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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Bisht, Ashmit Sen, Kuheli Jain, Neeraj Sharma, Ravi Pragya Shivahare, Basu Dev Galgotias University Scse Department of Computer Science & Engineering Greater Noida India Galgotias University Department of Electrical Electronics & Communication Engineering Greater Noida India

ISBN: (纸本)9798331508685

Traffic monitoring is critical for avoiding fatal accidents on the roads as well as essential for ensuring that traffic rules are followed. An effective traffic monitoring system is made possible by cutting-edge computer vision technology, raising the standard of urban traffic safety and lowering the possibility of traffic accidents. The field of urban safety management will benefit greatly from this creative idea. This paper aims to create an automated traffic vehicle monitoring system using an object detection algorithm known as You Only Look Once Version 8 (YOLOv8). The model's reliability has been enhanced by the use of the YOLOv8 framework for successful vehicle object detection, speed estimation within streaming videos, as well as for license plate recognition. YOLOv8 model is used to identify vehicle objects in every single frame. The model accurately detects vehicles, while the license plate recognition module extracts the text from diverse plate formats. The study's conclusions are important because they could lead to the development of a real- time, highly accurate, and reasonably priced traffic monitoring system. Road safety might get significantly improved by this research. © 2025 IEEE.

关键词： Video streaming

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Production Cost and Emissions Minimization Based on Renewable Energy Using Superiority of Feasible Solutions- Moth Flame Optimization 4

Production Cost and Emissions Minimization Based on Renewabl...

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4th International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2025

作者： Alam, Mohammad Khurshed Sayem, Shaoran Sulaiman, Mohd Herwan Ferdowsi, Asma Islam, Mishiba Islam, Mohammad Zohurul American International University-Bangladesh Faculty of Electrical & Electronics Engineering Technology Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Pahang Pekan26600 Malaysia Sir Salimullah Medical College Hospital Pharmacology Department Dhaka1206 Bangladesh University of Louisiana at Lafayette Department of Electrical and Computer Engineering LA United States

ISBN: (纸本)9798331530938

This paper introduces a refined version of the Moth Flame Optimization (MFO) algorithm, Superiority of Feasible Solutions-Moth Flame Optimization (SF-MFO), in recognition of the significant influence of renewable energy on both production costs and secretion reduction in energy structure. To improve the symmetry among scrutiny and fleecing also to resolve the limitations of the early MFO, SF-MFO implements two significant enhancements. The algorithm's objective is to reduce emissions and optimize production costs by utilizing renewable energy sources, resulting in a combined reduction in both. Its primary intention is to decrease the total operating expense of the production system, as well as to address active power loss, voltage deviations at load buses, and the cost and emissions of combined units. The SF-MFO is analyse on IEEE 30-bus and IEEE 118-bus structure by transforming this multi-objective problem into a single-objective function with specific constraints and utilizing a bus location index to refine the list of candidate buses. The effectiveness and benefits of the suggested SF-MFO are then highlighted by contrasting the outcomes with those of other published algorithms. © 2025 IEEE.

关键词： Premixed flames

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Leveraging Dual Output LUTs with Pipelining for Efficient BCD to Binary Converter on FPGA 38

Leveraging Dual Output LUTs with Pipelining for Efficient BC...

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38th International Conference on VLSI Design, VLSID 2025

作者： Kumar, Santosh Palchaudhuri, Ayan School of Electrical and Computer Sciences Indian Institute of Technology Bhubaneswar Department of Electronics & Communication Engineering Argul Odisha Khordha752050 India

ISBN: (纸本)9798331522445

Decimal operands expressed in BCD is often the convenient data format used in embedded systems and human-centric applications. For optimized arithmetic computation on hardware where binary arithmetic can be conveniently processed, input BCD operands should first be converted to binary. This paper explores the versatility of FPGA specific logic primitives, namely the dual output Look-Up Tables (LUTs) and carry chain for efficient realization of parallel and pipelined architectures to optimize throughput and resource utilization for BCD to binary conversion architectures. Primitive instantiation was adopted to ensure design optimization, which involved direct configuration of FPGA logic primitives. The utility ratio of the configured FPGA primitives was increased to extract the best performance. Experimental results demonstrate a good trade-off in speed and area for our proposed architectures when compared with existing designs. We have investigated multiple implementation variants for the addition tree, which serves as a crucial functional logic block in the converter design. © 2025 IEEE.

关键词： computer circuits

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Machine Learning for Wireless Network Optimization: Algorithms and Applications for Adaptive Communication 1

Machine Learning for Wireless Network Optimization: Algorith...

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1st International Conference on Advances in computer Science, electrical, electronics, and Communication Technologies, CE2CT 2025

作者： Srinivas, Madhira Surya, S. Thakar, Prachi Deepak Jain, Amit Kumar Department of Computer Science & Engineering Hyderabad501301 India Chennai600116 India KJ college of Engineering & Management Research Department of Computer Engineering Pune India Poornima University Department of Electrical & Electronics Engineering Rajasthan Jaipur303905 India

ISBN: (纸本)9798331518578

This project looks into the possibility of applying machine learning to optimize wireless networks for adaptive communication. Using 5G resource data, it applies preprocessing, exploratory analysis, and visualization to identify key trends: signal strength, latency, and bandwidth utilization. Machine learning algorithms predict resource allocation based on latency-sensitive and bandwidth-demanding applications. The approach emphasizes energy efficiency, scalability, and service quality, and dynamically adjusts communication strategies to meet diverse network demands. The findings indicate the machine learning importance in enhancing elasticity and robustness in a wireless network and its general function. © 2025 IEEE.

关键词： 5G Optimization Adaptive Communication Bandwidth Data Pre-processing EDA Energy Efficiency Latency Machine Learning Network Elasticity Predictive Models Resource Allocation Scalability Signal Strength Wireless Networks

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Fast Bit-Sliced VLSI Architectures on FPGA for Montgomery Domain Modular Inversion 38

Fast Bit-Sliced VLSI Architectures on FPGA for Montgomery Do...

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38th International Conference on VLSI Design, VLSID 2025

作者： Adhikary, Soham Palchaudhuri, Ayan Department of Electronics & Communication Engineering School of Electrical and Computer Sciences Indian Institute of Technology Bhubaneswar Argul Odisha Khordha752050 India

ISBN: (纸本)9798331522445

Modular inversion computation forms a crucial component in Elliptic Curve Cryptosystems. In this paper, we have proposed a fast VLSI architecture for Kaliski's Montgomery domain modular inversion al-gorithm using bit-sliced design paradigm. Logic sharing among functional modules and direct configuration of the FPGA logic primitives were adopted to reduce the critical path wherever possible. The hardwired carry chain fabric of FPGA was exploited to bring down the critical path delay. The entire design flow was implemented on modern day FPGA family and the post-implementation timing simulation results are reported. © 2025 IEEE.

关键词： computer circuits

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Automated Aeroponics System: IoT-Based Monitoring and Control for Optimized Plant Growth 4

Automated Aeroponics System: IoT-Based Monitoring and Contro...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Tanna, Rajesh Santhosh, Talada Gollavilli, Sanjay Kumar Demudu, Lagudu Suri Naik, Ratala Koteswara Raghuram, Cheepurupalli Department of Electrical and Electronics Engineering Vignan's Institute of Information Technology Visakhapatnam530049 India Department of Electrical and Electronics Engineering Visakha Institute of Engineering & Technology Visakhapatnam530027 India Department of Electrical and Electronics Engineering Vignan's Institute of Engineering for Women Visakhapatnam530046 India Department of Electrical and Electronics Engineering A. M. Reddy Memorial College of Engineering and Technology Narasaraopet522601 India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College Bhimavaram534204 India

ISBN: (纸本)9798331530402

Challenges of farming are getting worse due to pests, other environmental issues, and changes in the climate. Several novel technologies have been developed to improve plant growth and productivity, yet there are still numerous issues in this field. This suggests that daily improvements are needed. This paper provides an innovative automated system that uses a variety of environmental sensors, including as pH, TDS, and humidity, for hydroponics and aeroponics. These sensors continuously monitor and modify crucial components to provide the perfect growth environment. In addition, the system has autonomous nutrient dosing, which reduces the need for human intervention and boosts overall effectiveness. Better control over the farming process is made possible by the system's ability to scale and monitor in real-time thanks to the integration of IoT technologies. The study also looks at how machine learning (ML) and artificial intelligence (AI) might improve crop health monitoring and growth forecasts. Additionally, an LED-lit indoor farming setup is included in the system. © 2025 IEEE.

关键词： Agricultural robots

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Hybrid lightweight encryption for IoT: integrating chaotic key generators with Feistel and substitution-permutation networks for secure 3DV transmission

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Multimedia Tools and Applications 2025年 1-47页

作者： El-Shafai, Walid Mesrega, Ahmed K. Ahmed, Hossam Eldin H. Elbahnasawy, Nirmeen A. El-Samie, Fathi E. Abd Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Deptartment of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web of challenges, prominently centered around potential threats and data security implications. Recent cryptography techniques, such as DNA-based cryptography, 3D chaos-based cryptography, and optical cryptography, face challenges including large encryption times, high energy consumption, and suboptimal rather than optimal performance. Particularly, the burden of long encryption cycles strains the energy resources of typical low-power and compact IoT devices. These challenges render the devices vulnerable to unauthorized breaches, despite large storage capacities. The hallmark of the IoT ecosystem, characterized by its low-power compact devices, is the burgeoning volume of data they generate. This escalating data influx, while necessitating expansive storage, remains vulnerable to unauthorized access and breaches. Historically, encryption algorithms, with their multifaceted architectures, have been the bulwark against such intrusions. However, their inherently-complex nature, entailing multiple encryption cycles, strains the limited energy reserves of typical IoT devices. In response to this intricate dilemma, we present a hybrid lightweight encryption strategy. Our algorithm innovatively leverages both one-dimensional (1D) and two-dimensional (2D) chaotic key generators. Furthermore, it amalgamates a classical encryption philosophy, harmonizing the strengths of Feistel and substitution-permutation networks. The centerpiece of our strategy is achieving effective encryption in merely three rounds, tailored expressly for compressed Three-Dimensional Video (3DV) frames, ensuring their unwavering integrity. Our workflow commences with the H.264/MVC compression algorithm, setting the stage for the subsequent encryption phase. Through rigorous MATLAB simulations,

关键词： Image coding

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Reinforcement Learning for Quadrupedal Locomotion: Current Advancements and Future Perspectives 9

Reinforcement Learning for Quadrupedal Locomotion: Current A...

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9th International Conference on Mechanical engineering and Robotics Research, ICMERR 2025

作者： Gurram, Maurya Uttam, Prakash Kumar Ohol, Shantipal S. Computer Science and Engineering Vellore Institute of Technology AP Amaravati India Electrical Electronics and Computer Science Indian Institute of Science Bangalore India COEP University Pune India

ISBN: (纸本)9798331509262

In recent years, reinforcement learning (RL) based quadrupedal locomotion control has emerged as an extensively researched field, driven by the potential advantages of autonomous learning and adaptation compared to traditional control methods. This paper provides a comprehensive study of the latest research in applying RL techniques to develop locomotion controllers for quadrupedal robots. We present a detailed overview of the core concepts, methodologies, and key advancements in RL-based locomotion controllers, including learning algorithms, training curricula, reward formulations, and simulation-to-real transfer techniques. The study covers both gait-bound and gait-free approaches, highlighting their respective strengths and limitations. Additionally, we discuss the integration of these controllers with robotic hardware and the role of sensor feedback in enabling adaptive behavior. The paper also outlines future research directions, such as incorporating exteroceptive sensing, combining model-based and model-free techniques, and developing online learning capabilities. Our study aims to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art in RL-based locomotion controllers, enabling them to build upon existing work and explore novel solutions for enhancing the mobility and adaptability of quadrupedal robots in real-world environments. © 2025 IEEE.

关键词： Controllability

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Air Pollutant (CO) Forecasting: A Hybrid Approach Using Gooseneck Barnacle Optimization and Artificial Neural Networks 4

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Goos...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Ahmed, Marzia Mohamad, Ahmad Johari Hassan, Md Maruf Bhuiyan, Touhid Department of Software Engineering Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Malaysia Southeast University Department of Computer Science and Engineering Dhaka Bangladesh School of IT VA United States

ISBN: (纸本)9798331518882

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN's hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model's potential for accurate real-time air quality forecasting, offering a robust, sustainable solution. © 2025 IEEE.

关键词： Air Quality GBO Metaheuristic Neural Network Time series forecasting

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Sphere Decoding for Binary Polar Codes with the Modified Multiplicative Repetition Construction

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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 1229-1236页

作者： Haiqiang Chen Yuanbo Liu Shuping Dang Qingnian Li Youming Sun Xiangcheng Li School of Computer Electronics and InformationGuangxi UniversityNanning 530004China School of Electrical Electronic and Mechanical EngineeringUniversity of BristolBristol BS8 1TRUK Information and Engineering College Nanning UniversityNanning 530004China

Compared to the successive cancellation(SC)-based decoding algorithms,the sphere decoding(SD)algorithm can achieve better performance with reduced computational complexity,especially for short polar *** this paper,we propose a new method to construct the binary polar codes with the modified multiplicative repetition(MR)-based *** from the original construction,we first design a 2x2q-ary kernel to guarantee the single-level polarization ***,by replacing the new-designed binary companion matrix,a novel strategy is further developed to enhance the polarization in the bit level,resulting in a better distance ***,the SD-based Monte-Carlo(SDMC)method is used to construct MR-based binary polar codes,while the resulting codes without the butterfly pattern are decoded by the SD *** results show that the proposed method with the SD algorithm can achieve a maximum performance gain of 0.27 dB compared to the original method with slightly lower complexity.

关键词： polar codes sphere decoding generator matrix multiplicative repetition Monte-Carlo method

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