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Band Sampling of Hyperspectral Anomaly Detection in Effective Anomaly Space

IEEE Transactions on Geoscience and remote sensing 2024年 62卷 1-29页

作者： Chang, Chein-I Lin, Chien-Yu Hu, Peter Fuming Information and Technology College Dalian116026 China University of Maryland 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 University of Maryland School of Medicine R Adams Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

This article investigates four issues, background (BKG) suppression (BS), anomaly detectability, noise effect, and interband correlation reduction (IBCR), which have significant impacts on its performance. Despite that a recently developed effective anomaly space (EAS) was designed to use data sphering (DS) to remove the second-order data statistics characterized by BKG, enhance anomaly detectability, and reduce noise effect, it does not address the IBCR issue. To cope with this issue, this article introduces band sampling (BSam) into EAS to reduce IBCR and further suppress BKG more effectively. By implementing EAS in conjunction with BSam (EAS-BSam), these four issues can be resolved altogether for any arbitrary anomaly detector. It first modifies iterative spectral-spatial hyperspectral anomaly detection (ISSHAD) to develop a new variant of ISSHAD, called iterative spectral-spatial maximal map (ISSMax), and then generalizes ISSMax to EAS-ISSMax, which further enhances anomaly detectability and noise removal. Finally, EAS-BSam is implemented to reduce IBCR. As a result, combining EAS, BSam, and ISSMax yields four versions: EAS-BSam, EAS-ISSMax, BSam-SSMAX, and EAS-BSam-SSMax. Such integration presents a great challenge because all these four versions are derived from different aspects, iterative spectral-spatial feedback process, compressive sensing, and low-rank and sparse matrix decomposition. Experiments demonstrate that EAS-BSam and EAS-BSam-SSMax show their superiority to ISSHAD and many current existing hyperspectral anomaly detection (HAD) methods. © 1980-2012 IEEE.

关键词： Iterative methods

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computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

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Transportation Safety and Environment 2022年第4期4卷 3-18页

作者： Nachuan Ma Jiahe Fan Wenshuo Wang JinWu Yu Jiang Lihua Xie Rui Fan Department of Control Science and Engineering Frontiers Science Center for Intelligent Autonomous Systemsand State Key Laboratory of Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China Department of Civil Engineering McGill UniversityMontréalQC H3A 0C3Canada Department of Electronics and Computer Engineering the Hong Kong University of Science and TechnologyHong Kong SAR 999077P.R.China CTO Office ClearMotion Inc.BillericaMA 01821USA School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore

computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two ***,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these *** article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft *** then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole *** article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic *** believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

关键词： computer vision road imaging pothole detection deep learning image processing point cloud modelling convolutional neural networks

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National laboratory, Lemont, USA." Argonne National laboratory was left off the original publication. © The Author(s) 2025.

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Deep Learning-Based Estimation of Arterial Stiffness from PPG Spectrograms: A Novel Approach for Non-Invasive Cardiovascular Diagnostics

Deep Learning-Based Estimation of Arterial Stiffness from PP...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Kiana Pilevar Abrisham Khalil Alipour Bahram Tarvirdizadeh Mohammad Ghamari Department of Mechatronics Engineering School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology Advanced Service Robots (ASR) Laboratory University of Tehran Tehran Iran Department of Electrical and Computer Engineering Kettering University Flint Michigan USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cardiovascular diseases (CVDs), a leading cause of global mortality, are intricately linked to arterial stiffness, a key factor in cardiovascular health. Non-invasive assessment of arterial stiffness, particularly through Carotid-to-femoral Pulse Wave Velocity (cf-PWV) – the gold standard in this field – is vital for early detection and management of CVDs. This study introduces a novel approach, utilizing photoplethysmogram (PPG) signal spectrograms as inputs for deep learning models to estimate cf-PWV, a significant advancement over traditional methods. Employing a modified ResNet-18 architecture, we analyze PPG signals from digital, radial, and brachial arteries of a simulated dataset of 4374 healthy adults. Our methodology's innovation lies in its direct use of finely tuned spectrogram images, bypassing the complex feature extraction processes. This approach achieved R 2 (correlation coefficient) values of up to 0.9902 for the digital artery, 0.9898 for the radial artery, and 0.9825 for the brachial artery, coupled with significantly lower Mean Absolute Percentage Errors (MAPE) of approximately 1.61% for the digital, 1.87% for the radial, and 2.08% for the brachial artery. These findings highlight the efficacy of PPG spectrograms, especially from the digital artery, in providing an accurate, user-friendly, and non-invasive method for cf-PWV estimation, thereby enhancing the capabilities of non-invasive cardiovascular diagnostics.

关键词： Accuracy Noise Estimation Predictive models Feature extraction Brachytherapy Data models Arteries Spectrogram Monitoring

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Iterative Gaussian-Laplacian Pyramid Network for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and remote sensing 2024年 62卷 1-22页

作者： Chang, Chein-I Liang, Chia-Chen Hu, Peter Fuming Information and Technology College Dalian116026 China University of Maryland 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 University of Maryland School of Medicine R Adams Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

Gaussian pyramid (GP) is a commonly used image coding technique that encodes an image as a pyramid that is stacked by a set of images with Gaussian window-reduced sizes and multiple spatial resolutions. Associated with GP, a Laplacian pyramid (LP) can be also constructed to represent differential images between images in two consecutive layers of GP. Such resulting Gaussian-Laplacian pyramid (GLP) performs data compression in a lossless and lossy manner. A convolutional neural network (CNN) consists of a series of layers concatenated in a feedforward manner where each layer has a convolutional sublayer (CL) and a pooling sublayer (PL). Interestingly, each layer implemented by CL and PL in a CNN can be realized by a single layer in GP in the sense that CL and PL can be carried out by a low-pass Gaussian filter operated as a Gaussian kernel in a single layer of GP. This article develops a new approach to hyperspectral image classification (HSIC), called the GLP network (GLPN), which uses not only GP to realize CNN but also LP to capture differential information between two consecutive layers that CNN cannot. Furthermore, by incorporating an iterative process into GLPN, we can derive an iterative GLPN (IGLPN) that can be considered a companion of a recently developed iterative random training sampling CNN (IRTS-CNN) by replacing CNN with GLPN. Since GLPN can realize CNN in a better way, it is expected that IGLPN will perform better than IRTS-CNN and also significantly reduce computational efficiency compared to IRTS-CNN. © 1980-2012 IEEE.

关键词： Iterative methods

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models 22

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth IoanninaGR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory HeraklionGR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering HeraklionGR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 IEEE.

关键词： Deep learning

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Toward Process Controlled Medical Robotic System

arXiv

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arXiv 2023年

作者： Liu, Yihao Kheradmand, Amir Armand, Mehran The Biomechanical- and Image-Guided Surgical Systems Laboratory Laboratory for Computational Sensing & Robotics Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States The Department of Neurology Department of Neuroscience Laboratory for Computational Sensing & Robotics Johns Hopkins University BaltimoreMD21218 United States The Biomechanical- and Image-Guided Surgical Systems Laboratory Laboratory for Computational Sensing & Robotics Department of Orthopaedic Surgery Department of Mechanical Engineering Department of Computer Science Applied Physics Laboratory Johns Hopkins University BaltimoreMD21218 United States

Medical errors, defined as unintended acts either of omission or commission that cause the failure of medical actions, are the third leading cause of death in the United States. Medical errors can include communication breakdowns, diagnostic errors, poor judgment, and inadequate skills. The application of autonomy and robotics can alleviate some causes of medical errors by improving accuracy and providing means to accurately follow planned procedures. However, for the robotic applications to improve safety, they must maintain constant operating conditions in the presence of disturbances, and provide reliable measurements, evaluation, and control for each state of the procedure. This article addresses the need for process control in medical robotic systems, and proposes a standardized design cycle toward its automation. Monitoring and controlling the changing conditions in a medical or surgical environment necessitates a clear definition of workflows and their procedural dependencies. We propose integrating process control into medical robotic workflows using hierarchical Finite State Machines (hFSM) to identify changes in states of the system and environment, and execute possible operations or transitions to new states. Therefore, the system translates clinician experiences and procedure workflows into machine-interpretable languages. The design cycle using hFSM formulation can be a deterministic process, which opens up possibilities for higher-level automation in medical robotics. Shown in our work, with a standardized design cycle and software paradigm, we pave the way toward controlled workflows that can be automatically generated. Additionally, a modular design for a robotic system architecture that integrates hFSM can provide easy software and hardware integration. This article discusses the system design, software implementation, and example application to Robot-Assisted Transcranial Magnetic Stimulation (RATMS) and robot-assisted femoroplasty. We also provide

关键词： Navigation systems

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Dual-Band Slot Antenna for Miniaturized Biomedical Implants 2

Dual-Band Slot Antenna for Miniaturized Biomedical Implants

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2nd International Conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Benaissa, Mohamed Chaabane, Abdelhalim Aissaoui, Djelloul Attia, Hussein Ziane Achour University of Djelfa Telecommunications and Smart Systems Laboratory Faculty of Science and Technology Djelfa17000 Algeria Université8 Mai 1945 Guelma Laboratoire des Télécommunications-LT BP 401 Guelma24000 Algeria King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems Sensing and Electrical Engineering Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350314243

This paper presents a compact-size antenna designed for biomedical implants. The antenna operates at the dual-band of 910 MHz and 2.45 GHz, which fall within the industrial, scientific, and medical (ISM) band ranges. The proposed antenna achieves a significant size reduction with a minimal form factor measuring only 17.64 mm3(7 mm × 7 mm × 0.36 mm). This size reduction is accomplished by employing a printed antenna with a dual spiral-shape slot and incorporating an inverted U structure, contributing to a more compact design. Additionally, a superstrate layer is introduced to enhance the impedance matching and compactness of the antenna further. The proposed antenna showcases several notable attributes, such as its miniaturization, dual-band operation, and commendable gain values compared to alternative implantable antennas. To evaluate its performance, the antenna is simulated within a multi-layered enclosure comprised of skin, fat, and muscle. The proposed antenna's SARs (specific absorption rates) are calculated within the human tissue model at both operating frequencies. This analysis provides insights into the electromagnetic energy absorption by the surrounding tissues, ensuring the antenna's safety for use in biomedical applications. © 2023 IEEE.

关键词： Microstrip antennas

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Accurate Parameter Estimation for PMSMs using electrical and Mechanical Models Considering Temperature Effect and Inverter Distortion

Accurate Parameter Estimation for PMSMs using Electrical and...

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2022 Chinese Automation Congress, CAC 2022

作者： Tong, Zhe Meng, Bo Ding, Beichen Feng, Guodong Lai, Chunyan Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen China Sun Yat-sen University School of Intelligent Systems Engineering Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou510006 China Concordia University Department of Electrical and Computer Engineering Montreal Canada

ISBN: (纸本)9781665465335

This paper presents a decoupled parameter estimation method for PMSM considering temperature rise and inverter distortion. Both electrical and mechanical models are explored to derive the estimation model to achieve better parameter estimation accuracy. Specifically, the proposed approach firstly performs the initial estimation of the PM flux, winding resistance and friction coefficient by considering the temperature effect, and then derives the decoupled model for inductance estimation using both the electrical and mechanical measurements. Compared with other approaches, our decoupled approach employs the mechanical model and considers the temperature effect, which can make the parameter identification more accurate. The experiment is performed on a test PMSM to verify the estimation performance of the proposed approach. © 2022 IEEE.

关键词： Parameter estimation

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Physics-Informed Machine Learning for the Inverse Design of Wave Scattering Clusters

arXiv

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arXiv 2024年

作者： Tempelman, Joshua R. Weidemann, Tobias Flynn, Eric B. Matlack, Kathryn H. Vakakis, Alexander F. Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL United States Institute of Aircraft Propulsion Systems University of Stuttgart Germany Space Remote Sensing and Data Science Los Alamos National Laboratory Los AlamosNM United States

Clusters of wave-scattering oscillators offer the ability to passively control wave energy in elastic continua. However, designing such clusters to achieve a desired wave energy pattern is a highly nontrivial task. While the forward scattering problem may be readily analyzed, the inverse problem is very challenging as it is ill-posed, high-dimensional, and known to admit non-unique solutions. Therefore, the inverse design of multiple scattering fields and remote sensing of scattering elements remains a topic of great interest. Motivated by recent advances in physics-informed machine learning, we develop a deep neural network that is capable of predicting the locations of scatterers by evaluating the patterns of a target wavefield. We present a modeling and training formulation to optimize the multi-functional nature of our network in the context of inverse design, remote sensing, and wavefield engineering. Namely, we develop a multi-stage training routine with customized physics-based loss functions to optimize models to detect the locations of scatterers and predict cluster configurations that are physically consistent with the target wavefield. We demonstrate the efficacy of our model as a remote sensing and inverse design tool for three scattering problem types, and we subsequently applicability for designing clusters that direct waves along preferred paths or localize wave energy. Hence, we present an effective model for multiple scattering inverse design which may have diverse applications such as wavefield imaging or passive wave energy control. Copyright © 2024, The Authors. All rights reserved.

关键词： remote sensing

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