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Performance of Linear Receive Beamforming for Continuous Aperture Arrays (CAPAs)

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arXiv 2024年

作者： Ouyang, Chongjun Wang, Zhaolin Zhang, Xingqi Liu, Yuanwei The School of Electrical and Electronic Engineering UCD College of Engineering and Architecture University College DublinD04 V1W8 Ireland The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

The performance of linear receive beamforming in continuous aperture array (CAPA)-based uplink communications is analyzed. Continuous linear beamforming techniques are proposed for CAPA receivers under the criteria of maximum-ratio combining (MRC), zero-forcing (ZF), and minimum mean-square error (MMSE). i) For MRC beamforming, a closed-form expression for the beamformer is derived to maximize per-user signal power, and the achieved uplink rate and mean-square error (MSE) in detecting received data symbols are analyzed. ii) For ZF beamforming, a closed-form beamformer is derived using channel correlation to eliminate interference, with a function space interpretation demonstrating its optimality in maximizing signal power while ensuring zero inter-user interference. iii) For MMSE beamforming, it is proven to be the optimal linear receive approach for CAPAs in terms of maximizing per-user rate and minimizing MSE. A closed-form expression for the MMSE beamformer is then derived, along with the achievable sum-rate and sum-MSE. The proposed linear beamforming techniques are then compared with those for conventional spatially discrete arrays (SPDAs). Analytical and numerical results indicate that: i) for both CAPAs and SPDAs, the considered linear beamformers can be represented as weighted sums of each user’s spatial response, with weights determined by channel correlation;ii) CAPAs achieve higher sum-rates and lower sum-MSEs than SPDAs under ZF and MMSE beamforming;and iii) SPDAs may outperform CAPAs with MRC beamforming in interference-dominated scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

Toward Energy Efficient Multiuser IRS-Assisted URLLC Systems: A Novel Rank Relaxation Method

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Toward Energy Efficient Multiuser IRS-Assisted URLLC Systems...

IEEE Globecom Workshops

作者： Jalal Jalali Filip Lemic Hina Tabassum Rafael Berkvens Jeroen Famaey IDLab University of Antwerp - imec Antwerp Belgium AI-Driven Systems Lab i2Cat Foundation Barcelona Spain Department of Electrical Engineering and Computer Science York University Toronto ON Canada

This paper proposes an energy efficient resource allocation design algorithm for an intelligent reflecting surface (IRS)-assisted downlink ultra-reliable low-latency communication (URLLC) network. This setup features a multi-antenna base station (BS) transmitting data traffic to a group of URLLC users with short packet lengths. We maximize the total network's energy efficiency (EE) through the optimization of active beamformers at the BS and passive beamformers (a.k.a, phase shifts) at the IRS. The main non-convex problem is divided into two sub-problems. An alternating optimization (AO) approach is then used to solve the problem. Through the use of the successive convex approximation (SCA) with a novel iterative rank relaxation method, we construct a concave-convex objective function for each sub-problem. The first sub-problem is a fractional program that is solved using the Dinkelbach method and a penalty-based approach. The second sub-problem is then solved based on semi-definite programming (SDP) and the penalty-based approach. The iterative solution gradually approaches the rank-one for both the active beamforming and unit modulus IRS phase-shift sub-problems. Our results demonstrate the efficacy of the proposed solution compared to existing benchmarks.

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Collaborative Computing in Non-Terrestrial Networks: A Multi-Time-Scale Deep Reinforcement Learning Approach

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arXiv 2024年

作者： Cao, Yang Lien, Shao-Yu Liang, Ying-Chang Niyato, Dusit Shen, Xuemin School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China University of Electronic Science and Technology of China Chengdu611731 China Institute of Intelligent Systems National Yang Ming Chiao Tung University Tainan City711 Taiwan School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical and Computer Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada

Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks (NTNs) has been the most promising paradigm to enable global coverage. The limited computing capabilities on LEO satellites however render tackling resource optimization within a short duration a critical challenge. Although the sufficient computing capabilities of the ground infrastructures can be utilized to assist the LEO satellite, different time-scale control cycles and coupling decisions between the space- and ground-segments still obstruct the joint optimization design for computing agents at different segments. To address the above challenges, in this paper, a multi-time-scale deep reinforcement learning (DRL) scheme is developed for achieving the radio resource optimization in NTNs, in which the LEO satellite and user equipment (UE) collaborate with each other to perform individual decision-making tasks with different control cycles. Specifically, the UE updates its policy toward improving value functions of both the satellite and UE, while the LEO satellite only performs finite-step rollout for decision-makings based on the reference decision trajectory provided by the UE. Most importantly, rigorous analysis to guarantee the performance convergence of the proposed scheme is provided. Comprehensive simulations are conducted to justify the effectiveness of the proposed scheme in balancing the transmission performance and computational complexity. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

Occluded Human Pose Estimation based on Limb Joint Augmentation

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arXiv 2024年

作者： Han, Gangtao Song, Chunxiao Wang, Song Wang, Hao Chen, Enqing Wang, Guanghui School of Electrical and Information Engineering Zhengzhou University 100 Kexue Avenue Henan Zhengzhou450001 China School of Computer and Artificial Intelligence Zhengzhou University 100 Kexue Avenue Henan Zhengzhou450001 China Department of Computer Science Toronto Metropolitan University 350 Victoria Street TorontoONM5B 2K3 Canada

Human pose estimation aims at locating the specific joints of humans from the images or videos. While existing deep learning-based methods have achieved high positioning accuracy, they often struggle with generalization in occlusion scenarios. In this paper, we propose an occluded human pose estimation framework based on limb joint augmentation to enhance the generalization ability of the pose estimation model on the occluded human bodies. Specifically, the occlusion blocks are at first employed to randomly cover the limb joints of the human bodies from the training images, imitating the scene where the objects or other people partially occlude the human body. Trained by the augmented samples, the pose estimation model is encouraged to accurately locate the occluded keypoints based on the visible ones. To further enhance the localization ability of the model, this paper constructs a dynamic structure loss function based on limb graphs to explore the distribution of occluded joints by evaluating the dependence between adjacent joints. Extensive experimental evaluations on two occluded datasets, OCHuman and CrowdPose, demonstrate significant performance improvements without additional computation cost during inference. © 2024, CC BY-NC-SA.

关键词： Joints (anatomy)

Tactile Sensor-Based Detection of Partial Foothold for Balance Control in Humanoid Robots

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Tactile Sensor-Based Detection of Partial Foothold for Balan...

IEEE-RAS International Conference on Humanoid Robots

作者： Taro Tako Rafael Cisneros-Limón Hiroshi Kaminaga Kenji Kaneko Masaki Murooka Iori Kumagai Hiroaki Masuzawa Yohei Kakiuchi Department of Computer Science and Engineering Toyohashi University of Technology Toyohashi Japan CNRS-AIST JRL (Joint Robotics Laboratory) UMI3218/IRL National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Japan IRES2 Toyohashi University of Technology Toyohashi Japan

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

This paper proposes a tactile sensor array to detect partial footholds and a wrench control method for biped humanoid robots using detected partial footholds. The proposed tactile sensor adopts a high-density FSR (Force Sensitive Resistor) array. It can read data within milliseconds. Filtering methods for its high-speed data which provide accurate estimation of contact areas are also proposed. They enable the computation of optimal contact forces. A wrench control method for biped humanoid robots, based on contact area information from detected partial footholds, enables stable force control even in partial foothold situations. The effectiveness of this advanced control system is evaluated through stabilizing and climbing stepladder experiments on partial footholds using an actual humanoid robot.

关键词： Legged locomotion Torque Force measurement Force Tactile sensors Humanoid robots Estimation Torque measurement Foot Sensor arrays

Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment

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arXiv 2022年

作者： Lu, Zhichao Cheng, Ran Jin, Yaochu Tan, Kay Chen Deb, Kalyanmoy The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Nature Inspired Computing and Engineering Faculty of Technology Bielefeld University Bielefeld33615 Germany The Department of Computer Science University of Surrey Surrey GuildfordGU2 7XH United Kingdom The Department of Computing The Hong Kong Polytechnic University Hong Kong The Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States

The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging computer vision tasks. Meanwhile, the development of neural architecture search (NAS) has provided promising approaches to automating the design of network architectures for lower prediction error. Recently, the emerging application scenarios of deep learning (e.g., autonomous driving) have raised higher demands for network architectures considering multiple design criteria: number of parameters/weights, number of floating-point operations, inference latency, among others. From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems;hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them. Nonetheless, there is still a clear gap confining the related research along this pathway: on the one hand, there is a lack of a general problem formulation of NAS tasks from an optimization point of view;on the other hand, there are challenges in conducting benchmark assessments of EMO algorithms on NAS tasks. To bridge the gap: (i) we formulate NAS tasks into general multi-objective optimization problems and analyze the complex characteristics from an optimization point of view;(ii) we present an end-to-end pipeline, dubbed EvoXBench, to generate benchmark test problems for EMO algorithms to run efficiently - without the requirement of GPUs or Pytorch/Tensorflow;(iii) we instantiate two test suites comprehensively covering two datasets, seven search spaces, and three hardware devices, involving up to eight objectives. Based on the above, we validate the proposed test suites using six representative EMO algorithms and provide some empirical analyses. The code of EvoXBench is available at https://***/EMI-Group/EvoXBench. © 2022, CC BY.

关键词： Network architecture

Pmate: Pay More Attention to Edge Points in 3d Roof Wireframe Reconstruction

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SSRN

SSRN 2024年

作者： Hao, Qiaoqiao Liu, Yujun Huang, Shangfeng Han, Ting Zhu, Duxin Su, Jinhe Wu, Yundong Cai, Guorong Li, Jonathan School of Computer Engineering Jimei University Xiamen361000 China School of Geospatial Engineering and Science Sun Yat-Sen University Zhuhai519082 China School of Geospatial Engineering and Science University of Calgary CalgaryABT2N1N4 Canada The Department of Geography and Environmental Management University of Waterloo WaterlooONN2L 3G1 Canada Department of System Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The reconstruction of 3D Roof Wireframe from airborne LiDAR point clouds have attracted increasing attention in 3D vision and photogrammetry. Despite the advancements of deep learning-based methods in reconstructing roof wireframes, existing methods lead to subpar corner recall and incomplete results due to the difficulty of fully harness spatial structural information. An important structural characteristic of wireframes is that corner points are highly likely to be situated within the convex hull formed by the edge points. Meanwhile, the edges themselves are essentially confined within this convex hull. This structural information has not been fully exploited. In this paper, we propose to pays more attention to the edge points (Pmate) in 3D Roof Wireframe Reconstruction. Specifically, we utilize the Spatial Structure Generation module to generate an edge-weight attention for each point, representing the proximity of the point to the edge. Moreover, we develop a hybrid edge detection module based on edge point, which enhanced the accuracy of edge detection. Extensive experiments on the Building3D dataset demonstrate the effectiveness of Pmate, which improves edge recall by 10% compared to previous state-of-the-art methods with comparable edge precision. The source code of our approach are publicly available in https://***/onescholar/Pmate. © 2024, The Authors. All rights reserved.

关键词： Roofs

Human Factors in Model-Driven engineering: Future Research Goals and Initiatives for MDE

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arXiv 2024年

作者： Liebel, Grischa Klünder, Jil Hebig, Regina Lazik, Christopher Nunes, Inês Graßl, Isabella Steghöfer, Jan-Philipp Exelmans, Joeri Oertel, Julian Marquardt, Kai Juhnke, Katharina Schneider, Kurt Gren, Lucas Happe, Lucia Herrmann, Marc Wyrich, Marvin Tichy, Matthias Goulão, Miguel Wohlrab, Rebekka Kalantari, Reyhaneh Heinrich, Robert Greiner, Sandra Rukmono, Satrio Adi Chakraborty, Shalini Abrahão, Silvia Amaral, Vasco Department of Computer Science Reykjavik University Menntavegur 1 Reykjavik102 Iceland Software Engineering Group Leibniz University Hannover Welfengarten 1 Hannover30167 Germany Department of Software Engineering University of Rostock Albert-Einstein-Straße Rostock18057 Germany Department of Software Engineering Humboldt-Universität zu Berlin Unter den Linden 6 Berlin10099 Germany Frankfurt Germany Department of Software Engineering II University of Passau Innstraße 33 Passau94032 Germany XITASO GmbH Austraße 35 Augsburg86153 Germany Department of Computer Science University of Antwerp Middelheimlaan 1 Antwerp2020 Belgium Department of Informatics Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe75131 Germany SMT-EMI6 Carl Zeiss SMT GmbH Rudolf-Eber-Straße 2 Oberkochen73447 Germany Department of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg41127 Sweden Department of Interdisciplinary Didactics Karlsruhe Institute of Technology Engesserstr. 2 Karlsruhe75131 Germany Department of Computer Science Saarland University Saarland Informatics Campus Saarbrücken66123 Germany Institute of Software Engineering and Languages Ulm University James-Franck-Ring Ulm89081 Germany Department of Computer Science NOVA School of Science & Technology Campus de Caparica Caparica2829-516 Portugal Department of Engineering Design and Teaching Innovation University of Ottawa 800 King Edward Avenue OttawaK1N6N5 Canada KASTEL - Institute of Information Security and Dependability Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe75131 Germany Department of Computer Science University of Bern Bern3012 Switzerland Department of Mathematics and Computer Science Eindhoven University of Technology De Zaale Eindhoven5600 MB Netherlands Instituto Universitario Mixto de Tecnología Informática Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain

Purpose: Software modelling and Model-Driven engineering (MDE) is traditionally studied from a technical perspective. However, one of the core motivations behind the use of software models is inherently human-centred. Models aim to enable practitioners to communicate about software designs, make software understandable, or make software easier to write through domain-specific modelling languages. Several recent studies challenge the idea that these aims can always be reached and indicate that human factors play a role in the success of MDE. However, there is an under-representation of research focusing on human factors in modelling. Methods: During a GI-Dagstuhl seminar, topics related to human factors in modelling were discussed by 26 expert participants from research and industry. Results: In breakout groups, five topics were covered in depth, namely modelling human aspects, factors of modeller experience, diversity and inclusion in MDE, collaboration and MDE, and teaching human-aware MDE. Conclusion: We summarise our insights gained during the discussions on the five topics. We formulate research goals, questions, and propositions that support directing future initiatives towards an MDE community that is aware of and supportive of human factors and values. © 2024, CC BY.

关键词： Software design

Tacchi: A Pluggable and Low Computational Cost Elastomer Deformation Simulator for Optical Tactile Sensors

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arXiv 2023年

作者： Chen, Zixi Zhang, Shixin Luo, Shan Sun, Fuchun Fang, Bin The Department of Engineering King’s College London LondonWC2R 2LS United Kingdom The Institute for Artificial Intelligence Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Beijing100083 China

Simulation is widely applied in robotics research to save time and resources. There have been several works to simulate optical tactile sensors that leverage either a smoothing method or Finite Element Method (FEM). However, elastomer deformation physics is not considered in the former method, whereas the latter requires a massive amount of computational resources like a computer cluster. In this work, we propose a pluggable and low computational cost simulator using the Taichi programming language for simulating optical tactile sensors, named as Tacchi. It reconstructs elastomer deformation using particles, which allows deformed elastomer surfaces to be rendered into tactile images and reveals contact information without suffering from high computational costs. Tacchi facilitates creating realistic tactile images in simulation, e.g., ones that capture wear-and-tear defects on object surfaces. In addition, the proposed Tacchi can be integrated with robotics simulators for a robot system simulation. Experiment results showed that Tacchi can produce images with better similarity to real images and achieved higher Sim2Real accuracy compared to the existing methods. Moreover, it can be connected with MuJoCo and Gazebo with only the requirement of 1G memory space in GPU compared to a computer cluster applied for FEM. With Tacchi, physical robot simulation with optical tactile sensors becomes possible. All the materials in this paper are available at https://***/zixichen007115/Tacchi. Copyright © 2023, The Authors. All rights reserved.

关键词： Elastomers