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Machine Learning-Based Routing Protocol in Flying Ad Hoc Networks: A Review

computers, Materials & Continua 2025年第2期82卷 1615-1643页

作者： Priyanka Manjit Kaur Deepak Prashar Leo Mrsic Arfat Ahmad Khan Department of Computer Science and Engineering(CSE) Lovely Professional UniversityPhagwara144411India Department for Program Engineering Algebra UniversityGradiscanska 24Zagreb10000Croatia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian *** dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol *** the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high *** paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and *** research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant *** research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in *** compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance *** protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive *** comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in ***,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.

关键词： FANET protocol machine learning

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Breast Cancer Diagnosis via Swarm-Optimized Adaptive Neuro-Fuzzy Inference System 59

Breast Cancer Diagnosis via Swarm-Optimized Adaptive Neuro-F...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Greenlee, Aidan Palumbo, Neil F. Johns Hopkins University Electrical and Computer Engineering Program BaltimoreMD United States

ISBN: (纸本)9798331513269

This paper discusses a method for classification of breast cancer imaging data through the application of an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) for hyperparameter optimization of the ANFIS system. A robust parameter tuning method is used to select the optimal configuration for the ANFIS and PSO components without expert knowledge of the dataset. Using these methods, high classification accuracies can be achieved for both the original and diagnostic versions of the Wisconsin Breast Cancer Dataset. These results demonstrate the flexibility and potential of a joint ANFIS-PSO system for automated diagnosis while retaining system simplicity and linguistic interpretability to support clinical decision-making. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Variable-Length Feedback Codes Over Known and Unknown Channels With Non-Vanishing Error Probabilities

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IEEE Transactions on Information Theory 2025年第5期71卷 3271-3286页

作者： Yavas, Recep Can Tan, Vincent Y. F. Descartes Program CNRS@CREATE 138602 Singapore National University of Singapore Department of Computer Science Queenstown 117417 Singapore National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering Queenstown 119077 Singapore

We study variable-length feedback (VLF) codes with noiseless feedback for discrete memoryless channels. We present a novel non-asymptotic bound, which analyzes the average error probability and average decoding time of our modified Yamamoto-Itoh scheme. We then optimize the parameters of our code in the asymptotic regime where the average error probability ∈ remains a constant as the average decoding time N approaches infinity. Our second-order achievability bound is an improvement of Polyanskiy et al.’s (2011) achievability bound. We also develop a universal VLF code that does not rely on the knowledge of the underlying channel parameters. Our universal VLF code employs the empirical mutual information as its decoding metric and universalizes the code by Polyanskiy et al. (2011). We derive a second-order achievability bound for universal VLF codes. Our results for both VLF and universal VLF codes are extended to the additive white Gaussian noise channel with an average power constraint. The former yields an improvement over Truong and Tan’s (2017) achievability bound. The proof of our results for universal VLF codes uses a refined version of the method of types and an asymptotic expansion from the nonlinear renewal theory literature. © 1963-2012 IEEE.

关键词： White noise

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Rate Distortion Analysis of Wavefield Coding in Wireless Geophone Networks 8th

Rate Distortion Analysis of Wavefield Coding in Wireless G...

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8th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2024

作者： Khan, Hamood ur Rehman Khan, Farhan Sir Syed Center for Advanced Studies in Engineering Islamabad Pakistan Electrical and Computer Engineering Program Habib University Karachi Pakistan

ISBN: (纸本)9783031788055

Current and future trends in seismic acquisition point towards higher geophone densities (forecasted to be 1M nodes per survey). The geophones’ high operating precision and a sampling rate of a few milliseconds leads to a huge aggregate data rate in the geophone array. To handle this large data rate a hierarchy of multiplexed lines (including fiber optic cables) are used, resulting in substantial deployment costs. This work considers wireless geophone networks to mitigate these costs. Because of limited bandwidth of the wireless medium, compression of acquired data is needed. We assess lossy source coding (signal compression) performance using a rate-distortion tradeoff based on a physical model of the earth. The distortion criterion used is mean squared error. The earth’s physical model considered here consists of a randomly layered subsurface structure where the acoustic impedance of the earth varies as a homogeneous spatial random process. The rate distortion performance is assessed in terms of the parameters of this physical model e.g., the speed of sound underground and correlation length of acoustic impedance process. In comparison with previous work, this paper derives a closed form expression for the autocorrelation function for the reflection process for the 3D subsurface volume. We also show from the resulting rate-distortion curves that the compression performance improves both as the coding bock length increases and as the correlation length of the medium properties increases. The rate-distortion surface as a function of normalized MSE and sound of speed underground is also computed and validated through extensive simulations. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Signal distortion

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A Feasibility Study: Magnetic Field Monitoring in Cycled Lead Acid Cells for Stationary Applications

A Feasibility Study: Magnetic Field Monitoring in Cycled Lea...

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2025 IEEE Electrical Energy Storage Applications and Technologies Conference, EESAT 2025

作者： Vasconcelos, Scarleth Singh, Pritpal Lee, Ross Villanova University Sustainable Engineering Program Villanova United States Villanova University Dept of Electrical and Computer Engineering Villanova United States

ISBN: (纸本)9798331524852

This research introduces a novel, real-time, non-invasive, non-destructive, and accurate method for battery mon-itoring using magnetic field mapping. This technique detects stratification in flooded Lead Acid Batteries at different states of charge, providing early notification by monitoring variations in hydrogen ion concentration during cycling. The study tracked pH and magnetic field variations across H2SO4 electrolyte specific gravities ranging from 1.07 to 1.33, showing a 17%-20% pH reduction over 12 hours, indicating acid stratification. A proof-of-concept using air-core solenoid coils demonstrated a direct correlation between secondary coil output voltage and H2SO4 concentrations, influenced by the induced magnetic field at the primary coil and electrolyte concentration changes. The optimal AC input frequency for maximizing the induced magnetic field mapping response was identified as 30-33 kHz, depending on the physical properties of the magnetic field inductors. An inverse correlation was also observed between the induced magnetic field and the distance between the coils. This approach offers a promising alternative for lead acid battery monitoring, potentially improving battery management and lifespan. The experimental tests also included monitored variations in secondary coil output voltage in a single flooded lead acid cell during cycling. The preliminary results demonstrated a decrease in the magnetic field during charge and an increase during discharge. © 2025 IEEE.

关键词： Lead acid batteries

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FPGA implementation of low cost and low power chaotic encryption scheme based on a discrete-space chaotic map

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Multimedia Tools and Applications 2025年 1-22页

作者： Moreira Bezerra, João Inácio Machado, Gustavo Iankowski Soares, Rafael Valduga de Almeida Camargo, Vinícius Molter, Alexandre Post Graduate Program in Computer Science Universidade Federal de Pelotas Rio Grande do Sul Pelotas96010610 Brazil Graduate Program in Computer Engineering Universidade Federal de Pelotas Rio Grande do Sul Pelotas96010610 Brazil Post Graduate Program in Mathematical Modeling Universidade Federal de Pelotas Rio Grande do Sul Capão do Leão96160000 Brazil

The exponential growth of the number of devices connected to the Internet and the use of IoT applications increases the amount of data exchange over public channels in low-cost and low-power embedded systems. Images are the most widely shared files, and standard symmetric algorithms such as the Advanced Encryption Standard (AES) are optimized for text but not image encryption. Consequently, new approaches like chaotic encryption have attracted researchers’ interest. Chaotic encryption schemes have been analyzed mostly from a software standpoint, but recent advancements in the field, through the use of discrete chaotic maps, enabled the development of simpler hardware implementations of ciphers with promising reductions in power and cost and increased performance. This work presents the Field-programmable Gate Array (FPGA) implementation of a simultaneous permutation-diffusion chaotic encryption scheme utilizing a discrete-space chaotic map. The FPGA implementation of the cipher is analyzed considering area, maximum frequency, and power consumption, and comparisons are made with the AES. The proposed algorithm has a satisfactory trade-off between area, power and performance, obtaining a peak throughput 59% higher than state-of-the-art symmetric ciphers in the literature. Thus, the proposed cipher shows that chaotic ciphers are viable for both high-performance and low-power embedded devices. Tests verified the security of the proposed cipher against statistical, differential, brute-force, and chosen plain-text attacks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Cryptography

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A generalized adder for cell size homeostasis: Effects on stochastic clonal proliferation

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Biophysical Journal 2025年第9期124卷 1376-1386页

作者： Nieto, César Vargas-García, César Augusto Singh, Abhyudai Department of Electrical and Computer Engineering University of Delaware Newark DE United States AGROSAVIA - Corporacion Colombiana de Investigacion Agropecuaria Cundinamarca Mosquera Colombia Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Interdisciplinary Neuroscience Program University of Delaware Newark DE United States

Measurements of cell size dynamics have revealed phenomenological principles by which individual cells control their size across diverse organisms. One of the emerging paradigms of cell size homeostasis is the adder, where the cell cycle duration is established such that the cell size increase from birth to division is independent of the newborn cell size. We provide a mechanistic formulation of the adder, considering that cell size follows any arbitrary nonexponential growth law. Our results show that the main requirement to obtain an adder regardless of the growth law (the time derivative of cell size) is that cell cycle regulators are produced at a rate proportional to the growth law, and cell division is triggered when these molecules reach a prescribed threshold level. Among the implications of this generalized adder, we investigate fluctuations in the proliferation of single-cell-derived colonies. Considering exponential cell size growth, random fluctuations in clonal size show a transient increase and then eventually decay to zero over time (i.e., clonal populations become asymptotically more similar). In contrast, several forms of nonexponential cell size dynamics (with adder-based cell size control) yield qualitatively different results: clonal size fluctuations monotonically increase over time, reaching a nonzero value. These results characterize the interplay between cell size homeostasis at the single-cell level and clonal proliferation at the population level, explaining the broad fluctuations in clonal sizes seen in barcoded human cell lines. © 2025 Biophysical Society

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Government Digital Transformation: A Tailor-Made Digital Maturity Assessment Framework

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IEEE Access 2025年 13卷 71120-71132页

作者： Alfadhli, Muna Kucukvar, Murat Onat, Nuri C. Al-Maadeed, Somaya Abdessadok, Abdelhamid Engineering Management Program College of Engineering Qatar University Doha Qatar Daniels College of Business University of Denver DenverCO80208 United States Qatar Transportation and Traffic Safety Center College of Engineering Qatar University Doha Qatar Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Ministry of Social Development and Family Doha Qatar

Governments seek to implement digital transformation initiatives to improve the effectiveness of their public services. To achieve this, governments need to assess their IT environment and evaluate their digital maturity. The conventional digital maturity assessment frameworks, mainly constructed by consulting firms, often fail when applied to the public sector due to the differences between the business and government sectors in terms of their operational missions and activities. Therefore, a maturity assessment framework that accommodates the unique requirements of governments and their operating environments is necessary. The novelty of this research lies in its contribution to digital transformation in the public sector by presenting a framework specifically designed to assess the digital transformation maturity of government organizations. This research is based on analyzing the factors of maturity assessment models developed by industry leaders from three leading global consulting firms. Insights from the academic literature on these factors were extracted, and a comprehensive survey was conducted with 88 IT experts from government institutes across six countries to ensure their relevance and applicability in real-world government settings. In this research, we tested our hypothesis on whether there is a significant relationship between the importance of digital transformation factors and their implementation. The analysis indicated that there is no strong statistical relationship between the importance of digital transformation maturity factors and their implementation. The framework helps assess digital maturity and supports the formulation of effective strategies to drive successful digital transformation initiatives in government. © 2013 IEEE.

关键词： Digital transformation Organizations Government Capability maturity model Industries Biological system modeling Technological innovation Stakeholders Planning Current measurement

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TaxDiff: taxonomic-guided diffusion model for protein sequence generation

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Science China(Information Sciences) 2025年第4期68卷 385-386页

作者： Zongying LIN Hao LI Liuzhenghao LV Yu WANG Bin LIN Junwu ZHANG Zijun CHEN Calvin Yu-Chian CHEN Li YUAN Yonghong TIAN School of Electronic and Computer Engineering Peking University AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School Peng Cheng Laboratory School of Chemical Biology and Biotechnology Peking University

Protein design aims to generate protein variants with targeted biological functions, which is significant in multiple biological areas, including enzyme reaction catalysis, vaccine design, and fluorescence intensity. Protein design contains two paradigms: sequence generation and structure generation. Recently, EvoDiff [1] proposed a universal designing paradigm, combining structure and sequence generation using the diffusion framework, which improves the protein design efficiency.

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