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2024 IEEE Photonics Conference, IPC 2024

作者： Zhang, Xuguang Zhou, Zixuan Guo, Yijun Zhuang, Minxue Jin, Warren Shen, Bitao Chen, Yujun Huang, Jiahui Hu, Weiwei Shu, Haowen Bowers, John E. Wang, Xingjun Chang, Lin University of California Santa Barbara Department of Electrical and Computer Engineering Santa BarbaraCA United States Peking University State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Beijing China Peking University Frontiers Science Center for Nano-Optoelectronics Beijing China

ISBN: (纸本)9798350361957

We demonstrate a high-coherence parallel integrated light source utilizing a self-injection locked microcomb and injection locking amplification. Our strategy exhibits a record high on-chip gain of 60 dB, nearly 0 dB noise factor, which enable more than 30 highly coherent channels in integrated photonics whose linewidths down to 10 Hz, power exceeding 20 dBm per line and a wall plug efficiency of 19%. © 2024 IEEE.

关键词： Photonic integrated circuits

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Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images 8th

Compressed VGGNet for Automatic COVID-19 Disease Detection f...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Vinothini, R. Niranjana, G. Dhamanti, Inge Lailiyah, Syifa’ul Yakub, Fitri Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur 603203 India Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Faculty of Health Medicine and Life Sciences Universitas Airlangga East Java Indonesia Electronic System Engineering Department Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783031736162

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics. © IFIP International Federation for Information Processing 2025.

关键词： COVID-19

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 24

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent systems

作者： Xiaoqiang Wu Qingling Zhu Qiuzhen Lin Weineng Chen Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798400704864

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method.

关键词： evolutionary algorithm evolutionary reinforcement learning reinforcement learning

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A Scalable Solution for Pneumonia Diagnosis: Transfer Learning for Chest X-ray Analysis 7

A Scalable Solution for Pneumonia Diagnosis: Transfer Learni...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Haque, Rezaul Sultana, Shayma Prasad, Chandan Hasan, Sayed Fardin, Hasib Sakib, Abdullah Al Malik, Neeru Kaushik, Keshav East West University Department of Cse Dhaka Bangladesh International American University Department of Management Information System United States Pimpri Chinchwad University Department of Computer Science and Engineering Maharashtra Pune India Westcliff University Department of Engineering Management United States Westcliff University Department of Information Technology United States Pimpri Chinchwad University School of Engineering & Technology Maharashtra Pune India Amity University Amity School of Engineering and Technology Punjab India

ISBN: (纸本)9798350350067

The high death rate and rapid spread of pneumonia make it a serious global health concern. The precision of traditional diagnostic techniques is often compromised, particularly in resource-limited settings. Existing studies have predominantly focused on either binary or multi-class classification, often relying on limited datasets, which affects the generalizability and robustness of models. Additionally, the interpretability and transparency of deep learning models remain challenging in clinical environments. Addressing these challenges is crucial for advancing diagnostic methodologies to combat pneumonia. This study explores the use of transfer learning to improve the reliability of pneumonia classification using chest X-ray images. An automated classification system was developed, utilizing a combination of image preprocessing techniques, feature extraction with GLRLM, and various machine learning and transfer learning algorithms. Two datasets were employed: the GWCMCx dataset with 5856 chest X-rays and another dataset comprising 9207 chest X-rays. The system was designed for both binary and multi-class classification scenarios. CapsuleNet emerged as the most effective model, achieving an accuracy of 99.02% in binary classification and 90.43% in multi-class classification, significantly outperforming previous studies. Additionally, a user-friendly web application was developed to facilitate the integration of the classification system into clinical workflows, accelerating clinical decision-making. This study demonstrates the potential of transfer learning to automate and improve pneumonia diagnosis, offering a scalable solution that can alleviate radiologists' workload and improve patient outcomes. © 2024 IEEE.

关键词： Transfer learning

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Trust-based Authentication Aided Blockchain for Distributed Learning in UAV Swarms: Challenges and Solutions

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IEEE Network 2024年

作者： He, Daojing Song, Yuxiao Dai, Minghui Chan, Sammy Chen, Lei Guizani, Mohsen School Of Computer Science And Technology Shenzhen China Donghua University School Of Computer Science And Technology Shanghai China City University Of Hong Kong Department Of Electrical Engineering Hong Kong Hong Kong GuangDong Engineering Technology Research Center Of Cryptographic Product And System Evaluation Shenzhen China United Arab Emirates

The remarkable characteristics of unmanned aerial vehicles (UAVs), such as high flexibility and real-time data acquisition, make them a promising platform for providing services and applications like data collection and aerial computing. Furthermore, by integrating UAVs with decentralized learning, emerging machine learning (ML)-assisted services (e.g., urban sensing and natural resource exploration) can be enhanced. However, the limited computation and communication resources of UAVs and the potential security risks pose challenges to the reliability and security for distributed learning in UAV swarms. This necessitates suitable learning policy design and continuous supervision throughout the process of decentralized learning. In this article, motivated by the merits of blockchain, we propose a trust-based authentication aided blockchain for distributed learning in UAV swarms, along with a corresponding layered architecture to ensure effectiveness and security. This architecture includes a perception layer, an aerial layer, and a blockchain layer. Moreover, the trust values of UAVs are derived from their behaviors during the decentralized learning process. These values, combined with smart contracts, enable dynamic and robust authentication for UAV swarms. Based on this framework, we present its advantages and exemplified applications. Moreover, the potential challenges related to security and efficiency when applying the proposed framework in wireless networks are also discussed. Additionally, we present a case study on trust-based authentication aided blockchain for distributed learning in UAV swarms. This includes a detailed procedure and solutions to the associated challenges, demonstrating its performance in terms of learning efficiency and security. We finally discuss some open research directions regarding the blockchain-assisted decentralized learning for UAV swarms. © 1986-2012 IEEE.

关键词： Blockchain

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High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing

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Light(science & Applications) 2025年第1期14卷 227-237页

作者： Xiangyan Meng Nuannuan Shi Guojie Zhang Junshen Li Ye Jin Shiyou Sun Yichen Shen Wei Li Ninghua Zhu Ming Li Key Laboratory of Optoelectronic Materials and Devices Institute of SemiconductorsChinese Academy of SciencesBeijing100083China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of SciencesBeijing100049China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing100049China China Academy of Space Technology(Xi’an) Xi’anShaanxi710100China WeChat Pay Lab 33 Shenzhen Tencent Computer System Co.Ltd.Shenzhen518054China Lightelligence Group Hangzhou311121China

The burgeoning volume of parameters in artificial neural network models has posed substantial challenges to conventional tensor computing *** from the available optical multidimensional information entropy,optical intelligent computing is used as an alternative solution to address the emerging challenges of electrical *** limitations,in terms of device size and photonic integration scale,have hindered the performance of optical ***,an ultrahigh computing density optical tensor processing unit(OTPU),which is grounded in an individual microring resonator(MRR),is introduced to respond to these *** the independent tuning of multiwavelength lasers,the operational capabilities of an MRR are orchestrated,culminating in the formation of an optical tensor *** design facilitates the execution of tensor convolution operations via the lightwave and microwave multidomain hybrid multiplexing in terms of the time,wavelength,and frequency of *** experimental results for the MRR-based OTPU show an extraordinary computing density of 34.04 TOPS/***,the achieved accuracy rate in recognizing MNIST handwritten digits was 96.41%.These outcomes signify a significant advancement toward the realization of high-performance optical tensor processing chips.

关键词： tuning resonator tensor

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Terabit Ultra-high-speed NFDM Transmission over G.654E Fiber with 103Gbaud Probabilistic Shaped 64QAM Signals

Terabit Ultra-high-speed NFDM Transmission over G.654E Fiber...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chen, Xinyu Fang, Xiansong Cai, Xiang Zhou, Lingjun Li, Chao He, Zhixue Zhang, Fan State Key Laboratory of Advanced Optical Communication System and Networks Frontiers Science Center for Nano-optoelectronics School of Electronics Peking University Beijing100871 China School of Electronic and Computer Engineering Peking University Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China

We experimentally demonstrate a record ultra-high-speed 1.004 Tb/s dual-polarization NFDM transmission system over 300 km G.654E fiber with probabilistic shaped 64QAM signals, achieving a spectral efficiency at 7.65 b... 详细信息

ISBN: (纸本)9781957171258

关键词： Quadrature amplitude modulation

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Frequency-learning adversarial networks based on transfer learning for cross-scenario signal modulation classification

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Frontiers of Information Technology and Electronic engineering 2025年第5期26卷 816-832页

作者： Ma, Qinyan Xiao, Jing Shao, Zeqi Zhang, Duona Wang, Yufeng Ding, Wenrui School of Electronic and Information Engineering Beihang University Beijing100191 China School of Artificial Intelligence and Computer Science North China University of Technology Beijing100144 China Institute of Unmanned System Beihang University Beijing100191 China

Automatic modulation classification (AMC) serves a challenging yet crucial role in wireless communications. Despite deep learning-based approaches being widely used in signal processing, they are challenged by signal distribution variations, especially in various channel conditions. In this paper, we introduce an adversarial transfer framework named frequency-learning adversarial networks (FLANs) based on transfer learning for cross-scenario signal classification. This method uses the stability in the frequency spectrum by introducing a frequency adaptation (FA) technique to incorporate target channel information into source-domain signals. To address the unpredictable interference in the channel, a fitting channel adaptation (FCA) module is used to reduce the difference between the source and target domains caused by variations in the channel environment. Experimental results illustrate that FLANs outperforms state-of-the-art transfer approaches, demonstrating an improved top-1 classification accuracy by about 5.2 percentage points in high signal-to-noise ratio (SNR) scenes on a cross-scenario real collected dataset CSRC2023. © Zhejiang University Press 2025.

关键词： Signal modulation

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A Transient Switching Modeling Method for IGBT Based on Transfer Learning

A Transient Switching Modeling Method for IGBT Based on Tran...

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Data science and computer Application (ICDSCA), IEEE International Conference on

作者： Anning Yu Hao Xiang Linmei Chen Hang Wu Bo Zhou Liao Zhang School of Computer Science and Engineering (School of Artificial Intelligence) Chongqing University of Science & Technology Chongqing City China Transmission System Research Institute Chongqing Tsingshan Industrial Chongqing City China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

This paper proposes an IGBT transient switching modeling approach based on transfer learning, designed to enhance modeling efficiency and accuracy while reducing reliance on extensive data and complex parameter extraction. The study uses two IGBT modules with similar specifications as case examples. Data under various operating conditions were collected using the industrial simulation tool L Tspice, resulting in a source dataset and a target dataset, with the target dataset comprising only 13.43% of the size of the source dataset. Initially, a Multi-Layer Perceptron (MLP) neural network is pre-trained on the source dataset to capture the fundamental switching behaviors of the IGBTs. Subsequently, the pre-trained model is fine-tuned through transfer learning to adapt it to the specific target IGBT module using the target dataset. The model's performance is evaluated using the relative root mean square error (RMSE). The results demonstrate that this method can accurately replicate IGBT transient switching characteristics with limited data, offering an efficient and robust solution for IGBT transient modeling in power electronic systems.

关键词： Insulated gate bipolar transistors Adaptation models Accuracy Computational modeling Transfer learning Neural networks Switches Data models Transient analysis Root mean square

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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