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Designing a skilled soccer team for RoboCup: exploring skill-set-primitives through reinforcement learning

Neural Computing and Applications 2025年 1-36页

作者： Abreu, Miguel Reis, Luís Paulo Lau, Nuno LIACC/LASI/FEUP Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

The RoboCup 3D soccer simulation league serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase from scratch in Python after RoboCup 2021. The team’s performance relies on a set of skills centered around novel unifying primitives and a custom, symmetry-extended version of the proximal policy optimization algorithm. Our methods have been thoroughly tested in official RoboCup matches, where FC Portugal has won the last two main competitions, in 2022 and 2023. This paper presents our training framework, as well as a timeline of skills developed using our skill-set-primitives, which considerably improve the sample efficiency and stability of skills, and motivate seamless transitions. We start with a significantly fast Sprint-Kick developed in 2021 and progress to the most recent skill set, including a multi-purpose omnidirectional walk, a dribble with unprecedented ball control, a solid kick, and a push skill. The push addresses low-level collision scenarios and high-level strategies to increase ball possession. We address the resource-intensive nature of this task through an innovative multi-agent learning approach. Finally, we release the team’s codebase to the RoboCup community, providing other teams with a robust and modern foundation upon which they can build new features. © The Author(s) 2025.

关键词： Reinforcement learning

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A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

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computers, Materials & Continua 2024年第4期79卷 201-241页

作者： Naglaa F.Soliman Fatma E.Fadl-Allah Walid El-Shafai Mahmoud I.Aly Maali Alabdulhafith Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia ***-quality images require well-tuned communication *** Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and *** data need a space away fromunauthorized access,modification,or *** requirements are to be fulfilled by digital image watermarking and *** paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication *** introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver *** proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD *** addition,image encryption is implemented based on chaos and DNA *** encrypted watermarked images are then transmitted through the wireless SC-FDMA *** linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted *** subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this *** study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked *** addition,different variants of SC-FDMA bas

关键词： Cybersecurity applications image transmission channel models modulation techniques watermarking and encryption

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Analysis and Design of the Detection and Control System of Authorized and Unauthorized Users using Internet of Things and Machine Learning 9

Analysis and Design of the Detection and Control System of A...

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9th International Conference on Control, Instrumentation and Automation, ICCIA 2023

作者： Sarshar, Negar Nasiri, Ali Qom University of Technology Faculty of Electrical & Computer Engineering Department of Electronics Engineering Qom Iran Tarbiat Modares University Faculty of Electrical & Computer Engineering Department of Electronics Engineering Tehran Iran

ISBN: (纸本)9798350366013

In this article, a system has been designed using the Internet of Things and machine learning to detect equipment theft, monitor the entry and exit of people, as well as monitor people's activities to prevent equipment theft and provide security to the environment against possible theft. By using the motion detection sensor and the push buttons installed on the entrance doors, the entry and movement of people are monitored in real-time. Then, with machine vision and image processing algorithms, known and unknown people are cleaned and if necessary, a warning message including the video of the person's presence in the environment along with the person's image is sent to the relevant official. Also, the security doors will be locked until the relevant official confirms the safe situation. © 2023 IEEE.

关键词： Internet of things

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Stochastic Computing Convolutional Neural Network Architecture Reinvented for Highly Efficient Artificial Intelligence Workload on Field-Programmable Gate Array

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Research 2024年第2期2024卷 55-79页

作者： Yang Yang Lee Zaini Abdul Halim Mohd Nadhir Ab Wahab Tarik Adnan Almohamad School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal 14300 Penang Malaysia School of Computer Sciences Universiti Sains MalaysiaGelugor11800 PenangMalaysia Electrical-Electronics Engineering Department Faculty of EngineeringKarabuk University78050 KarabukTurkiye

Stochastic computing(SC)has a substantial amount of study on application-specific integrated circuit(ASIC)design for artificial intelligence(AI)edge computing,especially the convolutional neural network(CNN)***,SC has little to no optimization on field-programmable gate array(FPGA).Scaling up the ASIC logic without FPGA-oriented designs is inefficient,while aggregating thousands of bitstreams is still challenging in the conventional *** research has reinvented several FPGA-efficient 8-bit SC CNN computing architectures,i.e.,SC multiplexer multiply-accumulate,multiply-accumulate function generator,and binary rectified linear unit,and successfully scaled and implemented a fully parallel CNN model on Kintex7 *** proposed SC hardware only compromises 0.14%accuracy compared to binary computing on the handwriting Modified National Institute of Standards and Technology classification task and achieved at least 99.72%energy saving per image feedforward and 31?more data throughput than modern *** to SC,early decision termination pushed the performance baseline exponentially with minimum accuracy loss,making SC CNN extremely lucrative for AI edge computing but limited to classification *** SC's inherent noise heavily penalizes CNN regression performance,rendering SC unsuitable for regression tasks.

关键词： hardware Highly rendering

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A novel IoT-based arrhythmia detection system with ECG signals using a hybrid convolutional neural network and neural architecture search network

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International Journal of Applied Decision Sciences 2024年第5期17卷 636-656页

作者： Şentürk, Ümit Gülen, Ceren Polat, Kemal Department of Computer Engineering Faculty of Engineering Abant Izzet Baysal University Bolu Turkey Department of Computer Engineering Education of Graduate Institute Abant Izzet Baysal University Bolu Turkey Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu Turkey

Electrocardiogram (ECG) signals are the most common tool to evaluate the heart’s function in cardiovascular diagnosis. Irregular heartbeats (arrhythmia) found in the ECG play an essential role in diagnosing cardiovascular diseases (CVD). In this paper, we proposed an arrhythmia classification method with the neural architecture search network (NASNet) model, an optimised version of the convolutional neural networks (2D-CNN) model, which performs very well in visual information analysis classification. We aimed to use the arrhythmia classification problem in IoT and mobile devices with the NASNet model with low parameter numbers and processing capacity without performance loss. 2D input data have been obtained by converting the classified heart rate signals in the datasets into image files. The 2D data obtained have been classified by machine learning, CNN, and NASNet models. As a result of classification, 2D CNN accuracy was 97.51%, and the NASNet model was 96.89% accuracy. As a result of arrhythmia classification, the accuracy rates of the NASNet and the 2D CNN models were close. In conclusion, the proposed IoT-based arrhythmia detection system with ECG signals using a hybrid CNN and NASNet is a promising tool for the early detection of arrhythmias. Furthermore, it could help to reduce the mortality associated with these potentially fatal conditions. © 2024 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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A Comparative Study of Pure and Silver-Enhanced Graphene Antennas for Fat-Intrabody Communication 10

A Comparative Study of Pure and Silver-Enhanced Graphene Ant...

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10th IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024

作者： Subramaniam, Sree Aran Asan, Noor Badariah Aziz, Mohamad Zoinol Abidin Abd. Universiti Teknikal Malaysia Melaka Faculty of Electronics and Computer Technology & Engineering Engineering Department Durian Tunggal Melaka Malaysia Universiti Teknikal Malaysia Melaka Faculty of Electronics and Computer Technology & Engineering Technology Department Durian Tunggal Melaka Malaysia

ISBN: (纸本)9798350364163

This paper presents the design and comparative study of two implant antennas utilizing graphene material for fat-intrabody communication (fat-IBC) in the 5.8 GHz Industrial, Scientific, and Medical (ISM) band. Both antennas use pure graphene and silver-enhanced graphene as radiating elements, fabricated on a Taconic TLY-3 substrate. Copper-conducting elements were used as the substitute conductive material in CST Microwave Studio simulation for the optimized designs. These antennas were then measured in a fat-tissue-equivalent phantom model, and the S11reflection coefficients were recorded. These results show that the silver-enhanced graphene antenna has better performance in conductivity and resonance sharpness compared with the pure graphene antenna, which can be applied for the fat-IBC system. © 2024 IEEE.

关键词： Microwave antennas

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Multispectral point cloud superpoint segmentation

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Science China(Technological Sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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Investigating the impact of sensor axis combinations on activity recognition and fall detection: an empirical study

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Multimedia Tools and Applications 2024年 1-40页

作者： Kavuncuoğlu, Erhan Özdemir, Ahmet Turan Uzunhisarcıklı, Esma Department of Computer Technology Gemerek Vocational School Cumhuriyet University SivasTR-58840 Turkey Department of Electrical and Electronics Engineering Engineering Faculty Erciyes University KayseriTR-38039 Turkey Department of Electrical and Electronics Engineering Yozgat Bozok University Engineering Faculty YozgatTR-66900 Turkey

Activity recognition is a fundamental concept widely embraced within the realm of healthcare. Leveraging sensor fusion techniques, particularly involving accelerometers (A), gyroscopes (G), and magnetometers (M), this technology has undergone extensive development to effectively distinguish between various activity types, improve tracking systems, and attain high classification accuracy. This research is dedicated to augmenting the effectiveness of activity recognition by investigating diverse sensor axis combinations while underscoring the advantages of this approach. In pursuit of this objective, we gathered data from two distinct sources: 20 instances of falls and 16 daily life activities, recorded through the utilization of the Motion Tracker Wireless (MTw), a commercial product. In this particular experiment, we meticulously assembled a comprehensive dataset comprising 2520 tests, leveraging the voluntary participation of 14 individuals (comprising 7 females and 7 males). Additionally, data pertaining to 7 cases of falls and 8 daily life activities were captured using a cost-effective, environment-independent Activity Tracking Device (ATD). This alternative dataset encompassed a total of 1350 tests, with the participation of 30 volunteers, equally divided between 15 females and 15 males. Within the framework of this research, we conducted meticulous comparative analyses utilizing the complete dataset, which encompassed 3870 tests in total. The findings obtained from these analyses convincingly establish the efficacy of recognizing both fall incidents and routine daily activities. This investigation underscores the potential of leveraging affordable IoT technologies to enhance the quality of everyday life and their practical utility in real-world scenarios. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Wearable sensors

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Improving the Effectiveness of Image Classification Structural Methods by Compressing the Description According to the Information Content Criterion

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computers, Materials & Continua 2024年第8期80卷 3085-3106页

作者： Yousef Ibrahim Daradkeh Volodymyr Gorokhovatskyi Iryna Tvoroshenko Medien Zeghid Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Informatics Kharkiv National University of Radio ElectronicsKharkiv61166Ukraine Electronics and Micro-Electronics Laboratory Faculty of SciencesUniversity of MonastirMonastir5000Tunisia

The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint *** main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification *** class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric *** is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content *** informativeness of an etalon descriptor is estimated by the difference of the closest distances to its own and other *** developed method determines the relevance of the full description of the recognized object with the reduced description of the *** practical models of the classifier with different options for establishing the correspondence between object descriptors and etalons are *** results of the experimental modeling of the proposed methods for a database including images of museum jewelry are *** test sample is formed as a set of images from the etalon database and out of the database with the application of geometric transformations of scale and rotation in the field of *** practical problems of determining the threshold for the number of votes,based on which a classification decision is made,have been *** has revealed the practical possibility of tenfold reducing descriptions with full preservation of classification *** the descriptions by twenty times in the experiment leads to slightly decreased *** speed of the analysis increases in proportion to the degree of *** use of reduction by the informativeness criterion confirmed the possibility of obtaining the most significant subset of features for classification,which guarantees a decent

关键词： Description reduction description relevance descriptor image classification information content keypoint processing speed

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