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Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

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computers, Materials & Continua 2022年第6期71卷 4659-4676页

作者： Radek Martinek Jan Baros Rene Jaros Lukas Danys Jan Nedoma VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer ScienceDepartment of Cybernetics and Biomedical Engineering70800Ostrava-PorubaCzechia VSB Technical University of Ostrava Faculty of Electrical Engineering and Computer ScienceDepartment of Telecommunications70800Ostrava-PorubaCzechia

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise *** vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART *** speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground *** article presents the new concept of a hybrid system which is implemented as a virtual *** highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise *** study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual *** average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

关键词： 5G noise reduction hybrid algorithms speech recognition 5G data networks in-vehicle background noise

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Machine Learning Approach for Detecting Potential Anomalous Cosmic Rays Particles Tracks in Earth-Scale Cosmic Ray Extremely Distributed Observatory 15th

Machine Learning Approach for Detecting Potential Anomalous...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Tyc, Jan Zub, Marcin Hachaj, Tomasz Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Krakow Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031857027

In this paper, we present a novel contribution to the problem of the detection of potential anomalous cosmic rays particle tracks acquired by Earth-scale complex distributed observatory infrastructure. Detection of such signals provides an opportunity to analyze the performance of the entire research infrastructure of a complex system (for example, potential failures) and a chance to record unknown physical phenomena that can be recorded simultaneously with space radiation observations. The observatory comprises a worldwide network of CMOS detectors cooperating in the citizen science paradigm. We propose, evaluate, and compare several data processing, feature extractions, and potential anomaly discovery pipelines based on machine learning approaches. Although, to date, there have already been published works performing detection and analysis of anomalies in the Cosmic Ray Extremely Distributed Observatory Project (CREDO) dataset, they applied single methods without comparing the results with other possible approaches to analyze potential anomalies. In this work, for the first time, we have used and discussed many anomalous discovery approaches not previously applied to this issue to perform analysis of this extensive dataset (nearly 600,000 images). For this reason, the results published here are of significant cognitive importance to the problem under consideration. We have published both our research’s source codes and dataset so our results can be reproduced. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cosmic rays

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FSIW Cavity-Based Self-Packaging Filtering Slot Antennas with Wide-Stopband and High Frequency Selectivity

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3503-3516页

作者： Zhou, Xin Zhang, Gang Tam, Kam-Weng Pang, Ziyu Zhang, Zhuowei Lin, Huawei Hong, Jiasheng University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China Nanjing Normal University School of Electrical and Automation Engineering Nanjing210046 China Nanjing Normal University Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing School of Electrical and Automation Engineering Nanjing210046 China Heriot-Watt University School of Engineering and Physical Sciences Department of Electrical Electronic and Computer Engineering EdinburghEH14 4AS United Kingdom

This article proposes a novel design approach for miniaturized, highly selective, self-packaged, and wide-stopband filtering slot antennas based on C- and T-type folded substrate integrated waveguide (C-/T-FSIW) cavities. Firstly, two forms of FSIW cavity are explored and analyzed. A new coupled C-type half-mode FSIW (C-HMFSIW) cavities are developed. A filtering antenna is proposed, employing a C-HMFSIW cavity for feeding and a slot-loaded C-FSIW cavity for radiation. By introducing cross-coupling between different resonators, a radiation null (RN) is achieved. To further enhance bandwidth and frequency selectivity, a new antenna configuration is developed using a C-HMFSIW cavity for feeding and a slot-loaded T-FSIW cavity for radiation. After loading the radiation slot, the T-FSIW cavity serves as two parts: one as the main radiator and the other as a parasitic radiator opposite to it. This configuration increases antenna bandwidth and generates an additional RN. Interestingly, adjusting the position of the radiation slot allows the RN to switch between the upper and lower stopbands. The antennas proposed in this work exhibit good stopband performance and compact size. Three filtering antenna prototypes are designed, fabricated, and measured, showing consistent results between measurements and simulations. © 1963-2012 IEEE.

关键词： Radiators

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In Search for Model-Driven eXplainable Artificial Intelligence 1

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10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023

作者： Ligęza, Antoni Sepiolo, Dominik Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Applied Computer Science AGH University of Krakow al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9783031610691

ISBN: (纸本)9783031610684

This paper reports on ongoing and innovative research in the area of eXplainable Artificial Intelligence (XAI). A classical XAI task is considered as finding an explanation of the model generated via Machine Learning by identifying the most influential variables for local decision-making. Such an approach suffers from severe limitations. The proposed approach moves the explanatory process to a new, knowledge-level dimension. It is oriented towards Model Discovery, i.e. the internal structure and functions of the components. The concept of Model-Driven XAI is put forward and explained with examples. An experiment on Function Discovery via Grammatical Evolution is reported in brief. © IFIP International Federation for Information Processing 2024.

关键词： Decision making

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Revisiting the thermal ageing on the metallised polypropylene film capacitor:from device to dielectric film

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High Voltage 2023年第2期8卷 305-314页

作者： Yushuang He Feipeng Wang Guoqiang Du Lei Pan Kaizheng Wang Reimund Gerhard Ronald Plath Pawel Rozga Pavel Trnka State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqingChina Faculty of Electrical Engineering Kunming University of Science and TechnologyKunmingChina Soft Transducer Materials and Systems Universität PotsdamPotsdamGermany Faculty IV Electrical Engineering and Computer Science Technische Universität BerlinBerlinGermany Institute of Electrical Power Engineering Lodz University of TechnologyLodzPoland Faculty of Electrical Engineering University of West BohemiaPilsenCzech Republic

Metallised polypropylene film capacitors(MPPFCs)are widely used in power electronics and are generally degraded by elevated *** work aims to determine the relationships between the structural changes of MPPFC and the microstructural variations of the PP film during the thermal ageing of MPPFC at 100℃ for 38 *** capacitance of MPPFC has a slight decrease during thermal ***,the breakdown voltage of the MPPFC decreases by 39.4%by the *** partial discharge(PD)number of MPPFC increases linearly with ageing *** tear-down analysis of the MPPFC reveals that the molecular structure of the PP film has not been altered but has led to molecular chain scission and the generation of some polar fragments/***,the relative permittivity of the PP films rises as the ageing time ***,thermal ageing causes the conversion of aluminum to alumina in the metallised electrode,which is hydrophilic for polar groups and leads to an adhesion effect between the metallised electrodes and the PP *** angle measurements prove that the surface hydrophilicity of the PP sample increased after thermal ***,the PD/breakdown voltage in the MPPFC increases/decreases due to the uneven adhesion of the metallised PP film.

关键词： film thermal ageing

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of computer science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Fine-tuning Specialized NER Model for Symptom Extraction from Slovenian Medical Texts 9

Fine-tuning Specialized NER Model for Symptom Extraction fro...

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9th International Conference on Mathematics and computers in sciences and Industry, MCSI 2024

作者： Sallauka, Rigon Arioz, Umut Mlakar, Izidor University of Maribor Faculty of Electrical Engineering and Computer Science Laboratory of Digital Signal Processing Maribor Slovenia

ISBN: (纸本)9798350377224

In this study, we aim to develop a robust model capable of accurately extracting symptoms from clinical texts written in the Slovenian language. Our methodology involves several steps to achieve this objective. Initially, we identify datasets containing clinical texts in English and select a specialized model designed for symptom extraction from these texts. Then, we annotate the datasets and undertake the translation process into Slovenian, ensuring the preservation of annotations to maintain data integrity. Utilizing the translated dataset, we proceed to fine-tune the selected model specifically for Slovenian texts. Despite our efforts, optimal performance has not yet been attained. Challenges encountered include the translation methodology employed and the architectural limitations of the chosen model, which is primarily optimized for English texts. However, ongoing efforts are underway to address these challenges and enhance the model's performance. Our study represents a step towards improving automated symptom extraction in Slovenian clinical texts, with ongoing work focused on mitigating identified issues to achieve better outcomes. © 2024 IEEE.

关键词： Machine translation

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Multidimensional image morphing-fast image-based rendering of open 3D and VR environments

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 155-172页

作者： Simon SEIBT Bastian KUTH Bartosz von Rymon LIPINSKI Thomas CHANG Marc Erich LATOSCHIK Game Tech Laboratory Faculty of Computer ScienceNuremberg Institute of TechnologyNuremberg 90489Germany Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and ArtsCoburg 96450Germany Human-Computer Interaction Group Institute of Computer ScienceUniversity of WuerzburgWuerzburg 97074Germany

Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and ***,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains *** This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and ***,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image *** this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of *** rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image *** detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in *** addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light *** Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,*** achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering *** Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR ***,the handling of morphing artifacts in the parallax image regions remains a topic for future resea

关键词： computer graphics 3D real-time rendering computer vision Image morphing Virtual reality

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Fractal Dimension Analysis Demonstrates Overestimation and Underestimation of Time in EEG Signal 1

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13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Azari, Maryam Mollazadeh Sarbaz, Yashar Koohestani, Behrooz Farzamnia, Ali Modeling Biological System’s Laboratory Department of Biomedical Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Faculty of Engineering Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Keeping track of time is regarded as an essential human behavior. The question of how the brain deals with temporal information remains a subject of scholarly debate. The current investigation aims to explore the mechanism underlying time perception by extracting fractal dimension from an electroencephalogram (EEG) signal and its frequency bands. To accomplish this, Higuchi’s fractal dimension was calculated for 42 healthy subjects’ electroencephalogram (EEG) signal and its sub-bands during the time perception task. The EEG signal was recorded from 19 channels. Subsequently, a statistical analysis was conducted to compare participants who underestimated versus those who overestimated the elapsed time and significantly different channels were presented. The findings suggest an elevated level of fractal dimensionality in persons who displayed a tendency to overestimate time. The EEG signal and Gamma rhythm emerged as the most distinguishing signals between the two cohorts. The contrast in fractal dimension between the two groups was predominantly apparent in the parietal and central channels. To summarize, an increased level of complexity is discernible in the EEG signal and high-frequency rhythms when there is an overestimation of temporal duration. It can be asserted that the employment of FD yields presents an exceptional approach to comprehending cerebral functionalities, notably temporal perception. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electroencephalography

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Incident Detection with Pruned Residual Multilayer Perceptron Networks 18

Incident Detection with Pruned Residual Multilayer Perceptro...

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

作者： Soubra, Mohamad Dorohinicki, Marek-Kisiel Kurdziel, Marcin Zachara, Marek Agh University Faculty of Computer Science Electronics and Telecommunications Krakow Poland Agh University Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland

ISBN: (纸本)9788396744784

Internet of things (IoT) has opened new horizons in connecting all sorts of devices to the internet. However, continuous demand for connectivity increases the cybersecurity risks, rendering IoT devices more prone to cyberattacks. At the same time, rapid advances in Deep Learning (DL)-based algorithms provide state-of-the-art results in many classification tasks, including classification of network traffic or system logs. That said, deep learning algorithms are considered computationally expensive as they require substantial processing and storage capacity. Sadly, IoT devices have limited resources, making renowned DL models hard to implement in this environment. In this paper we present a Residual Neural Network inspired DL-based Intrusion Detection System (IDS) that incorporates weight pruning to make the model more compact in size and resource consumption. Additionally, the proposed system leverages feature selection algorithms to reduce the feature-space size. The model was trained on the NSL-KDD dataset benchmark. Experimental results show that the proposed system is effective, being able to classify network traffic with an F1 score of up to 98.9% before the pruning and an F1 score of up to 97.5% after pruning 90% of network weights. © 2023 Polish Information Processing Society.

关键词： Internet of things

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