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Photonic integrated spiking neuron chip based on a self-pulsating DFB laser with a saturable absorber

Photonics Research 2023年第8期11卷 1382-1389页

作者： YUECHUN SHI SHUIYING XIANG XINGXING GUO YAHUI ZHANG HONGJI WANG DIANZHUANG ZHENG YUNA ZHANG YANAN HAN YONG ZHAO XIAOJUN ZHU XIANGFEI CHEN XUN LI YUE HAO Yongjiang Laboratory Ningbo 315202China State Key Laboratory of Integrated Service Networks State Key Discipline Laboratory of Wide Bandgap Semiconductor TechnologyXidian UniversityXi’an 710071China Key Laboratory of Intelligent Optical Sensing and Manipulation Ministry of EducationNational Laboratory of Solid State MicrostructuresCollege of Engineering and Applied SciencesInstitute of Optical Communication EngineeringNanjing UniversityNanjing 210023China School of Science Jiangnan UniversityWuxi 214122China School of Information Science and Technology Nantong UniversityNantong 226019China Department of Electrical and Computer Engineering McMaster UniversityHamiltonOantario L8S 4K1Canada

We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback(DFB)laser chip with an intracavity saturable absorber(SA).The DFB laser with an intracavity SA(DFBSA)contains a gain region and an SA *** gain region is designed and fabricated by the asymmetric equivalentπ-phase shift based on the reconstruction-equivalent-chirp *** properly injected current in the gain region and reversely biased voltage in the SA region,periodic self-pulsation was experimentally observed due to the Q-switching *** self-pulsation frequency increases with the increase of the bias current and is within the range of several *** the bias current is below the self-pulsation threshold,neuronlike spiking responses appear when external optical stimulus pulses are *** results show that the spike threshold,temporal integration,and refractory period can all be observed in the fabricated DFB-SA *** numerically verify the experimental findings,a time-dependent coupled-wave equation model was developed,which described the physics processes inside the gain and SA *** numerical results agree well with the experimental *** further experimentally demonstrated that the weighted sum output can readily be encoded into the self-pulsation frequency of the DFB-SA *** also benchmarked the handwritten digit classification task with a simple single-layer fully connected neural *** using the experimentally measured dependence of the self-pulsation frequency on the bias current in the gain region as an activation function,we can achieve a recognition accuracy of 92.2%,which bridges the gap between the continuous valued artificial neural networks and spike-based neuromorphic *** the best of our knowledge,this is the first experimental demonstration of a photonic integrated spiking neuron based on a DFB-SA,which shows great potential to realizing large-scale mult

关键词： technique laser saturable

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Deep Robust Reversible Watermarking

arXiv

引用

arXiv 2025年

作者： Chen, Jiale Wang, Wei Shi, Chongyang Dong, Li Li, Yuanman Hu, Xiping School of Computer Science Beijing Institute of Technology Beijing100081 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China

Robust Reversible Watermarking (RRW) enables perfect recovery of cover images and watermarks in lossless channels while ensuring robust watermark extraction under lossy channels. However, existing RRW methods, mostly non-deep learning-based, suffer from complex designs, high computational costs, and poor robustness limiting their practical applications. To address these issues, this paper proposes Deep Robust Reversible Watermarking (DRRW), a deep learning-based RRW scheme. DRRW introduces an Integer Invertible Watermark Network (iIWN) to achieve an invertible mapping between integer data distributions, fundamentally addressing the limitations of conventional RRW approaches. Unlike traditional RRW methods requiring task-specific designs for different distortions, DRRW adopts an encoder-noise layer-decoder framework, enabling adaptive robustness against various distortions through end-to-end training. During inference, the cover image and watermark are mapped into an overflowed stego image and latent variables. Arithmetic coding efficiently compresses these into a compact bitstream, which is embedded via reversible data hiding to ensure lossless recovery of both the image and watermark. To reduce pixel overflow, we introduce an overflow penalty loss, significantly shortening the auxiliary bitstream while improving both robustness and stego image quality. Additionally, we propose an adaptive weight adjustment strategy that eliminates the need to manually preset the watermark loss weight, ensuring improved training stability and performance. Experiments on multiple datasets demonstrate that DRRW achieves notable performance advantages. Compared to state-of-the-art RRW methods, DRRW improves robustness and reduces embedding, extraction, and recovery complexities by 55.14×, 5.95×, and 3.57×, respectively. The auxiliary bitstream is shortened by 43.86×, and reversible embedding succeeds on 16,762 images in the PASCAL VOC 2012 dataset, marking a significant step toward pra

关键词： Image enhancement

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Precise Interception Flight Targets by Image-based Visual Servoing of Multicopter

arXiv

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arXiv 2024年

作者： Yan, Hailong Yang, Kun Cheng, Yixiao Wang, Zihao Li, Dawei The School of Computer Science Northwestern Polytechnical University Xi’an710129 China The School of Automation Science and Electrical Engineering The School of Future Aerospace Technology The Institute of Unmanned Systems Respectively Beihang University Beijing100191 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China

nterception of low-altitude intruding targets with low-cost drones equipped strapdown camera presents a competitive option. However, the malicious maneuvers by the non-cooperative target and the coupling of the camera make the task challenging. To solve this problem, an Image-Based Visual Servoing (IBVS) control algorithm based on proportional navigation guidance with field-of-view holding capability is designed. The proposed controller reduces the miss distance while improving the stability of the visual servo system during interception. Software-in-the-loop (SITL) simulation experiments show a 72.8% reduction in the circular error probability (CEP) compared to the most recent study. This improvement enhances interception accuracy from the decimeter to the centimeter level. Real-world experiments further validate the effectiveness of the proposed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual servoing

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Differential Privacy for Class-based Data: A Practical Gaussian Mechanism

arXiv

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arXiv 2023年

作者： Ramakrishna, Raksha Scaglione, Anna Wu, Tong Ravi, Nikhil Peisert, Sean Division of Network and Systems Engineering EECS KTH Royal Institute of Technology Stockholm Sweden The Department of Electrical and Computer Engineering Cornell Tech New York CityNY10044 United States The Computer Sciences Research and Development Department Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

In this paper, we present a notion of differential privacy (DP) for data that comes from different classes. Here, the class-membership is private information that needs to be protected. The proposed method is an output perturbation mechanism that adds noise to the release of query response such that the analyst is unable to infer the underlying class-label. The proposed DP method is capable of not only protecting the privacy of class-based data but also meets quality metrics of accuracy and is computationally efficient and practical. We illustrate the efficacy of the proposed method empirically while outperforming the baseline additive Gaussian noise mechanism. We also examine a real-world application and apply the proposed DP method to the autoregression and moving average (ARMA) forecasting method, protecting the privacy of the underlying data source. Case studies on the real-world advanced metering infrastructure (AMI) measurements of household power consumption validate the excellent performance of the proposed DP method while also satisfying the accuracy of forecasted power consumption measurements. © 2023, CC BY.

关键词： Electric power utilization

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Di...

引用

Energy, Materials and Communication engineering (ICEMCE), International Conference on

作者： D. Karthikeyan R. Nivasini Kiruba Thangam Raja D. Madhivadhani B. Sivakumar A.T.R. Krishna Priya Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Guvi Geek Networks IITM Research Park Chennai Tamilnadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Electronics and Communication Engineering Kings Engineering College Sriperumbudur Chennai Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari India

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer’s disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper.

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Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

arXiv

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arXiv 2024年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Singapore

The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 2024, CC BY.

关键词： Generative adversarial networks

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Fed-Haul: A Federated Learning Dual Band Point Multi-Point Backhaul Requirements in 5G Evolution and Beyond 23

Fed-Haul: A Federated Learning Dual Band Point Multi-Point B...

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6th International Conference on Networking, Intelligent systems and Security, NISS 2023

作者： Chehri, Abdellah Chaibi, Hasna Mettiti, Abderrahmane Saadane, Rachid Jeon, Gwanggil Dep. of Math. and Computer Science Royal Military College of Canada Kingston Canada Supmti Genius Laboratory Hassan Rabat Morocco It Rabat Center Faculty of Sciences Mohammed v University Rabat Morocco SIRC-LAGES-EHTP Hassania School of Public Works Casalanca Morocco Department of Embedded Systems Engineering Incheon National University Korea Republic of

ISBN: (纸本)9798400700194

The rapid adoption of smartphones and the explosive growth of data traffic due to these devices have been phenomenal. As the world anticipates more connected devices - the Internet of Things (IoT), vehicle-to-vehicle (V2V) communications, and wearable devices - and more value-added applications and services (ultra-high-definition video, 360° video, virtual reality, smart cars, etc.), leading industry experts are calling for the sixth generation (6G) networks. Federated learning is a common distributed machine learning framework. Through the training of the global model, the problems of large communication overhead and data privacy protection in traditional centralized machine learning are solved. Federated learning (FL) is essential in optimizing wireless communication networks' resources. On the other hand, wireless communications are crucial for FL. Therefore, the purpose of this survey paper is to bridge this gap in the literature by discussing the interdependency between FL and backhaul wireless communications. © 2023 ACM.

关键词： 5G mobile communication systems

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Defect Recognition based on Radon Transform in Pentacene organic thin-film Archimedean interdigitated spirals transistor

Defect Recognition based on Radon Transform in Pentacene org...

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International Conference on Clean electrical Power (ICCEP)

作者： Grazia Lo Sciuto Sivan Linde Rafi Shikler Paweł Kowol Salvo Coco Giacomo Capizzi Department of Mechatronics Silesian University of Technology Gliwice Poland Department of Electrical Electronics and Informatics Engineering University of Catania Catania Italy Department of Electrical and Computer Engineering School of Electrical and Computer Engineering Optoelectronic Organic Semiconductor Devices Laboratory Ben-Gurion University of the Negev Beer-Sheva Israel Ilse Katz Institute for Nanoscale Science and Technology Ben-Gurion University of the Negev Beer-Sheva Israel

In this paper the problem of automatic defects detection in thin film transistor is considered in order to increase the efficiency of electronic devices. An effective approach based on defect pattern recognition in organic transistor is developed using the Radon transform. The adopted design and technology in Pentacene organic thin-film transistor exhibits two interdigitated Archimedean spirals pattern. In the conducted experiments, the images acquisition is performed with a microscope camera to capture the various defects of surface morphology on the top of the examined transistors. The defects detection is obtained by using the Radon transform on each channel (R,G,B) of these images.

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An Experiment on 5G Open-RAN Platform with Sub-THz Backhauling

An Experiment on 5G Open-RAN Platform with Sub-THz Backhauli...

引用

IEEE Conference on Wireless Communications and Networking

作者： Norshahida Saba Efstathios Andrianopoulos Nikolaos K. Lyras Saimanoj Katta Mohammedadem Abdulkadir Evangelos Pikasis Garrit Schwanke Tianwen Qian Milan Deumer Simon Nellen Elias Tsirbas Eleftherios Loghis Georgios Megas Christos Tsokos Panos Groumas David De Felipe Robert B. Kohlhaas Maria Massaouti Christos Kouloumentas Dimitrios Kritharidis Norbert Keil Hercules Avramopoulos Jose Costa-Requena Riku Jäntti Department of Information and Communications Engineering Aalto University Espoo Finland Institute of Communication and Computer Systems Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Cumucore Espoo Finland Intracom Telecom Peania Athens Greece Fraunhofer Heinrich Hertz Institute Berlin Germany Optagon Photonics Agia Paraskevi Athens Greece

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The deployment of fifth-generation (5G) open radio access network (RAN) introduces new opportunities for wireless communication, particularly in the context of x-Haul wireless links. This paper presents a comprehensive demonstration of how photonic-generated sub- THz at 120 GHz was utilized for wireless backhaul within the 5G open RAN platform. The experimental setup highlighted the feasibility and effectiveness of establishing a sub- THz link as the backhaul, marking a significant advancement in this emerging field. Through extensive performance analysis, we evaluated 5G network's throughput, achieving approximately 957 Mbps for downlink with UDP and 269 Mbps with TCP. In the case of uplink, both UDP and TCP exhibited comparable speeds, both exceeding 80 Mbps. This work marks a significant achievement in advancing the development of photonic-generated sub- THz wireless links, holding the potential to enhance wireless transport capabilities, particularly in the context of advanced 5G networks and beyond..

关键词： Wireless communication 5G mobile communication Optical mixing Throughput Real-time systems Performance analysis Uplink

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Long Cheng Haoyang He Yan Gu Qingzhi Liu Zhiming Zhao Fang Fang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing China Information Technology Group Wageningen University and Research Netherlands Research Group of Multi-Scale Networked Systems University of Amsterdam Netherlands

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of $53.18 \%$ and costs by $61.98 \%$ compared to basic techniques, and achieving $20.26 \%$ and $25.71 \%$ improvements over the latest advanced methods, respectively.

关键词： Training Cloud computing Privacy Costs Scalability Deep reinforcement learning Dynamic scheduling Real-time systems Complexity theory Protection

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