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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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SEOE:an option graph based semantically embedding method for prenatal depression detection

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Frontiers of computer science 2024年第6期18卷 245-258页

作者： Xiaosong HAN Mengchen CAO Dong XU Xiaoyue FENG Yanchun LIANG Xiaoduo LANG Renchu GUAN College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012China Department of Electrical Engineering and Computer Science and Christopher S.Bond Life Sciences Center University of MissouriColumbiaMO 65211USA Department of Computer Science and Technology Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationZhuhai College of Science and TechnologyZhuhai 519041China Jilin Provincial Institute of Population Science and Technology Changchun 130000China

Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution *** studies have used questionnaires to screen for prenatal depression,but the existing methods lack *** diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire *** can quantitatively determine the relationship and patterns between options and *** first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on *** resort task is transformed into an optimization problem involving the traveling salesman ***,all questionnaire samples are used to train the options’vector using ***,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from *** verify the model,we compare it with other deep learning and traditional machine learning *** experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of *** most relevant factors of depression found by SEOE are also verified in the *** addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.

关键词： questionnaire analyses text classification graph representation learning traveling salesman problem prenatal depression

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An efficient weighted partial MaxSAT encoding for scheduling in overloaded real-time systems

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International Journal of computers and Applications 2024年第3期46卷 175-183页

作者： Pourhashem Kallehbasti, Mohammad Mehdi Pirgazi, Jamshid Ghanbari Sorkhi, Ali Kermani, Ali Department of Electrical and Computer Engineering University of Science and Technology of Mazandaran P.O. Box 48518-78195 Behshahr Iran

Scheduling tasks in overloaded real-time systems is a challenging problem that has received a significant amount of attention in recent years. The processor is overloaded with more tasks than its capacity, resulting in missed deadlines and degraded system performance. Therefore, scheduling algorithms play a critical role in ensuring that high-priority tasks are completed on time while minimizing the impact of incomplete lower-priority tasks. This paper proposes an efficient Weighted Partial MaxSAT(WPMS) encoding that returns an optimal solution in which the total weight of incomplete tasks is minimized in a single machine environment. To assess the efficiency of our proposed formulation, a comparative analysis is conducted alongside the state-of-the-art encoding. By examining the solving 75 distinct problems, it becomes evident that the WPMS encoding proposed herein exhibits a considerable advantage in terms of both time and memory efficiency. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Real time systems

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AC Optimal Power Flow Using a Learnable Weighted-Ensemble Neural Network

AC Optimal Power Flow Using a Learnable Weighted-Ensemble Ne...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Tran, Quan Mitra, Joydeep Nguyen, Nga University of Wyoming Electrical Engineering and Computer Science LaramieWY United States Michigan State University Electrical and Computer Engineering East LansingMI United States

ISBN: (纸本)9798331541125

Leveraging machine learning algorithms (ML) for AC optimal power flow (ACOPF) is an inevitable tendency in power system analysis and operation. A wide range of ML models' applications such as deep neural networks and convolutional neural networks are utilized to tackle the ACOPF more efficiently. For large-scale power systems, the disadvantages of deep neural networks (DNNs) are alternated by manipulating weighted-convolution neural networks (WCNNs) to input a whopping number of the entire system's parameters for efficient training. However, the strengths of DNNs are undeniable, and they should be exploited to improve their efficacy and efficiency. By combining DNNs and WCNNs within a model, named an ensemble-weighted convolutional neural network (EWCNN), this paper proposes a new machine-learning model based on DNNs and WCNNs to increase the model fidelity and accuracy. The proposed approach is tested on three IEEE case studies to exemplify its capability. The empirical experiments show an improvement in reducing the bias-variance tradeoff commonly found in machine learning problems and raising the prediction accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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A 28.8-mW Accelerator IC for Dark Channel Prior-Based Blind Image Deblurring

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IEEE Journal of Solid-State Circuits 2024年第6期59卷 1899-1911页

作者： Chen, Po-Shao Chen, Yen-Lung Lee, Yu-Chi Fu, Zih-Sing Yang, Chia-Hsiang Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Taipei10617 Taiwan

This work presents an accelerator that performs blind deblurring based on the dark channel prior. The alternating minimization algorithm is leveraged for latent image and blur kernel estimation. A 2-D Laplace equation solver is embedded to reduce the latency by 56% for boundary wrapping. For latent image estimation, gradient data locality is employed to reduce the latency by 57%. A sorting engine is designed to reduce the latency in data access by 96% for calculating the dark channel. A pipelined mixed-radix 1-D fast Fourier transform (FFT) engine is used for efficient latent image estimation and blur kernel estimation. By employing image size approximation, 85% of additions and 97% of multiplications for FFT can further be saved. In the blur kernel estimator, a 2-D convolution engine with a parallel architecture is implemented, reducing the latency by 79%. The accelerator supports blur kernels of 25times 25 and 49times 49 pixels for blurred images of 129times 129 and 257times 257 pixels, respectively. Fabricated in 40-nm CMOS, the accelerator's core area is 3.98 mm2. The chip dissipates 28.8 mW at 65 MHz from a 0.65-V supply. It can estimate a blur kernel of 25times 25 pixels for an image patch with 129times 129 pixels for deblurring a full-HD image in 1.7 s, achieving a 2562times shorter latency than a high-end CPU. Compared with the state-of-the-art design, the chip achieves a four times higher normalized area efficiency and a 7.5times higher normalized energy efficiency. © 1966-2012 IEEE.

关键词： CMOS integrated circuits

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Dynamic Artificial Neural Network-Assisted GPS-Less Navigation for IoT-Enabled Drones

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IEEE Internet of Things Magazine 2024年第3期7卷 92-99页

作者： Simsek, Murat Boukerche, Azzedine Kantarci, Burak Bitirgen, Rahman Hancer, Muhsin Bayezit, Ismail School of Electrical Engineering and Computer Science The University of Ottawa Ottawa Canada Istanbul Technical University Faculty of Aeronautics and Astronautics Turkey

Uncrewed Aerial Vehicles (UAVs) have enabled key duties in emergency preparedness, traffic monitoring, environmental monitoring, and public safety. Since the presence of GPS-enabled contexts is not always guaranteed, a grand challenge with the UAVs is the lack of accomplishing their tasks without the presence of GPS coordinates (latitude, longitude, and altitude). Hence, the performance of UAVs in GPS-denied environments is expected to degrade dramatically when compared to the UAVs employed in GPS-enabled environments. In this article, an alternative approach to the state-of-the-art, Dynamic Artificial Neural Network (D-ANN)-based solution is proposed to assist UAV navigation without GPS positions during a mission. Besides accelerometer and gyroscope data, Pulse Width Modulation (PWM) signals, which have been traditionally used in the design of UAV flight controllers, are proposed to be a part of the input for D-ANN-assisted UAV navigation without GPS data. Since the latitude, longitude, and altitude values of the UAV are not correlated, each position is obtained through a separate D-ANN system. The proposed D-ANN location of a quadrotor UAV assisted by D-ANN has less than 3m average destination error at the end of the testing trajectory and also less than 0.12 average normalized mean square error during the testing trajectory in terms of the 3D GPS coordinates. © 2018 IEEE.

关键词： Global positioning system

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EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN INDONESIAN PRISONS 22

EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN IN...

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22nd International Conference on e-Society 2024, ES 2024 and 20th International Conference on Mobile Learning 2024, ML 2024

作者： Imandeka, Ejo Hidayanto, Achmad Nizar Suhartanto, Heru Pidanic, Jan Department of Computer Science Faculty of Computer Science University of Indonesia Depok Indonesia Department of Electrical Engineering Faculty of Electrical Engineering and Informatics Pardubice University Pardubice Czech Republic

The exchange of knowledge is widely recognized as a crucial aspect of effective knowledge management. When it comes to sharing knowledge within Prison settings, things get complicated due to various challenges such as limited resources, social stigma, isolation, communication barriers, and security concerns. Additionally, there's a scarcity of resources dedicated to promoting knowledge sharing in secure environments. This study aims to dig deep into the key factors that influence how knowledge is shared in Indonesian prisons. To navigate this intricate terrain, the study uses questionnaires distributed among Prison officers to understand the complex relationship between the identified critical factors and knowledge sharing in Prison. Leadership support, organizational culture, organizational structure, incentives, and individual beliefs emerge as pivotal elements in knowledge sharing. It's also noted that the connection between these critical factors and the intention to share knowledge is significantly influenced by organizational structure. Recognizing and addressing critical factors in knowledge sharing within prisons not only improves rehabilitation within prison walls but also transforms the broader criminal justice system. This approach fosters evidence-based practices, better communication, and a comprehensive understanding of factors leading to successful rehabilitation and reduced recidivism, ultimately creating a more just and effective system. © 2024 International Conferences e-Society 2024 and Mobile Learning 2024. All rights reserved.

关键词： Critical Factors Indonesia Knowledge Knowledge Sharing Prison

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Contract-Based Incentive Mechanisms for UAV Networks in Covert Communications

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7378-7391页

作者： Tian, Wen Fan, Hao Liu, Guangjie Du, Miao Liu, Yinqiu Niyato, Dusit Han, Zhu Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing University of Information Science and Technology Key Laboratory of Intelligent Support Technology In Complex Environment Ministry of Education Nanjing210044 China Southeat University School of Computer Science and Engineering China Southeast University Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Covert communication is considered a promising technology for hiding transmission processes and activities from malicious eavesdropping. With the development of detection technology, the traditional point-to-point covert communication method cannot effectively guarantee the reliability of covert communication. In order to solve this problem, unmanned aerial vehicle (UAV) are widely adopted in covert communication as common relay equipment. However, the UAV maximizes its benefits by taking advantage of the information asymmetry between itself and the covert communication sender. Therefore, it is necessary to encourage UAV-assisted potential users (UPU) to transmit covert data honestly by designing an information asymmetry-based incentive mechanism. In this paper, we propose a UAV-assisted contract-theoretic (UACT) model to improve the covert communication performance, where UPU honestly transmits covert data and the throughput of UACT can be increased. Specifically, we first divide UPU into finite types and model the data transfer contract between the sender, UPU, and receiver. Then, the contract feasibility of UACT is derived with sufficient and necessary conditions. Finally, we analyze the contract optimization in the continuous case of UPUs. Numerical simulations show that the UACT can incentivize UPUs to transmit covert data through UAV assistance and make covert communication performance close to the information symmetry case. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Predefined-time distributed multiobjective optimization for network resource allocation

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science China(Information sciences) 2023年第7期66卷 58-72页

作者： Kunpeng ZHANG Lei XU Xinlei YI Zhengtao DING Karl H.JOHANSSON Tianyou CHAI Tao YANG State Key Laboratory of Synthetical Automation for Process Industries Northeastern University School of Electrical Engineering and Computer Science KTH Royal Institute of Technology School of Electrical and Electronic Engineering The University of Manchester

We consider the multiobjective optimization problem for the resource allocation of the multiagent network, where each agent contains multiple conflicting local objective functions. The goal is to find compromise solutions minimizing all local objective functions subject to resource constraints as much as possible, i.e., the Pareto optimums. To this end, we first reformulate the multiobjective optimization problem into one single-objective distributed optimization problem by using the weighted Lppreference index,where the weighting factors of all local objective functions are obtained from the optimization procedure so that the optimizer of the latter is the desired Pareto optimum of the former. Next, we propose novel predefined-time algorithms to solve the reformulated problem by time-based generators. We show that the reformulated problem is solved within a predefined time if the local objective functions are strongly convex and smooth. Moreover, the settling time can be arbitrarily preset since it does not depend on the initial values and designed parameters. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed algorithms.

关键词： distributed optimization multiobjective optimization predefined-time algorithms time-based generators weighted ${L_p}$ preference index

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Accurate, Yet Scalable: A SPICE-based Design and Optimization Framework for eNVM-based Analog In-memory Computing 24

Accurate, Yet Scalable: A SPICE-based Design and Optimizatio...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Mojahidul Ahsan, S.M. Shahriar, Muhammad Sakib Chowdhury, Mrittika Hossain, Tanvir Hasan, Md Sakib Hoque, Tamzidul Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Department of Electrical and Computer Engineering University of Mississippi OxfordMS United States

ISBN: (纸本)9798400710773

This paper introduces a scalable SPICE-based tool infrastructure designed to optimize analog compute-in-memory (ACIM) architectures utilizing emerging non-volatile resistive memory (eNVM) technologies. The inherent efficiency of analog eNVM crossbar arrays in performing matrix-vector multiplications significantly enhances the power, performance, and area efficiency of edge AI devices and other applications. Our framework addresses the challenges of accurately simulating ACIM architectures, which are highly susceptible to variations in process, voltage, temperature, and analog noise. The framework uses SPICE for accurate analog and mixed-signal circuit simulation. It automates the generation of SPICE-level ACIM designs for deep neural networks. Additionally, it speeds up the simulation runtime by up to 35× for large DNN models while maintaining the same SPICE-level accuracy. Moreover, it ensures simulation convergence for large netlists, facilitating SPICE simulation of large-scale eNVM crossbars that were previously impractical. We demonstrated that our framework is capable of simulating inference using netlists for MLPs with over 800,000 parameters trained on the MNIST dataset within acceptable runtime, where contemporary SPICE simulators do not even converge. We validated the simulation results in terms of inference accuracy, which shows less than a 3.8% accuracy drop compared to software-based inference results. Lastly, we demonstrated the integration of our framework with an architectural simulator, facilitating comprehensive system-level simulation. © 2024 Copyright is held by the owner/author(s).

关键词： Deep neural networks

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