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arXiv 2024年

作者： Kowalczyk, Krzysztof Wachel, Pawel Rojas, Cristian R. Department of Control Systems and Mechatronics CosmologyWroclaw University of Science and Technology Wroclaw Poland School of Electrical Engineering and Computer Science CosmologyKTH Royal Institute of Technology Stockholm Sweden

This paper addresses a kernel-based learning problem for a network of agents locally observing a latent multidimensional, nonlinear phenomenon in a noisy environment. We propose a learning algorithm that requires only mild a priori knowledge about the phenomenon under investigation and delivers a model with corresponding non-asymptotic high probability error bounds. Both non-asymptotic analysis of the method and numerical simulation results are presented and discussed in the paper. Copyright © 2024, The Authors. All rights reserved.

关键词： Asymptotic analysis

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Summary of the 2023 Low-Power Deep Learning Object Detection and Semantic Segmentation Multitask Model Compression Competition for Traffic Scene in Asian Countries

Summary of the 2023 Low-Power Deep Learning Object Detection...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Ni, Yu-Shu Tsai, Chia-Chi Chen, Chih-Cheng Kuo, Hsien-Kai Chen, Po-Yu Hu, Po-Chi Kuo, Ted T. Hwang, Jenq-Neng Guo, Jiun-In National Yang Ming Chiao Tung University Department of Institute of Electronics Hsinchu Taiwan National Cheng Kung University Department of Electronics Engineering Tainan Taiwan MediaTek Inc. Hsinchu Taiwan Hsinchu Taiwan National Yang Ming Chiao Tung University Department of Institute of Intelligent Systems Hsinchu Taiwan University of Washington Department of Electrical & Computer Engineering SeattleWA United States National Yang Ming Chiao Tung University Department of Institute of Electronic Hsinchu Taiwan

ISBN: (纸本)9798350313154

The competition endeavors to accomplish object detection and semantic segmentation traffic objects in Asia, notably in countries such as Taiwan, using low power while achieving a high mean intersection over union (mIOU) and high mean average precision (mAP). This task is challenging as it must be achieved in harsh driving environments. The targeted semantic segmentation objects include several types of lines such as dashed and single white and yellow lines, double dashed and double white and yellow lines, main and alter lanes and the targeted object detection objects include car, pedestrian, motorbike, and bike. To train the model, the participants utilized a total of 35,500 annotated images which were obtained from the iVS-ODSEG-Dataset, a revised version of the Berkeley Deep Drive 100K[1], along with 89,002 annotated training images from iVS-Dataset[2]. Additionally, 130 annotated images have been provided as validation examples from Asian road conditions. The contest evaluation process uses 9,612 testing images, of which 4,900 are used in the qualification stage competition, and the remaining are used in the final stage competition. The competition had 223 registered teams, with the top 15 teams that achieved the highest performance mIOU and mAP entering the final stage competition. Based on the score evaluation and the invited paper review result, the team 'Polybahn' produced the overall best model and the best INT8 model development, followed by team 'You Only Lowpower Once' and team 'ACVLab.' © 2023 IEEE.

关键词： Object detection

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Frequency Domain Auto-tuning of Structured LPV Controllers for High-Precision Motion Control

arXiv

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arXiv 2024年

作者： Broens, Yorick Butler, Hans Tóth, Roland The Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands ASML Veldhoven Netherlands The Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Hungary

Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput, requires novel control design methods that can go beyond the capabilities of traditional solutions. Traditional control methods often struggle with the complexity and position-dependent effects inherent in modern motion systems, leading to compromises in performance and a laborious task of controller design. This paper addresses these challenges by introducing a novel structured feedback control auto-tuning approach for multiple-input multiple-output (MIMO) motion systems. By leveraging frequency response function (FRF) estimates and the linear-parameter-varying (LPV) control framework, the proposed approach automates the controller design, while providing local stability and performance guarantees. Key innovations include norm-based magnitude optimization of the sensitivity functions, an automated stability check through a novel extended factorized Nyquist criterion, a modular structured MIMO LPV controller parameterization, and a controller discretization approach which preserves the continuous-time (CT) controller parameterization. The proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator *** Codes 37M15 © 2024, CC BY.

关键词： Continuous time systems

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Decision-Making in Dynamic Environment

arXiv

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arXiv 2024年

作者： Chowdhury, Jayabrata Shivaraman, Venkataramanan Dangi, Sumit Sundaram, Suresh Sujit, P.B. Robert Bosch Centre for Cyber-Physical Systems Indian Institute of Science Bangalore India The Department of Aerospace Engineering Indian Institute of Science Bangalore India The Department of Data Science & Engineering and Electrical Engineering & Computer Science Indian Institute of Science Education and Research Bhopal India

Autonomous Vehicle (AV) decision-making in urban environments is inherently challenging due to the dynamic interactions with surrounding vehicles. For safe planning, AV/ego must understand the weightage of various spatiotemporal interactions in a scene. Contemporary works use colossal transformer architectures to encode interactions mainly for trajectory prediction, resulting in increased computational complexity. To address this issue without compromising spatiotemporal understanding and performance, we propose the simple Deep Attention Driven Reinforcement Learning (DAD-RL) framework, which dynamically assigns and incorporates the significance of surrounding vehicles into the ego’s RL-driven decision-making process. We introduce an AV-centric spatiotemporal attention encoding (STAE) mechanism for learning the dynamic interactions with different surrounding vehicles. To understand map and route context, we employ a context encoder to extract features from context maps. The spatiotemporal representations combined with contextual encoding provide a comprehensive state representation. The resulting model is trained using the Soft-Actor Critic (SAC) algorithm. We evaluate the proposed framework on the SMARTS urban benchmarking scenarios without traffic signals to demonstrate that DAD-RL outperforms recent state-of-the-art methods. Furthermore, an ablation study underscores the importance of the context-encoder and spatio-temporal attention encoder in achieving superior performance. © 2024, CC BY-SA.

关键词： Deep reinforcement learning

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A Long-Term Voltage Stability Margin Index Based on Multiple Real Power Flow Solutions

A Long-Term Voltage Stability Margin Index Based on Multiple...

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IEEE General Meeting Power& Energy Society

作者： Bin Wang Dan Wu Xiaowen Su Kai Sun Le Xie Department of Electrical and Computer Engineering University of Texas at San Antonio San Antonio TX School of Electrical and Electronic Engineering Huazhong University of Science and Technology Lab for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA Department of Electrical Engineering and Computer Science University of Tennessee TN Department of Electrical and Computer Engineering Taxes A&M University College Station TX

In this paper, we demonstrate the distribution of real-valued power flow solutions and its application to the long-term voltage stability. It is the first time in power engineering we realize that even a small-scale IEEE standard power system can admit a humongous number of real-valued solutions for a single load and generation profile. For example, the IEEE 30-bus system can achieve 25686 many different real-valued solutions at a light loading condition. Furthermore, a mysterious class of “false” power flow solutions is reported and analyzed rigorously as legitimate numerical solutions. All solution sets investigated in this paper are posted online associated with this paper to support potential future applications [1]. Based on these extensive solved power flow solutions, we exhibit their occurrence patterns and distributions at different loading levels, and propose a long-term voltage stability margin index to quantify the long-term voltage stability of a given power flow condition. Numerical studies on a 5-bus system and a 57-bus system show the feasibility and effectiveness of the proposed index in assessing power system long-term voltage stability margin.

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A Continuous Off-Policy Reinforcement Learning Scheme for Optimal Motion Planning in Simply-Connected Workspaces

A Continuous Off-Policy Reinforcement Learning Scheme for Op...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Department of Electrical and Computer Engineering University of Patras Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in multiply connected manifolds, and is necessary for formulating continuous solutions. The current method generalizes and improves upon previous results, where parametrized controllers hindered the method in scope and results. Through enhancing the traditional reactive paradigm with RL, the proposed scheme is demonstrated to outperform both previous reactive methods as well as an RRT* method in path length, cost function values and execution times, indicating almost global optimality.

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Tunable Rectification of Diffusion-Wave Fields by Spatiotemporal Metamaterials

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Physical Review Letters 2022年第25期129卷 256601-256601页

作者： Jiaxin Li Zhanxiang Zhang Guoqiang Xu Haoran Sun Lizhou Dai Tianlong Li Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 China

The diffusion process is the basis of many branches of science and engineering, and generally obeys reciprocity between two ports of a linear time-invariant medium. Recent research on classical wave dynamics has explored the spatiotemporal modulation to exhibit preferred directions in photons and plasmons. Here we report a distinct rectification effect on diffusion-wave fields by modulating the conductivity and observe nonreciprocal transport of charges. We experimentally create a spatiotemporal diffusion metamaterial, in which a mode transition to zero frequency is realized. A direct current component thereby emerges, showcasing a biased effect on the charge diffusion when the incident fundamental frequency is a multiple of the system modulation frequency. These results may find applications spanning a plethora of diffusive fields in general.

关键词： Diffusion Metamaterials Bloch-Floquet theorem

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VersaSens: An Extendable Multimodal Platform for Next-Generation Edge-AI Wearables

IEEE Transactions on Circuits and Systems for Artificial Int...

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IEEE Transactions on Circuits and systems for Artificial Intelligence 2024年第1期1卷 83-96页

作者： Taraneh Aminosharieh Najafi José Angel Miranda Calero Jérôme Thevenot Benjamin Duc Stefano Albini Alireza Amirshahi Hossein Taji María José Belda Beneyto Antonio Affanni David Atienza Embedded Systems Laboratory (ESL) Institute of Electrical and Micro Engineering École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Polytechnic Department of Engineering and Architecture Udine Italy Computer Architecture and Automation Department Universidad Complutense de Madrid Madrid Spain

The transition of healthcare towards digitalization is closely related to the advancement of health-related technologies, including wearable sensors and edge computing. In this paper, we present VersaSens, a versatile and customizable platform concept and its real implementation as a tool to boost research in wearable sensors. The platform embodies the core attributes of the VersaSens concept: versatility, flexibility, and extendability across multiple aspects of hardware, software, and processing components. It features a modular design, consisting of sensor, processor, and co-processor modules, allowing for various configurations. To evaluate the efficiency of the platform, we tested three use cases: cough monitoring, heartbeat classification and epileptic seizure detection. In all cases, the results indicate that the platform effectively executes the applications, achieving low energy consumption. In particular, our findings indicates that the integration of a domain-specific edge-AI co-processor [i.e., HEEP ocrates (Machetti et al., 2024)] equipped with several hardware accelerators further improved the overall execution time and energy consumption of the system. These results demonstrate the potential of VersaSens to effectively support a diverse range of edge-AI applications and configurations, thereby providing a robust foundation for the research and development of novel smart wearable sensor systems.

关键词： Sensors Wearable devices Artificial intelligence Wearable sensors Medical services Intelligent sensors Monitoring

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Follow Everything: A Leader-Following and Obstacle Avoidance Framework with Goal-Aware Adaptation

arXiv

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arXiv 2025年

作者： Zhang, Qianyi Ma, Shijian Liu, Boyi Liu, Jingtai Jiao, Jianhao Kanoulas, Dimitrios Institute of Robotics and Automatic Information System Nankai University Tianjin300350 China Electrical and Computer Engineering Department Hong Kong University of Science and Technology China Centre for Data Science University of Macau China Department of Computer Science University College London United Kingdom

Robust and flexible leader-following is a critical capability for robots to integrate into human society. While existing methods struggle to generalize to leaders of arbitrary form and often fail when the leader temporarily leaves the robot’s field of view, this work introduces a unified framework addressing both challenges. First, traditional detection models are replaced with a segmentation model, allowing the leader to be anything. To enhance recognition robustness, a distance frame buffer is implemented that stores leader embeddings at multiple distances, accounting for the unique characteristics of leader-following tasks. Second, a goal-aware adaptation mechanism is designed to govern robot planning states based on the leader’s visibility and motion, complemented by a graph-based planner that generates candidate trajectories for each state, ensuring efficient following with obstacle avoidance. Simulations and real-world experiments with a legged robot follower and various leaders (human, ground robot, UAV, legged robot, stop sign) in both indoor and outdoor environments show competitive improvements in follow success rate, reduced visual loss duration, lower collision rate, and decreased leader-follower distance. Visit the website for the video and code. © 2025, CC BY.

关键词： Motion planning

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