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A survey on hyperspectral image restoration: from the view of low-rank tensor approximation

Science China(Information Sciences) 2023年第4期66卷 33-63页

作者： Na LIU Wei LI Yinjian WANG Ran TAO Qian DU Jocelyn CHANUSSOT School of Information and Electronics and Beijing Key Laboratory of Fractional Signals and Systems Beijing Institute of Technology Department of Electrical and Computer Engineering Mississippi State University GIPSA-Lab University of Grenoble Alpes Aerospace Information Research Institute Chinese Academy of Sciences

The ability to capture fine spectral discriminative information enables hyperspectral images(HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the captured HSIs may not represent the true distribution of ground objects and the received reflectance at imaging instruments may be degraded, owing to environmental disturbances, atmospheric effects, and sensors' hardware limitations. These degradations include but are not limited to complex noise, heavy stripes, deadlines,cloud/shadow occlusion, blurring and spatial-resolution degradation, etc. These degradations dramatically reduce the quality and usefulness of HSIs. Low-rank tensor approximation(LRTA) is such an emerging technique, having gained much attention in the HSI restoration community, with an ever-growing theoretical foundation and pivotal technological innovation. Compared to low-rank matrix approximation(LRMA),LRTA characterizes more complex intrinsic structures of high-order data and owns more efficient learning abilities, being established to address convex and non-convex inverse optimization problems induced by HSI restoration. This survey mainly attempts to present a sophisticated, cutting-edge, and comprehensive technical survey of LRTA toward HSI restoration, specifically focusing on the following six topics: denoising, fusion,destriping, inpainting, deblurring, and super-resolution. For each topic, state-of-the-art restoration methods are introduced, with quantitative and visual performance assessments. Open issues and challenges are also presented, including model formulation, algorithm design, prior exploration, and application concerning the interpretation requirements.

关键词： hyperspectral image image restoration low-rank tensor approximation multisource fusion remote sensing

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Spatio-Temporal Hybrid Fusion of CAE and SWin Transformers for Lung Cancer Malignancy Prediction 48

Spatio-Temporal Hybrid Fusion of CAE and SWin Transformers f...

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

作者： Khademi, Sadaf Heidarian, Shahin Afshar, Parnian Naderkhani, Farnoosh Oikonomou, Anastasia Plataniotis, Konstantinos N. Mohammadi, Arash Concordia University Concordia Institute for Information Systems Engineering Montreal Canada Concordia University Department of Electrical and Computer Engineering Montreal Canada Sunnybrook Health Sciences Centre Department of Medical Imaging Toronto Canada University of Toronto Department of Electrical and Computer Engineering Toronto Canada

ISBN: (纸本)9781728163277

The paper proposes a novel hybrid discovery Radiomics framework that simultaneously integrates temporal and spatial features extracted from non-thin chest Computed Tomography (CT) slices to predict Lung Adenocarcinoma (LUAC) malignancy with minimum expert involvement. Lung cancer is the leading cause of mortality from cancer worldwide and has various histologic types, among which LUAC has recently been the most prevalent. LUACs are classified as pre-invasive, minimally invasive, and invasive adenocarcinomas. Timely and accurate knowledge of the lung nodules malignancy leads to a proper treatment plan and reduces the risk of unnecessary or late surgeries. Currently, chest CT scan is the primary imaging modality to assess and predict the invasiveness of LUACs. However, the radiologists' analysis based on CT images is subjective and suffers from a low accuracy compared to the ground truth pathological reviews provided after surgical resections. The proposed hybrid framework, referred to as the CAET-SWin, consists of two parallel paths: (i) The Convolutional Auto-Encoder (CAE) Transformer path that extracts and captures informative features related to inter-slice relations via a modified Transformer architecture, and;(ii) The Shifted Window (SWin) Transformer path, which is a hierarchical vision transformer that extracts nodules' related spatial features from a volumetric CT scan. Extracted temporal (from the CAET path) and spatial (from the SWin path) are then fused through a fusion path to classify LUACs. Experimental results on our in-house dataset of 114 pathologically proven SubSolid Nodules (SSNs) demonstrate that the CAET-SWin significantly improves reliability of the invasiveness prediction task while achieving an accuracy of 82.65%, sensitivity of 83.66%, and specificity of 81.66% using 10-fold cross-validation. © 2023 IEEE.

关键词： Lung Adenocarcinoma Lung Nodule Invasiveness Self-Attention SubSolid Nodule Transformer

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GENERALIZED BAYES SURE FOR MULTIVARIATE NORMAL DISTRIBUTION UNDER BALANCED-QUADRATIC LOSS

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Journal of Mathematical Sciences (United States) 2024年第2期280卷 262-274页

作者： Karamikabir, Hamid Afshari, Mahmoud Karamikabir, Nasrin Kiaei, Seyed Faridoddin Department of Statistics Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran Department of Mathematics Hamedan Branch Islamic Azad University Hamedan Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

One of the most important subjects in statistics is the theory of estimation. In this paper, we consider the generalized Bayes shrinkage estimator of the mean vector for multivariate normal distribution with the unknown covariance matrix under balanced quadratic loss. Also, we find the minimax and admissible estimator of the mean vector based on the generalized Bayes estimator. We obtain Stein’s unbiased risk estimator (SURE) threshold with a generalized Bayes shrinkage estimator and we find the wavelet shrinkage generalized Bayes SURE. Finally, we present a simulation study to test the validity of the generalized Bayes SURE and the concrete compressive strength dataset is given to test the efficiency of this estimator in denoising. © 2024, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Balanced-quadratic loss Generalized bayes estimator Shrinkage estimaton Shrinkage wavelet SURE threshold

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Efficient and Unique Learning of The Complex Receiver Structure of Galileo E5 AltBOC Using an Educational Software in Matlab 4

Efficient and Unique Learning of The Complex Receiver Struct...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Ali, Irfan Khan, Subhan Ali, Mumtaz Mujeeb, Kashif Sukkur Iba University Computer Systems Engineering Department Sindh Sukkur Pakistan School of Electrical & Information Engineering University of Sydney Sydney Australia

ISBN: (纸本)9798350335316

In this paper, a unique and efficient method for learning the complex receiver structure of Galileo E5 AltBOC (Alternative Binary Offset Carrier) is presented. The methodology involves a software application designed in Matlab, which demonstrates the steps required to design a software receiver for Galileo E5 signal. The software application covers key concepts related to the Galileo E5 signal, such as signal acquisition, signal tracking, navigation data extraction, power spectral density (PSD) of the AltBOC (15,10), Fast Fourier Transform (FFT) of the E5a and E5b signals, and implementation of the subcarrier used for the AltBOC (15,10). Additionally, this paper presents a novel approach for extracting navigation data using the prompt channel of carrier tracking from the code loop discriminator. © 2023 IEEE.

关键词： MATLAB

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A Generic Load Flow Solution for Droop-Controlled AC Microgrids Considering Electric Vehicle Loads 11

A Generic Load Flow Solution for Droop-Controlled AC Microgr...

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11th IEEE International Conference on Power Electronics, Drives and Energy systems, PEDES 2024

作者： Meena, Ghanshyam Mathur, Akhilesh Singh, V.P. Murari, Krishna Kamalasadan, Sukumar Malaviya National Institute of Technology Department of Electrical Engineering Rajasthan Jaipur302017 India The University of Toledo Electrical Engineering and Computer Science Department ToledoOH43606 United States University of North Carolina at Charlotte Power Energy and Intelligent Systems Laboratory Epic Department of Electrical Engineering CharlotteNC28223 United States

Microgrids (MGs) have become more unpredictable due to integration of renewable generation sources. Several methods are used to solve load flow problems in MGs. Load flow analysis is a complex problem for islanded MGs due to the absence of slack bus and dependency of active power generation of droop-controlled DGs on frequency. In this paper, a Modified Newton Raphson (MNR) approach is used to perform load flow analysis of isolated MGs considering various types of electric vehicle (EV) loads. The presented method has been validated on 6-bus and 38-bus test systems of the islanded microgrid to give an accurate and simple approach for computing the load flow solutions. © 2024 IEEE.

关键词： Islanding

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Asymmetric Channel Reuse Model for Wi-Fi 6 19

Asymmetric Channel Reuse Model for Wi-Fi 6

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19th International Conference on Emerging Technologies, ICET 2024

作者： Syed, Aitizaz Uddin Marwat, Safdar Nawaz Khan Ministry of Justice Cyber Security Operations Doha Qatar University of Engineering and Technology Faculty of Electrical and Computer Engineering Department of Computer Systems Engineering Peshawar 25120 Pakistan

ISBN: (纸本)9798350379587

Channel reuse is an effective technique to reutilize the limited resource of the frequency radio spectrum to fulfill the ever-increasing demands of wireless communication. Implementing the channel reuse in Wi-Fi 6 by embedding the new enhancements of Channel Bonding, Beamforming, and utilizing reclaimed channels of the 5 GHz spectrum creates an asymmetric behavior. In this paper, we propose and investigate the Asymmetric Channel Reuse Model (ACRM) for 160 MHz communication in the presence and absence of Terminal Doppler Weather Radar (TDWR) channels at 5 GHz spectrum. In both conditions, we observe significant improvement in throughput and SINR compared to known symmetric channel reuse models. © 2024 IEEE.

关键词： Beamforming Channel Reuse Wi-Fi 6

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Exact Solution of the Full RMSA Problem in Elastic Optical Networks

IEEE Networking Letters

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IEEE Networking Letters 2024年第1期6卷 55-59页

作者： David, Fabio Rezende, Jose F. De Barbosa, Valmir C. Federal University of Rio de Janeiro Informatics and Computer Science Department Rio de Janeiro21941-914 Brazil Federal University of Rio de Janeiro Systems Engineering and Computer Science Program Centro de Tecnologia Rio de Janeiro21941-914 Brazil

Exact solutions of the Routing, Modulation, and Spectrum Allocation (RMSA) problem in Elastic Optical Networks (EONs), so that the number of admitted demands is maximized while those of regenerators and frequency slots used are minimized, require a complex ILP formulation taking into account frequency-slot continuity and contiguity. We introduce the first such formulation, ending a hiatus of some years since the last ILP formulation for a much simpler RMSA variation was introduced. By exploiting a number of problem and solver specificities, we use the NSFNET topology to illustrate the practicality and importance of obtaining exact solutions. © 2019 IEEE.

关键词： Topology

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OBSO Based Fractional PID for MPPT-Pitch Control of Wind Turbine systems

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computers, Materials & Continua 2022年第5期71卷 4001-4017页

作者： Ibrahim M.Mehedi Ubaid M.Al-Saggaf Mahendiran T.Vellingiri Ahmad H.Milyani Nordin Bin Saad Nor Zaihar Bin Yahaya Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical&Electronics Engineering Universiti Teknologi PETRONASMalaysia

In recent times,wind energy receives maximum attention and has become a significant green energy source *** wind turbine(WT)entered into several domains such as power electronics that are employed to assist the connection process of a wind energy system and *** turbulent characteristics of wind profile along with uncertainty in the design of WT make it highly challenging for prolific power *** pitch control angle is employed to effectively operate the WT at the above nominal wind ***,the pitch controller needs to be intelligent for the extraction of sustainable secure energy and keep WTs in a safe operating *** achieve this,proportional–integral–derivative(PID)controllers are widely used and the choice of optimal parameters in the PID controllers needs to be properly *** this motivation,this paper designs an oppositional brain storm optimization(OBSO)based fractional order PID(FOPID)design for sustainable and secure energy in WT *** proposed model aims to effectually extract the maximum power point(MPPT)in the low range of weather conditions and save the WT in high wind regions by the use of pitch *** OBSO algorithm is derived from the integration of oppositional based learning(OBL)concept with the traditional BSO algorithm in order to improve the convergence rate,which is then applied to effectively choose the parameters involved in the FOPID *** performance of the presented model is validated on the pitch control of a 5 MW WT and the results are examined under different *** simulation outcomes ensured the promising characteristics of the proposed model over the other methods.

关键词： Wind turbine wind energy pitch control brain storm optimization PID controller maximum power point

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Learning Media Advancement Using Virtual Reality Technology for Heavy Equipment Safety in Indonesian Vocational Schools

Learning Media Advancement Using Virtual Reality Technology ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Al Mubarok, Shofiyul Anam Nugroho, Supeno Mardi Susiki Muqtadiroh, Feby Artwodini Sumpeno, Surya Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Information Systems Surabaya Indonesia

ISBN: (纸本)9798350350821

Mining Geology is one of the study programs provided by Indonesian Vocational Schools that explores surface mining. Mining is a work in hazardous environments that poses numerous risks. The use of heavy equipment is a frequent cause of mining accidents. Therefore, enhancing vocational students' understanding of heavy equipment safety is essential. However, having heavy equipment for learning its safety is costly and complex maintenance, demanding substantial financial investment from schools. Moreover, many vocational schools still use traditional learning media that cannot provide an immersive experience in learning. Thus, this study developed an immersive and engaging virtual reality (VR)-based learning media for vocational students learning heavy equipment safety. The validity and feasibility of the learning media contents were evaluated by experts. The result concluded that the contents presented are valid and have good feasibility. Moreover, the learning media was tested in the learning process and the usability was assessed by vocational students. The result concluded that the learning media demonstrates significant potential to improve the educational experience in vocational schools, leading to better learning outcomes and increased student engagement. © 2024 IEEE.

关键词： Virtual environments

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Historical Arabic Images Classification and Retrieval Using Siamese Deep Learning Model

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computers, Materials & Continua 2022年第7期72卷 2109-2125页

作者： Manal M.Khayyat Lamiaa A.Elrefaei Mashael M.Khayyat Computer Science Department Umm Al-Qura UniversityMakkahSaudi Arabia Computer Science Department King Abdulaziz UniversityJeddahSaudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairoEgypt Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia

Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored ***,there were lots of efforts trying to automate the classification operation and retrieve similar images *** reach this goal,we developed a VGG19 deep convolutional neural network to extract the visual features from the images ***,the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural *** Siamese model built and trained at first from scratch but,it didn’t generated high evaluation ***,we re-built it from VGG19 pre-trained deep learning model to generate higher evaluation ***,three different distance metrics combined with the Sigmoid activation function are experimented looking for the most accurate method formeasuring the similarities among the retrieved *** that the highest evaluation parameters generated using the Cosine distance ***,the Graphics Processing Unit(GPU)utilized to run the code instead of running it on the Central Processing Unit(CPU).This step optimized the execution further since it expedited both the training and the retrieval time *** extensive experimentation,we reached satisfactory solution recording 0.98 and 0.99 F-score for the classification and for the retrieval,respectively.

关键词： Visual features vectors deep learning models distance methods similar image retrieval

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