Software and hardware co-design of lightweight authenticated ciphers ASCON for the internet of things by Jing WANG, Lesheng HE, Zhonghong LI, Luchi LI, Hang YANG

“…ASCON was the most promising algorithm to become an international standard in the 2021 NIST lightweight authenticated encryption call for proposals.The algorithm was designed to achieve the best performance in IoT resource-constrained environments, and there was no hardware IP core implementation based on this algorithm in the open literature.A software-hardware collaborative implementation method of ASCON was proposed, which improved the speed and reduced the memory footprint of ASCON in IoT security authentication applications through hardware means such as S-box optimization, prior calculation and advanced pipeline design.As a comparison, ASCON has been transplanted on the common IoT embedded processor platform.The results showed that the described method was more than 7.9 times faster, while the memory footprint was reduced by at least 90%.The schemes can be used for the design and implementation of IoT security application-specific integrated circuits or SoCs.…”
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Learned Shrinkage Approach for Low-Dose Reconstruction in Computed Tomography by Joseph Shtok, Michael Elad, Michael Zibulevsky

“…Our numerical simulations indicate that the proposed algorithm can manage well with noisy measurements, allowing a dose reduction by a factor of 4, while reducing noise and streak artifacts in the FBP reconstruction, comparable to the performance of a statistically based iterative algorithm.…”
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WSNs data acquisition by combining expected network coverage and clustered compressed sensing. by Zhouzhou Liu, Yangmei Zhang, Yang Bi, Jingxuan Wang, Yuanyuan Hou, Chao Liu, Guangyi Jiang, Shan Li

“…Combined with an optimized network coverage algorithm, a node scheduling strategy is introduced to focus on critical observation areas while minimizing overall energy consumption. …”
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DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants by Yixin Guo, Jinchao Pan, Xueying Wang, Hong Deng, Hong Deng, Mingliang Yang, Mingliang Yang, Enliang Liu, Qingshan Chen, Qingshan Chen, Rongsheng Zhu, Rongsheng Zhu

“…After 200 epochs, the recognition results of various object detection algorithms were compared to obtain the optimal model. …”
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Accelerated Tensor Robust Principal Component Analysis via Factorized Tensor Norm Minimization by Geunseop Lee

“…Experimental results demonstrate that our algorithm achieves significantly faster performance than existing reference methods known for efficient computation while maintaining high accuracy in recovering low-rank tensors for applications such as color image recovery and background subtraction.…”
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Fast Processing of Massive Hyperspectral Image Anomaly Detection Based on Cloud-Edge Collaboration by Yunchang Wang, Jin Sun, Yang Xu, Zhihui Wei, Shangdong Zheng, Zebin Wu

“…With the improvement of hyperspectral image resolution, existing anomaly detection algorithms find it challenging to quickly process large volumes of hyperspectral data while fully exploiting spectral information. …”
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A Study on Multi-ship Avoidance System for Unmanned Surface Vehicles Using the Quaternion Ship Domain and Collision Risk Index by Dong-Hun Lee, Mu-Yeong Seo, Hyo-Geun Lee, Won-Jun Yoo, Sanghyun Kim, Kwang-Jun Paik

“…This paper proposes a multi-ship collision avoidance algorithm that integrates the quaternion ship domain (QSD) and collision risk index (CRI) to assess collision risk and generate optimal avoidance trajectories. …”
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Application of RNA-seq for single nucleotide variation identification in a cohort of patients with hypertrophic cardiomyopathy by Anastasia Chumakova, Ivan Vlasov, Elena Filatova, Anna Klass, Andrey Lysenko, Gennady Salagaev, Maria Shadrina, Petr Slominsky

“…The algorithm was evaluated and the optimal quality threshold was determined based on allelic discrimination for the rs397516037 mutation (MYBPC3 c.3697 C > T) among patients. …”
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A Time- and Space-Integrated Expansion Planning Method for AC/DC Hybrid Distribution Networks by Yao Guo, Shaorong Wang, Dezhi Chen

“…A modified graph attention network (MGAT)-based deep reinforcement learning (DRL) algorithm is used for optimization, balancing economic and reliability objectives. …”
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Utilizing weak graph for edge consolidation-based efficient enhancement of network robustness by Wei Ding, Zhengdan Wang

“…We compare the proposed algorithm with optimal and approximate algorithms across graphs of varying scales. …”
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Zynq FPGA-Based Acceleration of Kernelized Correlation Filters via High-Level Synthesis of a Custom DFT Block by Enver Çavuş, Mustafa Yetiş

“…Within this framework, a custom combined DFT and inverse DFT IP, named CDFT, is developed and optimized on the Programmable Logic (PL) side of the Xilinx ZCU102 FPGA, whereas the rest of the KCF algorithm is run with customized Petalinux build on the (Processing System) side. …”
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Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem by Haneen Algethami

“…The routing solver in the Google OR-tools solver is utilised with three well-known metaheuristic methods for neighbourhood exploration: a guided local search (GLS), a tabu search (TS), and simulated annealing (SA), with two initialisation strategies, Clarke and Wright’s algorithm and the nearest neighbour algorithm. Results showed that optimal solutions are found in faster computational times than using only an IP solver, especially for large instances. …”
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Research on the cooperative offloading strategy of sensory data based on delay and energy constraints by Peiyan YUAN, Saike SHAO, Ran WEI, Junna ZHANG, Xiaoyan ZHAO

“…The edge offloading of the internet of things (IoT) sensing data was investigated.Multiple edge servers cooperatively offload all or part of the sensing data initially sent to the cloud center, which protects data privacy and improves user experience.In the process of cooperative offloading, the transmission of the sensing data and the information exchange among edge servers will consume system resources, resulting in the cost of cooperation.How to maximize the offloading ratio of the sensing data while maintaining a low collaboration cost is a challenging problem.A joint optimization problem of sensing data offload ratio and cooperative scale satisfying the constraints of network delay and system energy consumption was formulated.Subsequently, a distributed alternating direction method of multipliers (ADMM) via constraint projection and variable splitting was proposed to solve the problem.Finally, simulation experiments were carried out on MATLAB.Numerical results show that the proposed method improved the network delay and energy consumption compared to the fairness cooperation algorithm (FCA), the distributed optimization algorithm (DOA), and multi-subtasks-to-multi-servers offloading scheme (MTMS) algorithm.…”
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Research on GNNs with stable learning by Wenbin Li, Wenxuan Wei, Peiyang Wang, Li Pan, Bo Yang, Yanling Xu

“…The aim is to extract genuine causal features while eliminating spurious causal features. By introducing a feature sample weighting decorrelation technique in the random Fourier transform space and combining it with a baseline GNN model, a Stable-GNN model and a constrained sampling weight gradient update algorithm are designed. …”
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Research on filter-based adversarial feature selection against evasion attacks by Qimeng HUANG, Miaomiao WU, Yun LI

“…With the rapid development and widespread application of machine learning technology, its security has attracted increasing attention, leading to a growing interest in adversarial machine learning.In adversarial scenarios, machine learning techniques are threatened by attacks that manipulate a small number of samples to induce misclassification, resulting in serious consequences in various domains such as spam detection, traffic signal recognition, and network intrusion detection.An evaluation criterion for filter-based adversarial feature selection was proposed, based on the minimum redundancy and maximum relevance (mRMR) method, while considering security metrics against evasion attacks.Additionally, a robust adversarial feature selection algorithm was introduced, named SDPOSS, which was based on the decomposition-based Pareto optimization for subset selection (DPOSS) algorithm.SDPOSS didn’t depend on subsequent models and effectively handles large-scale high-dimensional feature spaces.Experimental results demonstrate that as the number of decompositions increases, the runtime of SDPOSS decreases linearly, while achieving excellent classification performance.Moreover, SDPOSS exhibits strong robustness against evasion attacks, providing new insights for adversarial machine learning.…”
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Three-Dimensional Extended Target Tracking and Shape Learning Based on Double Fourier Series and Expectation Maximization by Hongge Mao, Xiaojun Yang

“…Specifically, the 3D shape is modeled using a radial function estimated via double Fourier series (DFS) expansion, and orientation is represented using the compact, singularity-free axis-angle method. The ECM algorithm facilitates this joint estimation: an Unscented Kalman Smoother infers kinematics in the E-step, while the M-step estimates DFS shape parameters and rotation angles by minimizing regularized cost functions, promoting robustness and smoothness. …”
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Generalized Singular Value Decomposition-Based Secure Beam Hybrid Precoding for Millimeter Wave Massive Multiple-Input Multiple-Output Systems by Boqing Chen, Lijun Yang, Meng Wu

“…In a hybrid precoding system, the low-complexity GSVD-Sparsity algorithm can achieve a spectral efficiency close to that of the GSVD-based scheme in a fully digital system while maintaining anti-eavesdropping capabilities.…”
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Key Nodes Identification Method in Scale-Free Network Based on Structural Holes by Weiyan Liu

“…The proposed algorithm achieves higher accuracy in structural hole-aware ranking compared to benchmarks while maintaining <inline-formula> <tex-math notation="LaTeX">${ O}\left ({{ m+n.logn }}\right)$ </tex-math></inline-formula> complexity. …”
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Calibration and Uncertainty Analysis of Freundlich and Langmuir Isotherms Using the Markov Chain Monte Carlo (MCMC) Approach by Haniyeh Sharifi Moghadam, Saeed Alimohammadi

“…First, their parameters were estimated and calibrated using a simple optimization model. To analyze parameter uncertainty, a Bayesian approach employing the Markov Chain Monte Carlo method was adopted, utilizing the Metropolis-Hastings and Gibbs algorithms, and the results were compared. …”
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Adaptive dimensionality reduction for neural network-based online principal component analysis. by Nico Migenda, Ralf Möller, Wolfram Schenck

“…While the continuous update of the principal components is widely studied, the available algorithms for dimensionality adjustment are limited to an increment of one in neural network-based and incremental PCA. …”
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