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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Elazm, Lamiaa A. Abou Elazm, Atef Abou Siam, Ali I. Department of Microelectronics Cairo Egypt Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Egypt Kafrelsheikh University Faculty of Artificial Intelligence Department of Embedded Network Systems Technology Egypt

ISBN: (纸本)9798350376425

Modern digital communication relies heavily on text encryption to ensure secure communications and protect sensitive information. With the growth of the internet and digital communication, authorized and secure access has become more prevalent. Encryption is a way to protect this information by converting it into an unintelligible format, which can only be decrypted by authorized personnel with the appropriate encryption keys. In this paper, we propose a novel method for generating a new substitution lookup table that is resistant to known plaintext attacks and other cryptanalysis attacks. This method involves the use of cardinal tables to create a novel substitution lookup table. The proposed table provides a decimal code with a length varying from 3 to 5 digits for each alphabet letter. The nonlinear substitution of letters with new codes, generated from the proposed table, makes it suitable for data encryption and decryption. Sample messages are encoded with the proposed approach and the resulting encoded messages are compared with their ASCII codes. The obtained results with the novel table have been compared also with the conventional text encryption algorithms (Hill and Vigenere ciphering codes). The results show that the proposed lookup table gives the best histogram distribution, the lowest correlation coefficient, and the highest value of entropy results © 2024 IEEE.

关键词： Ciphertext

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Integral Reinforcement Reciprocal Zhang Neural Net for Time-Varying Linear Equations

Integral Reinforcement Reciprocal Zhang Neural Net for Time-...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Wang, Changyuan Chen, Long Zhang, Yunong Li, Shuai University of Macau Faculty of Science and Technology China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland Technical Research Center of Finland Oulu Finland

ISBN: (纸本)9798331517021

Solving time-varying linear equations is a com-mon challenge encountered in engineering and science. The development of neural dynamic systems has led to the creation of various feedback neural nets that effectively solve continuous time-varying linear equations. In this context, reciprocal Zhang neural net (RZNN) was developed, which is focused by this work, providing an explicit inverse-free continuous model for solving time-varying linear equations. However, the continuous model often struggle with noise interference, e.g., Gaussian noise, during the solving process. This paper enhances RZNN model with an integral reinforcement term to improve robustness of the explicit continuous models with noisy conditions. Furthermore, we optimize the RZNN solving process, making it easier for implementation in circuit systems and numerical simulations. © 2024 IEEE.

关键词： Continuous time systems

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Development and Validation of Robotic Ankle Exoskeleton With Parallel Nonlinear Elastic Actuator

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Journal of Mechanical Design 2024年第11期146卷 113304页

作者： Chen, Bing Shi, Chenpu Zi, Bin Qin, Ling Xu, Qingsong School of Mechanical Engineering Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Province Hefei230009 China School of Mechanical Engineering Hefei University of Technology Anhui Province Hefei230009 China Department of Orthopaedics and Traumatology The Chinese University of Hong Kong 999077 Hong Kong Department of Electromechanical Engineering Faculty of Science and Technology University of Macau 999078 China

This paper presents the development of a robotic ankle exoskeleton for human walking assistance. First, the biomechanical properties of a human ankle joint during walking are presented. Next, design of the robotic ankle exoskeleton is introduced. The exoskeleton is actuated by a novel parallel nonlinear elastic actuator. The cam-spring mechanism in the actuator can function as a parallel nonlinear spring with an adjustable stiffness, and the design of the cam profile curve is described. Additionally, an adaptive controller is proposed for the exoskeleton to generate a desired assistive torque according to the wearer’s total weight. Finally, experiments are conducted to validate the effectiveness of the developed robotic ankle exoskeleton. The experimental results demonstrate that during a gait cycle, reductions of 42.7% and 40.1% of the peak and average currents of the driving motor in the actuator are observed, respectively, with the designed cam-spring mechanism. A peak assistive torque of 23.9 Nm can be provided for the wearers by the exoskeleton during walking. With the assistance provided by the exoskeleton, the average and peak soleus activities of the wearers during a gait cycle are decreased by 25.42% and 31.94%, respectively. Copyright © 2024 by ASME.

关键词： Compliant mechanisms

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Mangrove forest drag and bed stabilisation effects on intertidal flat morphology (vol 49, pg 1117, 2024)

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EARTH SURFACE PROCESSES AND LANDFORMS 2024年第12期49卷 4074-4074页

作者： Gijsman, R. Horstman, E. M. Swales, A. MacDonald, I. T. Bouma, T. J. van der Wal, D. Wijnberg, K. M. Coastal Systems and Nature-Based Engineering Faculty of Engineering Technology University of Twente Enschede The Netherlands Coastal and Estuarine Physical Processes Group National Institute of Water and Atmospheric Research Hamilton New Zealand Department of Physical Geography Utrecht University Utrecht The Netherlands Department of Estuarine and Delta Systems NIOZ Royal Netherlands Institute for Sea Research Yerseke The Netherlands Department of Water Resources Faculty of Geo-Information Science and Earth Observation University of Twente Enschede The Netherlands

Mangrove trees influence their physical environment by exerting drag on tidal flows and waves while also stabilising the sediment bed of intertidal flats. These processes influence sediment accretion, the mangrove habitat and their resilience to sea level rise. However, little is known about the magnitude and spatial extent of the effects of mangrove forests on sediment transport and the morphology of the intertidal flat. We use manipulated simulations with an extended process-based numerical model, to study the influence of mangrove forests on intertidal flat morphology on a yearly timescale. The model includes the influence of mangrove trees on tidal flows, waves and sediment dynamics. The model is calibrated and validated with a comprehensive set of measurement data including hydrodynamics, sediment transport and morphological processes from an expanding mangrove forest in the sediment-rich Firth of Thames estuary in Aotearoa New *** accretion on the upper intertidal flat is predominantly influenced by the characteristic morphology of the established mangrove forest, with increased bed stability at higher mudflat elevations related to prolonged aerial exposure and drying of the bed. Our results show that, in comparison to the situation without mangroves, sediment accretion increases in the most seaward fringe area of the forest. The unvegetated intertidal flat fronting the mangrove forest captures less sediment compared to the situation without mangroves. The mangrove forest drag triggers the development of a steeper, convex-up-shaped, upper intertidal flat profile, especially during periods with higher water levels and waves. These effects are expected to influence the development and storm-recovery of natural and restored mangrove forests and may contribute to the resilience and persistence of mangrove-vegetated intertidal flats for coastal flood risk reduction.

关键词： ecosystem engineering flood risk reduction mangrove forests nature-based solutions persistence resilience

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A Survey on the Control Lyapunov Function and Control Barrier Function for Nonlinear-Affine Control systems

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 584-602页

作者： Boqian Li Shiping Wen Zheng Yan Guanghui Wen Tingwen Huang Australian AI Institute Faculty of Engineering and Information TechnologyUniversity of Technology SydneyNSW 2007Australia Department of Systems Science School of MathematicsSoutheast UniversityNanjing 210096China Science Program Texas A&M University at QatarDoha 23874Qatar IEEE

This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control *** problem of control is formulated as an optimization problem where the optimal control policy is derived by solving a constrained quadratic programming(QP)*** CLF and CBF respectively characterize the stability objective and the safety objective for the nonlinear control *** objectives imply important properties including controllability,convergence,and robustness of control *** this framework,optimal control corresponds to the minimal solution to a constrained QP *** uncertainties are explicitly considered,the setting of the CLF and CBF is proposed to study the input-to-state stability and input-to-state safety and to analyze the effect of *** recent theoretic progress and novel applications of CLF and CBF are systematically reviewed and discussed in this ***,we provide research directions that are significant for the advance of knowledge in this area.

关键词： Control barrier function(CBF) control Lyapunov function(CLF) nonlinear-affine control systems

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Blockchain-Based Decentralized Breast Cancer Patient Monitoring System for Remote Healthcare Security 5

Blockchain-Based Decentralized Breast Cancer Patient Monitor...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Dilshan, K.N. Rathnayake, H.A.C.K. Pathirana, L.P.T.L. Dhanapala, D.H.S.V. Fernando, Harinda Abeygunawardhana, Pradeep K.W Sri Lanka Institute of Information Technology Faculty of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

Breast cancer is a disease that is prevalent all over the world, and that is essential for the efficient treatment and care of breast cancer patients who are monitored remotely. However, traditional conventional centralized systems for patient monitoring face challenges in providing effective and efficient care for breast cancer patients, including limited access to medical records, ineffective communication, and patient participation. Fragmentation of records and difficulty in obtaining information can hinder patient care in terms of both privacy and data security. The proposed system uses machine learning, blockchain, smart contracts, hybrid access control, and hybrid intrusion prevention and detection to diagnose breast cancer decentralized. The technology analyses medical data using advanced machine learning algorithms to diagnose breast cancer accurately and provide healthcare practitioners with relevant insights. The blockchain stores patient information, diagnostic results, and metadata transparently and securely to protect data integrity and privacy. Smart contracts automate and enforce rules, securing data and improving diagnostic workflow. User roles and attributes are used to manage access to sensitive data and improve collaboration. Patients, doctors, and researchers have different permits and limits. Secures granular data and prevents unauthorized access. Scalability and network congestion are addressed via a hybrid network management system. The system optimizes resource allocation, load balancing, and data transfer by integrating centralized and decentralized methodologies. These features reduce delays and assure seamless operation. Experimental results reveal that the suggested system detects breast cancer more accurately than centralized methods. Blockchain, smart contracts, and access control ensure data integrity, privacy, and over-security. Scalability and reactivity are improved by the hybrid network management system, making breast cancer detec

关键词： Blockchain

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Machine Learning-Based Channel State Estimators for 5G Wireless Communication systems

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Computer Modeling in engineering & Sciences 2023年第4期135卷 755-778页

作者： Mohamed Hassan Essai Ali Fahad Alraddady Mo’ath Y.Al-Thunaibat Shaima Elnazer Department of Electrical Engineering Faculty of EngineeringAl-Azhar UniversityQena83513Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Management Information Systems Taif UniversityTaif21944Saudi Arabia

For a 5G wireless communication system,a convolutional deep neural network(CNN)is employed to synthesize a robust channel state estimator(CSE).The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state ***,it utilizes pilots to offer more helpful information about the communication *** proposedCNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory(BiLSTM/LSTM)NNs-based *** CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based *** three different loss function-based classification layers and the Adam optimization algorithm,a comparative study was conducted to assess the performance of the presented DNNs-based *** BiLSTM-CSE outperforms LSTM,CNN,conventional least squares(LS),and minimum mean square error(MMSE)*** addition,the computational and learning time complexities for DNN-CSEs are *** estimators are promising for 5G and future communication systems because they can analyze large amounts of data,discover statistical dependencies,learn correlations between features,and generalize the gotten knowledge.

关键词： DLNNs channel state estimator 5G and beyond communication systems robust loss functions

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A self-stabilizing distributed algorithm for the 1-MIS problem under the distance-3 model

A self-stabilizing distributed algorithm for the 1-MIS probl...

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作者： Kakugawa, Hirotsugu Kamei, Sayaka Shibata, Masahiro Ooshita, Fukuhito Faculty of Advanced Science and Technology Ryukoku University Shiga Otsu Japan Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashihiroshima Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka Japan Faculty of Engineering Fukui University of Technology Fukui Fukui Japan

Fault-tolerance and self-organization are critical properties in modern distributed systems. Self-stabilization is a class of fault-tolerant distributed algorithms which has the ability to recover from any kind and any finite number of transient faults and topology changes. In this article, we propose a self-stabilizing distributed algorithm for the 1-MIS problem under the unfair central daemon assuming the distance-3 model. Here, in the distance-3 model, each process can refer to the values of local variables of processes within three hops. Intuitively speaking, the 1-MIS problem is a variant of the maximal independent set (MIS) problem with improved local optimizations. The time complexity (convergence time) of our algorithm is (Formula presented.) steps and the space complexity is (Formula presented.) bits, where (Formula presented.) is the number of processes. Finally, we extend the notion of 1-MIS to (Formula presented.) -MIS for each nonnegative integer (Formula presented.), and compare the set sizes of (Formula presented.) -MIS ((Formula presented.)) and the maximum independent set. © 2024 John Wiley & Sons Ltd.

关键词： Consensus algorithm

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Optimization Techniques for Semi-Automated 3D Rigid Registration in Multimodal Image-Guided Deep Brain Stimulation

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Current Directions in Biomedical engineering 2023年第1期9卷 355-358页

作者： Al-Jaberi, Fadil Fachet, Melanie Hoeschen, Christoph Moeskes, Matthias Skalej, Martin Otto von Guericke University Faculty of Electrical Engineering and Information Technology Institute of Medical Technology Chair of Medical Systems Technology Magdeburg Germany Otto von Guericke University Medical Faculty Institute of Biometry and Medical Informatics Magdeburg Germany Martin Luther University Halle-Wittenberg Medical Faculty Neuroradiology Halle Germany

Multimodal image registration is vital in Deep Brain Stimulation (DBS) surgery. DBS treats movement disorders by implanting a neurostimulator device in the brain to deliver electrical impulses. Image registration between computed tomography (CT) and cone beam computed tomography (CBCT) involves fusing images with a specific field of view (FOV) to visualize individual electrode contacts. This contains important information about the location of segmented contacts that can reduce the time required for electrode programming. We performed a semi-automated multimodal image registration with different FOV between CT and CBCT images due to the tiny structures of segmented electrode contacts that necessitate high accuracy in the registration. In this work, we present an optimization workflow for multi-modal image registration using a combination of different similarity metrics, interpolators, and optimizers. Optimization-based rigid image registration (RIR) is a common method for registering images. The selection of appropriate interpolators and similarity metrics is crucial for the success of this optimization-based image registration *** rely on quantitative measures to compare their performance. Registration was performed on CT and CBCT images for DBS datasets with an image registration algorithm written in Python using the Insight Segmentation and Registration Toolkit (ITK). Several combinations of similarity metrics and interpolators were used, including mean square difference (MSD), mutual information (MI), correlation and nearest neighbors (NN), linear (LI), and B-Spline (SPI), respectively. The combination of a correlation as similarity metric, B-Spline interpolation, and GD optimizer performs the best in optimizing the 3D RIR algorithm, enhancing the visualization of segmented electrode contacts. Patients undergoing DBS therapy may ultimately benefit from this. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Image registration

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