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Advanced Electromagnetics 2025年第1期14卷 59-64页

作者： Karami, Pouya Yahya, Salah I. Palash, Behnam Chaudhary, Muhammad Akmal Assaad, Maher Parandin, Fariborz Roshani, Saeed Hazzazi, Fawwaz Roshani, Sobhan Department of Electrical Engineering Kermanshah Branch Islamic Azad University Kermanshah Iran Department of Communication and Computer Engineering Cihan University-Erbil Erbil44001 Iraq Department of Software Engineering Faculty of Engineering Koya University KOY45 Koya Iraq Department of Electrical and Computer Engineering College of Engineering and Information Technology Ajman University 346 Ajman United Arab Emirates Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj11492 Saudi Arabia

In this paper, design and modeling of an all-optical 2×1 multiplexer based on 2D photonic crystals and artificial neural networks (ANNs) are presented. The proposed structure aims to maximize the difference between the output powers in logical states, which is critical for enhancing the system ability to distinguish between these states. In this study, an ANN model is employed to accurately predict the normalized output power of the designed photonic crystal multiplexer, providing a time-efficient alternative to conventional simulation methods for analyzing multiplexer behavior across various logical states. The results demonstrate significant improvements in signal separation and overall performance compared to previous works. Additionally, a detailed comparison of the normalized output power for different logic states is provided, highlighting the advantages of the proposed design. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Silicon photonics

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An effective fault localization approach for Verilog based on enhanced contexts

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Frontiers of computer Science 2024年第5期18卷 223-225页

作者： Zhuo ZHANG Ya LI Lei XIA Jianxin XUE Jiang WU Xiaoguang MAO School of Computer Science and Engineering Xi'an University of TechnologyXi'an 710048China Ningbo Artificial Intelligence Institute Shanghai Jiao Tong UniversityNingbo 315000China No.83 Army Joint and Truma Disease Treatment Centre of PLA Xinxiang 453000China Department of Software Engineering Shanghai Second Polytechnic UniversityShanghai 201209China College of Computer National University of Defense TechnologyChangsha 410073China

New computer architecture innovationswith diverse functionalities and comprehensive features continue to emerge incessantly,resulting in a rising trend of incorporating a larger number of circuit devices into these products[1].In the case of a sophisticated and expansive integrated circuit chip,the presence of defective or malfunctioning components can significantly impact the overall performance of the *** situation may even result in costly repercussions.

关键词： Verilog computer circuit.

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Energy-Efficient Shop Scheduling Using Space-Cooperation Multi-Objective Optimization

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年第3期10卷 601-615页

作者： Ding, Jiepin Xia, Jun Yang, Yaning Zhou, Junlong Chen, Mingsong Li, Keqin East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai200062 China Ningxia Normal University School of Physics and Electronic Information Engineering Guyuan756500 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China State University of New York Department of Computer Science New PaltzNY12561 United States

Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521 s to 475 s. © 2016 IEEE.

关键词： Job shop scheduling

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Constructing Rough Set Based Unbalanced Binary Tree for Feature Selection

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Chinese Journal of Electronics 2025年第3期23卷 474-479页

作者： Zhengcai Lu Zheng Qin Qiao Jin Shengnan Li Department of Computer Science and Technology Tsinghua University Beijing China School of Software Tsinghua University Beijing China

Feature selection is one of the challenging problems facing data analysis in areas such as pattern recognition, data mining, and decision support. Many rough set algorithms for feature selection have been developed, most of which are essentially dependent on the definite information contained within the lower approximation. This paper proposes a novel approach, called Unbalanced binary tree based feature selection (UBT-FS), which utilizes the indefinite information contained within rough set boundary region for reduction. UBT-FS designs the underlying mechanism for obtaining the boundary region from the unbalanced binary tree and adopts the boundary region based significance for determining the optimal search path as well as the boundary region based evaluation criterion for identifying feature subsets. These allow UBT-FS to have considerable ability in finding an optimal or suboptimal reduct whilst simultaneously achieving obviously better computational efficiency than other available algorithms, which is also supported by the experimental results.

关键词： Data analysis Education Rough sets Binary trees Medical services Feature extraction Approximation algorithms Computational efficiency Data mining Time complexity

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Intelligent Sorting System 7

Intelligent Sorting System

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7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Mao, Yanghong Chen, Chuixin Pan, Zhengjun Guangzhou Institute of Science and Technology Department of Computer Science and Engineering Guangzhou China Institute of Guangzhou Department of Software Engineering Software Engineering Guangzhou China Department of Software Engineering Software Engineering Institute of Guangzhou Guangzhou China

ISBN: (纸本)9781665487115

In order to achieve the goal that the manipulator can automatically obtain any position within its working range, and can adjust the end actuator for grasping, a machine vision sorting system is designed. The system passes through image processing, target recognition and positioning, and finally realizes the sorting effect through trajectory planning. Aiming at the problem that the deep learning algorithm takes a long time, The traditional image processing algorithm and deep learning target detection algorithm are organically combined to reduce the use delay of image processing in the system. Because the training of deep learning model requires a large number of data sets, but the amount of data in manufacturing factories is small, and data labeling is time-consuming and laborious, YOLO v3 algorithm is used to improve the efficiency of training model under a small number of labeled data sets, SIFT algorithm is used to extract image features, and K-D tree algorithm is used to match image features. In the algorithm of position, the centroid is extracted as the positioning reference coordinate, the kinematics model of the manipulator is established. trajectory planning, straight-line and arc interpolation algorithms are used. The experimental results show that the system meets the design requirements. © 2022 IEEE.

关键词： Deep learning Training Trajectory planning Target recognition Object detection Feature extraction Manipulators

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Classifying Authentic and Fake Anger Based on High-Performance LSTM Networks Model, Fuzzy Logic Model and Fully Connected Neural Network 4

Classifying Authentic and Fake Anger Based on High-Performan...

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4th International Conference on Advances in Electrical Engineering and computer Applications, AEECA 2023

作者： Chen, Kangyu Peng, Zhiyuan The University of Hong Kong Department of Computer Science Hong Kong Nanjing University State Key Laboratory For Novel Software Technology Nanjing China

ISBN: (纸本)9798350308082

Understanding human's emotion is a vital task in the computer science and machine learning. People could easily distinguish the genuine and posed anger while it could be difficult for computers to judge the authenticity of anger. In this paper, apart from the fully connected Neural Networks built in the previous research, methods of LSTM (Long short-term memory) Networks and Fuzzy logic classification are also built for telling the posed angers from genuine ones using limited data. Then the paper compared the performance of these three popular classification methods. Carefully designed modifications have been made on these models to increase the performance. © 2023 IEEE.

关键词： Long short-term memory

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Research on Coding and Decoding Scheme for 5G Terminal Protocol Conformance Test Based on TTCN-3 6th

Research on Coding and Decoding Scheme for 5G Terminal Proto...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Jingyao, Cao Yadav, Amit Khan, Asif Ansar, Sharmin School of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India Department of Biological Sciences and Bioengineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9789819940707

Protocol conformance testing is an indispensable part of the marketization of commercial terminals. This paper elaborates and studies the architecture, test model, and design scheme of the 5G terminal protocol conformance testing system and proposes a 5G terminal protocol based on TTCN-3 Conformance test codec solution. The outcome of research shows that this solution has a positive role in promoting the industrialization of 5G. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： 5G mobile communication systems

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Person Re-Identification Based on Instance Segmentation and Pose Estimation 6

Person Re-Identification Based on Instance Segmentation and ...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Zhou, Yao Qin, Yusong Wang, Song Huang, Zengxi Zhang, Dazhi Xihua University School of Computer Science and Software Engineering Chengdu China Xihua University School of Computer and Software Engineering and the Sichuan Xihua Jiaotong Forensics Center Chengdu China Sichuan Police College Criminal Science and Technology Department Luzhou China

ISBN: (纸本)9798350325485

Person Re-Identification (ReID) is a crucial task in computer vision that aims to match persons captured from non-overlapping camera views. In this paper, to alleviate the impacts of background and occlusion, we propose to use instance segmentation and pose estimation methods to create masks for global feature extraction. Furthermore, we divide the pedestrian images into three regions according to pedestrian keypoints, trying to eliminate the alignment errors. This part-based matching strategy also helps to address the occlusion issue. Overall, we construct a deep learning network with three branches, including two global branches and a part-based branch. The two global branches extract global features using segmentation-based mask and the mask derived from pedestrian keypoint heatmaps, respectively. In the end, a weighted fusion strategy is used to combine the global scores and part-based scores for final classification. This network enables us to acquire robust global feature of pedestrians by excluding background and occlusion, and simultaneously address the alignment errors to some extent. Experimental results on these three widely used datasets demonstrate the effectiveness of our method: Specifically, it achieves 64.5% rank-1 accuracy and 54.3% mAP on Occluded-Duke, and 94.4% and 87.1% rank-1 accuracies on Market-1501 and DukeMTMC-reID, respectively. © 2023 IEEE.

关键词： Deep learning

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Deep Learning-Based Approaches for Accurate Brain Tumor Detection in MRI Images

Deep Learning-Based Approaches for Accurate Brain Tumor Dete...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Noori, Mohammed Ahmed, Mohsin Ibrahim, Amer Saeed, Ali Khishe, Mohammad Mahfuri, Mahmoud Al-ma'Moon University College Baghdad Iraq College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates Northern Technical University Computer Center Ninevah Iraq Yuan Ze University Innovation Center for Artificial Intelligence Applications Taiwan Jadara Research Center Jadara University Jordan International University for Science and Technology Computer and Information Technology Department Syria

ISBN: (纸本)9798350369106

Brain tumor classification is considered one of the major tasks in medical image analysis, where correct and timely diagnosis could be achieved to serve as the key to effective treatment. This research paper proposes a deep learning-based approach for automatic classification of brain tumors from MRI images by fine tuning the ResNet50V2 CNN model. The dataset is made up of four classes, namely: Glioma Tumor, Meningioma Tumor, No Tumor, and Pituitary Tumor, totaling 2,844 images. We have done some strategy data augmentation and class weighting in order to prevent class imbalance, which ensures good performance in all tumor classes. Pre-processing and augmentation of the input images are done via rotation, shifting, zooming, and flipping. It yielded 95.29% on the validation set, with the major highlights of performance metrics for the minority class being class precision equal to 0.97 and class recall equal to 0.92, hence proving the efficiency of performed class balancing. The range of F1-scores, from 0.93 to 0.97, means fantastic predictive capability across all tumor types. Other techniques were also used to make this model optimal, such as early stopping, learning rate reduction, and checkpointing. The high performance of this model shows great promise in helping clinicians toward the right diagnosis of brain tumors in an effective way. Future efforts will involve incorporating larger datasets, exploring advanced augmentation techniques to enhance further model generalizability. Model explainability tools such as Grad-CAM will be used to extract insight into the model's decision-making process. This enhances the clinical interpretability of the results. © 2024 IEEE.

关键词： Convolutional neural networks

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Contextual defeasible reasoning framework for heterogeneous knowledge sources

Contextual defeasible reasoning framework for heterogeneous ...

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作者： Mahfooz ul Haque, Hafiz Muhammad Akhtar, Salwa Uddin, Ijaz Department of Software Engineering University of Lahore Lahore Pakistan Department of Computer Science University of Lahore Lahore Pakistan Department of Computer Science City University of Science and Information Technology Peshawar Pakistan University of Lahore Lahore Pakistan

Recent years have witnessed the rapid advances of smart computing paradigms in a ubiquitous environment. These paradigms make human life much easier, comfortable, secure and hassle free. In a smart computing environment, it is a fact that human users interact with the systems dynamically with or without human intervention using different modalities. The core emphasize is given on the intelligent systems that run in a highly decentralized environment with different communication mechanism. Literature highlighted numerous formalisms to bridge the communication modalities for different knowledge sources. Among others, Multi-context System (MCS) has been advocated as one of the most suitable formalism to interlink different contexts (domains) dynamically in the distributed environment. However, interaction of these knowledge sources sometime may produce inconsistent and conflicting results. In this work, we presents a contextual defeasible reasoning based multi-agent formalism to handle the inconsistency issues. This framework relies on the semantic knowledge sources which allow us to model context-aware non-monotonic reasoning agents to infer the desired goals using the extracted rules from the ontologies and handles inconsistencies using conflicting contextual information. We illustrate the validity and correctness of the proposed formalism using a simple case study of a smart healthcare system with the prototypal implementation of the system. © 2021 John Wiley & Sons Ltd.

关键词： Multi agent systems

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