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SSRN 2023年

作者： Zhang, Xinyang Bu, Chao Wang, Jinsong Key Laboratory of Computer Vision System of Ministry of Education School of Computer Science and Engineering Tianjin University of Technology China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China

As a new computing paradigm, Mobile Edge Computing (MEC) sinks resources to the network edge to support real-time re-sponses of services. Facing the demand for migrating services caused by the frequent movement of the mobile device, it is a great challenge to reduce the operation costs of migrating services with user requirements satisfied, especially for the continu-ous optimization of services deployed in hot spot areas. In order to address the above challenge, we propose the mechanism of dynamically migrating and optimally deploying services, which optimizes the latency and energy consumption of the service mi-gration. Firstly, we introduce Markov Decision Process (MDP) to model changes of deployment locations of the single-user ser-vice, and devise the approach to determine whether to migrate a service at the current network status based on the dueling Deep Q-Network (DQN). Secondly, we propose the service pre-migration decision approach to determine where to migrate a ser-vice by combining the Long Short-Term Memory (LSTM) network and the attention mechanism, so as to determine the future moving area of the user and perform the migration decision in advance with the impact of migration service on service quality considered. Finally, we devise the distance-weighted matching algorithm based on the fuzzy logic to further optimize the selec-tion of the most appropriate services to migrate in hotspot areas and the corresponding service migration regions, thus, the load pressure of the edge computing node caused by the imbalanced deployment density of service among multiple regions. Simula-tion results show that the proposed mechanism has significant improvements in overall operating costs compared with the cur-rent state of the art. © 2023, The Authors. All rights reserved.

关键词： Fuzzy logic

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Quantum noise of dark pulse microcombs

Quantum noise of dark pulse microcombs

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CLEO: QELS_Fundamental science, QELS 2022

作者： Lao, Chenghao Jin, Xing Chang, Lin Xie, Weiqiang Shu, Haowen Wang, Xingjun Bowers, John E. Yang, Qi-Fan State Key Laboratory for Artificial Microstructure and Mesoscopic Physics Frontiers Science Center for Nano-Optoelectronics School of Physics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China

We investigate the fundamental timing jitter of dark pulse microcombs generated in an AlGaAs microresonator, where the quantum-limited frequency noise of the 91-GHz repetition rate is measured to be 0.5 Hz2/Hz. ©... 详细信息

ISBN: (纸本)9781557528209

关键词： Aluminum gallium arsenide

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Universal Low Bit-Rate Speech Steganalysis Integrating Domain-specific and Domain-shared Knowledge

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Tian, Hui Qiu, Yiqin Li, Haizhou Zhang, Xinpeng Vasilakos, Athanasios V. National Huaqiao University College of Computer Science and Technology Xiamen361021 China Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen361021 China School of Data Science Chinese University of Hong Kong Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 119077 Singapore Fudan University School of Computer Science Shanghai200433 China University of Agder Center for AI Research Grimstad4879 Norway

Universal low bit-rate speech steganalysis is a cutting-edge research task addressing real-world application needs and has garnered significant attention recently. However, the existing methods are still inadequate in extracting available information from various steganographic domains and fail to deliver interpretable forensic results for specific steganographic domains containing embedded information. In view of this, we present a novel universal low bit-rate speech steganalysis approach that seamlessly combines domain-specific and domain-shared information, enabling comprehensive and effective speech steganography detection. This approach comprises two vital components: the Matching Identification Network (MIN) and the Content Alignment Network (CAN). The MIN incorporates three effective separable backbones for capturing informative domain-specific embeddings, inherently unveiling local and global dependencies. In this network, we also design a cross-domain matching module to establish correlations among steganographic domains, thereby enhancing detection performance through multi-domain collaboration and facilitating effective forensics for the embedded domains. Moreover, the CAN acquires more informative domain-shared embeddings by using a metric learning-based Siamese architecture to process pairs of naive and recompressed speech samples. Experimental results demonstrate that the presented method not only significantly surpasses the existing universal steganalysis methods, but also competes with or even surpasses dedicated steganalysis methods in certain cases. In addition, our method can provide accurate forensic results regarding the existence of hidden information within each steganographic domain without relevant supervisory information, marking a significant milestone in pursuit of speech steganalysis. The source code for this work will be publicly available on GitHub. © 2004-2012 IEEE.

关键词： Steganography

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Developing a practical model for noise in entangled photon detection

arXiv

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arXiv 2025年

作者： Truong, Taman Arenz, Christian Lukens, Joseph M. School of Electrical Computer and Energy Engineering and Research Technology Office Arizona State University TempeAZ85287 United States Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

We develop a comprehensive model for the effective two-photon density matrix produced by a parametric source of entangled photon pairs under a variety of detector configurations commonly seen in a laboratory setting: two and four photon number-resolving (PNR) and threshold detectors. We derive the probability of obtaining a single coincidence assuming Poisson-distributed photon pairs, non-unit detection efficiency, and dark counts;obtain the effective density matrix;and use this quantity to compute the fidelity of the generated quantum state. The 4 PNR case admits an analytic result valid for any combination of parameters, while all other cases leverage low-efficiency approximations to arrive at closed-form expressions. Interestingly, our model reveals appreciable fidelity improvements from four detectors as opposed to two yet minimal advantages for PNR over threshold detectors in the regimes explored. Overall, our work provides a valuable tool for the quantitative design of two-photon experiments under realistic nonidealities. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum entanglement

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Hypercomplex Generative Adversarial Networks for Lightweight Semantic Labeling 3rd

Hypercomplex Generative Adversarial Networks for Lightweigh...

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3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Sfikas, Giorgos Retsinas, George Gatos, Basilis Nikou, Christophoros Computational Intelligence Laboratory Institute of Informatics and Telecommunications National Center for Scientific Research "Demokritos" Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Surveying and Geoinformatics Engineering University of West Attica Athens Greece Department of Computer Science and Engineering University of Ioannina Ioannina Greece

ISBN: (纸本)9783031090363

Following recent advances on parameterized hypercomplex multiplication [21], we explore the usefulness of hypercomplex convolutions and deconvolutions in a document labeling task. We show that the proposed Hypercomplex Generative Adversarial Networks achieve excellent results while requiring significantly less independent parameters than real-valued models. © 2022, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Classification-Detection of Medical Images by Visualizing Hidden-Layer Features of a Deep Learning Approach 12th

Classification-Detection of Medical Images by Visualizing Hi...

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12th International Conference on computer engineering and Networks, CENet 2022

作者： Tong, Ziyu Deng, Xing Shao, Haijian Wang, Xia School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China School of Automation Key Laboratory of Measurement and Control for CSE Ministry of Education Southeast University Nanjing Jiangsu210096 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Deep learning algorithms, such as CNN, have been widely used on a variety of computer vision problems due to their powerful capacity to extract nonlinear features for medical picture analysis. However, due to the decreased identification rate for medical images, CNNs continue to fail to offer an interpretable depiction of the hidden-layers’ features. Furthermore, the enormous amount of redundant parameters from the convolutional to fully connected layers will surely add to the computational complexity. This work provides a set of fast deep learning architectures for visualizing and analyzing hidden-layer properties in medical images. The clustered feature map is used to simplify the model parameters and minimize redundancy parameters in order to meet the goal of a streamlined trade-off between model performance and architecture. The testing results on the medial images reveal that 41% of redundant channels may be removed while 100% of performance can be preserved. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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An Automated and Interpretable computer-Aided Approach for Skin Cancer Diagnosis Using Genetic Programming

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IEEE Transactions on Evolutionary Computation 2024年

作者： Yu, Kunjie Lian, Jintao Bi, Ying Liang, Jing Xue, Bing Zhang, Mengjie Zhengzhou University School of Electrical and Information Engineering Zhengzhou China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang China Longmen Laboratory Luoyang China Zhongyuan University of Technology School of Electronic and Information Zhengzhou China Henan Institute of Technology School of Electrical Engineering and Automation Xinxiang China Victoria University of Wellington School of Engineering and Computer Science Centre for Data Science and Artificial Intelligence Wellington New Zealand

Malignant melanoma is a very deadly form of skin cancer and early diagnosis can significantly reduce the mortality rate. Many computer-aided diagnosis (CAD) systems have been developed as second opinion diagnostic aids to assist dermatologists in diagnosing malignant melanoma. However, traditional CAD systems often require domain knowledge for feature extraction, while neural network-based CAD systems require specialized knowledge for designing network structures and often have poor interpretability. In this paper, we propose a new skin cancer CAD system based on genetic programming (GP) to automatically learn effective features for classification with strong interpretability. The approach can automatically evolve variable-length models to extract informative features for describing skin cancer images based on a relatively simple program structure, a new function set, and a terminal set. In addition, compared with other GP methods, this approach employs a newly proposed duplicate subtree removing mechanism, which can effectively prevent the duplication of features, thereby simplifying the model and enhancing its interpretability. The proposed approach has been examined on five real-world skin cancer classification tasks. The results suggest that the proposed approach achieves better performance than GP-based, neural network-based feature learning comparison methods and traditional comparison methods in most cases. Further analysis shows that the proposed approach has employed a smaller tree structure and can automatically evolve/learn models with potentially high interpretability. © 1997-2012 IEEE.

关键词： Genetic programming

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Near-field communications:characteristics,technologies,and engineering

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Frontiers of Information Technology & Electronic engineering 2024年第12期25卷 1580-1626页

作者： Yajun ZHAO Linglong DAI Jianhua ZHANG Ran JI Mengnan JIAN Hao XUE Hongkang YU Yunqi SUN Yu LU Zidong WU Zhuo XU Jinke LI Haiyang MIAO Zhiqiang YUAN Pan TANG Jiayu SHEN Tierui GONG Haixia LIU Jiaqi HAN Qiang FENG Zhi CHEN Lingxiang LI Gang YANG Yong ZENG Cunhua PAN Wang LIU Kangda ZHI Weidong HU Yuanwei LIU Xidong MU Chau YUEN Mérouane DEBBAH Chongwen HUANG Long LI Ping ZHANG State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China ZTE Corporation Beijing 100192China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310007China Key Laboratory of High Speed Circuit Design and EMC of Ministry of Education School of Electronic EngineeringXidian UniversityXi’an 710071China Collaborative Innovation Center of Information Sensing and Understanding Xidian UniversityXi’an 710071China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of ChinaChengdu 611731China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211111China School of Electrical Engineering and Computer Science Technical University of BerlinBerlin 10623Germany School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Centre for Wireless Innovation(CWI) School of ElectronicsElectrical Engineering and Computer ScienceQueen’s University BelfastBelfast BT39DTUK School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore KU 6G Research Center Khalifa University of Science and TechnologyAbu DhabiUnited Arab Emirates

Near-field technology is increasingly recognized due to its transformative potential in communication systems,establishing it as a critical enabler for sixth-generation(6G)telecommunication *** paper presents a comprehensive survey of recent advancements in near-field technology ***,we explore the near-field propagation fundamentals by detailing definitions,transmission characteristics,and performance ***,we investigate various near-field channel models—deterministic,stochastic,and electromagnetic information theory based models,and review the latest progress in near-field channel testing,highlighting practical performance and *** evolving channel models,traditional mechanisms such as channel estimation,beamtraining,and codebook design require redesign and optimization to align with near-field propagation *** then introduce innovative beam designs enabled by near-field technologies,focusing on non-diffractive beams(such as Bessel and Airy)and orbital angular momentum(OAM)beams,addressing both hardware architectures and signal processing frameworks,showcasing their revolutionary potential in near-field communication ***,we highlight progress in both engineering and standardization,covering the primary 6G spectrum allocation,enabling technologies for near-field propagation,and network deployment ***,we conclude by identifying promising future research directions for near-field technology development that could significantly impact system *** comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

关键词： 6G Near-field technology Channel model Codebook Non-diffractive beams Orbital angular momentum engineering and standardization

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Open-Set Recognition Model for Distributed Fiber-Optic Vibration Sensor Based on Hierarchical Attention Network with Openmax

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IEEE Sensors Journal 2025年

作者： Zhou, Zhengxian Xie, Tianchang Wang, Xiankun Qu, Jun Shi, Jianping Han, Yashuai Yang, Xinyan Hu, Xing Zhang, Dawei Zhuang, Songlin Anhui Normal University Anhui Province Key Laboratory for Control and Applications of Optoelectronic Information Materials Wuhu241002 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

Recent advancements in deep learning have improved the signal recognition capabilities of distributed fiber-optic vibration sensors. However, most existing research focuses on closed-set scenarios, assuming that all potential events are known and accounted for during training. In practical applications, the environmental complexity surrounding sensing fibers makes it impractical to anticipate and train on every intrusion event. To address this, we propose a novel Hierarchical Attention Network with Openmax (HANO) model that integrates a hierarchical attention mechanism with the Openmax algorithm. This model effectively extracts and emphasizes discriminative features at multiple levels, enhancing the recognition of both known and unknown events. Incorporating the Openmax layer enhances the classifier’s ability to manage unknown classes, thereby facilitating open-set recognition. To validate the effectiveness of the HANO model, we conducted experiments involving human intrusion events within a fenced area. The network parameters were optimized accordingly, and HANO’s performance was compared against the Swin Transformer, as well as models utilizing Softmax and Sigmoid layers. Additionally, we benchmarked it against other open-set recognition algorithms. The experimental results demonstrate that the HANO model achieves an overall recognition accuracy exceeding 95%, outperforming the comparative methods. These results demonstrate that the HANO model enhances the recognition of open-set events, indicating its strong potential for practical applications, particularly in scenarios requiring precise intrusion detection, such as security monitoring and vibration detection. © 2001-2012 IEEE.

关键词： Convolutional neural networks

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A Lightweight Attention Network for Camouflaged Object Fixation Prediction

A Lightweight Attention Network for Camouflaged Object Fixat...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Qingbo Wu Guanxing Wu Shengyong Chen Key Laboratory of Computer Vision and Systems (Ministry of Education) Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System (Ministry of Education) Tianjin University of Technology Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

This paper proposes a novel encoder-decoder architecture for camouflaged object fixation prediction (COFP), which builds on a large-kernel decomposition technique and depth-wise separable convolution embedded attention. Specifically, the encoder with the large-kernel decomposition adopts a Visual Attention Network (VAN) as the backbone to extract more accurate features, and the decoder predicts fixation maps from concealed objects using the attention module incorporating depth-wise separable convolution. Both designs aim to reduce the number of learnable parameters, making the model lighter and simultaneously maintaining the performance of COFP. In addition, we propose using the reverse Kullback-Leibler Divergence (KLDiv) as the loss function. Extensive experiments demonstrate that the proposed method performs well against SOTA approaches in quality and quantity.

关键词： Industrial electronics Visualization Accuracy Convolution Feature extraction Decoding

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