This paper investigates the potential uses of graphene nanomechanical devices for NEMS applications fabricated using Helium Ion Microscope (HIM). Suspended nanomechanical graphene drum structures of diameter ( 2 - 3 m...
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This paper presents the development of an embedded device used for noninvasively measuring the tremor and related symptoms of disease in Parkinson *** system aims to provide an objective UPDRS *** this occasion,the mo...
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This paper presents the development of an embedded device used for noninvasively measuring the tremor and related symptoms of disease in Parkinson *** system aims to provide an objective UPDRS *** this occasion,the monitored signal can be scaled without bias by the physician operating the clinical *** embedded system consists of five main modules including a low-power consumption microcontroller,sensors,power management,detachable memory interface and wireless data communication *** device has been designed to be small,light weighted and compact to place on patient’s arm next to his/her *** sensor module is located at patient’s finger to monitor the *** monitoring signal of the module shows a promising result that the system can be used as a clinical trial.
In this paper the statistic distribution property of Synthetic Aperture Radar (SAR) image was analyzed. Then a fast Otsu segmentation algorithm for SAR images was proposed, which satisfies the requirements of SAR auto...
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In this paper the statistic distribution property of Synthetic Aperture Radar (SAR) image was analyzed. Then a fast Otsu segmentation algorithm for SAR images was proposed, which satisfies the requirements of SAR automatic target recognition on SAR image segmentation. In this proposed algorithm, the Constant False Alarm Rate (CFAR) technique is first employed for coarse segmentation, and then Otsu was adopted for fine segmentation. Experimental results show that the proposed algorithm is not only efficient and accurate, but also can also achieve higher objective evaluation values.
Keyword spotting refers to the detection of a limited number of given keywords in speech utterances. In this paper, first we review one of the large margin based keyword spotting approach that uses a discriminative me...
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Keyword spotting refers to the detection of a limited number of given keywords in speech utterances. In this paper, first we review one of the large margin based keyword spotting approach that uses a discriminative method for training the keyword spotter. Then, we evaluate the robustness of this approach in different noisy conditions. In addition; we compare the performance of this method with an HMM-based keyword spotter -which uses a generative training method- in the same noisy conditions. The experimental results show that the large-margin based keyword spotter is more robust than HMM-based system in noisy environments.
In this paper, a bi-Capon beamforming (BCB) algorithm for MIMO radar based on a simplified correlation matrix is investigated. By vectorising the received data matrix and its transpose and iteratively optimizing two l...
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In this paper, a bi-Capon beamforming (BCB) algorithm for MIMO radar based on a simplified correlation matrix is investigated. By vectorising the received data matrix and its transpose and iteratively optimizing two lower dimensional weight vectors, the bi-Capon beamforming (BCB) method can significantly decrease the computational complexity and the training samples requirement. Simulation results are presented to demonstrate the effectiveness of the proposed method.
To optimize thinned array pattern synthesis in multi-input multi-output radar, the distance perturbation is introduced and both transmit and receive antennas geometries are modified synchronously. Accordingly, pattern...
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To optimize thinned array pattern synthesis in multi-input multi-output radar, the distance perturbation is introduced and both transmit and receive antennas geometries are modified synchronously. Accordingly, pattern synthesis with a lower relative sidelobe level and maximum degrees of freedom are achieved．
Fisher's linear discriminant analysis (FLDA) is one of the most well-known linear subspace selection methods. However, FLDA suffers from the class separation problem. The projection to a subspace tends to merge cl...
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A new classification feature extraction method for Chinese-Korean spoken language identification was proposed in this paper. Firstly, speech signal was divided into frame serial and the number of frames was counted. F...
A new classification feature extraction method for Chinese-Korean spoken language identification was proposed in this paper. Firstly, speech signal was divided into frame serial and the number of frames was counted. Furthermore, the ratio between short-time zero-crossing rate and short-time energy, i.e. short-time-frequency-energy-ratio (STFER), was computed, and the mean STFER per frame was treated as the classification feature to implement Chinese-Korean spoken language identification. Finally, the classification threshold was determined using information gain. Experimental results show that the proposed method is simpler than MFCC feature parameters and has better ability to identify spoken language with lower complexity, can be adopted in preprocessing procedure of language recognition.
This paper proposes a new method for estimating the direction-of-arrival (DOA) of wideband signal, which employs particle filters to track array manifold at different frequency bands. Compared with the coherent signal...
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This paper proposes a new method for estimating the direction-of-arrival (DOA) of wideband signal, which employs particle filters to track array manifold at different frequency bands. Compared with the coherent signal-subspace method (CSM), the proposed method utilizes the current observed data and does not require the estimated covariance matrix, thus it performs better while considering a small sample set. In the meantime, this method does not require the preliminary DOA estimates. Moreover, the proposed method can localize completely correlated sources as it is based on the idea of maximum likelihood. Simulation results show that the performance of the propose method is better than CSM when the sample set is small, the signal to noise ratio (SNR) is low and the signal sources are correlated.
Keyword spotting (KWS) refers to detection of a limited number of given keywords in speech utterances. In this paper, we evaluate a robust keyword spotting system based on hidden markov models for speaker independent ...
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Keyword spotting (KWS) refers to detection of a limited number of given keywords in speech utterances. In this paper, we evaluate a robust keyword spotting system based on hidden markov models for speaker independent Persian conversational telephone speech. Performance of base line keyword spotter is improved by means of normalizing features using cepstral mean and variance normalization (CMVN) and cepstral gain normalization (CGN). And better performance is gained by applying auto-regressive moving average (ARMA) filter on normalized features. Experimental results show that although all these methods improve keyword spotting performance, CMVN and ARMA (MVA) processing of PLP features works much better on our Persian conversational telephone speech database and 41% improvement to baseline system is achieved at false alarm (FA) rate equal to 8.6 FA/KW/Hour.
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