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Hand Movement Recognition Using Cross Spectrum image Analysis of EMG signals-A Deep Learning Approach

Hand Movement Recognition Using Cross Spectrum Image Analysi...

引用

Sustainable Technology and Engineering applications (NCETSTEA), 2020 National conference on Emerging Trends on

作者： Sayanjit Singha Roy Kaniska Samanta Soumya Chatterjee Sayantan Dey Arnab Nandi Ronjoy Bhowmik Shoumosree Mondal Electrical Engineering Department Techno India University Kolkata India Electrical Engineering Department St. Thomas’ College of Engineering and Technology Kolkata India

ISBN: (数字)9781728143620

ISBN: (纸本)9781728143637

Accurate recognition of hand movements using electromyography (EMG) signals is important to develop robust human-machine interface system for upper limb prosthetic control. For accurate detection and classification of different movements, proper feature selection from EMG signals is necessary, failure of which may often lead to incorrect results. To this end, this article presents an image processing aided deep learning approach for detection and classification of hand movement EMG signals. In this study, EMG signals for both left hand and right hand movements were procured from an existing database. A reference signal for each category was chosen as reference and cross spectrum of the rest of the EMG signals was done with the reference signal. The resultant cross-wavelet spectrum images of both classes of EMG signals were fed to a pre-trained convolution neural network (CNN) for the purpose of detection and classification of hand movements. It has been observed that the proposed method returned an average classification accuracy of 97.6% in segregating different categories of EMG signals. Besides, the performance of the proposed method analyzed using different CNN architectures was also found to be robust. The proposed method can be implemented for real-time detection of hand movements.

关键词： Convolution neural network classification cross-wavelet transform electromyography and hand movement hand movement Taxonomy Electromyogram cross spectrum wavelet transforms signals Reference signals Movements

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Data compression by recurrent neural network dynamics

Data compression by recurrent neural network dynamics

引用

IEEE Region 10 International conference TENCON

作者： Leong Kwan Li Applied mathematics department Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)0780336798

Data compression is dominated by the Fourier or wavelet transforms which approximate the given function or sequence as a linear sum of the basis functions. In this paper, we discuss the use of dynamical systems for compression. Since leaky-integrator model neural nets can approximate arbitrary finite sequences, we propose to compress a 'not too wild' signal by a recurrent neural network. As an initial valued problem, the information to be stored are the parameters of the system and the initial states. Elementary analysis on error and compression ratio are also given.

关键词： Data compression Function approximation Neural networks image coding Discrete transforms Recurrent neural networks Feedforward neural networks Nonlinear dynamical systems Mathematical analysis Software packages

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Classifier-Based Feature Fusion for Texture Discrimination

Classifier-Based Feature Fusion for Texture Discrimination

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International conference on Information Engineering and Computer Science (ICIECS)

作者： Jianglin Ma Zhouwei Zhang Chengyi Wang Zhong Chen Remote Sensing Image Processing Laboratory Institute of Remote Sensing Applications Chinese Academy and Sciences Beijing China Institute for Pattern Recognition & Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424449934

A classifier-based method to select and fuse grey level co-occurrence matrix (GLCM), Gaussian Markov random field (GMRF) and discrete wavelet transform (DWT) features to improve texture discrimination is presented. Feature selection via wrapper approaches is applied to find the optimal combination of texture features. The fused features have obtained higher discrimination accuracy compared with individual features. The curse of dimensionality is shown to affect discrimination accuracy, and feature selection and reduction helps obtain higher accuracy. Overall our proposed classifier-based method obtains the highest discrimination accuracy compared to other feature reduction methods such as principal component analysis (PCA) and linear discriminant analysis (LDA). Meanwhile GLCM features are found to produce higher discrimination accuracy than GMRF and DWT, and LDA is demonstrated to obtain higher discrimination accuracy than PCA.

关键词： Discrete wavelet transforms Linear discriminant analysis signal processing algorithms Feature extraction Remote sensing Markov random fields Principal component analysis Classification algorithms Fuses wavelet analysis

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Regularized Shearlet Network for face recognition using single sample per person

Regularized Shearlet Network for face recognition using sing...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Mohamed Anouar Borgi Demetrio Labate Maher El'Arbi Chokri Ben Amar Research Groups on Intelligent Machines University of Sfax Sfax Tunisia Department of Mathematics University of Houston Houston TX USA

This paper presents an improved approach to face recognition, called Regularized Shearlet Network (RSN), which takes advantage of the sparse representation properties of shearlets in biometric applications. One of the novelties of our approach is that directional and anisotropic geometric features are efficiently extracted and used for the recognition step. In addition, our approach includes a module based on regularization theory (RSN) to control the trade-off between the fidelity to the data (gallery) and the smoothness of the solution (probe). In this work, we address the challenging problem of the single training sample per subject (STSS). We compare our new algorithm against different state-of-the-arts method using several facial databases, such as AR, FERET, FRGC, FEI, CK. Our tests show that the RSN approach is very competitive and outperforms several standard face recognition methods.

关键词： Face Face recognition Feature extraction Databases image coding wavelet transforms

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A study on VLSI architectures of lifting based discrete wavelet transform

A study on VLSI architectures of lifting based discrete wave...

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International conference on Advanced Computing and Communication Systems (ICACCS)

作者： K. Indira Devi R. Shanmugalakshmi Dept. of Electrical & Electronics Engg Sri Shakthi Inst. of Engg & Tech. Coimbatore Tamilnadu India Dept. of Computer Science & Engineering/IT Government College of Technology Coimbatore Tamilnadu India

To implement new algorithm assessing the previous work is an important part of system. The aim of this paper is to give a review of VLSI architectures for efficient methods for the implementation of DWT through Lifting schemes. The lifting scheme has wide applications and advantages over the DWT. Filters are complicated because of much number of multipliers. As multiplier decide the critical computation time and power dissipation of a signal processing architecture, designing of high throughput and power efficient multiplier is a major challenge. To reduce the complexity of the architecture and to increase the performance of the system this study will deriving an efficient method.

关键词： Discrete wavelet transforms Finite impulse response filters image coding Computer architecture Filter banks

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Estimation of Motor Imagination Based on Consumer-Grade EEG Device 3rd

Estimation of Motor Imagination Based on Consumer-Grade EEG ...

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3rd International conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Luo, Zhenzhen Hu, Zhongyi Li, Zuoyong College of Computer and Artificial Intelligence Wenzhou University Wenzhou China Intelligent Information Systems Institute Wenzhou University Wenzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9783030624590

Nowadays, classifying electroencephalogram (EEG) signals based motor imagery tasks is extensively used to control brain-computer interface applications, as a communication bridge between humans and computers. In this paper, we propose signal-to-image transformation and feature extraction methods for the classification of motor imagery. Specifically, a continuous wavelet transform is applied to decompose EEG signals into five rhythms and generate time-frequency images. Then, a gray-level co-occurrence matrix is used to extract global texture features on time-frequency images. Finally, the SVM classification model is optimized by using a grid search algorithm to select optimal parameter pairs. To confirm the validity of the proposed methods, we experimented on self-collected data, which is obtained using a consumer-grade EEG device. The experimental result showed that this proposed method can achieve an acceptable classification accuracy of 90% for a two-class problem (left/right-hand motor imagery). © 2020, Springer Nature Switzerland AG.

关键词： Electroencephalography

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Memory analysis and throughput enhancement for cost effective bit-plane coder in JPEG2000 applications

Memory analysis and throughput enhancement for cost effectiv...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Lien-Fei Chen Tai-Lun Huang Yeong-Kang Lai Department of Electrical Engineering National Chung Hsing University Taiwan

ISBN: (纸本)0780388747

A cost effective bit-plane coder with throughput enhancement in JPEG2000 applications is proposed. Many papers and the results of chip implementation show that memory requirement dominates the hardware cost of the bit-plane coder. In order to reduce the memory size, a memory-free algorithm is proposed to eliminate state variable memories by calculating three coding state variables (/spl gamma//sub p+1/[n], /spl sigma//sub p+1/[n], and /spl pi//sub p/[n]) on the fly. We also propose a stripe-column-based pass-parallel operation to perform three coding passes in pipeline operation and to encode four samples within the stripe-column concurrently for the high throughput requirement. Experimental results show that the hardware cost and memory size of the proposed architecture is smaller than other existing architectures because of the proposed memory-free algorithm. Furthermore, the proposed architecture has 3 times greater throughput than other familiar architectures.

关键词： Throughput Costs Hardware Discrete wavelet transforms IEC standards ISO standards Pipelines Standards development image coding Entropy coding

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Data fusion algorithms for horticulture classification using multi-sensory satellite images

Data fusion algorithms for horticulture classification using...

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IEEE India conference (INDICON)

作者： Anand N Khobragade Mukesh M Raghuwanshi Maharashtra Remote sensing Applications Centre Nagpur India Rajiv Gandhi College of Engineering & Research Nagpur India

ISBN: (纸本)9781479953653

With the advent of numerous remote sensing sensors available for the researcher, the fusion of digital image data has become a imperative tool for classifying remote sensing image and evaluation too. Remote sensing image fusion not only improves the spatial resolution of the original multispectral image, but also improves the spectral quality of merged product. Quantitative and qualitative digital image fusion is an emerging research domain that motivates the scholars for producing high quality image with best multi-spectral capabilities. PAN Sharpened images endow with increased interpretation capabilities as data with various distinctiveness are combined and process effectively. The objective of satellite data fusion is to reduce uncertainty and minimize redundancy in the merged image while maximizing relevant details particular to remote sensing applications. Horticulture in India having great impact on agro based economy. It motivates us for carrying research as very few attempts are made in order to address the issues pertaining with horticulture application of remote sensing. Referring to the results obtained from quantitative and qualitative analysis of fused images, it is obvious that Brovey and wavelet algorithms outperformed as compared to others.

关键词： Transforms Remote sensing image fusion Algorithm design and analysis Principal component analysis Spatial resolution signal processing algorithms

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Bearing Fault image Recognition in Strong Noise Based on Optimized CNN Architecture

Bearing Fault Image Recognition in Strong Noise Based on Opt...

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image processing and Computer applications (ICIPCA), IEEE International conference on

作者： Qiang Li Haowen Gai Kaijie Hu Northeastern University Shenyang China Tianjin College University of Science and Technology Beijing Tianjin China Zhonghuan Information College Tianjin University of Technology Tianjin China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

To address the problem of low accuracy and poor stability of bearing diagnostic models under strong background noise, a bearing fault image recognition method is proposed that reduces the randomness of the model by avoiding the need for artificial parameterization, which can introduce random factors. This method is based on hyperparameter optimization of the GoogLeNet convolutional neural network model and decision fusion. First of all, two-dimensional wavelet time-frequency variation of the original signal of the bearing vibration to construct an image dataset, one-dimensional classification is into a two-dimensional image problem; Secondly, three lightweight convolutional neural network architectures are selected for the noise immunity test, to get the best noise-resistant network architecture GoogLeNet; Finally, hyper-parameter optimization is performed for the network, comparing the mesh method, the progressive mesh method, and the group optimization algorithm, respectively, and getting The optimal parameters are then analyzed for decision fusion and visualization of the network. To verify the method proposed in this paper, we used the open Case Western Reserve University bearing dataset. The experimental verification demonstrates that the proposed method achieves a correctness rate of 94.33% in decision fusion under noise, with good accuracy and stability.

关键词： Accuracy image recognition Computer architecture Power system stability White noise Stability analysis Robustness Classification algorithms Convolutional neural networks Optimization

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Effect of Time-Frequency Representations for Fault Classification of Rolling Bearing in Noisy Conditions Using Deep Learning

Effect of Time-Frequency Representations for Fault Classific...

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Digital signal processing and its applications (DSPA), International conference on

作者： Prashant Kumar Sahu Rajiv Nandan Rai Subir Chowdhury School of Quality and Reliability Indian Institute of Technology Kharagpur Kharagpur India

Intelligent fault diagnosis techniques such as deep learning methods have gained immense attention recently for identifying bearing faults with higher accuracy and adaptability based on vibration analysis. This work proposes a fault diagnosis method for rolling bearings under extreme noise conditions based on the time-frequency representation (TFR) of signals with deep learning techniques. The bearing signals are masked with -5dB and -10dB of white Gaussian noise to create a noisy environment. Short-time Fourier transforms (STFT), Continuous wavelet transforms, and Gabor transforms (GT) are utilized to obtain spectrogram, magnitude scalogram, and constant-Q transform images to visualize the time-frequency relationship of the signal from one-dimensional vibration signals. These TFR images are directly given as input to VGG16-CNN deep learning architecture to classify the bearing faults. The effectiveness of each TFR is measured and compared based on classification accuracy. This work also studies the effect of TFR with and without overlapping the segmented signals. The case western reserve university (CWRU) standard-bearing dataset is used in this work and has achieved a satisfactory result. The result suggests that the magnitude scalogram of the vibration signal is an effective TFR that works efficiently with deep learning for bearing fault classification under different conditions, such as original signal with no added noise, -5dB and -10 dB of added noise, with an accuracy of 99.42%, 90.2 % and 85.03%, respectively when trained with 80% of the sample with 75% of the overlapping index.

关键词： Deep learning Fault diagnosis Vibrations image segmentation Time-frequency analysis Rolling bearings Noise measurement

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