognition, machine learning, and the analysisof sleep stages in the detection of sleep disorders using polysomnography (PSG data, includingelectroencephalography (EEG, breathing (Flow, and electro-oculogram (EOG signals. The proposedmethod is based on the classification of selected features by a neural network system with sigmoidaland softmax transfer functions using Bayesian methods for the evaluation of the probabilities of theseparate classes. The application is devoted to the analysis of the sleep stages of 184 individualswith different diagnoses, using EEG and further PSG signals. Data analysis points to an averageincrease of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of theRapid Eye Movement (REM stages by 0.8% per 10 years. The mean classification accuracy for givensets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1 and89.6% (STD:1.9, respectively. The proposed methods enable the use of adaptive learning processesfor the detection and classification of health disorders based on prior specialist experience andman–machine interaction.

  6. Effective and efficient Grassfinch kernel for SVM classification and its application to recognition based on image set

    International Nuclear Information System (INIS)

    Du, Genyuan; Tian, Shengli; Qiu, Yingyu; Xu, Chunyan

    2016-01-01

    This paper presents an effective and efficient kernel approach to recognize image set which is represented as a point on extended Grassmannian manifold. Several recent studies focus on the applicability of discriminant analysis on Grassmannian manifold and suffer from not obtaining the inherent nonlinear structure of the data itself. Therefore, we propose an extension of Grassmannian manifold to address this issue. Instead of using a linear data embedding with PCA, we develop a non-linear data embedding of such manifold using kernel PCA. This paper mainly consider three folds: 1) introduce a non-linear data embedding of extended Grassmannian manifold, 2) derive a distance metric of Grassmannian manifold, 3) develop an effective and efficient Grassmannian kernel for SVM classification. The extended Grassmannian manifold naturally arises in the application to recognition based on image set, such as face and object recognition. Experiments on several standard databases show better classification accuracy. Furthermore, experimental results indicate that our proposed approach significantly reduces time complexity in comparison to graph embedding discriminant analysis.

  7. Power-Efficient Beacon Recognition Method Based on Periodic Wake-Up for Industrial Wireless Devices.

    Science.gov (United States)

    Song, Soonyong; Lee, Donghun; Jang, Ingook; Choi, Jinchul; Son, Youngsung

    2018-04-17

    Energy harvester-integrated wireless devices are attractive for generating semi-permanent power from wasted energy in industrial environments. The energy-harvesting wireless devices may have difficulty in their communication with access points due to insufficient power supply for beacon recognition during network initialization. In this manuscript, we propose a novel method of beacon recognition based on wake-up control to reduce instantaneous power consumption in the initialization procedure. The proposed method applies a moving window for the periodic wake-up of the wireless devices. For unsynchronized wireless devices, beacons are always located in the same positions within each beacon interval even though the starting offsets are unknown. Using these characteristics, the moving window checks the existence of the beacon associated withspecified resources in a beacon interval, checks again for neighboring resources at the next beacon interval, and so on. This method can reduce instantaneous power and generates a surplus of charging time. Thus, the proposed method alleviates the problems of power insufficiency in the network initialization. The feasibility of the proposed method is evaluated using computer simulations of power shortage in various energy-harvesting conditions.

  8. Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

    Science.gov (United States)

    Deng, Ning

    2014-01-01

    In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity. PMID:24683317

  9. Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

    Directory of Open Access Journals (Sweden)

    Ying Chen

    2014-01-01

    Full Text Available In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region’s weights and then weighted different subregions’ matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1, demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.

  10. Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion.

    Science.gov (United States)

    Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; He, Fei; Wang, Hongye; Deng, Ning

    2014-01-01

    In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, and MMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.

  11. Efficient visual search from synchronized auditory signals requires transient audiovisual events.

    Directory of Open Access Journals (Sweden)

    Erik Van der Burg

    Full Text Available BACKGROUND: A prevailing view is that audiovisual integration requires temporally coincident signals. However, a recent study failed to find any evidence for audiovisual integration in visual search even when using synchronized audiovisual events. An important question is what information is critical to observe audiovisual integration. METHODOLOGY/PRINCIPAL FINDINGS: Here we demonstrate that temporal coincidence (i.e., synchrony of auditory and visual components can trigger audiovisual interaction in cluttered displays and consequently produce very fast and efficient target identification. In visual search experiments, subjects found a modulating visual target vastly more efficiently when it was paired with a synchronous auditory signal. By manipulating the kind of temporal modulation (sine wave vs. square wave vs. difference wave; harmonic sine-wave synthesis; gradient of onset/offset ramps we show that abrupt visual events are required for this search efficiency to occur, and that sinusoidal audiovisual modulations do not support efficient search. CONCLUSIONS/SIGNIFICANCE: Thus, audiovisual temporal alignment will only lead to benefits in visual search if the changes in the component signals are both synchronized and transient. We propose that transient signals are necessary in synchrony-driven binding to avoid spurious interactions with unrelated signals when these occur close together in time.

  12. Multimodal fusion framework: a multiresolution approach for emotion classification and recognition from physiological signals.

    Science.gov (United States)

    Verma, Gyanendra K; Tiwary, Uma Shanker

    2014-11-15

    The purpose of this paper is twofold: (i) to investigate the emotion representation models and find out the possibility of a model with minimum number of continuous dimensions and (ii) to recognize and predict emotion from the measured physiological signals using multiresolution approach. The multimodal physiological signals are: Electroencephalogram (EEG) (32 channels) and peripheral (8 channels: Galvanic skin response (GSR), blood volume pressure, respiration pattern, skin temperature, electromyogram (EMG) and electrooculogram (EOG)) as given in the DEAP database. We have discussed the theories of emotion modeling based on i) basic emotions, ii) cognitive appraisal and physiological response approach and iii) the dimensional approach and proposed a three continuous dimensional representation model for emotions. The clustering experiment on the given valence, arousal and dominance values of various emotions has been done to validate the proposed model. A novel approach for multimodal fusion of information from a large number of channels to classify and predict emotions has also been proposed. Discrete Wavelet Transform, a classical transform for multiresolution analysis of signal has been used in this study. The experiments are performed to classify different emotions from four classifiers. The average accuracies are 81.45%, 74.37%, 57.74% and 75.94% for SVM, MLP, KNN and MMC classifiers respectively. The best accuracy is for 'Depressing' with 85.46% using SVM. The 32 EEG channels are considered as independent modes and features from each channel are considered with equal importance. May be some of the channel data are correlated but they may contain supplementary information. In comparison with the results given by others, the high accuracy of 85% with 13 emotions and 32 subjects from our proposed method clearly proves the potential of our multimodal fusion approach. Copyright © 2013 Elsevier Inc. All rights reserved.

  13. Fatigue crack growth behavior and AE signal recognition from a composite patch repaired Ai thein plate

    International Nuclear Information System (INIS)

    Kim, Sung Jin; Kwon, Oh Yang

    2004-01-01

    The fatigue crack growth behavior of a fatigue-cracked and patch-repaired AA2024-T3 plate has been monitored. It was found that the overall crack growth rate was reduced and the crack propagation into the adjacent hole was also retarded. Signals due to crack growth after patch-repair and those due to debonding of the plate-patch interface were discriminated each other by using principal component analysis. The former showed higher center frequency and lower amplitude, whereas the latter showed longer rise time, lower frequency and higher amplitude.

  14. The conformational and subcellular compartmental dance of plant NLRs during viral recognition and defense signaling

    Science.gov (United States)

    Padmanabhan, Meenu S; Dinesh-Kumar, Savithramma P

    2014-01-01

    Plant innate immune response against viruses utilizes intracellular Nucleotide Binding domain Leucine Rich Repeat (NLR) class of receptors. NLRs recognize different viral proteins termed elicitors and initiate diverse signaling processes that induce programmed cell death (PCD) in infected cells and restrict virus spread. In this review we describe the recent advances made in the study of plant NLRs that detect viruses. We describe some of the physical and functional interactions these NLRs undertake. We elaborate on the intra-molecular and homotypic association of NLRs that function in self-regulation and activation. Nuclear role for some viral NLRs is discussed as well as the emerging importance of the RNAi pathway in regulating the NLR family. PMID:24906192

  15. Efficient Sparse Signal Transmission over a Lossy Link Using Compressive Sensing

    Directory of Open Access Journals (Sweden)

    Liantao Wu

    2015-08-01

    Full Text Available Reliable data transmission over lossy communication link is expensive due to overheads for error protection. For signals that have inherent sparse structures, compressive sensing (CS is applied to facilitate efficient sparse signal transmissions over lossy communication links without data compression or error protection. The natural packet loss in the lossy link is modeled as a random sampling process of the transmitted data, and the original signal will be reconstructed from the lossy transmission results using the CS-based reconstruction method at the receiving end. The impacts of packet lengths on transmission efficiency under different channel conditions have been discussed, and interleaving is incorporated to mitigate the impact of burst data loss. Extensive simulations and experiments have been conducted and compared to the traditional automatic repeat request (ARQ interpolation technique, and very favorable results have been observed in terms of both accuracy of the reconstructed signals and the transmission energy consumption. Furthermore, the packet length effect provides useful insights for using compressed sensing for efficient sparse signal transmission via lossy links.

  16. Recognition of emotions using multimodal physiological signals and an ensemble deep learning model.

    Science.gov (United States)

    Yin, Zhong; Zhao, Mengyuan; Wang, Yongxiong; Yang, Jingdong; Zhang, Jianhua

    2017-03-01

    Using deep-learning methodologies to analyze multimodal physiological signals becomes increasingly attractive for recognizing human emotions. However, the conventional deep emotion classifiers may suffer from the drawback of the lack of the expertise for determining model structure and the oversimplification of combining multimodal feature abstractions. In this study, a multiple-fusion-layer based ensemble classifier of stacked autoencoder (MESAE) is proposed for recognizing emotions, in which the deep structure is identified based on a physiological-data-driven approach. Each SAE consists of three hidden layers to filter the unwanted noise in the physiological features and derives the stable feature representations. An additional deep model is used to achieve the SAE ensembles. The physiological features are split into several subsets according to different feature extraction approaches with each subset separately encoded by a SAE. The derived SAE abstractions are combined according to the physiological modality to create six sets of encodings, which are then fed to a three-layer, adjacent-graph-based network for feature fusion. The fused features are used to recognize binary arousal or valence states. DEAP multimodal database was employed to validate the performance of the MESAE. By comparing with the best existing emotion classifier, the mean of classification rate and F-score improves by 5.26%. The superiority of the MESAE against the state-of-the-art shallow and deep emotion classifiers has been demonstrated under different sizes of the available physiological instances. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  17. Efficient representation of DNA data for pattern recognition using failure factor oracles

    NARCIS (Netherlands)

    Cleophas, Loek; Kourie, Derrick G.; Watson, Bruce W.

    2013-01-01

    In indexing of and pattern matching on DNA sequences, representing all factors of a sequence is important. One efficient, compact representation is the factor oracle (FO). At the same time, any classical deterministic finite automata (DFA) can be transformed to a so-called failure one (FDFA), which

  18. Word Recognition Processing Efficiency as a Component of Second Language Listening

    Science.gov (United States)

    Joyce, Paul

    2013-01-01

    This study investigated the application of the speeded lexical decision task to L2 aural processing efficiency. One-hundred and twenty Japanese university students completed an aural word/nonword task. When the variation of lexical decision time (CV) was correlated with reaction time (RT), the results suggested that the single-word recognition…

  19. Residual stress evaluation by Barkhausen signals with a magnetic field sensor for high efficiency electrical motors

    Science.gov (United States)

    Tsuchida, Yuji; Enokizono, Masato

    2018-04-01

    The iron loss of industrial motors increases by residual stress during manufacturing processes. It is very important to make clear the distribution of the residual stress in the motor cores to reduce the iron loss in the motors. Barkhausen signals which occur on electrical steel sheets can be used for the evaluation of the residual stress because they are very sensitive to the material properties. Generally, a B-sensor is used to measure Barkhausen signals, however, we developed a new H-sensor to measure them and applied it into the stress evaluation. It is supposed that the Barkhausen signals by using a H-sensor can be much effective to the residual stress on the electrical steel sheets by referring our results regarding to the stress evaluations. We evaluated the tensile stress of the electrical steel sheets by measuring Barkhausen signals by using our developed H-sensor for high efficiency electrical motors.

  20. Time lens based optical fourier transformation for advanced processing of spectrally-efficient OFDM and N-WDM signals

    DEFF Research Database (Denmark)

    Guan, Pengyu; Røge, Kasper Meldgaard; Morioka, Toshio

    2016-01-01

    We review recent progress in the use of time lens based optical Fourier transformation for advanced optical signal processing, with focus on all-optical generation, detection and format conversion of spectrally-efficient OFDM and N-WDM signals.......We review recent progress in the use of time lens based optical Fourier transformation for advanced optical signal processing, with focus on all-optical generation, detection and format conversion of spectrally-efficient OFDM and N-WDM signals....

  1. A novel stable 3D luminescent uranyl complex for highly efficient and sensitive recognition of Ru3+ and biomolecules

    Science.gov (United States)

    Tian, Hong-Hong; Chen, Liang-Ting; Zhang, Rong-Lan; Zhao, Jian-She; Liu, Chi-Yang; Weng, Ng Seik

    2018-02-01

    A novel highly stable 3D luminescent uranyl coordination polymer, namely {[UO2(L)]·DMA}n (1), was assembled with uranyl salt and a glycine-derivative ligand [6-(carboxymethyl-amino)-4-oxo-4,5-dihydro-[1,3,5]triazin-2-ylamino]-acetic acid (H2L) under solvothermal reaction. Besides, It was found that complex 1 possesses excellent luminescent properties, particularly the efficient selectivity and sensitivity in the recognition of Ru3+, biomacromolecule bovine serum albumin (BSA), biological small molecules dopamine (DA), ascorbic acid (AA) and uric acid (UA) in the water solution based on a "turn-off" mechanism. Accordingly, the luminescent explorations also demonstrated that complex 1 could be acted as an efficient luminescent probe with high quenching efficiency and low detection limit for selectively detecting Ru3+ and biomolecules (DA, AA, UA and BSA). It was noted that the framework structure of complex 1 still remains highly stable after quenching, which was verified by powder X-ray diffraction (PXRD).

  2. Arx: a toolset for the efficient simulation and direct synthesis of high-performance signal processing algorithms

    NARCIS (Netherlands)

    Hofstra, K.L.; Gerez, Sabih H.

    2007-01-01

    This paper addresses the efficient implementation of highperformance signal-processing algorithms. In early stages of such designs many computation-intensive simulations may be necessary. This calls for hardware description formalisms targeted for efficient simulation (such as the programming

  3. Call transmission efficiency in native and invasive anurans: competing hypotheses of divergence in acoustic signals.

    Science.gov (United States)

    Llusia, Diego; Gómez, Miguel; Penna, Mario; Márquez, Rafael

    2013-01-01

    Invasive species are a leading cause of the current biodiversity decline, and hence examining the major traits favouring invasion is a key and long-standing goal of invasion biology. Despite the prominent role of the advertisement calls in sexual selection and reproduction, very little attention has been paid to the features of acoustic communication of invasive species in nonindigenous habitats and their potential impacts on native species. Here we compare for the first time the transmission efficiency of the advertisement calls of native and invasive species, searching for competitive advantages for acoustic communication and reproduction of introduced taxa, and providing insights into competing hypotheses in evolutionary divergence of acoustic signals: acoustic adaptation vs. morphological constraints. Using sound propagation experiments, we measured the attenuation rates of pure tones (0.2-5 kHz) and playback calls (Lithobates catesbeianus and Pelophylax perezi) across four distances (1, 2, 4, and 8 m) and over two substrates (water and soil) in seven Iberian localities. All factors considered (signal type, distance, substrate, and locality) affected transmission efficiency of acoustic signals, which was maximized with lower frequency sounds, shorter distances, and over water surface. Despite being broadcast in nonindigenous habitats, the advertisement calls of invasive L. catesbeianus were propagated more efficiently than those of the native species, in both aquatic and terrestrial substrates, and in most of the study sites. This implies absence of optimal relationship between native environments and propagation of acoustic signals in anurans, in contrast to what predicted by the acoustic adaptation hypothesis, and it might render these vertebrates particularly vulnerable to intrusion of invasive species producing low frequency signals, such as L. catesbeianus. Our findings suggest that mechanisms optimizing sound transmission in native habitat can play a less

  4. Call transmission efficiency in native and invasive anurans: competing hypotheses of divergence in acoustic signals.

    Directory of Open Access Journals (Sweden)

    Diego Llusia

    Full Text Available Invasive species are a leading cause of the current biodiversity decline, and hence examining the major traits favouring invasion is a key and long-standing goal of invasion biology. Despite the prominent role of the advertisement calls in sexual selection and reproduction, very little attention has been paid to the features of acoustic communication of invasive species in nonindigenous habitats and their potential impacts on native species. Here we compare for the first time the transmission efficiency of the advertisement calls of native and invasive species, searching for competitive advantages for acoustic communication and reproduction of introduced taxa, and providing insights into competing hypotheses in evolutionary divergence of acoustic signals: acoustic adaptation vs. morphological constraints. Using sound propagation experiments, we measured the attenuation rates of pure tones (0.2-5 kHz and playback calls (Lithobates catesbeianus and Pelophylax perezi across four distances (1, 2, 4, and 8 m and over two substrates (water and soil in seven Iberian localities. All factors considered (signal type, distance, substrate, and locality affected transmission efficiency of acoustic signals, which was maximized with lower frequency sounds, shorter distances, and over water surface. Despite being broadcast in nonindigenous habitats, the advertisement calls of invasive L. catesbeianus were propagated more efficiently than those of the native species, in both aquatic and terrestrial substrates, and in most of the study sites. This implies absence of optimal relationship between native environments and propagation of acoustic signals in anurans, in contrast to what predicted by the acoustic adaptation hypothesis, and it might render these vertebrates particularly vulnerable to intrusion of invasive species producing low frequency signals, such as L. catesbeianus. Our findings suggest that mechanisms optimizing sound transmission in native habitat

  5. Oryza sativa Chloroplast Signal Recognition Particle 43 (OscpSRP43 Is Required for Chloroplast Development and Photosynthesis.

    Directory of Open Access Journals (Sweden)

    Xiang-guang Lv

    Full Text Available A rice chlorophyll-deficient mutant w67 was isolated from an ethyl methane sulfonate (EMS-induced IR64 (Oryza sativa L. ssp. indica mutant bank. The mutant exhibited a distinct yellow-green leaf phenotype in the whole plant growth duration with significantly reduced levels of chlorophyll and carotenoid, impaired chloroplast development and lowered capacity of photosynthesis compared with the wild-type IR64. Expression of a number of genes associated with chlorophyll metabolism, chloroplast biogenesis and photosynthesis was significantly altered in the mutant. Genetic analysis indicated that the yellow-green phenotype was controlled by a single recessive nuclear gene located on the short arm of chromosome 3. Using map-based strategy, the mutation was isolated and predicted to encode a chloroplast signal recognition particle 43 KD protein (cpSRP43 with 388 amino acid residuals. A single base substitution from A to T at position 160 resulted in a premature stop codon. OscpSRP43 was constitutively expressed in various organs with the highest level in the leaf. Functional complementation could rescue the mutant phenotype and subcellular localization showed that the cpSRP43:GFP fusion protein was targeted to the chloroplast. The data suggested that Oryza sativa cpSRP43 (OscpSRP43 was required for the normal development of chloroplasts and photosynthesis in rice.

  6. An efficient digital signal processing method for RRNS-based DS-CDMA systems

    Directory of Open Access Journals (Sweden)

    Peter Olsovsky

    2017-09-01

    Full Text Available This paper deals with an efficient method for achieving low power and high speed in advanced Direct-Sequence Code Division Multiple-Access (DS-CDMA wireless communication systems based on the Residue Number System (RNS. A modified algorithm for multiuser DS-CDMA signal generation in MATLAB is proposed and investigated. The most important characteristics of the generated PN code are also presented. Subsequently, a DS-CDMA system based on the combination of the RNS or the so-called Redundant Residue Number System (RRNS is proposed. The enhanced method using a spectrally efficient 8-PSK data modulation scheme to improve the bandwidth efficiency for RRNS-based DS-CDMA systems is presented. By using the C-measure (complexity measure of the error detection function, it is possible to estimate the size of the circuit. Error detection function in RRNSs can be efficiently implemented by LookUp Table (LUT cascades.

  7. Extracting time-frequency feature of single-channel vastus medialis EMG signals for knee exercise pattern recognition.

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    Full Text Available The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited. In this study, three types of knee exercises, i.e., flexion of the leg up (standing, hip extension from a sitting position (sitting and gait (walking are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded. Four types of lower-limb motions including standing, sitting, stance phase of walking, and swing phase of walking, are segmented. The Wavelet Transform (WT based Singular Value Decomposition (SVD approach is proposed for the classification of four lower-limb motions using a single-channel EMG signal from the muscle group of vastus medialis. Based on lower-limb motions from all subjects, the combination of five-level wavelet decomposition and SVD is used to comprise the feature vector. The Support Vector Machine (SVM is then configured to build a multiple-subject classifier for which the subject independent accuracy will be given across all subjects for the classification of four types of lower-limb motions. In order to effectively indicate the classification performance, EMG features from time-domain (e.g., Mean Absolute Value (MAV, Root-Mean-Square (RMS, integrated EMG (iEMG, Zero Crossing (ZC and frequency-domain (e.g., Mean Frequency (MNF and Median Frequency (MDF are also used to classify lower-limb motions. The five-fold cross validation is performed and it repeats fifty times in order to acquire the robust subject independent accuracy. Results show that the proposed WT-based SVD approach has the classification accuracy of 91.85%±0

  8. Computationally efficient SVM multi-class image recognition with confidence measures

    International Nuclear Information System (INIS)

    Makili, Lazaro; Vega, Jesus; Dormido-Canto, Sebastian; Pastor, Ignacio; Murari, Andrea

    2011-01-01

    Typically, machine learning methods produce non-qualified estimates, i.e. the accuracy and reliability of the predictions are not provided. Transductive predictors are very recent classifiers able to provide, simultaneously with the prediction, a couple of values (confidence and credibility) to reflect the quality of the prediction. Usually, a drawback of the transductive techniques for huge datasets and large dimensionality is the high computational time. To overcome this issue, a more efficient classifier has been used in a multi-class image classification problem in the TJ-II stellarator database. It is based on the creation of a hash function to generate several 'one versus the rest' classifiers for every class. By using Support Vector Machines as the underlying classifier, a comparison between the pure transductive approach and the new method has been performed. In both cases, the success rates are high and the computation time with the new method is up to 0.4 times the old one.

  9. High efficiency processing for reduced amplitude zones detection in the HRECG signal

    Science.gov (United States)

    Dugarte, N.; Álvarez, A.; Balacco, J.; Mercado, G.; Gonzalez, A.; Dugarte, E.; Olivares, A.

    2016-04-01

    Summary - This article presents part of a more detailed research proposed in the medium to long term, with the intention of establishing a new philosophy of electrocardiogram surface analysis. This research aims to find indicators of cardiovascular disease in its early stage that may go unnoticed with conventional electrocardiography. This paper reports the development of a software processing which collect some existing techniques and incorporates novel methods for detection of reduced amplitude zones (RAZ) in high resolution electrocardiographic signal (HRECG).The algorithm consists of three stages, an efficient processing for QRS detection, averaging filter using correlation techniques and a step for RAZ detecting. Preliminary results show the efficiency of system and point to incorporation of techniques new using signal analysis with involving 12 leads.

  10. Efficient retina formation requires suppression of both Activin and BMP signaling pathways in pluripotent cells

    Directory of Open Access Journals (Sweden)

    Kimberly A. Wong

    2015-03-01

    Full Text Available Retina formation requires the correct spatiotemporal patterning of key regulatory factors. While it is known that repression of several signaling pathways lead to specification of retinal fates, addition of only Noggin, a known BMP antagonist, can convert pluripotent Xenopus laevis animal cap cells to functional retinal cells. The aim of this study is to determine the intracellular molecular events that occur during this conversion. Surprisingly, blocking BMP signaling alone failed to mimic Noggin treatment. Overexpressing Noggin in pluripotent cells resulted in a concentration-dependent suppression of both Smad1 and Smad2 phosphorylation, which act downstream of BMP and Activin signaling, respectively. This caused a decrease in downstream targets: endothelial marker, xk81, and mesodermal marker, xbra. We treated pluripotent cells with dominant-negative receptors or the chemical inhibitors, dorsomorphin and SB431542, which each target either the BMP or Activin signaling pathway. We determined the effect of these treatments on retina formation using the Animal Cap Transplant (ACT assay; in which treated pluripotent cells were transplanted into the eye field of host embryos. We found that inhibition of Activin signaling, in the presence of BMP signaling inhibition, promotes efficient retinal specification in Xenopus tissue, mimicking the affect of adding Noggin alone. In whole embryos, we found that the eye field marker, rax, expanded when adding both dominant-negative Smad1 and Smad2, as did treating the cells with both dorsomorphin and SB431542. Future studies could translate these findings to a mammalian culture assay, in order to more efficiently produce retinal cells in culture.

  11. A preferential design approach for energy-efficient and robust implantable neural signal processing hardware.

    Science.gov (United States)

    Narasimhan, Seetharam; Chiel, Hillel J; Bhunia, Swarup

    2009-01-01

    For implantable neural interface applications, it is important to compress data and analyze spike patterns across multiple channels in real time. Such a computational task for online neural data processing requires an innovative circuit-architecture level design approach for low-power, robust and area-efficient hardware implementation. Conventional microprocessor or Digital Signal Processing (DSP) chips would dissipate too much power and are too large in size for an implantable system. In this paper, we propose a novel hardware design approach, referred to as "Preferential Design" that exploits the nature of the neural signal processing algorithm to achieve a low-voltage, robust and area-efficient implementation using nanoscale process technology. The basic idea is to isolate the critical components with respect to system performance and design them more conservatively compared to the noncritical ones. This allows aggressive voltage scaling for low power operation while ensuring robustness and area efficiency. We have applied the proposed approach to a neural signal processing algorithm using the Discrete Wavelet Transform (DWT) and observed significant improvement in power and robustness over conventional design.

  12. Energy-Efficient Crowdsensing of Human Mobility and Signal Levels in Cellular Networks

    Science.gov (United States)

    Foremski, Paweł; Gorawski, Michał; Grochla, Krzysztof; Polys, Konrad

    2015-01-01

    The paper presents a practical application of the crowdsensing idea to measure human mobility and signal coverage in cellular networks. Currently, virtually everyone is carrying a mobile phone, which may be used as a sensor to gather research data by measuring, e.g., human mobility and radio signal levels. However, many users are unwilling to participate in crowdsensing experiments. This work begins with the analysis of the barriers for engaging people in crowdsensing. A survey showed that people who agree to participate in crowdsensing expect a minimum impact on their battery lifetime and phone usage habits. To address these requirements, this paper proposes an application for measuring the location and signal strength data based on energy-efficient GPS tracking, which allows one to perform the measurements of human mobility and radio signal levels with minimum energy utilization and without any engagement of the user. The method described combines measurements from the accelerometer with effective management of the GPS to monitor the user mobility with the decrease in battery lifetime by approximately 20%. To show the applicability of the proposed platform, the sample results of signal level distribution and coverage maps gathered for an LTE network and representing human mobility are shown. PMID:26340633

  13. Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

    Directory of Open Access Journals (Sweden)

    John Jairo Villarejo Mayor

    2017-08-01

    Full Text Available Abstract Introduction Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi’s fractal dimension combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM, K-nearest neighbors (KNN and linear discriminant analysis (LDA were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees. Results The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications. Conclusion The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees.

  14. Longitudinal course of disease in a large cohort of myositis patients with autoantibodies recognizing the signal recognition particle

    Science.gov (United States)

    Werner, Jessie L.; Albayda, Jemyma; Paik, Julie; Danoff, Sonye K.; Casciola-Rosen, Livia; Christopher-Stine, Lisa; Mammen, Andrew L.

    2016-01-01

    Objective Patients with immune-mediated necrotizing myopathy (IMNM) often have autoantibodies recognizing the signal recognition particle (SRP) or HMG-CoA reductase (HMGCR). Here, we studied a cohort of anti-SRP patients to identify factors associated with disease severity and clinical improvement; we also compared the severity of weakness in those with anti-SRP versus anti-HMGCR autoantibodies. Methods All anti-SRP patients in the Johns Hopkins Myositis Cohort from 2002 to 2015 were included. Longitudinal information regarding proximal muscle strength, creatine kinase (CK) levels, and immunosuppressive therapy were recorded at each visit. Univariate and multivariate multilevel regression models were used to assess prognostic factors influencing recovery. Strength in the anti-SRP patients was compared to strength in 49 previously described anti-HMGCR subjects. Results Data from 37 anti-SRP patients and 380 total clinic visits was analyzed. Younger age at onset was associated with more severe weakness at the first visit (p=0.02) and all subsequent visits (p=0.002). Only 50% of patients reached near-full or full strength after 4 years of treatment and most of these continued to have elevated CK levels. Rituximab appeared to be effective in 13 of 17 anti-SRP patients. Anti-SRP patients were significantly weaker than those with anti-HMGCR autoantibodies (−1.3 strength points, p=0.001). Conclusions Younger age at onset is associated with more severe weakness in anti-SRP myositis. Furthermore, even among anti-SRP patients whose strength improved with immunosuppression, most had ongoing disease activity as demonstrated by elevated CK levels. Finally, anti-SRP patients were significantly weaker than anti-HMGCR patients, providing evidence that these autoantibodies are associated with distinct forms of IMNM. PMID:27111848

  15. Green signalling effects in the market for energy-efficient residential buildings

    International Nuclear Information System (INIS)

    Fuerst, Franz; Oikarinen, Elias; Harjunen, Oskari

    2016-01-01

    Highlights: • Energy efficiency (EE) levels are hypothesised to affect house transaction prices. • We estimate a hedonic model using Energy Performance Certificates from Finland. • A price premium is found for the most energy-efficient properties. • The empirical results are suggestive of a green signalling effect. • Demand for EE high performers appears to be segmented from lower tiers. - Abstract: Empirical evidence from recent studies suggests that the price premium on energy-efficient buildings is potentially higher than the pure capitalisation of energy savings but the empirical evidence on the size of the non-savings components is scant. This study aims to fill this research gap by investigating whether the mandatory energy efficiency ratings for residential properties imply benefits that go beyond energy savings. Using a sample of several thousand apartment transactions from Helsinki, Finland, we first test if higher ratings were significantly associated with higher prices. In addition to a large number of property and neighbourhood characteristics, this dataset contains information on building-level energy usage which allows us to distinguish between the cost savings effect of energy consumption and the value of more intangible factors associated with the energy label. The hedonic model yields a statistically significant 3.3% price premium for apartments in the top three energy-efficiency categories and 1.5% when a set of detailed neighbourhood characteristics are included. When maintenance costs containing energy usage costs are added, a robust and significant price premium of 1.3% persists whereas no differentiation is found for the medium and lower rating categories. These findings may be indicative of energy-efficient buildings having signalling value – and therefore an additional incentive to invest in such buildings – for ‘green’ consumers. However, a favourable energy rating did not appear to speed up the sales process in the

  16. Efficient secretory expression of recombinant proteins in Escherichia coli with a novel actinomycete signal peptide.

    Science.gov (United States)

    Cui, Yanbing; Meng, Yiwei; Zhang, Juan; Cheng, Bin; Yin, Huijia; Gao, Chao; Xu, Ping; Yang, Chunyu

    2017-01-01

    In well-established heterologous hosts, such as Escherichia coli, recombinant proteins are usually intracellular and frequently found as inclusion bodies-especially proteins possessing high rare codon content. In this study, successful secretory expression of three hydrolases, in a constructed inducible or constitutive system, was achieved by fusion with a novel signal peptide (Kp-SP) from an actinomycete. The signal peptide efficiently enabled extracellular protein secretion and also contributed to the active expression of the intracellular recombinant proteins. The thermophilic α-amylase gene of Bacillus licheniformis was fused with Kp-SP. Both recombinants, carrying inducible and constitutive plasmids, showed remarkable increases in extracellular and intracellular amylolytic activity. Amylase activity was observed to be > 10-fold in recombinant cultures with the constitutive plasmid, pBSPPc, compared to that in recombinants lacking Kp-SP. Further, the signal peptide enabled efficient secretion of a thermophilic cellulase into the culture medium, as demonstrated by larger halo zones and increased enzymatic activities detected in both constructs from different plasmids. For heterologous proteins with a high proportion of rare codons, it is difficult to obtain high expression in E. coli owing to the codon bias. Here, the fusion of an archaeal homologue of the amylase encoding gene, FSA, with Kp-SP resulted in > 5-fold higher extracellular activity. The successful extracellular expression of the amylase indicated that the signal peptide also contributed significantly to its active expression and signified the potential value of this novel and versatile signal peptide in recombinant protein production. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Risperidone reverses the spatial object recognition impairment and hippocampal BDNF-TrkB signalling system alterations induced by acute MK-801 treatment

    Science.gov (United States)

    Chen, Guangdong; Lin, Xiaodong; Li, Gongying; Jiang, Diego; Lib, Zhiruo; Jiang, Ronghuan; Zhuo, Chuanjun

    2017-01-01

    The aim of the present study was to investigate the effects of a commonly-used atypical antipsychotic, risperidone, on alterations in spatial learning and in the hippocampal brain-derived neurotrophic factor (BDNF)-tyrosine receptor kinase B (TrkB) signalling system caused by acute dizocilpine maleate (MK-801) treatment. In experiment 1, adult male Sprague-Dawley rats subjected to acute treatment of either low-dose MK801 (0.1 mg/kg) or normal saline (vehicle) were tested for spatial object recognition and hippocampal expression levels of BDNF, TrkB and the phophorylation of TrkB (p-TrkB). We found that compared to the vehicle, MK-801 treatment impaired spatial object recognition of animals and downregulated the expression levels of p-TrkB. In experiment 2, MK-801- or vehicle-treated animals were further injected with risperidone (0.1 mg/kg) or vehicle before behavioural testing and sacrifice. Of note, we found that risperidone successfully reversed the deleterious effects of MK-801 on spatial object recognition and upregulated the hippocampal BDNF-TrkB signalling system. Collectively, the findings suggest that cognitive deficits from acute N-methyl-D-aspartate receptor blockade may be associated with the hypofunction of hippocampal BDNF-TrkB signalling system and that risperidone was able to reverse these alterations. PMID:28451387

  18. Efficient Techniques of Sparse Signal Analysis for Enhanced Recovery of Information in Biomedical Engineering and Geosciences

    KAUST Repository

    Sana, Furrukh

    2016-11-01

    Sparse signals are abundant among both natural and man-made signals. Sparsity implies that the signal essentially resides in a small dimensional subspace. The sparsity of the signal can be exploited to improve its recovery from limited and noisy observations. Traditional estimation algorithms generally lack the ability to take advantage of signal sparsity. This dissertation considers several problems in the areas of biomedical engineering and geosciences with the aim of enhancing the recovery of information by exploiting the underlying sparsity in the problem. The objective is to overcome the fundamental bottlenecks, both in terms of estimation accuracies and required computational resources. In the first part of dissertation, we present a high precision technique for the monitoring of human respiratory movements by exploiting the sparsity of wireless ultra-wideband signals. The proposed technique provides a novel methodology of overcoming the Nyquist sampling constraint and enables robust performance in the presence of noise and interferences. We also present a comprehensive framework for the important problem of extracting the fetal electrocardiogram (ECG) signals from abdominal ECG recordings of pregnant women. The multiple measurement vectors approach utilized for this purpose provides an efficient mechanism of exploiting the common structure of ECG signals, when represented in sparse transform domains, and allows leveraging information from multiple ECG electrodes under a joint estimation formulation. In the second part of dissertation, we adopt sparse signal processing principles for improved information recovery in large-scale subsurface reservoir characterization problems. We propose multiple new algorithms for sparse representation of the subsurface geological structures, incorporation of useful prior information in the estimation process, and for reducing computational complexities of the problem. The techniques presented here enable significantly

  19. Understanding nitrate uptake, signaling and remobilisation for improving plant nitrogen use efficiency.

    Science.gov (United States)

    Kant, Surya

    2018-02-01

    The majority of terrestrial plants use nitrate as their main source of nitrogen. Nitrate also acts as an important signalling molecule in vital physiological processes required for optimum plant growth and development. Improving nitrate uptake and transport, through activation by nitrate sensing, signalling and regulatory processes, would enhance plant growth, resulting in improved crop yields. The increased remobilisation of nitrate, and assimilated nitrogenous compounds, from source to sink tissues further ensures higher yields and quality. An updated knowledge of various transporters, genes, activators, and microRNAs, involved in nitrate uptake, transport, remobilisation, and nitrate-mediated root growth, is presented. An enhanced understanding of these components will allow for their orchestrated fine tuning in efforts to improving nitrogen use efficiency in plants. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

  20. Multivariate Empirical Mode Decomposition Based Signal Analysis and Efficient-Storage in Smart Grid

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Lu [University of Tennessee, Knoxville (UTK); Albright, Austin P [ORNL; Rahimpour, Alireza [University of Tennessee, Knoxville (UTK); Guo, Jiandong [University of Tennessee, Knoxville (UTK); Qi, Hairong [University of Tennessee, Knoxville (UTK); Liu, Yilu [University of Tennessee (UTK) and Oak Ridge National Laboratory (ORNL)

    2017-01-01

    Wide-area-measurement systems (WAMSs) are used in smart grid systems to enable the efficient monitoring of grid dynamics. However, the overwhelming amount of data and the severe contamination from noise often impede the effective and efficient data analysis and storage of WAMS generated measurements. To solve this problem, we propose a novel framework that takes advantage of Multivariate Empirical Mode Decomposition (MEMD), a fully data-driven approach to analyzing non-stationary signals, dubbed MEMD based Signal Analysis (MSA). The frequency measurements are considered as a linear superposition of different oscillatory components and noise. The low-frequency components, corresponding to the long-term trend and inter-area oscillations, are grouped and compressed by MSA using the mean shift clustering algorithm. Whereas, higher-frequency components, mostly noise and potentially part of high-frequency inter-area oscillations, are analyzed using Hilbert spectral analysis and they are delineated by statistical behavior. By conducting experiments on both synthetic and real-world data, we show that the proposed framework can capture the characteristics, such as trends and inter-area oscillation, while reducing the data storage requirements

  1. Processing of cell-surface signalling anti-sigma factors prior to signal recognition is aconserved autoproteolytic mechanism that produces two functional domains.

    NARCIS (Netherlands)

    Bastiaansen, K.C.J.T.; Otero-Asman, J.R.; Luirink, J.; Bitter, W.; Llamas, M.A.

    2015-01-01

    Cell-surface signalling (CSS) enables Gram-negative bacteria to transduce an environmental signal into a cytosolic response. This regulatory cascade involves an outer membrane receptor that transmits the signal to an anti-sigma factor in the cytoplasmic membrane, allowing the activation of an

  2. Efficient Extracellular Expression of Phospholipase D in Escherichia Coli with an Optimized Signal Peptide

    Science.gov (United States)

    Yang, Leyun; Xu, Yu; Chen, Yong; Ying, Hanjie

    2018-01-01

    New secretion vectors containing the synthetic signal sequence (OmpA’) was constructed for the secretory production of recombinant proteins in Escherichia coli. The E. coli Phospholipase D structural gene (Accession number:NC_018658) fused to various signal sequence were expressed from the Lac promoter in E. coli Rosetta strains by induction with 0.4mM IPTG at 28°C for 48h. SDS-PaGe analysis of expression and subcellular fractions of recombinant constructs revealed the translocation of Phospholipase D (PLD) not only to the medium but also remained in periplasm of E. coli with OmpA’ signal sequence at the N-terminus of PLD. Thus the study on the effects of various surfactants on PLD extracellular production in Escherichia coli in shake flasks revealed that optimal PLD extracellular production could be achieved by adding 0.4% Triton X-100 into the medium. The maximal extracellular PLD production and extracellular enzyme activity were 0.23mg ml-1 and 16U ml-1, respectively. These results demonstrate the possibility of efficient secretory production of recombinant PLD in E. coli should be a potential industrial applications.

  3. Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients.

    Science.gov (United States)

    Shu, Xiaokang; Chen, Shugeng; Yao, Lin; Sheng, Xinjun; Zhang, Dingguo; Jiang, Ning; Jia, Jie; Zhu, Xiangyang

    2018-01-01

    Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10-50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and 10 healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately 1 min). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r = -0.732, p discrimination of BCI-inefficient users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-inefficiency phenomenon in stroke patients.

  4. Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients

    Science.gov (United States)

    Shu, Xiaokang; Chen, Shugeng; Yao, Lin; Sheng, Xinjun; Zhang, Dingguo; Jiang, Ning; Jia, Jie; Zhu, Xiangyang

    2018-01-01

    Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10–50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and 10 healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately 1 min). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r = −0.732, p discrimination of BCI-inefficient users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-inefficiency phenomenon in stroke patients. PMID:29515363

  5. Calix[4]arenes Containing a Ureido Functionality on the Lower Rim as Highly Efficient Receptors for Anion Recognition.

    Czech Academy of Sciences Publication Activity Database

    Klejch, T.; Slavíček, J.; Hudeček, O.; Eigner, V.; Gutierrez, Natalia Andrea; Cuřínová, Petra; Lhoták, P.

    2016-01-01

    Roč. 40, č. 9 (2016), s. 7935-7942 ISSN 1144-0546 Institutional support: RVO:67985858 Keywords : calix[4]arene * anion recognition * receptors Subject RIV: CC - Organic Chemistry Impact factor: 3.269, year: 2016

  6. Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients

    Directory of Open Access Journals (Sweden)

    Xiaokang Shu

    2018-02-01

    Full Text Available Motor imagery (MI based brain-computer interface (BCI has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10–50% of subjects are BCI-inefficient users (accuracy less than 70%. Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI and cortical activation strength (CAS, to predict MI-BCI performance. Twenty-four stroke patients and 10 healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG signals during paretic hand MI tasks (5 trials; approximately 1 min. LI values exhibited a statistically significant correlation with two-class BCI (left vs. right performance (r = −0.732, p < 0.001, and CAS values exhibited a statistically significant correlation with brain-switch BCI (task vs. idle performance (r = 0.641, p < 0.001. Furthermore, the BCI-inefficient users were successfully recognized with a sensitivity of 88.2% and a specificity of 85.7% in the two-class BCI. The brain-switch BCI achieved a sensitivity of 100.0% and a specificity of 87.5% in the discrimination of BCI-inefficient users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-inefficiency phenomenon in stroke patients.

  7. A Penalized Semialgebraic Deflation ICA Algorithm for the Efficient Extraction of Interictal Epileptic Signals.

    Science.gov (United States)

    Becker, Hanna; Albera, Laurent; Comon, Pierre; Kachenoura, Amar; Merlet, Isabelle

    2017-01-01

    As a noninvasive technique, electroencephalography (EEG) is commonly used to monitor the brain signals of patients with epilepsy such as the interictal epileptic spikes. However, the recorded data are often corrupted by artifacts originating, for example, from muscle activities, which may have much higher amplitudes than the interictal epileptic signals of interest. To remove these artifacts, a number of independent component analysis (ICA) techniques were successfully applied. In this paper, we propose a new deflation ICA algorithm, called penalized semialgebraic unitary deflation (P-SAUD) algorithm, that improves upon classical ICA methods by leading to a considerably reduced computational complexity at equivalent performance. This is achieved by employing a penalized semialgebraic extraction scheme, which permits us to identify the epileptic components of interest (interictal spikes) first and obviates the need of extracting subsequent components. The proposed method is evaluated on physiologically plausible simulated EEG data and actual measurements of three patients. The results are compared to those of several popular ICA algorithms as well as second-order blind source separation methods, demonstrating that P-SAUD extracts the epileptic spikes with the same accuracy as the best ICA methods, but reduces the computational complexity by a factor of 10 for 32-channel recordings. This superior computational efficiency is of particular interest considering the increasing use of high-resolution EEG recordings, whose analysis requires algorithms with low computational cost.

  8. Interpopulational Variations in Sexual Chemical Signals of Iberian Wall Lizards May Allow Maximizing Signal Efficiency under Different Climatic Conditions.

    Science.gov (United States)

    Martín, José; Ortega, Jesús; López, Pilar

    2015-01-01

    Sexual signals used in intraspecific communication are expected to evolve to maximize efficacy under a given climatic condition. Thus, chemical secretions of lizards might evolve in the evolutionary time to ensure that signals are perfectly tuned to local humidity and temperature conditions affecting their volatility and therefore their persistence and transmission through the environment. We tested experimentally whether interpopulational altitudinal differences in chemical composition of femoral gland secretions of male Iberian wall lizards (Podarcis hispanicus) have evolved to maximize efficacy of chemical signals in different environmental conditions. Chemical analyses first showed that the characteristics of chemical signals of male lizards differed between two populations inhabiting environments with different climatic conditions in spite of the fact that these two populations are closely related genetically. We also examined experimentally whether the temporal attenuation of the chemical stimuli depended on simulated climatic conditions. Thus, we used tongue-flick essays to test whether female lizards were able to detect male scent marks maintained under different conditions of temperature and humidity by chemosensory cues alone. Chemosensory tests showed that chemical signals of males had a lower efficacy (i.e. detectability and persistence) when temperature and dryness increase, but that these effects were more detrimental for signals of the highest elevation population, which occupies naturally colder and more humid environments. We suggest that the abiotic environment may cause a selective pressure on the form and expression of sexual chemical signals. Therefore, interpopulational differences in chemical profiles of femoral secretions of male P. hispanicus lizards may reflect adaptation to maximize the efficacy of the chemical signal in different climates.

  9. High antipredatory efficiency of insular lizards: a warning signal of excessive specimen collection?

    Directory of Open Access Journals (Sweden)

    Miguel Delibes

    Full Text Available We live-captured lizards on islands in the Gulf of California and the Baja California peninsula mainland, and compared their ability to escape predation. Contrary to expectations, endemic lizard species from uninhabited islands fled from humans earlier and more efficiently than those from peninsular mainland areas. In fact, 58.2% (n=146 of the lizards we tried to capture on the various islands escaped successfully, while this percentage was only 14.4% (n=160 on the peninsular mainland. Separate evidence (e.g., proportion of regenerated tails, low human population at the collection areas, etc. challenges several potential explanations for the higher antipredatory efficiency of insular lizards (e.g., more predation pressure on islands, habituation to humans on the peninsula, etc.. Instead, we suggest that the ability of insular lizards to avoid predators may be related to harvesting by humans, perhaps due to the value of endemic species as rare taxonomic entities. If this hypothesis is correct, predation-related behavioral changes in rare species could provide early warning signals of their over-exploitation, thus encouraging the adoption of conservation measures.

  10. Efficient transmission of subthreshold signals in complex networks of spiking neurons.

    Science.gov (United States)

    Torres, Joaquin J; Elices, Irene; Marro, J

    2015-01-01

    We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances--that naturally balances the network with excitatory and inhibitory synapses--and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.

  11. Efficient transmission of subthreshold signals in complex networks of spiking neurons.

    Directory of Open Access Journals (Sweden)

    Joaquin J Torres

    Full Text Available We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances--that naturally balances the network with excitatory and inhibitory synapses--and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.

  12. The role of electrical and jasmonate signalling in the recognition of captured prey in the carnivorous sundew plant Drosera capensis

    Czech Academy of Sciences Publication Activity Database

    Krausko, M.; Perůtka, M.; Šebela, M.; Šamajová, O.; Šamaj, J.; Novák, Ondřej; Pavlovič, A.

    2017-01-01

    Roč. 213, č. 4 (2017), s. 1818-1835 ISSN 0028-646X R&D Projects: GA MŠk(CZ) LO1204 Institutional support: RVO:61389030 Keywords : action potential * carnivorous plant * Drosera * electrical signal * enzymes * jasmonates * long-distance signalling * sundew Subject RIV: EB - Genetics ; Molecular Biology OBOR OECD: Plant sciences, botany Impact factor: 7.330, year: 2016

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

    Directory of Open Access Journals (Sweden)

    Seok-Won Lee

    2013-09-01

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

  14. Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones.

    Science.gov (United States)

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

    2013-09-27

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

  15. Efficient Sub-Bandgap Light Absorption and Signal Amplification in Silicon Photodetectors

    Science.gov (United States)

    Liu, Yu-Hsin

    This thesis focuses on two areas in silicon photodetectors, the first being enhancing the sub-bandgap light absorption of IR wavelenghts in silicon, and the second being intrinsic signal amplification in silicon photodetectors. Both of these are achieved using heavily doped p-n junction devices which create localized states that relax the k-selection rule of indirect bandgap material. The probability of transitions between impurity band and the conduction/valence band would be much more efficient than the one between band-to-band transition. The waveguide-coupled epitaxial p-n photodetector was demonstrated for 1310 nm wavelength detection. Incorporated with the Franz-Keldysh effect and the quasi-confined epitaxial layer design, an absorption coefficient around 10 cm-1 has been measured and internal quantum efficiency nearly 100% at -2.5V. The absorption coefficient is calculated from the wave function of the electron and hole in p-n diode. The heavily doped impurity wave function can be formulated as a delta function, and the quasi-confined conduction band energy states, and the wave function on each level can be obtained from the Silvaco software. The calculated theoretical absorption coefficient increases with the increasing applied bias and the doping concentration, which matches the experimental results. To solve the issues of large excess noise and high operation bias for avalanche photodiodes based on impact ionization, I presented a detector using the Cycling Excitation Process (CEP) for signal amplification. This can be realized in a heavily doped and highly compensated Si p-n junction, showing ultra high gain about 3000 at very low bias (<4 V), and possessing an intrinsic, phonon-mediated regulation process to keep the device stable without any quenching device required in today's Geiger-mode avalanche detectors. The CEP can be formulated with the rate equations in conduction bands and impurity states. The gain expression, which is a function of the

  16. Structures of SRP54 and SRP19, the two proteins that organize the ribonucleic core of the signal recognition particle from Pyrococcus furiosus.

    Directory of Open Access Journals (Sweden)

    Pascal F Egea

    Full Text Available In all organisms the Signal Recognition Particle (SRP, binds to signal sequences of proteins destined for secretion or membrane insertion as they emerge from translating ribosomes. In Archaea and Eucarya, the conserved ribonucleoproteic core is composed of two proteins, the accessory protein SRP19, the essential GTPase SRP54, and an evolutionarily conserved and essential SRP RNA. Through the GTP-dependent interaction between the SRP and its cognate receptor SR, ribosomes harboring nascent polypeptidic chains destined for secretion are dynamically transferred to the protein translocation apparatus at the membrane. We present here high-resolution X-ray structures of SRP54 and SRP19, the two RNA binding components forming the core of the signal recognition particle from the hyper-thermophilic archaeon Pyrococcus furiosus (Pfu. The 2.5 A resolution structure of free Pfu-SRP54 is the first showing the complete domain organization of a GDP bound full-length SRP54 subunit. In its ras-like GTPase domain, GDP is found tightly associated with the protein. The flexible linker that separates the GTPase core from the hydrophobic signal sequence binding M domain, adopts a purely alpha-helical structure and acts as an articulated arm allowing the M domain to explore multiple regions as it scans for signal peptides as they emerge from the ribosomal tunnel. This linker is structurally coupled to the GTPase catalytic site and likely to propagate conformational changes occurring in the M domain through the SRP RNA upon signal sequence binding. Two different 1.8 A resolution crystal structures of free Pfu-SRP19 reveal a compact, rigid and well-folded protein even in absence of its obligate SRP RNA partner. Comparison with other SRP19*SRP RNA structures suggests the rearrangement of a disordered loop upon binding with the RNA through a reciprocal induced-fit mechanism and supports the idea that SRP19 acts as a molecular scaffold and a chaperone, assisting the SRP

  17. Single-trial EEG-informed fMRI reveals spatial dependency of BOLD signal on early and late IC-ERP amplitudes during face recognition.

    Science.gov (United States)

    Wirsich, Jonathan; Bénar, Christian; Ranjeva, Jean-Philippe; Descoins, Médéric; Soulier, Elisabeth; Le Troter, Arnaud; Confort-Gouny, Sylviane; Liégeois-Chauvel, Catherine; Guye, Maxime

    2014-10-15

    Simultaneous EEG-fMRI has opened up new avenues for improving the spatio-temporal resolution of functional brain studies. However, this method usually suffers from poor EEG quality, especially for evoked potentials (ERPs), due to specific artifacts. As such, the use of EEG-informed fMRI analysis in the context of cognitive studies has particularly focused on optimizing narrow ERP time windows of interest, which ignores the rich diverse temporal information of the EEG signal. Here, we propose to use simultaneous EEG-fMRI to investigate the neural cascade occurring during face recognition in 14 healthy volunteers by using the successive ERP peaks recorded during the cognitive part of this process. N170, N400 and P600 peaks, commonly associated with face recognition, were successfully and reproducibly identified for each trial and each subject by using a group independent component analysis (ICA). For the first time we use this group ICA to extract several independent components (IC) corresponding to the sequence of activation and used single-trial peaks as modulation parameters in a general linear model (GLM) of fMRI data. We obtained an occipital-temporal-frontal stream of BOLD signal modulation, in accordance with the three successive IC-ERPs providing an unprecedented spatio-temporal characterization of the whole cognitive process as defined by BOLD signal modulation. By using this approach, the pattern of EEG-informed BOLD modulation provided improved characterization of the network involved than the fMRI-only analysis or the source reconstruction of the three ERPs; the latter techniques showing only two regions in common localized in the occipital lobe. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. Global 21 cm Signal Extraction from Foreground and Instrumental Effects. I. Pattern Recognition Framework for Separation Using Training Sets

    Science.gov (United States)

    Tauscher, Keith; Rapetti, David; Burns, Jack O.; Switzer, Eric

    2018-02-01

    The sky-averaged (global) highly redshifted 21 cm spectrum from neutral hydrogen is expected to appear in the VHF range of ∼20–200 MHz and its spectral shape and strength are determined by the heating properties of the first stars and black holes, by the nature and duration of reionization, and by the presence or absence of exotic physics. Measurements of the global signal would therefore provide us with a wealth of astrophysical and cosmological knowledge. However, the signal has not yet been detected because it must be seen through strong foregrounds weighted by a large beam, instrumental calibration errors, and ionospheric, ground, and radio-frequency-interference effects, which we collectively refer to as “systematics.” Here, we present a signal extraction method for global signal experiments which uses Singular Value Decomposition of “training sets” to produce systematics basis functions specifically suited to each observation. Instead of requiring precise absolute knowledge of the systematics, our method effectively requires precise knowledge of how the systematics can vary. After calculating eigenmodes for the signal and systematics, we perform a weighted least square fit of the corresponding coefficients and select the number of modes to include by minimizing an information criterion. We compare the performance of the signal extraction when minimizing various information criteria and find that minimizing the Deviance Information Criterion most consistently yields unbiased fits. The methods used here are built into our widely applicable, publicly available Python package, pylinex, which analytically calculates constraints on signals and systematics from given data, errors, and training sets.

  19. Optical signal processing for enabling high-speed, highly spectrally efficient and high capacity optical systems

    Science.gov (United States)

    Fazal, Muhammad Irfan

    may be possible. Recently, interest has increased in exploring the spatial dimension of light to increase capacity, both in fiber as well as free-space communication channels. The orbital angular momentum (OAM) of light, carried by Laguerre-Gaussian (LG) beams have the interesting property that, in theory, an infinite number of OAMs can be transmitted; which due to its inherent orthogonality will not affect each other. Thus, in theory, one can increase the channel capacity arbitrarily. However, in practice, the device dimensions will reduce the number of OAMs used. In addition to advanced modulation formats, it is expected that optical signal processing may play a role in the future development of more efficient optical transmission systems. The hope is that performing signal processing in the optical domain may reduce optical-to-electronic conversion inefficiencies, eliminate bottlenecks and take advantage of the ultrahigh bandwidth inherent in optics. While 40 to 50 Gbit/s electronic components are the peak of commercial technology and 100 Gbit/s capable RF components are still in their infancy, optical signal processing of these high-speed data signals may provide a potential solution. Furthermore, any optical processing system or sub-system must be capable of handling the wide array of data formats and data rates that networks may employ. The work presented in this Ph.D. dissertation attempts at addressing the issue of optical processing for advanced optical modulation formats, and particularly explores the state of the art in increasing the capacity of an optical link by a combination of wavelength/phase/polarization/OAM dimensions of light. Spatial multiplexing and demultiplexing of both coherently and directly detected signals at the 100 Gbit/s Ethernet standard is addressed. The application of a continuously tunable all-optical delay for all-optical functionality like time-slot interchange at high data-rates is presented. Moreover the interplay of chirp

  20. An Efficient Measure for Nonlinear Distortion Severity due to HPA in Downlink DS-CDMA Signals

    Directory of Open Access Journals (Sweden)

    Helaly TarekK

    2010-01-01

    Full Text Available This paper deals with the nonlinear distortion (NLD effects of high power amplifiers (HPAs on direct sequence-code division multiple access systems. Such a distortion drastically degrades the system performance in terms of bit error rate (BER degradation and spectral regrowth. Much effort has been conducted to minimize NLD. A key requirement to do so is to define a certain measure for the HPA nonlinearity, which when reduced often allows NLD to also be reduced. Several measures were proposed such as peak-to-average power ratio, instantaneous power variance, and cubic metric. In this paper, we show that such measures are not closely related to NLD and their reduction does not always lead to optimum performance. Hence, we introduce an efficient measure, namely, nonlinearity severity (NLS, to characterize NLD effects, as an alternative to the existing measures. The NLS is characterized by having direct link to the system performance as it is formulated based on the signal characteristics contributing to BER performance and spectral regrowth. Additionally, a major advantage of the NLS measure is that it is linked to the IBO level allowing the possibility of improving performance at all IBO levels of interest.

  1. Reliable and Efficient Parallel Processing Algorithms and Architectures for Modern Signal Processing. Ph.D. Thesis

    Science.gov (United States)

    Liu, Kuojuey Ray

    1990-01-01

    Least-squares (LS) estimations and spectral decomposition algorithms constitute the heart of modern signal processing and communication problems. Implementations of recursive LS and spectral decomposition algorithms onto parallel processing architectures such as systolic arrays with efficient fault-tolerant schemes are the major concerns of this dissertation. There are four major results in this dissertation. First, we propose the systolic block Householder transformation with application to the recursive least-squares minimization. It is successfully implemented on a systolic array with a two-level pipelined implementation at the vector level as well as at the word level. Second, a real-time algorithm-based concurrent error detection scheme based on the residual method is proposed for the QRD RLS systolic array. The fault diagnosis, order degraded reconfiguration, and performance analysis are also considered. Third, the dynamic range, stability, error detection capability under finite-precision implementation, order degraded performance, and residual estimation under faulty situations for the QRD RLS systolic array are studied in details. Finally, we propose the use of multi-phase systolic algorithms for spectral decomposition based on the QR algorithm. Two systolic architectures, one based on triangular array and another based on rectangular array, are presented for the multiphase operations with fault-tolerant considerations. Eigenvectors and singular vectors can be easily obtained by using the multi-pase operations. Performance issues are also considered.

  2. Efficient Maturation and Cytokine Production of Neonatal DCs Requires Combined Proinflammatory Signals

    Directory of Open Access Journals (Sweden)

    Doreen Krumbiegel

    2005-01-01

    Full Text Available Specific functional properties of dendritic cells (DCs have been suspected as being responsible for the impaired specific immune responses observed in human neonates. To analyze stimulatory requirements for the critical transition from immature, antigen-processing DCs to mature, antigen-presenting DCs, we investigated the effect of different proinflammatory mediators and antigens on phenotype and cytokine secretion of human neonatal DCs derived from hematopoietic progenitor cells (HPCs. Whereas single proinflammatory mediators were unable to induce the maturation of neonatal DCs, various combinations of IFNγ, CD40L, TNFα, LPS and antigens, induced the maturation of neonatal DCs documented by up-regulation of HLA-DR, CD83 and CD86. Combinations of proinflammatory mediators also increased cytokine secretion by neonatal DCs. Especially combined stimulation with LPS and IFNγ proved to be very efficient in inducing maturation and cytokine synthesis of neonatal DCs. In conclusion, neonatal DCs can be stimulated to express maturation as well as costimulatory surface molecules. However, induction of maturation requires combined stimulation with multiple proinflammatory signals.

  3. Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals

    Science.gov (United States)

    Yu, Lianchun; Liu, Liwei

    2014-03-01

    The generation and conduction of action potentials (APs) represents a fundamental means of communication in the nervous system and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in transferring pulse signals with APs. By analytically solving a bistable neuron model that mimics the AP generation with a particle crossing the barrier of a double well, we find the optimal number of ion channels that maximizes the energy efficiency of a neuron. We also investigate the energy efficiency of a neuron population in which the input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal number of neurons in neuron population, as well as the number of ion channels in each neuron that maximizes the energy efficiency. The energy efficiency also depends on the characters of the input signals, e.g., the pulse strength and the interpulse intervals. These results are confirmed by computer simulation of the stochastic Hodgkin-Huxley model with a detailed description of the ion channel random gating. We argue that the tradeoff between signal transmission reliability and energy cost may influence the size of the neural systems when energy use is constrained.

  4. Anti-synthetase syndrome associated with anti PL-12 and anti-Signal recognition particle antibodies and a necrotizing auto-immune myositis.

    Science.gov (United States)

    Malkan, Ashish; Cappelen-Smith, Cecilia; Beran, Roy; Griffith, Neil; Toong, Catherine; Wang, Min-Xia; Cordato, Dennis

    2015-02-01

    We report a 37-year-old woman with a 2 month history of proximal muscle weakness and extremely high creatine kinase (21,808 U/L) due to necrotizing auto-immune myositis (NAM) in association with anti-synthetase syndrome. Myositis-specific auto-immune antibody panel was positive for anti-Signal recognition particle and anti-PL-12. CT scan of the chest confirmed interstitial lung disease. Prednisolone, intravenous immunoglobulin and cyclophosphamide therapy was given with gradual improvement. This patient is notable for the unusual combination of NAM and anti-synthetase syndrome with the rare finding of two myositis-specific autoantibodies, which directed testing for associated extramuscular features and management with more aggressive immunotherapy. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Predicting the effect of spectral subtraction on the speech recognition threshold based on the signal-to-noise ratio in the envelope domain

    DEFF Research Database (Denmark)

    Jørgensen, Søren; Dau, Torsten

    2011-01-01

    rarely been evaluated perceptually in terms of speech intelligibility. This study analyzed the effects of the spectral subtraction strategy proposed by Berouti at al. [ICASSP 4 (1979), 208-211] on the speech recognition threshold (SRT) obtained with sentences presented in stationary speech-shaped noise....... The SRT was measured in five normal-hearing listeners in six conditions of spectral subtraction. The results showed an increase of the SRT after processing, i.e. a decreased speech intelligibility, in contrast to what is predicted by the Speech Transmission Index (STI). Here, another approach is proposed......, denoted the speech-based envelope power spectrum model (sEPSM) which predicts the intelligibility based on the signal-to-noise ratio in the envelope domain. In contrast to the STI, the sEPSM is sensitive to the increased amount of the noise envelope power as a consequence of the spectral subtraction...

  6. An energy-efficient communication method based on the relationships between biological signals for ubiquitous health monitoring.

    Science.gov (United States)

    Kwon, Hyok Chon; Na, Doosu; Ko, Byung Geun; Lee, Songjun

    2008-01-01

    Wireless sensor networks have been studied in the area of intelligent transportation systems, disaster perception, environment monitoring, ubiquitous healthcare, home network, and so on. For the ubiquitous healthcare, the previous systems collect the sensed health related data at portable devices without regard to correlations of various biological signals to determine the health conditions. It is not the energy-efficient method to gather a lot of information into a specific node to decide the health condition. Since the biological signals are related with each other to estimate certain body condition, it is necessary to be collected selectively by their relationship for energy efficiency of the networked nodes. One of researches about low power consumption is the reduction of the amount of packet transmission. In this paper, a health monitoring system, which allows the transmission of the reduced number of packets by means of setting the routing path considered the relations of biological signals, is proposed.

  7. Input/output Buffer based Vedic Multiplier Design for Thermal Aware Energy Efficient Digital Signal Processing on 28nm FPGA

    DEFF Research Database (Denmark)

    Goswami, Kavita; Pandey, Bishwajeet; Hussain, Dil muhammed Akbar

    2016-01-01

    Multiplier is used for multiplication of a signal and a constant in digital signal processing (DSP). 28nm technology based Vedic multiplier is implemented with use of VHDL HDL, Xilinx ISE, Kintex-7 FPGA and XPower Analyzer. Vedic multiplier gain speed improvements by parallelizing the generation...... Programmable Gate Array (FPGA) in order to reduce the development cost. The development cost for Application Specific Integrated Circuits (ASICs) are high in compare to FPGA. Selection of the most energy efficient IO standards in place of signal gating is the main design methodology for design of energy...... efficient Vedic multiplier.There is 68.51%, 69.86%, 74.65%, and 78.39% contraction in total power of Vedic multiplier on 28nm Kintex-7 FPGA, when we use HSTL_II in place of HSTL_II_DCI_18 at 56.7oC, 53.5oC, 40oC and 21oC respectively....

  8. Forensic speaker recognition

    NARCIS (Netherlands)

    Meuwly, Didier

    2013-01-01

    The aim of forensic speaker recognition is to establish links between individuals and criminal activities, through audio speech recordings. This field is multidisciplinary, combining predominantly phonetics, linguistics, speech signal processing, and forensic statistics. On these bases, expert-based

  9. Signal Peptide and Denaturing Temperature are Critical Factors for Efficient Mammalian Expression and Immunoblotting of Cannabinoid Receptors*

    Science.gov (United States)

    WANG, Chenyun; WANG, Yingying; WANG, Miao; CHEN, Jiankui; YU, Nong; SONG, Shiping; KAMINSKI, Norbert E.; ZHANG, Wei

    2013-01-01

    Summary Many researchers employed mammalian expression system to artificially express cannabinoid receptors, but immunoblot data that directly prove efficient protein expression can hardly be seen in related research reports. In present study, we demonstrated cannabinoid receptor protein was not able to be properly expressed with routine mammalian expression system. This inefficient expression was rescued by endowing an exogenous signal peptide ahead of cannabinoid receptor peptide. In addition, the artificially synthesized cannabinoid receptor was found to aggregate under routine sample denaturing temperatures (i.e., ≥95°C), forming a large molecular weight band when analyzed by immunoblotting. Only denaturing temperatures ≤75°C yielded a clear band at the predicted molecular weight. Collectively, we showed that efficient mammalian expression of cannabinoid receptors need a signal peptide sequence, and described the requirement for a low sample denaturing temperature in immunoblot analysis. These findings provide very useful information for efficient mammalian expression and immunoblotting of membrane receptors. PMID:22528237

  10. An efficient optimization method to improve the measuring accuracy of oxygen saturation by using triangular wave optical signal

    Science.gov (United States)

    Li, Gang; Yu, Yue; Zhang, Cui; Lin, Ling

    2017-09-01

    The oxygen saturation is one of the important parameters to evaluate human health. This paper presents an efficient optimization method that can improve the accuracy of oxygen saturation measurement, which employs an optical frequency division triangular wave signal as the excitation signal to obtain dynamic spectrum and calculate oxygen saturation. In comparison to the traditional method measured RMSE (root mean square error) of SpO2 which is 0.1705, this proposed method significantly reduced the measured RMSE which is 0.0965. It is notable that the accuracy of oxygen saturation measurement has been improved significantly. The method can simplify the circuit and bring down the demand of elements. Furthermore, it has a great reference value on improving the signal to noise ratio of other physiological signals.

  11. Damaged-self recognition in common bean (Phaseolus vulgaris shows taxonomic specificity and triggers signalling via reactive oxygen species (ROS

    Directory of Open Access Journals (Sweden)

    Dalia eDuran

    2014-10-01

    Full Text Available Plants require reliable mechanisms to detect injury. Danger signals or 'damage-associated molecular patterns' (DAMPs are released from stressed host cells and allow injury detection independently of enemy-derived molecules. We studied the response of common bean (Phaseolus vulgaris to the application of leaf homogenate as a source of DAMPs and measured the production of reactive oxygen species (ROS as an early response and the secretion of extrafloral nectar (EFN as a jasmonic acid (JA–dependent late response. We observed a strong taxonomic signal in the response to different leaf homogenates. ROS formation and EFN secretion were highly correlated and responded most strongly to leaf homogenates produced using the same cultivar or closely related accessions, less to a distantly related cultivar of common bean or each of the two congeneric species, P. lunatus and P. coccineus, and not at all to homogenates prepared from species in different genera, not even when using other Fabaceae. Interestingly, leaf homogenates also reduced the infection by the bacterial pathogen, Pseudomonas syringae, when they were applied directly before challenging, although the same homogenates exhibited no direct in vitro inhibitory effect in the bacterium. We conclude that ROS signaling is associated to the induction of EFN secretion and that the specific blend of DAMPs that are released from damaged cells allows the plant to distinguish the 'damaged self' from the damaged 'non-self'. The very early responses of plants to DAMPs can trigger resistance to both, herbivores and pathogens, which should be adaptive because injury facilitates infection, independently of its causal reason.

  12. Effect of filtration of signals of brain activity on quality of recognition of brain activity patterns using artificial intelligence methods

    Science.gov (United States)

    Hramov, Alexander E.; Frolov, Nikita S.; Musatov, Vyachaslav Yu.

    2018-02-01

    In present work we studied features of the human brain states classification, corresponding to the real movements of hands and legs. For this purpose we used supervised learning algorithm based on feed-forward artificial neural networks (ANNs) with error back-propagation along with the support vector machine (SVM) method. We compared the quality of operator movements classification by means of EEG signals obtained experimentally in the absence of preliminary processing and after filtration in different ranges up to 25 Hz. It was shown that low-frequency filtering of multichannel EEG data significantly improved accuracy of operator movements classification.

  13. Efficient Banknote Recognition Based on Selection of Discriminative Regions with One-Dimensional Visible-Light Line Sensor

    Directory of Open Access Journals (Sweden)

    Tuyen Danh Pham

    2016-03-01

    Full Text Available Banknote papers are automatically recognized and classified in various machines, such as vending machines, automatic teller machines (ATM, and banknote-counting machines. Previous studies on automatic classification of banknotes have been based on the optical characteristics of banknote papers. On each banknote image, there are regions more distinguishable than others in terms of banknote types, sides, and directions. However, there has been little previous research on banknote recognition that has addressed the selection of distinguishable areas. To overcome this problem, we propose a method for recognizing banknotes by selecting more discriminative regions based on similarity mapping, using images captured by a one-dimensional visible light line sensor. Experimental results with various types of banknote databases show that our proposed method outperforms previous methods.

  14. Efficient Banknote Recognition Based on Selection of Discriminative Regions with One-Dimensional Visible-Light Line Sensor.

    Science.gov (United States)

    Pham, Tuyen Danh; Park, Young Ho; Kwon, Seung Yong; Park, Kang Ryoung; Jeong, Dae Sik; Yoon, Sungsoo

    2016-03-04

    Banknote papers are automatically recognized and classified in various machines, such as vending machines, automatic teller machines (ATM), and banknote-counting machines. Previous studies on automatic classification of banknotes have been based on the optical characteristics of banknote papers. On each banknote image, there are regions more distinguishable than others in terms of banknote types, sides, and directions. However, there has been little previous research on banknote recognition that has addressed the selection of distinguishable areas. To overcome this problem, we propose a method for recognizing banknotes by selecting more discriminative regions based on similarity mapping, using images captured by a one-dimensional visible light line sensor. Experimental results with various types of banknote databases show that our proposed method outperforms previous methods.

  15. Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients

    OpenAIRE

    Xiaokang Shu; Shugeng Chen; Lin Yao; Xinjun Sheng; Dingguo Zhang; Ning Jiang; Jie Jia; Xiangyang Zhu

    2018-01-01

    Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10–50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variab...

  16. Designing informative warning signals: Effects of indicator type, modality, and task demand on recognition speed and accuracy

    Science.gov (United States)

    Stevens, Catherine J.; Brennan, David; Petocz, Agnes; Howell, Clare

    2009-01-01

    An experiment investigated the assumption that natural indicators which exploit existing learned associations between a signal and an event make more effective warnings than previously unlearned symbolic indicators. Signal modality (visual, auditory) and task demand (low, high) were also manipulated. Warning effectiveness was indexed by accuracy and reaction time (RT) recorded during training and dual task test phases. Thirty-six participants were trained to recognize 4 natural and 4 symbolic indicators, either visual or auditory, paired with critical incidents from an aviation context. As hypothesized, accuracy was greater and RT was faster in response to natural indicators during the training phase. This pattern of responding was upheld in test phase conditions with respect to accuracy but observed in RT only in test phase conditions involving high demand and the auditory modality. Using the experiment as a specific example, we argue for the importance of considering the cognitive contribution of the user (viz., prior learned associations) in the warning design process. Drawing on semiotics and cognitive psychology, we highlight the indexical nature of so-called auditory icons or natural indicators and argue that the cogniser is an indispensable element in the tripartite nature of signification. PMID:20523852

  17. The Peroxisomal Targeting Signal 1 in sterol carrier protein 2 is autonomous and essential for receptor recognition

    Directory of Open Access Journals (Sweden)

    Bond Charles S

    2011-03-01

    Full Text Available Abstract Background The majority of peroxisomal matrix proteins destined for translocation into the peroxisomal lumen are recognised via a C-terminal Peroxisomal Target Signal type 1 by the cycling receptor Pex5p. The only structure to date of Pex5p in complex with a cargo protein is that of the C-terminal cargo-binding domain of the receptor with sterol carrier protein 2, a small, model peroxisomal protein. In this study, we have tested the contribution of a second, ancillary receptor-cargo binding site, which was found in addition to the characterised Peroxisomal Target Signal type 1. Results To investigate the function of this secondary interface we have mutated two key residues from the ancillary binding site and analyzed the level of binding first by a yeast-two-hybrid assay, followed by quantitative measurement of the binding affinity and kinetics of purified protein components and finally, by in vivo measurements, to determine translocation capability. While a moderate but significant reduction of the interaction was found in binding assays, we were not able to measure any significant defects in vivo. Conclusions Our data therefore suggest that at least in the case of sterol carrier protein 2 the contribution of the second binding site is not essential for peroxisomal import. At this stage, however, we cannot rule out that other cargo proteins may require this ancillary binding site.

  18. Structural basis for IL-1α recognition by a modified DNA aptamer that specifically inhibits IL-1α signaling

    Energy Technology Data Exchange (ETDEWEB)

    Ren, Xiaoming; Gelinas, Amy D.; von Carlowitz, Ira; Janjic, Nebojsa; Pyle, Anna Marie (Yale); (SomaLogic)

    2017-10-09

    IL-1α is an essential cytokine that contributes to inflammatory responses and is implicated in various forms of pathogenesis and cancer. Here we report a naphthyl modified DNA aptamer that specifically binds IL-1α and inhibits its signaling pathway. By solving the crystal structure of the IL-1α/aptamer, we provide a high-resolution structure of this critical cytokine and we reveal its functional interaction interface with high-affinity ligands. The non-helical aptamer, which represents a highly compact nucleic acid structure, contains a wealth of new conformational features, including an unknown form of G-quadruplex. The IL-1α/aptamer interface is composed of unusual polar and hydrophobic elements, along with an elaborate hydrogen bonding network that is mediated by sodium ion. IL-1α uses the same interface to interact with both the aptamer and its cognate receptor IL-1RI, thereby suggesting a novel route to immunomodulatory therapeutics.

  19. Noise Suppression in ECG Signals through Efficient One-Step Wavelet Processing Techniques

    Directory of Open Access Journals (Sweden)

    E. Castillo

    2013-01-01

    Full Text Available This paper illustrates the application of the discrete wavelet transform (DWT for wandering and noise suppression in electrocardiographic (ECG signals. A novel one-step implementation is presented, which allows improving the overall denoising process. In addition an exhaustive study is carried out, defining threshold limits and thresholding rules for optimal wavelet denoising using this presented technique. The system has been tested using synthetic ECG signals, which allow accurately measuring the effect of the proposed processing. Moreover, results from real abdominal ECG signals acquired from pregnant women are presented in order to validate the presented approach.

  20. Efficient Techniques of Sparse Signal Analysis for Enhanced Recovery of Information in Biomedical Engineering and Geosciences

    KAUST Repository

    Sana, Furrukh

    2016-01-01

    precision technique for the monitoring of human respiratory movements by exploiting the sparsity of wireless ultra-wideband signals. The proposed technique provides a novel methodology of overcoming the Nyquist sampling constraint and enables robust

  1. Advanced life support therapy and on out-of-hospital cardiac arrest patients: Applying signal processing and pattern recognition methods

    Directory of Open Access Journals (Sweden)

    Trygve Eftestøl

    2005-10-01

    Full Text Available In the US alone, several hundred thousands die of sudden cardiac arrests each year. Basic life support defined as chest compressions and ventilations and early defibrillation are the only factors proven to increase the survival of patients with out-of-hospital cardiac arrest, and are key elements in the chain of survival defined by the American Heart Association. The current cardiopulmonary resuscitation guidelines treat all patients the same, but studies show need for more individualiza- tion of treatment. This review will focus on ideas on how to strengthen the weak parts of the chain of survival including the ability to measure the effects of therapy, improve time efficiency, and optimize the sequence and quality of the various components of cardiopulmonary resuscitation.

  2. Boronic acid recognition of non-interacting carbohydrates for biomedical applications: increasing fluorescence signals of minimally interacting aldoses and sucralose.

    Science.gov (United States)

    Resendez, Angel; Halim, Md Abdul; Singh, Jasmeet; Webb, Dominic-Luc; Singaram, Bakthan

    2017-11-22

    To address carbohydrates that are commonly used in biomedical applications with low binding affinities for boronic acid based detection systems, two chemical modification methods were utilized to increase sensitivity. Modified carbohydrates were analyzed using a two component fluorescent probe based on boronic acid-appended viologen-HPTS (4,4'-o-BBV). Carbohydrates normally giving poor signals (fucose, l-rhamnose, xylose) were subjected to sodium borohydride (NaBH 4 ) reduction in ambient conditions for 1 h yielding the corresponding sugar alcohols from fucose, l-rhamnose and xylose in essentially quantitative yields. Compared to original aldoses, apparent binding affinities were increased 4-25-fold. The chlorinated sweetener and colon permeability marker sucralose (Splenda), otherwise undetectable by boronic acids, was dechlorinated to a detectable derivative by reactive oxygen and hydroxide intermediates by the Fenton reaction or by H 2 O 2 and UV light. This method is specific to sucralose as other common sugars, such as sucrose, do not contain any carbon-chlorine bonds. Significant fluorescence response was obtained for chemically modified sucralose with the 4,4'-o-BBV-HPTS probe system. This proof of principle can be applied to biomedical applications, such as gut permeability, malabsorption, etc.

  3. c-Jun controls the efficiency of MAP kinase signaling by transcriptional repression of MAP kinase phosphatases

    International Nuclear Information System (INIS)

    Sprowles, Amy; Robinson, Dan; Wu Yimi; Kung, H.-J.; Wisdom, Ron

    2005-01-01

    The mammalian JNK signaling pathway regulates the transcriptional response of cells to environmental stress, including UV irradiation. This signaling pathway is composed of a classical MAP kinase cascade; activation results in phosphorylation of the transcription factor substrates c-Jun and ATF2, and leads to changes in gene expression. The defining components of this pathway are conserved in the fission yeast S. pombe, where the genetic studies have shown that the ability of the JNK homolog Spc1 to be activated in response to UV irradiation is dependent on the presence of the transcription factor substrate Atf1. We have used genetic analysis to define the role of c-Jun in activation of the mammalian JNK signaling pathway. Our results show that optimal activation of JNK requires the presence of its transcription factor substrate c-Jun. Mutational analysis shows that the ability of c-Jun to support efficient activation of JNK requires the ability of Jun to bind DNA, suggesting a transcriptional mechanism. Consistent with this, we show that c-Jun represses the expression of several MAP kinase phosphatases. In the absence of c-Jun, the increased expression of MAP kinase phosphatases leads to impaired activation of the ERK, JNK, and p38 MAP kinases after pathway activation. The results show that one function of c-Jun is to regulate the efficiency of signaling by the ERK, p38, and JNK MAP kinases, a function that is likely to affect cellular responses to many different stimuli

  4. Efficient algorithm for baseline wander and powerline noise removal from ECG signals based on discrete Fourier series.

    Science.gov (United States)

    Bahaz, Mohamed; Benzid, Redha

    2018-03-01

    Electrocardiogram (ECG) signals are often contaminated with artefacts and noises which can lead to incorrect diagnosis when they are visually inspected by cardiologists. In this paper, the well-known discrete Fourier series (DFS) is re-explored and an efficient DFS-based method is proposed to reduce contribution of both baseline wander (BW) and powerline interference (PLI) noises in ECG records. In the first step, the determination of the exact number of low frequency harmonics contributing in BW is achieved. Next, the baseline drift is estimated by the sum of all associated Fourier sinusoids components. Then, the baseline shift is discarded efficiently by a subtraction of its approximated version from the original biased ECG signal. Concerning the PLI, the subtraction of the contributing harmonics calculated in the same manner reduces efficiently such type of noise. In addition of visual quality results, the proposed algorithm shows superior performance in terms of higher signal-to-noise ratio and smaller mean square error when faced to the DCT-based algorithm.

  5. Statistical Pattern Recognition

    CERN Document Server

    Webb, Andrew R

    2011-01-01

    Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.  It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields,

  6. Signal Sampling for Efficient Sparse Representation of Resting State FMRI Data

    Science.gov (United States)

    Ge, Bao; Makkie, Milad; Wang, Jin; Zhao, Shijie; Jiang, Xi; Li, Xiang; Lv, Jinglei; Zhang, Shu; Zhang, Wei; Han, Junwei; Guo, Lei; Liu, Tianming

    2015-01-01

    As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain’s signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain’s signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority. PMID:26646924

  7. Towards novel efficient and stable nuclear import signals: synthesis and properties of trimethylguanosine cap analogs modified within the 5',5'-triphosphate bridge.

    Science.gov (United States)

    Zytek, Malgorzata; Kowalska, Joanna; Lukaszewicz, Maciej; Wojtczak, Blazej A; Zuberek, Joanna; Ferenc-Mrozek, Aleksandra; Darzynkiewicz, Edward; Niedzwiecka, Anna; Jemielity, Jacek

    2014-12-07

    A trimethylguanosine (TMG) cap is present at the 5' end of several small nuclear and nucleolar RNAs. Recently, it has been reported that the TMG cap is a potential nuclear import signal for nucleus-targeting therapeutic nucleic acids and proteins. The import is mediated by recognition of the TMG cap by the snRNA transporting protein, snurportin1. This work describes the synthesis and properties of a series of dinucleotide TMG cap (m3(2,2,7)GpppG) analogs modified in the 5',5'-triphosphate bridge as tools to study TMG cap-dependent biological processes. The bridge was altered at different positions by introducing either bridging (imidodiphosphate, O to NH and methylenebisphosphonate, O to CH2) or non-bridging (phosphorothioate, O to S and boranophosphate, O to BH3) modifications, or by elongation to tetraphosphate. The stability of novel analogs in blood serum was studied to reveal that the α,β-bridging O to NH substitution (m3(2,2,7)GppNHpG) confers the highest resistance. Short RNAs capped with analogs containing α,β-bridging (m3(2,2,7)GppNHpG) or β-non-bridging (m3(2,2,7)GppSpG D2) modifications were resistant to decapping pyrophosphatase, hNudt16. Preliminary studies on binding by human snurportin1 revealed that both O to NH and O to S substitutions support this binding. Due to favorable properties in all three assays, m3(2,2,7)GppNHpG was selected as a promising candidate for further studies on the efficiency of the TMG cap as a nuclear import signal.

  8. Recognition, signaling, and repair of DNA double-strand breaks produced by ionizing radiation in mammalian cells: the molecular choreography.

    Science.gov (United States)

    Thompson, Larry H

    2012-01-01

    The faithful maintenance of chromosome continuity in human cells during DNA replication and repair is critical for preventing the conversion of normal diploid cells to an oncogenic state. The evolution of higher eukaryotic cells endowed them with a large genetic investment in the molecular machinery that ensures chromosome stability. In mammalian and other vertebrate cells, the elimination of double-strand breaks with minimal nucleotide sequence change involves the spatiotemporal orchestration of a seemingly endless number of proteins ranging in their action from the nucleotide level to nucleosome organization and chromosome architecture. DNA DSBs trigger a myriad of post-translational modifications that alter catalytic activities and the specificity of protein interactions: phosphorylation, acetylation, methylation, ubiquitylation, and SUMOylation, followed by the reversal of these changes as repair is completed. "Superfluous" protein recruitment to damage sites, functional redundancy, and alternative pathways ensure that DSB repair is extremely efficient, both quantitatively and qualitatively. This review strives to integrate the information about the molecular mechanisms of DSB repair that has emerged over the last two decades with a focus on DSBs produced by the prototype agent ionizing radiation (IR). The exponential growth of molecular studies, heavily driven by RNA knockdown technology, now reveals an outline of how many key protein players in genome stability and cancer biology perform their interwoven tasks, e.g. ATM, ATR, DNA-PK, Chk1, Chk2, PARP1/2/3, 53BP1, BRCA1, BRCA2, BLM, RAD51, and the MRE11-RAD50-NBS1 complex. Thus, the nature of the intricate coordination of repair processes with cell cycle progression is becoming apparent. This review also links molecular abnormalities to cellular pathology as much a possible and provides a framework of temporal relationships. Copyright © 2012 Elsevier B.V. All rights reserved.

  9. Evaluation of Signal Regeneration Impact on the Power Efficiency of Long-Haul DWDM Systems

    Directory of Open Access Journals (Sweden)

    Pavlovs D.

    2017-10-01

    Full Text Available Due to potential economic benefits and expected environmental impact, the power consumption issue in wired networks has become a major challenge. Furthermore, continuously increasing global Internet traffic demands high spectral efficiency values. As a result, the relationship between spectral efficiency and energy consumption of telecommunication networks has become a popular topic of academic research over the past years, where a critical parameter is power efficiency. The present research contains calculation results that can be used by optical network designers and operators as guidance for developing more power efficient communication networks if the planned system falls within the scope of this paper. The research results are presented as average aggregated traffic curves that provide more flexible data for the systems with different spectrum availability. Further investigations could be needed in order to evaluate the parameters under consideration taking into account particular spectral parameters, e.g., the entire C-band.

  10. Evaluation of Signal Regeneration Impact on the Power Efficiency of Long-Haul DWDM Systems

    Science.gov (United States)

    Pavlovs, D.; Bobrovs, V.; Parfjonovs, M.; Alsevska, A.; Ivanovs, G.

    2017-10-01

    Due to potential economic benefits and expected environmental impact, the power consumption issue in wired networks has become a major challenge. Furthermore, continuously increasing global Internet traffic demands high spectral efficiency values. As a result, the relationship between spectral efficiency and energy consumption of telecommunication networks has become a popular topic of academic research over the past years, where a critical parameter is power efficiency. The present research contains calculation results that can be used by optical network designers and operators as guidance for developing more power efficient communication networks if the planned system falls within the scope of this paper. The research results are presented as average aggregated traffic curves that provide more flexible data for the systems with different spectrum availability. Further investigations could be needed in order to evaluate the parameters under consideration taking into account particular spectral parameters, e.g., the entire C-band.

  11. Efficient continuous-wave eye-safe region signal output from intra-cavity singly resonant optical parametric oscillator

    International Nuclear Information System (INIS)

    Li Bin; Ding Xin; Sheng Quan; Yin Su-Jia; Shi Chun-Peng; Li Xue; Wen Wu-Qi; Yao Jian-Quan; Yu Xuan-Yi

    2012-01-01

    We report an efficient continuous-wave (CW) tunable intra-cavity singly resonant optical parametric oscillator based on the multi-period periodically poled lithium niobate and using a laser diode (LD) end-pumped CW 1064 nm Nd:YVO 4 laser as the pump source. A highly efficiency CW operation is realized through a careful cavity design for mode matching and thermal stability. The signal tuning range is 1401–1500 nm obtained by varying the domain period. The maximum output power of 2.2 W at 1500 nm is obtained with a 17.1 W 808 nm LD power and the corresponding conversion efficiency is 12.9%. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

  12. Declining cost efficiency as a signal of increasing bank vulnerability: an entropy-based approach

    NARCIS (Netherlands)

    Balasubramanyan, L.; Stefanou, S.E.; Stokes, J.R.

    2010-01-01

    The mortgage crisis of 2007/08 has impacted the health of both small and large commercial banks in the financial services industry. The pressing question is how do regulators and bank monitors identify the warning signals of bank vulnerability and bank risk because of weakening credit and asset

  13. RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction

    Directory of Open Access Journals (Sweden)

    Michael M. Abdel-Sayed

    2016-11-01

    Full Text Available Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Compressed sensing initially adopted ℓ1 minimization for signal reconstruction which is computationally expensive. Several greedy recovery algorithms have been recently proposed for signal reconstruction at a lower computational complexity compared to the optimal ℓ1 minimization, while maintaining a good reconstruction accuracy. In this paper, the Reduced-set Matching Pursuit (RMP greedy recovery algorithm is proposed for compressed sensing. Unlike existing approaches which either select too many or too few values per iteration, RMP aims at selecting the most sufficient number of correlation values per iteration, which improves both the reconstruction time and error. Furthermore, RMP prunes the estimated signal, and hence, excludes the incorrectly selected values. The RMP algorithm achieves a higher reconstruction accuracy at a significantly low computational complexity compared to existing greedy recovery algorithms. It is even superior to ℓ1 minimization in terms of the normalized time-error product, a new metric introduced to measure the trade-off between the reconstruction time and error. RMP superior performance is illustrated with both noiseless and noisy samples.

  14. RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction.

    Science.gov (United States)

    Abdel-Sayed, Michael M; Khattab, Ahmed; Abu-Elyazeed, Mohamed F

    2016-11-01

    Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Compressed sensing initially adopted [Formula: see text] minimization for signal reconstruction which is computationally expensive. Several greedy recovery algorithms have been recently proposed for signal reconstruction at a lower computational complexity compared to the optimal [Formula: see text] minimization, while maintaining a good reconstruction accuracy. In this paper, the Reduced-set Matching Pursuit (RMP) greedy recovery algorithm is proposed for compressed sensing. Unlike existing approaches which either select too many or too few values per iteration, RMP aims at selecting the most sufficient number of correlation values per iteration, which improves both the reconstruction time and error. Furthermore, RMP prunes the estimated signal, and hence, excludes the incorrectly selected values. The RMP algorithm achieves a higher reconstruction accuracy at a significantly low computational complexity compared to existing greedy recovery algorithms. It is even superior to [Formula: see text] minimization in terms of the normalized time-error product, a new metric introduced to measure the trade-off between the reconstruction time and error. RMP superior performance is illustrated with both noiseless and noisy samples.

  15. Identification of amino acid residues in protein SRP72 required for binding to a kinked 5e motif of the human signal recognition particle RNA

    Directory of Open Access Journals (Sweden)

    Zwieb Christian

    2010-11-01

    Full Text Available Abstract Background Human cells depend critically on the signal recognition particle (SRP for the sorting and delivery of their proteins. The SRP is a ribonucleoprotein complex which binds to signal sequences of secretory polypeptides as they emerge from the ribosome. Among the six proteins of the eukaryotic SRP, the largest protein, SRP72, is essential for protein targeting and possesses a poorly characterized RNA binding domain. Results We delineated the minimal region of SRP72 capable of forming a stable complex with an SRP RNA fragment. The region encompassed residues 545 to 585 of the full-length human SRP72 and contained a lysine-rich cluster (KKKKKKKKGK at postions 552 to 561 as well as a conserved Pfam motif with the sequence PDPXRWLPXXER at positions 572 to 583. We demonstrated by site-directed mutagenesis that both regions participated in the formation of a complex with the RNA. In agreement with biochemical data and results from chymotryptic digestion experiments, molecular modeling of SRP72 implied that the invariant W577 was located inside the predicted structure of an RNA binding domain. The 11-nucleotide 5e motif contained within the SRP RNA fragment was shown by comparative electrophoresis on native polyacrylamide gels to conform to an RNA kink-turn. The model of the complex suggested that the conserved A240 of the K-turn, previously identified as being essential for the binding to SRP72, could protrude into a groove of the SRP72 RNA binding domain, similar but not identical to how other K-turn recognizing proteins interact with RNA. Conclusions The results from the presented experiments provided insights into the molecular details of a functionally important and structurally interesting RNA-protein interaction. A model for how a ligand binding pocket of SRP72 can accommodate a new RNA K-turn in the 5e region of the eukaryotic SRP RNA is proposed.

  16. Identification of amino acid residues in protein SRP72 required for binding to a kinked 5e motif of the human signal recognition particle RNA.

    Science.gov (United States)

    Iakhiaeva, Elena; Iakhiaev, Alexei; Zwieb, Christian

    2010-11-13

    Human cells depend critically on the signal recognition particle (SRP) for the sorting and delivery of their proteins. The SRP is a ribonucleoprotein complex which binds to signal sequences of secretory polypeptides as they emerge from the ribosome. Among the six proteins of the eukaryotic SRP, the largest protein, SRP72, is essential for protein targeting and possesses a poorly characterized RNA binding domain. We delineated the minimal region of SRP72 capable of forming a stable complex with an SRP RNA fragment. The region encompassed residues 545 to 585 of the full-length human SRP72 and contained a lysine-rich cluster (KKKKKKKKGK) at postions 552 to 561 as well as a conserved Pfam motif with the sequence PDPXRWLPXXER at positions 572 to 583. We demonstrated by site-directed mutagenesis that both regions participated in the formation of a complex with the RNA. In agreement with biochemical data and results from chymotryptic digestion experiments, molecular modeling of SRP72 implied that the invariant W577 was located inside the predicted structure of an RNA binding domain. The 11-nucleotide 5e motif contained within the SRP RNA fragment was shown by comparative electrophoresis on native polyacrylamide gels to conform to an RNA kink-turn. The model of the complex suggested that the conserved A240 of the K-turn, previously identified as being essential for the binding to SRP72, could protrude into a groove of the SRP72 RNA binding domain, similar but not identical to how other K-turn recognizing proteins interact with RNA. The results from the presented experiments provided insights into the molecular details of a functionally important and structurally interesting RNA-protein interaction. A model for how a ligand binding pocket of SRP72 can accommodate a new RNA K-turn in the 5e region of the eukaryotic SRP RNA is proposed.

  17. Inflammatory stress increases hepatic CD36 translational efficiency via activation of the mTOR signalling pathway.

    Directory of Open Access Journals (Sweden)

    Chuan Wang

    Full Text Available Inflammatory stress is an independent risk factor for the development of non-alcoholic fatty liver disease (NAFLD. Although CD36 is known to facilitate long-chain fatty acid uptake and contributes to NAFLD progression, the mechanisms that link inflammatory stress to hepatic CD36 expression and steatosis remain unclear. As the mammalian target of rapamycin (mTOR signalling pathway is involved in CD36 translational activation, this study was undertaken to investigate whether inflammatory stress enhances hepatic CD36 expression via mTOR signalling pathway and the underlying mechanisms. To induce inflammatory stress, we used tumour necrosis factor alpha (TNF-α and interleukin-6 (IL-6 stimulation of the human hepatoblastoma HepG2 cells in vitro and casein injection in C57BL/6J mice in vivo. The data showed that inflammatory stress increased hepatic CD36 protein levels but had no effect on mRNA expression. A protein degradation assay revealed that CD36 protein stability was not different between HepG2 cells treated with or without TNF-α or IL-6. A polysomal analysis indicated that CD36 translational efficiency was significantly increased by inflammatory stress. Additionally, inflammatory stress enhanced the phosphorylation of mTOR and its downstream translational regulators including p70S6K, 4E-BP1 and eIF4E. Rapamycin, an mTOR-specific inhibitor, reduced the phosphorylation of mTOR signalling pathway and decreased the CD36 translational efficiency and protein level even under inflammatory stress resulting in the alleviation of inflammatory stress-induced hepatic lipid accumulation. This study demonstrates that the activation of the mTOR signalling pathway increases hepatic CD36 translational efficiency, resulting in increased CD36 protein expression under inflammatory stress.

  18. Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

    Science.gov (United States)

    Bascil, M Serdar; Tesneli, Ahmet Y; Temurtas, Feyzullah

    2016-09-01

    Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.

  19. Molecularly Imprinted Polymer Enables High-Efficiency Recognition and Trapping Lithium Polysulfides for Stable Lithium Sulfur Battery.

    Science.gov (United States)

    Liu, Jie; Qian, Tao; Wang, Mengfan; Liu, Xuejun; Xu, Na; You, Yizhou; Yan, Chenglin

    2017-08-09

    Using molecularly imprinted polymer to recognize various target molecules emerges as a fascinating research field. Herein, we applied this strategy for the first time to efficiently recognize and trap long-chain polysulfides (Li 2 S x , x = 6-8) in lithium sulfur battery to minimize the polysulfide shuttling between anode and cathode, which enables us to achieve remarkable electrochemical performance including a high specific capacity of 1262 mAh g -1 at 0.2 C and superior capacity retention of over 82.5% after 400 cycles at 1 C. The outstanding performance is attributed to the significantly reduced concentration of long-chain polysulfides in electrolyte as evidenced by in situ UV/vis spectroscopy and Li 2 S nucleation tests, which were further confirmed by density functional theory calculations. The molecular imprinting is demonstrated as a promising approach to effectively prevent the free diffusion of long-chain polysulfides, providing a new avenue to efficiently recognize and trap lithium polysulfides for high-performance lithium sulfur battery with greatly suppressed shuttle effect.

  20. Type I interferon production during herpes simplex virus infection is controlled by cell-type-specific viral recognition through Toll-like receptor 9, the mitochondrial antiviral signaling protein pathway, and novel recognition systems

    DEFF Research Database (Denmark)

    Rasmussen, Simon Brandtoft; Sørensen, Louise Nørgaard; Malmgaard, Lene

    2007-01-01

    Recognition of viruses by germ line-encoded pattern recognition receptors of the innate immune system is essential for rapid production of type I interferon (IFN) and early antiviral defense. We investigated the mechanisms of viral recognition governing production of type I IFN during herpes...... simplex virus (HSV) infection. We show that early production of IFN in vivo is mediated through Toll-like receptor 9 (TLR9) and plasmacytoid dendritic cells, whereas the subsequent alpha/beta IFN (IFN-alpha/beta) response is derived from several cell types and induced independently of TLR9...

  1. ITAM-like signalling for efficient phagocytosis : The paradigm of the granulocyte receptor CEACAM3

    OpenAIRE

    Pils, Stefan

    2010-01-01

    Human CEACAM3 is a tailor-made receptor of the innate immune system to fight pathogens exploiting epithelial CEACAM-family members for colonisation and invasion of their host. Previous studies established CEACAM3 as the receptor facilitating rapid phagocytosis and elimination of N. gonorrhoeae by human granulocytes. The studies reported here set out to shed light on the evolution of this highly specialised receptor and the associated signalling machinery.CEACAM3 arose from exon shuffling afte...

  2. Traffic Efficiency Evaluation of Elliptical Roundabout Compared with Modern and Turbo Roundabouts Considering Traffic Signal Control

    Directory of Open Access Journals (Sweden)

    Hadi Hatami

    2017-02-01

    Full Text Available This paper compared the performance of elliptical roundabout with turbo and modern roundabouts. It considers the effects of increasing the central island radius and speed limit on delay and capacity. Three types of roundabouts (modern, turbo and elliptical roundabouts with different numbers of lanes (single lane, two-lane and three-lane were designed. Unsignalized and signalized controls were applied for these roundabouts. The robustness of the designed roundabouts was investigated for saturated and unsaturated flow conditions. Based on the obtained results, increasing the central island radius had both positive and negative effects on delay and capacity. However, a positive effect on these variables was observed in all roundabouts when increasing the speed limit. In unsignalized and signalized control under unsaturated flow conditions, a modern roundabout had lower delay time than an elliptical roundabout. Moreover, in saturated flow, the elliptical roundabout had the best performance in terms of delay. Overall, in comparison with the turbo roundabouts, modern and elliptical roundabouts had the highest capacities in unsignalized and signalized controls. This study can provide useful information for engineers who decide to design a roundabout.

  3. Efficient Secretion of Recombinant Proteins from Rice Suspension-Cultured Cells Modulated by the Choice of Signal Peptide.

    Science.gov (United States)

    Huang, Li-Fen; Tan, Chia-Chun; Yeh, Ju-Fang; Liu, Hsin-Yi; Liu, Yu-Kuo; Ho, Shin-Lon; Lu, Chung-An

    2015-01-01

    Plant-based expression systems have emerged as a competitive platform in the large-scale production of recombinant proteins. By adding a signal peptide, αAmy3sp, the desired recombinant proteins can be secreted outside transgenic rice cells, making them easy to harvest. In this work, to improve the secretion efficiency of recombinant proteins in rice expression systems, various signal peptides including αAmy3sp, CIN1sp, and 33KDsp have been fused to the N-terminus of green fluorescent protein (GFP) and introduced into rice cells to explore the efficiency of secretion of foreign proteins. 33KDsp had better efficiency than αAmy3sp and CIN1sp for the secretion of GFP from calli and suspension-cultured cells. 33KDsp was further applied for the secretion of mouse granulocyte-macrophage colony-stimulating factor (mGM-CSF) from transgenic rice suspension-cultured cells; approximately 76%-92% of total rice-derived mGM-CSF (rmGM-CSF) was detected in the culture medium. The rmGM-CSF was bioactive and could stimulate the proliferation of a murine myeloblastic leukemia cell line, NSF-60. The extracellular yield of rmGM-CSF reached 31.7 mg/L. Our study indicates that 33KDsp is better at promoting the secretion of recombinant proteins in rice suspension-cultured cell systems than the commonly used αAmy3sp.

  4. Hardware-efficient signal generation of layered/enhanced ACO-OFDM for short-haul fiber-optic links.

    Science.gov (United States)

    Wang, Qibing; Song, Binhuang; Corcoran, Bill; Boland, David; Zhu, Chen; Zhuang, Leimeng; Lowery, Arthur J

    2017-06-12

    Layered/enhanced ACO-OFDM is a promising candidate for intensity modulation and direct-detection based short-haul fiber-optic links due to its both power and spectral efficiency. In this paper, we firstly demonstrate a hardware-efficient real-time 9.375 Gb/s QPSK-encoded layered/enhanced asymmetrical clipped optical OFDM (L/E-ACO-OFDM) transmitter using a Virtex-6 FPGA. This L/E-ACO-OFDM signal is successfully transmitted over 20-km uncompensated standard single-mode fiber (S-SMF) using a directly modulated laser. Several methods are explored to reduce the FPGA's logic resource utilization by taking advantage of the L/E-ACO-OFDM's signal characteristics. We show that the logic resource occupation of L/E-ACO-OFDM transmitter is almost the same as that of DC-biased OFDM transmitter when they achieve the same spectral efficiency, proving its great potential to be used in a real-time short-haul optical transmission link.

  5. A Novel Efficient Cluster-Based MLSE Equalizer for Satellite Communication Channels with -QAM Signaling

    Directory of Open Access Journals (Sweden)

    Dalakas Vassilis

    2006-01-01

    Full Text Available In satellites, nonlinear amplifiers used near saturation severely distort the transmitted signal and cause difficulties in its reception. Nevertheless, the nonlinearities introduced by memoryless bandpass amplifiers preserve the symmetries of the -ary quadrature amplitude modulation ( -QAM constellation. In this paper, a cluster-based sequence equalizer (CBSE that takes advantage of these symmetries is presented. The proposed equalizer exhibits enhanced performance compared to other techniques, including the conventional linear transversal equalizer, Volterra equalizers, and RBF network equalizers. Moreover, this gain in performance is obtained at a substantially lower computational cost.

  6. Evaluating music emotion recognition

    DEFF Research Database (Denmark)

    Sturm, Bob L.

    2013-01-01

    A fundamental problem with nearly all work in music genre recognition (MGR)is that evaluation lacks validity with respect to the principal goals of MGR. This problem also occurs in the evaluation of music emotion recognition (MER). Standard approaches to evaluation, though easy to implement, do...... not reliably differentiate between recognizing genre or emotion from music, or by virtue of confounding factors in signals (e.g., equalization). We demonstrate such problems for evaluating an MER system, and conclude with recommendations....

  7. Signal-to-noise ratio and detective quantum efficiency determination by and alternative use of photographic detectors

    International Nuclear Information System (INIS)

    Burgudzhiev, Z.; Koleva, D.

    1986-01-01

    A known theoretical model of an alternative use of silver-halogenid pnotographic emulsions in which the number of the granulas forming the photographic image is used as a detector output instead of the microdensiometric blackening density is applied to some real photographic emulsions. It is found that by this use the Signal-to-Noise ratio of the photographic detector can be increased to about 5 times while its detective quantum efficiency can reach about 20%, being close to that of some photomultipliers

  8. Capacity-Approaching Modulation Formats for Optical Transmission Systems: Signal shaping and advanced de/muxing for efficient resource exploitation

    DEFF Research Database (Denmark)

    Estaran Tolosa, Jose Manuel

    Aiming for efficient fiber-optic data transport, this thesis addresses three scenario-specific modulation and/or multiplexing techniques which, leveraging digital signal processing, can further exploit the available resources.The considered environments are: (i) (ultra) long-haul networks, where we...... focus on improving the receiver sensitivity; (ii) metropolitan area networks, where the target is providing spectral and rate adaptability with fine granularity and easy reconfigurability; and (iii) short-haul networks, where facilitating more affordable throughput scaling is pursued. Functioning...

  9. Optimization of E r-density profile for efficient pumping and high signal gain in Erbium-doped fiber amplifiers

    International Nuclear Information System (INIS)

    Arzi, E.; Hassani, A.; Esmaili Seraji, F.

    2000-01-01

    Recently, the Erbium-Doped Fiber Amplifier has been shown to have a great potentiality in Fiber-Optics Communication. A model is suggested for calculating the E r-density profile, using the propagation and rate equations of a homogeneous two-level laser medium in Erbium-Doped Fiber Amplifier, such that efficient pumping and high signal gain is achieved for different fiber waveguide structure. The result of this numerical calculation shows that the gain, compared with the gain of the existing Erbium-Doped Fiber Amplifier, is higher by a factor of 3.5. This model is applicable in all active waveguides and any other dopant as well

  10. Communication efficiency and congestion of signal traffic in large-scale brain networks.

    Science.gov (United States)

    Mišić, Bratislav; Sporns, Olaf; McIntosh, Anthony R

    2014-01-01

    The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a "rich club" of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication.

  11. Development and application of VISAR probe with higher signal-collecting efficiency and adjusted depth of field

    International Nuclear Information System (INIS)

    Zhao Jianheng; Sun Chengwei; Ma Ruchao

    2001-01-01

    The new design of optical fiber VISAR probes has been described. It consists of optical fibers and two lenses, and has simpler structure and lower cost than others. If the size of the image near the end face of collecting fiber is larger than the diameter of the core fiber, the distance between the probe and the target can be decreased. Requirement for the precision in the design is lower in this way, and is easier to build up. At the same time, the signal-collecting efficiency is improved in some degree. During the process of designing the probe, the technique for manufacturing the lens of plexiglass is developed. The lens of plexiglass is used to replace the lens of glass, which can reduce the cost of the probe. The factors affecting the collecting efficiency are analyzed

  12. Pattern recognition

    CERN Document Server

    Theodoridis, Sergios

    2003-01-01

    Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to ""learn"" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10

  13. Simple and efficient method for region of interest value extraction from picture archiving and communication system viewer with optical character recognition software and macro program.

    Science.gov (United States)

    Lee, Young Han; Park, Eun Hae; Suh, Jin-Suck

    2015-01-01

    The objectives are: 1) to introduce a simple and efficient method for extracting region of interest (ROI) values from a Picture Archiving and Communication System (PACS) viewer using optical character recognition (OCR) software and a macro program, and 2) to evaluate the accuracy of this method with a PACS workstation. This module was designed to extract the ROI values on the images of the PACS, and created as a development tool by using open-source OCR software and an open-source macro program. The principal processes are as follows: (1) capture a region of the ROI values as a graphic file for OCR, (2) recognize the text from the captured image by OCR software, (3) perform error-correction, (4) extract the values including area, average, standard deviation, max, and min values from the text, (5) reformat the values into temporary strings with tabs, and (6) paste the temporary strings into the spreadsheet. This principal process was repeated for the number of ROIs. The accuracy of this module was evaluated on 1040 recognitions from 280 randomly selected ROIs of the magnetic resonance images. The input times of ROIs were compared between conventional manual method and this extraction module-assisted input method. The module for extracting ROI values operated successfully using the OCR and macro programs. The values of the area, average, standard deviation, maximum, and minimum could be recognized and error-corrected with AutoHotkey-coded module. The average input times using the conventional method and the proposed module-assisted method were 34.97 seconds and 7.87 seconds, respectively. A simple and efficient method for ROI value extraction was developed with open-source OCR and a macro program. Accurate inputs of various numbers from ROIs can be extracted with this module. The proposed module could be applied to the next generation of PACS or existing PACS that have not yet been upgraded. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

  14. Detection of boiling by Piety's on-line PSD-pattern recognition algorithm applied to neutron noise signals in the SAPHIR reactor

    International Nuclear Information System (INIS)

    Spiekerman, G.

    1988-09-01

    A partial blockage of the cooling channels of a fuel element in a swimming pool reactor could lead to vapour generation and to burn-out. To detect such anomalies, a pattern recognition algorithm based on power spectra density (PSD) proposed by Piety was further developed and implemented on a PDP 11/23 for on-line applications. This algorithm identifies anomalies by measuring the PSD on the process signal and comparing them with a standard baseline previously formed. Up to 8 decision discriminants help to recognize spectral changes due to anomalies. In our application, to detect boiling as quickly as possible with sufficient sensitivity, Piety's algorithm was modified using overlapped Fast-Fourier-Transform-Processing and the averaging of the PSDs over a large sample of preceding instantaneous PSDs. This processing allows high sensitivity in detecting weak disturbances without reducing response time. The algorithm was tested with simulation-of-boiling experiments where nitrogen in a cooling channel of a mock-up of a fuel element was injected. Void fractions higher than 30 % in the channel can be detected. In the case of boiling, it is believed that this limit is lower because collapsing bubbles could give rise to stronger fluctuations. The algorithm was also tested with a boiling experiment where the reactor coolant flow was actually reduced. The results showed that the discriminant D5 of Piety's algorithm based on neutron noise obtained from the existing neutron chambers of the reactor control system could sensitively recognize boiling. The detection time amounts to 7-30 s depending on the strength of the disturbances. Other events, which arise during a normal reactor run like scrams, removal of isotope elements without scramming or control rod movements and which could lead to false alarms, can be distinguished from boiling. 49 refs., 104 figs., 5 tabs

  15. Hallucinogenic 5-HT2AR agonists LSD and DOI enhance dopamine D2R protomer recognition and signaling of D2-5-HT2A heteroreceptor complexes.

    Science.gov (United States)

    Borroto-Escuela, Dasiel O; Romero-Fernandez, Wilber; Narvaez, Manuel; Oflijan, Julia; Agnati, Luigi F; Fuxe, Kjell

    2014-01-03

    Dopamine D2LR-serotonin 5-HT2AR heteromers were demonstrated in HEK293 cells after cotransfection of the two receptors and shown to have bidirectional receptor-receptor interactions. In the current study the existence of D2L-5-HT2A heteroreceptor complexes was demonstrated also in discrete regions of the ventral and dorsal striatum with in situ proximity ligation assays (PLA). The hallucinogenic 5-HT2AR agonists LSD and DOI but not the standard 5-HT2AR agonist TCB2 and 5-HT significantly increased the density of D2like antagonist (3)H-raclopride binding sites and significantly reduced the pKiH values of the high affinity D2R agonist binding sites in (3)H-raclopride/DA competition experiments. Similar results were obtained in HEK293 cells and in ventral striatum. The effects of the hallucinogenic 5-HT2AR agonists on D2R density and affinity were blocked by the 5-HT2A antagonist ketanserin. In a forskolin-induced CRE-luciferase reporter gene assay using cotransfected but not D2R singly transfected HEK293 cells DOI and LSD but not TCB2 significantly enhanced the D2LR agonist quinpirole induced inhibition of CRE-luciferase activity. Haloperidol blocked the effects of both quinpirole alone and the enhancing actions of DOI and LSD while ketanserin only blocked the enhancing actions of DOI and LSD. The mechanism for the allosteric enhancement of the D2R protomer recognition and signalling observed is likely mediated by a biased agonist action of the hallucinogenic 5-HT2AR agonists at the orthosteric site of the 5-HT2AR protomer. This mechanism may contribute to the psychotic actions of LSD and DOI and the D2-5-HT2A heteroreceptor complex may thus be a target for the psychotic actions of hallunicogenic 5-HT2A agonists. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. Highly sensitive photoelectrochemical biosensor for kinase activity detection and inhibition based on the surface defect recognition and multiple signal amplification of metal-organic frameworks.

    Science.gov (United States)

    Wang, Zonghua; Yan, Zhiyong; Wang, Feng; Cai, Jibao; Guo, Lei; Su, Jiakun; Liu, Yang

    2017-11-15

    A turn-on photoelectrochemical (PEC) biosensor based on the surface defect recognition and multiple signal amplification of metal-organic frameworks (MOFs) was proposed for highly sensitive protein kinase activity analysis and inhibitor evaluation. In this strategy, based on the phosphorylation reaction in the presence of protein kinase A (PKA), the Zr-based metal-organic frameworks (UiO-66) accommodated with [Ru(bpy) 3 ] 2+ photoactive dyes in the pores were linked to the phosphorylated kemptide modified TiO 2 /ITO electrode through the chelation between the Zr 4+ defects on the surface of UiO-66 and the phosphate groups in kemptide. Under visible light irradiation, the excited electrons from [Ru(bpy) 3 ] 2+ adsorbed in the pores of UiO-66 injected into the TiO 2 conduction band to generate photocurrent, which could be utilized for protein kinase activities detection. The large surface area and high porosities of UiO-66 facilitated a large number of [Ru(bpy) 3 ] 2+ that increased the photocurrent significantly, and afforded a highly sensitive PEC analysis of kinase activity. The detection limit of the as-proposed PEC biosensor was 0.0049UmL -1 (S/N!=!3). The biosensor was also applied for quantitative kinase inhibitor evaluation and PKA activities detection in MCF-7 cell lysates. The developed visible-light PEC biosensor provides a simple detection procedure and a cost-effective manner for PKA activity assays, and shows great potential in clinical diagnosis and drug discoveries. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Tuning differentiation signals for efficient propagation and in vitro validation of rat embryonic stem cell cultures.

    Science.gov (United States)

    Meek, Stephen; Sutherland, Linda; Burdon, Tom

    2015-01-01

    The rat is one of the most commonly used laboratory animals in biomedical research and the recent isolation of genuine pluripotent rat embryonic stem (ES) cell lines has provided new opportunities for applying contemporary genetic engineering techniques to the rat and enhancing the use of this rodent in scientific research. Technical refinements that improve the stability of the rat ES cell cultures will undoubtedly further strengthen and broaden the use of these stem cells in biomedical research. Here, we describe a relatively simple and robust protocol that supports the propagation of germ line competent rat ES cells, and outline how tuning stem cell signaling using small molecule inhibitors can be used to both stabilize self-renewal of rat ES cell cultures and aid evaluation of their differentiation potential in vitro.

  18. DMT efficiently inhibits hepatic gluconeogenesis by regulating the Gαq signaling pathway.

    Science.gov (United States)

    Zhou, Ting-Ting; Ma, Fei; Shi, Xiao-Fan; Xu, Xin; Du, Te; Guo, Xiao-Dan; Wang, Gai-Hong; Yu, Liang; Rukachaisirikul, Vatcharin; Hu, Li-Hong; Chen, Jing; Shen, Xu

    2017-08-01

    Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease with complicated pathogenesis and targeting gluconeogenesis inhibition is a promising strategy for anti-diabetic drug discovery. G protein-coupled receptors (GPCRs) are classified as distinct families by heterotrimeric G proteins, primarily including Gαs, Gαi and Gαq. Gαs-coupled GPCRs function potently in the regulation of hepatic gluconeogenesis by activating cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA) pathway and Gαi-coupled GPCRs exhibit inhibitory effect on adenylyl cyclase and reduce intracellular cAMP level. However, little is known about the regulation of Gαq-coupled GPCRs in hepatic gluconeogenesis. Here, small-molecule 2-(2,4-dimethoxy-3-methylphenyl)-7-(thiophen-2-yl)-9-(trifluoromethyl)-2,3-dihydropyrido[3',2':4,5]thieno[3,2-d]pyrimidin-4( 1H )-one (DMT) was determined to suppress hepatic glucose production and reduce mRNA levels of gluconeogenic genes. Treatment of DMT in db/db mice decreased fasting blood glucose and hemoglobin A1C (HbA1c) levels, while improved glucose tolerance and pyruvate tolerance. Mechanism study demonstrated that DMT-inhibited gluconeogenesis by regulating the Gαq/phospholipase C (PLC)/inositol-1,4,5-triphosphate receptor (IP3R)-mediated calcium (Ca 2+ )/calmodulin (CaM)/phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)/protein kinase B (AKT)/forkhead box protein O1 (FOXO1) signaling pathway. To our knowledge, DMT might be the first reported small molecule able to suppress hepatic gluconeogenesis by regulating Gαq signaling, and our current work has also highlighted the potential of DMT in the treatment of T2DM. © 2017 Society for Endocrinology.

  19. Mixed-Signal Architectures for High-Efficiency and Low-Distortion Digital Audio Processing and Power Amplification

    Directory of Open Access Journals (Sweden)

    Pierangelo Terreni

    2010-01-01

    Full Text Available The paper addresses the algorithmic and architectural design of digital input power audio amplifiers. A modelling platform, based on a meet-in-the-middle approach between top-down and bottom-up design strategies, allows a fast but still accurate exploration of the mixed-signal design space. Different amplifier architectures are configured and compared to find optimal trade-offs among different cost-functions: low distortion, high efficiency, low circuit complexity and low sensitivity to parameter changes. A novel amplifier architecture is derived; its prototype implements digital processing IP macrocells (oversampler, interpolating filter, PWM cross-point deriver, noise shaper, multilevel PWM modulator, dead time compensator on a single low-complexity FPGA while off-chip components are used only for the power output stage (LC filter and power MOS bridge; no heatsink is required. The resulting digital input amplifier features a power efficiency higher than 90% and a total harmonic distortion down to 0.13% at power levels of tens of Watts. Discussions towards the full-silicon integration of the mixed-signal amplifier in embedded devices, using BCD technology and targeting power levels of few Watts, are also reported.

  20. Biometric Methods for Secure Communications in Body Sensor Networks: Resource-Efficient Key Management and Signal-Level Data Scrambling

    Science.gov (United States)

    Bui, Francis Minhthang; Hatzinakos, Dimitrios

    2007-12-01

    As electronic communications become more prevalent, mobile and universal, the threats of data compromises also accordingly loom larger. In the context of a body sensor network (BSN), which permits pervasive monitoring of potentially sensitive medical data, security and privacy concerns are particularly important. It is a challenge to implement traditional security infrastructures in these types of lightweight networks since they are by design limited in both computational and communication resources. A key enabling technology for secure communications in BSN's has emerged to be biometrics. In this work, we present two complementary approaches which exploit physiological signals to address security issues: (1) a resource-efficient key management system for generating and distributing cryptographic keys to constituent sensors in a BSN; (2) a novel data scrambling method, based on interpolation and random sampling, that is envisioned as a potential alternative to conventional symmetric encryption algorithms for certain types of data. The former targets the resource constraints in BSN's, while the latter addresses the fuzzy variability of biometric signals, which has largely precluded the direct application of conventional encryption. Using electrocardiogram (ECG) signals as biometrics, the resulting computer simulations demonstrate the feasibility and efficacy of these methods for delivering secure communications in BSN's.

  1. Snake venom VEGF Vammin induces a highly efficient angiogenic response in skeletal muscle via VEGFR-2/NRP specific signaling.

    Science.gov (United States)

    Toivanen, Pyry I; Nieminen, Tiina; Laakkonen, Johanna P; Heikura, Tommi; Kaikkonen, Minna U; Ylä-Herttuala, Seppo

    2017-07-17

    Vascular Endothelial Growth Factors (VEGFs) are promising molecules for the treatment of ischemic diseases by pro-angiogenic therapy. Snake venom VEGFs are a novel subgroup with unique receptor binding profiles and as such are potential new therapeutic agents. We determined the ligand-receptor interactions, gene regulation and angiogenic properties of Vipera ammodytes venom VEGF, Vammin, and compared it to the canonical angiogenic factor VEGF-A to evaluate the use of Vammin for therapeutic angiogenesis. Vammin efficiently induced VEGFR-2 mediated proliferation and expression of genes associated with proliferation, migration and angiogenesis. VEGF-A 165 and especially VEGF-A 109 induced less pronounced effects. Vammin regulates a number of signaling pathways by inducing the expression of NR4A family nuclear receptors and regulators of calcium signaling and MAP kinase pathways. Interestingly, MARC1, which encodes an enzyme discovered to catalyze reduction of nitrate to NO, was identified as a novel VEGFR-2 regulated gene. In rabbit skeletal muscle adenoviral delivery of Vammin induced prominent angiogenic responses. Both the vector dose and the co-receptor binding of the ligand were critical parameters controlling the type of angiogenic response from sprouting angiogenesis to vessel enlargement. Vammin induced VEGFR-2/NRP-1 mediated signaling more effectively than VEGF-A, consequently it is a promising candidate for development of pro-angiogenic therapies.

  2. Biometric Methods for Secure Communications in Body Sensor Networks: Resource-Efficient Key Management and Signal-Level Data Scrambling

    Directory of Open Access Journals (Sweden)

    Dimitrios Hatzinakos

    2008-03-01

    Full Text Available As electronic communications become more prevalent, mobile and universal, the threats of data compromises also accordingly loom larger. In the context of a body sensor network (BSN, which permits pervasive monitoring of potentially sensitive medical data, security and privacy concerns are particularly important. It is a challenge to implement traditional security infrastructures in these types of lightweight networks since they are by design limited in both computational and communication resources. A key enabling technology for secure communications in BSN's has emerged to be biometrics. In this work, we present two complementary approaches which exploit physiological signals to address security issues: (1 a resource-efficient key management system for generating and distributing cryptographic keys to constituent sensors in a BSN; (2 a novel data scrambling method, based on interpolation and random sampling, that is envisioned as a potential alternative to conventional symmetric encryption algorithms for certain types of data. The former targets the resource constraints in BSN's, while the latter addresses the fuzzy variability of biometric signals, which has largely precluded the direct application of conventional encryption. Using electrocardiogram (ECG signals as biometrics, the resulting computer simulations demonstrate the feasibility and efficacy of these methods for delivering secure communications in BSN's.

  3. Efficient and Robust Detection of GFSK Signals under Dispersive Channel, Modulation Index, and Carrier Frequency Offset Conditions

    Directory of Open Access Journals (Sweden)

    Stephan Weiss

    2005-09-01

    Full Text Available Gaussian frequency shift keying is the modulation scheme specified for Bluetooth. Signal adversities typical in Bluetooth networks include AWGN, multipath propagation, carrier frequency, and modulation index offsets. In our effort to realise a robust but efficient Bluetooth receiver, we adopt a high-performance matched-filter-based detector, which is near optimal in AWGN, but requires a prohibitively costly filter bank for processing of K bits worth of the received signal. However, through filtering over a single bit period and performing phase propagation of intermediate results over successive single-bit stages, we eliminate redundancy involved in providing the matched filter outputs and reduce its complexity by up to 90% (for K=9. The constant modulus signal characteristic and the potential for carrier frequency offsets make the constant modulus algorithm (CMA suitable for channel equalisation, and we demonstrate its effectiveness in this paper. We also introduce a stochastic gradient-based algorithm for carrier frequency offset correction, and show that the relative rotation between successive intermediate filter outputs enables us to detect and correct offsets in modulation index.

  4. Efficient expression of nattokinase in Bacillus licheniformis: host strain construction and signal peptide optimization.

    Science.gov (United States)

    Wei, Xuetuan; Zhou, Yinhua; Chen, Jingbang; Cai, Dongbo; Wang, Dan; Qi, Gaofu; Chen, Shouwen

    2015-02-01

    Nattokinase (NK) possesses the potential for prevention and treatment of thrombus-related diseases. In this study, high-level expression of nattokinase was achieved in Bacillus licheniformis WX-02 via host strain construction and signal peptides optimization. First, ten genes (mpr, vpr, aprX, epr, bpr, wprA, aprE, bprA, hag, amyl) encoding for eight extracellular proteases, a flagellin and an amylase were deleted to obtain B. licheniformis BL10, which showed no extracellular proteases activity in gelatin zymography. Second, the gene fragments of P43 promoter, Svpr, nattokinase and TamyL were combined into pHY300PLK to form the expression vector pP43SNT. In BL10 (pP43SNT), the fermentation activity and product activity per unit of biomass of nattokinase reached 14.33 FU/mL and 2,187.71 FU/g respectively, which increased by 39 and 156 % compared to WX-02 (pP43SNT). Last, Svpr was replaced with SsacC and SbprA, and the maximum fermentation activity (33.83 FU/mL) was achieved using SsacC, which was 229 % higher than that of WX-02 (pP43SNT). The maximum NK fermentation activity in this study reaches the commercial production level of solid state fermentation, and this study provides a promising engineered strain for industrial production of nattokinase, as well as a potential platform host for expression of other target proteins.

  5. Gravitational waves from inspiralling compact binaries: Hexagonal template placement and its efficiency in detecting physical signals

    International Nuclear Information System (INIS)

    Cokelaer, T.

    2007-01-01

    Matched filtering is used to search for gravitational waves emitted by inspiralling compact binaries in data from the ground-based interferometers. One of the key aspects of the detection process is the design of a template bank that covers the astrophysically pertinent parameter space. In an earlier paper, we described a template bank that is based on a square lattice. Although robust, we showed that the square placement is overefficient, with the implication that it is computationally more demanding than required. In this paper, we present a template bank based on an hexagonal lattice, which size is reduced by 40% with respect to the proposed square placement. We describe the practical aspects of the hexagonal template bank implementation, its size, and computational cost. We have also performed exhaustive simulations to characterize its efficiency and safeness. We show that the bank is adequate to search for a wide variety of binary systems (primordial black holes, neutron stars, and stellar-mass black holes) and in data from both current detectors (initial LIGO, Virgo and GEO600) as well as future detectors (advanced LIGO and EGO). Remarkably, although our template bank placement uses a metric arising from a particular template family, namely, stationary phase approximation, we show that it can be used successfully with other template families (e.g., Pade resummation and effective one-body approximation). This quality of being effective for different template families makes the proposed bank suitable for a search that would use several of them in parallel (e.g., in a binary black hole search). The hexagonal template bank described in this paper is currently used to search for nonspinning inspiralling compact binaries in data from the Laser Interferometer Gravitational-Wave Observatory (LIGO)

  6. Age-related differences in signaling efficiency of human lens cells underpin differential wound healing response rates following cataract surgery.

    Science.gov (United States)

    Dawes, Lucy Jean; Duncan, George; Wormstone, Ian Michael

    2013-01-14

    Cataract surgery is blighted by posterior capsule opacification (PCO), which is more severe and frequent in the young than the elderly (>60 years). Our aim was to understand the biological basis for these age-related differences in PCO/wound healing rates. Human capsular bags were prepared by cataract surgery on donor lenses (young [60 years] groups) and maintained in serum-free Eagle's minimum essential medium. Cell growth was determined using the MTS assay. Fibroblast growth factor (FGF) and hepatocyte growth factor (HGF) levels were determined using ELISA. Protein synthesis rates were elucidated by 35S-methionine incorporation. U0126, SB203580, and SP600125 were used to disrupt ERK-, p38-, and JNK-mediated signaling, respectively. Level of total and phospho-ERK, -c-jun, -P38, and -JNK plus cytokines were detected using a BIOPLEX array system. Following a 2-day culture period, significant decreases in IL-1β and IL-6, and increases in IL-10, IL-12, IL-13, and VEGF in the >60 years group were observed compared with their younger counterparts. Capsular bags (cells and capsule) from aged donors contained greater than or equal levels of HGF and FGF than younger counterparts and had greater rates of protein synthesis. Inhibition of ERK, p38, and JNK signaling significantly suppressed cell coverage on the posterior capsule. pERK, p-c-jun, p-p38, and pJNK were consistently lower in aged cell populations; total signaling protein expression was unaffected by age. Serum stimulation increased pERK, p-c-jun, and pJNK levels in cells of all ages; p-p38 was significantly increased in the >60 years group only. Ligand availability to cells is not a limiting factor as we age, but the ability to convert this resource into signaling activity is. We therefore propose that overall signaling efficiency is reduced as a function of age, which consequently limits wound-healing response rates after injury.

  7. Combinatorial Modulation of Signaling Pathways Reveals Cell-Type-Specific Requirements for Highly Efficient and Synchronous iPSC Reprogramming

    Directory of Open Access Journals (Sweden)

    Simon E. Vidal

    2014-10-01

    Full Text Available The differentiated state of somatic cells provides barriers for the derivation of induced pluripotent stem cells (iPSCs. To address why some cell types reprogram more readily than others, we studied the effect of combined modulation of cellular signaling pathways. Surprisingly, inhibition of transforming growth factor β (TGF-β together with activation of Wnt signaling in the presence of ascorbic acid allows >80% of murine fibroblasts to acquire pluripotency after 1 week of reprogramming factor expression. In contrast, hepatic and blood progenitors predominantly required only TGF-β inhibition or canonical Wnt activation, respectively, to reprogram at efficiencies approaching 100%. Strikingly, blood progenitors reactivated endogenous pluripotency loci in a highly synchronous manner, and we demonstrate that expression of specific chromatin-modifying enzymes and reduced TGF-β/mitogen-activated protein (MAP kinase activity are intrinsic properties associated with the unique reprogramming response of these cells. Our observations define cell-type-specific requirements for the rapid and synchronous reprogramming of somatic cells.

  8. Damage localization using a power-efficient distributed on-board signal processing algorithm in a wireless sensor network

    International Nuclear Information System (INIS)

    Liu, Lei; Liu, Shuntao; Yuan, Fuh-Gwo

    2012-01-01

    A distributed on-board algorithm that is embedded and executed within a group of wireless sensors to locate structural damages in isotropic plates is presented. The algorithm is based on an energy-decay model of Lamb waves and singular value decomposition (SVD) to determine damage locations. A sensor group consists of a small number of sensors, each of which independently collects wave signals and evaluates wave energy upon an external triggering signal sent from a base station. The energy values, usually a few bytes in length, are then sent to the base station to determine the presence and location of damages. In comparison with traditional centralized approaches in which whole datasets are required to be transmitted, the proposed algorithm yields much less wireless communication traffic, yet with a modest amount of computation required within sensors. Experiments have shown that the algorithm is robust to locate damage for isotropic plate structures and is very power efficient, with more than an order-of-magnitude power saving

  9. The progesterone-induced enhancement of object recognition memory consolidation involves activation of the extracellular signal-regulated kinase (ERK) and mammalian target of rapamycin (mTOR) pathways in the dorsal hippocampus

    Science.gov (United States)

    Orr, Patrick T.; Rubin, Amanda J.; Fan, Lu; Kent, Brianne A.; Frick, Karyn M.

    2012-01-01

    Although much recent work has elucidated the biochemical mechanisms underlying the modulation of memory by 17β-estradiol, little is known about the signaling events through which progesterone (P) regulates memory. We recently demonstrated that immediate post-training infusion of P into the dorsal hippocampus enhances object recognition memory consolidation in young ovariectomized female mice (Orr et al., 2009). The goal of the present study was to identify the biochemical alterations that might underlie this mnemonic enhancement. We hypothesized that the P-induced enhancement of object recognition would be dependent on activation of the ERK and mTOR pathways. In young ovariectomized mice, we found that bilateral dorsal hippocampal infusion of P significantly increased levels of phospho-p42 ERK and the mTOR substrate S6K in the dorsal hippocampus 5 minutes after infusion. Phospho-p42 ERK levels were downregulated 15 minutes after infusion and returned to baseline 30 minutes after infusion, suggesting a biphasic effect of P on ERK activation. Dorsal hippocampal ERK and mTOR activation were necessary for P to facilitate memory consolidation, as suggested by the fact that inhibitors of both pathways infused into the dorsal hippocampus immediately after training blocked the P-induced enhancement of object recognition. Collectively, these data provide the first demonstration that the ability of P to enhance memory consolidation depends on the rapid activation of cell signaling and protein synthesis pathways in the dorsal hippocampus. PMID:22265866

  10. De Novo Transcriptome Analysis Shows That SAV-3 Infection Upregulates Pattern Recognition Receptors of the Endosomal Toll-Like and RIG-I-Like Receptor Signaling Pathways in Macrophage/Dendritic Like TO-Cells

    Directory of Open Access Journals (Sweden)

    Cheng Xu

    2016-04-01

    Full Text Available A fundamental step in cellular defense mechanisms is the recognition of “danger signals” made of conserved pathogen associated molecular patterns (PAMPs expressed by invading pathogens, by host cell germ line coded pattern recognition receptors (PRRs. In this study, we used RNA-seq and the Kyoto encyclopedia of genes and genomes (KEGG to identify PRRs together with the network pathway of differentially expressed genes (DEGs that recognize salmonid alphavirus subtype 3 (SAV-3 infection in macrophage/dendritic like TO-cells derived from Atlantic salmon (Salmo salar L headkidney leukocytes. Our findings show that recognition of SAV-3 in TO-cells was restricted to endosomal Toll-like receptors (TLRs 3 and 8 together with RIG-I-like receptors (RLRs and not the nucleotide-binding oligomerization domain-like receptors NOD-like receptor (NLRs genes. Among the RLRs, upregulated genes included the retinoic acid inducible gene I (RIG-I, melanoma differentiation association 5 (MDA5 and laboratory of genetics and physiology 2 (LGP2. The study points to possible involvement of the tripartite motif containing 25 (TRIM25 and mitochondrial antiviral signaling protein (MAVS in modulating RIG-I signaling being the first report that links these genes to the RLR pathway in SAV-3 infection in TO-cells. Downstream signaling suggests that both the TLR and RLR pathways use interferon (IFN regulatory factors (IRFs 3 and 7 to produce IFN-a2. The validity of RNA-seq data generated in this study was confirmed by quantitative real time qRT-PCR showing that genes up- or downregulated by RNA-seq were also up- or downregulated by RT-PCR. Overall, this study shows that de novo transcriptome assembly identify key receptors of the TLR and RLR sensors engaged in host pathogen interaction at cellular level. We envisage that data presented here can open a road map for future intervention strategies in SAV infection of salmon.

  11. Hidden Markov models in automatic speech recognition

    Science.gov (United States)

    Wrzoskowicz, Adam

    1993-11-01

    This article describes a method for constructing an automatic speech recognition system based on hidden Markov models (HMMs). The author discusses the basic concepts of HMM theory and the application of these models to the analysis and recognition of speech signals. The author provides algorithms which make it possible to train the ASR system and recognize signals on the basis of distinct stochastic models of selected speech sound classes. The author describes the specific components of the system and the procedures used to model and recognize speech. The author discusses problems associated with the choice of optimal signal detection and parameterization characteristics and their effect on the performance of the system. The author presents different options for the choice of speech signal segments and their consequences for the ASR process. The author gives special attention to the use of lexical, syntactic, and semantic information for the purpose of improving the quality and efficiency of the system. The author also describes an ASR system developed by the Speech Acoustics Laboratory of the IBPT PAS. The author discusses the results of experiments on the effect of noise on the performance of the ASR system and describes methods of constructing HMM's designed to operate in a noisy environment. The author also describes a language for human-robot communications which was defined as a complex multilevel network from an HMM model of speech sounds geared towards Polish inflections. The author also added mandatory lexical and syntactic rules to the system for its communications vocabulary.

  12. Recognition of fractal graphs

    NARCIS (Netherlands)

    Perepelitsa, VA; Sergienko, [No Value; Kochkarov, AM

    1999-01-01

    Definitions of prefractal and fractal graphs are introduced, and they are used to formulate mathematical models in different fields of knowledge. The topicality of fractal-graph recognition from the point of view, of fundamental improvement in the efficiency of the solution of algorithmic problems

  13. Effects of training on recognition of musical instruments presented through cochlear implant simulations.

    Science.gov (United States)

    Driscoll, Virginia D; Oleson, Jacob; Jiang, Dingfeng; Gfeller, Kate

    2009-01-01

    The simulation of the CI (cochlear implant) signal presents a degraded representation of each musical instrument, which makes recognition difficult. To examine the efficiency and effectiveness of three types of training on recognition of musical instruments as presented through simulations of the sounds transmitted through a CI. Participants were randomly assigned to one of three training conditions: repeated exposure, feedback, and direct instruction. Sixty-six adults with normal hearing. Each participant completed three training sessions per week, over a five-week time period, in which they listened to the CI simulations of eight different musical instruments. Analyses on percent of instruments identified correctly showed statistically significant differences between recognition accuracy of the three training conditions (p different types of training are differentially effective with regard to improving recognition of musical instruments presented through a degraded signal, which has practical implications for the auditory rehabilitation of persons who use cochlear implants.

  14. Non-classical nuclear localization signal peptides for high efficiency lipofection of primary neurons and neuronal cell lines.

    Science.gov (United States)

    Ma, H; Zhu, J; Maronski, M; Kotzbauer, P T; Lee, V M-Y; Dichter, M A; Diamond, S L

    2002-01-01

    Gene transfer into CNS is critical for potential therapeutic applications as well as for the study of the genetic basis of neural development and nerve function. Unfortunately, lipid-based gene transfer to CNS cells is extremely inefficient since the nucleus of these post-mitotic cells presents a significant barrier to transfection. We report the development of a simple and highly efficient lipofection method for primary embryonic rat hippocampal neurons (up to 25% transfection) that exploits the M9 sequence of the non-classical nuclear localization signal of heterogeneous nuclear ribonucleoprotein A1 for targeting beta(2)-karyopherin (transportin-1). M9-assistant lipofection resulted in 20-100-fold enhancement of transfection over lipofection alone for embryonic-derived retinal ganglion cells, rat pheochromocytoma (PC12) cells, embryonic rat ventral mesencephalon neurons, as well as the clinically relevant human NT2 cells or retinoic acid-differentiated NT2 neurons. This technique can facilitate the implementation of promoter construct experiments in post-mitotic cells, stable transformant generation, and dominant-negative mutant expression techniques in CNS cells.

  15. Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0

    Science.gov (United States)

    Lundberg, J.; Conrad, J.; Rolke, W.; Lopez, A.

    2010-03-01

    A C++ class was written for the calculation of frequentist confidence intervals using the profile likelihood method. Seven combinations of Binomial, Gaussian, Poissonian and Binomial uncertainties are implemented. The package provides routines for the calculation of upper and lower limits, sensitivity and related properties. It also supports hypothesis tests which take uncertainties into account. It can be used in compiled C++ code, in Python or interactively via the ROOT analysis framework. Program summaryProgram title: TRolke version 2.0 Catalogue identifier: AEFT_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEFT_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: MIT license No. of lines in distributed program, including test data, etc.: 3431 No. of bytes in distributed program, including test data, etc.: 21 789 Distribution format: tar.gz Programming language: ISO C++. Computer: Unix, GNU/Linux, Mac. Operating system: Linux 2.6 (Scientific Linux 4 and 5, Ubuntu 8.10), Darwin 9.0 (Mac-OS X 10.5.8). RAM:˜20 MB Classification: 14.13. External routines: ROOT ( http://root.cern.ch/drupal/) Nature of problem: The problem is to calculate a frequentist confidence interval on the parameter of a Poisson process with statistical or systematic uncertainties in signal efficiency or background. Solution method: Profile likelihood method, Analytical Running time:<10 seconds per extracted limit.

  16. Isolation of Fully Human Antagonistic RON Antibodies Showing Efficient Block of Downstream Signaling and Cell Migration1

    Science.gov (United States)

    Gunes, Zeynep; Zucconi, Adriana; Cioce, Mario; Meola, Annalisa; Pezzanera, Monica; Acali, Stefano; Zampaglione, Immacolata; De Pratti, Valeria; Bova, Luca; Talamo, Fabio; Demartis, Anna; Monaci, Paolo; La Monica, Nicola; Ciliberto, Gennaro; Vitelli, Alessandra

    2011-01-01

    RON belongs to the c-MET family of receptor tyrosine kinases. As its well-known family member MET, RON and its ligand macrophage-stimulating protein have been implicated in the progression and metastasis of tumors and have been shown to be overexpressed in cancer. We generated and tested a large number of human monoclonal antibodies (mAbs) against human RON. Our screening yielded three high-affinity antibodies that efficiently block ligand-dependent intracellular AKT and MAPK signaling. This effect correlates with the strong reduction of ligand-activated migration of T47D breast cancer cell line. By cross-competition experiments, we showed that the antagonistic antibodies fall into three distinct epitope regions of the RON extracellular Sema domain. Notably, no inhibition of tumor growth was observed in different epithelial tumor xenografts in nude mice with any of the antibodies. These results suggest that distinct properties beside ligand antagonism are required for anti-RON mAbs to exert antitumor effects in vivo. PMID:21286376

  17. Speech Recognition on Mobile Devices

    DEFF Research Database (Denmark)

    Tan, Zheng-Hua; Lindberg, Børge

    2010-01-01

    in the mobile context covering motivations, challenges, fundamental techniques and applications. Three ASR architectures are introduced: embedded speech recognition, distributed speech recognition and network speech recognition. Their pros and cons and implementation issues are discussed. Applications within......The enthusiasm of deploying automatic speech recognition (ASR) on mobile devices is driven both by remarkable advances in ASR technology and by the demand for efficient user interfaces on such devices as mobile phones and personal digital assistants (PDAs). This chapter presents an overview of ASR...

  18. Recognition of power quality events by using multiwavelet-based neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kaewarsa, Suriya; Attakitmongcol, Kitti; Kulworawanichpong, Thanatchai [School of Electrical Engineering, Suranaree University of Technology, 111 University Avenue, Muang District, Nakhon Ratchasima 30000 (Thailand)

    2008-05-15

    Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a novel approach for the recognition of power quality disturbances using multiwavelet transform and neural networks. The proposed method employs the multiwavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different power quality signal types efficiency. (author)

  19. Feature selection in classification of eye movements using electrooculography for activity recognition.

    Science.gov (United States)

    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.

  20. Signaling efficiency of Gαq through its effectors p63RhoGEF and GEFT depends on their subcellular location.

    Science.gov (United States)

    Goedhart, Joachim; van Unen, Jakobus; Adjobo-Hermans, Merel J W; Gadella, Theodorus W J

    2013-01-01

    The p63RhoGEF and GEFT proteins are encoded by the same gene and both members of the Dbl family of guanine nucleotide exchange factors. These proteins can be activated by the heterotrimeric G-protein subunit Gαq. We show that p63RhoGEF is located at the plasma membrane, whereas GEFT is confined to the cytoplasm. Live-cell imaging studies yielded quantitative information on diffusion coefficients, association rates and encounter times of GEFT and p63RhoGEF. Calcium signaling was examined as a measure of the signal transmission, revealing more efficient signaling through the membrane-associated p63RhoGEF. A rapamycin dependent recruitment system was used to dynamically alter the subcellular location and concentration of GEFT, showing efficient signaling through GEFT only upon membrane recruitment. Together, our results show efficient signal transmission through membrane located effectors, and highlight a role for increased concentration rather than increased encounter times due to membrane localization in the Gαq mediated pathways to p63RhoGEF and PLCβ.

  1. Efficiency test of filtering methods for the removal of transcranial magnetic stimulation artifacts on human electroencephalography with artificially transcranial magnetic stimulation-corrupted signals

    Science.gov (United States)

    Zilber, Nicolas A.; Katayama, Yoshinori; Iramina, Keiji; Erich, Wintermantel

    2010-05-01

    A new approach is proposed to test the efficiency of methods, such as the Kalman filter and the independent component analysis (ICA), when applied to remove the artifacts induced by transcranial magnetic stimulation (TMS) from electroencephalography (EEG). By using EEG recordings corrupted by TMS induction, the shape of the artifacts is approximately described with a model based on an equivalent circuit simulation. These modeled artifacts are subsequently added to other EEG signals—this time not influenced by TMS. The resulting signals prove of interest since we also know their form without the pseudo-TMS artifacts. Therefore, they enable us to use a fit test to compare the signals we obtain after removing the artifacts with the original signals. This efficiency test turned out very useful in comparing the methods between them, as well as in determining the parameters of the filtering that give satisfactory results with the automatic ICA.

  2. Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds.

    Science.gov (United States)

    Park, Subok; Gallas, Bradon D; Badano, Aldo; Petrick, Nicholas A; Myers, Kyle J

    2007-04-01

    A previous study [J. Opt. Soc. Am. A22, 3 (2005)] has shown that human efficiency for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds is approximately 4%. This human efficiency is much less than the reported 40% efficiency that has been documented for Gaussian-distributed lumpy backgrounds [J. Opt. Soc. Am. A16, 694 (1999) and J. Opt. Soc. Am. A18, 473 (2001)]. We conducted a psychophysical study with a number of changes, specifically in display-device calibration and data scaling, from the design of the aforementioned study. Human efficiency relative to the ideal observer was found again to be approximately 5%. Our variance analysis indicates that neither scaling nor display made a statistically significant difference in human performance for the task. We conclude that the non-Gaussian distributed lumpy background is a major factor in our low human-efficiency results.

  3. Evaluating music emotion recognition:Lessons from music genre recognition?

    OpenAIRE

    Sturm, Bob L.

    2013-01-01

    A fundamental problem with nearly all work in music genre recognition (MGR)is that evaluation lacks validity with respect to the principal goals of MGR. This problem also occurs in the evaluation of music emotion recognition (MER). Standard approaches to evaluation, though easy to implement, do not reliably differentiate between recognizing genre or emotion from music, or by virtue of confounding factors in signals (e.g., equalization). We demonstrate such problems for evaluating an MER syste...

  4. Automatic radar target recognition of objects falling on railway tracks

    International Nuclear Information System (INIS)

    Mroué, A; Heddebaut, M; Elbahhar, F; Rivenq, A; Rouvaen, J-M

    2012-01-01

    This paper presents an automatic radar target recognition procedure based on complex resonances using the signals provided by ultra-wideband radar. This procedure is dedicated to detection and identification of objects lying on railway tracks. For an efficient complex resonance extraction, a comparison between several pole extraction methods is illustrated. Therefore, preprocessing methods are presented aiming to remove most of the erroneous poles interfering with the discrimination scheme. Once physical poles are determined, a specific discrimination technique is introduced based on the Euclidean distances. Both simulation and experimental results are depicted showing an efficient discrimination of different targets including guided transport passengers

  5. Sudden Event Recognition: A Survey

    Directory of Open Access Journals (Sweden)

    Mohd Asyraf Zulkifley

    2013-08-01

    Full Text Available 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.

  6. Speaker Recognition

    DEFF Research Database (Denmark)

    Mølgaard, Lasse Lohilahti; Jørgensen, Kasper Winther

    2005-01-01

    Speaker recognition is basically divided into speaker identification and speaker verification. Verification is the task of automatically determining if a person really is the person he or she claims to be. This technology can be used as a biometric feature for verifying the identity of a person...

  7. Time Lens based Optical Fourier Transformation for All-Optical Signal Processing of Spectrally-Efficient Data

    DEFF Research Database (Denmark)

    Guan, Pengyu; Røge, Kasper Meldgaard; Lillieholm, Mads

    2017-01-01

    We review recent progress in the use of time lens based optical Fourier transformation for advanced all-optical signal processing. A novel time lens based complete optical Fourier transformation (OFT) technique is introduced. This complete OFT is based on two quadratic phase-modulation stages using...... four-wave mixing (FWM), separated by a dispersive medium, which enables time-to-frequency and frequency-to-time conversions simultaneously, thus performing an exchange between the temporal and spectral profiles of the input signal. Using the proposed complete OFT, several advanced all-optical signal......, such as orthogonal frequency division multiplexing (OFDM), Nyquist wavelength-division multiplexing (Nyquist-WDM) and Nyquist optical time division multiplexing (Nyquist-OTDM) signals....

  8. Characterization and optimization of a high-efficiency AlGaAs-On-Insulator-based wavelength converter for 64- and 256-QAM signals

    DEFF Research Database (Denmark)

    Da Ros, Francesco; Yankov, Metodi Plamenov; Porto da Silva, Edson

    2017-01-01

    of the wavelength converter is reported, including the optimization of the AlGaAsOI nano-waveguide in terms of conversion efficiency and associated bandwidth and the analysis of the impact of the converter pump quality and power as well as the signal input power. The optimized converter enables generating idlers......In this paper, we demonstrate wavelength conversion of advanced modulation formats such as 10-GBd 64-QAM and 256-QAM with high conversion efficiency over a 29-nm spectral window by using four-wave mixing in an AlGaAs-On-Insulator (AlGaAsOI) nano-waveguide. A thorough characterization...

  9. Development of an efficient signal amplification strategy for label-free enzyme immunoassay using two site-specific biotinylated recombinant proteins

    International Nuclear Information System (INIS)

    Tang, Jin-Bao; Tang, Ying; Yang, Hong-Ming

    2015-01-01

    Highlights: • An efficient signal amplification strategy for label-free EIA is proposed. • Divalent biotinylated AP and monovalent biotinylated ZZ were prepared via Avitag–BirA system. • The above site-specific biotinylated fusion proteins form complex via SA–biotin interaction. • The mechanism relies on the ZZ–Avi-B/SA/AP–(Avi-B) 2 complex. • The analytical signals are enhanced (32-fold) by the proposed strategy. - Abstract: Constructing a recombinant protein between a reporter enzyme and a detector protein to produce a homogeneous immunological reagent is advantageous over random chemical conjugation. However, the approach hardly recombines multiple enzymes in a difunctional fusion protein, which results in insufficient amplification of the enzymatic signal, thereby limiting its application in further enhancement of analytical signal. In this study, two site-specific biotinylated recombinant proteins, namely, divalent biotinylated alkaline phosphatase (AP) and monovalent biotinylated ZZ domain, were produced by employing the Avitag–BirA system. Through the high streptavidin (SA)–biotin interaction, the divalent biotinylated APs were clustered in the SA–biotin complex and then incorporated with the biotinylated ZZ. This incorporation results in the formation of a functional macromolecule that involves numerous APs, thereby enhancing the enzymatic signal, and in the production of several ZZ molecules for the interaction with immunoglobulin G (IgG) antibody. The advantage of this signal amplification strategy is demonstrated through ELISA, in which the analytical signal was substantially enhanced, with a 32-fold increase in the detection sensitivity compared with the ZZ–AP fusion protein approach. The proposed immunoassay without chemical modification can be an alternative strategy to enhance the analytical signals in various applications involving immunosensors and diagnostic chips, given that the label-free IgG antibody is suitable for

  10. Development of an efficient signal amplification strategy for label-free enzyme immunoassay using two site-specific biotinylated recombinant proteins

    Energy Technology Data Exchange (ETDEWEB)

    Tang, Jin-Bao [School of Pharmacy, Weifang Medical University, Weifang 261053 (China); Tang, Ying [Affiliated Hospital of Weifang Medical University, Weifang 261041 (China); Yang, Hong-Ming, E-mail: yanghongming2006@sohu.com [School of Pharmacy, Weifang Medical University, Weifang 261053 (China)

    2015-02-15

    Highlights: • An efficient signal amplification strategy for label-free EIA is proposed. • Divalent biotinylated AP and monovalent biotinylated ZZ were prepared via Avitag–BirA system. • The above site-specific biotinylated fusion proteins form complex via SA–biotin interaction. • The mechanism relies on the ZZ–Avi-B/SA/AP–(Avi-B){sub 2} complex. • The analytical signals are enhanced (32-fold) by the proposed strategy. - Abstract: Constructing a recombinant protein between a reporter enzyme and a detector protein to produce a homogeneous immunological reagent is advantageous over random chemical conjugation. However, the approach hardly recombines multiple enzymes in a difunctional fusion protein, which results in insufficient amplification of the enzymatic signal, thereby limiting its application in further enhancement of analytical signal. In this study, two site-specific biotinylated recombinant proteins, namely, divalent biotinylated alkaline phosphatase (AP) and monovalent biotinylated ZZ domain, were produced by employing the Avitag–BirA system. Through the high streptavidin (SA)–biotin interaction, the divalent biotinylated APs were clustered in the SA–biotin complex and then incorporated with the biotinylated ZZ. This incorporation results in the formation of a functional macromolecule that involves numerous APs, thereby enhancing the enzymatic signal, and in the production of several ZZ molecules for the interaction with immunoglobulin G (IgG) antibody. The advantage of this signal amplification strategy is demonstrated through ELISA, in which the analytical signal was substantially enhanced, with a 32-fold increase in the detection sensitivity compared with the ZZ–AP fusion protein approach. The proposed immunoassay without chemical modification can be an alternative strategy to enhance the analytical signals in various applications involving immunosensors and diagnostic chips, given that the label-free IgG antibody is suitable

  11. On the (In)Efficiency of the Cross-Correlation Statistic for Gravitational Wave Stochastic Background Signals with Non-Gaussian Noise and Heterogeneous Detector Sensitivities

    OpenAIRE

    Lionel, Martellini; Tania, Regimbau

    2015-01-01

    Under standard assumptions including stationary and serially uncorrelated Gaussian gravitational wave stochastic background signal and noise distributions, as well as homogenous detector sensitivities, the standard cross-correlation detection statistic is known to be optimal in the sense of minimizing the probability of a false dismissal at a fixed value of the probability of a false alarm. The focus of this paper is to analyze the comparative efficiency of this statistic, versus a simple alt...

  12. On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals.

    Science.gov (United States)

    Sahoo, Prasan Kumar; Thakkar, Hiren Kumar; Lin, Wen-Yen; Chang, Po-Cheng; Lee, Ming-Yih

    2018-01-28

    Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the

  13. On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

    Directory of Open Access Journals (Sweden)

    Prasan Kumar Sahoo

    2018-01-01

    Full Text Available Cardiovascular disease (CVD is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI, computerized tomography scan (CT scan, and echocardiography (Echo are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL. In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to

  14. Pattern-Recognition Receptor Signaling Regulator mRNA Expression in Humans and Mice, and in Transient Inflammation or Progressive Fibrosis

    Science.gov (United States)

    Günthner, Roman; Kumar, Vankayala Ramaiah Santhosh; Lorenz, Georg; Anders, Hans-Joachim; Lech, Maciej

    2013-01-01

    The cell type-, organ-, and species-specific expression of the pattern-recognition receptors (PRRs) are well described but little is known about the respective expression profiles of their negative regulators. We therefore determined the mRNA expression levels of A20, CYLD, DUBA, ST2, CD180, SIGIRR, TANK, SOCS1, SOCS3, SHIP, IRAK-M, DOK1, DOK2, SHP1, SHP2, TOLLIP, IRF4, SIKE, NLRX1, ERBIN, CENTB1, and Clec4a2 in human and mouse solid organs. Humans and mice displayed significant differences between their respective mRNA expression patterns of these factors. Additionally, we characterized their expression profiles in mononuclear blood cells upon bacterial endotoxin, which showed a consistent induction of A20, SOCS3, IRAK-M, and Clec4a2 in human and murine cells. Furthermore, we studied the expression pattern in transient kidney ischemia-reperfusion injury versus post-ischemic atrophy and fibrosis in mice. A20, CD180, ST2, SOCS1, SOCS3, SHIP, IRAK-M, DOK1, DOK2, IRF4, CENTB1, and Clec4a2 were all induced, albeit at different times of injury and repair. Progressive fibrosis was associated with a persistent induction of these factors. Thus, the organ- and species-specific expression patterns need to be considered in the design and interpretation of studies related to PRR-mediated innate immunity, which seems to be involved in tissue injury, tissue regeneration and in progressive tissue scarring. PMID:24009023

  15. Cleavable DNA-protein hybrid molecular beacon: A novel efficient signal translator for sensitive fluorescence anisotropy bioassay.

    Science.gov (United States)

    Hu, Pan; Yang, Bin

    2016-01-15

    Due to its unique features such as high sensitivity, homogeneous format, and independence on fluorescent intensity, fluorescence anisotropy (FA) assay has become a hotspot of study in oligonucleotide-based bioassays. However, until now most FA probes require carefully customized structure designs, and thus are neither generalizable for different sensing systems nor effective to obtain sufficient signal response. To address this issue, a cleavable DNA-protein hybrid molecular beacon was successfully engineered for signal amplified FA bioassay, via combining the unique stable structure of molecular beacon and the large molecular mass of streptavidin. Compared with single DNA strand probe or conventional molecular beacon, the DNA-protein hybrid molecular beacon exhibited a much higher FA value, which was potential to obtain high signal-background ratio in sensing process. As proof-of-principle, this novel DNA-protein hybrid molecular beacon was further applied for FA bioassay using DNAzyme-Pb(2+) as a model sensing system. This FA assay approach could selectively detect as low as 0.5nM Pb(2+) in buffer solution, and also be successful for real samples analysis with good recovery values. Compatible with most of oligonucleotide probes' designs and enzyme-based signal amplification strategies, the molecular beacon can serve as a novel signal translator to expand the application prospect of FA technology in various bioassays. Copyright © 2015 Elsevier B.V. All rights reserved.

  16. Facial recognition in education system

    Science.gov (United States)

    Krithika, L. B.; Venkatesh, K.; Rathore, S.; Kumar, M. Harish

    2017-11-01

    Human beings exploit emotions comprehensively for conveying messages and their resolution. Emotion detection and face recognition can provide an interface between the individuals and technologies. The most successful applications of recognition analysis are recognition of faces. Many different techniques have been used to recognize the facial expressions and emotion detection handle varying poses. In this paper, we approach an efficient method to recognize the facial expressions to track face points and distances. This can automatically identify observer face movements and face expression in image. This can capture different aspects of emotion and facial expressions.

  17. Efficient trigger signal generation from wasted backward amplified stimulated emission at optical amplifiers for optical coherence tomography

    Directory of Open Access Journals (Sweden)

    Kim Seung Taek

    2015-01-01

    Full Text Available This paper propose an optical structure to generate trigger signals for optical coherence tomography (OCT using backward light which is usually disposed. The backward light is called backward amplified stimulated emission generated from semiconductor optical amplifier (SOA when using swept wavelength tunable laser (SWTL. A circulator is applied to block undesirable lights in the SWTL instead of an isolator in common SWTL. The circulator also diverts backward amplified spontaneous lights, which finally bring out trigger signals for a high speed digitizer. The spectra of the forward lights at SOA and the waveform of the backward lights were measured to check the procedure of the trigger formation in the experiment. The results showed that the trigger signals from the proposed SWTL with the circulator was quite usable in OCT.

  18. Automated pattern recognition system for noise analysis

    International Nuclear Information System (INIS)

    Sides, W.H. Jr.; Piety, K.R.

    1980-01-01

    A pattern recognition system was developed at ORNL for on-line monitoring of noise signals from sensors in a nuclear power plant. The system continuousy measures the power spectral density (PSD) values of the signals and the statistical characteristics of the PSDs in unattended operation. Through statistical comparison of current with past PSDs (pattern recognition), the system detects changes in the noise signals. Because the noise signals contain information about the current operational condition of the plant, a change in these signals could indicate a change, either normal or abnormal, in the operational condition

  19. Pedestrian recognition using automotive radar sensors

    OpenAIRE

    A. Bartsch; F. Fitzek; R. H. Rasshofer

    2012-01-01

    The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insight...

  20. Bipartite recognition of DNA by TCF/Pangolin is remarkably flexible and contributes to transcriptional responsiveness and tissue specificity of wingless signaling.

    Directory of Open Access Journals (Sweden)

    Hilary C Archbold

    2014-09-01

    Full Text Available The T-cell factor (TCF family of transcription factors are major mediators of Wnt/β-catenin signaling in metazoans. All TCFs contain a High Mobility Group (HMG domain that possesses specific DNA binding activity. In addition, many TCFs contain a second DNA binding domain, the C-clamp, which binds to DNA motifs referred to as Helper sites. While HMG and Helper sites are both important for the activation of several Wnt dependent cis-regulatory modules (W-CRMs, the rules of what constitutes a functional HMG-Helper site pair are unknown. In this report, we employed a combination of in vitro binding, reporter gene analysis and bioinformatics to address this question, using the Drosophila family member TCF/Pangolin (TCF/Pan as a model. We found that while there were constraints for the orientation and spacing of HMG-Helper pairs, the presence of a Helper site near a HMG site in any orientation increased binding and transcriptional response, with some orientations displaying tissue-specific patterns. We found that altering an HMG-Helper site pair from a sub-optimal to optimal orientation/spacing dramatically increased the responsiveness of a W-CRM in several fly tissues. In addition, we used the knowledge gained to bioinformatically identify two novel W-CRMs, one that was activated by Wnt/β-catenin signaling in the prothoracic gland, a tissue not previously connected to this pathway. In sum, this work extends the importance of Helper sites in fly W-CRMs and suggests that the type of HMG-Helper pair is a major factor in setting the threshold for Wnt activation and tissue-responsiveness.

  1. Scheme for efficient extraction of low-frequency signal beyond the quantum limit by frequency-shift detection.

    Science.gov (United States)

    Yang, R G; Zhang, J; Zhai, Z H; Zhai, S Q; Liu, K; Gao, J R

    2015-08-10

    Low-frequency (Hz~kHz) squeezing is very important in many schemes of quantum precision measurement. But it is more difficult than that at megahertz-frequency because of the introduction of laser low-frequency technical noise. In this paper, we propose a scheme to obtain a low-frequency signal beyond the quantum limit from the frequency comb in a non-degenerate frequency and degenerate polarization optical parametric amplifier (NOPA) operating below threshold with type I phase matching by frequency-shift detection. Low-frequency squeezing immune to laser technical noise is obtained by a detection system with a local beam of two-frequency intense laser. Furthermore, the low-frequency squeezing can be used for phase measurement in Mach-Zehnder interferometer, and the signal-to-noise ratio (SNR) can be enhanced greatly.

  2. HPV18 Persistence Impairs Basal and DNA Ligand-Mediated IFN-β and IFN-λ1 Production through Transcriptional Repression of Multiple Downstream Effectors of Pattern Recognition Receptor Signaling.

    Science.gov (United States)

    Albertini, Silvia; Lo Cigno, Irene; Calati, Federica; De Andrea, Marco; Borgogna, Cinzia; Dell'Oste, Valentina; Landolfo, Santo; Gariglio, Marisa

    2018-03-15

    Although it is clear that high-risk human papillomaviruses (HPVs) can selectively infect keratinocytes and persist in the host, it still remains to be unequivocally determined whether they can escape antiviral innate immunity by interfering with pattern recognition receptor (PRR) signaling. In this study, we have assessed the innate immune response in monolayer and organotypic raft cultures of NIKS cells harboring multiple copies of episomal HPV18 (NIKSmcHPV18), which fully recapitulates the persistent state of infection. We show for the first time, to our knowledge, that NIKSmcHPV18, as well as HeLa cells (a cervical carcinoma-derived cell line harboring integrated HPV18 DNA), display marked downregulation of several PRRs, as well as other PRR downstream effectors, such as the adaptor protein stimulator of IFN genes and the transcription factors IRF1 and 7. Importantly, we provide evidence that downregulation of stimulator of IFN genes, cyclic GMP-AMP synthase, and retinoic acid-inducible gene I mRNA levels occurs at the transcriptional level through a novel epigenetic silencing mechanism, as documented by the accumulation of repressive heterochromatin markers seen at the promoter region of these genes. Furthermore, stimulation of NIKSmcHPV18 cells with salmon sperm DNA or poly(deoxyadenylic-deoxythymidylic) acid, two potent inducers of PRR signaling, only partially restored PRR protein expression. Accordingly, the production of IFN-β and IFN-λ 1 was significantly reduced in comparison with the parental NIKS cells, indicating that HPV18 exerts its immunosuppressive activity through downregulation of PRR signaling. Altogether, our findings indicate that high-risk human papillomaviruses have evolved broad-spectrum mechanisms that allow simultaneous depletion of multiple effectors of the innate immunity network, thereby creating an unreactive cellular milieu suitable for viral persistence. Copyright © 2018 by The American Association of Immunologists, Inc.

  3. Thermal efficiency on welding of AA6061-T6 alloy by modified indirect electric arc and current signals digitalisation

    International Nuclear Information System (INIS)

    Ambriz, R. R.; Barrera, G.; Garcia, R.; Lopez, V. H.

    2009-01-01

    The results of the thermal efficiency on welding by modified indirect electric arc technique (MIEA) [1] of the 6061- T6 aluminum alloy are presented. These values are in a range of 90 to 94 %, which depend of the preheating employed. Thermal efficiency was obtained by means of a balance energy which considers the heat input, the amount of melted mass of the welding profiles, and welding parameters during the joining, especially of the arc current data acquisition. Also, some dimensionless parameters were employed in order to determine the approximation grade of the melted pool, the heat affected zone (HAZ), and their corresponding values with the experimental results. (Author) 13 refs

  4. A CMOS power-efficient low-noise current-mode front-end amplifier for neural signal recording.

    Science.gov (United States)

    Wu, Chung-Yu; Chen, Wei-Ming; Kuo, Liang-Ting

    2013-04-01

    In this paper, a new current-mode front-end amplifier (CMFEA) for neural signal recording systems is proposed. In the proposed CMFEA, a current-mode preamplifier with an active feedback loop operated at very low frequency is designed as the first gain stage to bypass any dc offset current generated by the electrode-tissue interface and to achieve a low high-pass cutoff frequency below 0.5 Hz. No reset signal or ultra-large pseudo resistor is required. The current-mode preamplifier has low dc operation current to enhance low-noise performance and decrease power consumption. A programmable current gain stage is adopted to provide adjustable gain for adaptive signal scaling. A following current-mode filter is designed to adjust the low-pass cutoff frequency for different neural signals. The proposed CMFEA is designed and fabricated in 0.18-μm CMOS technology and the area of the core circuit is 0.076 mm(2). The measured high-pass cutoff frequency is as low as 0.3 Hz and the low-pass cutoff frequency is adjustable from 1 kHz to 10 kHz. The measured maximum current gain is 55.9 dB. The measured input-referred current noise density is 153 fA /√Hz , and the power consumption is 13 μW at 1-V power supply. The fabricated CMFEA has been successfully applied to the animal test for recording the seizure ECoG of Long-Evan rats.

  5. DETECCIÓN Y RECONOCIMIENTO DE SEÑALES DE TRÁNSITO UTILIZANDO MATCHING DE CHAMFER DETECTION AND RECOGNITION OF TRAFFIC SIGNALS USING MATCHING OF CHAMFER

    Directory of Open Access Journals (Sweden)

    Cristián Arriagada García

    2007-08-01

    available details at first sight etc, in our case a prototype is presented which allows the opportunity to help a car driver to pay attention to the traffic signs on the road, attempting to assist the driver, and at the same time to avoid traffic infractions and accidents. The prototype developed with computer vision techniques, allows the detection and recognition of signposts that are on the road and to inform its nature to the driver through an audible sign or a visual projection. The research was mainly centered on the phases of initial detection; with the objective of taking into account a quick heuristic, taking advantage of the segmentation by color, with their characteristics of invariability of system HSV (Brightness, Saturation, Value [10], and/or initial detection by borders, making use of the improved algorithm of Chamfer [1], finally to detect and recognize the symbols of the sign, using transformation of distance techniques and hierarchical matching of Chamfer[1], conditioned to this kind of application. The prototype in the phase of proof was implemented in Matlab, with the initial purpose of proving the effectiveness of the methods that were used. Once they are proved an OpenCV was used to verify its functioning in real time.

  6. The recognition heuristic : A review of theory and tests

    OpenAIRE

    Pachur, T.; Todd, P.; Gigerenzer, G.; Schooler, L.; Goldstein, D.

    2011-01-01

    The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) th...

  7. Overexpression of AtABCG25 enhances the abscisic acid signal in guard cells and improves plant water use efficiency.

    Science.gov (United States)

    Kuromori, Takashi; Fujita, Miki; Urano, Kaoru; Tanabata, Takanari; Sugimoto, Eriko; Shinozaki, Kazuo

    2016-10-01

    In addition to improving drought tolerance, improvement of water use efficiency is a major challenge in plant physiology. Due to their trade-off relationships, it is generally considered that achieving stress tolerance is incompatible with maintaining stable growth. Abscisic acid (ABA) is a key phytohormone that regulates the balance between intrinsic growth and environmental responses. Previously, we identified AtABCG25 as a cell-membrane ABA transporter that export ABA from the inside to the outside of cells. AtABCG25-overexpressing plants showed a lower transpiration phenotype without any growth retardation. Here, we dissected this useful trait using precise phenotyping approaches. AtABCG25 overexpression stimulated a local ABA response in guard cells. Furthermore, AtABCG25 overexpression enhanced drought tolerance, probably resulting from maintenance of water contents over the common threshold for survival after drought stress treatment. Finally, we observed enhanced water use efficiency by overexpression of AtABCG25, in addition to drought tolerance. These results were consistent with the function of AtABCG25 as an ABA efflux transporter. This unique trait may be generally useful for improving the water use efficiency and drought tolerance of plants. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  8. Monitoring Activity for Recognition of Illness in Experimentally Infected Weaned Piglets Using Received Signal Strength Indication ZigBee-based Wireless Acceleration Sensor

    Directory of Open Access Journals (Sweden)

    Sonia Tabasum Ahmed

    2016-01-01

    Full Text Available In this experiment, we proposed and implemented a disease forecasting system using a received signal strength indication ZigBee-based wireless network with a 3-axis acceleration sensor to detect illness at an early stage by monitoring movement of experimentally infected weaned piglets. Twenty seven piglets were divided into control, Salmonella enteritidis (SE infection, and Escherichia coli (EC infection group, and their movements were monitored for five days using wireless sensor nodes on their backs. Data generated showed the 3-axis movement of piglets (X-axis: left and right direction, Y-axis: anteroposterior direction, and Z-axis: up and down direction at five different time periods. Piglets in both infected groups had lower weight gain and feed intake, as well as higher feed conversion ratios than the control group (p<0.05. Infection with SE and EC resulted in reduced body temperature of the piglets at day 2, 4, and 5 (p<0.05. The early morning X-axis movement did not differ between groups; however, the Y-axis movement was higher in the EC group (day 1 and 2, and the Z-axis movement was higher in the EC (day 1 and SE group (day 4 during different experimental periods (p<0.05. The morning X and Y-axis movement did not differ between treatment groups. However, the Z-axis movement was higher in both infected groups at day 1 and lower at day 4 compared to the control (p<0.05. The midday X-axis movement was significantly lower in both infected groups (day 4 and 5 compared to the control (p<0.05, whereas the Y-axis movement did not differ. The Z-axis movement was highest in the SE group at day 1 and 2 and lower at day 4 and 5 (p<0.05. Evening X-axis movement was highest in the control group throughout the experimental period. During day 1 and 2, the Z-axis movement was higher in both of the infected groups; whereas it was lower in the SE group during day 3 and 4 (p<0.05. During day 1 and 2, the night X-axis movement was lower and the Z

  9. Man machine interface based on speech recognition

    International Nuclear Information System (INIS)

    Jorge, Carlos A.F.; Aghina, Mauricio A.C.; Mol, Antonio C.A.; Pereira, Claudio M.N.A.

    2007-01-01

    This work reports the development of a Man Machine Interface based on speech recognition. The system must recognize spoken commands, and execute the desired tasks, without manual interventions of operators. The range of applications goes from the execution of commands in an industrial plant's control room, to navigation and interaction in virtual environments. Results are reported for isolated word recognition, the isolated words corresponding to the spoken commands. For the pre-processing stage, relevant parameters are extracted from the speech signals, using the cepstral analysis technique, that are used for isolated word recognition, and corresponds to the inputs of an artificial neural network, that performs recognition tasks. (author)

  10. Blind Recognition of Binary BCH Codes for Cognitive Radios

    Directory of Open Access Journals (Sweden)

    Jing Zhou

    2016-01-01

    Full Text Available A novel algorithm of blind recognition of Bose-Chaudhuri-Hocquenghem (BCH codes is proposed to solve the problem of Adaptive Coding and Modulation (ACM in cognitive radio systems. The recognition algorithm is based on soft decision situations. The code length is firstly estimated by comparing the Log-Likelihood Ratios (LLRs of the syndromes, which are obtained according to the minimum binary parity check matrixes of different primitive polynomials. After that, by comparing the LLRs of different minimum polynomials, the code roots and generator polynomial are reconstructed. When comparing with some previous approaches, our algorithm yields better performance even on very low Signal-Noise-Ratios (SNRs with lower calculation complexity. Simulation results show the efficiency of the proposed algorithm.

  11. Histogram Equalization to Model Adaptation for Robust Speech Recognition

    Directory of Open Access Journals (Sweden)

    Suh Youngjoo

    2010-01-01

    Full Text Available We propose a new model adaptation method based on the histogram equalization technique for providing robustness in noisy environments. The trained acoustic mean models of a speech recognizer are adapted into environmentally matched conditions by using the histogram equalization algorithm on a single utterance basis. For more robust speech recognition in the heavily noisy conditions, trained acoustic covariance models are efficiently adapted by the signal-to-noise ratio-dependent linear interpolation between trained covariance models and utterance-level sample covariance models. Speech recognition experiments on both the digit-based Aurora2 task and the large vocabulary-based task showed that the proposed model adaptation approach provides significant performance improvements compared to the baseline speech recognizer trained on the clean speech data.

  12. Pattern Recognition Control Design

    Science.gov (United States)

    Gambone, Elisabeth A.

    2018-01-01

    Spacecraft control algorithms must know the expected vehicle response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach was used to investigate the relationship between the control effector commands and spacecraft responses. Instead of supplying the approximated vehicle properties and the thruster performance characteristics, a database of information relating the thruster ring commands and the desired vehicle response was used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands was analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center to analyze flight dynamics Monte Carlo data sets through pattern recognition methods was used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands was established, it was used in place of traditional control methods and gains set. This pattern recognition approach was compared with traditional control algorithms to determine the potential benefits and uses.

  13. 联合频偏估计与循环矩的MPSK信号调制识别算法%Joint Frequency Offset Estimation and Cyclic Moments MPSK Signal Modulation Recognition Algoritm

    Institute of Scientific and Technical Information of China (English)

    吴涛; 狄旻珉; 黄国策

    2014-01-01

    To overcome the problem of sensitive to frequency offset while using cyclic cumulants to classify QPSK and 8PSK, by jointing frequency offset estimation and 2/4 order cyclic moments,this paper proposed a modulation classification algorithm in allusion to the BPSK,QPSK and 8PSK signals,making use of the cyclic moments which has similar classification features with cy-clic cumulants but with lower calculation complexity.Both theoretical analysis and simulation results show that this algorithm is ro-bust to frequency offset and the recognition ratio of BPSK achieved 100%in SNR=-11 dB.%根据循环累积量分类QPSK和8PSK时易受频偏影响的问题,利用循环矩和循环累积量类似的分类特征且计算复杂度低的特点,提出了联合频偏估计与二/四阶循环矩的针对BPSK、QPSK和8PSK信号的调制识别算法。理论分析和仿真结果表明该算法对载波频偏具有强鲁棒性,且在SNR=-11 dB时对BPSK的识别率达到100%。

  14. Flexible Piezoelectric Sensor-Based Gait Recognition

    Directory of Open Access Journals (Sweden)

    Youngsu Cha

    2018-02-01

    Full Text Available Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %.

  15. Investigations on search methods for speech recognition using weighted finite state transducers

    OpenAIRE

    Rybach, David

    2014-01-01

    The search problem in the statistical approach to speech recognition is to find the most likely word sequence for an observed speech signal using a combination of knowledge sources, i.e. the language model, the pronunciation model, and the acoustic models of phones. The resulting search space is enormous. Therefore, an efficient search strategy is required to compute the result with a feasible amount of time and memory. The structured statistical models as well as their combination, the searc...

  16. Adult Word Recognition and Visual Sequential Memory

    Science.gov (United States)

    Holmes, V. M.

    2012-01-01

    Two experiments were conducted investigating the role of visual sequential memory skill in the word recognition efficiency of undergraduate university students. Word recognition was assessed in a lexical decision task using regularly and strangely spelt words, and nonwords that were either standard orthographically legal strings or items made from…

  17. Toward more efficient fabrication of high-density 2-D VCSEL arrays for spatial redundancy and/or multi-level signal communication

    Science.gov (United States)

    Roscher, Hendrik; Gerlach, Philipp; Khan, Faisal Nadeem; Kroner, Andrea; Stach, Martin; Weigl, Alexander; Michalzik, Rainer

    2006-04-01

    We present flip-chip attached high-speed VCSELs in 2-D arrays with record-high intra-cell packing densities. The advances of VCSEL array technology toward improved thermal performance and more efficient fabrication are reviewed, and the introduction of self-aligned features to these devices is pointed out. The structure of close-spaced wedge-shaped VCSELs is discussed and their static and dynamic characteristics are presented including an examination of the modal structure by near-field measurements. The lasers flip-chip bonded to a silicon-based test platform exhibit 3-dB and 10-dB bandwidths of 7.7 GHz and 9.8 GHz, respectively. Open 12.5 Gbit/s two-level eye patterns are demonstrated. We discuss the uses of high packing densities for the increase of the total amount of data throughput an array can deliver in the course of its life. One such approach is to provide up to two backup VCSELs per fiber channel that can extend the lifetimes of parallel transmitters through redundancy of light sources. Another is to increase the information density by using multiple VCSELs per 50 μm core diameter multimode fiber to generate more complex signals. A novel scheme using three butt-coupled VCSELs per fiber for the generation of four-level signals in the optical domain is proposed. First experiments are demonstrated using two VCSELs butt-coupled to the same standard glass fiber, each modulated with two-level signals to produce four-level signals at the photoreceiver. A four-level direct modulation of one VCSEL within a triple of devices produced first 20.6 Gbit/s (10.3 Gsymbols/s) four-level eyes, leaving two VCSELs as backup sources.

  18. Iris Recognition Using Wavelet

    Directory of Open Access Journals (Sweden)

    Khaliq Masood

    2013-08-01

    Full Text Available Biometric systems are getting more attention in the present era. Iris recognition is one of the most secure and authentic among the other biometrics and this field demands more authentic, reliable and fast algorithms to implement these biometric systems in real time. In this paper, an efficient localization technique is presented to identify pupil and iris boundaries using histogram of the iris image. Two small portions of iris have been used for polar transformation to reduce computational time and to increase the efficiency of the system. Wavelet transform is used for feature vector generation. Rotation of iris is compensated without shifts in the iris code. System is tested on Multimedia University Iris Database and results show that proposed system has encouraging performance.

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

    Science.gov (United States)

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

    2016-12-01

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

  20. An effective approach for iris recognition using phase-based image matching.

    Science.gov (United States)

    Miyazawa, Kazuyuki; Ito, Koichi; Aoki, Takafumi; Kobayashi, Koji; Nakajima, Hiroshi

    2008-10-01

    This paper presents an efficient algorithm for iris recognition using phase-based image matching--an image matching technique using phase components in 2D Discrete Fourier Transforms (DFTs) of given images. Experimental evaluation using CASIA iris image databases (versions 1.0 and 2.0) and Iris Challenge Evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier Phase Code (FPC) for representing iris information. The 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art Digital Signal Processing (DSP) technology.

  1. Quantitative SERS Detection of Dopamine in Cerebrospinal Fluid by Dual-Recognition-Induced Hot Spot Generation.

    Science.gov (United States)

    Zhang, Kun; Liu, Yu; Wang, Yuning; Zhang, Ren; Liu, Jiangang; Wei, Jia; Qian, Hufei; Qian, Kun; Chen, Ruoping; Liu, Baohong

    2018-05-09

    Reliable profiling of the extracellular dopamine (DA) concentration in the central nervous system is essential for a deep understanding of its biological and pathological functions. However, quantitative determination of this neurotransmitter remains a challenge because of the extremely low concentration of DA in the cerebrospinal fluid (CSF) of patients. Herein, on the basis of the specific recognition of boronate toward diol and N-hydroxysuccinimide ester toward the amine group, a simple and highly sensitive strategy was presented for DA detection by using surface-enhanced Raman scattering (SERS) spectroscopy as a signal readout. This was realized by first immobilizing 3,3'-dithiodipropionic acid di( N-hydroxysuccinimide ester) on gold thin film surfaces to capture DA, followed by introducing 3-mercaptophenylboronic acid (3-MPBA)-functionalized silver nanoparticles to generate numerous plasmonic "hot spots" with the nanoparticle-on-mirror geometry. Such a dual-recognition mechanism not only avoids complicated bioelement-based manipulations but also efficiently decreases the background signal. With the direct use of the recognition probe 3-MPBA as a Raman reporter, the "signal-on" SERS method was employed to quantify the concentration of DA from 1 pM to 1 μM with a detection limit of 0.3 pM. Moreover, our dual-recognition-directed SERS assay exhibited a high resistance to cerebral interference and was successfully applied to monitoring of DA in CSF samples of patients.

  2. Red Bell Pepper Chromoplasts Exhibit in Vitro Import Competency and Membrane Targeting of Passenger Proteins from the Thylakoidal Sec and ΔpH Pathways but Not the Chloroplast Signal Recognition Particle Pathway1

    Science.gov (United States)

    Summer, Elizabeth J.; Cline, Kenneth

    1999-01-01

    Chloroplast to chromoplast development involves new synthesis and plastid localization of nuclear-encoded proteins, as well as changes in the organization of internal plastid membrane compartments. We have demonstrated that isolated red bell pepper (Capsicum annuum) chromoplasts contain the 75-kD component of the chloroplast outer envelope translocon (Toc75) and are capable of importing chloroplast precursors in an ATP-dependent fashion, indicating a functional general import apparatus. The isolated chromoplasts were able to further localize the 33- and 17-kD subunits of the photosystem II O2-evolution complex (OE33 and OE17, respectively), lumen-targeted precursors that utilize the thylakoidal Sec and ΔpH pathways, respectively, to the lumen of an internal membrane compartment. Chromoplasts contained the thylakoid Sec component protein, cpSecA, at levels comparable to chloroplasts. Routing of OE17 to the lumen was abolished by ionophores, suggesting that routing is dependent on a transmembrane ΔpH. The chloroplast signal recognition particle pathway precursor major photosystem II light-harvesting chlorophyll a/b protein failed to associate with chromoplast membranes and instead accumulated in the stroma following import. The Pftf (plastid fusion/translocation factor), a chromoplast protein, integrated into the internal membranes of chromoplasts during in vitro assays, and immunoblot analysis indicated that endogenous plastid fusion/translocation factor was also an integral membrane protein of chromoplasts. These data demonstrate that the internal membranes of chromoplasts are functional with respect to protein translocation on the thylakoid Sec and ΔpH pathways. PMID:9952453

  3. Detection of Acute Myocardial Infarction in a Pig Model Using the SAN-Atrial-AVN-His (SAAH) Electrocardiogram (ECG), Model PHS-A10, an Automated and Integrated Signals Recognition System.

    Science.gov (United States)

    Zhao, Wenjiao; Lu, Guihua; Liu, Li; Sun, Zhishan; Wu, Mingxin; Yi, Wenyan; Chen, Haiyan; Li, Yanhui; Tang, Lilong; Zeng, Jianping

    2018-03-04

    BACKGROUND The aim of this study was to compare the use of the standard 12-lead electrocardiogram (ECG) with the SAN-Atrial-AVN-His (SAAH) ECG (Model PHS-A10), a new automated and integrated signals recognition system that detects micro-waveforms within the P, QRS, and T-wave, in a pig model of acute myocardial infarction (MI). MATERIAL AND METHODS Six medium-sized domestic Chinese pigs underwent general anesthesia, and an angioplasty balloon was placed and dilated for 120 minutes in the first diagonal coronary artery arising from the left anterior descending (LAD) coronary artery. A standard ECG and a SAAH ECG (Model PHS-A10) were used to evaluate: 1) the number of wavelets in ST-T segment in lead V5; 2) the duration of the repolarization initial (Ri), or duration of the wavelets starting from the J-point to the endpoint of the wavelets in the ST interval; 3) the duration of the repolarization terminal (Rt), of the wavelets, starting from the endpoint of the wavelets in the ST interval to the cross-point of the T-wave and baseline; 4) the ratio Ri: Rt. RESULTS Following coronary artery occlusion, duration of Ri and Ri/Rt increased, and Rt decreased, which was detected by the SAAH ECG (Model PHS-A10) within 12 seconds, compared with standard ECG that detected ST segment depression at 24 seconds following coronary artery occlusion. CONCLUSIONS The findings from this preliminary study in a pig model of acute MI support the need for clinical studies to evaluate the SAAH ECG (Model PHS-A10) for the early detection of acute MI.

  4. Physics of Automatic Target Recognition

    CERN Document Server

    Sadjadi, Firooz

    2007-01-01

    Physics of Automatic Target Recognition addresses the fundamental physical bases of sensing, and information extraction in the state-of-the art automatic target recognition field. It explores both passive and active multispectral sensing, polarimetric diversity, complex signature exploitation, sensor and processing adaptation, transformation of electromagnetic and acoustic waves in their interactions with targets, background clutter, transmission media, and sensing elements. The general inverse scattering, and advanced signal processing techniques and scientific evaluation methodologies being used in this multi disciplinary field will be part of this exposition. The issues of modeling of target signatures in various spectral modalities, LADAR, IR, SAR, high resolution radar, acoustic, seismic, visible, hyperspectral, in diverse geometric aspects will be addressed. The methods for signal processing and classification will cover concepts such as sensor adaptive and artificial neural networks, time reversal filt...

  5. Hardware-efficient robust biometric identification from 0.58 second template and 12 features of limb (Lead I) ECG signal using logistic regression classifier.

    Science.gov (United States)

    Sahadat, Md Nazmus; Jacobs, Eddie L; Morshed, Bashir I

    2014-01-01

    The electrocardiogram (ECG), widely known as a cardiac diagnostic signal, has recently been proposed for biometric identification of individuals; however reliability and reproducibility are of research interest. In this paper, we propose a template matching technique with 12 features using logistic regression classifier that achieved high reliability and identification accuracy. Non-invasive ECG signals were captured using our custom-built ambulatory EEG/ECG embedded device (NeuroMonitor). ECG data were collected from healthy subjects (10), between 25-35 years, for 10 seconds per trial. The number of trials from each subject was 10. From each trial, only 0.58 seconds of Lead I ECG data were used as template. Hardware-efficient fiducial point detection technique was implemented for feature extraction. To obtain repeated random sub-sampling validation, data were randomly separated into training and testing sets at a ratio of 80:20. Test data were used to find the classification accuracy. ECG template data with 12 extracted features provided the best performance in terms of accuracy (up to 100%) and processing complexity (computation time of 1.2ms). This work shows that a single limb (Lead I) ECG can robustly identify an individual quickly and reliably with minimal contact and data processing using the proposed algorithm.

  6. A stilbene synthase allele from a Chinese wild grapevine confers resistance to powdery mildew by recruiting salicylic acid signalling for efficient defence.

    Science.gov (United States)

    Jiao, Yuntong; Xu, Weirong; Duan, Dong; Wang, Yuejin; Nick, Peter

    2016-10-01

    Stilbenes are central phytoalexins in Vitis, and induction of the key enzyme stilbene synthase (STS) is pivotal for disease resistance. Here, we address the potential for breeding resistance using an STS allele isolated from Chinese wild grapevine Vitis pseudoreticulata (VpSTS) by comparison with its homologue from Vitis vinifera cv. 'Carigane' (VvSTS). Although the coding regions of both alleles are very similar (>99% identity on the amino acid level), the promoter regions are significantly different. By expression in Arabidopsis as a heterologous system, we show that the allele from the wild Chinese grapevine can confer accumulation of stilbenes and resistance against the powdery mildew Golovinomyces cichoracearum, whereas the allele from the vinifera cultivar cannot. To dissect the upstream signalling driving the activation of this promoter, we used a dual-luciferase reporter system in a grapevine cell culture. We show elevated responsiveness of the promoter from the wild grape to salicylic acid (SA) and to the pathogen-associated molecular pattern (PAMP) flg22, equal induction of both alleles by jasmonic acid (JA), and a lack of response to the cell death-inducing elicitor Harpin. This elevated SA response of the VpSTS promoter depends on calcium influx, oxidative burst by RboH, mitogen-activated protein kinase (MAPK) signalling, and JA synthesis. We integrate the data in the context of a model where the resistance of V. pseudoreticulata is linked to a more efficient recruitment of SA signalling for phytoalexin synthesis. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology.

  7. Efficient visual object and word recognition relies on high spatial frequency coding in the left posterior fusiform gyrus: evidence from a case-series of patients with ventral occipito-temporal cortex damage.

    Science.gov (United States)

    Roberts, Daniel J; Woollams, Anna M; Kim, Esther; Beeson, Pelagie M; Rapcsak, Steven Z; Lambon Ralph, Matthew A

    2013-11-01

    Recent visual neuroscience investigations suggest that ventral occipito-temporal cortex is retinotopically organized, with high acuity foveal input projecting primarily to the posterior fusiform gyrus (pFG), making this region crucial for coding high spatial frequency information. Because high spatial frequencies are critical for fine-grained visual discrimination, we hypothesized that damage to the left pFG should have an adverse effect not only on efficient reading, as observed in pure alexia, but also on the processing of complex non-orthographic visual stimuli. Consistent with this hypothesis, we obtained evidence that a large case series (n = 20) of patients with lesions centered on left pFG: 1) Exhibited reduced sensitivity to high spatial frequencies; 2) demonstrated prolonged response latencies both in reading (pure alexia) and object naming; and 3) were especially sensitive to visual complexity and similarity when discriminating between novel visual patterns. These results suggest that the patients' dual reading and non-orthographic recognition impairments have a common underlying mechanism and reflect the loss of high spatial frequency visual information normally coded in the left pFG.

  8. A small and efficient dimerization/packaging signal of rat VL30 RNA and its use in murine leukemia virus-VL30-derived vectors for gene transfer.

    Science.gov (United States)

    Torrent, C; Gabus, C; Darlix, J L

    1994-02-01

    Retroviral genomes consist of two identical RNA molecules associated at their 5' ends by the dimer linkage structure located in the packaging element (Psi or E) necessary for RNA dimerization in vitro and packaging in vivo. In murine leukemia virus (MLV)-derived vectors designed for gene transfer, the Psi + sequence of 600 nucleotides directs the packaging of recombinant RNAs into MLV virions produced by helper cells. By using in vitro RNA dimerization as a screening system, a sequence of rat VL30 RNA located next to the 5' end of the Harvey mouse sarcoma virus genome and as small as 67 nucleotides was found to form stable dimeric RNA. In addition, a purine-rich sequence located at the 5' end of this VL30 RNA seems to be critical for RNA dimerization. When this VL30 element was extended by 107 nucleotides at its 3' end and inserted into an MLV-derived vector lacking MLV Psi +, it directed the efficient encapsidation of recombinant RNAs into MLV virions. Because this VL30 packaging signal is smaller and more efficient in packaging recombinant RNAs than the MLV Psi + and does not contain gag or glyco-gag coding sequences, its use in MLV-derived vectors should render even more unlikely recombinations which could generate replication-competent viruses. Therefore, utilization of the rat VL30 packaging sequence should improve the biological safety of MLV vectors for human gene transfer.

  9. Modulation of pathogen recognition by autophagy

    Directory of Open Access Journals (Sweden)

    Ji Eun eOh

    2012-03-01

    Full Text Available Autophagy is an ancient biological process for maintaining cellular homeostasis by degradation of long-lived cytosolic proteins and organelles. Recent studies demonstrated that autophagy is availed by immune cells to regulate innate immunity. On the one hand, cells exert direct effector function by degrading intracellular pathogens; on the other hand, autophagy modulates pathogen recognition and downstream signaling for innate immune responses. Pathogen recognition via pattern recognition receptors induces autophagy. The function of phagocytic cells is enhanced by recruitment of autophagy-related proteins. Moreover, autophagy acts as a delivery system for viral replication complexes to migrate to the endosomal compartments where virus sensing occurs. In another case, key molecules of the autophagic pathway have been found to negatively regulate immune signaling, thus preventing aberrant activation of cytokine production and consequent immune responses. In this review, we focus on the recent advances in the role of autophagy in pathogen recognition and modulation of innate immune responses.

  10. Kazakh Traditional Dance Gesture Recognition

    Science.gov (United States)

    Nussipbekov, A. K.; Amirgaliyev, E. N.; Hahn, Minsoo

    2014-04-01

    Full body gesture recognition is an important and interdisciplinary research field which is widely used in many application spheres including dance gesture recognition. The rapid growth of technology in recent years brought a lot of contribution in this domain. However it is still challenging task. In this paper we implement Kazakh traditional dance gesture recognition. We use Microsoft Kinect camera to obtain human skeleton and depth information. Then we apply tree-structured Bayesian network and Expectation Maximization algorithm with K-means clustering to calculate conditional linear Gaussians for classifying poses. And finally we use Hidden Markov Model to detect dance gestures. Our main contribution is that we extend Kinect skeleton by adding headwear as a new skeleton joint which is calculated from depth image. This novelty allows us to significantly improve the accuracy of head gesture recognition of a dancer which in turn plays considerable role in whole body gesture recognition. Experimental results show the efficiency of the proposed method and that its performance is comparable to the state-of-the-art system performances.

  11. Efficient CEPSTRAL Normalization for Robust Speech Recognition

    National Research Council Canada - National Science Library

    Liu, Fu-Hua; Stern, Richard M; Huang, Xuedong; Acero, Alejandro

    1993-01-01

    .... We compare the performance of these algorithms with the very simple RASTA and cepstral mean normalization procedures, describing the performance of these algorithms in the context of the 1992 DARPA...

  12. Automatic modulation format recognition for the next generation optical communication networks using artificial neural networks

    Science.gov (United States)

    Guesmi, Latifa; Hraghi, Abir; Menif, Mourad

    2015-03-01

    A new technique for Automatic Modulation Format Recognition (AMFR) in next generation optical communication networks is presented. This technique uses the Artificial Neural Network (ANN) in conjunction with the features of Linear Optical Sampling (LOS) of the detected signal at high bit rates using direct detection or coherent detection. The use of LOS method for this purpose mainly driven by the increase of bit rates which enables the measurement of eye diagrams. The efficiency of this technique is demonstrated under different transmission impairments such as chromatic dispersion (CD) in the range of -500 to 500 ps/nm, differential group delay (DGD) in the range of 0-15 ps and the optical signal tonoise ratio (OSNR) in the range of 10-30 dB. The results of numerical simulation for various modulation formats demonstrate successful recognition from a known bit rates with a higher estimation accuracy, which exceeds 99.8%.

  13. Efficient and Robust Signal Approximations

    Science.gov (United States)

    2009-05-01

    gains of MrICA over the non- adaptive wavelet method for these images are: 2.43 bpp , 0.62 bpp , 2.91 bpp , 2.78 bpp , 3.39 bpp , and 2.69 bpp . Figure 3.6...shows six examples of 64 × 64 images encoded at 20dB. The coding gain values of the adaptive method are in this case 1.5 bpp , 1.48 bpp , 0.33 bpp , 0.23... bpp , 0.45 bpp , and 1.23 bpp . (For both figures, the colormaps are maximally stretched to enhance visibility.) As a general conclusion, MrICA obtains a

  14. Pattern recognition and classification an introduction

    CERN Document Server

    Dougherty, Geoff

    2012-01-01

    The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer visi

  15. Acoustic Pattern Recognition on Android Devices

    DEFF Research Database (Denmark)

    Møller, Maiken Bjerg; Gaarsdal, Jesper; Steen, Kim Arild

    2013-01-01

    an Android application developed for acoustic pattern recognition of bird species. The acoustic data is recorded using a built-in microphone, and pattern recognition is performed on the device, requiring no network connection. The algorithm is implemented in C++ as a native Android module and the Open......CV library is used for signal processing. We conclude that the approach presented here is a viable solution to pattern recognition problems. Since it requires no network connection, it shows promise in fields such as wildlife research....

  16. Automated recognition system for power quality disturbances

    Science.gov (United States)

    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

  17. The automaticity of emotion recognition.

    Science.gov (United States)

    Tracy, Jessica L; Robins, Richard W

    2008-02-01

    Evolutionary accounts of emotion typically assume that humans evolved to quickly and efficiently recognize emotion expressions because these expressions convey fitness-enhancing messages. The present research tested this assumption in 2 studies. Specifically, the authors examined (a) how quickly perceivers could recognize expressions of anger, contempt, disgust, embarrassment, fear, happiness, pride, sadness, shame, and surprise; (b) whether accuracy is improved when perceivers deliberate about each expression's meaning (vs. respond as quickly as possible); and (c) whether accurate recognition can occur under cognitive load. Across both studies, perceivers quickly and efficiently (i.e., under cognitive load) recognized most emotion expressions, including the self-conscious emotions of pride, embarrassment, and shame. Deliberation improved accuracy in some cases, but these improvements were relatively small. Discussion focuses on the implications of these findings for the cognitive processes underlying emotion recognition.

  18. Track recognition in the central drift chamber of the SAPHIR detector at ELSA and first reconstruction of real tracks

    International Nuclear Information System (INIS)

    Korn, P.

    1991-02-01

    The FORTRAN program for pattern recognition in the central drift chamber of SAPHIR has been modified in order to find tracks with more than one missing wire signal and has been optimized in resolving the left/right ambiguities. The second part of this report deals with the reconstruction of some real tracks (γ → e + e - ), which were measured with SAPHIR. The efficiency of the central drift chamber and the space-to-drift time-relation are discussed. (orig.)

  19. Physiological arousal in processing recognition information

    Directory of Open Access Journals (Sweden)

    Guy Hochman

    2010-07-01

    Full Text Available The recognition heuristic (RH; Goldstein and Gigerenzer, 2002 suggests that, when applicable, probabilistic inferences are based on a noncompensatory examination of whether an object is recognized or not. The overall findings on the processes that underlie this fast and frugal heuristic are somewhat mixed, and many studies have expressed the need for considering a more compensatory integration of recognition information. Regardless of the mechanism involved, it is clear that recognition has a strong influence on choices, and this finding might be explained by the fact that recognition cues arouse affect and thus receive more attention than cognitive cues. To test this assumption, we investigated whether recognition results in a direct affective signal by measuring physiological arousal (i.e., peripheral arterial tone in the established city-size task. We found that recognition of cities does not directly result in increased physiological arousal. Moreover, the results show that physiological arousal increased with increasing inconsistency between recognition information and additional cue information. These findings support predictions derived by a compensatory Parallel Constraint Satisfaction model rather than predictions of noncompensatory models. Additional results concerning confidence ratings, response times, and choice proportions further demonstrated that recognition information and other cognitive cues are integrated in a compensatory manner.

  20. Degraded character recognition based on gradient pattern

    Science.gov (United States)

    Babu, D. R. Ramesh; Ravishankar, M.; Kumar, Manish; Wadera, Kevin; Raj, Aakash

    2010-02-01

    Degraded character recognition is a challenging problem in the field of Optical Character Recognition (OCR). The performance of an optical character recognition depends upon printed quality of the input documents. Many OCRs have been designed which correctly identifies the fine printed documents. But, very few reported work has been found on the recognition of the degraded documents. The efficiency of the OCRs system decreases if the input image is degraded. In this paper, a novel approach based on gradient pattern for recognizing degraded printed character is proposed. The approach makes use of gradient pattern of an individual character for recognition. Experiments were conducted on character image that is either digitally written or a degraded character extracted from historical documents and the results are found to be satisfactory.

  1. Activity recognition from minimal distinguishing subsequence mining

    Science.gov (United States)

    Iqbal, Mohammad; Pao, Hsing-Kuo

    2017-08-01

    Human activity recognition is one of the most important research topics in the era of Internet of Things. To separate different activities given sensory data, we utilize a Minimal Distinguishing Subsequence (MDS) mining approach to efficiently find distinguishing patterns among different activities. We first transform the sensory data into a series of sensor triggering events and operate the MDS mining procedure afterwards. The gap constraints are also considered in the MDS mining. Given the multi-class nature of most activity recognition tasks, we modify the MDS mining approach from a binary case to a multi-class one to fit the need for multiple activity recognition. We also study how to select the best parameter set including the minimal and the maximal support thresholds in finding the MDSs for effective activity recognition. Overall, the prediction accuracy is 86.59% on the van Kasteren dataset which consists of four different activities for recognition.

  2. Face Detection and Recognition

    National Research Council Canada - National Science Library

    Jain, Anil K

    2004-01-01

    This report describes research efforts towards developing algorithms for a robust face recognition system to overcome many of the limitations found in existing two-dimensional facial recognition systems...

  3. Predicting automatic speech recognition performance over communication channels from instrumental speech quality and intelligibility scores

    NARCIS (Netherlands)

    Gallardo, L.F.; Möller, S.; Beerends, J.

    2017-01-01

    The performance of automatic speech recognition based on coded-decoded speech heavily depends on the quality of the transmitted signals, determined by channel impairments. This paper examines relationships between speech recognition performance and measurements of speech quality and intelligibility

  4. Wavelet analysis and it's applications to recognition of nuclear explosion and lightning

    International Nuclear Information System (INIS)

    Zhang Zhongshan; Zhang Enshan; Gao Chunxia

    1999-01-01

    An approach to feature extraction and recognition of the characteristic signal is studied. And the method is applied to recognition of nuclear explosions and lightning. The results show the method is valid

  5. Automatic speech recognition (zero crossing method). Automatic recognition of isolated vowels

    International Nuclear Information System (INIS)

    Dupeyrat, Benoit

    1975-01-01

    This note describes a recognition method of isolated vowels, using a preprocessing of the vocal signal. The processing extracts the extrema of the vocal signal and the interval time separating them (Zero crossing distances of the first derivative of the signal). The recognition of vowels uses normalized histograms of the values of these intervals. The program determines a distance between the histogram of the sound to be recognized and histograms models built during a learning phase. The results processed on real time by a minicomputer, are relatively independent of the speaker, the fundamental frequency being not allowed to vary too much (i.e. speakers of the same sex). (author) [fr

  6. Graphical symbol recognition

    OpenAIRE

    K.C. , Santosh; Wendling , Laurent

    2015-01-01

    International audience; The chapter focuses on one of the key issues in document image processing i.e., graphical symbol recognition. Graphical symbol recognition is a sub-field of a larger research domain: pattern recognition. The chapter covers several approaches (i.e., statistical, structural and syntactic) and specially designed symbol recognition techniques inspired by real-world industrial problems. It, in general, contains research problems, state-of-the-art methods that convey basic s...

  7. Combination of minimum enclosing balls classifier with SVM in coal-rock recognition

    Science.gov (United States)

    Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

    2017-01-01

    Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition. PMID:28937987

  8. Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

    Science.gov (United States)

    Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

    2017-01-01

    Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

  9. Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

    Directory of Open Access Journals (Sweden)

    QingJun Song

    Full Text Available Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB algorithm plus Support vector machine (SVM is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

  10. Recognition of Handwriting from Electromyography

    Science.gov (United States)

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

    2009-01-01

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

  11. Robust recognition via information theoretic learning

    CERN Document Server

    He, Ran; Yuan, Xiaotong; Wang, Liang

    2014-01-01

    This Springer Brief represents a comprehensive review of information theoretic methods for robust recognition. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy.The?authors?resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. For computational efficiency,?the brief?introduces the additive and multip

  12. Pattern recognition in spectra

    International Nuclear Information System (INIS)

    Gebran, M; Paletou, F

    2017-01-01

    We present a new automated procedure that simultaneously derives the effective temperature T eff , surface gravity log g , metallicity [ Fe/H ], and equatorial projected rotational velocity v e sin i for stars. The procedure is inspired by the well-known PCA-based inversion of spectropolarimetric full-Stokes solar data, which was used both for Zeeman and Hanle effects. The efficiency and accuracy of this procedure have been proven for FGK, A, and late type dwarf stars of K and M spectral types. Learning databases are generated from the Elodie stellar spectra library using observed spectra for which fundamental parameters were already evaluated or with synthetic data. The synthetic spectra are calculated using ATLAS9 model atmospheres. This technique helped us to detect many peculiar stars such as Am, Ap, HgMn, SiEuCr and binaries. This fast and efficient technique could be used every time a pattern recognition is needed. One important application is the understanding of the physical properties of planetary surfaces by comparing aboard instrument data to synthetic ones. (paper)

  13. Recognition and Toleration

    DEFF Research Database (Denmark)

    Lægaard, Sune

    2010-01-01

    Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms...... or interpretations of recognition and toleration are considered, confusing and problematic uses of the terms are noted, and the compatibility of toleration and recognition is discussed. The article argues that there is a range of legitimate and importantly different conceptions of both toleration and recognition...

  14. A Feature-Based Structural Measure: An Image Similarity Measure for Face Recognition

    Directory of Open Access Journals (Sweden)

    Noor Abdalrazak Shnain

    2017-08-01

    Full Text Available Facial recognition is one of the most challenging and interesting problems within the field of computer vision and pattern recognition. During the last few years, it has gained special attention due to its importance in relation to current issues such as security, surveillance systems and forensics analysis. Despite this high level of attention to facial recognition, the success is still limited by certain conditions; there is no method which gives reliable results in all situations. In this paper, we propose an efficient similarity index that resolves the shortcomings of the existing measures of feature and structural similarity. This measure, called the Feature-Based Structural Measure (FSM, combines the best features of the well-known SSIM (structural similarity index measure and FSIM (feature similarity index measure approaches, striking a balance between performance for similar and dissimilar images of human faces. In addition to the statistical structural properties provided by SSIM, edge detection is incorporated in FSM as a distinctive structural feature. Its performance is tested for a wide range of PSNR (peak signal-to-noise ratio, using ORL (Olivetti Research Laboratory, now AT&T Laboratory Cambridge and FEI (Faculty of Industrial Engineering, São Bernardo do Campo, São Paulo, Brazil databases. The proposed measure is tested under conditions of Gaussian noise; simulation results show that the proposed FSM outperforms the well-known SSIM and FSIM approaches in its efficiency of similarity detection and recognition of human faces.

  15. 8 CFR 1292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

    Science.gov (United States)

    2010-01-01

    ...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 1292.2...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization...

  16. Development of a System for Automatic Recognition of Speech

    Directory of Open Access Journals (Sweden)

    Roman Jarina

    2003-01-01

    Full Text Available The article gives a review of a research on processing and automatic recognition of speech signals (ARR at the Department of Telecommunications of the Faculty of Electrical Engineering, University of iilina. On-going research is oriented to speech parametrization using 2-dimensional cepstral analysis, and to an application of HMMs and neural networks for speech recognition in Slovak language. The article summarizes achieved results and outlines future orientation of our research in automatic speech recognition.

  17. Hybrid Speaker Recognition Using Universal Acoustic Model

    Science.gov (United States)

    Nishimura, Jun; Kuroda, Tadahiro

    We propose a novel speaker recognition approach using a speaker-independent universal acoustic model (UAM) for sensornet applications. In sensornet applications such as “Business Microscope”, interactions among knowledge workers in an organization can be visualized by sensing face-to-face communication using wearable sensor nodes. In conventional studies, speakers are detected by comparing energy of input speech signals among the nodes. However, there are often synchronization errors among the nodes which degrade the speaker recognition performance. By focusing on property of the speaker's acoustic channel, UAM can provide robustness against the synchronization error. The overall speaker recognition accuracy is improved by combining UAM with the energy-based approach. For 0.1s speech inputs and 4 subjects, speaker recognition accuracy of 94% is achieved at the synchronization error less than 100ms.

  18. Window Size Impact in Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Oresti Banos

    2014-04-01

    Full Text Available 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.

  19. Application of robust face recognition in video surveillance systems

    Science.gov (United States)

    Zhang, De-xin; An, Peng; Zhang, Hao-xiang

    2018-03-01

    In this paper, we propose a video searching system that utilizes face recognition as searching indexing feature. As the applications of video cameras have great increase in recent years, face recognition makes a perfect fit for searching targeted individuals within the vast amount of video data. However, the performance of such searching depends on the quality of face images recorded in the video signals. Since the surveillance video cameras record videos without fixed postures for the object, face occlusion is very common in everyday video. The proposed system builds a model for occluded faces using fuzzy principal component analysis (FPCA), and reconstructs the human faces with the available information. Experimental results show that the system has very high efficiency in processing the real life videos, and it is very robust to various kinds of face occlusions. Hence it can relieve people reviewers from the front of the monitors and greatly enhances the efficiency as well. The proposed system has been installed and applied in various environments and has already demonstrated its power by helping solving real cases.

  20. Optical Pattern Recognition

    Science.gov (United States)

    Yu, Francis T. S.; Jutamulia, Suganda

    2008-10-01

    Contributors; Preface; 1. Pattern recognition with optics Francis T. S. Yu and Don A. Gregory; 2. Hybrid neural networks for nonlinear pattern recognition Taiwei Lu; 3. Wavelets, optics, and pattern recognition Yao Li and Yunglong Sheng; 4. Applications of the fractional Fourier transform to optical pattern recognition David Mendlovic, Zeev Zalesky and Haldum M. Oxaktas; 5. Optical implementation of mathematical morphology Tien-Hsin Chao; 6. Nonlinear optical correlators with improved discrimination capability for object location and recognition Leonid P. Yaroslavsky; 7. Distortion-invariant quadratic filters Gregory Gheen; 8. Composite filter synthesis as applied to pattern recognition Shizhou Yin and Guowen Lu; 9. Iterative procedures in electro-optical pattern recognition Joseph Shamir; 10. Optoelectronic hybrid system for three-dimensional object pattern recognition Guoguang Mu, Mingzhe Lu and Ying Sun; 11. Applications of photrefractive devices in optical pattern recognition Ziangyang Yang; 12. Optical pattern recognition with microlasers Eung-Gi Paek; 13. Optical properties and applications of bacteriorhodopsin Q. Wang Song and Yu-He Zhang; 14. Liquid-crystal spatial light modulators Aris Tanone and Suganda Jutamulia; 15. Representations of fully complex functions on real-time spatial light modulators Robert W. Cohn and Laurence G. Hassbrook; Index.

  1. Effects of Pre-Experimental Knowledge on Recognition Memory

    Science.gov (United States)

    Bird, Chris M.; Davies, Rachel A.; Ward, Jamie; Burgess, Neil

    2011-01-01

    The influence of pre-experimental autobiographical knowledge on recognition memory was investigated using as memoranda faces that were either personally known or unknown to the participant. Under a dual process theory, such knowledge boosted both recollection- and familiarity-based recognition judgements. Under an unequal variance signal detection…

  2. Testing Theories of Recognition Memory by Predicting Performance Across Paradigms

    Science.gov (United States)

    Smith, David G.; Duncan, Matthew J. J.

    2004-01-01

    Signal-detection theory (SDT) accounts of recognition judgments depend on the assumption that recognition decisions result from a single familiarity-based process. However, fits of a hybrid SDT model, called dual-process theory (DPT), have provided evidence for the existence of a second, recollection-based process. In 2 experiments, the authors…

  3. From Off-line to On-line Handwriting Recognition

    NARCIS (Netherlands)

    Lallican, P.; Viard-Gaudin, C.; Knerr, S.

    2004-01-01

    On-line handwriting includes more information on time order of the writing signal and on the dynamics of the writing process than off-line handwriting. Therefore, on-line recognition systems achieve higher recognition rates. This can be concluded from results reported in the literature, and has been

  4. Pattern recognition & machine learning

    CERN Document Server

    Anzai, Y

    1992-01-01

    This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

  5. The recognition heuristic: a review of theory and tests.

    Science.gov (United States)

    Pachur, Thorsten; Todd, Peter M; Gigerenzer, Gerd; Schooler, Lael J; Goldstein, Daniel G

    2011-01-01

    The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) that recognition supersedes further cue knowledge; (d) that its use can produce the less-is-more effect - the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference).

  6. The Recognition Heuristic: A Review of Theory and Tests

    Directory of Open Access Journals (Sweden)

    Thorsten ePachur

    2011-07-01

    Full Text Available The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a that recognition is often an ecologically valid cue; (b that people often follow recognition when making inferences; (c that recognition supersedes further cue knowledge; (d that its use can produce the less-is-more effect—the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference.

  7. The Recognition Heuristic: A Review of Theory and Tests

    Science.gov (United States)

    Pachur, Thorsten; Todd, Peter M.; Gigerenzer, Gerd; Schooler, Lael J.; Goldstein, Daniel G.

    2011-01-01

    The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) that recognition supersedes further cue knowledge; (d) that its use can produce the less-is-more effect – the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference). PMID:21779266

  8. Electromyographic Grasp Recognition for a Five Fingered Robotic Hand

    Directory of Open Access Journals (Sweden)

    Nayan M. Kakoty

    2012-09-01

    Full Text Available This paper presents classification of grasp types based on surface electromyographic signals. Classification is through radial basis function kernel support vector machine using sum of wavelet decomposition coefficients of the EMG signals. In a study involving six subjects, we achieved an average recognition rate of 86%. The electromyographic grasp recognition together with a 8-bit microcontroller has been employed to control a fivefingered robotic hand to emulate six grasp types used during 70% daily living activities.

  9. Support vector machine for automatic pain recognition

    Science.gov (United States)

    Monwar, Md Maruf; Rezaei, Siamak

    2009-02-01

    Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

  10. Picture languages formal models for picture recognition

    CERN Document Server

    Rosenfeld, Azriel

    1979-01-01

    Computer Science and Applied Mathematics: Picture Languages: Formal Models for Picture Recognition treats pictorial pattern recognition from the formal standpoint of automata theory. This book emphasizes the capabilities and relative efficiencies of two types of automata-array automata and cellular array automata, with respect to various array recognition tasks. The array automata are simple processors that perform sequences of operations on arrays, while the cellular array automata are arrays of processors that operate on pictures in a highly parallel fashion, one processor per picture element. This compilation also reviews a collection of results on two-dimensional sequential and parallel array acceptors. Some of the analogous one-dimensional results and array grammars and their relation to acceptors are likewise covered in this text. This publication is suitable for researchers, professionals, and specialists interested in pattern recognition and automata theory.

  11. Embedded Face Detection and Recognition

    Directory of Open Access Journals (Sweden)

    Göksel Günlü

    2012-10-01

    Full Text Available The need to increase security in open or public spaces has in turn given rise to the requirement to monitor these spaces and analyse those images on-site and on-time. At this point, the use of smart cameras – of which the popularity has been increasing – is one step ahead. With sensors and Digital Signal Processors (DSPs, smart cameras generate ad hoc results by analysing the numeric images transmitted from the sensor by means of a variety of image-processing algorithms. Since the images are not transmitted to a distance processing unit but rather are processed inside the camera, it does not necessitate high-bandwidth networks or high processor powered systems; it can instantaneously decide on the required access. Nonetheless, on account of restricted memory, processing power and overall power, image processing algorithms need to be developed and optimized for embedded processors. Among these algorithms, one of the most important is for face detection and recognition. A number of face detection and recognition methods have been proposed recently and many of these methods have been tested on general-purpose processors. In smart cameras – which are real-life applications of such methods – the widest use is on DSPs. In the present study, the Viola-Jones face detection method – which was reported to run faster on PCs – was optimized for DSPs; the face recognition method was combined with the developed sub-region and mask-based DCT (Discrete Cosine Transform. As the employed DSP is a fixed-point processor, the processes were performed with integers insofar as it was possible. To enable face recognition, the image was divided into sub-regions and from each sub-region the robust coefficients against disruptive elements – like face expression, illumination, etc. – were selected as the features. The discrimination of the selected features was enhanced via LDA (Linear Discriminant Analysis and then employed for recognition. Thanks to its

  12. An Ultrasonic Pattern Recognition Approach to Welding Defect Classification

    International Nuclear Information System (INIS)

    Song, Sung Jin

    1995-01-01

    Classification of flaws in weldments from their ultrasonic scattering signals is very important in quantitative nondestructive evaluation. This problem is ideally suited to a modern ultrasonic pattern recognition technique. Here brief discussion on systematic approach to this methodology is presented including ultrasonic feature extraction, feature selection and classification. A stronger emphasis is placed on probabilistic neural networks as efficient classifiers for many practical classification problems. In an example probabilistic neural networks are applied to classify flaws in weldments into 3 classes such as cracks, porosity and slag inclusions. Probabilistic nets are shown to be able to exhibit high performance of other classifiers without any training time overhead. In addition, forward selection scheme for sensitive features is addressed to enhance network performance

  13. Binary zone-plate array for a parallel joint transform correlator applied to face recognition.

    Science.gov (United States)

    Kodate, K; Hashimoto, A; Thapliya, R

    1999-05-10

    Taking advantage of small aberrations, high efficiency, and compactness, we developed a new, to our knowledge, design procedure for a binary zone-plate array (BZPA) and applied it to a parallel joint transform correlator for the recognition of the human face. Pairs of reference and unknown images of faces are displayed on a liquid-crystal spatial light modulator (SLM), Fourier transformed by the BZPA, intensity recorded on an optically addressable SLM, and inversely Fourier transformed to obtain correlation signals. Consideration of the bandwidth allows the relations among the channel number, the numerical aperture of the zone plates, and the pattern size to be determined. Experimentally a five-channel parallel correlator was implemented and tested successfully with a 100-person database. The design and the fabrication of a 20-channel BZPA for phonetic character recognition are also included.

  14. Paradigms in object recognition

    International Nuclear Information System (INIS)

    Mutihac, R.; Mutihac, R.C.

    1999-09-01

    A broad range of approaches has been proposed and applied for the complex and rather difficult task of object recognition that involves the determination of object characteristics and object classification into one of many a priori object types. Our paper revises briefly the three main different paradigms in pattern recognition, namely Bayesian statistics, neural networks, and expert systems. (author)

  15. Infant Visual Recognition Memory

    Science.gov (United States)

    Rose, Susan A.; Feldman, Judith F.; Jankowski, Jeffery J.

    2004-01-01

    Visual recognition memory is a robust form of memory that is evident from early infancy, shows pronounced developmental change, and is influenced by many of the same factors that affect adult memory; it is surprisingly resistant to decay and interference. Infant visual recognition memory shows (a) modest reliability, (b) good discriminant…

  16. Recognition and Toleration

    DEFF Research Database (Denmark)

    Lægaard, Sune

    2010-01-01

    Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms or inter...

  17. Gender recognition from vocal source

    Science.gov (United States)

    Sorokin, V. N.; Makarov, I. S.

    2008-07-01

    Efficiency of automatic recognition of male and female voices based on solving the inverse problem for glottis area dynamics and for waveform of the glottal airflow volume velocity pulse is studied. The inverse problem is regularized through the use of analytical models of the voice excitation pulse and of the dynamics of the glottis area, as well as the model of one-dimensional glottal airflow. Parameters of these models and spectral parameters of the volume velocity pulse are considered. The following parameters are found to be most promising: the instant of maximum glottis area, the maximum derivative of the area, the slope of the spectrum of the glottal airflow volume velocity pulse, the amplitude ratios of harmonics of this spectrum, and the pitch. On the plane of the first two main components in the space of these parameters, an almost twofold decrease in the classification error relative to that for the pitch alone is attained. The male voice recognition probability is found to be 94.7%, and the female voice recognition probability is 95.9%.

  18. Signals for the initiation and termination of synthesis of the viral strand of bacteriophage f1

    International Nuclear Information System (INIS)

    Dotto, G.P.; Horiuchi, K.; Jakes, K.S.; Zinder, N.D.

    1983-01-01

    In this paper the sequence around the plus origin that is required for efficient plus-strand synthesis as well as that necessary for gene-II-protein recognition is described. Results which demonstrate that the nucleotide sequence of the f1 plus origin contains two overlapping but distinct signals, one for initiation and the other for termination of plus-strand synthesis is presented. 29 references, 6 figures, 1 table

  19. Challenging ocular image recognition

    Science.gov (United States)

    Pauca, V. Paúl; Forkin, Michael; Xu, Xiao; Plemmons, Robert; Ross, Arun A.

    2011-06-01

    Ocular recognition is a new area of biometric investigation targeted at overcoming the limitations of iris recognition performance in the presence of non-ideal data. There are several advantages for increasing the area beyond the iris, yet there are also key issues that must be addressed such as size of the ocular region, factors affecting performance, and appropriate corpora to study these factors in isolation. In this paper, we explore and identify some of these issues with the goal of better defining parameters for ocular recognition. An empirical study is performed where iris recognition methods are contrasted with texture and point operators on existing iris and face datasets. The experimental results show a dramatic recognition performance gain when additional features are considered in the presence of poor quality iris data, offering strong evidence for extending interest beyond the iris. The experiments also highlight the need for the direct collection of additional ocular imagery.

  20. Individual recognition based on communication behaviour of male fowl.

    Science.gov (United States)

    Smith, Carolynn L; Taubert, Jessica; Weldon, Kimberly; Evans, Christopher S

    2016-04-01

    Correctly directing social behaviour towards a specific individual requires an ability to discriminate between conspecifics. The mechanisms of individual recognition include phenotype matching and familiarity-based recognition. Communication-based recognition is a subset of familiarity-based recognition wherein the classification is based on behavioural or distinctive signalling properties. Male fowl (Gallus gallus) produce a visual display (tidbitting) upon finding food in the presence of a female. Females typically approach displaying males. However, males may tidbit without food. We used the distinctiveness of the visual display and the unreliability of some males to test for communication-based recognition in female fowl. We manipulated the prior experience of the hens with the males to create two classes of males: S(+) wherein the tidbitting signal was paired with a food reward to the female, and S (-) wherein the tidbitting signal occurred without food reward. We then conducted a sequential discrimination test with hens using a live video feed of a familiar male. The results of the discrimination tests revealed that hens discriminated between categories of males based on their signalling behaviour. These results suggest that fowl possess a communication-based recognition system. This is the first demonstration of live-to-video transfer of recognition in any species of bird. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Automatic Modulation Recognition by Support Vector Machines Using Wavelet Kernel

    Energy Technology Data Exchange (ETDEWEB)

    Feng, X Z; Yang, J; Luo, F L; Chen, J Y; Zhong, X P [College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha (China)

    2006-10-15

    Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method.

  2. Automatic Modulation Recognition by Support Vector Machines Using Wavelet Kernel

    International Nuclear Information System (INIS)

    Feng, X Z; Yang, J; Luo, F L; Chen, J Y; Zhong, X P

    2006-01-01

    Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method

  3. An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition.

    Science.gov (United States)

    Rasouli, Mahdi; Chen, Yi; Basu, Arindam; Kukreja, Sunil L; Thakor, Nitish V

    2018-04-01

    Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic

  4. A Hierarchical Representation for Human Activity Recognition with Noisy Labels

    NARCIS (Netherlands)

    Hu, N.; Englebienne, G.; Lou, Z.; Kröse, B.

    2015-01-01

    Human activity recognition is an essential task for robots to effectively and efficiently interact with the end users. Many machine learning approaches for activity recognition systems have been proposed recently. Most of these methods are built upon a strong assumption that the labels in the

  5. Recognition of social identity in ants

    DEFF Research Database (Denmark)

    Bos, Nick; d'Ettorre, Patrizia

    2012-01-01

    Recognizing the identity of others, from the individual to the group level, is a hallmark of society. Ants, and other social insects, have evolved advanced societies characterized by efficient social recognition systems. Colony identity is mediated by colony specific signature mixtures, a blend...

  6. Post-editing through Speech Recognition

    DEFF Research Database (Denmark)

    Mesa-Lao, Bartolomé

    (i.e. typing, handwriting and speaking) to improve the efficiency and accuracy of the translation process. However, further studies need to be conducted to build up new knowledge about the way in which state-of-the-art speech recognition software can be applied to the post-editing process...

  7. Handwriting Moroccan regions recognition using Tifinagh character

    Directory of Open Access Journals (Sweden)

    B. El Kessab

    2015-09-01

    In this context we propose a data set for handwritten Tifinagh regions composed of 1600 image (100 Image for each region. The dataset can be used in one hand to test the efficiency of the Tifinagh region recognition system in extraction of characteristics significatives and the correct identification of each region in classification phase in the other hand.

  8. Random clustering ferns for multimodal object recognition

    OpenAIRE

    Villamizar Vergel, Michael Alejandro; Garrell Zulueta, Anais; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc

    2017-01-01

    The final publication is available at link.springer.com We propose an efficient and robust method for the recognition of objects exhibiting multiple intra-class modes, where each one is associated with a particular object appearance. The proposed method, called random clustering ferns, combines synergically a single and real-time classifier, based on the boosted assembling of extremely randomized trees (ferns), with an unsupervised and probabilistic approach in order to recognize efficient...

  9. An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition

    Directory of Open Access Journals (Sweden)

    Ming Liu

    2017-09-01

    Full Text Available Algorithms for locomotion mode recognition (LMR based on surface electromyography and mechanical sensors have recently been developed and could be used for the neural control of powered prosthetic legs. However, the variations in input signals, caused by physical changes at the sensor interface and human physiological changes, may threaten the reliability of these algorithms. This study aimed to investigate the effectiveness of applying adaptive pattern classifiers for LMR. Three adaptive classifiers, i.e., entropy-based adaptation (EBA, LearnIng From Testing data (LIFT, and Transductive Support Vector Machine (TSVM, were compared and offline evaluated using data collected from two able-bodied subjects and one transfemoral amputee. The offline analysis indicated that the adaptive classifier could effectively maintain or restore the performance of the LMR algorithm when gradual signal variations occurred. EBA and LIFT were recommended because of their better performance and higher computational efficiency. Finally, the EBA was implemented for real-time human-in-the-loop prosthesis control. The online evaluation showed that the applied EBA effectively adapted to changes in input signals across sessions and yielded more reliable prosthesis control over time, compared with the LMR without adaptation. The developed novel adaptive strategy may further enhance the reliability of neurally-controlled prosthetic legs.

  10. Contact-Free Heartbeat Signal for Human Identification and Forensics

    DEFF Research Database (Denmark)

    Nasrollahi, Kamal; Haque, Mohammad Ahsanul; Irani, Ramin

    2017-01-01

    The heartbeat signal, which is one of the physiological signals, is of great importance in many real-world applications, for example, in patient monitoring and biometric recognition. The traditional methods for measuring such this signal use contact-based sensors that need to be installed...... been developed for contact-free extraction of the heartbeat signal. We have recently used the contact-free measured heartbeat signal, for bio- metric recognition, and have obtained promising results, indicating the importance of these signals for biometrics recognition and also for forensics...

  11. Retinoic acid signalling is required for the efficient differentiation of CD4+ T cells into pathogenic effector cells during the development of intestinal inflammation

    DEFF Research Database (Denmark)

    Rivollier, Aymeric Marie Christian; Pool, Lieneke; Frising, Ulrika

    Epidemiological studies of vitamin A-deficient populations have illustrated the importance of the vitamin A metabolite retinoic acid (RA) in mucosal immune responses. However, RA seems to be a double-edge sword in CD4+ T cell biology. While it sustains the development of foxp3+ regulatory T cells......, it was also very recently reported to be essential for the stability of the Th1 lineage and to prevent transition to a Th17 program. Here we explored the role of RA signalling in CD4+ T cells during the development of intestinal inflammation in the T cell transfer colitis model. We found that RA signalling......-deficient CD4+ T cells are less potent at inducing intestinal inflammation compared to their RA signalling-competent counterparts and exhibit a differentiation skewing towards more IFNγ- IL-17+, IL-17+IFNγ+ and foxp3+ cells, while their capacity to differentiate into IL-17-IFNγ+ Th1 cells is compromised...

  12. An Introduction to Face Recognition Technology

    Directory of Open Access Journals (Sweden)

    Shang-Hung Lin

    2000-01-01

    Full Text Available Recently face recognition is attracting much attention in the society of network multimedia information access.  Areas such as network security, content indexing and retrieval, and video compression benefits from face recognition technology because "people" are the center of attention in a lot of video.  Network access control via face recognition not only makes hackers virtually impossible to steal one's "password", but also increases the user-friendliness in human-computer interaction.  Indexing and/or retrieving video data based on the appearances of particular persons will be useful for users such as news reporters, political scientists, and moviegoers.  For the applications of videophone and teleconferencing, the assistance of face recognition also provides a more efficient coding scheme.  In this paper, we give an introductory course of this new information processing technology.  The paper shows the readers the generic framework for the face recognition system, and the variants that are frequently encountered by the face recognizer.  Several famous face recognition algorithms, such as eigenfaces and neural networks, will also be explained.

  13. 8 CFR 292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

    Science.gov (United States)

    2010-01-01

    ...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 292.2...; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization established in the United...

  14. Very deep recurrent convolutional neural network for object recognition

    Science.gov (United States)

    Brahimi, Sourour; Ben Aoun, Najib; Ben Amar, Chokri

    2017-03-01

    In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.

  15. [Diagnostic efficiency of decline rate of signal intensity and apparent diffusion coefficient with different b values for differentiating benign and malignant breast lesions on diffusion-weighted 3.0T magnetic resonance imaging].

    Science.gov (United States)

    Jiang, Jing; Liu, Wanhua; Ye, Yuanyuan; Wang, Rui; Li, Fengfang; Peng, Chengyu

    2014-06-17

    To investigate the diagnostic efficiency of decline rate of signal intensity and apparent diffusion coefficient with different b values for differentiating benign and malignant breast lesions on diffusion-weighted 3.0 T magnetic resonance imaging. A total of 152 patients with 162 confirmed histopathologically breast lesions (85 malignant and 77 benign) underwent 3.0 T diffusion-weighted magnetic resonance imaging. Four b values (0, 400, 800 and 1 000 s/mm²) were used. The signal intensity and ADC values of breast lesions were measured respectively. The signal intensity decline rate (SIDR) and apparent diffusion coefficient decline rate (ADCDR) were calculated respectively. SIDR = (signal intensity of lesions with low b value-signal intensity of lesions with high b value)/signal intensity of lesions with low b value, ADCDR = (ADC value of lesions with low b value-ADC value of lesions with high b value) /ADC value of lesions with low b value. The independent sample t-test was employed for statistical analyses and the receiver operating characteristic (ROC) curve for evaluating the diagnosis efficiency of SIDR and ADCDR values. Significant differences were observed in SIDR between benign and malignant breast lesions with b values of 0-400, 400-800 and 800-1 000 s/mm². The sensitivities of SIDR for differentiating benign and malignant breast lesions were 61.2%, 68.2% and 67.1%, the specificities 74.0%, 85.7% and 67.5%, the diagnosis accordance rates 67.3%, 76.5% and 67.3%, the positive predictive values 72.2%, 84.1% and 69.5% and the negative predictive values 63.3%, 71.0% and 65.0% respectively. Significant differences were observed in ADCDR between benign and malignant breast lesions with b values of 400-800 s/mm² and 800-1 000 s/mm². The sensitivities of SDR for differentiating benign and malignant breast lesions were 80.0% and 65.9%, the specificities 72.7% and 65.0%, the diagnostic accordance rates 76.5% and 65.4%, the positive predictive values 76.4% and 67

  16. Speech emotion recognition methods: A literature review

    Science.gov (United States)

    Basharirad, Babak; Moradhaseli, Mohammadreza

    2017-10-01

    Recently, attention of the emotional speech signals research has been boosted in human machine interfaces due to availability of high computation capability. There are many systems proposed in the literature to identify the emotional state through speech. Selection of suitable feature sets, design of a proper classifications methods and prepare an appropriate dataset are the main key issues of speech emotion recognition systems. This paper critically analyzed the current available approaches of speech emotion recognition methods based on the three evaluating parameters (feature set, classification of features, accurately usage). In addition, this paper also evaluates the performance and limitations of available methods. Furthermore, it highlights the current promising direction for improvement of speech emotion recognition systems.

  17. Accessing Specific Peptide Recognition by Combinatorial Chemistry

    DEFF Research Database (Denmark)

    Li, Ming

    Molecular recognition is at the basis of all processes for life, and plays a central role in many biological processes, such as protein folding, the structural organization of cells and organelles, signal transduction, and the immune response. Hence, my PhD project is entitled “Accessing Specific...... Peptide Recognition by Combinatorial Chemistry”. Molecular recognition is a specific interaction between two or more molecules through noncovalent bonding, such as hydrogen bonding, metal coordination, van der Waals forces, π−π, hydrophobic, or electrostatic interactions. The association involves kinetic....... Combinatorial chemistry was invented in 1980s based on observation of functional aspects of the adaptive immune system. It was employed for drug development and optimization in conjunction with high-throughput synthesis and screening. (chapter 2) Combinatorial chemistry is able to rapidly produce many thousands...

  18. Harmonization versus Mutual Recognition

    DEFF Research Database (Denmark)

    Jørgensen, Jan Guldager; Schröder, Philipp

    The present paper examines trade liberalization driven by the coordination of product standards. For oligopolistic firms situated in separate markets that are initially sheltered by national standards, mutual recognition of standards implies entry and reduced profits at home paired with the oppor......The present paper examines trade liberalization driven by the coordination of product standards. For oligopolistic firms situated in separate markets that are initially sheltered by national standards, mutual recognition of standards implies entry and reduced profits at home paired...... countries and three firms, where firms first lobby for the policy coordination regime (harmonization versus mutual recognition), and subsequently, in case of harmonization, the global standard is auctioned among the firms. We discuss welfare effects and conclude with policy implications. In particular......, harmonized standards may fail to harvest the full pro-competitive effects from trade liberalization compared to mutual recognition; moreover, the issue is most pronounced in markets featuring price competition....

  19. CASE Recognition Awards.

    Science.gov (United States)

    Currents, 1985

    1985-01-01

    A total of 294 schools, colleges, and universities received prizes in this year's CASE Recognition program. Awards were given in: public relations programs, student recruitment, marketing, program pulications, news writing, fund raising, radio programming, school periodicals, etc. (MLW)

  20. Models of Recognition, Repetition Priming, and Fluency : Exploring a New Framework

    Science.gov (United States)

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

    2012-01-01

    We present a new modeling framework for recognition memory and repetition priming based on signal detection theory. We use this framework to specify and test the predictions of 4 models: (a) a single-system (SS) model, in which one continuous memory signal drives recognition and priming; (b) a multiple-systems-1 (MS1) model, in which completely…

  1. The Recognition Of Fatigue

    DEFF Research Database (Denmark)

    Elsass, Peter; Jensen, Bodil; Mørup, Rikke

    2007-01-01

    Elsass P., Jensen B., Morup R., Thogersen M.H. (2007). The Recognition Of Fatigue: A qualitative study of life-stories from rehabilitation clients. International Journal of Psychosocial Rehabilitation. 11 (2), 75-87......Elsass P., Jensen B., Morup R., Thogersen M.H. (2007). The Recognition Of Fatigue: A qualitative study of life-stories from rehabilitation clients. International Journal of Psychosocial Rehabilitation. 11 (2), 75-87...

  2. Signaling Mechanisms in Pattern-Triggered Immunity (PTI)

    KAUST Repository

    Bigeard, Jean; Colcombet, Jean; Hirt, Heribert

    2015-01-01

    In nature, plants constantly have to face pathogen attacks. However, plant disease rarely occurs due to efficient immune systems possessed by the host plants. Pathogens are perceived by two different recognition systems that initiate the so-called pattern-triggered immunity (PTI) and effector-triggered immunity (ETI), both of which are accompanied by a set of induced defenses that usually repel pathogen attacks. Here we discuss the complex network of signaling pathways occurring during PTI, focusing on the involvement of mitogen-activated protein kinases. © 2015 The Author.

  3. Modeling Fan Effects on the Time Course of Associative Recognition

    Science.gov (United States)

    Schneider, Darryl W.; Anderson, John R.

    2012-01-01

    We investigated the time course of associative recognition using the response signal procedure, whereby a stimulus is presented and followed after a variable lag by a signal indicating that an immediate response is required. More specifically, we examined the effects of associative fan (the number of associations that an item has with other items…

  4. Gender Recognition from Unconstrained and Articulated Human Body

    Science.gov (United States)

    Wu, Qin; Guo, Guodong

    2014-01-01

    Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition. PMID:24977203

  5. Gender recognition from unconstrained and articulated human body.

    Science.gov (United States)

    Wu, Qin; Guo, Guodong

    2014-01-01

    Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.

  6. An Embedded Application for Degraded Text Recognition

    Directory of Open Access Journals (Sweden)

    Thillou Céline

    2005-01-01

    Full Text Available This paper describes a mobile device which tries to give the blind or visually impaired access to text information. Three key technologies are required for this system: text detection, optical character recognition, and speech synthesis. Blind users and the mobile environment imply two strong constraints. First, pictures will be taken without control on camera settings and a priori information on text (font or size and background. The second issue is to link several techniques together with an optimal compromise between computational constraints and recognition efficiency. We will present the overall description of the system from text detection to OCR error correction.

  7. Why recognition is rational

    Directory of Open Access Journals (Sweden)

    Clintin P. Davis-Stober

    2010-07-01

    Full Text Available The Recognition Heuristic (Gigerenzer and Goldstein, 1996; Goldstein and Gigerenzer, 2002 makes the counter-intuitive prediction that a decision maker utilizing less information may do as well as, or outperform, an idealized decision maker utilizing more information. We lay a theoretical foundation for the use of single-variable heuristics such as the Recognition Heuristic as an optimal decision strategy within a linear modeling framework. We identify conditions under which over-weighting a single predictor is a mini-max strategy among a class of a priori chosen weights based on decision heuristics with respect to a measure of statistical lack of fit we call ``risk''. These strategies, in turn, outperform standard multiple regression as long as the amount of data available is limited. We also show that, under related conditions, weighting only one variable and ignoring all others produces the same risk as ignoring the single variable and weighting all others. This approach has the advantage of generalizing beyond the original environment of the Recognition Heuristic to situations with more than two choice options, binary or continuous representations of recognition, and to other single variable heuristics. We analyze the structure of data used in some prior recognition tasks and find that it matches the sufficient conditions for optimality in our results. Rather than being a poor or adequate substitute for a compensatory model, the Recognition Heuristic closely approximates an optimal strategy when a decision maker has finite data about the world.

  8. Perceptual Plasticity for Auditory Object Recognition

    Science.gov (United States)

    Heald, Shannon L. M.; Van Hedger, Stephen C.; Nusbaum, Howard C.

    2017-01-01

    In our auditory environment, we rarely experience the exact acoustic waveform twice. This is especially true for communicative signals that have meaning for listeners. In speech and music, the acoustic signal changes as a function of the talker (or instrument), speaking (or playing) rate, and room acoustics, to name a few factors. Yet, despite this acoustic variability, we are able to recognize a sentence or melody as the same across various kinds of acoustic inputs and determine meaning based on listening goals, expectations, context, and experience. The recognition process relates acoustic signals to prior experience despite variability in signal-relevant and signal-irrelevant acoustic properties, some of which could be considered as “noise” in service of a recognition goal. However, some acoustic variability, if systematic, is lawful and can be exploited by listeners to aid in recognition. Perceivable changes in systematic variability can herald a need for listeners to reorganize perception and reorient their attention to more immediately signal-relevant cues. This view is not incorporated currently in many extant theories of auditory perception, which traditionally reduce psychological or neural representations of perceptual objects and the processes that act on them to static entities. While this reduction is likely done for the sake of empirical tractability, such a reduction may seriously distort the perceptual process to be modeled. We argue that perceptual representations, as well as the processes underlying perception, are dynamically determined by an interaction between the uncertainty of the auditory signal and constraints of context. This suggests that the process of auditory recognition is highly context-dependent in that the identity of a given auditory object may be intrinsically tied to its preceding context. To argue for the flexible neural and psychological updating of sound-to-meaning mappings across speech and music, we draw upon examples

  9. Evaluation of blind signal separation methods

    NARCIS (Netherlands)

    Schobben, D.W.E.; Torkkola, K.; Smaragdis, P.

    1999-01-01

    Recently many new Blind Signal Separation BSS algorithms have been introduced Authors evaluate the performance of their algorithms in various ways Among these are speech recognition rates plots of separated signals plots of cascaded mixingunmixing impulse responses and signal to noise ratios Clearly

  10. Improvement of emotional healthcare system with stress detection from ECG signal.

    Science.gov (United States)

    Tivatansakul, S; Ohkura, M

    2015-01-01

    Our emotional healthcare system is designed to cope with users' negative emotions in daily life. To make the system more intelligent, we integrated emotion recognition by facial expression to provide appropriate services based on user's current emotional state. Our emotion recognition by facial expression has confusion issue to recognize some positive, neutral and negative emotions that make the emotional healthcare system provide a relaxation service even though users don't have negative emotions. Therefore, to increase the effectiveness of the system to provide the relaxation service, we integrate stress detection from ECG signal. The stress detection might be able to address the confusion issue of emotion recognition by facial expression to provide the service. Indeed, our results show that integration of stress detection increases the effectiveness and efficiency of the emotional healthcare system to provide services.

  11. Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine

    Science.gov (United States)

    Yang, Zhutian; Wu, Zhilu; Yin, Zhendong; Quan, Taifan; Sun, Hongjian

    2013-01-01

    Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, namely the primary signal recognition and the advanced signal recognition. In the former step, a novel rough k-means classifier, which comprises three regions, i.e., certain area, rough area and uncertain area, is proposed to cluster the samples of radar emitter signals. In the latter step, the samples within the rough boundary are used to train the relevance vector machine (RVM). Then RVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and presents lower computational complexity than traditional approaches. PMID:23344380

  12. Recall and recognition hypermnesia for Socratic stimuli.

    Science.gov (United States)

    Kazén, Miguel; Solís-Macías, Víctor M

    2016-01-01

    In two experiments, we investigate hypermnesia, net memory improvements with repeated testing of the same material after a single study trial. In the first experiment, we found hypermnesia across three trials for the recall of word solutions to Socratic stimuli (dictionary-like definitions of concepts) replicating Erdelyi, Buschke, and Finkelstein and, for the first time using these materials, for their recognition. In the second experiment, we had two "yes/no" recognition groups, a Socratic stimuli group presented with concrete and abstract verbal materials and a word-only control group. Using signal detection measures, we found hypermnesia for concrete Socratic stimuli-and stable performance for abstract stimuli across three recognition tests. The control group showed memory decrements across tests. We interpret these findings with the alternative retrieval pathways (ARP) hypothesis, contrasting it with alternative theories of hypermnesia, such as depth of processing, generation and retrieve-recognise. We conclude that recognition hypermnesia for concrete Socratic stimuli is a reliable phenomenon, which we found in two experiments involving both forced-choice and yes/no recognition procedures.

  13. Pattern recognition and modelling of earthquake registrations with interactive computer support

    International Nuclear Information System (INIS)

    Manova, Katarina S.

    2004-01-01

    The object of the thesis is Pattern Recognition. Pattern recognition i.e. classification, is applied in many fields: speech recognition, hand printed character recognition, medical analysis, satellite and aerial-photo interpretations, biology, computer vision, information retrieval and so on. In this thesis is studied its applicability in seismology. Signal classification is an area of great importance in a wide variety of applications. This thesis deals with the problem of (automatic) classification of earthquake signals, which are non-stationary signals. Non-stationary signal classification is an area of active research in the signal and image processing community. The goal of the thesis is recognition of earthquake signals according to their epicentral zone. Source classification i.e. recognition is based on transformation of seismograms (earthquake registrations) to images, via time-frequency transformations, and applying image processing and pattern recognition techniques for feature extraction, classification and recognition. The tested data include local earthquakes from seismic regions in Macedonia. By using actual seismic data it is shown that proposed methods provide satisfactory results for classification and recognition.(Author)

  14. Speech pattern recognition for forensic acoustic purposes

    OpenAIRE

    Herrera Martínez, Marcelo; Aldana Blanco, Andrea Lorena; Guzmán Palacios, Ana María

    2014-01-01

    The present paper describes the development of a software for analysis of acoustic voice parameters (APAVOIX), which can be used for forensic acoustic purposes, based on the speaker recognition and identification. This software enables to observe in a clear manner, the parameters which are sufficient and necessary when performing a comparison between two voice signals, the suspicious and the original one. These parameters are used according to the classic method, generally used by state entit...

  15. Novel DNA packaging recognition in the unusual bacteriophage N15

    Energy Technology Data Exchange (ETDEWEB)

    Feiss, Michael [Department of Microbiology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242 (United States); Geyer, Henriette, E-mail: henriettegeyer@gmail.com [Division of Viral Infections, Robert Koch Institute, Berlin (Germany); Division of Viral Infections, Robert Koch Institute, Berlin (Germany); Klingberg, Franco, E-mail: franco.klingberg@thermofisher.com [Flow Cytometry, Imaging & Microscopy, Thermo Fisher Scientific, Frankfurter Strasse 129B 64293 Darmstadt (Germany); Flow Cytometry, Imaging & Microscopy, Thermo Fisher Scientific, Frankfurter Strasse 129B 64293 Darmstadt (Germany); Moreno, Norma, E-mail: nmoreno@islander.tamucc.edu [Texas A& M University – Corpus Christi, 6300 Ocean Drive, Corpus Christi, TX 78412, United States. (United States); Texas A& M University – Corpus Christi, 6300 Ocean Drive, Corpus Christi, TX 78412, United States. (United States); Forystek, Amanda, E-mail: eamanda-forystek@uiowa.edu [Flow Cytometry, Imaging & Microscopy, Thermo Fisher Scientific, Frankfurter Strasse 129B 64293 Darmstadt (Germany); Room # 2911 JPP, Dept. of Psychiatry, The University of Iowa, 200 Hawkins Drive, Iowa City, Iowa, 52242 (United States); Maluf, Nasib Karl, E-mail: fKarl.Maluf@ap-lab.com [Flow Cytometry, Imaging & Microscopy, Thermo Fisher Scientific, Frankfurter Strasse 129B 64293 Darmstadt (Germany); Alliance Protein Laboratories, Inc. 6042 Cornerstone Court West, Suite ASan Diego, CA 92121, USA. (United States); Sippy, Jean [Department of Microbiology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA 52242 (United States)

    2015-08-15

    Phage lambda's cosB packaging recognition site is tripartite, consisting of 3 TerS binding sites, called R sequences. TerS binding to the critical R3 site positions the TerL endonuclease for nicking cosN to generate cohesive ends. The N15 cos (cos{sup N15}) is closely related to cos{sup λ}, but whereas the cosB{sup N15} subsite has R3, it lacks the R2 and R1 sites and the IHF binding site of cosB{sup λ}. A bioinformatic study of N15-like phages indicates that cosB{sup N15} also has an accessory, remote rR2 site, which is proposed to increase packaging efficiency, like R2 and R1 of lambda. N15 plus five prophages all have the rR2 sequence, which is located in the TerS-encoding 1 gene, approximately 200 bp distal to R3. An additional set of four highly related prophages, exemplified by Monarch, has R3 sequence, but also has R2 and R1 sequences characteristic of cosB–λ. The DNA binding domain of TerS-N15 is a dimer. - Highlights: • There are two classes of DNA packaging signals in N15-related phages. • Phage N15's TerS binding site: a critical site and a possible remote accessory site. • Viral DNA recognition signals by the λ-like bacteriophages: the odd case of N15.

  16. Anticipatory coarticulation facilitates word recognition in toddlers.

    Science.gov (United States)

    Mahr, Tristan; McMillan, Brianna T M; Saffran, Jenny R; Ellis Weismer, Susan; Edwards, Jan

    2015-09-01

    Children learn from their environments and their caregivers. To capitalize on learning opportunities, young children have to recognize familiar words efficiently by integrating contextual cues across word boundaries. Previous research has shown that adults can use phonetic cues from anticipatory coarticulation during word recognition. We asked whether 18-24 month-olds (n=29) used coarticulatory cues on the word "the" when recognizing the following noun. We performed a looking-while-listening eyetracking experiment to examine word recognition in neutral vs. facilitating coarticulatory conditions. Participants looked to the target image significantly sooner when the determiner contained facilitating coarticulatory cues. These results provide the first evidence that novice word-learners can take advantage of anticipatory sub-phonemic cues during word recognition. Copyright © 2015 Elsevier B.V. All rights reserved.

  17. Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures

    Science.gov (United States)

    Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany

    2016-07-01

    Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.

  18. Page Recognition: Quantum Leap In Recognition Technology

    Science.gov (United States)

    Miller, Larry

    1989-07-01

    No milestone has proven as elusive as the always-approaching "year of the LAN," but the "year of the scanner" might claim the silver medal. Desktop scanners have been around almost as long as personal computers. And everyone thinks they are used for obvious desktop-publishing and business tasks like scanning business documents, magazine articles and other pages, and translating those words into files your computer understands. But, until now, the reality fell far short of the promise. Because it's true that scanners deliver an accurate image of the page to your computer, but the software to recognize this text has been woefully disappointing. Old optical-character recognition (OCR) software recognized such a limited range of pages as to be virtually useless to real users. (For example, one OCR vendor specified 12-point Courier font from an IBM Selectric typewriter: the same font in 10-point, or from a Diablo printer, was unrecognizable!) Computer dealers have told me the chasm between OCR expectations and reality is so broad and deep that nine out of ten prospects leave their stores in disgust when they learn the limitations. And this is a very important, very unfortunate gap. Because the promise of recognition -- what people want it to do -- carries with it tremendous improvements in our productivity and ability to get tons of written documents into our computers where we can do real work with it. The good news is that a revolutionary new development effort has led to the new technology of "page recognition," which actually does deliver the promise we've always wanted from OCR. I'm sure every reader appreciates the breakthrough represented by the laser printer and page-makeup software, a combination so powerful it created new reasons for buying a computer. A similar breakthrough is happening right now in page recognition: the Macintosh (and, I must admit, other personal computers) equipped with a moderately priced scanner and OmniPage software (from Caere

  19. An improved PSO-SVM model for online recognition defects in eddy current testing

    Science.gov (United States)

    Liu, Baoling; Hou, Dibo; Huang, Pingjie; Liu, Banteng; Tang, Huayi; Zhang, Wubo; Chen, Peihua; Zhang, Guangxin

    2013-12-01

    Accurate and rapid recognition of defects is essential for structural integrity and health monitoring of in-service device using eddy current (EC) non-destructive testing. This paper introduces a novel model-free method that includes three main modules: a signal pre-processing module, a classifier module and an optimisation module. In the signal pre-processing module, a kind of two-stage differential structure is proposed to suppress the lift-off fluctuation that could contaminate the EC signal. In the classifier module, multi-class support vector machine (SVM) based on one-against-one strategy is utilised for its good accuracy. In the optimisation module, the optimal parameters of classifier are obtained by an improved particle swarm optimisation (IPSO) algorithm. The proposed IPSO technique can improve convergence performance of the primary PSO through the following strategies: nonlinear processing of inertia weight, introductions of the black hole and simulated annealing model with extremum disturbance. The good generalisation ability of the IPSO-SVM model has been validated through adding additional specimen into the testing set. Experiments show that the proposed algorithm can achieve higher recognition accuracy and efficiency than other well-known classifiers and the superiorities are more obvious with less training set, which contributes to online application.

  20. Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices.

    Science.gov (United States)

    Liu, Wei; Kulin, Merima; Kazaz, Tarik; Shahid, Adnan; Moerman, Ingrid; De Poorter, Eli

    2017-09-12

    Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals' modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI's probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.

  1. Applications of Hilbert Spectral Analysis for Speech and Sound Signals

    Science.gov (United States)

    Huang, Norden E.

    2003-01-01

    A new method for analyzing nonlinear and nonstationary data has been developed, and the natural applications are to speech and sound signals. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. This method invention can be used to process all acoustic signals. Specifically, it can process the speech signals for Speech synthesis, Speaker identification and verification, Speech recognition, and Sound signal enhancement and filtering. Additionally, as the acoustical signals from machinery are essentially the way the machines are talking to us. Therefore, the acoustical signals, from the machines, either from sound through air or vibration on the machines, can tell us the operating conditions of the machines. Thus, we can use the acoustic signal to diagnosis the problems of machines.

  2. Development and Comparative Study of Effects of Training Algorithms on Performance of Artificial Neural Network Based Analog and Digital Automatic Modulation Recognition

    Directory of Open Access Journals (Sweden)

    Jide Julius Popoola

    2015-11-01

    Full Text Available This paper proposes two new classifiers that automatically recognise twelve combined analog and digital modulated signals without any a priori knowledge of the modulation schemes and the modulation parameters. The classifiers are developed using pattern recognition approach. Feature keys extracted from the instantaneous amplitude, instantaneous phase and the spectrum symmetry of the simulated signals are used as inputs to the artificial neural network employed in developing the classifiers. The two developed classifiers are trained using scaled conjugate gradient (SCG and conjugate gradient (CONJGRAD training algorithms. Sample results of the two classifiers show good success recognition performance with an average overall recognition rate above 99.50% at signal-to-noise ratio (SNR value from 0 dB and above with the two training algorithms employed and an average overall recognition rate slightly above 99.00% and 96.40% respectively at - 5 dB SNR value for SCG and CONJGRAD training algorithms. The comparative performance evaluation of the two developed classifiers using the two training algorithms shows that the two training algorithms have different effects on both the response rate and efficiency of the two developed artificial neural networks classifiers. In addition, the result of the performance evaluation carried out on the overall success recognition rates between the two developed classifiers in this study using pattern recognition approach with the two training algorithms and one reported classifier in surveyed literature using decision-theoretic approach shows that the classifiers developed in this study perform favourably with regard to accuracy and performance probability as compared to classifier presented in previous study.

  3. Rational design of DKK3 structure-based small peptides as antagonists of Wnt signaling pathway and in silico evaluation of their efficiency.

    Directory of Open Access Journals (Sweden)

    Mansour Poorebrahim

    Full Text Available Dysregulated Wnt signaling pathway is highly associated with the pathogenesis of several human cancers. Dickkopf proteins (DKKs are thought to inhibit Wnt signaling pathway through binding to lipoprotein receptor-related protein (LRP 5/6. In this study, based on the 3-dimensional (3D structure of DKK3 Cys-rich domain 2 (CRD2, we have designed and developed several peptide inhibitors of Wnt signaling pathway. Modeller 9.15 package was used to predict 3D structure of CRD2 based on the Homology modeling (HM protocol. After refinement and minimization with GalaxyRefine and NOMAD-REF servers, the quality of selected models was evaluated utilizing VADAR, SAVES and ProSA servers. Molecular docking studies as well as literature-based information revealed two distinct boxes located at CRD2 which are actively involved in the DKK3-LRP5/6 interaction. A peptide library was constructed conducting the backrub sequence tolerance scanning protocol in Rosetta3.5 according to the DKK3-LRP5/6 binding sites. Seven tolerated peptides were chosen and their binding affinity and stability were improved by some logical amino acid substitutions. Molecular dynamics (MD simulations of peptide-LRP5/6 complexes were carried out using GROMACS package. After evaluation of binding free energies, stability, electrostatic potential and some physicochemical properties utilizing computational approaches, three peptides (PEP-I1, PEP-I3 and PEP-II2 demonstrated desirable features. However, all seven improved peptides could sufficiently block the Wnt-binding site of LRP6 in silico. In conclusion, we have designed and improved several small peptides based on the LRP6-binding site of CRD2 of DKK3. These peptides are highly capable of binding to LRP6 in silico, and may prevent the formation of active Wnt-LRP6-Fz complex.

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

    Directory of Open Access Journals (Sweden)

    Houzet Dominique

    2005-01-01

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

  5. Are Haar-like Rectangular Features for Biometric Recognition Reducible?

    DEFF Research Database (Denmark)

    Nasrollahi, Kamal; Moeslund, Thomas B.

    2013-01-01

    Biometric recognition is still a very difficult task in real-world scenarios wherein unforeseen changes in degradations factors like noise, occlusion, blurriness and illumination can drastically affect the extracted features from the biometric signals. Very recently Haar-like rectangular features...... which have usually been used for object detection were introduced for biometric recognition resulting in systems that are robust against most of the mentioned degradations [9]. The problem with these features is that one can define many different such features for a given biometric signal...... and it is not clear whether all of these features are required for the actual recognition or not. This is exactly what we are dealing with in this paper: How can an initial set of Haar-like rectangular features, that have been used for biometric recognition, be reduced to a set of most influential features...

  6. Forensic Speaker Recognition Law Enforcement and Counter-Terrorism

    CERN Document Server

    Patil, Hemant

    2012-01-01

    Forensic Speaker Recognition: Law Enforcement and Counter-Terrorism is an anthology of the research findings of 35 speaker recognition experts from around the world. The volume provides a multidimensional view of the complex science involved in determining whether a suspect’s voice truly matches forensic speech samples, collected by law enforcement and counter-terrorism agencies, that are associated with the commission of a terrorist act or other crimes. While addressing such topics as the challenges of forensic case work, handling speech signal degradation, analyzing features of speaker recognition to optimize voice verification system performance, and designing voice applications that meet the practical needs of law enforcement and counter-terrorism agencies, this material all sounds a common theme: how the rigors of forensic utility are demanding new levels of excellence in all aspects of speaker recognition. The contributors are among the most eminent scientists in speech engineering and signal process...

  7. Virus-mediated suppression of host non-self recognition facilitates horizontal transmission of heterologous viruses.

    Directory of Open Access Journals (Sweden)

    Songsong Wu

    2017-03-01

    Full Text Available Non-self recognition is a common phenomenon among organisms; it often leads to innate immunity to prevent the invasion of parasites and maintain the genetic polymorphism of organisms. Fungal vegetative incompatibility is a type of non-self recognition which often induces programmed cell death (PCD and restricts the spread of molecular parasites. It is not clearly known whether virus infection could attenuate non-self recognition among host individuals to facilitate its spread. Here, we report that a hypovirulence-associated mycoreovirus, named Sclerotinia sclerotiorum mycoreovirus 4 (SsMYRV4, could suppress host non-self recognition and facilitate horizontal transmission of heterologous viruses. We found that cell death in intermingled colony regions between SsMYRV4-infected Sclerotinia sclerotiorum strain and other tested vegetatively incompatible strains was markedly reduced and inhibition barrage lines were not clearly observed. Vegetative incompatibility, which involves Heterotrimeric guanine nucleotide-binding proteins (G proteins signaling pathway, is controlled by specific loci termed het (heterokaryon incompatibility loci. Reactive oxygen species (ROS plays a key role in vegetative incompatibility-mediated PCD. The expression of G protein subunit genes, het genes, and ROS-related genes were significantly down-regulated, and cellular production of ROS was suppressed in the presence of SsMYRV4. Furthermore, SsMYRV4-infected strain could easily accept other viruses through hyphal contact and these viruses could be efficiently transmitted from SsMYRV4-infected strain to other vegetatively incompatible individuals. Thus, we concluded that SsMYRV4 is capable of suppressing host non-self recognition and facilitating heterologous viruses transmission among host individuals. These findings may enhance our understanding of virus ecology, and provide a potential strategy to utilize hypovirulence-associated mycoviruses to control fungal diseases.

  8. Magneto-sensor circuit efficiency incremented by Fourier-transformation

    KAUST Repository

    Talukdar, Abdul Hafiz Ibne; Useinov, Arthur; Hussain, Muhammad Mustafa

    2011-01-01

    In this paper detection by recognized intelligent algorithm for different magnetic films with the aid of a cost-effective and simple high efficient circuit are realized. Well-known, magnetic films generate oscillating frequencies when they stay a part of an LC- oscillatory circuit. These frequencies can be further analyzed to gather information about their magnetic properties. For the first time in this work we apply the signal analysis in frequency domain to create the Fourier frequency spectra which was used to detect the sample properties and their recognition. In this paper we have summarized both the simulation and experimental results. © 2011 Elsevier Ltd. All rights reserved.

  9. Magneto-sensor circuit efficiency incremented by Fourier-transformation

    KAUST Repository

    Talukdar, Abdul Hafiz Ibne

    2011-10-01

    In this paper detection by recognized intelligent algorithm for different magnetic films with the aid of a cost-effective and simple high efficient circuit are realized. Well-known, magnetic films generate oscillating frequencies when they stay a part of an LC- oscillatory circuit. These frequencies can be further analyzed to gather information about their magnetic properties. For the first time in this work we apply the signal analysis in frequency domain to create the Fourier frequency spectra which was used to detect the sample properties and their recognition. In this paper we have summarized both the simulation and experimental results. © 2011 Elsevier Ltd. All rights reserved.

  10. Automatic Emotion Recognition in Speech: Possibilities and Significance

    Directory of Open Access Journals (Sweden)

    Milana Bojanić

    2009-12-01

    Full Text Available Automatic speech recognition and spoken language understanding are crucial steps towards a natural humanmachine interaction. The main task of the speech communication process is the recognition of the word sequence, but the recognition of prosody, emotion and stress tags may be of particular importance as well. This paper discusses thepossibilities of recognition emotion from speech signal in order to improve ASR, and also provides the analysis of acoustic features that can be used for the detection of speaker’s emotion and stress. The paper also provides a short overview of emotion and stress classification techniques. The importance and place of emotional speech recognition is shown in the domain of human-computer interactive systems and transaction communication model. The directions for future work are given at the end of this work.

  11. Ordinal measures for iris recognition.

    Science.gov (United States)

    Sun, Zhenan; Tan, Tieniu

    2009-12-01

    Images of a human iris contain rich texture information useful for identity authentication. A key and still open issue in iris recognition is how best to represent such textural information using a compact set of features (iris features). In this paper, we propose using ordinal measures for iris feature representation with the objective of characterizing qualitative relationships between iris regions rather than precise measurements of iris image structures. Such a representation may lose some image-specific information, but it achieves a good trade-off between distinctiveness and robustness. We show that ordinal measures are intrinsic features of iris patterns and largely invariant to illumination changes. Moreover, compactness and low computational complexity of ordinal measures enable highly efficient iris recognition. Ordinal measures are a general concept useful for image analysis and many variants can be derived for ordinal feature extraction. In this paper, we develop multilobe differential filters to compute ordinal measures with flexible intralobe and interlobe parameters such as location, scale, orientation, and distance. Experimental results on three public iris image databases demonstrate the effectiveness of the proposed ordinal feature models.

  12. Probabilistic Open Set Recognition

    Science.gov (United States)

    Jain, Lalit Prithviraj

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

  13. Touchless palmprint recognition systems

    CERN Document Server

    Genovese, Angelo; Scotti, Fabio

    2014-01-01

    This book examines the context, motivation and current status of biometric systems based on the palmprint, with a specific focus on touchless and less-constrained systems. It covers new technologies in this rapidly evolving field and is one of the first comprehensive books on palmprint recognition systems.It discusses the research literature and the most relevant industrial applications of palmprint biometrics, including the low-cost solutions based on webcams. The steps of biometric recognition are described in detail, including acquisition setups, algorithms, and evaluation procedures. Const

  14. Simultaneous suppression of TGF-β and ERK signaling contributes to the highly efficient and reproducible generation of mouse embryonic stem cells from previously considered refractory and non-permissive strains.

    Science.gov (United States)

    Hassani, Seyedeh-Nafiseh; Totonchi, Mehdi; Farrokhi, Ali; Taei, Adeleh; Larijani, Mehran Rezaei; Gourabi, Hamid; Baharvand, Hossein

    2012-06-01

    Mouse embryonic stem cells (ESCs) are pluripotent stem cell lines derived from pre-implantation embryos. The efficiency of mESC generation is affected by genetic variation in mice; that is, some mouse strains are refractory or non-permissive to ESC establishment. Developing an efficient method to derive mESCs from strains of various genetic backgrounds should be valuable for establishment of ESCs in various mammalian species. In the present study, we identified dual inhibition of TGF-β and ERK1/2, by SB431542 and PD0325901, respectively led to the highly efficient and reproducible generation of mESC lines from NMRI, C57BL/6, BALB/c, DBA/2, and FVB/N strains, which previously considered refractory or non-permissive for ESC establishment. These mESCs expressed pluripotency markers and retained the capacity to differentiate into derivatives of all three germ layers. The evaluated lines exhibited high rates of chimerism when reintroduced into blastocysts. To our knowledge, this is the first report of efficient (100%) mESC lines generation from different genetic backgrounds. The application of these two inhibitors will not only solve the problems of mESC derivation but also clarifies new signaling pathways in pluripotent mESCs.

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

    Directory of Open Access Journals (Sweden)

    Joongrock Kim

    2014-03-01

    Full Text Available 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.

  16. SignalR blueprints

    CERN Document Server

    Ingebrigtsen, Einar

    2015-01-01

    This book is designed for software developers, primarily those with knowledge of C#, .NET, and JavaScript. Good knowledge and understanding of SignalR is assumed to allow efficient programming of core elements and applications in SignalR.

  17. [Prosopagnosia and facial expression recognition].

    Science.gov (United States)

    Koyama, Shinichi

    2014-04-01

    This paper reviews clinical neuropsychological studies that have indicated that the recognition of a person's identity and the recognition of facial expressions are processed by different cortical and subcortical areas of the brain. The fusiform gyrus, especially the right fusiform gyrus, plays an important role in the recognition of identity. The superior temporal sulcus, amygdala, and medial frontal cortex play important roles in facial-expression recognition. Both facial recognition and facial-expression recognition are highly intellectual processes that involve several regions of the brain.

  18. Granular neural networks, pattern recognition and bioinformatics

    CERN Document Server

    Pal, Sankar K; Ganivada, Avatharam

    2017-01-01

    This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinf...

  19. Optical Character Recognition.

    Science.gov (United States)

    Converso, L.; Hocek, S.

    1990-01-01

    This paper describes computer-based optical character recognition (OCR) systems, focusing on their components (the computer, the scanner, the OCR, and the output device); how the systems work; and features to consider in selecting a system. A list of 26 questions to ask to evaluate systems for potential purchase is included. (JDD)

  20. Facial Expression Recognition

    NARCIS (Netherlands)

    Pantic, Maja; Li, S.; Jain, A.

    2009-01-01

    Facial expression recognition is a process performed by humans or computers, which consists of: 1. Locating faces in the scene (e.g., in an image; this step is also referred to as face detection), 2. Extracting facial features from the detected face region (e.g., detecting the shape of facial

  1. Control of Target Molecular Recognition in a Small Pore Space with Biomolecule-Recognition Gating Membrane.

    Science.gov (United States)

    Okuyama, Hiroto; Oshiba, Yuhei; Ohashi, Hidenori; Yamaguchi, Takeo

    2018-05-01

    A biomolecule-recognition gating membrane, which introduces thermosensitive graft polymer including molecular recognition receptor into porous membrane substrate, can close its pores by recognizing target biomolecule. The present study reports strategies for improving both versatility and sensitivity of the gating membrane. First, the membrane is fabricated by introducing the receptor via a selectively reactive click reaction improving the versatility. Second, the sensitivity of the membrane is enhanced via an active delivering method of the target molecules into the pores. In the method, the tiny signal of the target biomolecule is amplified as obvious pressure change. Furthermore, this offers 15 times higher sensitivity compared to the previously reported passive delivering method (membrane immersion to sample solution) with significantly shorter recognition time. The improvement will aid in applying the gating membrane to membrane sensors in medical fields. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. Neural Mechanisms and Information Processing in Recognition Systems

    Directory of Open Access Journals (Sweden)

    Mamiko Ozaki

    2014-10-01

    Full Text Available 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.

  3. Pretreatment of Sialic Acid Efficiently Prevents Lipopolysaccharide-Induced Acute Renal Failure and Suppresses TLR4/gp91-Mediated Apoptotic Signaling

    Directory of Open Access Journals (Sweden)

    Shih-Ping Hsu

    2016-05-01

    Full Text Available Background/Aims: Lipopolysaccharides (LPS binding to Toll-like receptor 4 (TLR4 activate NADPH oxidase gp91 subunit-mediated inflammation and oxidative damage. Recognizing the high binding affinity of sialic acid (SA with LPS, we further explored the preventive potential of SA pretreatment on LPS-evoked acute renal failure (ARF. Methods: We determined the effect of intravenous SA 30 min before LPS-induced injury in urethane-anesthetized female Wistar rats by evaluating kidney reactive oxygen species (ROS responses, renal and systemic hemodynamics, renal function, histopathology, and molecular mechanisms. Results: LPS time-dependently reduced arterial blood pressure, renal microcirculation, and increased blood urea nitrogen and creatinine in the rats. LPS enhanced monocyte/macrophage infiltration and ROS production, and subsequently impaired kidneys with the enhancement of TLR4/NADPH oxidase gp91/Caspase 3/poly-(ADP-ribose-polymerase (PARP-mediated apoptosis in the kidneys. SA pretreatment effectively alleviated LPS-induced ARF. The levels of LPS-increased ED-1 infiltration and ROS production in the kidney were significantly depressed by SA pretreatment. Furthermore, SA pretreatment significantly depressed TLR4 activation, gp91 expression, and Caspase 3/PARP induced apoptosis in the kidneys. Conclusion: We suggest that pretreatment of SA significantly and preventively attenuated LPS-induced detrimental effects on systemic and renal hemodynamics, renal ROS production and renal function, as well as, LPS-activated TLR4/gp91/Caspase3 mediated apoptosis signaling.

  4. Efficient and accurate simulations of two-dimensional electronic photon-echo signals: Illustration for a simple model of the Fenna-Matthews-Olson complex

    International Nuclear Information System (INIS)

    Sharp, Leah Z.; Egorova, Dassia; Domcke, Wolfgang

    2010-01-01

    Two-dimensional (2D) photon-echo spectra of a single subunit of the Fenna-Matthews-Olson (FMO) bacteriochlorophyll trimer of Chlorobium tepidum are simulated, employing the equation-of-motion phase-matching approach (EOM-PMA). We consider a slightly extended version of the previously proposed Frenkel exciton model, which explicitly accounts for exciton coherences in the secular approximation. The study is motivated by a recent experiment reporting long-lived coherent oscillations in 2D transients [Engel et al., Nature 446, 782 (2007)] and aims primarily at accurate simulations of the spectroscopic signals, with the focus on oscillations of 2D peak intensities with population time. The EOM-PMA accurately accounts for finite pulse durations as well as pulse-overlap effects and does not invoke approximations apart from the weak-field limit for a given material system. The population relaxation parameters of the exciton model are taken from the literature. The effects of various dephasing mechanisms on coherence lifetimes are thoroughly studied. It is found that the experimentally detected multiple frequencies in peak oscillations cannot be reproduced by the employed FMO model, which calls for the development of a more sophisticated exciton model of the FMO complex.

  5. MR imaging of the knee: Improvement of signal and contrast efficiency of T1-weighted turbo spin echo sequences by applying a driven equilibrium (DRIVE) pulse

    Energy Technology Data Exchange (ETDEWEB)

    Radlbauer, Rudolf, E-mail: rudolf.radlbauer@stpoelten.lknoe.a [MR Physics Group, Department of Radiology, Landesklinikum St. Poelten, Propst Fuehrer Strasse 4, 3100 St. Poelten (Austria); Lomoschitz, Friedrich, E-mail: friedrich.lomoschitz@stpoelten.lknoe.a [MR Physics Group, Department of Radiology, Landesklinikum St. Poelten, Propst Fuehrer Strasse 4, 3100 St. Poelten (Austria); Salomonowitz, Erich, E-mail: erich.salomonowitz@stpoelten.lknoe.a [MR Physics Group, Department of Radiology, Landesklinikum St. Poelten, Propst Fuehrer Strasse 4, 3100 St. Poelten (Austria); Eberhardt, Knut E., E-mail: info@mrt-kompetenzzentrum.d [MRT Competence Center Schloss Werneck, Balthasar-Neumann-Platz 2, 97440 Werneck (Germany); Stadlbauer, Andreas, E-mail: andi@nmr.a [MR Physics Group, Department of Radiology, Landesklinikum St. Poelten, Propst Fuehrer Strasse 4, 3100 St. Poelten (Austria); Department of Neurosurgery, University of Erlangen-Nuremberg, Schwabachanlage 6, 91054 Erlangen (Germany)

    2010-08-15

    The purpose of this study was to assess the effect of a driven equilibrium (DRIVE) pulse incorporated in a standard T1-weighted turbo spin echo (TSE) sequence as used in our routine MRI protocol for examination of pathologies of the knee. Sixteen consecutive patients with knee disorders were examined using the routine MRI protocol, including T1-weighted TSE-sequences with and without a DRIVE pulse. Signal-to-noise ratios (SNRs) and contrast-to-noise ratio (CNR) of anatomical structures and pathologies were calculated and compared for both sequences. The differences in diagnostic value of the T1-weighted images with and without DRIVE pulse were assessed. SNR was significantly higher on images acquired with DRIVE pulse for fluid, effusion, cartilage and bone. Differences in the SNR of meniscus and muscle between the two sequences were not statistically significant. CNR was significantly increased between muscle and effusion, fluid and cartilage, fluid and meniscus, cartilage and meniscus, bone and cartilage on images acquired using the DRIVE pulse. Diagnostic value of the T1-weighted images was found to be improved for delineation of anatomic structures and for diagnosing a variety of pathologies when a DRIVE pulse is incorporated in the sequence. Incorporation of a DRIVE pulse into a standard T1-weighted TSE-sequence leads to significant increase of SNR and CNR of both, anatomical structures and pathologies, and consequently to an increase in diagnostic value within the same acquisition time.

  6. MR imaging of the knee: Improvement of signal and contrast efficiency of T1-weighted turbo spin echo sequences by applying a driven equilibrium (DRIVE) pulse

    International Nuclear Information System (INIS)

    Radlbauer, Rudolf; Lomoschitz, Friedrich; Salomonowitz, Erich; Eberhardt, Knut E.; Stadlbauer, Andreas

    2010-01-01

    The purpose of this study was to assess the effect of a driven equilibrium (DRIVE) pulse incorporated in a standard T1-weighted turbo spin echo (TSE) sequence as used in our routine MRI protocol for examination of pathologies of the knee. Sixteen consecutive patients with knee disorders were examined using the routine MRI protocol, including T1-weighted TSE-sequences with and without a DRIVE pulse. Signal-to-noise ratios (SNRs) and contrast-to-noise ratio (CNR) of anatomical structures and pathologies were calculated and compared for both sequences. The differences in diagnostic value of the T1-weighted images with and without DRIVE pulse were assessed. SNR was significantly higher on images acquired with DRIVE pulse for fluid, effusion, cartilage and bone. Differences in the SNR of meniscus and muscle between the two sequences were not statistically significant. CNR was significantly increased between muscle and effusion, fluid and cartilage, fluid and meniscus, cartilage and meniscus, bone and cartilage on images acquired using the DRIVE pulse. Diagnostic value of the T1-weighted images was found to be improved for delineation of anatomic structures and for diagnosing a variety of pathologies when a DRIVE pulse is incorporated in the sequence. Incorporation of a DRIVE pulse into a standard T1-weighted TSE-sequence leads to significant increase of SNR and CNR of both, anatomical structures and pathologies, and consequently to an increase in diagnostic value within the same acquisition time.

  7. Local Feature Learning for Face Recognition under Varying Poses

    DEFF Research Database (Denmark)

    Duan, Xiaodong; Tan, Zheng-Hua

    2015-01-01

    In this paper, we present a local feature learning method for face recognition to deal with varying poses. As opposed to the commonly used approaches of recovering frontal face images from profile views, the proposed method extracts the subject related part from a local feature by removing the pose...... related part in it on the basis of a pose feature. The method has a closed-form solution, hence being time efficient. For performance evaluation, cross pose face recognition experiments are conducted on two public face recognition databases FERET and FEI. The proposed method shows a significant...... recognition improvement under varying poses over general local feature approaches and outperforms or is comparable with related state-of-the-art pose invariant face recognition approaches. Copyright ©2015 by IEEE....

  8. Face recognition increases during saccade preparation.

    Science.gov (United States)

    Lin, Hai; Rizak, Joshua D; Ma, Yuan-ye; Yang, Shang-chuan; Chen, Lin; Hu, Xin-tian

    2014-01-01

    Face perception is integral to human perception system as it underlies social interactions. Saccadic eye movements are frequently made to bring interesting visual information, such as faces, onto the fovea for detailed processing. Just before eye movement onset, the processing of some basic features, such as the orientation, of an object improves at the saccade landing point. Interestingly, there is also evidence that indicates faces are processed in early visual processing stages similar to basic features. However, it is not known whether this early enhancement of processing includes face recognition. In this study, three experiments were performed to map the timing of face presentation to the beginning of the eye movement in order to evaluate pre-saccadic face recognition. Faces were found to be similarly processed as simple objects immediately prior to saccadic movements. Starting ∼ 120 ms before a saccade to a target face, independent of whether or not the face was surrounded by other faces, the face recognition gradually improved and the critical spacing of the crowding decreased as saccade onset was approaching. These results suggest that an upcoming saccade prepares the visual system for new information about faces at the saccade landing site and may reduce the background in a crowd to target the intended face. This indicates an important role of pre-saccadic eye movement signals in human face recognition.

  9. Pedestrian recognition using automotive radar sensors

    Science.gov (United States)

    Bartsch, A.; Fitzek, F.; Rasshofer, R. H.

    2012-09-01

    The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insights into the object classification process. The impact of raw radar data properties can be directly observed in every layer of the classification system by avoiding machine learning and tracking. This gives information on the limiting factors of raw radar data in terms of classification decision making. To accomplish the very challenging distinction between pedestrians and static objects, five significant and stable object features from the spatial distribution and Doppler information are found. Experimental results with data from a 77 GHz automotive radar sensor show that over 95% of pedestrians can be classified correctly under optimal conditions, which is compareable to modern machine learning systems. The impact of the pedestrian's direction of movement, occlusion, antenna beam elevation angle, linear vehicle movement, and other factors are investigated and discussed. The results show that under real life conditions, radar only based pedestrian recognition is limited due to insufficient Doppler frequency and spatial resolution as well as antenna side lobe effects.

  10. Compression of a Deep Competitive Network Based on Mutual Information for Underwater Acoustic Targets Recognition

    Directory of Open Access Journals (Sweden)

    Sheng Shen

    2018-04-01

    Full Text Available The accuracy of underwater acoustic targets recognition via limited ship radiated noise can be improved by a deep neural network trained with a large number of unlabeled samples. However, redundant features learned by deep neural network have negative effects on recognition accuracy and efficiency. A compressed deep competitive network is proposed to learn and extract features from ship radiated noise. The core idea of the algorithm includes: (1 Competitive learning: By integrating competitive learning into the restricted Boltzmann machine learning algorithm, the hidden units could share the weights in each predefined group; (2 Network pruning: The pruning based on mutual information is deployed to remove the redundant parameters and further compress the network. Experiments based on real ship radiated noise show that the network can increase recognition accuracy with fewer informative features. The compressed deep competitive network can achieve a classification accuracy of 89.1 % , which is 5.3 % higher than deep competitive network and 13.1 % higher than the state-of-the-art signal processing feature extraction methods.

  11. Efficient secretion of small proteins in mammalian cells relies on Sec62-dependent posttranslational translocation

    Science.gov (United States)

    Lakkaraju, Asvin K. K.; Thankappan, Ratheeshkumar; Mary, Camille; Garrison, Jennifer L.; Taunton, Jack; Strub, Katharina

    2012-01-01

    Mammalian cells secrete a large number of small proteins, but their mode of translocation into the endoplasmic reticulum is not fully understood. Cotranslational translocation was expected to be inefficient due to the small time window for signal sequence recognition by the signal recognition particle (SRP). Impairing the SRP pathway and reducing cellular levels of the translocon component Sec62 by RNA interference, we found an alternate, Sec62-dependent translocation path in mammalian cells required for the efficient translocation of small proteins with N-terminal signal sequences. The Sec62-dependent translocation occurs posttranslationally via the Sec61 translocon and requires ATP. We classified preproteins into three groups: 1) those that comprise ≤100 amino acids are strongly dependent on Sec62 for efficient translocation; 2) those in the size range of 120–160 amino acids use the SRP pathway, albeit inefficiently, and therefore rely on Sec62 for efficient translocation; and 3) those larger than 160 amino acids depend on the SRP pathway to preserve a transient translocation competence independent of Sec62. Thus, unlike in yeast, the Sec62-dependent translocation pathway in mammalian cells serves mainly as a fail-safe mechanism to ensure efficient secretion of small proteins and provides cells with an opportunity to regulate secretion of small proteins independent of the SRP pathway. PMID:22648169

  12. Galeotti on recognition as inclusion

    DEFF Research Database (Denmark)

    Lægaard, Sune

    2008-01-01

    Anna Elisabetta Galeotti's theory of 'toleration as recognition' has been criticised by Peter Jones for being conceptually incoherent, since liberal toleration presupposes a negative attitude to differences, whereas multicultural recognition requires positive affirmation hereof. The paper spells ...

  13. School IPM Recognition and Certification

    Science.gov (United States)

    Schools and school districts can get support and recognition for implementation of school IPM. EPA is developing a program to provide recognition for school districts that are working towards or have achieved a level of success with school IPM programs.

  14. Stereotype Associations and Emotion Recognition

    NARCIS (Netherlands)

    Bijlstra, Gijsbert; Holland, Rob W.; Dotsch, Ron; Hugenberg, Kurt; Wigboldus, Daniel H. J.

    We investigated whether stereotype associations between specific emotional expressions and social categories underlie stereotypic emotion recognition biases. Across two studies, we replicated previously documented stereotype biases in emotion recognition using both dynamic (Study 1) and static

  15. Heme as a danger molecule in pathogen recognition.

    Science.gov (United States)

    Wegiel, Barbara; Hauser, Carl J; Otterbein, Leo E

    2015-12-01

    Appropriate control of redox mechanisms are critical for and effective innate immune response, which employs multiple cell types, receptors and molecules that recognize danger signals when they reach the host. Recognition of pathogen-associated pattern molecules (PAMPs) is a fundamental host survival mechanism for efficient elimination of invading pathogens and resolution of the infection and inflammation. In addition to PAMPs, eukaryotic cells contain a plethora of intracellular molecules that are normally secured within the confines of the plasma membrane, but if liberated and encountered in the extracellular milieu can provoke rapid cell activation. These are known as Alarmins or Danger-Associated Molecular Patterns (DAMPs) and can be released actively by cells or passively as a result of sterile cellular injury after trauma, ischemia, or toxin-induced cell rupture. Both PAMPs and DAMPs are recognized by a series of cognate receptors that increase the generation of free radicals and activate specific signaling pathways that result in regulation of a variety of stress response, redox sensitive genes. Multiple mediators released, as cells die include, but are not limited to ATP, hydrogen peroxide, heme, formyl peptides, DNA or mitochondria provide the second signal to amplify immune responses. In this review, we will focus on how sterile and infective stimuli activate the stress response gene heme oxygenase-1 (Hmox1, HO-1), a master gene critical to an appropriate host response that is now recognized as one with enormous therapeutic potential. HO-1 gene expression is regulated in large part by redox-sensitive proteins including but not limited to nrf2. Both PAMPs and DAMPs increase the activation of nrf2 and HO-1. Heme is a powerful pro-oxidant and as such should be qualified as a DAMP. With its degradation by HO-1a molecule of carbon monoxide (CO) is generated that in turn serves as a bioactive signaling molecule. PAMPs such as bacterial endotoxin activate HO-1

  16. Character Recognition Using Genetically Trained Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    amount of noise significantly degrades character recognition efficiency, some of which can be overcome by adding noise during training and optimizing the form of the network's activation fimction.

  17. A REVIEW: OPTICAL CHARACTER RECOGNITION

    OpenAIRE

    Swati Tomar*1 & Amit Kishore2

    2018-01-01

    This paper presents detailed review in the field of Optical Character Recognition. Various techniques are determine that have been proposed to realize the center of character recognition in an optical character recognition system. Even though, sufficient studies and papers are describes the techniques for converting textual content from a paper document into machine readable form. Optical character recognition is a process where the computer understands automatically the image of handwritten ...

  18. Hand Gesture Recognition Using Ultrasonic Waves

    KAUST Repository

    AlSharif, Mohammed Hussain

    2016-04-01

    Gesturing is a natural way of communication between people and is used in our everyday conversations. Hand gesture recognition systems are used in many applications in a wide variety of fields, such as mobile phone applications, smart TVs, video gaming, etc. With the advances in human-computer interaction technology, gesture recognition is becoming an active research area. There are two types of devices to detect gestures; contact based devices and contactless devices. Using ultrasonic waves for determining gestures is one of the ways that is employed in contactless devices. Hand gesture recognition utilizing ultrasonic waves will be the focus of this thesis work. This thesis presents a new method for detecting and classifying a predefined set of hand gestures using a single ultrasonic transmitter and a single ultrasonic receiver. This method uses a linear frequency modulated ultrasonic signal. The ultrasonic signal is designed to meet the project requirements such as the update rate, the range of detection, etc. Also, it needs to overcome hardware limitations such as the limited output power, transmitter, and receiver bandwidth, etc. The method can be adapted to other hardware setups. Gestures are identified based on two main features; range estimation of the moving hand and received signal strength (RSS). These two factors are estimated using two simple methods; channel impulse response (CIR) and cross correlation (CC) of the reflected ultrasonic signal from the gesturing hand. A customized simple hardware setup was used to classify a set of hand gestures with high accuracy. The detection and classification were done using methods of low computational cost. This makes the proposed method to have a great potential for the implementation in many devices including laptops and mobile phones. The predefined set of gestures can be used for many control applications.

  19. Superficial Priming in Episodic Recognition

    Science.gov (United States)

    Dopkins, Stephen; Sargent, Jesse; Ngo, Catherine T.

    2010-01-01

    We explored the effect of superficial priming in episodic recognition and found it to be different from the effect of semantic priming in episodic recognition. Participants made recognition judgments to pairs of items, with each pair consisting of a prime item and a test item. Correct positive responses to the test item were impeded if the prime…

  20. Word Recognition in Auditory Cortex

    Science.gov (United States)

    DeWitt, Iain D. J.

    2013-01-01

    Although spoken word recognition is more fundamental to human communication than text recognition, knowledge of word-processing in auditory cortex is comparatively impoverished. This dissertation synthesizes current models of auditory cortex, models of cortical pattern recognition, models of single-word reading, results in phonetics and results in…