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arXiv 2023年

作者： Vlahakis, Eleftherios E. Lindemann, Lars Dimarogonas, Dimos V. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Thomas Lord Department of Computer Science Viterbi School of Engineering University of Southern California Los AngelesCA90089 United States

We consider the synthesis problem of a multi-agent system under signal temporal logic (STL) specifications representing bounded-time tasks that need to be satisfied recurrently over an infinite horizon. Motivated by the limited approaches to handling recurring STL systematically, we tackle the infinite-horizon control problem with a receding horizon scheme equipped with additional STL constraints that introduce minimal complexity and a backward-reachability-based terminal condition that is straightforward to construct and ensures recursive feasibility. Subsequently, we decompose the global receding horizon optimization problem into agent-level programs the objectives of which are to minimize local cost functions subject to local and joint STL constraints. We propose a scheduling policy that allows individual agents to sequentially optimize their control actions while maintaining recursive feasibility. This results in a distributed strategy that can operate online as a model predictive controller. Last, we illustrate the effectiveness of our method via a multi-agent system example assigned a surveillance task. © 2023, CC BY.

关键词： Temporal logic

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Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge

arXiv

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arXiv 2023年

作者： Risso, Matteo Burrello, Alessio Conti, Francesco Lamberti, Lorenzo Chen, Yukai Benini, Luca Macii, Enrico Poncino, Massimo Pagliari, Daniele Jahier The Department of Control and Computer Engineering Politecnico di Torino Turin10129 Italy The Interuniversity Department of Regional and Urban Studies and Planning Politecnico di Torino Turin10129 Italy The Department of Electrical Electronic and Information Engineering University of Bologna Bologna40136 Italy The Department of Information Technology and Electrical Engineering The ETH Zurich Zurich8092 Switzerland

Neural Architecture Search (NAS) is quickly becoming the go-to approach to optimize the structure of Deep Learning (DL) models for complex tasks such as Image Classification or Object Detection. However, many other relevant applications of DL, especially at the edge, are based on time-series processing and require models with unique features, for which NAS is less explored. This work focuses in particular on Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged as a promising alternative to more complex recurrent architectures. We propose the first NAS tool that explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive-field and number of features in each layer. The proposed approach searches for networks that offer good trade-offs between accuracy and number of parameters/operations, enabling an efficient deployment on embedded platforms. Moreover, its fundamental feature is that of being lightweight in terms of search complexity, making it usable even with limited hardware resources. We test the proposed NAS on four real-world, edge-relevant tasks, involving audio and bio-signals: (i) PPG-based Heart-Rate Monitoring, (ii) ECG-based Arrythmia Detection, (iii) sEMG-based Hand-Gesture Recognition, and (iv) Keyword Spotting. Results show that, starting from a single seed network, our method is capable of obtaining a rich collection of Pareto optimal architectures, among which we obtain models with the same accuracy as the seed, and 15.9-152× fewer parameters. Moreover, the NAS finds solutions that Pareto-dominate state-of-the-art hand-tuned models for 3 out of the 4 benchmarks, and are Pareto-optimal on the fourth (sEMG). Compared to three state-of-the-art NAS tools, ProxylessNAS, MorphNet and FBNetV2, our method explores a larger search space for TCNs (up to 1012×) and obtains superior solutions, while requiring low GPU memory and search time. We d

关键词： Energy efficiency

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Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing

Continual Learning for Robust Gate Detection under Dynamic L...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhongzheng Qiao Xuan Huy Pham Savitha Ramasamy Xudong Jiang Erdal Kayacan Andriy Sarabakha ERI@N Interdisciplinary Graduate Programme Nanyang Technological University Singapore Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Institute of Infocomm Research (I2R) Agency for Science Technology and Research (A*STAR) Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Automatic Control Group (RAT) Paderborn University Paderborn Germany

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In autonomous and mobile robotics, a principal challenge is resilient real-time environmental perception, particularly in situations characterized by unknown and dynamic elements, as exemplified in the context of autonomous drone racing. This study introduces a perception technique for detecting drone racing gates under illumination variations, which is common during high-speed drone flights. The proposed technique relies upon a lightweight neural network backbone augmented with capabilities for continual learning. The envisaged approach amalgamates predictions of the gates' positional coordinates, distance, and orientation, encapsulating them into a cohesive pose tuple. A comprehensive number of tests serve to underscore the efficacy of this approach in confronting diverse and challenging scenarios, specifically those involving variable lighting conditions. The proposed methodology exhibits notable robustness in the face of illumination variations, thereby substantiating its effectiveness.

关键词： Continuing education Robot kinematics Neural networks Lighting Logic gates Robustness Real-time systems

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A Practical Cross-Device Federated Learning Framework over 5G Networks

arXiv

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arXiv 2022年

作者： Yang, Wenti Wang, Naiyu Guan, Zhitao Wu, Longfei Du, Xiaojiang Guizani, Mohsen School of Control and Computer Engineering North China Electric Power University China Department of Mathematics & Computer Science Fayetteville State University United States Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ United States Department of Computer Science and Engineering Qatar University Qatar

The concept of federated learning (FL) was first proposed by Google in 2016. Thereafter, FL has been widely studied for the feasibility of application in various fields due to its potential to make full use of data without compromising the privacy. However, limited by the capacity of wireless data transmission, the employment of federated learning on mobile devices has been making slow progress in practical. The development and commercialization of the 5th generation (5G) mobile networks has shed some light on this. In this paper, we analyze the challenges of existing federated learning schemes for mobile devices and propose a novel cross-device federated learning framework, which utilizes the anonymous communication technology and ring signature to protect the privacy of participants while reducing the computation overhead of mobile devices participating in FL. In addition, our scheme implements a contribution-based incentive mechanism to encourage mobile users to participate in FL. We also give a case study of autonomous driving. Finally, we present the performance evaluation of the proposed scheme and discuss some open issues in federated learning. Copyright © 2022, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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A Benchmark and Frequency Compression Method for Infrared Few-Shot Object Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Ruiheng Yang, Biwen Xu, Lixin Huang, Yan Xu, Xiaofeng Zhang, Qi Jiang, Zhizhuo Liu, Yu Beijing Institute of Technology State Key Laboratory of Electromechanical Dynamic Control Beijing100081 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia Anhui Polytechnic University School of Computer and Information Wuhan241000 China Technical University of Munich Data Science in Earth Observation Munich85521 Germany Tsinghua University Shenzhen International Graduate School Shenzhen518055 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Infrared few-shot object detection (IFSOD) aims to detect infrared objects with limited labeled examples. Current infrared datasets, however, suffer from limited diversity in object types and classes, hindering robust evaluation of model generalization on novel classes. To systematically assess dataset quality, we propose metrics for class diversity, instance variability, and object density. By integrating three widely used infrared datasets, we construct the first dataset specifically tailored for IFSOD, increasing instance density to 4.8 (a 1.1 improvement) and expanding the number of classes to 18 (a 5-class increase) compared to the source datasets. Furthermore, frequency analysis of spatial features reveals that sparse annotations introduce spectral bias in the frequency domain. Directly transforming spatial features to the frequency domain, however, mixes background noise with object features, causing spectral leakage and impairing the learning of discriminative features for novel classes. To address these issues, we propose the frequency compression few-shot detection (FC-fsd) method, which incorporates a frequency compression (FC) module. The FC module leverages Discrete Cosine Transform (DCT) within localized windows to reduce spectral leakage and enhance feature clarity. With minimal additional computational overhead, FC-fsd significantly outperforms state-of-the-art methods, achieving nAP50 scores of 28.57 (+13.37) and 35.63 (+2.59) in 1-shot and 2-shot settings, respectively. Our dataset is published at https://***/RuihengZhang/IFSOD-dataset. © 2025 IEEE.

关键词： Zero-shot learning

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Influence of the variables describing brain signals on the performance of the Naive Bayesian Classifier

Influence of the variables describing brain signals on the p...

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Progress in Applied electrical engineering (PAEE)

作者： Aleksandra Kawala-Sterniuk Jaroslaw Zygarlicki Adam Lysiak Barbara Grochowicz Mariusz Pelc Waldemar Bauer Dawid Baczkowicz Krzysztof Krecisz Katarzyna A. Dylag Wiktoria Wieczorek Radana Kahankova Radek Martinek Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland Faculty of Physical Education and Physiotherapy Opole University of Technology Opole Poland St. Louis Children Hospital Krakow Poland Department of Pathophysiology Jagiellonian University in Krakow–Collegium Medicum Krakow Poland Department of Bioinformatics and Telemedicine Jagiellonian University in Krakow–Collegium Medicum Krakow Poland Department of Cybernetics and Biomedical Engineering–FEECS VSB—Technical University Ostrava Ostrava–Poruba Czech Republic

ISBN: (数字)9781665455152

ISBN: (纸本)9781665455169

In this paper various variables describing brain signals such as near-infrared spectroscopy (NIRS) and electroencephalography (EEG) on the Naive Bayesian Classifier were presented. Analysis of biomedical signals is a very challenging task – therefore the authors of this work decided to analyse two types of brain signals – EEG and NIRS, which have different features, character, nature and are differently prone to various artifacts occurrence. The applied classifiers can be applied for various features extraction and pattern comparisons, which is useful in diagnostic processes.

关键词： electrical engineering Feature extraction Electroencephalography Bayes methods Task analysis Functional near-infrared spectroscopy

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EMG Onset and Offset Detection via a Modified Threshold Crossings Algorithm

EMG Onset and Offset Detection via a Modified Threshold Cros...

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Mediterranean Conference on control and Automation (MED)

作者： Vasiliki Theofili Nikolaidi George Andrikopoulos Dimitris Tsipianitis Dimosthenis Kazakos Department of Electrical and Computer Engineering University of Patras Greece Unit of Mechatronics and Embedded Control Systems KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

In this study, we propose a novel family of onset and offset detection algorithms for electromyographic (EMG) signals, based on the Teager-Kaiser Energy Operator (TKEO). These algorithms are derived from an existing double-threshold statistical detector, which is modified to use Shifted Skew Log Laplace Distribution (SSLLD) probabilities and likelihoods to take advantage of the improved TKEO SNR ratio. The performance of the proposed algorithms are compared against existing approaches on synthetic EMG signals generated using an heteroscedastic autoregressive Gaussian model.

关键词： Automation Detectors Benchmark testing Probability Approximation algorithms Electromyography Real-time systems

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An Efficient Post-Quantum Multi-Signature Scheme for the Internet of Vehicles

An Efficient Post-Quantum Multi-Signature Scheme for the Int...

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IEEE International Conference on Communications (ICC)

作者： Qianyi Zhang Shuai Yuan Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power University Beijing China Department of Finance Operations and Information Systems (FOIS) Brock University CANADA Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

Multi-signature scheme is a unique type of digital signature where a group of participants are capable of producing a signature interactively on a shared message, thus significantly reducing the signature size. This is especially important for Internet of Vehicles (IoV) systems where higher efficiency and lower costs are required during the communication. Most approaches so far, however, are developed by traditional methods such as the integer factoring assumption, which result in potential vulnerability to quantum computing attacks. Although a few lattice-based multi-signature candidates have been proposed, they either rely on hash-and-sign process with higher costs or may be compromised by larger size of public key and signature. Motivated by the Bimodal Lattice Signature Scheme (BLISS) model [1], we propose a new lattice-based multi-signature scheme (Multi-BLISS, MB) in this paper. Our scheme can also be transformed into an aggregate signature scheme (Aggregate MB, AMB) with similar level of performance. We evaluate both schemes by setting security levels of 128, 160 and 192 bits in the experiments, and the results demonstrate significant improvement on security and efficiency comparing to existing lattice-based multi-signature schemes.

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Reputation-Based Resilient Consensus with Differential Privacy Guarantees

SSRN

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SSRN 2024年

作者： Ramos, Guilherme Pequito, Sérgio Silvestre, Daniel Departamento de Informática Instituto Superior Técnico Universidade de Lisboa Portugal Instituto de Telecomunicações Lisbon1049-001 Portugal Division of Systems and Control Department of Information Technology Uppsala University Sweden Department of Electrical and Computer Engineering Faculty of Sciences and Technology NOVA University of Lisbon Portugal

Reputation-based consensus methods have gained significant attention in distributed systems to mitigate the impact of malicious agents and ensure reliable decision-making. However, privacy concerns arise when sensitive information is shared among participants. In this paper, we propose a reputation-based consensus method that incorporates resilience and differential privacy guarantees, providing a balance between preserving participants' privacy and maintaining the accuracy of the resilient consensus process. Our approach leverages the notion of differential privacy to add noise to the agents' states, ensuring that individual contributions are masked while still enabling the detection of malicious behavior. We present a detailed analysis of the privacy-accuracy trade-off and demonstrate the effectiveness of our method through simulations. The results show that our reputation-based consensus method with differential privacy guarantees offers robustness against attacks while preserving the privacy of participants, making it a promising solution for privacy-conscious resilient distributed systems. © 2024, The Authors. All rights reserved.

关键词： Decision making

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Assessment of Integrating Evacuated Tube-Based Domestic Electric/Solar Water Heater with an Aluminum Reflector for Managing electrical Consumption

SSRN

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SSRN 2023年

作者： Sabry, Ahmad H. Al-Dujaili, Ayad Q. Oleiwi, Bushra Kadhim Alkubaisi, Yaser Mahmood Humaidi, Amjad J. Electrical Engineering Technical College Middle Technical University Baghdad Iraq Department of Control and Systems Engineering University of Technology-Iraq Baghdad Iraq Department of Computer Engineering Al-Nahrain University Al Jadriyah Bridge Baghdad64074 Iraq Department of Sustainability Management Dubai Academic Health Corporation Dubai4545 United Arab Emirates Control and Systems Eng. Dept. University of Technology Baghdad Iraq

Most places in the world have seen rapid increases in the need for power. It is essential to look for alternatives and energy-saving technologies because traditional fossil fuel power plants emit pollution in addition to the electricity they produce. This work considers a multi-generational heating system with the hybrid Electric/Solar Water Heaters (ESWHs) to replace electric water heaters (EWH)by combining the ESWH with an Aluminum reflector. This work experimental discusses the performance of the ESWH and the influence of placing a reflector plate underneath the evacuated tubes in Baghdad-Iraq. A test procedure to heat the water to a specified heating temperature over the whole day period was conducted to analyze the daily power consumption profile of the system. Four operational scenarios have been conducted including the mode;the ESWH as a solar water heater, ESWH as an electric water heater, ESWH without a reflector, and ESWH with a reflector plate. The experimental analysis confirmed that the presented multi-generational heating configuration could contribute to energy sustainably. The developed multi-generational heating system achieved an improvement in annual energy consumption of 15.4% over the hybrid electric/solar water heater and 46.2% over the traditional electrical water heater. © 2023, The Authors. All rights reserved.

关键词： Energy conservation

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