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Channels Selection for pattern recognition of Five Fingers Motor Imagery Electroencephalography Signals 3

Channels Selection for Pattern Recognition of Five Fingers M...

引用

3rd international conference on Emerging Electrical Energy, Electronics and computing Technologies 2021, ICE4CT 2021

作者： Azizah, R.N. Zakaria, H. Hermanto, B.R. Department of Biomedical Engineering Bandung Institute of Technology Bandung Indonesia

Research about pattern recognition on electroencephalography (EEG) signal of finger motor imagery (MI) plays a critical role in Brain-Computer Interfaces (BCI) based hand prosthetics development. However, the previous research still used irrelevant channels to finger MI. This work proposed optimal EEG channels combination for five-finger MI. It is achieved by subject-dependence channel selection using One versus Rest Common Spatial pattern (CSP-OVR) combined with sequential searching algorithms due to specific neural activation areas of MI. Optimal channels combinations are of great importance to reduce channels number. It supports the development of practical BCI-based hand prosthetics that can help hand handicapped to do daily activities easier. Experimental results show 4 out of 19 channels are relevant to five-finger MI with 0,6% accuracy degradation compared with EEG-MI pattern recognition using 19 channels. This result is better than the Principal Component Analysis (PCA) channel selection method that only selects 11 out of 19 channels with 1 % accuracy degradation. © Published under licence by IOP Publishing Ltd.

关键词： Electroencephalography

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MA-LSTM: A Multi-Attention Based LSTM for Complex pattern Extraction 25

MA-LSTM: A Multi-Attention Based LSTM for Complex Pattern Ex...

引用

25th international conference on pattern recognition (ICPR)

作者： Guo, Jingjie Tian, Kelang Ye, Kejiang Xu, Cheng-Zhong Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Univ Macau Fac Sci & Technol State Key Lab IoTSC Zhuhai Peoples R China

ISBN: (纸本)9781728188089

With the improvement of data volume, computing power and algorithms, deep learning has achieved rapid development and showing excellent performance. Recently, many deep learning models are proposed to solve the problems in different areas. A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior, which makes it applicable to tasks such as handwriting recognition or speech recognition. However, the RNN relies heavily on the automatic learning ability to update parameters that concentrate on the data flow but seldom considers the feature extraction capability of the gate mechanism. In this paper, we propose a novel architecture to build the forget gate which is generated by multiple bases. Instead of using the traditional single-layer fully-connected network, we use a Multiple Attention (MA) based network to generate the forget gate which refines the optimization space of gate function and improve the granularity of the recurrent neural network to approximate the map in the ground truth. Due to the benefit of MA structure on the gate mechanism, the proposed MA-LSTM model achieves better feature extraction capability than other known models.

关键词： Multi-Attention traffic prediction handwriting recognition language model

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Artificial Intelligence for the Classification of Neuromuscular Diseases Using Dominant MUAP 8

Artificial Intelligence for the Classification of Neuromuscu...

引用

8th international conference on Smart Structures and Systems, ICSSS 2022

作者： Maheshwary, Priti Vinu, W. Velvadivu, P. Shukla, Surendra Kumar Srivastava, Prabhat Kumar Pareek, Prakash Centre for Internet of Things and Advance Computing Rabindranath Tagore University Bhopal India Annamalai University Department of Physical Education Tamilnadu Chidambaram India Coimbatore Institute of Technology Department of Computing-Data Science Tamilnadu Coimbatore India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Quantum University Department of Computer Science and Engineering Uttrakhand Roorkee India Vishnu Institute of Technology Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India

ISBN: (纸本)9781665497619

Motor-unit-action-potentials in an electromyographic isolated signals shape & sounds are valuable diagnosis information for neuro-muscular disorders-treatment & management. An expert can analyse these parameters qualitatively or quantitative by pattern recognition techniques. Because of benefits of the quantitatively method of EMG, producing robust automated MUAP types is investigated, & many systems is produced for this purpose, but correctness of the previous methods isn't peak enough to use in clinical settings. The developed system uses an EMG signal decomposition mechanism to extract both time & frequency domain for M-U-A-P's retrieved from E-M-G. Un-similar type of algorithms was studied, including 1 & many types with many sub-sets characteristics. The multi-classifier methods proposed here performed well in experiments by real set EMG. The multi-classifier, which aggregates base classifiers using multiple feature sets & both trainable & non-trainable fusion techniques, performed the best of the methods studied, with an average precision is 98%. © 2022 IEEE.

关键词： Support vector machines Time-frequency analysis Shape Neuromuscular Morphology Electromyography pattern recognition

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Securing Automotives with Iris pattern recognition

Securing Automotives with Iris Pattern Recognition

引用

Emerging Smart computing and Informatics (ESCI), conference on

作者： Kiran B. Ingle Manasvi Patil Pranav Patil Payal Powar Rahul Daga Parth Rajopadhye Vishwakarma Institute of Technology Pune Maharashtra Dept. of Information Technology Vishwakarma Institute of Technology Pune Maharashtra

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Cars and automotive theft are increasingly affecting today’s world hence implementing a smart locking system for luxury is must. Currently, there is a trend of biometric security and hence we have implemented an Iris pattern identification based locking system for the security systems of the car. Iris scans are based on the techniques of studying eye patterns and segmentations. Since biometric security provides maximum security, Iris scans are the most secure among all. The iris patterns are converted into unique mathematical forms that abdicate unambiguous positive recognizable proof of a person. Iris Acknowledgment viability is seldom hindered by glasses or contact focal points. This Biometric Security system for our automotive is the most efficient and optimal method in terms of security and other domains with stability as a key advantage. We have the Gabor wavelet transform based on Daugman’s algorithm for iris scans. As a result, auto motor optimization can be achieved by this technique and providing an immense level of security for the cars.

关键词： Wavelet transforms User experience Robustness pattern recognition Security Automobiles Optimization Iris recognition Automotive engineering Testing

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The Classification of Timor Weaving Using Artificial Neural Network

The Classification of Timor Weaving Using Artificial Neural ...

引用

international conference on Informatics and computing (ICIC)

作者： Mendarissan Aritonang Adi Setiawan Krismiyati Theophilus Wellem Dept of Computer Science Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Dept. Mathematics and Data Science Faculty of Science and Mathematics Satya Wacana Christian University Salatiga Indonesia Dept. Informatics & Computer Engineering Education Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Department of Informatics Satya Wacana Christian University Salatiga Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The number of woven fabric on the island of Timor is very large and varied, making it difficult to distinguish between types and origins. Many woven fabric motifs appear similar but represent different types. Therefore, this classification was used to perform pattern recognition of woven fabric in the system. The process allowed the system’s ability to be tested by applying the pattern recognition algorithm to woven images. In this context, the feature extraction method used was gray level co-occurrence matrix (GLCM), while the method used for classification was artificial neural network (ANN). The results showed an accuracy of $87.5 \%$ in the system. Finally, GUI system was developed, enabling tests to be performed on woven images for the identification of Timor Weaving image patterns.

关键词： Training Accuracy Social networking (online) Neurons Artificial neural networks Feature extraction Weaving Fabrics pattern recognition Web sites

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Communication Signal Modulation recognition Algorithm Based on soft Threshold and Hole Convolution

Communication Signal Modulation Recognition Algorithm Based ...

引用

international conference on Communication Technology (ICCT)

作者： Minghui Gao Yanjie Ren Binquan Zhang Lu Wang Yuepeng Li Xiaogang Tang School of Space Information Space Engineering University Beijing China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

The current communication signal modulation recognition based on end-to-end deep learning methods is susceptible to signal noise, resulting in poor recognition performance under low signal-to-noise ratio(SNR) conditions. To address this issue, this paper presents a modulation recognition convolutional neural network that combines soft threshold and hole convolution(STCNN). Firstly, adaptive filtering of useless features such as noise is achieved through a soft threshold layer and attention mechanism. Secondly, by using dilated convolutional networks to extract multi-scale time-frequency domain features of signals, the number of model parameters is reduced while the accuracy of signal classification is improved. Finally, simulation analysis was conducted based on the RML2016.10a dataset, and the highest recognition rate of 62.7% was achieved for 11 modulation signals under different SNR conditions, which is 10.9%, 4.5%, and 1.6% higher than that of CNN, LSTM, and SCRNN algorithms, respectively. Furthermore, the number of parameters is reduced by 98.8%, 82.5%, and 91.2%, respectively, which is simulated to verify the effectiveness and recognition performance of the proposed method.

关键词： Time-frequency analysis Convolution Filtering Neural networks Modulation pattern classification Feature extraction Classification algorithms Convolutional neural networks Signal to noise ratio

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Multi-view Ensemble Clustering Based on BLS-Autoencoder

Multi-view Ensemble Clustering Based on BLS-Autoencoder

引用

pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Yukun Yang Yifan Shi School of Engineering Huaqiao University Quanzhou China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

With the richness of data sources, data from various modalities reflect the features of samples from different views. To uncover the cluster structure of these data in an unsupervised setting, multi-view clustering approaches have garnered extensive attention. However, the current multi-view clustering approaches usually learn a shared data representation or graph embedding for the final clustering process, which may lead to the loss of view-specific information. Therefore, in this paper, we propose a multi-view ensemble clustering framework based on BLS-autoencoder(MVEC-BLSAE). In generation phase, a multi-view BLS-autoencoder(MVBLSAE) considering both the cross-view subspace structure and the within-view feature projection is designed, which can extract compressed feature subspaces and contribute to producing multiple basic partitions. In integration phase, based on the basic partitions, the sample-cluster relevance is captured through computing fuzzy partition matrices, which are then leveraged to achieve a better consensus partition via graph-cut algorithm on the constructed bipartite graph. According to the experimental results of parameters analysis, comparative analysis and ablation study, MVEC-BLSAE achieves more satisfactory performance compared to several existing multi-view clustering approaches.

关键词： Learning systems soft sensors Design methodology Clustering methods Clustering algorithms Data visualization Transforms Feature extraction Partitioning algorithms Bipartite graph

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Data Augmentation in Medical Materials Science: A Review

Data Augmentation in Medical Materials Science: A Review

引用

pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Wenhao Lin Yifeng Lin Yuer Yang College of Information Science and Technology Jinan University Guangzhou China College of Cyber Security Jinan University Guangzhou China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

With the rapid development of computing methods, traditional methods of medical materials science analysis are unable to keep pace with today's development, especially when multiple chemical factors are contributing at the same time. Most artificial intelligence (AI) techniques in medical materials science are based on traditional machine learning (ML) and deep learning (DL). Nonetheless, the two methods are limited by the amount of data. Thus, data augmentation (DA) was introduced to medical materials science to solve this problem. This paper aims to provide an overview of DA applications in medical materials science. A general procedure is stated. Crucial scientific guidance for future innovations in medical materials science based on DA is also presented.

关键词： Deep learning Materials science and technology Technological innovation Reviews Lead Data augmentation pattern recognition Chemicals

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Multi-Scale Features Integration for Handwritten Mathematical Expression recognition 11

Multi-Scale Features Integration for Handwritten Mathematica...

引用

11th international conference on computing and pattern recognition, ICCPR 2022

作者： Liu, Xianghao Wang, Da-Han Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Handwritten mathematical expression recognition (HMER) is a challenging task due to the complex two-dimensional structure of mathematical expressions and the similarity of handwritten texts. Most existing methods for HMER only consider single-scale features while ignoring multi-scale features that are very important to HMER. Few works have explored the fusion of multi-scale features in HMER, but exhibited an extra branch that brings more parameters and computation. In this paper, we propose an end-to-end method to integrate multi-scale features using a unified model. Specifically, we customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture the multi-scale features of the input image meanwhile expanding the receptive fields. Moreover, we added a symbol classifier using focal loss to better discriminate and recognize similar symbols, to further improve the performance of HMER. Experiments on the Competition on recognition of Online Handwritten Mathematical Expressions (CROHME) 2014, 2016 and 2019 shows that the proposed method achieves superior performance to most state-of-the-art methods, demonstrating the effectiveness of the proposed method. © 2022 ACM.

关键词： Character recognition

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IET conference Proceedings

IET Conference Proceedings

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8th international conference on computing in Engineering and Technology, ICCET 2023

ISBN: (纸本)9781839539237

The proceedings contain 85 papers. The topics discussed include: performance analysis of radial system with renewable sources;artificial intelligence framework for rice blast disease detection and classification using recurrent neural networks;road anomaly detection using self-supervised label generator with vertical disparity maps;sEMG signal analysis and classification for precision and pulp to pulp grip recognition;Internet of Things and robotics: a review of concept, added value and applications;a deep learning based approach to enhance the Quality of Service (QoS) of next generation wireless systems by large intelligent surfaces;new applications of morphological methodology for the early detection of cancer;IoT based automatic fuel level monitoring system for automobiles;evolutionary technique for high utility sequential pattern mining;high utility sequence mining using cuckoo search algorithm;image restoration and object detection in unfavorable weather conditions for autonomous vehicles using deep learning approaches: a review;and a review of image super-resolution approaches based on deep learning methods.

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