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International Conference on Information Fusion

作者： John-Joseph Brady Yuhui Luo Wenwu Wang Víctor Elvira Yunpeng Li Computer Science Research Centre University of Surrey Guildford United Kingdom Data Science Department National Physical Laboratory Teddington United Kingdom Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom School of Mathematics University of Edinburgh Edinburgh United Kingdom

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Differentiable particle filters are an emerging class of models that combine sequential Monte Carlo techniques with the flexibility of neural networks to perform state space inference. This paper concerns the case where the system may switch between a finite set of state-space models, i.e. regimes. No prior approaches effectively learn both the individual regimes and the switching process simultaneously. In this paper, we propose the neural network based regime learning differentiable particle filter (RLPF) to address this problem. We further design a training procedure for the RLPF and other related algorithms. We demonstrate competitive performance compared to the previous state-of-the-art algorithms on a pair of numerical experiments.

关键词： Training Monte Carlo methods Heuristic algorithms Neural networks Switches Information filters Particle filters Numerical models State-space methods

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Regime Learning for Differentiable Particle Filters

arXiv

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arXiv 2024年

作者： Brady, John-Joseph Luo, Yuhui Wang, Wenwu Elvira, Víctor Li, Yunpeng Computer Science Research Centre University of Surrey Guildford United Kingdom Data Science Department National Physical Laboratory Teddington United Kingdom Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom School of Mathematics University of Edinburgh Edinburgh United Kingdom

关键词： Monte Carlo methods

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A Comparison of Approximate Analytic and Neural Network Solutions for Effective Mass Schrodinger Equation With Yukawa's Potential 2

A Comparison of Approximate Analytic and Neural Network Solu...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Fotso Kamsu, Brigitte Vubangsi, Mercel Tchoffo, M. Al-Turjman, Fadi University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Faculty of Science Cameroon Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Htttc Bambili Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

We model a compositionally graded heterostructure using the effective mass approach with a screened Coulomb potential. Approximate analytical solutions to the model are compared with a Neural Network output and good agreement is achieved, paving the way for Artificial Intelligence modeling of systems with graded composition. © 2022 IEEE.

关键词： Grading

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40 Gb/s High-speed mode-division multiplexing transmission employing NRZ modulation format

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Journal of Optical Communications 2023年第S1期44卷 S1157-S1165页

作者： Sabitu, Rabiu Imam Khan, Nafizah Goriman Malekmohammadi, Amin Affiliated to the George Green Institute for Electromagnetics Research Nottingham United Kingdom Photonics Communication Systems and Signal Processing Research Group Kano University of Science and Technology Wudil KanoPMB 3244 Nigeria Semenyih Malaysia Department of Electrical and Computer Engineering California State University BakersfieldCA93311 United States Photonics Communication Systems and Signal Processing Research Group Kano University of Science and Technology Wudil KanoPMB 3244 Nigeria

This report examines the performance of a high-speed MDM transmission system supporting four nondegenerate spatial modes at 10 Gb/s. The analysis adopts the NRZ modulation format to evaluate the system performance in terms of a minimum power required (PN) and the nonlinear threshold power (PTH) at a BER of 10−9. The receiver sensitivity, optical signal-to-noise ratio, and the maximum transmission distance were investigated using the direct detection by employing a multimode erbium-doped amplifier (MM-EDFA). It was found that by properly optimizing the MM-EDFA, the system performance can significantly be improved. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Erbium doped fiber amplifiers

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A Variable Step-Size l0-PRLS Algorithm and its Application in Sparse Channel Estimations

A Variable Step-Size l0-PRLS Algorithm and its Application i...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yu Wang Zhen Qin Jun Tao Ming Jiang Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education School of Information Science and Engineering Southeast University Nanjing China Department of Computer Science and Engineering The Ohio State University Columbus OH USA Pengcheng Laboratory Shenzhen China

We recently proposed a sparsity-aware recursive least squares (RLS) adaptive filtering algorithm, named the proportionate RLS with l 0 norm regularization (l 0 -PRLS). It shows better performance than its non-sparse counterparts under sparse systems. The l 0 -PRLS algorithm, however, employs a fixed step size which trades off the convergence speed and steady-state performance. To simultaneously obtain fast convergence and low steady-state error, we resort to the variable step size (VSS) technique and propose a VSS-l 0 -PRLS adaptive filtering algorithm in this paper. The superiority of the VSS-l 0 -PRLS was verified by simulation results of sparse system identification as well as experimental results of channel estimation for underwater acoustic (UWA) communications.

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Robust Optimization-based Neural Architectures Against Adversarial Attacks

Robust Optimization-based Neural Architectures Against Adver...

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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

引用

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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