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Butler matrix enabled multi-beam optical phased array for two-dimensional beam-steering and ranging

Photonics Research 2024年第5期12卷 912-920页

作者： ZUOYU ZHOU WEIHAN XU CHUXIN LIU RUIYANG XU CHEN ZHU XINHANG LI LIANGJUN LU JIANPING CHEN LINJIE ZHOU State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Laboratory of Navigation and Location ServicesShanghai Institute for Advanced Communication and Data ScienceDepartment of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu 314200China State Grid Sichuan Electric Power Company Chengdu Xinjin Power Supply Branch Xinjin 611430China

Based on the wavelength transparency of the Butler matrix(BM)beamforming network,we demonstrate a multibeam optical phased array(MOPA)with an emitting aperture composed of grating couplers at a 1.55μm pitch for wavelength-assisted two-dimensional *** device is capable of simultaneous multi-beam operation in a field of view(FOV)of 60°×8°in the phased-array scanning axis and the wavelength-tuning scanning axis,*** typical beam divergence is about 4°on both *** multiple linearly chirped lasers,multibeam frequency-modulated continuous wave(FMCW)ranging is realized with an average ranging error of 4 cm.A C-shaped target is imaged for proof-of-concept 2D scanning and ranging.

关键词： optical tuning beam

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A Novel Remaining Useful Life Prediction Model Based on Adaptive Feature Fusion and its Application to Rolling Bearings

SSRN

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SSRN 2024年

作者： Guo, Kai Ma, Jun Wu, Jiande Xiong, Xin Faculty of Information Engineering & Automation Kunming University of Science and Technology Kunming650500 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming650500 China

As a key component in the transmission system of high-speed trains, once a failure occurs, it will directly affect the safety of train operation. Therefore, it is of great significance to construct a degradation index that can comprehensively characterize the running state of bearings and establish a reliable remaining useful life model to ensure the safety of train operation. Aiming at the problems that the redundant features in the existing multi-feature fusion process affect the diagnosis performance and the spurious fluctuations phenomenon of the fusion features causes the inaccurate determination of the start fault time and leads to the low prediction accuracy of remaining useful life (RUL), A method based on adaptive feature fusion and autoregressive integrated moving average model is proposed for rolling bearings prediction. The research in this study demonstrates that by comparing and verifying with two sets of public data, the proposed method is effective in accurately identifying the features that are most sensitive to degradation trends, accurately determine the initial fault moment of bearings, achieve effective prediction of the RUL of bearings, and the related research results can provide reference basis for predictive maintenance of train bearings. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Optimisation and Performance Computation of a Phase Frequency Detector Module for IoT Devices

arXiv

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arXiv 2024年

作者： Hemel, Md. Shahriar Khan Reaz, Mamun Bin Ibne Ali, Sawal Hamid Bin Md Bhuiyan, Mohammad Arif Sobhan Miraz, Mahdi H. Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Malaysia Department of Electrical and Electronic Engineering Independent University Bangladesh School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Malaysia School of Computing and Data Science Xiamen University Malaysia Malaysia Faculty of Arts Science and Technology Wrexham University United Kingdom Faculty of Computing Engineering and Science University of South Wales United Kingdom

The Internet of Things (IoT) is pivotal in transforming the way we live and interact with our surroundings. To cope with the advancement in technologies, it is vital to acquire accuracy with the speed. A phase frequency detector (PFD) is a critical device to regulate and provide accurate frequency in IoT devices. Designing a PFD poses challenges in achieving precise phase detection, minimising dead zones, optimising power consumption, and ensuring robust performance across various operational frequencies, necessitating complex engineering and innovative solutions. This study delves into optimising a PFD circuit, designed using 90 nm standard CMOS technology, aiming to achieve superior operational frequencies. An efficient and high-frequency PFD design is crafted and analysed using cadence virtuoso. The study focused on investigating the impact of optimising PFD design. With the optimised PFD, an operational frequency of 5 GHz has been achieved, along with a power consumption of only 29 µW. The dead zone of the PFD was only 25 ps. © 2024, CC BY.

关键词： Phase locked loops

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Design of an adaptive fuzzy-neural inference system-based control approach for robotic manipulators

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Applied Soft Computing 2023年第PartA期149卷

作者： Barhaghtalab, Mojtaba Hadi Sepestanaki, Mohammadreza Askari Mobayen, Saleh Jalilvand, Abolfazl Fekih, Afef Meigoli, Vahid Department of Electrical Engineering Faculty of Engineering University of Zanjan Zanjan Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin640301 Taiwan Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States Department of Electrical Engineering Faculty of Engineering Persian Gulf University Bushehr Iran

This paper proposes an adaptive fuzzy-neural inference system (ANFIS)-based control approach for a six degrees of freedom (6-DoF) robotic manipulator. Its main objective is to guarantee the error convergence of the controlled system in the presence of uncertainties and unknown disturbances. The suggested controller is a parallel combination of an ANFIS network with a proportional-integral-derivative (PID) controller. The ANFIS system is used as an estimator to approximate a part of the system and then applied as the feedback linearization in the suggested control structure. The convergence of system errors to zero was proven using Barbalat's lemma. The suggested control law combines the simplicity and ease of implementation of PID control with the estimation properties of ANFIS networks. The suggested approach was evaluated using a simulation study and further validated experimentally using the 6-DoF IRB-120 robotic manipulator (IRB-120-RM). The obtained results confirmed its superior performance and suitability for practical implementation to industrial actuators. © 2023 Elsevier B.V.

关键词： Feedback linearization

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Interval Type-2 Enhanced Possibilistic Fuzzy C-Means Noisy Image Segmentation Algorithm Amalgamating Weighted Local Information

SSRN

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SSRN 2024年

作者： Huang, Chengquan Lei, Huan Chen, Yang Cai, Jianghai Qin, Xiaosu Peng, Jialei Zhou, Lihua Zheng, Lan Engineering Training Center Guizhou Minzu University Guiyang550025 China School of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University Guiyang550025 China

The fuzzy clustering algorithms based on interval type-2 are effective methods for data clustering and image segmentation with some potential advantages in dealing with higher-order uncertainty. However, the fuzzy clustering algorithms based on interval type-2 have limited accuracy in noisy image segmentation. Therefore, we propose a new noisy image segmentation algorithm based on weighted local information for interval type-2 enhanced possibilistic fuzzy C-means clustering. Firstly, a new possibilistic fuzzy weighted local information factor, which amalgamates the mutually guided image filtering, is devised to control the relationship between mutually guided image filtering and the original image utilizing the absolute difference image between the original image and mutually guided image filtering. Additionally, two weighted sum functions are also introduced into the new possibilistic fuzzy weighted local information factor to calculate the greyscale differences between the current pixel and its neighborhood pixels. Secondly, the distance of the objective function is corrected using mutually guided image filtering to obtain the local features and global information of the image. Finally, this paper introduces the new possibilistic fuzzy weighted local information factor into the interval type-2 possibilistic fuzzy C-means clustering algorithm and proposes a new interval type-2 enhanced possibilistic fuzzy C-means clustering algorithm with local information and mutually guided image filtering constraints to improve noisy image segmentation results. Through extensive experiments on noisy synthetic images and noisy real images, the proposed algorithm can achieve higher performance and more accurate segmentation results compared with several existing algorithms. © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Augmented Reality Display of Robot with Graphs of Property Response Using Its USD Model

Augmented Reality Display of Robot with Graphs of Property R...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Kazuki Tsukamoto Masanobu Koga Department of Creative Informatics Kyushu Institute of Technology Department of Intelligent and Control Systems Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology

This study proposes a method that can easily grasp the relationship between the actual machine and the graphs. In recent years, there has been a lot of research on augmented reality displays. The fields of research range from education to welfare. In the development of control systems, when evaluating the performance of a system by simulation or experiment, the results are often checked as graphs. Since the graphs are checked on a PC using CAD or other means, it is difficult to know which part of the actual machine each graph corresponds to. Therefore, we developed a tool that displays graphs in augmented reality around the actual machine through a camera on a mobile terminal. To display graphs in augmented reality, it is important to obtain the coordinates of the actual machine and display them in a location associated with the device. Therefore, a USD model with the same shape and size as the actual machine is used. This is achieved by displaying the USD model in augmented reality so that it is superimposed on the actual machine. The accuracy of the tool was also examined and its usefulness was evaluated.

关键词： Solid modeling Automation Shape Robot kinematics Robot vision systems Education Control systems

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

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IEEE Wireless Communications Letters 2025年第6期14卷 1618-1622页

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems China The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of HongKong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent algorithm to iteratively optimize the antenna position vector, array rotation vector, receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio. Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. © 2025 IEEE. All rights reserved.

关键词： Aircraft communication

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Light-EvoOPT: A Lightweight Evolutionary Optimization Framework for Ultra-Large-Scale Mixed Integer Linear Programs

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IEEE Transactions on Evolutionary Computation 2025年

作者： Ye, Huigen Xu, Hua Coello, Carlos A. Coello Tsinghua University State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Beijing100084 China Department of Computer Science D.F México07300 Mexico Tecnologico de Monterrey Faculty of Excellence The School of Engineering and Sciences N.L Monterrey Mexico

Machine Learning (ML)-based optimization frameworks emerge as a promising technique for solving large-scale Mixed Integer Linear Programs (MILPs), as they can capture the mapping between problem structures and optimal solutions to expedite their solution process. However, existing solution frameworks often suffer from high model computation costs, incomplete problem reduction, and reliance on large-scale solvers, leading to performance bottlenecks in ultra-large-scale problems with complex constraints. To address these issues, this paper proposes Light-EvoOPT, a Lightweight Evolutionary Optimization Framework for Ultra-Large-Scale Mixed Integer Linear Programs, which can be divided into four stages: (1) Problem Formulation for problem division to reduce model computational costs, (2) Model-based Initial Solution Prediction for predicting and constructing the initial solution using a small-scale training dataset, (3) Problem Reduction for both variable and constraint reduction, and (4) Evolutionary Optimization for current solution improvement employing a lightweight optimizer. Experiments on four benchmark datasets with tens of millions of variables and constraints and a real-world problem show that the proposed framework based on the sole use of a lightweight optimizer, trained on only one-thousandth of the scale of ultra-large-scale problems, is able to outperform state-of-the-art ML-based frameworks and advanced solvers (e.g. Gurobi) within a specified computational time, validating the feasibility and effectiveness of our proposed ML-based evolutionary optimization framework for ultra-large-scale MILPs. © 2025 IEEE. All rights reserved.

关键词： Mixed-integer linear programming

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Infer the Whole from a Glimpse of a Part: Keypoint-Based Knowledge Graph for Vehicle Re-Identification 39

Infer the Whole from a Glimpse of a Part: Keypoint-Based Kno...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lv, Kai Li, Yunlong Chen, Zhuo Wang, Shuo Han, Sheng Lin, Youfang School of Computer Science & Technology Beijing Jiaotong University China Beijing Key Laboratory of Traffic Data Mining Embodied Intelligence China Zhejiang University China CSSC Intelligent Innovation Research Institute China CSSC Systems Engineering Research Institute China

ISBN: (纸本)157735897X

Vehicle re-identification aims to match vehicles across non-overlapping camera views. Many existing methods extract features from one specific image, and these methods lack view-invariance when comparing vehicles of different orientations. As a result, discriminative parts obscured by viewpoint changes cannot contribute effectively to matching. This work presents a novel keypoint-based framework for vehicle Re-ID. We propose to explicitly model the intrinsic structural relationships between vehicle components via knowledge graph. By establishing connection between keypoints, our approach aims to leverage such prior to match vehicles even when some parts are not directly comparable due to orientation inconsistencies. Specifically, given query and gallery images, we first detect visible keypoints. Then, a transformer-based model infers features for non-overlapped keypoints by conditioning on visible correspondences defined in the knowledge graph. The final representation integrates visible and inferred features. Extensive experiments demonstrate our method outperforms state-of-the-arts on standard benchmarks under cross-view matching scenarios. To our knowledge, this is the first work introducing structural priors via keypoint knowledge graphs for view-invariant vehicle re-identification. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge engineering

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Discretizing Continuous Action Space With Unimodal Probability Distributions for On-Policy Reinforcement Learning

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IEEE Transactions on Neural Networks and Learning systems 2024年第6期36卷 11285-11297页

作者： Yuanyang Zhu Zhi Wang Yuanheng Zhu Chunlin Chen Dongbin Zhao Department of Control Science and Intelligence Engineering School of Management and Engineering and the Laboratory of Data Intelligence and Interdisciplinary Innovation Nanjing University Nanjing China Department of Control Science and Intelligence Engineering School of Management and Engineering and the Research Center for Novel Technology of Intelligent Equipment Nanjing University Nanjing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

For on-policy reinforcement learning (RL), discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without considering the inherent ordering between the discrete atomic actions, the explosion in the number of discrete actions can possess undesired properties and induce a higher variance for the policy gradient (PG) estimator. In this article, we introduce a straightforward architecture that addresses this issue by constraining the discrete policy to be unimodal using Poisson probability distributions. This unimodal architecture can better leverage the continuity in the underlying continuous action space using explicit unimodal probability distributions. We conduct extensive experiments to show that the discrete policy with the unimodal probability distribution provides significantly faster convergence and higher performance for on-policy RL algorithms in challenging control tasks, especially in highly complex tasks such as Humanoid. We provide theoretical analysis on the variance of the PG estimator, which suggests that our attentively designed unimodal discrete policy can retain a lower variance and yield a stable learning process.

关键词： Task analysis Probability distribution Aerospace electronics Reinforcement learning Gaussian distribution Technological innovation Learning systems

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