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2021 IEEE International Conference on Pervasive Computing and Communications, PerCom 2021 2021年

作者： Sandhu, Muhammad Moid Khalifa, Sara Geissdoerfer, Kai Jurdak, Raja Portmann, Marius The University of Queensland School of Information Technology and Electrical Engineering Brisbane Australia Australia School of Computer Science and Engineering University of New South Wales Sydney Australia TU Networked Embedded Systems Lab Dresden Germany School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia

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End-to-End Design for Self-Reconfigurable Heterogeneous Robotic Swarms

End-to-End Design for Self-Reconfigurable Heterogeneous Robo...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Jorge Peña Queralta Li Qingqing Tuan Nguyen Gia Hong-Linh Truong Tomi Westerlund Turku Intelligent Embedded and Robotic Systems (TIERS) Lab University of Turku Turku Finland Department of Computer Science Aalto University Finland

The following topics are dealt with: Internet of Things; wireless sensor networks; mobile robots; autonomous aerial vehicles; learning (artificial intelligence); control engineering computing; mobile computing; protoc... 详细信息

ISBN: (数字)9781728143514

ISBN: (纸本)9781728198040

关键词： Robot sensing systems Drones Robot kinematics Collaboration Cloud computing

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Motion planner augmented reinforcement learning for robot manipulation in obstructed environments

arXiv

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arXiv 2020年

作者： Yamada, Jun Lee, Youngwoon Salhotra, Gautam Pertsch, Karl Pflueger, Max Sukhatme, Gaurav S. Lim, Joseph J. Englert, Peter Cognitive Learning for Vision and Robotics Lab United States Robotic Embedded Systems Laboratory Department of Computer Science University of Southern California United States

Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in environments with many obstacles that complicate exploration. In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment. To combine the benefits of both approaches, we propose motion planner augmented RL (MoPA-RL) which augments the action space of an RL agent with the long-horizon planning capabilities of motion planners. Based on the magnitude of the action, our approach smoothly transitions between directly executing the action and invoking a motion planner. We evaluate our approach on various simulated manipulation tasks and compare it to alternative action spaces in terms of learning efficiency and safety. The experiments demonstrate that MoPA-RL increases learning efficiency, leads to a faster exploration, and results in safer policies that avoid collisions with the environment. Videos and code are available at https://***/mopa-rl. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion planning

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Distributed nonlinear model predictive control and metric learning for heterogeneous vehicle platooning with cut-in/cut-out maneuvers

arXiv

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arXiv 2020年

作者： Basiri, Mohammad Hossein Ghojogh, Benyamin Azad, Nasser L. Fischmeister, Sebastian Karray, Fakhri Crowley, Mark The Department of Systems Design Engineering University of Waterloo ON Canada The Department of Electrical and Computer Engineering University of Waterloo ON Canada The Smart Hybrid and Electric Vehicles Systems Lab The Real-Time Embedded Software Lab The Machine Learning Lab The Centre for Pattern Analysis and Machine Intelligence

Vehicle platooning has been shown to be quite fruitful in the transportation industry to enhance fuel economy, road throughput, and driving comfort. Model Predictive Control (MPC) is widely used in literature for platoon control to achieve certain objectives, such as safely reducing the distance among consecutive vehicles while following the leader vehicle. In this paper, we propose a Distributed Nonlinear MPC (DNMPC), based upon an existing approach, to control a heterogeneous dynamic platoon with unidirectional topologies, handling possible cut-in/cut-out maneuvers. The introduced method addresses a collision-free driving experience while tracking the desired speed profile and maintaining a safe desired gap among the vehicles. The time of convergence in the dynamic platooning is derived based on the time of cut-in and/or cut-out maneuvers. In addition, we analyze the improvement level of driving comfort, fuel economy, and absolute and relative convergence of the method by using distributed metric learning and distributed optimization with Alternating Direction Method of Multipliers (ADMM). Simulation results on a dynamic platoon with cut-in and cut-out maneuvers and with different unidirectional topologies show the effectiveness of the introduced method. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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Continual Learning in the Presence of Repetition

arXiv

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arXiv 2024年

作者： Hemati, Hamed Pellegrini, Lorenzo Duan, Xiaotian Zhao, Zixuan Xia, Fangfang Masana, Marc Tscheschner, Benedikt Veas, Eduardo Zheng, Yuxiang Zhao, Shiji Li, Shao-Yuan Huang, Sheng-Jun Lomonaco, Vincenzo van de Ven, Gido M. Institute for Computer Science University of St. Gallen Rosenbergstrasse 30 St. Gallen9000 Switzerland Department of Computer Science University of Bologna Via dell’Università 50 Cesena47521 Italy The University of Chicago 5801 S Ellis Ave Chicago60637 United States Argonne National Laboratory 9700 S Cass Ave Lemont60439 United States Graz University of Technology Rechbauerstraße 12 Graz8010 Austria TU Graz - SAL Dependable Embedded Systems Lab Silicon Austria Labs Graz8010 Austria Know-Center GmbH Sandgasse 36/4 Graz8010 Austria MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science University of Pisa Piano Secondo Largo Bruno Pontecorvo 3 Pisa56127 Italy Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 Leuven3001 Belgium

Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often considered in standard benchmarks for CL. Unlike with the rehearsal mechanism in buffer-based strategies, where sample repetition is controlled by the strategy, repetition in the data stream naturally stems from the environment. This report provides a summary of the CLVision challenge at CVPR 2023, which focused on the topic of repetition in class-incremental learning. The report initially outlines the challenge objective and then describes three solutions proposed by finalist teams that aim to effectively exploit the repetition in the stream to learn continually. The experimental results from the challenge highlight the effectiveness of ensemble-based solutions that employ multiple versions of similar modules, each trained on different but overlapping subsets of classes. This report underscores the transformative potential of taking a different perspective in CL by employing repetition in the data stream to foster innovative strategy design. © 2024, CC BY.

关键词： Contrastive Learning

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Towards energy positive sensing using kinetic energy harvesters

arXiv

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arXiv 2020年

作者： Sandhu, Muhammad Moid Geissdoerfer, Kai Khalifa, Sara Jurdak, Raja Portmann, Marius Kusy, Brano School of Information Technology & Electrical Engineering University of Queensland Brisbane Australia Australia Networked Embedded Systems Lab TU Dresden Germany School of Computer Science & Engineering University of New South Wales Sydney Australia School of Electrical Engineering & Computer Science Queensland University of Technology Brisbane Australia

—Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once depleted, have to be replaced or recharged. Kinetic Energy Harvesting (KEH) allows to convert ambient motion and vibration into usable electricity and can enable batteryless, maintenance free operation of motion sensors. The signal from a KEH transducer correlates with the underlying motion and may thus directly be used for context detection, saving space, cost and energy by omitting the accelerometer. Previous work uses the open circuit or the capacitor voltage for sensing without using the harvested energy to power a load. In this paper, we propose to use other sensing points in the KEH circuit that offer information-rich sensing signals while the energy from the harvester is used to power a load. We systematically analyze multiple sensing signals available in different KEH architectures and compare their performance in a transport mode detection case study. To this end, we develop four hardware prototypes, conduct an extensive measurement campaign and use the data to train and evaluate different classifiers. We show that sensing the harvesting current signal from a transducer can be energy positive, delivering up to ten times as much power as it consumes for signal acquisition, while offering comparable detection accuracy to the accelerometer signal for most of the considered transport modes. Copyright © 2020, The Authors. All rights reserved.

关键词： Kinetics

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Competition: Low-Power Wireless Bus Baseline 19

Competition: Low-Power Wireless Bus Baseline

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Proceedings of the 2019 International Conference on embedded Wireless systems and Networks

作者： Fabian Mager Romain Jacob Reto Da Forno Marco Zimmerling Networked Embedded Systems Lab TU Dresden Germany Computer Engineering and Networks Laboratory ETH Zurich Switzerland

No abstract available.

ISBN: (纸本)9780994988638

No abstract available.

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Open real-time, non-invasive fish detection and size estimation utilizing binocular camera system in a Portuguese river affected by hydropeaking

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Ecological Informatics 2025年

作者： Jürgen Soom Isabel Boavida Renan Leite Maria João Costa Gert Toming Mairo Leier Jeffrey A. Tuhtan Embedded AI Research Lab Tallinn University of Technology Akadeemia tee 15A Tallinn 12618 Harjumaa Estonia Department of Computer Systems Akadeemia tee 15A Tallinn 12618 Harjumaa Estonia Civil Engineering Research and Innovation for Sustainability Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Forest Research Centre TERRA Associate Laboratory School of Agriculture University of Lisbon Lisboa Portugal

The need for efficient approaches to track and assess fish behavior in rivers impacted by hydropeaking is increasing. Nonetheless, employing an automated camera system for underwater monitoring requires that the algorithms function under highly variable environmental conditions, which affect the ability to detect and assess fish size. Additionally, there is a lack of openly accessible freshwater fish classification and size estimation datasets. To address these limitations, we propose a binocular underwater fish monitoring system capable of real-time fish detection and size estimation. The system was deployed and tested over one week in two Portuguese rivers affected by hydropeaking. The week-long analysis also provided new insights regarding wild fish behavior in rivers affected by hydropeaking. Results indicate that hydropeaking strongly influences how fish may use instream flow refuges during hydropeaking. Fish were less frequently detected in the flow refuge during peak flow events, suggesting that the flow conditions created habitat instability and difficulty accessing the flow refuge. In contrast, fish in the non-hydropeaking river consistently used refuge areas, reinforcing their importance as shelter during natural flow variations. This study demonstrates the potential of a computer vision-based pipeline for real-time, fully automated fish monitoring of hydropeaking’s impacts on riverine fish. Additionally, we provide PTFish, an open dataset with 18,523 manually annotated frames featuring infrared and color video frames. These findings emphasize that automated, camera-based solutions for hydropeaking monitoring can be used to develop evidence-based mitigation strategies to sustain fish populations in rivers impacted by hydropeaking.

关键词： Fish migration Size estimation Binocular vision Hydropeaking Edge computing

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Towards Optimal Kinetic Energy Harvesting for the Batteryless IoT

arXiv

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arXiv 2020年

—Traditional Internet of Things (IoT) sensors rely on batteries that need to be replaced or recharged frequently which impedes their pervasive deployment. A promising alternative is to employ energy harvesters that convert the environmental energy into electrical energy. Kinetic Energy Harvesting (KEH) converts the ambient motion/vibration energy into electrical energy to power the IoT sensor nodes. However, most previous works employ KEH without dynamically tracking the optimal operating point of the transducer for maximum power output. In this paper, we systematically analyze the relation between the operating point of the transducer and the corresponding energy yield. To this end, we explore the voltage-current characteristics of the KEH transducer to find its Maximum Power Point (MPP). We show how this operating point can be approximated in a practical energy harvesting circuit. We design two hardware circuit prototypes to evaluate the performance of the proposed mechanism and analyze the harvested energy using a precise load shaker under a wide set of controlled conditions typically found in human-centric applications. We analyze the dynamic current-voltage characteristics and specify the relation between the MPP sampling rate and harvesting efficiency which outlines the need for dynamic MPP tracking. The results show that the proposed energy harvesting mechanism outperforms the conventional method in terms of generated power and offers at least one order of magnitude higher power than the latter. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy harvesting

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Correction: Privacy preservation in the internet of vehicles using local differential privacy and IOTA ledger

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Cluster Computing 2023年第6期26卷 3379-3379页

作者： Iftikhar, Zainab Anjum, Adeel Khan, Abid Shah, Munam Ali Jeon, Gwanggil Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Institute of Information Technology Quaid i Azam University Islamabad Pakistan College of Science and Engineering University of Derby Derby UK Department of Embedded Systems Engineering Incheon National University Incheon Republic of Korea Energy Excellence & Smart City Lab Incheon National University Incheon Republic of Korea

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