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Lecture Notes in Networks and Systems 2023年 606卷 v-vi页

作者： Srujan Raju, K. Sellathurai, Mathini Ashoka Reddy, K. George, Boby Rama Devi, B. Kakatiya Institute of Technology and Science Warangal India Department of Electrical and Electronics Engineering Indian Institute of Technology Tamil Nadu Chennai India Department of Signal Processing Heriot-Watt University Edinburgh United Kingdom Department of Electronics and Communication Engineering Kakatiya Institute of Technology and Science Warangal India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India

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Interferometric radar for activity recognition and benchmarking in different radar geometries 5

Interferometric radar for activity recognition and benchmark...

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5th IET International Radar Conference, IET IRC 2020

作者： Zhou, Boyu Le Kernec, Julien Yang, Shufan Fioranelli, Francesco Romain, Olivier Zhao, Zhiqin Department of Computer Science The University of Hong Kong Hong Kong Hong Kong School of Information and Communication University of Electronic Science and Technology of China Chengdu China James Watt School of Engineering University of Glasgow Glasgow United Kingdom ETIS – Signal and Information Processing lab University Cergy-Pontoise Cergy France MS3-Microwave Sensing Signals and Systems TU Delft Delft Netherlands

ISBN: (纸本)9781839535406

Radar micro-Doppler signatures have been proposed for human activity classification for surveillance and ambient assisted living in healthcare-related applications. A known issue is the performance reduction when the target is moving tangentially to the line-of-sight of the radar. Multiple techniques have been proposed to address this, such as multistatic radar and to some extent, interferometric radar. A simulator is presented to generate synthetic data representative of 8 different radar systems (including configurations as monostatic, multistatic, and interferometric) to quantify classification performances as a function of aspect angles and deployment geometries. This simulator allows an unbiased performance evaluation of the different radar systems. 6 human activities are considered with signatures originating from motion-captured data of 14 different subjects. The results show that interferometric radar data with fusion outperforms the other methods with over 97.6% accuracy consistently across all aspect angles, as well as the potential for simplified indoor deployment. © 2020 IET Conference Proceedings. All rights reserved.

关键词： Machine learning

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Effect of Quantization Error and SQNR on the ADC Using Truncating Method to the Nearest Integer Bit

Effect of Quantization Error and SQNR on the ADC Using Trunc...

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2019 International Conference on Advanced science and Engineering, ICOASE 2019

作者： Hasso, Alaaldin Jacksi, Karwan Smith, Kevin Department of Computer Science University of Zakho Zakho Kurdistan Region Iraq M.Sc. Digital Signal Processing for Communication Systems University of Westminster Alumni London United Kingdom

ISBN: (纸本)9781538693438

This paper considers the effects of the quantization error and signal Quantization Noise Ratio (SQNR) on the performance of an Analog Digital Converter of the sine signal using the Truncating method to the nearest integer bit. This Truncating method assigns each sample of the sine signal to the quantization level below it. By using MATLAB program, the experimental SQNRTruncating was always lower than the theoretical value as the number of bits increased in the digital word. This was expected because truncating the data leads to less accurate quantization of the signal and a lower SQNR. StatGraphics software was used to analyze simple regressions of the quantization error, and compare between Standard Deviation and Mean Absolute Deviation of the quantization error. StatGraph Plus program has been used to analyze the effect of the correlation coefficient statistic for truncating. Analysis Regression-Linear model using the StatGraph Plus program has been performed, then analysis of the effect of the statistic correlation coefficient and the equation of the regression line of dependent value (SQNR)Truncating on the independent value 'number of bits (n)' is accomplished. Results show that increasing the value of (n) by one bit in digital word, the value of SQNR increases by 6 Decibels (dB). © 2019 IEEE.

关键词： MATLAB

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DD-CNN: DEPTHWISE DISOUT CONVOLUTIONAL NEURAL NETWORK FOR LOW:COMPLEXITY ACOUSTIC SCENE CLASSIFICATION

arXiv

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arXiv 2020年

作者： Zhao, Jingqiao Feng, Zhen-Hua Kong, Qiuqiang Song, Xiaoning Wu, Xiao-Jun School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Department of Computer Science University of Surrey Guildford United Kingdom Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom ByteDance AI Lab Shanghai China

This paper presents a Depthwise Disout Convolutional Neural Network (DD-CNN) for the detection and classification of urban acoustic scenes. Specifically, we use log-mel as feature representations of acoustic signals for the inputs of our network. In the proposed DD-CNN, depthwise separable convolution is used to reduce the network complexity. Besides, SpecAugment and Disout are used for further performance boosting. Experimental results demonstrate that our DD-CNN can learn discriminative acoustic characteristics from audio fragments and effectively reduce the network complexity. Our DD-CNN was used for the low-complexity acoustic scene classification task of the DCASE2020 Challenge, which achieves 92.04% accuracy on the validation set. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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The Importance of Gender Specification for Detection of Driver Fatigue using a Single EEG Channel

The Importance of Gender Specification for Detection of Driv...

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Biomedical Engineering International Conference (BMEiCON)

作者： Mohammad Shahbakhti Matin Beiramvand Erfan Nasiri Wei Chen Jordi Solé-Casals Michal Wierzchon Anna Broniec-Wójcik Piotr Augustyniak Vaidotas Marozas Biomedical Engineering Institute Kaunas University of Technology Kaunas Lithuania Faculty of Information Technology and Communication Tampere University Tempere Finland Faculty of Statistics Mathematics and Computer Allameh Tabataba’i University Tehran Iran Center for Intelligent Medical Electronics School of Information Science and Technology the Human Phenome Institute Fudan University Shanghai China Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain Institute of Psychology Jagiellonian University Krakow Poland Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9781665489041

Although detection of the driver fatigue using a single electroencephalography (EEG) channel has been addressed in literature, the gender differentiation for applicability of the model has not been investigated heretofore. Motivated accordingly, we address the detection of driver fatigue based the gender-segregated datasets, where each of them contains 8 subjects. After splitting the EEG signal into its sub-bands (delta, theta, alpha, beta, and gamma) using discrete wavelet transform, the log energy entropy of each band is computed to form the feature vector. Afterwards, the feature vector is randomly split into 50% for training and 50% for the unseen testing, and fed to a support vector machine model. When comparing the classification results of fatigue driving detection between the gender segregated and non-gender segregated datasets, the former achieved the accuracy 78% and 77% for male and female subjects, respectively, than the latter (71%). The obtained results show the importance of gender-specification for the driver fatigue detection.

关键词： Training Support vector machines Computational modeling Transforms Fatigue Brain modeling Electroencephalography

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Digging Deeper into Egocentric Gaze Prediction

arXiv

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arXiv 2019年

作者： Tavakoli, Hamed R. Rahtu, Esa Kannala, Juho Borji, Ali Department of Computer Science Aalto University Department of Signal Processing Tampere University of Technology

This paper digs deeper into factors that influence egocentric gaze. Instead of training deep models for this purpose in a blind manner, we propose to inspect factors that contribute to gaze guidance during daily tasks. Bottom-up saliency and optical flow are assessed versus strong spatial prior baselines. Task-specific cues such as vanishing point, manipulation point, and hand regions are analyzed as representatives of top-down information. We also look into the contribution of these factors by investigating a simple recurrent neural model for ego-centric gaze prediction. First, deep features are extracted for all input video frames. Then, a gated recurrent unit is employed to integrate information over time and to predict the next fixation. We also propose an integrated model that combines the recurrent model with several top-down and bottom-up cues. Extensive experiments over multiple datasets reveal that (1) spatial biases are strong in egocentric videos, (2) bottom-up saliency models perform poorly in predicting gaze and underperform spatial biases, (3) deep features perform better compared to traditional features, (4) as opposed to hand regions, the manipulation point is a strong influential cue for gaze prediction, (5) combining the proposed recurrent model with bottom-up cues, vanishing points and, in particular, manipulation point results in the best gaze prediction accuracy over egocentric videos, (6) the knowledge transfer works best for cases where the tasks or sequences are similar, and (7) task and activity recognition can benefit from gaze prediction. Our findings suggest that (1) there should be more emphasis on hand-object interaction and (2) the egocentric vision community should consider larger datasets including diverse stimuli and more subjects. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Erratum: Perception of loudness and envelopment for different orchestral dynamics [J. Acoust. Soc. Am. 148, 2137-2145 (2020)]

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The Journal of the Acoustical Society of America 2022年第5期151卷 2843页

作者： Tapio Lokki Laura McLeod Antti Kuusinen Aalto Acoustics Lab Department of Signal Processing and Acoustics Aalto University Espoo Finland. Arup Manchester United Kingdom. Aalto Acoustics Lab Department of Computer Science Aalto University Espoo Finland.

This erratum concerns Fig. 2 in the previously published paper.1 Due to a programming error, the values of G and LJ were computed from one source only, not the

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Socially Pertinent Robots in Gerontological Healthcare

arXiv

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arXiv 2024年

作者： Alameda-Pineda, Xavier Addlesee, Angus García, Daniel Hernández Reinke, Chris Arias, Soraya Arrigoni, Federica Auternaud, Alex Blavette, Lauriane Beyan, Cigdem Camara, Luis Gomez Cohen, Ohad Conti, Alessandro Dacunha, Sébastien Dondrup, Christian Ellinson, Yoav Ferro, Francesco Gannot, Sharon Gras, Florian Gunson, Nancie Horaud, Radu D’Incà, Moreno Kimouche, Imad Lemaignan, Séverin Lemon, Oliver Liotard, Cyril Marchionni, Luca Moradi, Mordehay Pajdla, Tomas Pino, Maribel Polic, Michal Py, Matthieu Rado, Ariel Ren, Bin Ricci, Elisa Rigaud, Anne-Sophie Rota, Paolo Romeo, Marta Sebe, Nicu Sieińska, Weronika Tandeitnik, Pinchas Tonini, Francesco Turro, Nicolas Wintz, Timothée Yu, Yanchao RobotLearn Team Inria at Univ. Grenoble Alpes CNRS LJK 655 Avenue de l’Europe Montbonnot38334 France Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Jugoslávských partyzánů 1580/3 Dejvice 160 00 Czech Republic Acoustic Signal Processing Laboratory Bar-Ilan University Ramat-Gan5290002 Israel Department of Information and Computer Science University of Trento Via Sommarive 9 Trento38123 Italy Interaction Lab Mathematical and Computer Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom ERM Automatismes 561 allée Bellecour Carpentras84200 France PAL Robotics C/ Pujades 77-79 Barcelona08005 Spain Lusage Living Lab Assistance Publique - Hopitaux de Paris 54-56 Rue Pascal Paris75013 France

Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary. While several robotic platforms have been used in gerontological healthcare, the question of whether or not an autonomous social interactive robot with multi-modal conversational capabilities will be useful and accepted in real-life facilities is yet to be answered. This paper is an attempt to partially answer this question, via two waves of experiments including patients and companions in a day-care gerontological facility in Paris with a full-sized humanoid robot endowed with social and conversational interaction capabilities. The software architecture, developed during the H2020 SPRING project, together with the experimental protocol, allowed us to evaluate the acceptability (AES) and usability (SUS) with more than 60 end-users. Overall, the users are receptive to this technology, especially when the robot’s perception and action skills are robust to environmental clutter and flexible to handle a plethora of different interactions. We also report and discuss some concerns and general comments of the users. © 2024, CC BY.

关键词： Microrobots

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Artificial Intelligence-Aided Kalman Filters: AI-Augmented Designs for Kalman-Type Algorithms

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IEEE signal processing Magazine 2025年

作者： Shlezinger, Nir Revach, Guy Ghosh, Anubhab Chatterjee, Saikat Tang, Shuo Imbiriba, Tales Dunik, Jindrich Straka, Ondrej Closas, Pau Eldar, Yonina C. Ben-Gurion University Beersheba8410501 Israel Technion–Israel Institute of Technology Department of Electrical and Computer Engineering Haifa Israel Institute for Signal and Information Processing ETH Zürich Zürich8092 Switzerland KTH Royal Institute of Technology StockholmSE-100 44 Sweden Indian Institute of Science Bengaluru560012 India Northeastern University BostonMA02115 United States Federal University of Santa Catarina Department of Electrical Engineering Florianopolis Brazil University of Massachusetts Boston Department of Computer Science BostonMA01003 United States University of West Bohemia Plzeň30100 Czech Republic University of Barcelona Barcelona Spain Weizmann Institute of Science Department of Mathematics and Computer Science Rehovot7610001 Israel

The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on mathematical representations in the form of simple state-space (SS) models, which may be crude and inaccurate descriptions of the underlying dynamics. Emerging data-centric artificial intelligence (AI) techniques tackle these tasks using deep neural networks (DNNs), which are model-agnostic. Recent developments illustrate the possibility of fusing DNNs with classic Kalman-type filtering, obtaining systems that learn to track in partially known dynamics. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms. We review both generic and dedicated DNN architectures suitable for state estimation and provide a systematic presentation of techniques for fusing AI tools with KFs and for leveraging partial SS modeling and data, categorizing design approaches into task-oriented and SS model-oriented. The usefulness of each approach in preserving the individual strengths of model-based KFs and data-driven DNNs is investigated in a qualitative and quantitative study (whose code is publicly available), illustrating the gains of hybrid model-based/data-driven designs. We also discuss existing challenges and future research directions that arise from fusing AI and Kalman-type algorithms. © 1991-2012 IEEE.

关键词： Deep neural networks

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Real-time deep learning for multi-label retinal disease diagnosis with embedded system

Computational and Structural Biotechnology Reports

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Computational and Structural Biotechnology Reports 2025年 2卷

作者： Feudjio Ghislain Saha Tchinda Beaudelaire Romain Atangana Tchiotsop Daniel Research Unit of Automation and Applied Computer (UR-AIA) Electrical Engineering Department of IUT-FV University of Dschang P.O. Box: 134 Bandjoun Cameroon Research Unit of Condensed Matter of Electronics and Signal Processing (UR‑MACETS) Department of Physics Faculty of Sciences University of Dschang P.O. Box 67 Dschang Cameroon Department of Computer Science Higher Teacher Training College University of Bertoua-Cameroon P.O.Box 652 Bertoua Cameroon

Background Ophthalmological consultations are essential for the early detection of retinal pathologies. The development of advanced diagnostic devices that combine portability, ease of access, and energy efficiency offers significant benefits, especially in rural or peri-urban areas, which often struggle with limited medical resources and time constraints. Method In this study, we develop a classification platform for various retinal diseases using a Raspberry Pi 4 board. The system relies on a lightweight deep learning model based on convolutional neural networks (CNNs). To enhance the model's efficiency, we integrate biorthogonal wavelet transforms, which facilitate the effective extraction of relevant features from the input images. This approach reduces computational complexity while maintaining the quality of the extracted information. The model, optimized for real-time deployment, was trained on a dataset containing 4303 retinal images, representing four classes of pathologies. Results The proposed embedded model achieves an accuracy of 0.9806 across the four classes of pathologies collected from the two public databases ODIR (Ocular Disease Intelligence Recognition) and RFMiD (Retinal Fundus Multi-Disease Image Dataset). Conclusions Compared to several state-of-the-art methods published to date, the outstanding performance of the proposed embedded system demonstrates its potential as a valuable tool for clinicians to diagnose various ocular diseases in underprivileged healthcare settings.

关键词： Embedded board Real-time classification Lightweight CNN model Wavelet transforms

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