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Hyperchaos on the dynamics of memristive Tabu learning neuron model under influence of electromagnetic radiation: Application in biomedical data privacy

Franklin Open

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Franklin Open 2025年 10卷

作者： Boya, Bertrand Frederick Boui A Kengne, Jacques Nanfak, Arnaud Muni, Sishu Shankar Nkapkop, Jean de Dieu Kenmoe, Germaine Djuidje Babenko, Lyudmila Klimentyevna Unité de Recherche d'Automatique et d'Informatique Appliquée (UR-AIA) IUT-FV Bandjoun University of Dschang Bandjoun P.O. Box 134 Cameroon Laboratory of Energy-Electric and Electronic Systems Department of Physics Faculty of Science University of Yaoundé I Yaoundé P.O. Box 812 Cameroon Department of Physics Faculty of Science University of Yaoundé 1 Yaoundé Cameroon Institute of Computer Technology and Information Security Southern Federal University P.O. Box 347922 Taganrog Russian Federation Laboratory for electronics electrical engineering automation and telecommunications National Higher Polytechnic School of Douala University of Douala Douala PO Box 2701 Cameroon School of Digital Sciences Digital University Kerala Technocity campus Kerala Mangalapuram India Indian Institute of Information Technology and Management Kerala Technopark road Kerala India Department of Electrical Engineering and Industrial Computing University Institute of Technology Douala P.O. Box 8698 Cameroon

This paper presents a novel biological neural networks based on memristive Tabu learning neuron (MTLN) model influenced by electromagnetic radiation. Despite the model having an unstable equilibrium plane, numerical investigation without the influence of electromagnetic radiation reveal that when increasing the memristor strength induces complex phenomena, including the coexistence of infinite bubbles of bifurcations, offset plane coexistence of various attractors. Furthermore, electromagnetic radiation can significantly impact the system's behavior, giving rise to hyperchaotic dynamics. A decent understanding is seen between the mathematical outcomes, approved through trial execution on an Arduino microcontroller-based. The insights gained from neural dynamics could pave the way for applications in neuromorphic computing, engineering, and brain-machine interfaces. Based on the complex dynamics of the model, a data privacy of biomedical data has been build and reveals good results, and enable to resist to robust attacks. © 2024 The Author(s)

关键词： Data privacy Electromagnetic radiation Hyperchaos Microcontroller design MTLN model Offset plane coexisting behaviors

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Efficient visual recognition with deep neural networks: A survey on recent advances and new directions

arXiv

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arXiv 2021年

作者： Wu, Yang Wang, Dingheng Lu, Xiaotong Yang, Fan Li, Guoqi Dong, Weisheng Shi, Jianbo Institute for Research Initiatives Nara Institute of Science and Technology Takayama-cho Ikoma Nara630-0192 Japan School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'An Jiaotong University Shaanxi Xi'an710049 China School of Artificial Intelligence Xidian University China Division of Information Science Nara Institute of Science and Technology Japan Department of Precision Instrumentation Center for Brain Inspired Computing Research Beijing Innovation Center for Future Chip Tsinghua University Beijing100084 China Department of Computer and Information Science University of Pennsylvania United States

Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence. It has great fundamental importance and strong industrial needs. Deep neural networks (DNNs) have largely boosted their performances on many concrete tasks, with the help of large amounts of training data and new powerful computation resources. Though recognition accuracy is usually the first concern for new progresses, efficiency is actually rather important and sometimes critical for both academic research and industrial applications. Moreover, insightful views on the opportunities and challenges of efficiency are also highly required for the entire community. While general surveys on the efficiency issue of DNNs have been done from various perspectives, as far as we are aware, scarcely any of them focused on visual recognition systematically, and thus it is unclear which progresses are applicable to it and what else should be concerned. In this paper, we present the review of the recent advances with our suggestions on the new possible directions towards improving the efficiency of DNNrelated visual recognition approaches. We investigate not only from the model but also the data point of view (which is not the case in existing surveys), and focus on three most studied data types (images, videos and points). This paper attempts to provide a systematic summary via a comprehensive survey which can serve as a valuable reference and inspire both researchers and practitioners who work on visual recognition problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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An Efficient Evolutionary Algorithm for High-Speed Train Rescheduling Under a Partial Station Blockage

SSRN

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SSRN 2023年

作者： Wang, Rongsheng Zhang, Qi Dai, Xuewu Yuan, Zhiming Zhang, Tao Ding, Shuxin Jin, Yaochu China Academy of Railway Sciences Corporation Limited Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Train Operation Control Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing100081 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Faculty of Technology Bielefeld University BielefeldD-33615 Germany Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. An HSTR model that is subject to train operation constraints is established to minimize the total train delay. The PS-SEGA framework consists of the following main steps. Firstly, a permutation-based encoding method is developed to define an efficient search space based on a train departure sequence. A decoding method is employed to output the rescheduled timetable and objective value based on the departure sequence. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of Evolutionary algorithms (EAs). Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced EAs and their improved variants also using the PS-SEGA framework, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX can not obtain optimal solutions within 10 minutes, PS-SEGA can all provide quasi-optimal solutions in real-time. Furthermore, compared with the other two strategies (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can find the best departure sequence that can output the timetable with a quasi-optimal solution. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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Data and Physics Driven Learning Models for Fast MRI - Fundamentals and Methodologies from CNN, GAN to Attention and Transformers

arXiv

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arXiv 2022年

作者： Huang, Jiahao Fang, Yingying Nan, Yang Wu, Huanjun Wu, Yinzhe Gao, Zhifan Li, Yang Wang, Zidong Lio, Pietro Rueckert, Daniel Eldar, Yonina C. Yang, Guang The National Heart and Lung Institute Imperial College London Guy Scadding Building Dovehouse St LondonSW3 6LY United Kingdom The Department of Bioengineering Imperial College London South Kensington LondonSW7 2AZ United Kingdom The School of Biomedical Engineering Sun Yat-Sen University Guangdong Guangzhou510006 China The School of Automation Sciences and Electrical Engineering Beihang University Beijing China The Department of Computer Science Brunel University London LondonUB8 3PH United Kingdom The Computer Laboratory University of Cambridge CambridgeCB3 0FD United Kingdom The Department of Computing Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom Technical University of Munich Munich80333 Germany Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e.g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning. However, deep learning models are not the sovereign remedy for medical image analysis when the upstream imaging is not being conducted properly (with artefacts). This has been manifested in MRI studies, where the scanning is typically slow, prone to motion artefacts, with a relatively low signal to noise ratio, and poor spatial and/or temporal resolution. Recent studies have witnessed substantial growth in the development of deep learning techniques for propelling fast MRI. This article aims to (1) introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods, (2) survey the attention and transformer based models for speeding up MRI reconstruction, and (3) detail the research in coupling physics and data driven models for MRI acceleration. Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies, and discuss common pitfalls in current research and recommendations for future research directions. © 2022, CC BY-NC-ND.

关键词： Signal to noise ratio

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Adaptive multi-modal fusion hashing via hadamard matrix

arXiv

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arXiv 2020年

作者： Yu, Jun Zhang, Donglin Shu, Zhenqiu Chen, Feng The College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China The Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China The Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China The School of Computer Science and Technology Anhui University of Technology Ma'anshan China

Hashing plays an important role in information retrieval, due to its low storage and high speed of processing. Among the techniques available in the literature, multi-modal hashing, which can encode heterogeneous multi-modal features into compact hash codes, has received particular attention. Most of the existing multi-modal hashing methods adopt the fixed weighting factors to fuse multiple modalities for any query data, which cannot capture the variation of different queries. Besides, many methods introduce hyper-parameters to balance many regularization terms that make the optimization harder. Meanwhile, it is time-consuming and labor-intensive to set proper parameter values. The limitations may significantly hinder their promotion in real applications. In this paper, we propose a simple, yet effective method that is inspired by the Hadamard matrix. The proposed method captures the multi-modal feature information in an adaptive manner and preserves the discriminative semantic information in the hash codes. Our framework is flexible and involves a very few hyper-parameters. Extensive experimental results show the method is effective and achieves superior performance compared to state-of-the-art algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Hash functions

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Fine-grained distributed averaging for large-scale radio interferometric measurement sets

arXiv

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arXiv 2020年

作者： Wei, Shoulin Luo, Kaida Wang, Feng Deng, Hui Mei, Ying Faculty of Information Engineering and Automation Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming650500 China Center for Astrophysics Guangzhou University Guangzhou510006 China

The Square Kilometre Array (SKA) would be the worlds largest radio telescope with eventually over a square kilometre of collecting area. However, there are enormous challenges in its data processing. The using of modern distributed computing techniques to solve the problem of massive data processing in SKA is one of the most important challenges. In this study, basing on the Dask distribution computational framework, and taking the visibility function integral processing as an example, we adopt a multi-level parallelism method to implement distributed averaging over time and channel. Dask Array was used to implement super large matrix or arrays with supported parallelism. To maximize the usage of memory, we further exploit the data parallelism provided by Dask that intelligently distributes the computational load across a network of computer agents and has a built-in fault tolerance mechanism. The validity of the proposed pattern was also verified by using the Common Astronomy Software Application (CASA), wherein we analyze the smearing effects on images reconstructed from different resolution visibilities. Copyright © 2020, The Authors. All rights reserved.

关键词： Visibility

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Model-Free Optimization Scheme for Efficiency Improvement of Wind Farm Using Decentralized Reinforcement Learning ⁎

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IFAC-PapersOnLine 2020年第2期53卷 12103-12108页

作者： Zhiwei Xu Hua Geng Bing Chu Menghao Qian Ni Tan Department of Automation Tsinghua University and Beijing National Research Center for Information Science and Technology Beijing 100190 China School of Electronics and Computer Science Faculty of Physical Sciences and Engineering University of Southampton Southampton SO17 1BJ U.K. Department of Electrical Engineering Tsinghua University Beijing 100190 China

Wake interactions caused by the complex wakes between the turbines within a wind farm have significant adverse effect on the total power generation of the wind farm. To mitigate the effect of wake interactions and optimize the total power output of wind farm, this paper proposes a model-free control scheme using reinforcement learning by developing a decentralized Q learning method. The proposed approach guarantees that the output power of wind farm converges to the optimal total power under different wind conditions, and further ensures the gradual changes of control variables of wind turbines and thus avoids the unexpected sharp drop of the power generation performance of wind farm. Simulation results are provided to demonstrate the effectiveness of the proposed method.

关键词： Wind farm power optimization model-free approach decentralized control Q learning method

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Corrigendum to “Realistic acceleration of neural networks with fine-grained tensor decomposition” [Neurocomputing 512 (2022) 52–68]

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Neurocomputing 2023年 527卷 47-47页

作者： Rui Lv Dingheng Wang Jiangbin Zheng Yefan Xie Zhao-Xu Yang School of Computer Science Northwestern Polytechnical University Xi’an Shaanxi China Northwest Institute of Mechanical & Electrical Engineering Xianyang Shaanxi China School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an Shaanxi China State Key Laboratory for Strength and Vibration of Mechanical Structures Shaanxi Key Laboratory of Environment and Control for Flight Vehicle and School of Aerospace Engineering Xi’an Jiaotong University Xi’an Shaanxi China

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Adaptive binarization based on fuzzy integrals

arXiv

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arXiv 2020年

作者： Bardozzo, Francesco de la Osa, Borja L'ubomíra, Horanská Fumanal-Idocin, Javier delli Priscoli, Mattia Troiano, Luigi Tagliaferri, Roberto Fernandez, Javier Bustince, Humberto SA Italy Department of Statistics Computer Science and Mathematics Public University of Navarra Pamplona Spain Institute of Information Engineering Automation and Mathematics Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Slovakia

Adaptive binarization methodologies threshold the intensity of the pixels with respect to adjacent pixels exploiting the integral images. In turn, the integral images are generally computed optimally using the summed-area-table algorithm (SAT). This document presents a new adaptive binarization technique based on fuzzy integral images through an efficient design of a modified SAT for fuzzy integrals. We define this new methodology as FLAT (Fuzzy Local Adaptive Thresholding). The experimental results show that the proposed methodology have produced an image quality thresholding often better than traditional algorithms and saliency neural networks. We propose a new generalization of the Sugeno and CF1,2 integrals to improve existing results with an efficient integral image computation. Therefore, these new generalized fuzzy integrals can be used as a tool for grayscale processing in real-time and deep-learning *** Codes 68W25 Copyright © 2020, The Authors. All rights reserved.

关键词： Integral equations

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A one-stage control strategy for planar underactuated manipulators 4

A one-stage control strategy for planar underactuated manipu...

引用

4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019

作者： Wu, Jundong Wang, Yawu Ye, Wenjun Faculty of Engineering and Computer Science Concordia University MontrealQCH3G 1M8 Canada School of Automation China University of Geosciences Wuhan Hubei430074 China

ISBN: (纸本)9781728100647

A one-stage control strategy is proposed for planar underactuated manipulators, which have a passive link not located at the first link. The control objective is to control the end-effector from an initial position to a given target position. To achieve the control objective, a general form of controllers for the active links is proposed. To ensure that the system can be stabilized at its target state, parameters of the proposed controllers are calculated using the differential evolution algorithm. Simulations on specific systems are performed to show the validity of the proposed control strategy. © 2019 IEEE.

关键词： Controllers

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