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Two simple memristive maps with adaptive energy regulation and digital signal process verification

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2024年第5期25卷 382-394页

作者： Feifei YANG Lujie REN Jun MA Zhigang ZHU College of Electrical and information Engineering Lanzhou University of TechnologyLanzhou 730050China department of Physics Lanzhou University of TechnologyLanzhou 730050China School of information Science and Engineering Dalian Polytechnic UniversityDalian 116034China

Mathematical models can produce desired dynamics and statistical properties with the insertion of suitable nonlinear terms,while energy characteristics are crucial for practical application because any hardware realizations of nonlinear systems are relative to energy *** involvement of memristive terms relative to memristors enables multistability and initial-dependent property in memristive *** this study,two kinds of memristors are used to couple a capacitor or an inductor,along with a nonlinear resistor,to build different neural *** corresponding circuit equations are derived to develop two different types of memristive oscillators,which are further converted into two kinds of memristive maps after linear *** Hamilton energy function for memristive oscillators is obtained by applying the Helmholz theorem or by mapping from the field energy of the memristive *** Hamilton energy functions for both memristive maps are obtained by replacing the gains and discrete variables for the memristive oscillator with the corresponding parameters and *** two memristive maps have rich dynamic behaviors including coherence resonance under noisy excitation,and an adaptive growth law for parameters is presented to express the self-adaptive property of the memristive maps.A digital signal process(DSP)platform is used to verify these *** scheme will provide a theoretical basis and experimental guidance for oscillator-to-map transformation and discrete map-energy calculation.

关键词： Hamilton energy Discrete memristor Self-adaptive regulation Digital signal process(DSP)implementation

Proof of Two Supercongruences of Truncated Hypergeometric Series_(4)F_(3)

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Acta Mathematica Sinica,English Series 2024年第4期40卷 1015-1028页

作者： Guo Shuai MAO department of Mathematics Nanjing University of Information Science and TechnologyNanjing 210044P.R.China

In this paper,we prove two supercongruences conjectured by Z.-*** via the Wilf-Zeilberger *** of them is,for any prime p>3,4F3[7/6 1/2 1/2 1/2 1/6 1 1]-1/8]-1/2≡p(-2/p)+p^(3)/4(2/p)Ep-3 (mod p^(4))where(·/p)stands f... 详细信息

关键词： Supercongruence truncated hypergeometric series Wilf–Zeilberger method Euler numbers Legendre symbol

CNN demodulation model with cascade parallel crossing for CPM signals

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The Journal of China Universities of Posts and Telecommunications 2024年第3期31卷 30-42页

作者： Yang Jiachen Duan Ruifeng Li Chengju School of information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry-Oriented Intelligent information Processing of National Forestry and Grassland administration Beijing 100083China

The continuous phase modulation(CPM)technique is widely used in range telemetry due to its high spectral efficiency and power ***,the demodulation performance of the traditional maximum likelihood sequence detection(MLSD)algorithm significantly deteriorates in non-ideal synchronization or fading *** address this issue,this work proposes a convolutional neural network(CNN)called the cascade parallel crossing network(CPCNet)to enhance the robustness of CPM signals *** CPCNet model employs a multiple parallel structure and feature fusion to extract richer features from CPM *** approach constructs feature maps at different levels,resulting in a more comprehensive training of the model and improved demodulation *** results show that under Gaussian channel,the proposed CPCNet achieves the same bit error rate(BER)performance as MLSD method when there is no timing error,but with 1/4 symbol period timing error,the proposed method has 2 dB demodulation gain compared with CNN and convolutional long short-term memory deep neural network(CLDNN).In addition,under Rayleigh channel,the BER of the proposed method is reduced by 5%-87%compared to that of MLSD in the wide signal-to-noise ratio(SNR)region.

关键词： continuous phase modulation(CPM) convolutional neural network(CNN) maximum likelihood sequence detection(MLSD) Rayleigh fading timing error

WavEnhancer: Unifying Wavelet and Transformer for Image Enhancement

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Journal of Computer Science & Technology 2024年第2期39卷 336-345页

作者：李梓诺陈绪行郭淑娜王书强潘治文 department of Computer and information Science University of MacaoMacao 999078China Research Center for Biomedical information Technology Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Image enhancement is a widely used technique in digital image processing that aims to improve image aesthetics and visual quality. However, traditional methods of enhancement based on pixel-level or global-level modifications have limited effectiveness. Recently, as learning-based techniques gain popularity, various studies are now focusing on utilizing networks for image enhancement. However, these techniques often fail to optimize image frequency domains. This study addresses this gap by introducing a transformer-based model for improving images in the wavelet domain. The proposed model refines various frequency bands of an image and prioritizes local details and high-level features. Consequently, the proposed technique produces superior enhancement results. The proposed model’s performance was assessed through comprehensive benchmark evaluations, and the results suggest it outperforms the state-of-the-art techniques.

关键词： transformer wavelet transform image enhancement

Enhanced Object Detection and Classification via Multi-Method Fusion

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Computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： Muhammad Waqas Ahmed Nouf Abdullah Almujally Abdulwahab Alazeb Asaad Algarni Jeongmin Park department of Computer Science Air UniversityIslamabad44000Pakistan department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

Security of quantum key distribution with virtual mutually unbiased bases

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Science China(Physics,Mechanics & Astronomy) 2024年第7期67卷 13-21页

作者： Hong-Wei Li Chen-Peng Hao Zhi-Jiang Chen Li Gong Yi-Fei Lu Yang Wang Jia-Ji Li Chun-Mei Zhang Rong Wang Zhen-Qiang Yin Qing-Yu Cai Henan Key Laboratory of Quantum information and Cryptography SSF IEUZhengzhou 450000China Institute of Quantum information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China department of Physics University of Hong KongHong Kong 999077China Key Laboratory of Quantum information University of Science and Technology of ChinaHefei 230026China School of information and Communication Engineering Hainan UniversityHaikou 570228China

In a perfect quantum key distribution(QKD)protocol,quantum states should be prepared and measured with mutually unbiased bases(MUBs).However,in a practical QKD system,quantum states are generally prepared and measured with imperfect MUBs using imperfect devices,possibly reducing the secret key rate and transmission *** analyze the security of a QKD system with imperfect MUBs,we propose virtual MUBs to characterize the quantum channel against collective attack,and analyze the corresponding secret key rate under imperfect state preparation and measurement *** generally,we apply the advantage distillation method for analyzing the security of QKD with imperfect MUBs,where the error tolerance and transmission distance can be sharply *** analysis method can be applied to benchmark and standardize a practical QKD system,elucidating the security analysis of different QKD protocols with imperfect devices.

关键词： quantum key distribution mutually unbiased bases advantage distillation

A Hybrid Deep Learning and Machine Learning-Based Approach to Classify Defects in Hot Rolled Steel Strips for Smart Manufacturing

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Computers, Materials & Continua 2024年第8期80卷 2099-2119页

作者： Tajmal Hussain Jungpyo Hong Jongwon Seok department of information and Communication Engineering Changwon National UniversityChangwon51140Republic of Korea

Smart manufacturing is a process that optimizes factory performance and production quality by utilizing various technologies including the Internet of Things(IoT)and artificial intelligence(AI).Quality control is an important part of today’s smart manufacturing process,effectively reducing costs and enhancing operational *** technology in the industry becomes more advanced,identifying and classifying defects has become an essential element in ensuring the quality of products during the manufacturing *** this study,we introduce a CNN model for classifying defects on hot-rolled steel strip surfaces using hybrid deep learning techniques,incorporating a global average pooling(GAP)layer and a machine learning-based SVM classifier,with the aim of enhancing ***,features are extracted by the VGG19 convolutional ***,after processing through the GAP layer,the extracted features are fed to the SVM classifier for *** this purpose,we collected images from publicly available datasets,including the Xsteel surface defect dataset(XSDD)and the NEU surface defect(NEU-CLS)datasets,and we employed offline data augmentation techniques to balance and increase the size of the *** outcome of experiments shows that the proposed methodology achieves the highest metrics score,with 99.79%accuracy,99.80%precision,99.79%recall,and a 99.79%F1-score for the NEU-CLS ***,it achieves 99.64%accuracy,99.65%precision,99.63%recall,and a 99.64%F1-score for the XSDD dataset.A comparison of the proposed methodology to the most recent study showed that it achieved superior results as compared to the other studies.

关键词： Smart manufacturing steel defect detection deep learning CNN

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

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Computers, Materials & Continua 2024年第6期79卷 4081-4105页

作者： Yasser Hachaichi Ayman E.Khedr Amira M.Idrees College of Computing and information Technology at Khulais Department of Information SystemsUniversity of JeddahJeddah21959Saudi Arabia Faculty of Computers and information Technology Future University in EgyptCairo11835Egypt

The diversity of data sources resulted in seeking effective manipulation and *** challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of *** of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search *** research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO *** the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain *** proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant *** confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve ***,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different *** proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

关键词： Optimization particle swarm optimization algorithm feature selection logistics supply chain management backlogs

A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

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Computers, Materials & Continua 2024年第4期79卷 1631-1657页

作者： Maria Gul Imran Ali Khan Gohar Zaman Atta Rahman Jamaluddin Mir Sardar Asad Ali Biabani May IssaAldossary Mustafa Youldash Ashraf Saadeldeen Maqsood Mahmud Asiya Abdus Salam Dania Alkhulaifi Abdullah AlTurkey31department of Computer Science COMSATS University IslamabadAbbottabad CampusAbbottabad22010Pakistan department of Computer Science Abbottabad University of Science and TechnologyAbbottabad22010Pakistan department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Science and Technology Unit Umm Al-Qura UniversityMakkah21955Saudi Arabia Deanship of Scientific Research Umm Al-Qura UniversityMakkah21955Saudi Arabia department of Computer information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia department of Computer Science Faculty of Computer Science and Information TechnologyOmdurman Islamic UniversityOmdurman14415Sudan Business Analytic Program Department of Management and MarketingCollege of Business AdministrationUniversity of BahrainSakhir32038Bahrain

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route *** proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.

关键词： Safe crowd evacuation public safety emergency transition probability cooperation