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International Joint Conference on Computer Science and Software engineering (JCSSE)

作者： Possathon Teerakantapirut Rungrat Viratikul Charnchai Pluempittiwiriyawej Department of Electrical Engineering Multimedia Data Analytics and Processing Research Unit Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Telecommunication Networking and Systems Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

This study introduces an advanced methodology to overcome the intrinsic challenges associated with Light Detection and Ranging (LiDAR) technology integrated into Apple's devices, specifically focusing on optimizing depth map accuracy through multiview angle analysis. Despite the extensive utility of LiDAR in generating detailed three-dimensional models for various applications, from autonomous vehicle navigation to environmental conservation, the efficiency of the technology is often compromised by difficulties in accurately rendering complex geometries from multiple perspectives and by its substantial demands on computational resources. To address these limitations, depth map data are systematically collected and evaluated from four distinct viewing angles (0, 90, 180, and 270 degrees) against established ground truth benchmarks. With a threshold of Intersection over Union (IoU) set at 0.8, it is inferred that the predicted shape exhibits over 80% similarity to the actual shape. When only one view is utilized, an IoU of 0.95 is achieved, alongside a size reduction of 73.07%, a Perimeter Accuracy reduction of 1.56%, and an increase in Root Mean Square Error (RMSE) of 29.37%. This comprehensive analysis highlights the critical need for precise geometric representation and marks a significant step toward enhancing the fidelity of LiDAR measurements. The results of this research indicate a substantial improvement in the accuracy of three-dimensional representations, suggesting a potential shift in the utilization of LiDAR technology across various scientific and infrastructural domains. By proposing a novel framework that optimizes data precision and processing efficiency, this paper contributes to the ongoing discourse on improving 3D modeling and analysis techniques.

关键词： Geometry Solid modeling Laser radar Accuracy Three-dimensional displays Shape Shape measurement

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Cascaded Noise Reduction and Acoustic Echo Cancellation Based on an Extended Noise Reduction

Cascaded Noise Reduction and Acoustic Echo Cancellation Base...

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European Signal processing Conference (EUSIPCO)

作者： Arnout Roebben Toon Van Waterschoot Marc Moonen Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In many speech recording applications, the recorded desired speech is corrupted by both noise and acoustic echo, such that combined noise reduction (NR) and acoustic echo cancellation (AEC) is called for. A common cascaded design corresponds to NR filters preceding AEC filters. These NR filters aim at reducing the near-end room noise (and possibly partially the echo) and operate on the microphones only, consequently re-quiring the AEC filters to model both the echo paths and the NR filters. In this paper, however, we propose a design with extended NR (NRext) filters preceding AEC filters under the assumption of the echo paths being additive maps, thus preserving the addition operation. Here, the NR ext filters aim at reducing both the near-end room noise and the far-end room noise component in the echo, and operate on both the microphones and loudspeakers. We show that the succeeding AEC filters remarkably become independent of the NR ext filters, such that the AEC filters are only required to model the echo paths, improving the AEC performance. Further, the degrees of freedom in the NR ext filters scale with the number of loudspeakers, which is not the case for the NR filters, resulting in an improved NR performance.

关键词： Loudspeakers Filters Echo cancellers Additives Noise Noise reduction Acoustics Recording Standards Microphones

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Exact Characterization of the Global Optima of Least Squares Realization of Autonomous LTI Models as a Multiparameter Eigenvalue Problem

Exact Characterization of the Global Optima of Least Squares...

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European Control Conference (ECC)

作者： Sibren Lagauw Lukas Vanpoucke Bart De Moor Department of Electrical Engineering (ESAT) KU Leuven Center for Dynamical Systems Signal Processing and Data Analytics (STADIUS) Leuven Belgium

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We consider the problem of finding the best least squares realization of an autonomous single-output linear time- invariant dynamical system, given a sequence of non-model- compliant output data. We characterize the solution set of the identification problem and derive novel properties of the optimal models. We show how the global minima of the problem follow from the eigentuples of a multiparameter eigenvalue problem and illustrate this result using several numerical 'toy examples' in which we compute the globally optimal solution(s) explicitly.

关键词： Linear systems Computational modeling Toy manufacturing industry Europe Eigenvalues and eigenfunctions Numerical models Dynamical systems

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Machine Learning-Based Classification of Mental Health State Using the DASS-21 Profile 16

Machine Learning-Based Classification of Mental Health State...

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16th Biomedical engineering International Conference, BMEiCON 2024

作者： Duangchaemkarn, Khanita Khammarew, Patiphan Aramvith, Supavadee School of Pharmaceutical Sciences University of Phayao Department of Pharmacy Practice Phayao56000 Thailand School of Pharmaceutical Sciences University of Phayao Pharmacy Research Extern Under the Doctor of Pharmacy Program Phayao56000 Thailand Chulalongkorn University Multimedia Data Analytic and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Bangkok10330 Thailand

ISBN: (纸本)9798331505431

This study aimed to determine the most effective machine learning model for classifying emotional states of depression, anxiety, and stress based on the DASS-21 profile. Six machine learning models were developed and tested using 2,602 records from the University of Phayao Counselling Center, collected between 23 July and 16 August 2020. The results demonstrate that the deep learning model provided the highest accuracy and precision, exceeding 90% for all three emotional categories, with an AUC greater than 99%. These findings introduce an innovative approach for classifying emotional states of depression, anxiety, and stress using the DASS-21 profile as a benchmark. Incorporating additional features into the model could further enhance its utility in clinical decision-making, improving the accuracy of mental health screening and diagnosis. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Learning-Based Golf Swing Sequence Analysis

Deep Learning-Based Golf Swing Sequence Analysis

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IEEE Region 10 International Conference TENCON

作者： Amir Hajian Karit Sookpreedee Kingrak Phairoh Watchara Ruangsang Supavadee Aramvith Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Multimedia Data Analytic and Processing Research Unit Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Multimedia Data Analytics and Processing Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand

Golf is widely recognized as one of the most popular sports globally. However, one drawback of playing golf is the relatively high cost of equipment and coaching. While numerous training programs are available to assist players in their practice, there is currently no swing analysis program developed by Thai professionals. In this project, advanced deep learning models were employed: SwingNet, capable of predicting the sequence of eight golf swing events in videos and determining the confidence level of each swing, and MoveN et, designed to identify joint positions on the body and represent them as skeletons. These models were integrated into a customized template-matching algorithm that utilized angle-based measurements to analyze the sequence of golf swings. This analysis assessed the similarity score, represented as a percentage, between two individuals for each golf swing event. Furthermore, various techniques were implemented to enhance the efficiency of SwingN et. Through performance evaluation, it was observed that the efficiency of SwingN et surpassed by one percent compared to the pre-trained model.

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Efficient Deep Attentive Pixels Network in Face Super-Resolution at Scale Factor of 16

Efficient Deep Attentive Pixels Network in Face Super-Resolu...

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International Conference on electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Hein Htet Aung Supavadee Aramvith Department of Electrical Engineering Multimedia Data Analytics and Processing Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand

Nowadays, Face Super-Resolution (FSR) models utilize the fusion approach, which combines the attention technique with the super-resolution network. The fusion approach has been proposed and solves the problem of FSR. Facial attributes have been effectively used to guide low-level information of the face to perform robust face image restoration. Iterative techniques appraised the value of facial landmarks to boost the reconstruction capability of the super-resolution network. Nevertheless, the network parameters in FSR are high, while the learning rate is still low. This paper proposes an attention mechanism combined with the Face Alignment Network (FAN). The proposed attention mechanism consists of channel attention and a non-local module. Our proposed model outperforms at the scale of $\times 16$ compared to the other state-of-the-art models.

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Epilepsy Detection Grand Challenge 48

Epilepsy Detection Grand Challenge

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Chatzichristos, Christos Bhagubai, Miguel Van Paesschen, Wim De Vos, Maarten Un. Hospital Leuven Lab. for Epilepsy Research KU Leuven & Department of Neurology Belgium Signal Processing and Data Analytics KU Leuven STADIUS Center for Dynamical Systems Department of Electrical Engineering Leuven Belgium KU Leuven Department Development and Regeneration Leuven Belgium

ISBN: (纸本)9781728163277

In this paper, we describe the epilepsy detection grand challenge, in association with ICASSP 2023. The challenge was centered on seizure detection using wearable behind-the-ear EEG. Two separate tasks were set for the participants: 1) obtain the best overall performance in seizure detection;2) systematically engineer the data in a data-centric task to optimize a given AI model for seizure detection. © 2023 IEEE.

关键词： Neurology

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Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

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IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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Model-Free Decentralized Training for Deep Learning Based Resource Allocation in Communication Networks

Model-Free Decentralized Training for Deep Learning Based Re...

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European Signal processing Conference (EUSIPCO)

作者： Pourya Behmandpoor Panagiotis Patrinos Marc Moonen Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven

Decentralized deep learning (DL) based resource allocation (RA) in communication networks guarantees scalability and higher communication bandwidth efficiency compared to centralized RA. Although the RA is decentralized in such approaches, the policies are mostly trained in a centralized manner. In this paper, we investigate a decentralized model-free training approach based on zeroth-order optimization methods. Each user trains its individual policy-possibly with a unique structure-to guarantee the maximum global utility, e.g., sum rate (SR) of users. More importantly, during the training, the users need to share only scalar quantities with their neighbors, avoiding a large communication overhead. The training is also robust against a certain level of asynchrony between the users. The proposed approach relaxes the need for a computationally complex central server and offers the possibility for (re)training in dynamic environments in a model-free manner using the computational power at the edge. Numerical experiments show a competitive performance compared to centralized and federated training approaches.

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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