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2025 IEEE International Conference on Computational, Communication and information technology, ICCCIT 2025

作者： Jain, Upma Boob, Nandini Shirish Anandhi, R.J. Sehgal, Archana Bhavana, Munugapati Kumar, Yogendra Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India

ISBN: (纸本)9798331512965

When attempting to make underwater images better, there are a lot of obstacles to overcome, including water's inherent color distortion, inadequate visibility, and light absorption and dispersion noise. The difficulties in accurately analyzing images caused by these problems apply to marine research, underwater robots, and environmental monitoring are only a few areas which are impacted. To address the limitations of existing picture enhancement techniques, this paper proposes a new hybrid deep learning CNN + GAN model for enhancing underwater photographs. The hybrid model extracts features with GANs and noise reduces with CNNs to produce better photos with better clarity and color correction. I train and validate the model using an underwater picture dataset and it defeats the state of the art approaches on visibility, color correctness, contrast and contrast. In quantitative and qualitative assessments our hybrid deep learning method yields superior generalization in different undersea environments relative to traditional methods. A suggested approach can potentially improve the underwater photography, marine setting data collecting with more precision, as well as better underwater exploration. This work now provides confidence in research progress into autonomous underwater exploration systems and underwater image processing. © 2025 IEEE.

关键词： Convolutional neural networks

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Scene Parsing via Tree Structure Enhancement Lightweight Network

Scene Parsing via Tree Structure Enhancement Lightweight Net...

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2022 IEEE International Conference on Intelligent Education and Intelligent Research, IEIR 2022

作者： Huang, Wenxin Liu, Wenxuan Jia, Xuemei Hubei University School of Computer Science and Information Engineering Wuhan China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Wuhan University School of Computer Wuhan China

ISBN: (纸本)9798350345629

Scene parsing is a hot topic in the field of computer vision communities. It has extensive applications in visual perception e.g. education system, human-object robots, etc. However, there exists a huge size difference among objects in the scene image because of the diversity of objects and the influence of observation distance and other factors. How to better solve the varying scale problem has become a challenging problem in scene parsing. Thus, a tree-structure is proposed to handle the varying scale problem, where the feature maps of different levels are gradually nested and connected, which strengthens the connection between multiple feature maps, and captures more representative information. For real-time, we propose a framework named tree structure enhancement lightweight network (TSELight), which introduces the depth-wise separable dilated convolution (DSDC) into the tree structure and decomposes the middle nodes in the tree structure along the channel direction, thus improving the efficiency. Experimental results demonstrate that our TSELight architecture outperforms state-of-the-art methods on Cityscapes dataset, and provides consistent improvements on the real-time scene parsing performance. © 2022 IEEE.

关键词： computer vision

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Algorithm-based Study on Transformation Combination for Carry-free Modified Signed Digit(MSD) Addition 10

Algorithm-based Study on Transformation Combination for Carr...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Qi, Baofeng Sun, Shaojiang Tong, Yihui Zhang, Jie Wang, Zhehe Wang, Xianchao Fuyang Normal University School of Computer and Information Engineering Anhui Fuyang China Fuyang Normal University School of Mathematics and Statistics Anhui Fuyang China Hainan Tropical Ocean University School of Computer Science and Technology Hainan Sanya China

ISBN: (纸本)9798350312461

Addition is the most basic operation in arithmetic. And the binary number system is used to implement the adder in modern electronic computer. The inherent properties of binary make it difficult to implement carry-free addition. And the redundancy of Modified Signed Digit(MSD) number system has been applied to implementing carry-free addition by use of four logic transformations. This paper explores in depth the reason why the MSD number system can be used to implement carry-free addition and obtain 16 pairs of transformation which can be used to implement MSD addition. Moreover, we obtain all transformation combinations for three-step MSD addition by designing relevant algorithms. Meanwhile, we find the test data set with minimum number of trits for MSD addition. Finally, to improve computational efficiency, we explore the optimal transformation combinations for implementation of MSD adder. And we find that there are four optimal transformation combinations, i.e. (5, 3, 0), (5, 3, 1), (10,2,0), and (10, 2, 4) which all use the same number of basic operating units(BOUs). © 2023 IEEE.

关键词： Adders

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Pareto Optimization for Fair Subset Selection: A Case Study on Personalized Recommendation

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IEEE Transactions on Evolutionary Computation 2024年

作者： Zhang, Lei Wang, Zhanpeng Yang, Haipeng Sun, Xiang Cheng, Fan Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Hefei230039 China

Subset selection is a fundamental problem across a wide range of applications. In this study, we explore scenarios where the variables within the original dataset are divided into distinct groups. Subsequently, we investigate an optimization problem that includes extra fairness constraints (i.e., partition matroid constraints), restricting the selection of a specified number of variables from each group, which is known as the fair subset selection (FSS) problem. Firstly, for the case where the existing Pareto optimization algorithms do not have the ability to well handle the fairness constraints, due to they do not consider fairness constraints in the process of solutions generation. In this paper, a Pareto Optimization algorithm named POFSS is proposed for FSS, by introducing a designed fairness balance flip operator. Also, we prove that POFSS has the approximation ability of max{α/l(1-e-γ),γ/k,1-erk/k} in polynomial time when l © 1997-2012 IEEE.

关键词： Optimization algorithms

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Rethinking How to Capture Long-range Dependency in 3D Object Detection

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wang, Haoyu Wang, Fasheng Wang, Mengyin Sun, Fuming Li, Haojie Dalian Minzu University School of Information and Communication Engineering Dalian116600 China Shandong University of Science and Technology School of Computer Science and Engineering Qingdao266590 China

LiDAR-based 3D object detection is essential for autonomous driving. Existing high-performance 3D object detectors usually design complex structures in the 3D backbone to capture long-range dependencies among features. However, introducing these complex structures into the 3D backbone significantly increases computational cost and inference latency, limiting the efficiency and feasibility of detectors in practical applications. In this work, we rethink the long-range dependency capturing problem from a new perspective, that is transferring this task from 3D backbone to 2D feature space. To accomplish this goal, we propose a Long-Range Dense Feature Capture Network (LDFCNet). LDFCNet retains the basic structure of the 3D backbone to extract preliminary 3D features but shifts the complex long-range dependency capturing task to be processed on a 2D dense feature map, thereby enhancing the detection performance while reducing the computational cost. Importantly, a robust 2D dense feature capture (2D-DFC) backbone is devised to effectively and efficiently capture the long-range dependencies. In addition, we introduce a re-parameterization technique to decouple the training and inference of the 2D backbone, further reducing inference latency. We conduct extensive experiments on the Waymo Open and nuScenes datasets and the experimental results show that LDFCNet demonstrates competitive performance. Notably, LDFCNet is 1.5× faster than the state-of-the-art hybrid detector HEDNet and 2.1× faster than the transformer-based detector DSVT. Codes and results are released on https://***/asd291614761/LDFCNet. © 1991-2012 IEEE.

关键词： Parameterization

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A robust multi-objective optimization algorithm for accurate parameter estimation for solar cell models

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Soft Computing 2024年第19期28卷 11265-11277页

作者： Singla, Manish Kumar Gupta, Jyoti Alsharif, Mohammed H. Kim, Mun-Kyeom Aljaidi, Mohammad Safaraliev, Murodbek Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering & ampTechnology Chitkara University Punjab India Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology K. R. Mangalam University Gurgaon Haryana122003 India Department of Electrical Engineering College of Electronics and Information Engineering Sejong University 209 Neungdong-Ro Gwangjin-Gu Seoul05006 Korea Republic of School of Energy System Engineering Chung-Ang University 84 Heukseok-Ro Dongjak-Gu Seoul06974 Korea Republic of Department of Computer Science Zarqa University Zarqa13110 Jordan Department of Automated Electrical Systems Ural Federal University Yekaterinburg620002 Russia

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality. © The Author(s) 2024.

关键词： Solar cells

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Exploring Quantum Key Distribution for Secure Communication: A Comprehensive Survey

Exploring Quantum Key Distribution for Secure Communication:...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Krishna, Marrapu Hari Juttiga, Alekhya Shobharani, Meesala Deepa, D. Anilkumar, Chunduru Sarabu, Ashok Gmr Institute of Technology Department of Information Technology Ap India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Shamshabad TS Hyderabad India Reva University School of Computer Science and Engineering Karnataka Bangalore India Kongu Engineering College Department of Computer Science and Engineering Perundurai Tamilnadu Erode India Bvrit Hyderabad College of Engineering for Women Department of Cse Hyderabad India

ISBN: (纸本)9798350393378

Modern-day secure communication is made possible via cryptography. Modern cryptographic algorithms are based on the process of factoring large integers into their primes, as they are intractable. But the cryptography we use nowadays is vulnerable to technological advances in computing power like quantum computing and evolution in math to quickly reverse one-way functions like factorization of large integers. Incorporating quantum physics concepts into cryptography is the answer, which results in an assessment of quantum cryptography. A unique type of cryptography known as quantum cryptography makes advantage of quantum mechanics to provide complete protection against the transmitted message. Quantum Key Distribution (QKD), a random binary key distribution used in quantum cryptography, enables communication participants to recognize unauthorized listeners. Quantum Key Distribution (QKD) is likely the most advanced quantum technology currently accessible with full-stack systems already in use. This project's goal is to develop secure and encrypted communication between the parties with the help of a web application using the BB84 protocol. © 2023 IEEE.

关键词： Quantum cryptography

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Prediction of diabetic patients in Iraq using binary dragonfly algorithm with long-short term memory neural network

AIMS Electronics and Electrical Engineering

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AIMS Electronics and Electrical engineering 2023年第3期7卷 217-230页

作者： Alhakeem, Zaineb M. Hakim, Heba Hasan, Ola A. Laghari, Asif Ali Jumani, Awais Khan Jasm, Mohammed Nabil Chemical Engineering and Oil Refining Department Oil and Gas Engineering College Basrah University for Oil and Gas Basrah Iraq Computer Engineering Department Engineering College University of Basrah Basrah Iraq Real Estate Bank of Iraq Basrah Iraq Software college Shenyang Normal University Shenyang China School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou China Department of Computer Science ILMA University Karachi Sindh Pakistan Basra Oil Company Basrah Iraq Computer Technology Engineering Department Iraq University College Basrah Iraq

Over the past 20 years, there has been a surge of diabetes cases in Iraq. Blood tests administered in the absence of professional medical judgment have allowed for the early detection of diabetes, which will fasten disease detection and lower medical costs. This work focuses on the use of a Long-Short Term Memory (LSTM) neural network for diabetes classification in Iraq. Some medical tests and body features were used as classification features. The most relevant features were selected using the Binary Dragon Fly Algorithm (BDA) Binary version of the selection method because the features either selected or not. To reduce the number of features that are used in prediction,features without effects will be eliminated. This effects the classification accuracy, which is very important in both the computation time of the method and the cost of medical test that the individual will take during annual check *** work found out that among 11 features, only five features are most relevant to the disease. These features provide a classification accuracy up to 98% among three classes: diabetic, non diabetic and pre-diabetic. © 2023 the Author(s)

关键词： Long short-term memory

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Intra-cluster aggregation aware routing for distributed training in wireless sensor networks

Intra-cluster aggregation aware routing for distributed trai...

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作者： Chen, Zhaohong Long, Xin Chen, Long Wu, Yalan Wu, Jigang Liu, Shuangyin School of Computer Science and Technology Guangdong University of Technology Guangzhou China Guangzhou Key Laboratory of Agricultural Products Quality Safety Traceability Information Technology Zhongkai University of Agriculture and Engineering Guangzhou China

In wireless sensor networks (WSNs), wireless sensor nodes can be equipped with deep neural network accelerators to deal with the computation challenges in distributed training. However, the communication overhead of distributed training and the limited battery capacity of sensor nodes still impedes the broad deployment of distributed training applications. This article investigates the distributed training in WSNs by formulating an aggregation-aware routing problem into a non-linear integer programming problem. The objective of the formulated problem is to reduce the training time using data aggregation-aware routing under the constraints of memory size and energy cost. Meanwhile, the NP-Hardness of the formulated problem is proved in this article. Then, an intra-cluster aggregation-aware routing algorithm is proposed. The proposed algorithm accelerates the transmission of the data packet by integrating the K-Means clustering and shortest path routing to choose the aggregators and the route paths. Extensive experiments demonstrate that the proposed algorithm outperforms two classical clustering routing algorithms UC-LEACH and K-Means by 29% and 37% in terms of average training time, and reducing the energy consumption by 21% and 15%, respectively. © 2021 John Wiley & Sons, Ltd.

关键词： Routing algorithms

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Embedded Machine Learning with Tensorflow Lite Micro

Embedded Machine Learning with Tensorflow Lite Micro

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Mamtha, G.N. Sharma, Sunil Sing, Navjot Department of Electronics and Communication Engineering Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineeing & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

In this review, we examined what different nanofillers meant for the electronic and warm properties of epoxy microcomposites. Specifically, carbon nanotubes (CNTs), the nanoplatelets of graphene (GNPs), and the nitride boron nanosheets (BNNSs) were examined. Warm conductivity expanded with the consideration of CNTs and GNPs, with BNNSs showing the best upgrade at lower pressing focuses.. In a similar vein, adding nanofillers increased the composites' electrical conductivity, with GNPs showing the most rise. SEM pictures showed good interfacial adhesion due to well-dispersed nanofillers that were uniformly distributed in the epoxy matrix. This work emphasizes that adding nanofillers to epoxy microcomposites is a useful way to improve their thermal and electrical characteristics. This method has the potential to be used in several areas, including the automobile, aircraft, and gadget industries. To better choose and balance the amount of nanofillers suited for certain uses, further research is necessary. © 2023 IEEE.

关键词： Combination Channels Dielectric Spectroscopy and Breakdown Strength Electrical Execution Epoxy Miniature Composites Nanofillers Warm Execution

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