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A&I-ED-TSP: Association and Integration Encoder-Decoder for Traveling Shortest Path Planning

IEEE ACCESS 2024年 12卷 129601-129610页

作者： Hu, Bin Zhang, Renjun Hebei Petr Univ Technol Sci & Technol Dept Chengde 067000 Peoples R China Hebei Petr Univ Technol Dept Foreign Languages & Tourism Chengde 067000 Peoples R China

Traveling shortest path planning, encompassing the Traveling Salesman Problem (TSP) in graph theory, holds profound significance. The motivation for addressing the TSP stems from its critical application in real-world scenarios, such as logistics, where optimal routing can substantially reduce costs and improve service efficiency. Furthermore, TSP-like challenges play a pivotal role in assisting travelers to chart the optimal itinerary, encompassing all landmarks in the least distance or time and concluding at the departure site. This optimization not only streamlines travel routes but also economizes time and energy, ensuring maximal sightseeing within a confined timeframe. Recognizing the limitations of current solutions in achieving high efficiency and accuracy simultaneously, we propose an innovative Association & Integration-based Encoder-Decoder structure tailored for solving the Traveling Salesman Problem, i.e., A&*** proposed structure comprises four blocks: the information linkage space, dual-path integration encoder, node encoder, and representation decoder. Specifically, the information linkage space constructs associations among hidden information between input sequence samples. The dual-path integration encoder extracts and merges the original representations of the sequence with associated representations. The node encoder extracts current sequence representations, while the representation decoder block computes the probability distribution of sequence samples, completing the combinatorial optimization of the entire sequence. In the experimental evaluation, we utilized three different metrics: Average Tour Length (ATL), Optimality Gap (OG), and Evaluation Time (ET). We compared the proposed method with classical approximation methods and various state-of-the-art deep learning approaches. The experimental results show that our A&I-ED-TSP structure achieved the best ATLs of 5.704, 12.770, 17.981, 21.979, and 25.293 for TSP instances of TSP50, TS

关键词： Heuristic algorithms Decoding Optimization approximation algorithms Couplings Transformers Encoding Deep learning Traveling salesman problems Path planning TSP encoder-decoder structure deep learning travel planning

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Multipath Component Power Delay Profile Based Ranging

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2024年第5期18卷 950-963页

作者： Xiao, Fangqing Zhao, Zilu Slock, Dirk T. M. Eurecom Dept Commun Syst F-06410 Biot France

Precision ranging technology has become indispensable for ensuring efficient, reliable, and low-latency fifth-generation (5G) networks. In this paper, we propose a novel ranging method which is multipath component (MPC) power delay profile (PDP) based ranging. Whereas the Received Signal Strength (RSS) only summarizes the PDP into a single characteristic, we aim to furthermore exploit the range dependent curvature of the PDP envelope over its delay spread. However, the multipath propagation only allows to sample the PDP envelope at the path delays and suffers from (slow) fading. Hence our approach involves constructing a statistical fading model of the PDP and establishing a relationship between the distribution parameters and the propagation distance. To theoretically validate the feasibility of our proposed method, we adopt the widely accepted Nakagami-m fading model, which renders traditional estimation methods difficult to apply. Therefore we introduce the Expectation Maximization (EM)-Revisited Vector Approximate Message Passing (ReVAMP) algorithm. This algorithm is specifically designed to handle difficulties in parameter estimation for Gaussian linear models (GLMs) with hidden random variables and intractable posterior distributions. Extensive numerical simulation results have been conducted which exhibit preliminary evidence of the effectiveness of our MPCPDP-based ranging method compared to the received signal strength (RSS)-based method. Moreover, the versatility of the EM-ReVAMP algorithm allows for its extension to other statistical fading models beyond the Nakagami-m model with minor modifications, which opens the door to potential improvements based on more accurate statistical fading models. Nevertheless, the applicability of our MPCPDP-based ranging method should be validated in real-world scenarios in future studies.

关键词： Distance measurement Fading channels Delays Vectors Attenuation Signal processing algorithms approximation algorithms Hardware Direction-of-arrival estimation Accuracy Ranging estimation multipath component power delay profile expectation maximization revisited vector approximate message passing algorithm

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Toward Efficient Sensor Deployment in Internet of Things for Target Coverage and Sensor Connectivity

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第3期70卷 5432-5440页

作者： Balaji, S. Pavithra, R. Arivudainambi, D. Varunkumar, K. A. Suresh, A. Omar, Marwan Bashir, Ali Kashif Vellore Inst Technol Sch Adv Sci Math Div Chennai Campus Chennai 600127 India Anna Univ Dept Math Chennai 600025 India SRM Inst Sci & Technol Fac E&T Sch Comp Dept Networking & Commun Kattankulathur 603203 India IIT Informat Technol & Management Warrenville IL 60555 USA Manchester Metropolitan Univ Dept Comp & Math Manchester M15 6BH England Woxsen Univ Woxsen Sch Business Hyderabad 502345 India

The Internet of Things (IoT) has become widely popular due to its rapid progress and broad range of practical uses, which have significantly impacted our daily lives. One such key utilization of IoT is making consumer electronics interactive, interconnected and self-responsible by means of sensors and Internet. Sensors play a crucial role as the primary technology that enables the Internet of Things (IoT) and its various applications. The inherent difficulty hinders the further growth of IoT. One such problem is utilizing minimum number of sensors to achieve maximum coverage and connectivity in the network. The most accurate method to encounter this issue is optimal Sensor Placement (OSP). OSP predetermines the sensor's position for its deployment to attain maximum network coverage and connectivity. Hence, this paper proposes a coverage and connectivity-preserving hierarchical algorithm based on discrete Haar wavelet transform for optimal sensor placement algorithm. The proposed algorithm utilizes discrete Haar wavelet transform, for identifying sensor's position and to ensure connectivity Breadth first search algorithm is used. Further, to enhance the coverage, proposed algorithm determines redundant sensors and redeploy it in determined spots. The results obtained from the proposed algorithm effectively address the research problem and attain better results compared with the existing efficient methods.

关键词： Internet of Things Sensor placement Monitoring Discrete wavelet transforms Base stations approximation algorithms Wireless sensor networks Breadth first search algorithm discrete haar wavelet transform optimal sensor placement target coverage problem

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Variance Reduced Random Relaxed Projection Method for Constrained Finite-Sum Minimization Problems

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 2188-2203页

作者： Yang, Zhichun Xia, Fu-quan Tu, Kai Yue, Man-Chung Sichuan Normal Univ Sch Math Sci Chengdu 610068 Peoples R China Shenzhen Univ Sch Math Sci Shenzhen 518060 Peoples R China Univ Hong Kong Musketeers Fdn Inst Data Sci Hong Kong Peoples R China Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Peoples R China

For many applications in signal processing and machine learning, we are tasked with minimizing a large sum of convex functions subject to a large number of convex constraints. In this paper, we devise a new random projection method (RPM) to efficiently solve this problem. Compared with existing RPMs, our proposed algorithm features two useful algorithmic ideas. First, at each iteration, instead of projecting onto the set defined by one of the constraints, our algorithm only requires projecting onto a half-space approximation of the set, which significantly reduces the computational cost as it admits a closed-form formula. Second, to exploit the structure that the objective is a sum, variance reduction is incorporated into our algorithm to further improve the performance. As theoretical contributions, under a novel error bound condition and other standard assumptions, we prove that the proposed RPM converges to an optimal solution and that both optimality and feasibility gaps vanish at a sublinear rate. In particular, via a new analysis framework, we show that our RPM attains a faster convergence rate in optimality gap than existing RPMs when the objective function has a Lipschitz continuous gradient, capitalizing the benefit of the variance reduction. We also provide sufficient conditions for the error bound condition to hold. Experiments on a beamforming problem and a robust classification problem are presented to demonstrate the superiority of our RPM over existing ones.

关键词： Signal processing algorithms Convergence approximation algorithms Optimization Convex functions Indexes Standards Constrained optimization finite-sum minimization random projection method relaxed projection variance reduction convergence rate analysis

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Minimizing Service Latency Through Image-Based Microservice Caching and Randomized Request Routing in Mobile Edge Computing

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IEEE INTERNET OF THINGS JOURNAL 2024年第18期11卷 30054-30068页

作者： Sun, Xiao Wang, Desheng Zhang, Weizhe Lou, Guanqing Wang, Jiayin Yadav, Rahul Harbin Inst Technol Sch Cyberspace Sci Harbin 150001 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China Peng Cheng Lab Cyberspace Secur Res Ctr Shenzhen 518066 Peoples R China Harbin Engn Univ Coll Comp Sci & Technol Harbin 150009 Peoples R China

In the context of mobile edge computing (MEC), the traditional method of requesting microservices from a central cloud can result in increased delay for users due to the physical distance between the user and the cloud server. To address this issue, MEC advocates for placing servers closer to the users at the edge of the network. However, this approach is constrained by the storage capacity and computing resources of edge servers (ESs). Therefore, it is crucial to devise a strategy for processing user requests that minimizes the average request delay. To address this problem, This article formulates the microservice caching problem as an image-based microservice placement and task request routing problem. We model the problem as an integer linear programming problem with multicondition constraints. Considering the limited resources of ESs, we propose a microservice placement algorithm called approximate algorithm based on randomized task request routing. The proposed algorithm is designed to provide near-optimal solutions in polynomial time, leveraging Chernoff's theorem. Our approach is evaluated through comparisons with two existing algorithms: 1) the image-pull-based microservice cache request algorithm and 2) the greedy-based microservice cache and request routing algorithm. The results demonstrate that our algorithm exhibits superior performance compared to existing methods.

关键词： approximation algorithms approximation algorithm microservice cache mobile edge computing (MEC) task request routing

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A Real-Time Subaperture Preprocessing for Multireceiver Wide-Beam SAS Imaging

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Zhang, Jiafeng Cheng, Guangli Tang, Jinsong Wu, Haoran Naval Univ Engn Inst Elect Engn Wuhan 430000 Peoples R China

The multireceiver synthetic aperture sonar (SAS) data are usually converted to equivalent monostatic data through the displaced phase center approximation (DPCA) before the monostatic imaging. However, the DPCA error is azimuth-variant in the wide-beam case, resulting in the traditional algorithms compensating for the DPCA error in the extended Doppler domain of each receiver, which obviously increases the computational complexity. To solve the problem, this letter analyzes the space-variant characteristics of the DPCA error and discovers that the DPCA error exhibits significant receiver-variant and weak azimuth-variant characteristics. Based on this, a subaperture preprocessing is proposed to reduce computational complexity without sacrificing imaging accuracy. The proposed algorithm compensates for the DPCA error uniformly within the subaperture using the DPCA error at the center of the subaperture and then superposes all subapertures coherently to obtain equivalent monostatic data. The weak azimuth-variant characteristic ensures that the subapertures are very sparse, Additionally, the algorithm allows data recording and preprocessing to be synchronized, significantly reducing the imaging waiting time further. The simulated data and actual data experiments verify the effectiveness of the proposed algorithm.

关键词： Receivers Synthetic aperture sonar Signal processing algorithms Imaging Doppler effect Azimuth approximation algorithms Apertures Transmitters Geoscience and remote sensing Azimuth-variant characteristic displaced phase center approximation (DPCA) preprocessing real time subaperture synthetic aperture sonar (SAS) wide-beam

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Constructing Highly Nonlinear Cryptographic Balanced Boolean Functions on Learning Capabilities of Recurrent Neural Networks

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IEEE ACCESS 2024年 12卷 150255-150267页

作者： Muhammad Waseem, Hafiz Asfand Hafeez, Muhammad Ahmad, Shabir David Deebak, Bakkiam Munir, Noor Majeed, Abdul Hwang, Seoung Oun Gachon Univ Dept Comp Engn Seongnam Si 13120 South Korea Kansas State Univ Dept Comp Sci Manhattan KS 66502 USA Gachon Univ Dept Smart Secur Seongnam Si 13120 South Korea

This study presents a novel approach to cryptographic algorithm design that harnesses the power of recurrent neural networks. Unlike traditional mathematical-based methods, neural networks offer nonlinear models that excel at capturing chaotic behavior within systems. We employ a recurrent neural network trained on Monte Carlo estimation to predict future states and generate confusion components. The resulting highly nonlinear substitution boxes exhibit exceptional characteristics, with a maximum nonlinearity of 114 and low linear and differential probabilities. To evaluate the efficacy of our methodology, we employ a comprehensive range of traditional and advanced metrics for assessing randomness and cryptanalytics. Comparative analysis against state-of-the-art methods demonstrates that our developed nonlinear confusion component offers remarkable efficiency for block-cipher applications.

关键词： Cryptography Recurrent neural networks Monte Carlo methods Estimation Ciphers Boolean functions approximation algorithms Vectors Training Time series analysis Block ciphers confusion components Monte Carlo estimation recurrent neural networks substitution boxes

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Novel Multi-Stage Feedback Technique for Time Skew Calibration in Time-Interleaved ADCs

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IEEE ACCESS 2024年 12卷 188940-188956页

作者： Hu, Min Yi, Pengxing Hao, Zhengxu Zhou, Zhitong Huazhong Univ Sci & Technol Dept Mech Sci & Engn Wuhan 430074 Peoples R China

This paper introduces a novel low-complexity algorithm for time skew calibration, which is crucial in ensuring undistorted signal acquisition in a time-interleaved analog-to-digital conversion system. By approximating the absolute value of the difference during linear approximation, the algorithm decouples derivatives from the step size factor, significantly reducing computational complexity. An enhanced multi-stage feedback approach is employed to minimize Taylor series approximation errors without increasing the number of derivative calculations. Simulation results demonstrate notable improvements, with an SNDR above 66 dB and an SFDR above 69 dB achieved for single-tone sinusoidal signals after calibration. The algorithm remains effective for multi-tone and frequency modulation signals, demonstrating its versatility. Real-world experimental measurements validate the algorithm's efficacy, highlighting its potential for practical applications with reasonable resource utilization. The proposed multi-stage feedback calibration technique consumes 1509 look-up tables (LUTs), 1409 flip flops (FFs), 33 Inputs/Outputs (I/Os), 1 buffer-global-clock (BUFG), 33 digital signal processors (DSPs), and 16.5 block random access memories (BRAMs) in the implementation, making it a practical solution for various applications.

关键词： Calibration Mathematical models Iterative methods Taylor series Signal processing algorithms Estimation approximation algorithms Interleaved codes Time-interleaved digital calibration time skew multi-stage feedback approach fewer derivatives

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An Efficient Sum-Rate Maximization Algorithm for Fluid Antenna-Assisted ISAC System

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IEEE COMMUNICATIONS LETTERS 2025年第1期29卷 200-204页

作者： Zhang, Qian Shao, Mingjie Zhang, Tong Chen, Gaojie Liu, Ju Ching, P. C. Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China Chinese Acad Sci Inst Syst Sci Acad Math & Syst Sci AMSS Key Lab Syst & Control Beijing 100149 Peoples R China Harbin Inst Technol Inst Intelligent Ocean Engn Shenzhen 518055 Peoples R China Sun Yat Sen Univ Sch Flexible Elect SoFE Shenzhen 518107 Peoples R China Sun Yat Sen Univ State Key Lab Optoelect Mat & Technol OEMT Shenzhen 518107 Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

This letter investigates a fluid antenna (FA)-assisted integrated sensing and communication (ISAC) system, with joint antenna position optimization and waveform design. We consider enhancing the sum-rate maximization (SRM) and sensing performance with the aid of FAs. Although the introduction of FAs brings more degrees of freedom for performance optimization, its position optimization poses a non-convex programming problem and brings great computational challenges. This letter contributes to building an efficient design algorithm by the block successive upper bound minimization and majorization-minimization principles, with each step admitting closed-form update for the ISAC waveform design. In addition, the extrapolation technique is exploited further to speed up the empirical convergence of FA position design. Simulation results show that the proposed design can achieve state-of-the-art sum-rate performance with at least 60% computation cutoff compared to existing works with successive convex approximation (SCA) and particle swarm optimization (PSO) algorithms.

关键词： Optimization Fluids approximation algorithms Receiving antennas Minimization Upper bound Transmitting antennas Spectral efficiency Reconfigurable intelligent surfaces Fluid antenna integrated sensing and communication proximal distance extrapolated projected gradient extrapolated projected gradient

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Maintaining the Giant Component in Networks With Edge Weighted Augmentation Given Causal Failures

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IEEE ACCESS 2024年 12卷 136588-136598页

作者： Zhang, Zuyuan Radhakrishnan, Sridhar Barker, Kash Gonzalez, Andres D. Univ Oklahoma Sch Comp Sci Norman OK 73019 USA Univ Oklahoma Sch Ind & Syst Engn Norman OK 73019 USA

Understanding the relationship between various nodes of a network is critical for building a robust and resilient network. Studying and understanding the causes of network failures is vital to prevent electric grid blackouts, mitigate supply chain failures, and keep transportation systems functional, among others. Failure of one or more nodes may cause other nodes in the network to fail as well, that is, failures are causal. In general, these failure relationships extend beyond the immediate neighborhood of failed nodes. When any of the causal failures are applied (the nodes of the causal failures are removed), the network could be disconnected. One can add edges to the original network (augmentation problem) in such a way that the network remains connected after applying each of the causal failures, or the largest connected component in the disconnected network is at least a given specified size alpha x n ( alpha -giant component), where n is the number of nodes in the original network. By choosing this size, we guarantee that the network is active for a large population of entities represented by the nodes in the giant component. More formally, we consider the network augmentation problem when faced with causal failures as follows. Given a network G=(V, E) , its complement G=(V, E) with a cost function c: E -> R+ and the causality set C , find a subset of E with a minimum total cost such that the network maintains at least one alpha -giant component when each causal failure in C is applied to the augmented graph. We prove the NP-hardness of this problem and present a mixed integer linear programming model to provide the exact solution to the problem. Furthermore, we design a heuristic algorithm by checking the connected components when applying each causality. Finally, experiments are conducted on synthetic Erd & odblac;s-R & eacute;nyi networks, and we demonstrate the efficacy and efficiency of the heuristic algorithm relat

关键词： Cause effect analysis Robustness approximation algorithms Heuristic algorithms Costs Mixed integer linear programming Measurement Causal failure robustness connectivity augmentation NP-hardness heuristic algorithms

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