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arXiv 2024年

作者： Wu, Haijun Mata-Cervera, Nilo Wang, Haiwen Zhu, Zhihan Qiu, Cheng-Wei Shen, Yijie Wang Da-Heng Center Heilongjiang Key Laboratory of Quantum Control Harbin University of Science and Technology Harbin150080 China Centre for Disruptive Photonic Technologies School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Applied Physics Stanford University StanfordCA94305 United States School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore

Creation and control of topological complex excitations play crucial roles in both fundamental physics and modern information science. Torons are a sophisticated class of 3D chiral polar topological structures with both skyrmionic quasiparticle textures and monopole point defects, so far only observed in liquid crystal nonpolar models. Here, we experimentally construct torons with the photonic spin of vector structured light and demonstrate the topological phase transitions among diverse topological states: torons, hopfions, skyrmioniums and monopole pairs. We can also continually tune the toron’s chirality and the helical spin textures of emerging monopole pairs. The birth of photonic torons and tunable monopoles opens a flexible platform for studying nontrivial light-matter interaction and topological informatics. © 2024, CC0.

关键词： Topology

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Adaptive Block Elevation Mapping for Large-scale Scene

Adaptive Block Elevation Mapping for Large-scale Scene

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IEEE International Conference on Robotics and Biomimetics

作者： Yang Zhou Shiqiang Zhu Huang Huang Yuehua Li Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Beijing Institute of Control Engineering Beijing China Beijing Research Institute of Zhejiang Laboratory Beijing China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory.

关键词： Geometry Laser radar Navigation Motion segmentation Memory management Robot sensing systems Trajectory

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Signatures of Open and Noisy Quantum Systems in Single-Qubit Quantum Annealing

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Physical Review Applied 2023年第3期19卷 034053-034053页

作者： Zachary Morrell Marc Vuffray Andrey Y. Lokhov Andreas Bärtschi Tameem Albash Carleton Coffrin Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Electrical & Computer Engineering University of New Mexico Albuquerque New Mexico 87131 USA Department of Physics & Astronomy University of New Mexico Albuquerque New Mexico 87131 USA Center for Quantum Information and Control (CQuIC) University of New Mexico Albuquerque New Mexico 87131 USA

We propose a quantum annealing protocol that effectively probes the dynamics of a single qubit on D-Wave’s quantum annealing hardware. This protocol uses D-Wave’s h-gain schedule functionality, which allows the rapid suppression of the longitudinal magnetic field at arbitrary points during the anneal. This feature enables us to distinguish between open and closed-system dynamics as well as the presence and absence of longitudinal magnetic field noise. We show that thermal fluctuations alone are not sufficient to explain the system’s dynamics and that a prominent role is played by magnetic field fluctuations, which need to be included in an open quantum system description. Moreover, our protocol only requires single-qubit measurements, which makes it suitable as an exploration and calibration tool for large-scale quantum annealing hardware.

关键词： Adiabatic quantum optimization Open quantum systems Quantum benchmarking Quantum fluctuations & noise Quantum information with solid state qubits Quantum verification Superconducting qubits Simulated annealing

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Optimization Strategy of Bio-Inspired Metaheuristic Algorithms Tuned PID controller for PMBDC Actuated Robotic Manipulator

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Procedia computer Science 2020年 171卷 2040-2049页

作者： Sudarshan K. Valluru Madhusudan Singh Center for Control of Dynamical Systems and Computation Department of Electrical Engineering Delhi Technological University Delhi-110042 India

The present article makes an attempt to evaluate the computational performance of genetic meta heuristic optimized control algorithms. Here, the multi-objective bio inspired algorithm (MOGA) and adaptive particle swarm optimization (APSO) algorithm are used to tune linear PID (L-PID) and nonlinear PID(NL-PID) controllers to implement performance and execution control of permanent magnet brushed DC (PMBDC) motor actuated robotic manipulator. The MOGA, APSO optimised nonlinear and linear PID controllers have been validated for their response efficacy and compared in respect of the steady-state error, overshoot and settling time of PMBDC driven robotic Manipulator. The efficacy of metaheuristics such as APSO and MOGA tuned NL-PID controller are better as compared to the L- PID controlled objects. Experimental results show that the NL-PID controller mends the controlled objects’ performace with a reduction in both the overshoot as well as the settling time, if tuned either with MOGA or APSO for L-PID controllers while NL-PID controller tuned with APSO gives satisfactory dynamic response of the system.

关键词： L-PID NL-PID MOGA APSO Robotic Manipulator

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Output consensus control for linear port-Hamiltonian systems

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IFAC-PapersOnLine 2022年第30期55卷 230-235页

作者： Shuai Feng Yu Kawano Michele Cucuzzella Jacquelien M.A. Scherpen School of Automation Nanjing University of Science and Technology Nanjing 210094 China Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima 739-8527 Japan Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia PV Italy Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen 9747 AG Groningen the Netherlands

In this paper, we study output consensus of coupled linear port-Hamiltonian systems on graphs in the presence of constant disturbances, where couplings are allowed to be both static and dynamic. Utilizing port-Hamiltonian structures, we present dynamic controllers achieving output consensus where the consensus values are determined by the disturbances. Finally, the utility of the proposed controller is illustrated by applying it to current sharing of DC microgrids.

关键词： port-Hamiltonian systems networked systems output consensus

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Visuotactile Sensor Enabled Pneumatic Device Towards Compliant Oropharyngeal Swab Sampling

Visuotactile Sensor Enabled Pneumatic Device Towards Complia...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Shoujie Li Mingshan He Wenbo Ding Linqi Ye Xueqian Wang Junbo Tan Jinqiu Yuan Xiao-Ping Zhang Tsinghua Shenzhen International Graduate School Shenzhen China Department of Mechanical Engineering Seoul National University Seoul Korea Department of Intelligent Control Qianyuan Institute of Science Hangzhou China Clinical Research Center The Seventh Affiliated Hospital Sun Yat-sen University Shenzhen Guangdong China Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto ON Canada

Manual oropharyngeal (OP) swab sampling is an intensive and risky task. In this article, a novel OP swab sampling device of low cost and high compliance is designed by combining the visuotactile sensor and the pneumatic actuator-based gripper. Here, a concave visuotactile sensor called CoTac is first proposed to address the problems of high cost and poor reliability of traditional multi-axis force sensors. Besides, by imitating the doctor's fingers, a soft pneumatic actuator with a rigid skeleton structure is designed, which is demonstrated to be reliable and safe via finite element modeling and experiments. Furthermore, we propose a sampling method that adopts a compliant control algorithm based on the adaptive virtual force to enhance the safety and compliance of the swab sampling process. The effectiveness of the device has been verified through sampling experiments as well as in vivo tests, indicating great application potential. The cost of the device is around 30 US dollars and the total weight of the functional part is less than 0.1 kg, allowing the device to be rapidly deployed on various robotic arms.

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Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach

Real-Time Object Detection and Robotic Manipulation for Agri...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Hongyu Zhao Zezhi Tang Zhenhong Li Yi Dong Yuancheng Si Mingyang Lu George Panoutsos The Department of Physics Imperial College London United Kingdom The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom The Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom The Department of Electronics and Computer Science University of Southampton Southampton United Kingdom The Department of Economics Fudan University Shanghai China The Center for Nondestructive Evaluation (CNDE) Iowa State University Ames IA United States

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

The optimization of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialization. Nowadays, the utilization of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of Convolutional Neural Networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The You Only Look Once(YOLO) algorithmic framework is employed with traditional Rectangular Bounding Boxes (R-Bbox) for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation.

关键词： YOLO Visualization Crops Grasping Production Robustness Real-time systems

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When showing your hand pays off: Announcing strategic intentions in Colonel Blotto games

arXiv

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arXiv 2020年

作者： Chandan, Rahul Paarporn, Keith Marden, Jason R. Department of Electrical and Computer Engineering Center of Control Dynamical Systems and Computation UC SantaBarbara United States

— In competitive adversarial environments, it is often advantageous to obfuscate one’s strategies or capabilities. However, revealing one’s strategic intentions may shift the dynamics of the competition in complex ways. Can it ever be advantageous to reveal strategic intentions to an opponent? In this paper, we consider three-stage Colonel Blotto games in which one player can choose whether or not to pre-commit resources to a single battlefield before play begins. This pre-commitment is public knowledge. In response, the opponent can either secure the battlefield by matching the pre-commitment with its own forces, or withdraw. In a two-player setting, we show that a weaker player never has an incentive to pre-commit any amount of resources to a battlefield regardless of how valuable it is. We then consider a three-player setting in which two players fight against a common adversary on separate fronts. Only one of the two players facing the adversary has the option of pre-committing. We find there are instances where this player benefits from pre-committing. The analysis indicates that under non-cooperative team settings and no possibility of forming alliances, there can be incentives to publicly announce one’s strategic intentions to an adversary. Copyright © 2020, The Authors. All rights reserved.

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A Trustless Federated Framework for Decentralized and Confidential Deep Learning

A Trustless Federated Framework for Decentralized and Confid...

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Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Chao Li Qiuyu Shen Cheng Xiang Bharath Ramesh Department of Electrical and Computer Engineering Advanced Control Technology Lab National University of Singapore Singapore International Center for Neuromorphic Systems The MARCS Institute for Brain Behaviour and Development Western Sydney University Sydney Australia

Nowadays, deep learning models can be trained on large amounts of web data on power hungry servers and be deployment-ready for specific real-world applications. With a state-of-the-art model architecture and a large publicly available dataset for pre-training, convolutional neural network models can be further fine-tuned via transfer learning for a related task. Nonetheless, the training process required of privacy-sensitive applications needs to protect data confidentiality and simultaneously boost performance using limited training data. For such data-deprived and privacy-centric learning, we introduce a trustless federated learning framework that seamlessly integrates deep learning models from different edge nodes using a blockchain-based architecture. Our framework performs federated learning without the need of a central server by leveraging a smart contract blockchain platform with a distributed file system for model storage. Users can locally train on their data while routinely benefiting from an enhanced model obtained by merging the models from all users in a decentralized fashion. Most importantly, this framework is free of the potential single point of failure in centralized federated learning. Besides, our framework has in-built incentive mechanisms to prevent model corruption and temper bad actors. We tested our framework on various computer vision datasets. The experimental results show that the merged model accuracy is on-par compared to a centralized federated training setup. To the best of our knowledge, this work represents the first systematic attempt at building a blockchain-based federated deep learning framework for computer vision. The code is publicly made available at: https://***/s-elo/DNN-Blockchain.

关键词： Deep learning Training Data privacy Federated learning computational modeling Smart contracts Data models

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All electrical control and Temperature Dependence of the Spin and Valley Hall Effect in Monolayer WSe2 Transistors

arXiv

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arXiv 2022年

作者： Li, Xintong Liu, Zhida Liu, Yihan Karki, Suyogya Li, Xiaoqin Akinwande, Deji Incorvia, Jean Anne C. Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Department of Physics Center for Dynamics and Control of Materials The University of Texas at Austin AustinTX78712 United States

Heavy metal-based two-dimensional van der Waals materials have a large, coupled spin and valley Hall effect (SVHE) that has potential use in spintronics and valleytronics. Optical measurements of the SVHE have largely been performed below 30 K and understanding of the SVHE-induced spin/valley polarizations that can be electrically generated is limited. Here, we study the SVHE in monolayer p-type tungsten diselenide (WSe2). Kerr rotation (KR) measurements show the spatial distribution of the SVHE at different temperatures, its persistence up to 160 K, and that it can be electrically modulated via gate and drain bias. A spin/valley drift and diffusion model together with reflection spectra data is used to interpret the KR data and predict a lower-bound spin/valley lifetime of 4.1 ns below 90 K and 0.26 ns at 160 K. The excess spin and valley per unit length along the edge is calculated to be 109 μm-1 at 45 K, which corresponds to a spin/valley polarization on the edge of 6%. These results are important steps towards practical use of the SVHE. © 2022, CC BY.

关键词： Monolayers

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