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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Wei Shi Kui Xiong Shiyuan Wang College of Electronic and Information Engineering Southwest University The Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

As a nonlinear measure similarity developed in a kernel space, the generalized correntropic loss (GC-Loss) has been successfully used for signal processing and machine learning in non-Gaussian situations thanks to its ability of extracting high-order statistical properties of data. However, a highly non-convex performance surface in GC-Loss leads to poor optimization performance. To address this issue, we propose a new similarity measure developed in the kernel space by combining a generalized Gaussian distributed kernel function into the Cauchy loss. By minimizing the proposed loss function and using the kernel method, a novel kernel recursive generalized Cauchy kernel loss (KRGCKL) algorithm in the reproducing kernel Hilbert space (RKHS) is proposed. Simulations on different examples under non-Gaussian noises show the superiorities of KRGCKL over other representative algorithms in terms of filtering accuracy and robustness to large outliers.

关键词： Generalized Cauchy kernel loss kernel method robustness non-Gaussian noise kernel method non-Gaussian noise Seeds similar measure Kernel attribute data Cauchy Robustness Recursive

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A hybrid intelligent system for assisting low-vision people with over-the-counter medication 12

A hybrid intelligent system for assisting low-vision people ...

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12th International Conference on information Science and Technology, ICIST 2022

作者： Leung, Man-Fai Che, Hangjun Kwok, Chin-Hung Chan, Lewis Faculty of Science and Engineering Anglia Ruskin University Cambridge United Kingdom College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China School of Science and Technology Hong Kong Metropolitan University Hong Kong Hong Kong

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

Because people with low vision have difficulty pur-chasing and taking the correct medicine and dosages on time, this paper presents a system with a Flask Server and Android application that assists low-vision people with using over-the-counter (OTC) medication correctly. The system is mainly divided into three parts: an Android application, a Flask server and a MongoDB database. The application provides a medication time reminder, medicine information retrieval and image capture for recognition functions. A server recognizes the medication package by combining optical character recognition (OCR) and an image classification convolutional neural network (CNN). A database is used to store and provide medicine information. The experimental results show that the recognition performance has up to 96.1% accuracy. Moreover, the approach is shown to be able to handle out-of-distribution images. © 2022 IEEE.

关键词： Image classification

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A Multiband Frequency Reconfigurable Antenna for Wireless Communication

A Multiband Frequency Reconfigurable Antenna for Wireless Co...

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第43届中国控制会议

作者： Rongkai Liang Chuandong Li Youxun Su Weiwei Liang Menggang Chen Zhen Luo College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In order to enhance the adaptability of wireless devices across diverse applications,it becomes imperative for an antenna to embody a multitude of functionalities,including the capacity to dynamically reconfigure the operating frequency,polarization mode,and radiation *** paper introduces a frequency-reconfigurable antenna that incorporates an L-U shaped slot band with a Split-Ring Resonator(SRR).Fabricated on a 1.2 mm low-cost FR4 substrate using a partially grounded design approach,which measures 40 x 35 *** manipulating the bias states of the two PIN diodes,the radiation element current and field distributions can be altered,resulting in reconfigurable bands spanning 2.45 GHz,3.3 GHz,4.5 GHz,7.4GHz,and 9.4 GHz,with an expansive Ultra-Wide Bandwidth(UWB) of 4230 *** antenna design caters to a range of wireless applications,including WLAN(Wireless Local Area Network),WiMAX(Worldwide Interoperability for Microwave Access),Remote Radio,and X-band Satellite *** meticulous simulation optimization,four reconfiguration scenarios are thoroughly examined by presenting and analyzing the simulation results.

关键词： Frequency Bandwidth Reconfigurable Multi-Band Antenna Ultra Wide Band Antenna PIN Diode

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A Multi-Kernel-Based Multi-View Deep non-Negative Matrix Factorization for Enhanced Healthcare Data Clustering

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Che, Hangjun Yang, Xuanhao College of Electronic and Information Engineering Southwest University and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China

Multi-view clustering methods based on deep matrix factorization play a vital role in data analysis within the healthcare sector. However, existing methods predominantly conduct deep matrix factorization in the original data space, which is not conducive to addressing non-linear and complex data patterns. To address this issue, the Multi-kernel based Multi-view Deep non-negative Matrix Factorization with Optimal Consensus Graph (OGMKMDNMF) is introduced. This approach utilizes deep non-negative matrix factorization after projecting the data matrix into a high-dimensional kernel space. Additionally, it employs optimal consensus graph to alleviate the detrimental effects arising from misassigned nearest neighbors during the construction of similarity matrix. An innovative iterative optimization algorithm is developed for OGMKMDNMF. The experimental results demonstrate the effectiveness and competitive advantage of OGMKMDNMF in addressing multi-view healthcare data clustering tasks. IEEE

关键词： Cluster analysis

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A Novel Privacy-Preserving Data Integrity Verification by Partial Delegation

A Novel Privacy-Preserving Data Integrity Verification by Pa...

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International Conference on information Science and Technology (ICIST)

作者： Runze Ji Nankun Mu Xiaofeng Liao The Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing P.R.China

Recently, cloud storage is widely concerned due to the properties of low cost, advanced technology and efficient business model, and has been made a rapid development. However, as a new storage model, there are known security and privacy issues in migrating data to the cloud. Therefore, how to ensure the integrity and correctness of data storage is the main research direction and development of cloud storage technology. Moreover, the main purpose of the data integrity verification mechanism is to verify that the data is completely stored in the cloud server and is not modified maliciously or deleted. Sometimes the user can't perform data integrity verification for some reason, and the verification right need to be delegate to a third party auditor. In this paper, we proposed: (1) a new data integrity verification by partial delegation(PDIPD) is proposed, which delegates the remote data possession checking to a third party auditor. Compared with the existing data integrity verification scheme, the new scheme has less computational overhead and the flexibility at the same security level. (2) Extensive security analyses show this scheme is provable secure in random oracle. Estimating the cost of basic cryptographic and analyzing the experiment show, the proposed schemes are highly efficient and practical.

关键词： Data integrity Cloud computing Servers Data models Games Public key

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Effects of Acute Moderate Intensity Exercise on Emotion Based on beta Power in EEG 2019

Effects of Acute Moderate Intensity Exercise on Emotion Base...

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Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence

作者： Pengfei Gao Zhengji Long Zhangyan Xiao Fu Yang Jihai Zhang Guangyuan Liu School of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Processing Chongqing China

ISBN: (纸本)9781450376273

According to previous studies, high anxiety leads to an increase in beta power, while pleasure and relaxation of emotion reduces beta power. In this experiment, the International Standard Picture Stimulation Library(IAPS) was used to induce positive and negative emotions of subjects, and EEG signals were collected when subjects were looking at pictures. Comparing the beta power of positive and negative EEG induced by before and after exercise, we found that the power of beta stimulated by positive and negative pictures before exercise is generally greater than that after exercise. It indicates that subjects were happier when facing positive stimuli than before exercise, and less negative emotions were produced when facing negative stimuli than before exercise. In short, acute moderate intensity exercise can improve people's negative emotion in the face of negative stimuli. The purpose of this study is to explore the mechanism of the effect of acute moderate intensity exercise on emotion by analyzing beta power in late positive potential (LPP).

关键词： moderate emotion exercise beta power

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Synchronization of Reaction-Diffusion Neural Networks with Sampled-Data Control Via a New Two-Sided Looped Functional

SSRN

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SSRN 2022年

作者： Huan, Mingchen Li, Chuandong Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China

In this paper, the exponential synchronization problem of reaction-diffusion neural networks with sampled-data control is addressed via looped-functional approach. Considering the transmission delay of sampled-data controller, a two-sided looped functional is designed to obtain thesynchronization conditions, which are less conservative than those with the traditional Lyapunov method. The research results are applied to Takagi-Sugeno (T-S) fuzzy models with reactiondiffusion terms. Three numerical examples are presented to showthe feasibility and effectivenessof our methods by using looped functional. © 2022, The Authors. All rights reserved.

关键词： Lyapunov methods

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Another Two-Timescale Duplex Neurodynamic Approach to Portfolio Selection 11

Another Two-Timescale Duplex Neurodynamic Approach to Portfo...

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11th International Conference on intelligent Control and information processing, ICICIP 2021

作者： Leung, Man-Fai Wang, Jun Che, Hangjun Hong Kong Metropolitan University School of Science and Technology Hong Kong City University of Hong Kong Department of Computer Science School of Data Science Hong Kong Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing China

ISBN: (纸本)9781665425155

This paper is concerned with portfolio selection based on the Markowitz mean-variance framework using neurodynamic optimization. The portfolio optimization problem is formulated as a biconvex optimization problem. A two-timescale duplex neurodynamic approach is then applied for solving the profolio selection problem. The approach makes use of two recurrent neural networks (RNNs) which operate at different timescales for local search. A particle swarm optimization algorithm is employed to update the neuronal states of the two RNNs for global optima. Experimental results on four stock market datasets show the superior performance of the neurodynamic approach in terms of long-term expected returns. © 2021 IEEE.

关键词： Recurrent neural networks

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Kernel Least Mean Square Based on the Sparse Nyström Method

Kernel Least Mean Square Based on the Sparse Nyström Method

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IEEE International Symposium on circuits and Systems (ISCAS)

作者： Haonan Zhang Han Jiang Shiyuan Wang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing 400715 China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Kernel least mean square based on the Nyström method (NysKLMS) has been proposed to fix the network structure of kernel least mean square (KLMS) by approximating a large Gram matrix with a low-rank matrix generated by sampling from input vectors. However, the computational burden of NysKLMS increases with the growth of the input dimension. To alleviate this computational burden, a novel KLMS based on a sparse Nyström method (SNKLMS) algorithm is proposed in this paper. Unlike NysKLMS using the k-means sampling to obtain k centroids from input data, the proposed SNKLMS algorithm first divides input data into several clusters, and then selects k centroids from the obtained clusters equally. According to the relation between the Gaussian kernel and the distance of different clusters, partial submatrices in the low-rank matrix of SNKLMS can be omitted to further reduce the operations of multiplication and addition, and thus a sparse low-rank matrix is obtained. Simulations on the channel equalization and chaotic time-series prediction validate the superiorities of SNKLMS.

关键词： Kernel Sparse matrices Signal processing algorithms Approximation algorithms Clustering algorithms Matrix decomposition Training data

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Mean Square Exponential Stabilization Analysis of Stochastic Neural Networks with Saturated Impulsive Input

SSRN

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SSRN 2023年

作者： Deng, Hao Li, Chuandong Chang, Fei Wang, Yinuo Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China

The exponential stabilization of stochastic neural networks in mean square sense with saturated impulsive input is investigated in this paper. Firstly, the saturated term is handled by polyhedral representation method. When the impulsive sequence is determined by average impulsive interval, impulsive density and mode-dependent impulsive density, the sufficient conditions for stability are proposed, respectively. Then, the ellipsoid or the polyhedron are used to estimate the attractive domain, respectively. By transforming the estimation of the attractive domain into a convex optimization problem, a relatively optimum domain of attraction is obtained. Finally, a three-dimensional continuous time Hopfield neural network example is provided to illustrate the effectiveness and rationality of our proposed theoretical results. © 2023, The Authors. All rights reserved.

关键词： Hopfield neural networks

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