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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Kani, Mathina M. Mahalakshmi, R. Shunmugavel, V. Kareem, Abdul M. Department of Computer Science and Engineering Sethu Institute of Technology Virudhunagar India Department of Electrical and Electronics Engineering Quantanics TechServ pvt Ltd Madurai India

ISBN: (纸本)9798331522667

In today’s technological landscape, manual methods are increasingly being replaced by automated computer systems, streamlining tasks and enhancing efficiency. Within the realm of agricultural research, two critical areas plant identification and disease classification have shifted toward computerized solutions. With millions of plant species globally, medicinal plants hold particular significance due to their healing properties in traditional medicine. These readily accessible and culturally important remedies not only contribute to affordable healthcare but also sustain livelihoods in indigenous and rural communities. To address these challenges, our study evaluates the performance of two widely used pre-trained deep learning models U-Net and F-Net. By comparing their accuracy levels, we aim to enhance disease detection. Additionally, we present performance metrics such as accuracy and error rates, visualized through graphs. Our user interface (UI) leverages Kivy, an open-source multiplatform GUI development library for Python. Kivy enables cross-device app development, emphasizing code reusability and rapid prototyping, making it a valuable tool for interaction design across iOS, Android, Windows, OS X, and GNU/Linux platforms. © 2025 IEEE.

关键词： Medicinal chemistry

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An Analysis Approach for Crowds with Varying Density and Complexity Using Image Data 29

An Analysis Approach for Crowds with Varying Density and Com...

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29th International Conference on Information Technology, IT 2025

作者： Menevse, M. Mert Gozet, Melisa Yilmaz, Asim Egemen Karakose, Mehmet Firat University Computer Engineering Elazig Turkey Ostim Technical University Artificial Intelligence Engineering Ankara Turkey Ankara University Electrical Electronics Engineering Ankara Turkey

ISBN: (纸本)9798331517649

Smart cities represent urban environments that leverage advanced technologies to optimize infrastructure and services. Crowd density analysis, a critical component of smart cities, employs sensors, cameras, and data analytics to monitor population density and movement in public spaces. Deep Learning (DL) techniques enable precise and automated crowd density evaluation, transforming traditional methods. This study introduces CILHOF-CDC, a CLAHE-InceptionV3-LSTM Hyperparameter Optimization Framework for Crowd Density Classification. The framework enhances image contrast using the CLAHE method, employs Inception V3 for feature extraction, and integrates a Bidirectional LSTM model for crowd density detection and classification. Reinforcement Learning (RL) is utilized for hyperparameter optimization to further improve system performance. Experimental evaluations on a crowd density image dataset demonstrate the efficacy of the proposed framework, achieving an impressive accuracy of 99.4%, outperforming recently developed DL models. Furthermore, comparative analyses with existing models in the literature reveal that CILHOF-CDC achieves superior performance in terms of classification accuracy and adaptability across diverse crowd density scenarios. © 2025 IEEE.

关键词： Deep reinforcement learning

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Low-Profile Leaky-Wave Antenna with Mechanical Beam Steering Using Sliding Slot Arrays for K-Band Communications 6

Low-Profile Leaky-Wave Antenna with Mechanical Beam Steering...

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6th Australian Microwave Symposium, AMS 2025

作者： Jasim, Ahmed Sultan, Kamel Fumeaux, Christophe Abbosh, Amin University of Queensland School of Electrical Engineering and Computer Science BrisbaneQLD4072 Australia Ninevah University College of Electronics Engineering Ninewa41001 Iraq

ISBN: (纸本)9798350379969

A low-profile leaky-wave antenna with mechanical beam steering is presented for K-band applications. The antenna comprises two radiating layers: A fixed layer and a movable layer. Four sets of slots with different periodicities are etched at the surface of both layers. Sliding the movable layer in four steps exposes different sets of slots on the fixed layer, shifting the main beam at four angles. The radiating layers are excited using a pillbox feeding network based on two substrate-integrated waveguide horn antennas through a coupling slot. Simulations demonstrate that beam steering is realized by moving the top layer in four 0.6 mm steps. The antenna is designed to operate at 20 GHz with 0.7 GHz bandwidth, a maximum gain of 20 dBi, with 3 dB scan loss and-9 dB side lobe level. © 2025 IEEE.

关键词： Microwave antennas

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Sum-Rate Maximization of RIS-Aided Digital and Holographic Beamformers in MU-MISO Systems

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IEEE Transactions on Communications 2025年第5期73卷 3106-3118页

作者： Kumar Gadamsetty, Pavan Hari, K.V.S. Hanzo, Lajos Indian Institute of Science Department of Electrical Communication Engineering Bengaluru560012 India University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Reconfigurable holographic surfaces (RHS) are intrinsically amalgamated with reconfigurable intelligent surfaces (RIS), for beneficially ameliorating the signal propagation environment. This potent architecture significantly improves the system performance in non-line-of-sight scenarios at a low power consumption. Briefly, the RHS technology integrates ultra-thin, lightweight antennas onto the transceiver, for creating sharp, high-gain directional beams. We formulate a user sum-rate maximization problem for our RHS-RIS-based hybrid beamformer. Explicitly, we jointly design the digital, holographic, and passive beamformers for maximizing the sum-rate of all user equipment (UE). To tackle the resultant nonconvex optimization problem, we propose an alternating maximization (AM) framework for decoupling and iteratively solving the subproblems involved. Specifically, we employ the zero-forcing criterion for the digital beamformer, leverage fractional programming to determine the radiation amplitudes of the RHS and utilize the Riemannian conjugate gradient algorithm for optimizing the RIS phase shift matrix of the passive beamformer. Our simulation results demonstrate that the proposed RHS-RIS-based hybrid beamformer outperforms its conventional counterpart operating without an RIS in multi-UE scenarios. The sum-rate improvement attained ranges from 8 bps/Hz to 13 bps/Hz for various transmit powers at the base station (BS) and at the UEs, which is significant. © 1972-2012 IEEE.

关键词： Beamforming

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Intelligent control algorithms for industrial automation systems

Intelligent control algorithms for industrial automation sys...

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2025 IEEE International Students' Conference on electrical, electronics and computer Science, SCEECS 2025

作者： Sarathkumar, D. Anil, A.R. Sivadasan, J. Jayakumar, T. Manivel, Murugesan Anandhakumar, C. Engineering Kongu Engineering College Department of Electrical and Electronics Tamil Nadu 638 060 India Sree Buddha College of Engineering Pattoor Department of Computer Science &engineering Kerala Alappuzha India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Sri Ramakrishna Institute of Technology Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641010 India

ISBN: (纸本)9798331529833

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem. © 2025 IEEE.

关键词： Genetic algorithms

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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High quality dehazed image and video based on enhanced multi-scale guided filtering dehazing technique

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Cluster Computing 2025年第5期28卷 1-24页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. El-Rabaie, El-Sayed M. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Haze is one of the most common challenges faced by outdoor imaging systems. Hazy images are characterized by low contrast and a white overlay, which make it difficult to produce high-quality results. To address this issue, researchers have developed various haze removal methods aimed at achieving superior image enhancement, efficient processing, and cost-effectiveness. This paper introduces an enhanced Multi-scale Guided Filtering (MGF) technique for dehazing. The proposed technique incorporates several key components: a fast dehazing method, homomorphic processing, and Contrast Limited Adaptive Histogram Equalization (CLAHE) as preprocessing steps, followed by a fuzzy logic approach to enhance contrast. The core of the technique is the MGF dehazing method, which is supported by fast dehazing and fuzzy logic proessing as enhancement strategies to reduce noise and improve the dynamic range of images. The MGF dehazing process gets the transmission map and atmospheric light at the coarsest level, and then guided filtering is employed iteratively to produce a smoothed transmission map, resulting in dehazed images free from artifacts. The haze model is applied to obtain the final dehazed image. This technique effectively combines two traditional approaches—fast dehazing and MGF dehazing—into a unified framework. The performance of the proposed technique is evaluated using visible and Near Infrared (NIR) video frames, as well as real hazy images. To demonstrate its efficacy, its results are compared with those of the standalone MGF dehazing method. Additionally, a comparative analysis is conducted with other dehazing techniques using both visible and NIR frames. Evaluation metrics include both referenced and non-referenced quantitative measures such as Peak Signal-to-Noise Ratio (PSNR), correlation (to measure similarity between hazy and dehazed images), entropy (quantifying the amount of information), Feature Similarity Index (FSIM), Chronic Feature Similarity index (FSI

关键词： Image enhancement

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A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues, and More

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 84-107页

作者： Hassija, Vikas Chamola, Vinay De, Rajdipta Das, Soham Chakrabarti, Arjab Sangwan, Kuldip Singh Pandey, Amit Deemed to be University School of Computer Engineering Bhubaneswar751024 India BITS-Pilani Department of Electrical and Electronics Engineering Pilani Campus Pilani333031 India BITS Pilani Department of Mechanical Engineering Pilani Campus Pilani333031 India Bennett University Computer Science Engineering and Technology Department Greater Noida201310 India

Digital Twins, sophisticated digital replicas of physical entities, have been gaining significant attention, especially after NASA's endorsement, and are poised to revolutionize numerous fields, such as medicine and construction. These advanced models offer dynamic, real-time simulations, leveraging enabling technologies, such as artificial intelligence, machine learning, IoT, cloud computing, and Big Data analytics to enhance their functionality and applicability. In the medical field, Digital Twins facilitate personalized treatment plans and predictive maintenance of medical equipment by simulating human organs with precision. In construction, they enable efficient building design and urban planning, optimizing resource use, and reducing costs through predictive maintenance. Startups are innovatively employing Digital Twins in various sectors, from smart cities - where they optimize traffic flow, energy consumption, and waste management - to industrial machinery, ensuring predictive maintenance and minimizing downtime. This survey delves into the diverse use cases, market potential, and challenges of Digital Twins, such as data security and interoperability, while emphasizing their transformative impact on industries. The future prospects are promising, with continuous advancements in AI, ML, IoT, and cloud computing driving further expansion and application of Digital Twin technologies. © 2024 IEEE.

关键词： Predictive maintenance

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Safe and Reliable Live-Streaming Traffic Monitoring System using OpenCV and Yolov8

Safe and Reliable Live-Streaming Traffic Monitoring System u...

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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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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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