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Digitally tunable optical delay line based on thinfilm lithium niobate featuring high switching speed and low optical loss

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Photonics Research 2022年第11期10卷 2575-2583页

作者： WEI KE YANMEI LIN MINGBO HE MENGYUE XU JIAXIANG ZHANG ZHONGJIN LIN SIYUAN YU XINLUN CAI State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510275China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200092China Department of Electrical and Computer Engineering The University of British ColumbiaVancouverBritish Columbia V6T 1Z4Canada

A tunable optical delay line(ODL) featuring high switching speed and low optical loss is highly desirable in many fields. Here, based on the thin-film lithium niobate platform, we demonstrate a digitally tunable on-chip ODL that includes five Mach–Zehnder interferometer optical switches, four flip-chip photodetectors, and four delayline waveguides. The proposed optical switches can achieve a switching speed of 13 ns and an extinction ratio of34.9 dB. Using a modified Euler-bend-based spiral structure, the proposed delay-line waveguide can simultaneously achieve a small footprint and low optical propagation loss. The proposed ODL can provide a maximum delay time of 150 ps with a resolution of 10 ps and feature a maximum insertion loss of 3.4 dB.

关键词： tunable optical interferometer

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Performance Evaluation of WMN-PSOSA-DGA Simulation System Considering Uniform and Chi-Square Client Distributions 23rd

Performance Evaluation of WMN-PSOSA-DGA Simulation System Co...

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23rd International Conference on Network-Based information Systems, NBiS 2020

作者： Barolli, Admir Sakamoto, Shinji Ampririt, Phudit Ohara, Seiji Barolli, Leonard Takizawa, Makoto Department of Information Technology Aleksander Moisiu University of Durres L.1 Rruga e Currilave Durres Albania Department of Computer and Information Science Seikei University 3-3-1 Kichijoji-Kitamachi Musashino-shiTokyo180-8633 Japan Graduate School of Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-KuFukuoka811-0295 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-KuFukuoka811-0295 Japan Department of Advanced Sciences Faculty of Science and Engineering Hosei University Kajino-Machi Koganei-ShiTokyo184-8584 Japan

ISBN: (纸本)9783030578107

The Wireless Mesh Networks (WMNs) have many advantages such as low cost and high-speed wireless Internet connectivity. The connectivity and stability are important factors in the performance of WMNs. In our previous work, we implemented a simulation system considering Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Distributed Genetic Algorithm (DGA), called WMN-PSOSA-DGA. In this paper, we evaluate the performance of WMNs using WMN-PSOSA-DGA simulation system considering Uniform and Chi-square distributions of mesh clients. Simulation results show that a good performance is achieved for Chi-square distribution compared with the case of Uniform distribution. © 2021, Springer Nature Switzerland AG.

关键词： Simulated annealing

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Role of activity-based learning and ChatGPT on students' performance in education

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computers and Education: Artificial Intelligence 2024年 6卷

作者： Al Shloul, Tamara Mazhar, Tehseen Abbas, Qamar Iqbal, Muhammad Ghadi, Yazeed Yasin Shahzad, Tariq Mallek, Fatma Hamam, Habib General Education Department Liwa College Abu Dhabi15222 United Arab Emirates Department of Computer Science Virtual University of Pakistan Lahore51000 Pakistan Department of Computer Science Faculty of Computing and Information Technology International Islamic University Islamabad 44000 Pakistan Pakistan Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates Department of Computer Sciences COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Faculty of Engineering University de Moncton MonctonNBE1A3E9 Canada Hodmas University College Taleh Area Mogadishu Somalia Bridges for Academic Excellence Tunis Tunisia School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa

Purpose: This study investigates the impact of activity-based learning and the utilization of ChatGPT on students' academic performance within the educational framework. Objectives: The study aims to assess the effectiveness of activity-based learning in comparison to traditional methods, while also evaluating the potential benefits and drawbacks of integrating ChatGPT as an educational tool. Methods: The study employs a comparative approach, analyzing the outcomes of students exposed to activity-based learning versus those using conventional methods. Additionally, the study examines the usage of ChatGPT in education through surveys and trials to determine its contribution to personalized feedback, interactive learning, and innovative teaching methods. Results: The findings reveal that activity-based learning enhances students' engagement, motivation, and critical thinking skills. Students participating in activity-based learning demonstrate improved academic achievement, which is attributed to their active involvement and practical application of knowledge. Similarly, the integration of ChatGPT offers novel avenues for interactive learning and individualized assistance, fostering students' understanding and exploration of complex concepts. Conclusion: In conclusion, activity-based learning proves to be a student-centered approach that enhances learning outcomes by fostering active participation and practical engagement. The utilization of ChatGPT in education showcases its potential to enhance educational experiences through interactive conversations and innovative teaching methodologies, despite considerations regarding potential limitations and ethical implications. © 2024 The Authors

关键词： Students

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CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels

arXiv

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arXiv 2024年

作者： Zeng, Ye Qiao, Li Gao, Zhen Qin, Tong Wu, Zhonghuai Khalaf, Emad Chen, Sheng Guizani, Mohsen School of Information and Electronics Beijing Institute of Technology Beijing100081 China Electrical and Computer Engineering Department Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom King Abdulaziz University Jeddah21589 Saudi Arabia United Arab Emirates

In massive multiple-input multiple-output (MIMO) systems, how to reliably acquire downlink channel state information (CSI) with low overhead is challenging. In this work, by integrating the generative pre-trained Transformer (GPT) with federated-tuning, we propose a CSI-GPT approach to realize efficient downlink CSI acquisition. Specifically, we first propose a Swin Transformer-based channel acquisition network (SWTCAN) to acquire downlink CSI, where pilot signals, downlink channel estimation, and uplink CSI feedback are jointly designed. Furthermore, to solve the problem of insufficient training data, we propose a variational auto-encoder-based channel sample generator (VAE-CSG), which can generate sufficient CSI samples based on a limited number of high-quality CSI data obtained from the current cell. The CSI dataset generated from VAE-CSG will be used for pre-training SWTCAN. To fine-tune the pre-trained SWTCAN for improved performance, we propose an online federated-tuning method, where only a small amount of SWTCAN parameters are unfrozen and updated using over-the-air computation, avoiding the high communication overhead caused by aggregating the complete CSI samples from user equipment (UEs) to the BS for centralized fine-tuning. Simulation results verify the advantages of the proposed SWTCAN and the communication efficiency of the proposed federated-tuning method. Our code is publicly available at https://***/ BIT-ZY/CSI-GPT Copyright © 2024, The Authors. All rights reserved.

关键词： Channel estimation

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Performance evaluation of wmn-psosa-dga simulation system considering linearly decreasing vmax method and rational decreament of vmax method 14th

Performance evaluation of wmn-psosa-dga simulation system co...

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14th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2020

作者： Barolli, Admir Sakamoto, Shinji Ohara, Seiji Barolli, Leonard Takizawa, Makoto Department of Information Technology Aleksander Moisiu University of Durres L.1 Rruga e Currilave Durres Albania Department of Computer and Information Science Seikei University 3-3-1 Kichijoji-Kitamachi Musashino-shi Tokyo180-8633 Japan Graduate School of Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan Department of Advanced Sciences Faculty of Science and Engineering Hosei University Kajino-Machi Koganei-Shi Tokyo184-8584 Japan

ISBN: (纸本)9783030503987

The Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure because they have many advantages such as low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented a simulation system considering Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Distributed Genetic Algorithm (DGA), called WMN-PSOSA-DGA. In this paper, we present the performance of WMNs using WMN-PSOSA-DGA simulation system considering Linearly Decreasing Vmax Method (LDVM) and Rational Decrement of Vmax Method (RDVM). Simulation results show that a good performance is achieved for RDVM compared with the case of LDVM. © Springer Nature Switzerland AG 2021.

关键词： Simulated annealing

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Deep Learning-Based Decision-Making of Autonomous Vehicles to Predict Accidents

Deep Learning-Based Decision-Making of Autonomous Vehicles t...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Abida Khanum Chao-Yang Lee Chu-Sing Yang Department of Electrical Engineering National Cheng Kung University No.1 Tainan City Taiwan (R.O.C) Department of Computer Science and Information Engineering National Yunlin University of Science and Technology National Formosa University Yunlin Country Taiwan (R.O.C)

The autonomous vehicle system ensures safe driving by certainly controlling a vehicle's motion in the lane to reduce the risk of a collision. The proposed architecture is based on convolutional neural network (CNN) deep learning-based with three convolutional blocks. As output, the CNN framework can predict three different risk factor evaluations, i.e., high risk, medium risk, and low risk, used to predict the accident of the AV. The deep learning-based network had a better accuracy rate and computing time for all MTs with a 70% accuracy rate and an 8-ms computing time for risk prediction in autonomous vehicles. The experimental accuracy demonstrates that our trained method has fruitfully accurate results. The self-driving vehicle model successfully predicts the accident risk during driving a vehicle on the road.

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Enhancement of Computational Efficiency for Deadlock Recovery of Flexible Manufacturing Systems Using Improved Generating and Comparing Aiding Matrix Algorithms

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Processes 2023年第10期11卷 3026页

作者： Pan, Yen-Liang Tseng, Ching-Yun Chen, Ju-Chin Mathematics and Physics Division General Education Center R.O.C. Air Force Academy No. Sisou 1 Jieshou W. Rd. Gangshan Dist Kaohsiung City820009 Taiwan Department of Aeronautical Engineering Chaoyang University of Technology Taichung413310 Taiwan Department of Electronic Engineering National Kaohsiung University of Sciences and Technology Kaohsiung City807618 Taiwan Department of Computer Science and Information Engineering National Kaohsiung University of Sciences and Technology Kaohsiung City807618 Taiwan

After the fourth industrial evolution, precision and automatic manufacturing have become increasingly widely accepted in production. With highly variable productivity and flexibility, flexible manufacturing systems (FMS) lower production costs and increase efficiency. Due to its resource shareability, unexpected system deadlock may occur in some specific situations. Many existing works use deadlock prevention as the primary control methodology in research on system deadlock control, while this type of control policy would constrain the transportation resources and reduce the system’s liveness. This paper adopts a new transition-based deadlock recovery policy as the direct control strategy, which uses generating and comparing aiding matrix (GCAM) to determine the optimal control transition. We also improve the existing GCAM-based method by reducing the computational redundancy. This kind of control strategy and its benefit could be demonstrated through two typical systems of simple sequential processes with resource (S3PR) nets and their Petri nets model. © 2023 by the authors.

关键词： Petri nets

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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A Survey of Graph-Based Resource Management in Wireless Networks -Part I: Optimization Approaches

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Dai, Yanpeng Lyu, Ling Cheng, Nan Sheng, Min Liu, Junyu Wang, Xiucheng Cui, Shuguang Cai, Lin Shen, Xuemin Dalian Maritime University School of Information Science and Technology Dalian116026 China Xidian University State Key Laboratory of ISN School of Telecommunications Engineering Xian710071 China Guangdong Provincial Key Laboratory of Future Networks of Intelligence Shenzhen518066 China University of Victoria Department of Electrical and Computer Engineering VictoriaBCV8P 5C2 Canada University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

The evolution of wireless communications and networking technologies has led significantly expansion of the dimensionality of network resources, which compels innovations in resource management. Graphs, a classic discrete mathematical tool, have long been widely used for resource management thanks to their capabilities to model complex relationships and interactions among elements in wireless networks. Recently, resource management over graphs embraces various advanced approaches of graph optimization and graph learning, aligned with evolving demands in future wireless networks. To better learn recent research landscape and explore important trends, this twopart survey provides a comprehensive overview for resource management via graph optimization and learning. Part I presents the fundamentals of graph optimization and provides a recent literature review of graph optimization for resource management in various wireless communication scenarios, including cellular networks, device-to-device communications, multi-hop networks, multi-antenna systems, edge caching and computing, and nonterrestrial networks. Part II gives the basics of graph learning and provides a state-of-the-art literature review of graph learning in wireless networks for addressing various resource management issues, covering power control, spectrum management, beamforming design, task scheduling, and aerial coverage planning. A discussion of technical challenges and future research directions is covered in Part II. © 2015 IEEE.

关键词： graph resource management Wireless networks

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Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learning

arXiv

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arXiv 2023年

作者： An, Xuming Fan, Rongfei Zuo, Shiyuan Hu, Han Jiang, Hai Zhang, Ning The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The School of Cyberspace Science and Technology Beijing100081 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada The Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

Federated learning (FL) has emerged as an appealing machine learning approach to deal with massive raw data generated at multiple mobile devices, which needs to aggregate the training model parameter of every mobile device at one base station (BS) iteratively. For parameter aggregating in FL, over-the-air computation is a spectrum-efficient solution, which allows all mobile devices to transmit their parameter-mapped signals concurrently to a BS. Due to heterogeneous channel fading and noise, there exists difference between the BS's received signal and its desired signal, measured as the mean-squared error (MSE). To minimize the MSE, we propose to jointly optimize the signal amplification factors at the BS and the mobile devices as well as the data size (the number of data samples involved in local training) at every mobile device. The formulated problem is challenging to solve due to its non-convexity. To find the optimal solution, with some simplification on cost function and variable replacement, which still preserves equivalence, we transform the changed problem to be a bi-level problem equivalently. For the lower-level problem, optimal solution is found by enumerating every candidate solution from the Karush-Kuhn-Tucker (KKT) condition. For the upper-level problem, the optimal solution is found by exploring its piecewise convexity. Numerical results show that our proposed method can greatly reduce the MSE and can help to improve the training performance of FL compared with benchmark methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Fading channels

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