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Silicon photonics for high-capacity data communications

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Photonics Research 2022年第9期10卷 I0002-I0030页

作者： YAOCHENG SHI YONG ZHANG YATING WAN YU YU YUGUANG ZHANG XIAO HU XI XIAO HONGNAN XU LONG ZHANG BINGCHENG PAN Centre for Optical and Electromagnetic Research State Key Laboratory for Modern Optical InstrumentationCollege of Optical Science and EngineeringZhejiang UniversityZijingang CampusHangzhou 310058China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Institute for Energy Efficiency University of California Santa BarbaraSanta BarbaraCalifornia 93106USA Electrical and Computer Engineering Department King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Wuhan National Laboratory for Optoelectronics&School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China National Information Optoelectronics Innovation Center China Information and Communication Technologies Group Corporation(CICT)Wuhan 430074China State Key Laboratory of Optical Communication Technologies and Networks China Information and Communication Technologies Group Corporation(CICT)Wuhan 430074China Department of Electronic Engineering The Chinese University of Hong KongShatinNew TerritoriesHong KongChina

In recent years,optical modulators,photodetectors,(de)multiplexers,and heterogeneously integrated lasers based on silicon optical platforms have been *** performance of some devices even surpasses the traditional III-V and photonic integrated circuit(PIC)platforms,laying the foundation for large-scale photonic *** photonic technology can overcome the limitations of traditional transceiver technology in high-speed transmission networks to support faster interconnection between data *** this article,we will review recent progress for silicon *** first part gives an overview of recent achievements in silicon *** second part introduces the silicon photonic building blocks,including low-loss waveguides,passive devices,modulators,photodetectors,heterogeneously integrated lasers,and so *** the third part,the recent progress on high-capacity silicon photonic transceivers is *** the fourth part,we give a review of high-capacity silicon photonic networks on chip.

关键词： interconnection passive networks

Accelerating the Evolution of Personalized Automated Lane Change Through Lesson Learning

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SSRN

SSRN 2024年

作者： Hu, Jia Lei, Mingyue Li, Duo Li, Zhenning So, Jaehyun Wang, Haoran Key Laboratory of Road and Traffic Engineering Ministry of Education Institute for Advanced Study Tongji University 4800 Cao’an Road Shanghai China Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University 4800 Cao’an Road Shanghai China Department of Civil and Geospatial Engineering Newcastle University Newcastle upon TyneNE1 7RU United Kingdom State Key Laboratory of Internet of Things for Smart City Departments of Civil and Environmental Engineering and Computer and Information Science University of Macau Avenida da Universidade Taipa China Department of Transportation Systems Engineering Ajou University 206 World cup-ro Yeongtong-gu Gyeonggi-do Suwon-si16499 Korea Republic of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China

Personalization is crucial for the widespread adoption of advanced driver assistance system. To match up with each user's preference, the online evolution capability is a must. However, conventional evolution methods learn from naturalistic driving data, which requires a lot computing power and cannot be applied online. To address this challenge, this paper proposes a lesson learning approach: learning from driver's takeover interventions. By leveraging online takeover data, the driving zone is generated to ensure perceived safety using Gaussian discriminant analysis. Real-time corrections to trajectory planning rewards are enacted through apprenticeship learning. Guided by the objective of optimizing rewards within the constraints of the driving zone, this approach employs model predictive control for trajectory planning. This lesson learning framework is highlighted for its faster evolution capability, adeptness at experience accumulating, assurance of perceived safety, and computational efficiency. Simulation results demonstrate that the proposed system consistently achieves a successful customization without further takeover interventions. Accumulated experience yields a 24% enhancement in evolution efficiency. The average number of learning iterations is only 13.8. The average computation time is 0.08 seconds. © 2024, The Authors. All rights reserved.

关键词： Discriminant analysis

Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning

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arXiv 2024年

作者： Hu, Jia Lei, Mingyue Li, Duo Li, Zhenning So, Jaehyun Wang, Haoran Key Laboratory of Road and Traffic Engineering The Ministry of Education Institute for Advanced Study Tongji University 4800 Cao’an Road Shanghai China Key Laboratory of Road and Traffic Engineering The Ministry of Education Tongji University 4800 Cao’an Road Shanghai China Department of Civil and Geospatial Engineering Newcastle University Newcastle upon TyneNE1 7RU United Kingdom State Key Laboratory of Internet of Things for Smart City Departments of Civil and Environmental Engineering and Computer and Information Science University of Macau Avenida da Universidade Taipa China Department of Transportation Systems Engineering Ajou University 206 World cup-ro Yeongtong-gu Gyeonggi-do Suwon-si16499 Korea Republic of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China

关键词： Discriminant analysis

Embedding 3D models in offline physical environments

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Embedding 3D models in offline physical environments

作者： Ertugrul, Egemen Zhang, Han Zhu, Fang Lu, Ping Li, Ping Sheng, Bin Wu, Enhua Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China ZTE Corporation China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Faculty of Science and Technology University of Macau China

This article introduces a novel approach for embedding 3D models in offline physical environments using quick response (QR) codes. Unlike conventional methods, we consider settings where 3D models cannot be retrieved from a remote server. Our method involves generating octree models from voxelized 3D models and storing them in QR codes using a space-efficient data structure. This allows storing 3D models that are both intelligible and purposeful on standard QR codes while addressing the major storage constraint that is present in offline situations. Furthermore, we explore 3D convolutional neural networks (CNN) and autoencoders (AE) to compress 3D models with high resolutions where using octrees alone does not suffice. To the best of our knowledge, our AE network is the first to employ octrees to further compress its encoded data. Through user-friendly desktop and mobile applications, we allow users to encode, decode and visualize 3D models in augmented reality (AR) using QR codes, thus experiment with our methods. The proposed approach enables unique applications and future research in ubiquitous computing, 3D data compression and transmission, 3D AEs, AR and Virtual Reality, low-cost autonomous robots, and 3D printing. © 2020 John Wiley & Sons, Ltd.

关键词： Digital storage

The influence of limited visual sensing on the Reynolds flocking algorithm 3

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The influence of limited visual sensing on the Reynolds floc...

3rd IEEE International Conference on Robotic computing (IRC)

作者： Soria, Enrica Schiano, Fabrizio Floreano, Dario Department of Computer Science and Technology the Key Laboratory of Embedded System Service Computing Shanghai China

ISBN: (纸本)9781538692455

The interest in multi-drone systems flourished in the last decade and their application is promising in many fields. We believe that in order to make drone swarms flying smoothly and reliably in real-world scenarios we need a first intermediate step which consists in the analysis of the effects of limited sensing on the behavior of the swarm. In nature, the central sensor modality often used for achieving flocking is vision. In this work, we study how the reduction in the field of view and the orientation of the visual sensors affect the performance of the Reynolds flocking algorithm used to control the swarm. To quantify the impact of limited visual sensing, we introduce different metrics such as (i) order, (ii) safety, (iii) union and (iv) connectivity. As Nature suggests, our results confirm that lateral vision is essential for coordinating the movements of the individuals. Moreover, the analysis we provide will simplify the tuning of the Reynolds flocking algorithm which is crucial for real-world deployment and, especially for aerial swarms, it depends on the envisioned application. We achieve the results presented in this paper through extensive Monte-Carlo simulations and integrate them with the use of genetic algorithm optimization.

关键词： Robot sensing systems Visualization Animals Nickel Azimuth

RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation

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arXiv 2022年

作者： Zhang, Yao Xiong, Yun Sun, Yiheng Shan, Caihua Lu, Tian Song, Hui Zhu, Yangyong Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Tencent Weixin Group China Microsoft Research Asia China Department of Information Systems W. P. Carey School of Business Arizona State University United States

Risk scoring systems have been widely deployed in many applications, which assign risk scores to users according to their behavior sequences. Though many deep learning methods with sophisticated designs have achieved promising results, the black-box nature hinders their applications due to fairness, explainability, and compliance consideration. Rule-based systems are considered reliable in these sensitive scenarios. However, building a rule system is labor-intensive. Experts need to find informative statistics from user behavior sequences, design rules based on statistics and assign weights to each rule. In this paper, we bridge the gap between effective but black-box models and transparent rule models. We propose a two-stage method, RuDi, that distills the knowledge of black-box teacher models into rule-based student models. We design a Monte Carlo tree search-based statistics generation method that can provide a set of informative statistics in the first stage. Then statistics are composed into logical rules with our proposed neural logical networks by mimicking the outputs of teacher models. We evaluate RuDi on three real-world public datasets and an industrial dataset to demonstrate its effectiveness. Copyright © 2022, The Authors. All rights reserved.

关键词： Statistics

When Brain-inspired AI Meets AGI

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arXiv 2023年

作者： Zhao, Lin Zhang, Lu Wu, Zihao Chen, Yuzhong Dai, Haixing Yu, Xiaowei Liu, Zhengliang Zhang, Tuo Hu, Xintao Jiang, Xi Li, Xiang Zhu, Dajiang Shen, Dinggang Liu, Tianming School of Computing The University of Georgia Athens30602 United States Department of Computer Science and Engineering The University of Texas at Arlington Arlington76019 United States MOE Key Laboratory for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China School of Automation Northwestern Polytechnical University Xi’an710072 China Department of Radiology Massachusetts General Hospital Harvard Medical School Boston02115 United States School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from the perspective of AGI. We begin with the current progress in brain-inspired AI and its extensive connection with AGI. We then cover the important characteristics for both human intelligence and AGI (e.g., scaling, multimodality, and reasoning). We discuss important technologies toward achieving AGI in current AI systems, such as in-context learning and prompt tuning. We also investigate the evolution of AGI systems from both algorithmic and infrastructural perspectives. Finally, we explore the limitations and future of AGI. © 2023, CC BY.

关键词： Brain

Reconfigurable Distributed Antennas and Reflecting Surface: A New Architecture for Wireless Communications

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arXiv 2023年

作者： Ma, Chengzhi Yang, Xi Wang, Jintao Yang, Guanghua Zhang, Wei Ma, Shaodan The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China The Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China The School of Intelligent Systems Science and Engineering GBA and B&R International Joint Research Center for Smart Logistics Jinan University Zhuhai519070 China The School of Electrical Engineering & Telecommunications University of New South Wales Sydney Australia

Distributed Antenna systems (DASs) employ multiple antenna arrays in remote radio units to achieve highly directional transmission and provide great coverage performance for future-generation networks. However, the utilization of fully digital or hybrid active antenna arrays results in a significant increase in hardware costs and power consumption for DAS. To address these issues, integrating DAS with Reconfigurable Intelligent Surfaces (RIS) offers a viable approach to ensure coverage and transmission performance while maintaining low hardware costs and power consumption. To incorporate the merits of RIS into the DAS from practical consideration, a novel architecture of "Reconfigurable Distributed Antennas and Reflecting Surfaces (RDARS)" is proposed in this paper. Specifically, based on the design of the additional direct-through state together with the existing high-quality fronthaul link, any element of the RDARS can be dynamically programmed to connect with the base station (BS) via fibers and perform the connected mode as remote distributed antennas of the BS to receive or transmit signals. Additionally, RDARS also inherits the low-cost and low-energy-consumption benefits of fully passive RISs by default configuring the elements as passive to perform the reflection mode. As a result, RDARS encompasses both DAS and RIS as special cases, offering flexible control over the trade-off between distribution gain and reflection gain to enhance performance. To unveil the potential of such architecture, the ergodic achievable rate under the RDARS architecture is analyzed and closed-form expression with meaningful insights is derived. The theoretical analysis proves that the RDARS can achieve a higher achievable rate than both DAS and fully passive RIS with the passive beamforming gain provided by elements acting reflection mode while combating the "multiplicative fading" suffered by RISs through the connected mode performed at the RDARS. Simulation results also demonstr

关键词： Reconfigurable architectures

RDARS Empowered Massive MIMO System: Two-Timescale Transceiver Design with Imperfect CSI

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arXiv 2023年

作者： Ma, Chengzhi Wang, Jintao Yang, Xi Yang, Guanghua Zhang, Wei Ma, Shaodan State Key Laboratory of Internet of Things for Smart City the Department of Electrical and Computer Engineering University of Macau China Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China School of Intelligent Systems Science and Engineering and GBA B&R International Joint Research Center for Smart Logistics Jinan University Zhuhai519070 China School of Electrical Engineering & Telecommunications University of New South Wales Sydney Australia

In this paper, we investigate a novel reconfigurable distributed antennas and reflecting surface (RDARS) aided multi-user massive multiple-input multiple-output (MIMO) system with imperfect channel state information (CSI) and propose a practical two-timescale (TTS) transceiver design to reduce the communication overhead and computational complexity of the system. In the RDARS-aided system, not only distribution gain but also reflection gain can be obtained by a flexible combination of the distributed antennas and reflecting surface, which differentiates the system from the others and also makes the TTS design challenging. To enable the optimal TTS transceiver design, the achievable rate of the system is first derived in closed-form. The rate expression is general and covers that of the distributed antenna systems (DAS) and reconfigurable intelligent surface (RIS) aided systems as special cases. Then the TTS design aiming at the weighted sum rate maximization is considered. To solve the challenging non-convex optimization problem with high order design variables, i.e., the transmit powers and the phase shifts at the RDARS, a block coordinate descent based method is proposed to find the optimal solutions in semi-closed forms iteratively. Specifically, two efficient algorithms are proposed with provable convergence for the optimal phase shift design, i.e., Riemannian Gradient Ascent based algorithm by exploiting the unit-modulus constraints, and Two-Tier Majorization-Minimization based algorithm with closed-form optimal solutions in each iteration. Simulation results validate the effectiveness of the proposed algorithm and demonstrate the superiority of deploying RDARS in massive MIMO systems to provide substantial rate improvement with a significantly reduced total number of active antennas/RF chains and lower transmit power when compared to the DAS and RIS-aided systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Convex optimization