IN this paper, the feature vector of a few dimensions for the electromyograph (EMG) recognition systems is extracted. We aim at the construction of the comprehensive operation equipment to which the operation used fre...
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IN this paper, the feature vector of a few dimensions for the electromyograph (EMG) recognition systems is extracted. We aim at the construction of the comprehensive operation equipment to which the operation used frequently was summarized. Important frequency bands of EMG signals are selected by using a genetic algorithm. The EMG signals are a kind of the living organism signal. The EMG signals based on 7 operations at a wrist are measured and recognized. We perform a recognition experiment of EMG signals by neural network using the selected frequency band. We show the effectiveness of this method by means of computer simulations.
We present blind equalization techniques for ETSI standard distributed speech recognition (DSR) frontend which compensate for acoustic mismatch caused by input devices. The DSR front-end employs vector quantization (V...
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We present blind equalization techniques for ETSI standard distributed speech recognition (DSR) frontend which compensate for acoustic mismatch caused by input devices. The DSR front-end employs vector quantization (VQ) for feature parameter compression so that the mismatch does not only cause a shift of parameters but also increases VQ distortion. Although cepstral mean subtraction (CMS) is one of the most effective methods to compensate for the shift, it can not decrease VQ distortion in DSR. To compensate for the shift and decrease VQ distortion simultaneously, the proposed methods estimate the shift in the input data necessary to match the VQ codebook distribution. The methods do not need the acoustic likelihood which is calculated in a decoder on the server side. Therefore, they are applicable to the DSR front-end. Japanese Newspaper Article Sentences database (JNAS) was used for the equalization experiments. While the word error rate (WER) for ETSI standard DSR frontend was 18.6 % under acoustic mismatched condition, our proposed method yielded a rate of 12.3 %.
In this paper, a feature vector is extracted from an electromyography (EMG) signal at a wrist, and the EMG signals based on 7 motions are recognized. In order to perform good pattern recognition, it is desirable that ...
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In this paper, a feature vector is extracted from an electromyography (EMG) signal at a wrist, and the EMG signals based on 7 motions are recognized. In order to perform good pattern recognition, it is desirable that the distance in feature vector between classes is far, and that the variance in a class is small. In consideration of these, important frequency bands of EMG signals are selected by using a genetic algorithm. We use the selected frequency band to perform the recognition experiment of EMG signal by a neural network. Finally, the effectiveness of this method is demonstrated by means of computer simulations.
In this study we present blind equalization techniques for ETSI standard Distributed Speech Recognition (DSR) front-end which compensate for acoustic mismatch caused by input *** DSR front-end employs vector quantizat...
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In this study we present blind equalization techniques for ETSI standard Distributed Speech Recognition (DSR) front-end which compensate for acoustic mismatch caused by input *** DSR front-end employs vector quantization(VQ) for feature parameter compression so that the mismatch does not only cause a shift of parameters but also increases VQ distortion. Although CMS is one of the most effective methods to compensate for the shift,it can not decrease VQ distortion in *** compensate for the shift and decrease VQ distortion simultaneously,the proposed methods estimate the shift in the input data necessary to match the VQ codebook *** methods do not need the acoustic likelihood which is calculated in a decoder on the server ***, they are applicable to the DSR *** Newspaper Article Sentences database(JNAS) was used for the equalization *** the word error rate(WER) for ETSI standard DSR front-end was 18.6%under acoustic mismatched condition,our propsed method yielded a rate of 12.3%.
The purpose of this paper is to evaluate an immune optimization algorithm using a biological immune co-evolutionary phenomenon and cell-cooperation. The co-evolutionary model searches solutions through the interaction...
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The purpose of this paper is to evaluate an immune optimization algorithm using a biological immune co-evolutionary phenomenon and cell-cooperation. The co-evolutionary model searches solutions through the interactions between two kinds of agents, on the agent is called immune agent, which optimizes the cost of its own work. The other is call antigen agent, which realized the equal work assignment. This algorithm solves the division-of-labor problems in multi-agent system (MAS) through the three kinds of interactions: division-and-integration processing is used for optimization of the work-cost of immune agents and immune cell-cooperation is used to perform equal work assignment as a result of evolving the antigen agents. To investigate the validity, this algorithm is applied to "n-th agent's traveling salesman problem" as a typical problem of MAS. The good property on solving for MAS will be clarified by some simulations.
We propose a new real-time self-localization method for a mobile robot equipped with an omni-directional camera in a dynamically changing environment. This method uses direction of two landmarks and dead reckoning. Mu...
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We propose a new real-time self-localization method for a mobile robot equipped with an omni-directional camera in a dynamically changing environment. This method uses direction of two landmarks and dead reckoning. Multiple localization process in parallel results robust and accurate localization. The proposed method applies to the soccer robot in the RoboCup middle-size league and an experimental result indicates that the approach is reliable.
This paper reports an evaluation of European Telecommunications Standards Institute (ETSI) standard Distributed Speech Recognition (DSR) front-end through continuous word recognition on a Japanese speech corpus and pr...
This paper reports an evaluation of European Telecommunications Standards Institute (ETSI) standard Distributed Speech Recognition (DSR) front-end through continuous word recognition on a Japanese speech corpus and proposes a method, the Bias Removal Method (BRM), that reduces the distortion between feature vector and VQ codebook. Experimental results show that using nonquantized features in acoustic model training procedure can improve the recognition performance of DSR fornt-end features and that the proposed method can improve recognition performances of DSR front-end feature.
The paper proposed a new syntactic annotation scheme - functional chunk, which tried to represent information about grammatical relations between sentence-level predicates and their arguments. Under this scheme, we bu...
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The paper proposed a new syntactic annotation scheme - functional chunk, which tried to represent information about grammatical relations between sentence-level predicates and their arguments. Under this scheme, we built a Chinese chunk bank with about two million Chinese characters, and developed some learned models for automatically annotating fresh text with functional chunks. We also proposed a two-stages approach to build Chinese tree bank on the top of chunk bank, and gave some experimental results of chunk-based syntactic parser to show the advantage of functional chunk for parsing performance increase. All these work lays good foundations for further research project to build a large scale Chinese tree bank.
Visual tracking could be treated as target state representation and target state inference problem in an image sequence. Moreover, in cluttered and dynamic environments the better probabilities of accurate tracking de...
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Visual tracking could be treated as target state representation and target state inference problem in an image sequence. Moreover, in cluttered and dynamic environments the better probabilities of accurate tracking depend on richer representation and more robust inference. Target state representation could be considered as color segmentation, contour detection and position mark and target state inference could be treated as an evaluation from old states to new one in fuzzy logic at every step of an image sequence. This paper presents a special tracking system based on factored sampling model in order to resolve difficult and complicated visual tracking problem, such as a changing of target's representation, a clutter of environments and an interaction of target and camera. This tracking system is applied to changeful target tracking by handling the related information to sample-set between every two time-steps in an image sequence and implemented in real time system at around 20 Hz with 640*480 pixels image. Specially, color and position distribution of a target have been used in this system to estimate the target situation. The results show the robust, real-time system is able to track a target with enough accuracy and automatically control the camera's pan, tilt and zoom to remain the object centered in the field of vision.
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