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LOS and NLOS identification in real indoor environment using deep learning approach

Digital Communications and Networks 2024年第5期10卷 1305-1312页

作者： Alicja Olejniczak Olga Blaszkiewicz Krzysztof K.Cwalina Piotr Rajchowski Jaroslaw Sadowski Faculty of Electronics Telecommunications and InformaticsGdansk University of TechnologyGdansk 80-233Poland

Visibility conditions between antennas,***-of-Sight(LOS)and Non-Line-of-Sight(NLOS)can be crucial in the context of indoor localization,for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and *** this paper a Deep Learning(DL)model to classify LOS/NLOS condition while analyzing two Channel Impulse Response(CIR)parameters:Total Power(TP)[dBm]and First Path Power(FP)[dBm]is *** experiments were conducted using DWM1000 DecaWave radio module based on measurements collected in a real indoor environment and the proposed architecture provides LOS/NLOS identification with an accuracy of more than 100%and 95%in static and dynamic senarios,*** proposed model improves the classification rate by 2-5%compared to other Machine Learning(ML)methods proposed in the literature.

关键词： Deep learning Machine learning LOS NLOS UWB

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Comparison of 3D Point Cloud Completion Networks for High Altitude Lidar Scans of Buildings

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Photogrammetric Engineering and Remote Sensing 2024年第1期90卷 55-64页

作者： Kulawiak, Marek Department of Geoinformatics Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Poland

High altitude lidar scans allow for rapid acquisition of big spatial data representing entire city blocks. Unfortunately, the raw point clouds acquired by this method are largely incomplete due to object occlusions and restrictions in scanning angles and sensor resolution, which can negatively affect the obtained results. In recent years, many new solutions for 3D point cloud completion have been created and tested on various objects;however, the application of these methods to high-altitude lidar point clouds of buildings has not been properly investigated yet. In the above context, this paper presents the results of applying several state-of-the-art point cloud completion networks to various building exteriors acquired by simulated airborne laser scanning. Moreover, the output point clouds generated from partial data are compared with complete ground-truth point clouds. The performed tests show that the SeedFormer network trained on the ShapeNet-55 data set provides promising shape completion results. © 2024 American Society for Photogrammetry and Remote Sensing.

关键词： Optical radar

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A comprehensive survey on weed and crop classification using machine learning and deep learning

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Artificial Intelligence in Agriculture 2024年第3期13卷 45-63页

作者： Faisal Dharma Adhinata Wahyono Raden Sumiharto Department of Computer Science and Electronics Faculty of Mathematic and Natural SciencesUniversitas Gadjah MadaYogyakartaIndonesia Faculty of Informatics Institut Teknologi Telkom PurwokertoCentral JavaIndonesia

Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or *** technology plays a crucial role in facilitating the transition from conventional to precision agriculture,particularly in the context of weed *** agriculture,which previously relied on manual efforts,has now embraced the use of smart devices for more efficient weed ***,several challenges are associated with weed detection,including the visual similarity between weed and crop,occlusion and lighting effects,as well as the need for early-stage weed ***,this study aimed to provide a comprehensive review of the application of both traditional machine learning and deep learning,as well as the combination of the two methods,for weed detection across different crop *** results of this review show the advantages and disadvantages of using machine learning and deep ***,deep learning produced superior accuracy compared to machine learning under various *** learning required the selection of the right combination of features to achieve high accuracy in classifyingweed and crop,particularly under conditions consisting of lighting and early growth ***,a precise segmentation stage would be required in cases of *** learning had the advantage of achieving real-time processing by producing smaller models than deep learning,thereby eliminating the need for additional ***,the development of GPU technology is currently rapid,so researchers are more often using deep learning for more accurate weed identification.

关键词： Deep learning Machine learning Precision agriculture Weed control Weed detection

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

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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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Analysis of Centrality Metrics for Geographical Road Networks under Topological Representation

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Transactions of the Japanese Society for Artificial Intelligence 2025年第1期40卷 1-11页

作者： Kumano, Masahito Saito, Masaki Kimura, Masahiro Faculty of Advanced Science and Technology Ryukoku University Japan Division of Electronics and Informatics Graduate School of Science and Technology Ryukoku University Japan

Aiming at enhancing geographical road network analysis from a network science perspective, we introduce a novel problem of analyzing the road network in a city, and consider providing a new network centrality metric that could be useful for that problem. The problem addresses vehicular evacuation in urban settings. During emergency and disaster scenarios of vehicular evacuation, the shortest distance routes might not always be the most optimal. Instead, routes that are easier to traverse can be more crucial, even if they involve detours. Also, destinations for evacuation do not necessarily have to be restricted to traditional facilities;broad and well-maintained streets might also serve as suitable alternatives. We focus on the streets as the basic units of the road network to be investigated, and consider a scenario in which people efficiently move from starting intersections around their current places to designated goal streets, following the routes of easiest traversal. For the road network, we employ its topological representation, where vertices and edges correspond to streets and intersections between them, respectively. We thus represent the road network as a vertex-weighted graph, where the weight of each vertex reflects its ease of traversal. By appropriately extending the recently developed edge-centrality metric, "salience", to this vertex-weighted graph, we construct a new network centrality metric to detect critical streets for the newly introduced problem. Using a toy model of road network and real-world urban road networks obtained from OpenStreetMap, we experimentally reveal its distinctive characteristics by comparing it with several baselines. Moreover, we demonstrate that the proposed network centrality metric can successfully find critical streets for vehicular evacuation, which are difficult to detect using baseline methods. © 2025, Japanese Society for Artificial Intelligence. All rights reserved.

关键词： Network topology

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Measurement Performance Verification of Asynchronous Method for Simultaneous Estimation of Object Position and Orientation 100

Measurement Performance Verification of Asynchronous Method ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Sadowski, Jaroslaw Blaszkiewicz, Olga Cwalina, Krzysztof Olejniczak, Alicja Rajchowski, Piotr Stefanski, Jacek Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gdansk Poland

ISBN: (纸本)9798331517786

The paper describes the results of a measurement verification of the effectiveness of an asynchronous method of locating an object on a plane using localization signals sent simultaneously from two transmitters placed on that object at a known distance from each other. The advantage of proposed solution is ability to estimate position of mobile object by set of reference receivers that can work asynchronously, which simplifies the construction of reference nodes. © 2024 IEEE.

关键词： asynchronous systems radio localization radio navigation

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Electric Generator Model Research (Stage Efficiency of Electromechanical Part) 15

Electric Generator Model Research (Stage Efficiency of Elect...

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15th International Scientific and Practical Conference on Environment. Technology. Resources, ETR 2024

作者： Vaidotas, Matutis Loreta, Savulioniene Tomas, Kasperavicius Dainius, Savulionis Faculty of electronics and informatics Vilniaus Kolegija/Higher Education Institution Vilnius Lithuania

ISBN: (纸本)9781713899570

The article presents interim results of an ongoing continued study (https://***/10.17770/etr2023vol3.7261). At this stage, measurements are made on a model in which two cavities for magnets and coils are already provided in the turned-wing impeller. The cavities contain mounted magnets that rotate with the impeller around to the housing stationary mounted coils. We already have two independent power generators. In the initial stage, these two generators were selected with coils of different inductance and the voltage and current generated by them were measured. The goal is to find out the possibilities of efficiency using a comparative method, as well as the dependence of the parameters of the generated energy on the speed of the air flow. In the experiments, the speed of the air flow, the area of the air inlet and the area of the air outlet were varied. A rather large amount of data was received, the processing of which will still have to be worked on, but the general trends of the dependence of the extracted electricity parameters not only on the air flow speed, but also on the geometric parameters of the ducts (in this case, the areas of the openings) became apparent. The general parameters of the generated current are already obtained at values suitable for practical use, because in the previous stage of research these values were insufficient to achieve the level of the practical efficiency factor. The article discusses the results of the experiments, observed trends, presents generalizations and conclusions, and predicts directions for further research. © 2024 Vaidotas Matutis, Loreta Savulioniene, Tomas Kasperavicius, Dainius Savulionis.

关键词： Air

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Modeling Parking Occupancy Using Algorithm of 3D Visibility Network

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IEEE Access 2025年 13卷 84424-84434页

作者： Babič, Matej Šter, Branko Hluchý, Ladislav Baiamonte, Giuliana Calì, Michele Faculty of Information Studies Novo Mesto8000 Slovenia University of Ljubljana Faculty of Computer and Informatics Ljubljana1000 Slovenia Institute of Informatics Slovak Academy of Sciences Bratislava845 07 Slovakia University of Catania Electric Electronics and Computer Engineering Department Catania95125 Italy

The modernization of the information communication infrastructure of the regional data transmission network has advanced in order to increase the maximum transmission speed of existing transport routes, ensuring the quality of the service and its reliability. In the present research work, a new approach for the 3D visibility network algorithm has been developed, showing how graph theory applied to wireless sensor networks (WSNs) enables technological development for new solutions in areas such as public infrastructure. The possibility of determining in such networks whether an area of interest is sufficiently covered by a given set of sensors by means of the Voronoi diagram is discussed. The parking dynamics and parking system were modeled with cellular neural networks (CNNs) based on weather conditions, and magnetic parking sensors were replaced with pillar sensors. The proposed method has proven its effectiveness in determining the position of the minimum sensors covering the area of interest, in order to find a solution in the occupation of parking spaces in the presence of different weather conditions. The proposed approach and experimental results offer potential applications in various fields such as lighting and rendering, motion planning, pattern recognition, computer graphics and computational geometry, in order to conduct studies on problems and perspectives of pillar sensor technology while reducing costs compared to magnetic ones. © 2013 IEEE.

关键词： Rendering (computer graphics)

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Fluctuation-enhanced sensing of organic solvent vapors mixture by machine learning

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IEEE Sensors Journal 2025年第11期25卷 20415-20422页

作者： Smulko, J. Kwiatkowski, A. Drozdowska, K. Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Metrology and Optoelectronics G. Narutowicza 11/12 Gdansk80-233 Poland

Graphene back-gated FETs operate as gas sensors and can detect different gases by the fluctuation-enhanced gas sensing (FES) method, utilizing low-frequency noise measurements. Individual Lorentzian components are observed in low-frequency noise and identified by their corner frequency fc, which is characteristic of the adsorbed-desorbed gas molecules. The sensor was irradiated by UV light (275 nm) to enhance its sensing properties and reduce humidity impact. The estimated resistance noise power spectral density can evaluate gas mixture components by applying a detection algorithm. We proposed an efficient method of power spectra processing to realize this task. It utilizes a support vector machine (SVM) detection algorithm implemented in a MATLAB environment. The power spectrum is an input data vector comprising flicker noise and additive Lorentzians having intensities proportional to the gas concentration responsible for its generation. The results confirmed that a single graphene back-gated FET can determine the components of the studied gas mixture (chloroform, acetone). The accuracy of gas detection depends on the intensity of the generated noise components related to gas molecules' activity. The selected algorithm was the most efficient among other machine learning algorithms available in MATLAB software. We underline that the proposed method is characterized by a low limit of gas detection (LOD) and robust against gas sensor drift in time because it only considers noise components related to the selected gases. © 2025 IEEE.

关键词： MATLAB

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Increasing Productivity in Vapour Phase Soldering Using Vertical Stacking of Boards 47

Increasing Productivity in Vapour Phase Soldering Using Vert...

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47th International Spring Seminar on electronics Technology, ISSE 2024

作者： Havellant, Gergo Illes, Balazs Geczy, Attila Budapest University of Technology and Informatics Faculty of Electrical Engineering and Informatics Department of Electronics Technology BudapestH 1111 Hungary

ISBN: (纸本)9798350385472

During vapour phase soldering, the lateral size of the specimen tray determines the number of assemblies that can be soldered at once. Increased productivity, thus less power consumption can be achieved by increasing the size of the tray, and the workspace of the oven;therefore, also the size of the machine. This means significant additional costs both in terms of machine design and the amount of heat transfer fluid used. The aim of our research was to find out how we can increase the productivity of an industrial grade oven and improve the technology for a more energy efficient production, without making any significant retrofitting modifications. By stacking multiple printed circuit boards (PCB), we could increase the number of PCBs that could be soldered at once up to three times. We designed a sample holder with reduced mass, which shortened the cycle time. We performed comparative tests in several vertical configurations, compared to the default setup. We examined the resulting thermal profiles, performed shear tests, examined the surface of the cracks with optical microscopy, and also performed a cross section analysis. The results show that the solder joint quality was degraded due to increased void formation;however, the overall product quality might fit the requirements of commercial electronics. © 2024 IEEE.

关键词： Printed circuit boards

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