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Computation-Efficient Distributed Algorithm for Convex Optimization Over Time-Varying Networks With Limited Bandwidth Communication

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IEEE TRANSACTIONS ON SIGNAL AND informatION PROCESSING OVER NETWORKS 2020年 6卷 140-151页

作者： Li, Huaqing Huang, Chicheng Wang, Zheng Chen, Guo Umar, Hafiz Gulfam Ahmad Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Ghazi Univ Dept Comp Sci & IT Dg Khan 32260 Pakistan

A novel computation-efficient quantized distributed optimization algorithm is presented in this article for solving a class of convex optimization problems over time-varying undirected networks with limited communication capacity. These convex optimization problems are usually relevant to the minimization of a sum of local convex objective functions using only local communication and local computation. In most of the existing distributed optimization algorithms, each agent needs to calculate the subgradient of its local convex objective function at each time step, which leads to extremely heavy computation. The proposed algorithm incorporates random sleep scheme into procedures of agents' updates in a probabilistic form to reduce the computation load, and further allows for uncoordinated step-sizes of all agents. The quantized strategy is also applied, which overcomes the limitation of communication capacity. Theoretical analysis indicates that the convex optimization problems can be solved and numerical analysis shows that the computation load of subgradient can be significantly reduced by the proposed algorithm. The boundedness of the quantization levels at each time step has been explicitly characterized. Simulation examples are presented to demonstrate the effectiveness of the algorithm and the correctness of the theoretical results.

关键词： Convex optimization distributed algorithm computation-efficient quantization limited communication capacity

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Recognition of the Impulse of Love at First Sight Based on Electrocardiograph Signal

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021年第1期2021卷 6631616-6631616页

作者： Zhang, Jin Yuan, Guangjie Lu, Huan Liu, Guangyuan Southwest Univ Coll Elect & Informat Engn Chongqing Peoples R China Southwest Univ Inst Affect Comp & Informat Proc Chongqing Peoples R China Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China

The impulse of love at first sight (ILFS) is a well known but rarely studied phenomenon. Despite the privacy of these emotions, knowing how attractive one finds a partner may be beneficial for building a future relationship in an open society, where partners are accepting each other. Therefore, this study adopted the electrocardiograph (ECG) signal collection method, which has been widely used in wearable devices, to collect signals and conduct corresponding recognition analysis. First, we used photos to induce ILFS and obtained ECG signals from 46 healthy students (24 women and 22 men) in a laboratory. Second, we extracted the time-and frequency-domain features of the ECG signals and performed a nonlinear analysis. We subsequently used a feature selection algorithm and a set of classifiers to classify the features. Combined with the sequence floating forward selection and random forest algorithms, the identification accuracy of the ILFS was 69.07%. The sensitivity, specificity, F1, and area under the curve of the other parameters were all greater than 0.6. The classification of ECG signals according to their characteristics demonstrated that the signals could be recognized. Through the information provided by the ECG signals, it can be determined whether the participant possesses the desire to fall in love, helping to determine the right partner in the fastest time, this is conducive to establishing a romantic relationship.

关键词： nonlinear analysis

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Energy preserving relaxation method for space-fractional nonlinear Schrodinger equation

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APPLIED NUMERICAL MATHEMATICS 2020年 152卷 480-498页

作者： Cheng, Bianru Wang, Dongling Yang, Wei Northwest Univ Dept Math Xian 710075 Shaanxi Peoples R China Northwest Univ Ctr Nonlinear Studies Xian 710075 Shaanxi Peoples R China Xiangtan Univ Hunan Key Lab Computat & Simulat Sci & Engn Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Hunan Peoples R China

In this paper, we develop and analyze an energy preserving relaxation scheme for a class of space fractional nonlinear Schrodinger equations (F-NLS) with periodic boundary condition. This scheme can preserve the discrete mass and energy for F-NLS with general power nonlinearity. The second order convergence in time direction for the numerical method is rigorously proved for the cubic nonlinear F-NLS. Some numerical simulations for 1D and 2D cases based on the Fourier spectral approximation in space are presented to validate the theoretical analysis of the proposed method. (C) 2019 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： Fractional nonlinear Schrodinger equation Relaxation method Energy conservation

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Evaluating cognitive task result through heart rate pattern analysis

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HEALTHCARE TECHNOLOGY LETTERS 2020年第2期7卷 41-44页

作者： Yu Juan Liu Guang Yuan Wen Wan Hui Chen Chuan Wu Southwest Univ Sch Elect & Informat Engn Chongqing 400700 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400700 Peoples R China Chongqing Collaborat Innovat Ctr Brain Sci Chongqing 400700 Peoples R China

The measurement of the right and wrong results of cognitive tasks has important applications in many commercial and educational areas such as the drivers' training system, the simulation training and online learning system. This Letter aims to distinguish the heartbeat pattern of cognitively wrong responses to that of cognitively right responses based on the electrocardiogram (ECG) through 36 subjects with different professional backgrounds. The experimental design methods were double-digit and five-digit addition/subtraction, which were blindly selected by subjects from a black box. Through the R-R interval (RRI) series obtained from the ECG data, some linear, nonlinear and moment features were extracted to evaluate the cognitive task results by using pattern recognition methods. The binary classification of RRI datasets indicated that autonomic nerve patterns of the right and wrong cognitive heartbeat responses were distinguishable.

关键词： learning (artificial intelligence) electrocardiography cognition neurophysiology pattern classification pattern recognition feature extraction medical signal processing educational areas drivers simulation training learning system heartbeat pattern cognitively wrong responses cognitively right responses different professional backgrounds experimental design methods cognitive task result pattern recognition methods autonomic nerve patterns wrong cognitive heartbeat responses heart rate pattern analysis wrong results cognitive tasks commercial areas

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Two-timescale neurodynamic approaches to supervised feature selection based on alternative problem formulations

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NEURAL NETWORKS 2021年 142卷 180-191页

作者： Wang, Yadi Wang, Jun Che, Hangjun Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Henan Univ Sch Comp & Informat Engn Inst Data & Knowledge Engn Kaifeng 475004 Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China City Univ Hong Kong Sch Data Sci Kowloon Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Guangdong Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

Feature selection is a crucial step in data processing and machine learning. While many greedy and sequential feature selection approaches are available, a holistic neurodynamics approach to supervised feature selection is recently developed via fractional programming by minimizing feature redundancy and maximizing relevance simultaneously. In view that the gradient of the fractional objective function is also fractional, alternative problem formulations are desirable to obviate the fractional complexity. In this paper, the fractional programming problem formulation is equivalently reformulated as bilevel and bilinear programming problems without using any fractional function. Two two-timescale projection neural networks are adapted for solving the reformulated problems. Experimental results on six benchmark datasets are elaborated to demonstrate the global convergence and high classification performance of the proposed neurodynamic approaches in comparison with six mainstream feature selection approaches. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Feature selection Recurrent neural networks Neurodynamic optimization Two-timescale neurodynamics

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Maximum Total Complex Correntropy for Adaptive Filter

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年第0期68卷 978-989页

作者： Qian, Guobing Wang, Shiyuan Iu, Herbert H. C. Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Crawley WA 6009 Australia

Nowadays, complex Correntropy has been widely used for adaptive filtering in the complex domain. Compared with the second order statistics methods, the complex correntropy based algorithms have shown the superiority in the non-Gaussian noise, especially the impulsive noise. However, the current complex correntropy based adaptive filtering algorithms have not taken the input noise into consideration, and the performances will be deteriorated when the input signals are also corrupted by the noise. In this article, we focus on the errors-in-variables (EIV) model and propose an adaptive algorithm based on the maximum total complex correntropy (MTCC). More importantly, we present the local stability analysis and derive the theoretical weight error power. Simulation results confirm the validity of the theoretical analysis and illustrate the superior performance of the propose algorithm in the EIV cases.

关键词： Complex domain adaptive filtering total complex correntropy steady-state mean square

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Open-set gas recognition: A case-study based on an electronic nose dataset

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SENSORS AND ACTUATORS B-CHEMICAL 2022年第0期360卷 131652-131652页

作者： Qu, Cheng Liu, Chuanjun Gu, Yun Chai, Shuiqin Feng, Changhao Chen, Bin Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuit & Intelligent Chongqing 400715 Peoples R China Kyushu Univ Grad Sch Informat Sci & Elect Engn Dept Elect Fukuoka 8190395 Japan Chongqing Univ Sci & Technol Coll Chem & Chem Engn 20 East Daxuecheng Rd Chongqing Peoples R China

Electronic nose (E-Nose) has been widely used in detection and classification of gases. The learning models of traditional E-Noses are generally limited in closed-set environment: the training and test samples share the same label spaces. However, a more challenging and realistic scenario of E-Noses is open-set learning, where the test samples contains classes unseen during the model training. This study investigated the possibility of open-set learning models for the recognition and classification of gases based on a public electronic nose datasets. The dataset includes the response of a 72-channels MOS sensor array on 10 gaseous substances. The original data was preprocessed by two methods: one is to manually extract features from the response curve of each sample, and the other is to down-sample the original sample into a matrix. Then multilayer perceptron (MLP) and convolution neural network (CNN) were used to extract the feature vectors of the data processed by the two processing methods respectively. The performance of four different open-set recognition models, including softmax threshold (ST), OpenMax, extreme value machine (EVM) and class anchor clustering (CAC), was compared based on the feature vectors obtained from two neural networks. To understand the effect of sensor drift on the models, we also validated the models on a commonly used sensor drift dataset. The results demonstrated that for the open-set detection task, the CNN-based CAC (CAC-CNN) outperformed the other methods. For the closed-set recognition task, the CNN-based classification model achieved higher accuracy. On sensor drift dataset, the performance of open-set recognition models has decreased a lot, and it seems that drift has a large negative impact on the open-set gas recognition.

关键词： Open-set Gas recognition Electronic nose CNN

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Secure resource allocation for green two-way relay networks with mutually untrusted relay and mobile users

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IET NETWORKS 2020年第1期9卷 1-6页

作者： Wang, Fei Qin, Changchun Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuit & Intelligent Chongqing 400715 Peoples R China Chongqing Coll Elect Engn Dept Artificial Intelligence & Big Data Chongqing 401331 Peoples R China

The authors investigate secure resource allocation for an orthogonal frequency division multiple access two-way relay network, which has several pairs of mobile users (MUs) and one energy harvesting (EH) relay. In this network, the relay is untrusted and each MU pair may also overhear the other MU pairs' confidential information. In particular, to forward information for each MU, the relay adopts power splitting technique to harvest energy from radio frequency signals emitted by each MU. For this scenario, which has hardly been considered, the joint problem of subcarrier allocation, power control, and power splitting ratio selection is formulated, with the aim to maximise the total secrecy rate of all MUs. A suboptimal algorithm with low complexity and satisfactory performance is proposed to solve the formulated non-convex problem.

关键词： concave programming power control resource allocation relay networks (telecommunication) OFDM modulation energy harvesting telecommunication security frequency division multiple access telecommunication control telecommunication power management secure resource allocation mutually untrusted relay mobile users energy harvesting relay power splitting technique radio frequency signals subcarrier allocation power splitting ratio selection green two-way relay networks orthogonal frequency division multiple access two-way relay network MU pair confidential information power control suboptimal algorithm nonconvex problem

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Lag synchronization of nonlinear dynamical systems via asymmetric saturated impulsive control

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CHAOS SOLITONS & FRACTALS 2021年 152卷

作者： Wu, Hongjuan Li, Chuandong He, Zhilong Wang, Yinuo He, Yingying Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Three Gorges Univ Sch Comp Sci & Engn Chongqing 404120 Peoples R China

In this paper, an asymmetric saturated impulsive control scheme is put forward and is suggested to realize the lag synchronization of two coupled dynamical systems in the master-slave pattern. We analyze the stability and then estimate the domain of attraction of the error system between slave system and master system based on Lyapunov stability theory, sector nonlinearity model approach, matrix inequality, set invariance, Schur complement. In addition, some sufficient conditions are also given for the lag synchronization of two nonlinear dynamical systems under this impulsive control scheme without actuator saturation. Finally, simulation results for a new chaotic oscillator and a Hopfield-type dynamic neural network are presented to show the feasibility and effectiveness of the obtained results. (c) 2021 Published by Elsevier Ltd.

关键词： nonlinear dynamical system Asymmetric saturation Impulsive control Lag synchronization Domain of attraction

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Robust least mean logarithmic square adaptive filtering algorithms

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JOURNAL OF THE FRANKLIN INSTITUTE 2019年第1期356卷 654-674页

作者： Xiong, Kui Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China

The conventional logarithm cost-based adaptive filters, e.g., the least mean logarithmic square (LMLS) algorithm, cannot combat impulsive noises at the filtering process. To address this issue, we present a novel robust least mean logarithmic square (RLMLS) algorithm by using a generalized logarithmic cost function. The proposed RLMLS algorithm can provide robustness against impulsive noises with the improvement of filtering accuracy. For theoretical analysis, the mean square analysis of RLMLS is provided in terms of the mean square deviation (MSD) and excess mean-square error (EMSE) with a white Gaussian reference. For further performance improvement in different noises, the variable step-size RLMLS (VSSRLMLS) based on the statistics of error is proposed to improve the convergence rate and steady-state mean square error, simultaneously. Analytical results and superiorities of RLMLS and VSSRLMLS in the context of system identification are supported by simulations from the aspects of filtering accuracy and robustness in Gaussian and impulse noises. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： Impulse noise

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