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IEEE SSD International Multi-Conference on Systems, signals and Devices

作者： Khaoula Ben Ali Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Noureddine Liouane Laboratory of Automatic Signal Image Processing National Engineering School Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Recent years have seen a rapid development in Machine Learning, which has profoundly influenced many areas of science and engineering. Among them, computer vision takes the leading place, where important tasks are image classifications powered by CNNs. Despite the great performance of CNNs in complicated scenarios, they remain sensitive to so-called adversarial attacks, and deliberate perturbations leading them to incorrect predictions. Besides more innocuous consequences, this has serious security implications for critical applications, in-cluding medical diagnostics, where misclassifications might result in disastrous outcomes. This research work discusses adversarial attacks on CNNs and other DNNs in computer vision, studying a full range of the generation and detection methods with details while discussing intrinsic vulnerability and robustness. It also proposes a learning framework that will enhance the robustness and security of DNNs and CNNs against such adversarial perils. The ultimate goal is directed to an improvement in the reliability of such models in absolutely critical scenarios for safe deployment into applications where accuracy is crucial.

关键词： computer vision Accuracy Perturbation methods computer network reliability Machine learning Robustness Security Medical diagnosis Resilience Image classification

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A benchmark study on room acoustic simulations with various material inputs 51

A benchmark study on room acoustic simulations with various ...

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51st International Congress and Exposition on Noise Control Engineering, Internoise 2022

作者： Li, Yue Meyer, Julie Lokki, Tapio Cuenca, Jacques Atak, Onur Desmet, Wim Siemens Digital Industries Software Interleuvenlaan 68 LeuvenB-3001 Belgium Department of Computer Science Aalto University Konemiehentie 29 EspooFI-02150 Finland Department of Signal Processing and Acoustics Aalto University Maarintie 8 EspooFI-02150 Finland Department of Mechanical Engineering KU Leuven Celestijnenlaan 300 LeuvenB-3001 Belgium

ISBN: (纸本)9781906913427

This work investigates and evaluates the capabilities of an explicit finite-difference time-domain (FDTD), a fast multipole indirect boundary element method (FMBEM), and a ray acoustic solver in the context of room acoustic modeling and simulations. In room acoustic simulations, the wave-based FDTD and FMBEM methods are known for generating accurate results at low frequencies, while the ray acoustic technique is in principle more valid at higher frequencies. The numerical aspects of setting up the solution for each solver are discussed. Special attention is given to the influence of material input data of various degrees of detail. Single/multiple frequency-independent/dependent materials are considered in the model setup. The modeling capabilities of the three solvers in handling material input with various complexities are analyzed. Numerical results are evaluated in both frequency and time domains. Room acoustic parameters, including the reverberation time, early decay time, clarity, and definition, are compared. These results are also compared with available measurement data. Last but not least, the computational efficiencies of the three solvers are briefly discussed. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.

关键词： Materials handling

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Stable Extended U-Net for Noise-Robust Speaker Verification

Stable Extended U-Net for Noise-Robust Speaker Verification

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zonghui Wang Zhihua Fang Liang He School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China School of Intelligence Science and Technology Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With advancements in deep learning, speaker verification systems have significantly improved their performance in noisy environments. Researchers typically demonstrate the effectiveness of their improved models by comparing performance on specific datasets, such as the VoxCeleb benchmark. However, in diverse real-world noise conditions, the out-of-domain generalization ability is also a crucial factor in evaluating a model’s performance improvement. Research on stable learning indicates that eliminating the spurious correlation between training and testing data can enhance the generalization of the model. Building on this idea, we propose an improved speaker verification system with high generalization based on the extended U-Net (ExU-Net). It uses the sample reweighting method from stable learning to eliminate sample correlations and retains more effective speaker information through subpixel convolutions and coordinate attention mechanisms. We validate the effectiveness of this approach through extensive evaluations on VoxCeleb1, VOiCES, and other out-of-domain noise test sets, highlighting its generalization capability and model robustness.

关键词： Training Correlation Attention mechanisms Convolution Data models Stability analysis Robustness Noise measurement Speech processing signal to noise ratio

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Rate Maximization in Sweeping Robot-Assisted Reconfigurable Intelligent Surface Communication Systems

Rate Maximization in Sweeping Robot-Assisted Reconfigurable ...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Zhongyu Wang Gengxin Zhao Yashuai Cao Jiawei Zhang Zheng Chang Hebei Key Laboratory of Information Transmission and Signal Processing School of Information Science and Engineering Yanshan University Qinhuangdao China Department of Electronic Engineering Tsinghua University Beijing China School of Computer Science University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyäaskylä Jyväskylä Finland

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Reconfigurable intelligent surface (RIS) assisted communication is recognized as a promising technology in academia and industry because it offers a novel and promising solution for implementing both secure and good-quality wireless communication networks. The paper proposes installing an RIS on a sweeping robot to act as the mobile passive relay, which can flexibly deploy the location of RIS and enhance communication capacity. In this paper, the maximization problem of user throughput is formulated, and we jointly optimize transmission power at AP and reflection phase shifts at RIS. It is difficult to deal with the optimization problem. Therefore, we separate the two variables into two subproblems, i.e., the transmit power optimization problem and the reflection phase-shift optimization problem. We can solve these problems by semidefinite relaxation (SDR) and alternating optimization algorithms. The simulation results demonstrate the advantages of the proposed algorithm, which enhances the communication capacity efficiently.

关键词： Wireless communication Industries Service robots Simulation Conferences Reconfigurable intelligent surfaces Throughput Reflection Relays Optimization

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A Self-Supervised Model for Multi-modal Stroke Risk Prediction

arXiv

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arXiv 2024年

作者： Delgrange, Camille Demler, Olga Mora, Samia Menze, Bjoern de la Rosa, Ezequiel Davoudi, Neda Signal Processing Institute EPFL University Lausanne Switzerland Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland ETH AI Center Department of Computer Science Department of Quantitative Biomedicine University of Zurich Zurich Switzerland

Predicting stroke risk is a complex challenge that can be enhanced by integrating diverse clinically available data modalities. This study introduces a self-supervised multimodal framework that combines 3D brain imaging, clinical data, and image-derived features to improve stroke risk prediction prior to onset. By leveraging large unannotated clinical datasets, the framework captures complementary and synergistic information across image and tabular data modalities. Our approach is based on a contrastive learning framework that couples contrastive language-image pretraining with an image-tabular matching module, to better align multimodal data representations in a shared latent space. The model is trained on the UK Biobank, which includes structural brain MRI and clinical data. We benchmark its performance against state-of-the-art unimodal and multimodal methods using tabular, image, and image-tabular combinations under diverse frozen and trainable model settings. The proposed model outperformed self-supervised tabular (image) methods by 2.6% (2.6%) in ROC-AUC and by 3.3% (5.6%) in balanced accuracy. Additionally, it showed a 7.6% increase in balanced accuracy compared to the best multimodal supervised model. Through interpretable tools, our approach demonstrated better integration of tabular and image data, providing richer and more aligned embeddings. Gradient-weighted Class Activation Mapping heatmaps further revealed activated brain regions commonly associated in the literature with brain aging, stroke risk, and clinical outcomes. This robust self-supervised multimodal framework surpasses state-of-the-art methods for stroke risk prediction and offers a strong foundation for future studies integrating diverse data modalities to advance clinical predictive modeling. © 2024, CC BY-NC-SA.

关键词： Brain mapping

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A comprehensive review of visualization methods for association rule mining: Taxonomy, Challenges, Open problems and Future ideas

arXiv

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arXiv 2023年

作者： Fister, Iztok Fister, Iztok Fister, Dušan Podgorelec, Vili Salcedo-Sanz, Sancho Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 Maribor2000 Slovenia Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares Madrid28805 Spain

Association rule mining is intended for searching for the relationships between attributes in transaction databases. The whole process of rule discovery is very complex, and involves pre-processing techniques, a rule mining step, and post-processing, in which visualization is carried out. Visualization of discovered association rules is an essential step within the whole association rule mining pipeline, to enhance the understanding of users on the results of rule mining. Several association rule mining and visualization methods have been developed during the past decades. This review paper aims to create a literature review, identify the main techniques published in peer-reviewed literature, examine each method’s main features, and present the main applications in the field. Defining the future steps of this research area is another goal of this review paper. Copyright © 2023, The Authors. All rights reserved.

关键词： Visualization

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Constrained Energy Minimization (CEM) for Hyperspectral Target Detection: Theory and Generalizations

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷

作者： Chang, Chein-I. Information and Technology College Dalian116026 China University of Maryland Baltimore County Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan

Target detection is a fundamental task of hyperspectral imaging where constrained energy minimization (CEM) has been widely used for subpixel target detection techniques. Due to its effectiveness, CEM has been generalized to various versions, such as kernel CEM (KCEM), kernel target-constrained interference-minimized filter (KTCIMF), ensemble cascaded CEM (ECEM), and hierarchical CEM (HCEM). Unfortunately, these generalizations overlooked the key design rationale behind CEM. This article revisits CEM for hyperspectral target detection (HTD) and proves how and why it works mathematically. Specifically, several new CEM generalizations are derived and particularly noteworthy. By including spatial information in an iterative process, KCEM, ECEM, and HCEM can be generalized to iterative KCEM (IKCEM), iterative KTCIMF (IKTCIMF), iterative ECEM (IECEM), and iterative HCEM (IHCEM). Also, by utilizing an iterative random training sampling (IRTS) to generate the desired target signature to be detected, these algorithms are further generalized to IRTS KCEM (IRTS-KCEM), IRTS ECEM (IRTS-ECEM), and IRTS HCEM (IRTS-HCEM). A comprehensive analysis along with comparative study on these generalizations is conducted through extensive experiments to demonstrate the effectiveness of IKCEM, IHCEM, and IECEM. © 1980-2012 IEEE.

关键词： Iterative methods

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Multi-Granularity Prosodic Speech Synthesis with Grammar Information

SSRN

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SSRN 2024年

作者： Zhou, Jian Liu, Jiahui Fan, Cunhang Zheng, Wenming Lv, Zhao Tao, Liang Kwan, Hon Keung The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Ministry of Education School of Biological Science and Medical Engineering Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering University of Windsor WindsorN9B 3P4 Canada

Personalized speech synthesis involves learning an individual's unique speaking patterns from a reference audio to generate speech that mimics the speaker's habitual rhythm. However, the one-to-many mapping between text and speech, combined with the dynamic nature of personalized speech style characteristics in the reference audio, poses significant challenges for accurate personalized speech synthesis. In this paper, we propose a Grammar Infused Multi-granularity Prosody Network (GMG ProsodyNet) for personalized speech synthesis. Specifically, 1) We model prosodic features hierarchically at the levels of utterance, content syntax, word, and phoneme. We utilize the utterance-level prosodic feature to guide the prediction of fine-grained prosodic features according to the hierarchical property of prosodic features. We introduce a context syntax encoder to improve the prediction accuracy of duration, pitch, and energy characteristics in the synthesized audio. The proposed word-level prosodic encoder can efficiently extract valuable pitch dynamic and speech continuity features from the word spectrum. For controlling subtle prosodic nuances, we employ phoneme-level prosodic modeling. 2) We extract syntax level prosodic features from the syntax graph constructed by Graph Neural Network (GNN) with word as node, exploiting grammatical dependencies between distant words. Experimental results demonstrate that the proposed GMG ProsodyNet can effectively encode delicate and personalized prosodic features, leading to improved speech synthesis quality, fluency, and naturalness. © 2024, The Authors. All rights reserved.

关键词： Speech synthesis

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On the Secrecy Performance of Reconfigurable Intelligent Surfaces-Assisted Satellite Networks Under Shadow-Rician Channels

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6794-6808页

作者： Le, Si-Phu Nguyen, Tan N. Le-Tien, Thuong Duy, Tran Trung Nguyen, Tien-Tung Ng, Derrick Wing Kwan Tu, Lam-Thanh Voznak, Miroslav Ton Duc Thang University Communication and Signal Processing Research Group Faculty of Electrical and Electronics Engineering Ho Chi Minh72900 Viet Nam VSB-Technical University of Ostrava Department of Telecommunications Faculty of Electrical Engineering and Computer Science Ostrava70800 Czech Republic University of Technology Vietnam National University Ho Chi Minh72900 Viet Nam Posts and Telecommunications Institute of Technology Faculty of Telecommunications 2 Ho Chi Minh72900 Viet Nam Industrial University of Ho Chi Minh City Faculty of Electronics Technology Ho Chi Minh70000 Viet Nam University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia

In this study, we investigate the secrecy performance of reconfigurable intelligent surfaces-assisted satellite (RISAS) networks. Specifically, we analytically derive closed-form expressions of the secrecy outage probability (SOP) and the tight lower bound secrecy rate (SR) exploiting the moment matching method (MMM). Furthermore, we explore the asymptotic behavior of the SOP as the satellite transmit power is sufficiently large, offering valuable insights into network performance. Numerical simulations are conducted to validate the established framework and elucidate the influence of critical parameters such as transmission distance, transmit power, and the number of reconfigurable intelligent surfaces (RIS) elements on SOP and SR. Our findings highlight the efficacy of increasing the number of phase shifters of the RIS to mitigate the adverse effects of fading, shadowing, and transmission environments. Finally, we unveil the adaptability of the proposed networks, illustrating their suitability for various satellite systems, including low earth orbit (LEO) and geostationary earth orbit (GEO). Among these, LEO offers superior performance due to its shorter transmission distance compared to medium earth orbit (MEO) and GEO. © 1965-2011 IEEE.

关键词： Satellite communication systems

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Technology acceptance model (TAM) for mobile durian plantation apps(Pro Durian);the analysis for Malaysian farmers

Technology acceptance model (TAM) for mobile durian plantati...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Noraziahtulhidayu Kamarudin Aiman Yusoff Abd Samad Hasan Basari Noor Zuraidin bin Mohd Safar Mohd Hatta Hussin Department of Multimedia Multimedia and Signal Processing Focus Group Faculty of Computer Science and Information Universiti Tun Hussein Onn Malaysia Department of Multimedia Faculty of Computer Science and Information Universiti Tun Hussein Onn Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Department of Information Security and Web Technology Faculty of Computer Science and Information Universiti Tun Hussein Onn Malaysia Johor Darul Takzim Pertubuhan Peladang Senggarang

During the application development process, it is important for a developer to analyze user requirements. This is crucial to the success of interactive systems and is an essential component of the design of information systems. User requirement analysis aids developers in understanding and analyzing users' needs and technology requirements. Throughout this case study, it is able to forecast the understanding, and technology acceptance, and at the same time improve the quality of work, reducing training and support costing and perennially improve customer or user satisfaction. The method of research selected is questionnaires that focused on collecting the data directly from Malaysian farmers through Google Forms as an online medium. The method was chosen because it is easy to implement and inexpensive due to the current situation of Covid-19. The questionnaire is designed with two sections where; a) includes the participant's demography to analyze information of the intended audience while b)focused on the knowledge of participants about recognition images. Results demonstrate the farmer's opinion and knowledge of the technology of image recognition in the agriculture sector. The quantitative research was undertaken using an online platform due to the Covid-19 pandemic. From the analysis, it is justified that the Technology Acceptance Model (TAM) together with the mobile farming image recognition app is useful in collecting farmers' feedback and positive results have an outcome on the challenges faced by the durian farmers.

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