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

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

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

Adversarial Domain Adaptation Guided by Farthest Distance for Open Set Electronic Nose Drift Compensation

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SSRN 2024年

作者： Pan, Yong Li, Chuandong Xiong, Jiang Hou, Ziye Yao, Youbin College of Computer Science and Engineering Chongqing Three Gorges University Chongqing404020 China College of Electronic and Information Engineering Southwest University Chongqing400715 China College of Computer Science Chongqing University Chongqing440044 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing China

With advancements in modern science and technology, electronic noses (ENs) have gained significant attention for their applications in environmental monitoring, food quality inspection, and medical equipment. ENs mimic biological olfactory systems to classify gases using arrays of sensors and pattern recognition models. However, gas sensor drift poses a major challenge, leading to performance degradation in EN systems. To address this, Domain Adaptation (DA) methods have been introduced, aligning source domain data with target domain drift data. While traditional DA methods assume identical class compositions in both domains, this is often unrealistic in practice, leading to suboptimal results. Open Set Domain Adaptation (OSDA) methods have been developed to handle unknown categories in the target domain, but they often become overly focused on distinguishing unknown classes, neglecting the accurate recognition of known classes. To overcome these limitations, we propose an Adversarial Domain Adaptation Guided by Farthest Distance (ADA-FDG) approach. ADA-FDG combines a Farthest Distance Guide Set Selection algorithm and Confidence Normalized Adaptive Factor, enhancing EN performance by efficiently using labeled data, robustly aligning domain distributions, and improving drift compensation. Our approach demonstrates superior performance on two datasets, effectively handling both known and unknown gases, and significantly enhancing the robustness of EN systems in dynamic environments. © 2024, The Authors. All rights reserved.

关键词： Electronic medical equipment

(Rectified Version) The Barzilai-Borwein Method for Distributed Optimization over Unbalanced Directed Networks

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arXiv

arXiv 2023年

作者： Hu, Jinhui Chen, Xin Zheng, Lifeng Zhang, Ling Li, Huaqing Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Big Data & Software Engineering Chongqing University Chongqing401331 PR China

This paper studies optimization problems over multi-agent systems, in which all agents cooperatively minimize a global objective function expressed as a sum of local cost functions. Each agent in the systems uses only local computation and communication in the overall process without leaking their private information. Based on the Barzilai-Borwein (BB) method and multi-consensus inner loops, a distributed algorithm with the availability of larger step-sizes and accelerated convergence, namely ADBB, is proposed. Moreover, owing to employing only row-stochastic weight matrices, ADBB can resolve the optimization problems over unbalanced directed networks without requiring the knowledge of neighbors’ out-degree for each agent. Via establishing contraction relationships between the consensus error, the optimality gap, and the gradient tracking error, ADBB is theoretically proved to converge linearly to the globally optimal solution. A real-world data set is used in simulations to validate the correctness of the theoretical analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

Robust variable normalization least mean p-power algorithm

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Science China(information Sciences) 2020年第9期63卷 294-302页

作者： Kui XIONG Yu ZHANG Shiyuan WANG College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Faculty of Computer and Information Science Southwest University

Dear editor,Adaptive filtering algorithms have been widely applied in system identification, channel equalization, and echo cancellation over the past decades [1–3]. Generally, adaptive filtering algorithms can be ge... 详细信息

关键词： Least mean p-power algorithm variable normalization sigmoid function steady-state performance analysis noise robustness

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

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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

Finite-Time Stability of Solutions for non-Instantaneous Impulsive Systems and Application to non-Instantaneous Impulsive Neural Networks

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SSRN 2022年

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

This work extends the finite-time stability (FTS) results to non-instantaneous impulsive time-varying systems on the basis of general impulsive systems via Lyapunov theory. The impacts of impulse numbers, impulse time sequences and impulse amplitudes on FTS and settling-time estimation are discussed. The effects of different types of impulses on settling-time are discussed when the impulse number is known or pre-given. The average impulsive interval is introduced to extend the above results to the case when the impulse number is unknown, and the derivative of Lyapunov function is allowed to be indefinite instead of being negative definite or semi-negative definite. Moreover, the theoretical results are applied to non-instantaneous impulsive neural networks. Finally, we provide two numerical examples to demonstrate the effectiveness and feasibility of the obtained results. © 2022, The Authors. All rights reserved.

关键词： Lyapunov functions

A Proportionate Maximum Total Complex Correntropy Algorithm for Sparse Systems

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SSRN 2023年

作者： Huang, Sifan Qian, Guobing Wang, Xin Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Cyber Science and Engineering Sichuan University Chengdu610065 China

While much attention has been paid to the practical application of adaptive filters, two significant problems that have a great impact on performance need to be solved. One is that the sparsity of the system, and the other is the additive interference noise is impulsive noise. With the purpose of solving these issues at the same time, by introducing the proportionate matrix, a proportionate maximum total complex correntropy algorithm based on the errors-in-variables (EIV) model is proposed in this paper. We derive the theoretical analysis of the steady-state weight error power on the premise of impulsive noise. Meanwhile, the performance comparison in system identification is discussed and the robustness of the proposed algorithm is illustrated. Finally, the simulation with the stereophonic acoustic echo cancellation (SAEC) is carried out, and the result validates that the proposed PMTCC algorithm has obvious advantages. © 2023, The Authors. All rights reserved.

关键词： Impulse noise

Neurodynamics-based Iteratively Reweighted Convex Optimization for Sparse Signal Reconstruction 12

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Neurodynamics-based Iteratively Reweighted Convex Optimizati...

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

作者： Che, Hangjun Wang, Jun Cichocki, Andrzej College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China City University of Hong Kong Department of Computer Science and School of Data Science Hong Kong Hong Kong Skolkovo Institute of Science and Technology Moscow143026 Russia

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

In this paper, sparse signal reconstruction is for-mulated a q-ratio minimization problem subjecting to linear underdetermined equations. In view of the nonconvexity of the objective function, the q-ratio formulation with q=2 is approximately reformulated as an iteratively reweighted convex optimization problem in the majorization-minimization frame-work. A neurodynamic optimization approach is introduced to solve the formulated problem iteratively. The experimental results on sparse signal reconstruction are discussed to demonstrate the performance of the proposed approach. © 2022 IEEE.

关键词： Convex optimization

Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition

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arXiv

arXiv 2023年

作者： Cheng, Huqiang Liao, Xiaofeng Li, Huaqing Zhao, You Key Laboratory of Dependable Services Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing400044 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China

Distributed optimization is manifesting great potential in multiple fields, e.g., machine learning, control, and resource allocation. Existing decentralized optimization algorithms require sharing explicit state information among the agents, which raises the risk of private information leakage. To ensure privacy security, combining information security mechanisms, such as differential privacy and homomorphic encryption, with traditional decentralized optimization algorithms is a commonly used means. However, this would either sacrifice optimization accuracy or incur heavy computational burden. To overcome these shortcomings, we develop a novel privacy-preserving decentralized optimization algorithm, called PPSD, that combines gradient tracking with a state decomposition mechanism. Specifically, each agent decomposes its state associated with the gradient into two substates. One substate is used for interaction with neighboring agents, and the other substate containing private information acts only on the first substate and thus is entirely agnostic to other agents. For the strongly convex and smooth objective functions, PPSD attains a R-linear convergence rate. Moreover, the algorithm can preserve the agents’ private information from being leaked to honest-but-curious neighbors. Simulations further confirm the results. © 2023, CC BY.

关键词： Optimization