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An Efficient AES 32-Bit Architecture Resistant to Fault Attacks

computers, Materials & Continua 2022年第2期70卷 3667-3683页

作者： Hassen Mestiri Imen Barraj Abdullah Alsir Mohamed Mohsen Machhout Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Institute of Applied Sciences and Technology of Sousse University of SousseTunisia Electronics and Micro-Electronics Laboratory Faculty of Sciences of MonastirUniversity of MonastirTunisia METS Research Group Electrical Engineering DepartmentNational Engineers School of SfaxUniversity of SfaxTunisia Higher Institute of Computer Science and Multimedia of Gabes University of GabesTunisia

The Advanced Encryption Standard cryptographic algorithm,named AES,is implemented in cryptographic circuits to ensure high security level to any system which required confidentiality and secure information *** of the most effective physical attacks against the hardware implementation of AES is fault attacks which can extract secret *** now,a several AES fault detection schemes against fault injection attacks have been *** this paper,so as to ensure a high level of security against fault injection attacks,a new efficient fault detection scheme based on the AES architecture modification has been *** this reason,the AES 32-bit round is divided into two half rounds and input and pipeline registers are implemented between *** proposed scheme is independent of the procedure the AES is ***,it can be implemented to secure the pipeline and iterative *** evaluate the robustness of the proposed fault detection scheme against fault injection attacks,we conduct a transient and permanent fault attacks and then we determine the fault detection capability;it is about 99.88585%and 99.9069%for transient and permanent faults *** have modeled the AES fault detection scheme using VHDL hardware language and through hardware FPGA *** FPGA results demonstrate that our scheme can efficiently protect the AES hardware implementation against fault *** can be simply implemented with low *** addition,the FPGA implementation performances prove the low area overhead and the high efficiency and working frequency for the proposed AES detection scheme.

关键词： Security cryptographic circuits AES hardware implementation fault detection fault attacks

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A multi-context Severity-based SLO Violation Detector

A multi-context Severity-based SLO Violation Detector

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IEEE Symposium on computers and Communications (ISCC)

作者： Andreas Tsagkaropoulos Yiannis Verginadis Gregoris Mentzas Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Zografou Athens Greece Institute of Communication and Computer Systems Athens University of Business and Economics Zografou Athens Greece

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

As cloud computing continuum services become ever more important, the need of platforms that facilitate and manage their proper operation is unquestionable. An integral part of these platforms is a series of tools which will complement day one and day two operations in the context of the application software lifecycle. This work introduces an approach towards a Service Level Objective (SLO) Violation Detection system, based on the perceived Severity of an imminent or predicted violation. This system leverages insights to stay operational and triggers appropriate reconfiguration actions by continuously considering the required conditions of good operation. The detailed architecture of the system, its operation overview as well as the required interactions with other components – parts of an adaptation ecosystem of a cloud platform, are provided. Finally, potential future improvements are discussed. Keywords—application reconfiguration, modelling, elasticity, cloud computing, service adaptation

关键词： Measurement computers Cloud computing DevOps Computational modeling Biological system modeling Ecosystems Detectors computer architecture Elasticity

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Study on neural information decoding of ground and flight motion state of pigeons in free-moving in the field 22

Study on neural information decoding of ground and flight mo...

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7th International Conference on Cyber Security and Information engineering, ICCSIE 2022

作者： Ma, Erteng Yang, Lifang Shang, Zhigang School of Electrical Engineering Zhengzhou University Zhengzhou China Henan Key Laboratory of Brain-Computer Interface Technology Zhengzhou China

ISBN: (纸本)9781450397414

The neural information decoding of birds in natural flight is very significant for the development of new biological robots, but there is little research at present. In this paper, we recorded the local field potential signals of pigeons under free movement in the wild;and constructed the brain function connectivity network underground and flight motion state by using the coherence between channels. The analysis shows that the information transmission of pigeons' brains is higher during flight, and its gamma-band contains more movement-related information. Finally, the clustering coefficients and global efficiency features of the gamma band are extracted for decoding. The average decoding accuracy of the three pigeons is 82.66%. This work can provide theoretical support for bird neural information decoding in the future natural environment. © 2022 ACM.

关键词： Birds

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Quantum Driven Dynamic Passivity Based Neuromechanical Control for Wrist Rehabilitation Robot

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical Robotics and Bionics 2025年

作者： Khan, Naveed Ahmad Hussain, Fahad Goyal, Tanishka Jamwal, Prashant K. Hussain, Shahid University of Canberra School of Information Technology and Systems ACT Canberra Australia Nazarbayev University Department of Electrical and Computer Engineering Astana010000 Kazakhstan

Robotic-assisted rehabilitation for wrist movements demands adaptive systems capable of balancing patient autonomy with robotic support. The integration of artificial intelligence (AI) into robotic-assisted rehabilitation offers transformative potential in delivering personalized, dynamic, and effective therapeutic interventions. This study introduces a novel neuromechanical control framework integrating a passivity observer with Quantum-Enhanced Deep Reinforcement Learning (QDRL) for adaptive impedance scaling in wrist rehabilitation robotics. The passivity observer continuously monitors energy exchanges to classify patient states into passive (patient requiring robotic assistance) and non-passive (patient actively participating) categories, dynamically guiding the robot’s impedance adjustments. Experiments were conducted with ten unimpaired human subjects (eight male and two female), who were instructed to simulate rehabilitation scenarios, focusing on three key wrist movements, flexion/extension (FL/EX), abduction/adduction (AB/AD), and pronation/supination (PR/SU). Experimental results showed high correlations ( > 0.83 ) between energy-based and electromyography (EMG)-based passivity classifications, confirming the reliability of the proposed approach. Furthermore, the designed QDRL model significantly outperformed traditional reinforcement learning methods, achieving superior adaptability, stability, and higher average rewards during robotic impedance control. The framework offers advancement in optimizing robotic assistance during motor recovery, promoting personalized rehabilitation by tailoring interventions to the specific needs of each patient. © 2018 IEEE.

关键词： Electromyography

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Correction to: Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes (Neural Computing and Applications, (2025), 10.1007/s00521-025-10991-3)

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Neural Computing and Applications 2025年第16期37卷 9309-9310页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

In this article, the author’s name Stephen Ojo was incorrectly written as Stepehn Ojo and the affiliation details for author Stephen Ojo was incorrectly given as ‘Department of electrical and computer engineering, College of engineering Anderson, Anderson University, Anderson, SC 29621, USA’ but should have been ‘Department of electrical and computer engineering, College of engineering, Anderson University, Anderson, SC 29621, USA’. In addition, the affiliation details for author Gabriel Avelino Sampedro were incorrectly given as ‘College of computer Studies, De La Salle University, 2401 Taft Ave., Malate, 1004 Manila, Philippines and Faculty of Information and Communication Studies, University of the Philippines Open University, 4031 Los Baños, Philippines’ but should have been ‘Department of computer Science, University of the Philippines Diliman, 1101 Quezon City, Philippines’. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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LF-CNN:Deep Learning-Guided Small Sample Target Detection for Remote Sensing Classification

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computer Modeling in engineering & Sciences 2022年第4期131卷 429-444页

作者： Chengfan Li Lan Liu Junjuan Zhao Xuefeng Liu School of Computer Engineering and Science Shanghai UniversityShanghai200444China Key Laboratory for Digital Land and Resources of Jiangxi Province East China University of TechnologyNanchang330013China School of Electronic and Electrical Engineering Shanghai University of Engineering ScienceShanghai201620China School of Communication&Information Engineering Shanghai UniversityShanghai200444China

Target detection of small samples with a complex background is always difficult in the classification of remote sensing *** propose a new small sample target detection method combining local features and a convolutional neural network(LF-CNN)with the aim of detecting small numbers of unevenly distributed ground object targets in remote sensing *** k-nearest neighbor method is used to construct the local neighborhood of each point and the local neighborhoods of the features are extracted one by one from the convolution *** the local features are aggregated by maximum pooling to obtain global feature *** classification probability of each category is then calculated and classified using the scaled expected linear units function and the full connection *** experimental results show that the proposed LF-CNN method has a high accuracy of target detection and classification for hyperspectral imager remote sensing data under the condition of small *** drawbacks in both time and complexity,the proposed LF-CNN method can more effectively integrate the local features of ground object samples and improve the accuracy of target identification and detection in small samples of remote sensing images than traditional target detection methods.

关键词： Small samples local features convolutional neural network(CNN) k-nearest neighbor(KNN) target detection

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Activated Parameter Locating via Causal Intervention for Model Merging

arXiv

引用

arXiv 2024年

作者： Kong, Fanshuang Zhang, Richong Wang, Ziqiao SKLSDE School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

Model merging combines multiple homologous models into one model, achieving convincing generalization without the necessity of additional training. A key challenge in this problem is resolving parameter redundancies and conflicts across multiple models. Existing models have demonstrated that dropping a portion of delta parameters can alleviate conflicts while maintaining performance. However, these methods often drop parameters either randomly or based on magnitude, overlooking task-specific information embedded in fine-tuned models. In this paper, we propose an Activated Parameter Locating (APL) method that utilizes causal intervention to estimate parameter importance, enabling more precise parameter drops and better conflict mitigation. Moreover, to reduce the computational complexity associated with a large number of parameter partitions, we also introduce a theoretically supported gradient approximation strategy for APL. Experiments on model merging within both in-domain and out-of-domain settings, along with associated analyses, showcase the effectiveness of APL. Copyright © 2024, The Authors. All rights reserved.

关键词： APL (programming language)

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Weighted AoI Minimization for RIS-Assisted Multi-User mmWave Communications 23

Weighted AoI Minimization for RIS-Assisted Multi-User mmWave...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Guo, Ziqi Niu, Yong Wen, Shuxian Fang, Ying Han, Zhu Mao, Shiwen Guan, Shunji Wang, Ning Ai, Bo Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Auburn University Department of Electrical and Computer Engineering AuburnAL36849-5201 United States School of Information Engineering Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9798350325959

In this paper, we focus on a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communications system, where a RIS is deployed to create reliable reflection links and alleviate multi-user interference. In the system, each user equipment (UE) is concerned with the freshness of the information received from the base station (BS) and has a certain transmission requirement. The age of information (AoI) is adopted as the metric for information freshness and on this basis, we define the weighted AoI to take into account the satisfaction of UE transmission requirements. Then, an average weighted AoI minimization problem is formulated by jointly optimizing the precoding, the discrete RIS phase shifts, and the scheduling strategy. The problem is decomposed into two subproblems. For the first precoding and RIS design subproblem, we aim to satisfy the transmission requirements of the scheduled UEs with minimum power consumption under a given scheduling strategy. We use the zero-force (ZF)-based precoder and adopt the local search method for the RIS design. For the second scheduling strategy design subproblem, we propose a heuristic scheduling algorithm to minimize the system's average weighted AoI. Simulation results verify the effectiveness of our proposed method over several baseline schemes. © 2023 IEEE.

关键词： millimeter-wave (mmWave) communication Reconfigurable intelligent surface (RIS) scheduling strategy weighted age of information (AoI)

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Learning Probabilistic Coordinate Fields for Robust Correspondences

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 12004-12021页

作者： Zhao, Weiyue Lu, Hao Ye, Xinyi Cao, Zhiguo Li, Xin Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan430074 China West Virginia University The Lane Department of Computer Science and Electrical Engineering MorgantownWV26506-6109 United States

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know when and where to trust the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification. © 1979-2012 IEEE.

关键词： Image registration

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Deep Neural Networks for the Qualitative Analysis of Myocardial Perfusion Emission Computed Tomography Images 15

Deep Neural Networks for the Qualitative Analysis of Myocard...

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15th International Conference on Information Technology and electrical engineering, ICITEE 2023

作者： Pruthipanyasakul, Nareekarn Kanungsukkasem, Nont Urruty, Thierry Leelanupab, Teerapong Bangkok10520 Thailand University of Poitiers Xlim Laboratory Umr Cnrs 7252 Poitiers France School of Electrical Engineering and Computer Science The University of Queensland BrisbaneQLD4072 Australia

ISBN: (纸本)9798350304466

Integrating AI into medical diagnosis can provide a more accurate diagnosis when medical staff make treatment decisions. This paper studied on several deep neural networks, re-used with further training for a specific task in classifying the stenosis of a patient's coronary artery. From a 4DM-SPECT application, we collected polar map images that report, for example, myocardial perfusion, function and defect severity from cardiac emission computed tomography examination. We conducted a comparative study to identify the optimal combination of various state-of-the-art pre-trained models (i.e., VGG19, ResNet50, DenseNet121, and EfficientNetB0-B3) and eight different modalities of the myocardial perfusion images for classifying the stenosis of the coronary artery. © 2023 IEEE.

关键词： Deep neural networks

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