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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Deniziak, Stanislaw Plaza, Miroslaw Arcab, Lukasz Kielce University of Technology Faculty of Electrical Engineering Automatic Control and Computer Science Kielce Poland

ISBN: (纸本)9788396744784

Internet of Things solutions typically involve interaction between sensors, actuators, the cloud, embedded systems and user applications. Often in such cases, there are time constraints specifying the maximum response time to a request. This time depends on the calculation time and transmission time. Existing Internet communication solutions do not ensure the implementation of transmissions in a way that guarantees meeting the set time constraints. This paper proposes a new model of Internet communication dedicated to real-time Internet of Things systems, which includes a communication protocol, as well as a transmission scheduling and routing method. The protocol takes into account information about transmission time constraints, which is used for packet scheduling by routers, allowing to increase quality of service. In addition, the proposed static routing mechanism makes it possible to parallelize transmissions if time constraints are still exceeded. Also presented are preliminary results of experiments showing to what extent the proposed methods allow improving the quality of service in real-time Internet of Things systems. © 2023 Polish Information Processing Society.

关键词： Internet of things

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EEG Multi-Objective Feature Selection using a Genetic Procedure with Hybrid Mutation Operator 24

EEG Multi-Objective Feature Selection using a Genetic Proced...

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24th International Conference on control Systems and computer science, CSCS 2023

作者： Cimpanu, Corina Gheorge Asachi Technical University of Iai Faculty of Automatic Control and Computer Science Computer Science Department Iai Romania

ISBN: (纸本)9798350313390

computer science developments in medical data processing increase modeling accuracy, speed up data analysis, and obtain automated diagnostics. As a result, the role of machine learning algorithms becomes significant, as well as the need to implement faster and more accurate systems. For example, classification is a common component in computer-aided diagnostics, and choosing the proper set of predictors is critical for training a classifier model. Usually, medical datasets are highly dimensional, and their characteristical data correlations require reducing the set of features used for classification. Besides supervised grouping algorithms, genetic optimization procedures are well known for their robust verification of competing attributes by directly estimating their usefulness on a specific classifier. This paper discusses the assessment of Electroencephalogram data samples acquired during working memory load n-back scenarios using a new embedded feature selection procedure based on Multi-Objective Optimization via Genetic Algorithms enhanced with a hybrid mutation operator. Experimental results focus on both the hybrid mutation operator performances, and obtaining overall higher Support Vector Machine classification accuracy while reducing the number of selected features. © 2023 IEEE.

关键词： Genetic algorithms

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RISC-V Extension for Optimized PWM control 27

RISC-V Extension for Optimized PWM Control

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27th International Conference on System Theory, control and Computing, ICSTCC 2023

作者： Popovici, Cosmin-Andrei Stan, Andrei Manta, Vasile-Ion Department of Computer Engineering Faculty of Automatic Control and Computer Engineering Technical University "Gheorghe Asachi" Iași Romania

ISBN: (纸本)9798350337983

This paper proposes a RISC-V extension, named SigWavy, meant to optimize the PWM control for general purpose or application specific designs. The RISC-V extension named above is a PWM control Unit with a dedicated ISA extension set designed for configuring and driving up to 32 PWM signals. The extension is integrated into RiscPwm, an updated version of our previous work, the RisCanFd SoC, for taking advantage of CAN-FD, a massively used protocol in the areas of automation and mobility. Being configured with the dedicated ISA extension or with parameters directly extracted from CAN-FD commands, the proposed solution manages to configure/reconfigure PWM channels between 4.79x and 9.18x times faster than an ARM Cortex-M7 processor, although our SoC operates with a 6x lower frequency. © 2023 IEEE.

关键词： Pulse width modulation

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Joint V2V Group Association and Transmission Power control in Federated Vehicular Networks

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IEEE Internet of Things Journal 2025年第11期12卷 18167-18182页

作者： Liu, Su Guan, Peiyuan Li, Yushuai Li, Tianyi Zolfagharian, Zolaikha Yu, Jiong Huang, Tingwen Xinjiang University School of Computer Science and Technology Urumqi830046 China University of Oslo Department of Informatics Oslo0316 Norway Aalborg University Department of Computer Science Aalborg9220 Denmark University of Tehran School of Electrical and Computer Engineering Iran Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

With growing awareness of privacy protection, Federated Learning (FL) in vehicular network scenarios effectively addresses privacy concerns, leading to the development of Federated Vehicular Networks (FVN). In FVN, vehicles maintain a global model by transmitting local models and iterative processing, resulting in significant communication overhead. In the Internet of Vehicles (IoV), as the majority of the spectrum resources are allocated to Vehicle-to-Vehicle (V2V) communication, vehicles engaged in FL encounter uplink interference when these resources are reused. This compromises the efficacy of FL vehicle communication. To address this issue, we propose FedCDC, a new Federated learning framework with adaptive weight Clustering with knowledge Distillation and Channel sharing-based resource allocation. First, we implement a communication compression strategy based on clustering and distillation to alleviate transmission load. Then, we develop a resource allocation strategy to maximize the Signal-to-Interference-plus-Noise Ratio (SINR) for both FL vehicles and V2V groups, which investigates two critical components: channel pairing and power coordination. We formulate the optimization issue as a multi-objective optimization problem that is solved offline using the MIDACO solver. Due to the dynamic characteristics of FVN, we employ a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to enhance efficiency. Finally, extensive experiments demonstrate that our approach significantly reduces data transmission and offers an efficient resource coordination plan, thus improving FL communication efficiency and ensuring the Quality of Service (QoS) for both V2V groups and FL vehicles. © 2014 IEEE.

关键词： Differential privacy

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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Microservice Deployment for Satellite Edge AI Inference via Deep Reinforcement Learning 35

Microservice Deployment for Satellite Edge AI Inference via ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Yu, Zhiyong Cheng, Hei Victor Yang, Zhanpeng Liu, Xin Jiang, Yuning Zhou, Yong Shi, Yuanming ShanghaiTech University School of Information Science and Technology Shanghai201210 China Aarhus University Department of Electrical and Computer Engineering Denmark Automatic Control Laboratory Switzerland

ISBN: (纸本)9798350362244

Artificial intelligence (AI) is critical in evolving 5G and developing 6G networks, running on edge devices, and solving resource management challenges. The burgeoning number of edge devices draws attention to the potential of low-earth orbit (LEO) satellite networks with their onboard computing capabilities for edge inference. This paper explores LEO scenarios where multiple remote sensing edge AI inference tasks concurrently process data from a single source. However, due to there being parts with the same functions between different AI applications, traditional monolithic edge AI architecture must be deployed repeatedly and falls short in efficiently harnessing the heterogeneous resources of LEO satellite networks. To solve this problem, we utilize the microservice architecture to decouple a single AI application into several independent microservices to reuse these same functions. However, due to the high latency caused by multiple microservices' communication, we need to design a deployment strategy to fully utilize resources to reduce the service latency. We present a microservice deployment model to minimize the total service latency across all AI applications and meet resource constraints with the constraints of hardware, energy, and memory limitations. This latency optimization problem is rewritten as a Markov decision process (MDP) to effectively deal with the challenge posed by the time-varying transmission rate caused by satellite mobility. To increase the training data utilization, we employ a Proximal Policy Optimization (PPO) based reinforcement learning algorithm to meet the dynamic environment challenge. Finally, we obtain a sub-optimal solution with minimal accuracy loss and an acceptable solution time. © 2024 IEEE.

关键词： Markov processes

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Radar style transfer for metric robot localisation on lidar maps

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 139-148页

作者： Huan Yin Yue Wang Jun Wu Rong Xiong College of Control Science and Engineering Zhejiang UniversityHangzhouChina Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologyKowloonHong KongChina

Lidar and visual data are affected heavily in adverse weather conditions due to sensing mechanisms,which bring potential safety hazards for vehicle *** sensing is desirable to build a more robust navigation *** this paper,a cross-modality radar localisation on prior lidar maps is ***,the proposed workflow consists of two parts:first,bird's-eye-view radar images are transferred to fake lidar images by training a generative adversarial network *** with online radar scans,a Monte Carlo localisation framework is built to track the robot pose on lidar *** whole online localisation system only needs a rotating radar sensor and a pre-built global lidar *** the experimental section,the authors conduct an ablation study on image settings and test the proposed system on Oxford Radar Robot Car *** promising results show that the proposed localisation system could track the robot pose successfully,thus demonstrating the feasibility of radar style transfer for metric robot localisation on lidar maps.

关键词： lidar local maps

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation 26

Out-of-distribution Object Detection through Bayesian Uncert...

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26th International Conference on Information Fusion, FUSION 2023

作者： Zhang, Tianhao Wang, Shenglin Bouaynaya, Nidhal Calinescu, Radu Mihaylova, Lyudmila The University of Sheffield Automatic Control and Systems Engineering United Kingdom Rowan University Department of Electrical and Computer Engineering United States University of York Department of Computer Science United Kingdom

ISBN: (纸本)9798890344854

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset. © 2023 International Society of Information Fusion.

关键词： Object detection

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Real-Time Multi-object Tracking Using YOLOv8 and SORT on a SoC FPGA 21st

Real-Time Multi-object Tracking Using YOLOv8 and SORT on a...

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21st International Symposium on Applied Reconfigurable Computing, ARC 2025

作者： Danilowicz, Michal Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031879944

Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation on low-power and real-time embedded platforms is highly desirable. Modern MOT algorithms should be able to track objects of a given class (e.g. people or vehicles). In addition, the number of objects to be tracked is not known in advance, and they may appear and disappear at any time, as well as be obscured. For these reasons, the most popular and successful approaches have recently been based on the tracking paradigm. Therefore, the presence of a high quality object detector is essential, which in practice accounts for the vast majority of the computational and memory complexity of the whole MOT system. In this paper, we propose an FPGA (Field-Programmable Gate Array) implementation of an embedded MOT system based on a quantized YOLOv8 detector and the SORT (Simple Online Realtime Tracker) tracker. We use a modified version of the FINN framework to utilize external memory for model parameters and to support operations necessary required by YOLOv8. We discuss the evaluation of detection and tracking performance using the COCO and MOT15 datasets, where we achieve 0.21 mAP and 38.9 MOTA respectively. As the computational platform, we use an MPSoC system (Zynq UltraScale+ device from AMD/Xilinx) where the detector is deployed in reprogrammable logic and the tracking algorithm is implemented in the processor system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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Ant Colony Optimization Algorithm for Finding the Maximum Number of d-Size Cliques in a Graph with Not All m Edges between Its d Parts 18th

Ant Colony Optimization Algorithm for Finding the Maximum Nu...

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Dependable computer Systems and Networks - Proceedings of the Eighteenth International Conference on Dependability of computer Systems DepCoS-RELCOMEX

作者： Schiff, Krzysztof Department of Automatic Control and Computer Science Cracow University of Technology Cracow31-155 Poland

ISBN: (纸本)9783031377198

A graph with m vertices in each of its d sections is a d-partite graph. In sections of a d-partite graph, the vertices are not connected by any edges. Each pair of vertices from different sections is always connected by an edge. If in a d-partite graph each section has m vertices, then such a graph can be divided into m d-vertex cliques if it has all edges between each pair of vertices from different sections. If in a graph with m vertices in each section there are no edges between each pair of vertices from different sections of the graph, then such a graph cannot be divided into m cliques. Such a graph can be easily divided into cliques, but their number does not have to be the maximum number of cliques into which such a graph can be divided. So this article deals with the problem of how to divide such a graph into cliques so that their number is the maximum number of cliques. For this purpose ant algorithms without and with two different dynamic desire function were used. The achieved results were compared and discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ant colony optimization

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