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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Hailong Kang Liang Xu Tuoyu Shen Jun Li Marco Martorella The Hangzhou Institute of Technology Xidian University Hangzhou China The National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Radar and Surveillance Systems (RaSS) Laboratory (CNIT) Pisa Italy Department of Electronic Electrical and Systems Engineering University of Birmingham Birmingham UK

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Autofocusing is an important step for inverse synthetic aperture radar (ISAR) imaging, while traditional autofocusing algorithms often fail in sparse aperture (SA) case. The residual phase error due to the imperfect motion compensation cannot be ignored for the compressive sensing (CS) based SA ISAR imaging. To tackle this issue, an autofocus-coupled approximate message passing with unitary transformation (AFUTAMP) algorithm for SA ISAR imaging is proposed in this paper. The phase error estimation process is introduced to the UTAMP framework for autofocusing, which enable UTAMP to simultaneously complete ISAR image reconstruction and autofocus, thereby leading to excellent performance of imaging and autofocusing. The effectiveness and superiority of the proposed algorithm are verified by the simulation results and experimental results based on measured data.

关键词： Simulation Message passing Imaging Geoscience and remote sensing Apertures Radar imaging Approximation algorithms

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Neural Source Coding For Bandwidth-Efficient Brain-Computer Interfacing With Wireless Neuro-Sensor Networks

Neural Source Coding For Bandwidth-Efficient Brain-Computer ...

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

作者： Thomas Strypsteen Alexander Bertrand Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Leuven.AI KU Leuven Institute for AI Leuven Belgium

Neural Source Coding (NSC) is a technique that exploits the modelling power of (deep) neural network for the purpose of source coding. Its goal is to transform the data into a space of low entropy, where they can be coded by classic entropy coding schemes. In this paper, our goal is to investigate the use of NSC in so-called neuro-sensor networks, i.e., a type of body-sensor network consisting of a collection of wireless sensor nodes that record brain activity at different scalp locations, e.g., via electroencephalography (EEG) sensors. All nodes wirelessly transmit their data to a fusion center, where inference is then performed on the joint sensor signals by a given deep neural network. The NSC parameters and inference network are learned jointly, optimizing the trade-off between accuracy and bitrate for a given application. We validate this method on a motor execution task in an emulated EEG sensor network and compare the resulting trade-offs with those obtained by directly quantizing the transmitted data to low-bit precision. We demonstrate that NSC yields more favorable trade-offs than straightforward quantization for very low bit depths and allows for large bandwidth gains at little loss in accuracy on the investigated brain-computer interface (BCI) task.

关键词： Wireless communication Deep learning Wireless sensor networks Source coding Transforms Electroencephalography Brain-computer interfaces

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Raspberry Pi-Based Monitoring System for Grid-Connected PV systems using Deep Learning Technique*

Raspberry Pi-Based Monitoring System for Grid-Connected PV S...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Zahra Yahyaoui Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The evolution of embedded systems has demonstrated their reliability as a solution for monitoring and controlling industrial systems, particularly in renewable energy conversion systems like photovoltaic (PV) energy. The increasing adoption of PV systems highlights the critical need for effective fault diagnosis to ensure their reliable operation. In this paper, we present a novel fault diagnosis approach utilizing Long Short-Term Memory (LSTM) networks optimized through Bayesian optimization techniques. Our methodology is implemented on a Raspberry Pi platform, demonstrating the feasibility of deploying sophisticated fault diagnosis algorithms in resource-constrained environments. Through extensive experiments, we demonstrate the effectiveness of our approach to accurately diagnose faults in grid-connected photovoltaic systems, thereby improving the reliability and efficiency of integrated environmental monitoring *** obtained results highlight the potential of combining advanced deep learning techniques with embedded systems to address complex diagnostic challenges, as demonstrated by achieving a 100% accuracy rate.

关键词： Fault diagnosis Deep learning Photovoltaic systems Embedded systems Accuracy Economic indicators Bayes methods Environmental monitoring Long short term memory Optimization

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SACA Net: Cybersickness Assessment of Individual Viewers for VR Content via Graph-Based Symptom Relation Embedding 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Lee, Sangmin Kim, Jung Uk Kim, Hak Gu Kim, Seongyeop Ro, Yong Man Image and Video Systems Lab School of Electrical Engineering KAIST Daejeon Korea Republic of Signal Processing Lab Institute of Electrical Engineering EPFL Lausanne Switzerland

ISBN: (数字)9783030585921

ISBN: (纸本)9783030585914

Recently, cybersickness assessment for VR content is required to deal with viewing safety issues. Assessing physical symptoms of individual viewers is challenging but important to provide detailed and personalized guides for viewing safety. In this paper, we propose a novel symptom-aware cybersickness assessment network (SACA Net) that quantifies physical symptom levels for assessing cybersickness of individual viewers. The SACA Net is designed to utilize the relational characteristics of symptoms for complementary effects among relevant symptoms. The proposed network consists of three main parts: a stimulus symptom context guider, a physiological symptom guider, and a symptom relation embedder. The stimulus symptom context guider and the physiological symptom guider extract symptom features from VR content and human physiology, respectively. The symptom relation embedder refines the stimulus-response symptom features to effectively predict cybersickness by embedding relational characteristics with graph formulation. For validation, we utilize two public 360-degree video datasets that contain cybersickness scores and physiological signals. Experimental results show that the proposed method is effective in predicting human cybersickness with physical symptoms. Further, latent relations among symptoms are interpretable by analyzing relational weights in the proposed network. © 2020, Springer Nature Switzerland AG.

关键词： Embeddings

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Graph learning and augmentation based interpolation of signal strength for location-aware communications 28

Graph learning and augmentation based interpolation of signa...

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28th European signal processing Conference, EUSIPCO 2020

作者： Chiu, Hong-Ming Fung, Carrson C. Ortega, Antonio Dept. of Electronics Engineering National Chiao Tung University Hsinchu Taiwan Institute of Electronics National Chiao Tung University Hsinchu Taiwan Signal and Image Processing Institute Department of Electrical and Computer Engineering University of Southern California Los Angeles United States

ISBN: (纸本)9789082797053

A graph learning and augmentation (GLA) technique is proposed herein to solve the received signal power interpolation problem, which is important for preemptive resource allocation in location-aware communications. A graph parameterization results in the proposed GLA interpolator having superior mean-squared error performance and lower computational complexity than the traditional Gaussian process method. Simulation results and analytical complexity analysis are used to prove the efficacy of the GLA interpolator. © 2021 European signal processing Conference, EUSIPCO. All rights reserved.

关键词： Interpolation

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A technical primer on the physical modeling of diffusion-encoded magnetic resonance experiments: A random process perspective

arXiv

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arXiv 2021年

作者： Haldar, Justin P. Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California

Diffusion-encoded magnetic resonance (MR) experiments can provide important insights into the microstructural characteristics of a variety of biological tissues and other fluid- or gas-filled porous media. The physics of diffusion encoding has been studied extensively over the span of many decades, and many excellent descriptions can be found in the literature - see, e.g., Refs. [1-5]. However, many of these descriptions spend relatively little time focusing on random process descriptions of the diffusion process, instead relying on different abstractions. In this primer, we describe diffusion-encoded MR experiments from a random process perspective. While the results we derive from this perspective are quite standard (and match the results obtained with other arguments), we expect that the alternative derivations may be insightful for some readers. This primer is intended for technical readers who have a graduate-level understanding of random processes. Readers are also expected to already have good familiarity with the basics of MR, and we anticipate that a signal processing perspective on MR [6] will be especially complementary to the random process perspectives presented herein. © 2021, CC BY-NC-ND.

关键词： Magnetic resonance

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A Distributed Adaptive Algorithm for Non-Smooth Spatial Filtering Problems

A Distributed Adaptive Algorithm for Non-Smooth Spatial Filt...

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

作者： Charles Hovine Alexander Bertrand Department of Electrical Engineering (ESAT) KU Leuven Leuven Belgium Stadius Center for Dynamical Systems Signal Processing and Data Analytics Leuven Belgium KU Leuven Institute for Artificial Intelligence (Leuven.AI) Leuven Belgium

Computing the optimal solution to a spatial filtering problems in a Wireless Sensor Network can incur large bandwidth and computational requirements if an approach relying on data centralization is used. The so-called distributed adaptive signal fusion (DASF) algorithm solves this problem by having the nodes collaboratively solve low-dimensional versions of the original optimization problem, relying solely on the exchange of compressed views of the sensor data between the nodes. However, the DASF algorithm has only been shown to converge for filtering problems that can be expressed as smooth optimization problems. In this paper, we explore an extension of the DASF algorithm to a family of non-smooth spatial filtering problems, allowing the addition of non-smooth regularizers to the optimization problem, which could for example be used to perform node selection, and eliminate nodes not contributing to the filter objective, therefore further reducing communication costs. We provide a convergence proof of the non-smooth DASF algorithm and validate its convergence via simulations in both a static and adaptive setting.

关键词： Wireless sensor networks Costs Filtering signal processing algorithms Distributed databases signal processing Filtering algorithms

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Deep Learning based Fault Diagnosis in a Grid-Connected Photovoltaic systems 19

Deep Learning based Fault Diagnosis in a Grid-Connected Phot...

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19th IEEE International Multi-Conference on systems, signals and Devices, SSD 2022

作者： Hichri, Amal Hajji, Mansour Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Higher Institute of Applied Sciences and Technology of Kasserine Kasserine Tunisia Electrical and Computer Engineering Program Texas A&m University at Qatar Doha23874 Qatar Prince Sultan University Department of Mathematics and Sciences Riyadh11586 Saudi Arabia Signal Processing and Image National Engineering School of Monastir Research Laboratory of Automation 5019 Tunisia Chemical Engineering Program Texas A&m University at Qatar Doha23874 Qatar

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Photovoltaic (PV) system fault diagnosis is crucial because it helps PV system operators reduce energy and income losses. It also decreases the risk of fire and electric shock from PV system failures. Thus, the implementation of fault diagnosis tool is required. The most well-known techniques are machine learning models. Although these techniques provided well in terms of diagnosis and classification accuracy, they rely on manual feature extraction, which is time consuming, expensive, and requires diagnostic skill. Manual feature extraction is a problem that has to be addressed. This paper therefore presents a PV system fault diagnosis techniques that can automatically extract features from raw data in order to classify PV system faults. The implemented methods include long short-term memory (LSTM) bidirectional long short-term memory (BiLSTM) networks, which are deep learning based algorithms. Finally, the presented fault diagnosis approaches showed a good diagnosis performances when applied to an emulated GCPV system. © 2022 IEEE.

关键词： Failure analysis

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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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Stimulus-Informed Generalized Canonical Correlation Analysis of Stimulus-Following Brain Responses

Stimulus-Informed Generalized Canonical Correlation Analysis...

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European signal processing Conference (EUSIPCO)

作者： Simon Geirnaert Tom Francart Alexander Bertrand Department of Electrical Engineering (ESAT) KU Leuven STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Leuven Belgium Department of Neurosciences KU Leuven ExpORL Leuven Belgium KU Leuven Institute for Artificial Intelligence (Leuven.AI) Leuven Belgium

In brain-computer interface or neuroscience applications, generalized canonical correlation analysis (GCCA) is often used to extract correlated signal components in the neural activity of different subjects attending to the same stimulus. This allows quantifying the so-called inter-subject correlation or boosting the signal-to-noise ratio of the stimulus-following brain responses with respect to other (non-)neural activity. GCCA is, however, stimulus-unaware: it does not take the stimulus information into account and does therefore not cope well with lower amounts of data or smaller groups of subjects. We propose a novel stimulus-informed GCCA algorithm based on the MAXVAR-GCCA framework. We show the superiority of the proposed stimulus-informed GCCA method based on the inter-subject correlation between electroencephalography responses of a group of subjects listening to the same speech stimulus, especially for lower amounts of data or smaller groups of subjects.

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