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2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023

作者： Samaddar, Poulami Gaddam, Sunil Rahman, Md Mirazur Dey, Shuvashis Roy, Sayan Mitra, Dipankar Arunachalam, Shivaram P. Microwave Engineering & Imaging Laboratory Division of Gastroenterology and Hepatology Department of Medicine Mayo Clinic RochesterMN United States North Dakota State University Department of Electrical and Computer Engineering FargoND United States University of Missouri-Kansas City Division of Energy Matter and Systems School of Science and Engineering Kansas CityMO United States University of Wisconsin-La Crosse Department of Computer Science & Computer Engineering La CrosseWI United States

ISBN: (纸本)9798350328264

In this work, we studied the response of an antipodal Vivaldi antenna in the presence of different types of mice liver models. The scope of the study was limited to two types of liver diseases, i.e., nonalcoholic steatohepatitis (NASH) and fibrosis. The complex dielectric properties of each liver type were measured using the open-ended coaxial probe dielectric spectroscopy method. A Vivaldi antenna for near-field sensing was designed and then simulated in CST Studio to determine the antenna characteristics. The antenna performance from the simulation matched the reflection coefficients (S11) measurements collected with the fabricated antenna in an anechoic chamber. This verified antenna was used for further measurements of different liver models placed near the antenna. The antenna response, S11 measurements, varied from one tissue type to another, demonstrating the potential of using the Vivaldi antenna as a near-field sensor to differentiate liver disease models from the healthy liver model. © 2023 IEEE.

关键词： Antipodal Vivaldi antenna complex permittivity dielectric property fibrosis liver disease microstrip antenna Nonalcoholic steatohepatitis

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Toward Scene Understanding with Depth and Object-Aware Clustering in Contested Environment 22

Toward Scene Understanding with Depth and Object-Aware Clust...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Bhurtel, Manish Siwakoti, Yuba R. Rawat, Danda B. Sadler, Brian M. Fossaceca, John M. Rice, Daniel O. Howard University Department of Electrical Engineering and Computer Science WashingtonDC United States U.S. Army Research Laboratory AdelphiMD United States Bae Systems Fast Labs BurlingtonMA United States

ISBN: (纸本)9798350345346

Scene understanding in a contested battlefield is one of the very difficult tasks for detecting and identifying threats. In a complex battlefield, multiple autonomous robots for multi-domain operations are likely to track the activities of the same threat/objects leading to inefficient and redundant tasks. To address this problem, we propose a novel and effective object clustering framework that takes into account the position and depth of objects scattered in the scene. This framework enables the robot to focus solely on the objects of interest. Our system model incorporates YOLOv5 object detection model (ODM) and a pre-trained depth estimation model (DEM), whose outputs are fed into the KMeans Clustering (KM C) model. To train YOLOv5, we create our own battlefield dataset entitled 'Common Objects in Battlefield (COBA)' which is split into a training dataset and a validation dataset using our customized object-aware stratified 7-fold cross-validation technique. We present the creation of minimum optimal clusters assuming the minimum number of available battlefield robots. These clusters exhibit accurate relative distances among objects, further validating the effectiveness of our approach for depth and object-aware clustering for scene understanding. Overall, our proposed approach facilitates efficient work division among robots, enhances decision-making capabilities, and helps to improve situational awareness in battlefield scenarios. © 2023 IEEE.

关键词： clustering depth estimation object detection scene understanding situational awareness task allocation

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Exploration of Data Scene Characterization and 3D ROC Evaluation for Hyperspectral Anomaly Detection

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remote sensing 2024年第1期16卷

作者： Chang, Chein-I Chen, Shuhan Zhong, Shengwei Shi, Yidan Information and Technology College Dalian Maritime University Dalian116026 China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland BaltimoreMD21250 United States Department of Electrical Engineering National Cheng Kung University Tainan70101 Taiwan Department of Electrical Engineering Zhejiang University Hangzhou310027 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Whether or not a hyperspectral anomaly detector is effective is determined by two crucial issues, anomaly detectability and background suppressibility (BS), both of which are very closely related to two factors, the datasets used for a selected hyperspectral anomaly detector and detection measures used for its performance evaluation. This paper explores how anomaly detectability and BS play key roles in hyperspectral anomaly detection (HAD). To address these two issues, we investigate three key elements attributed to HAD. One is a selected hyperspectral anomaly detector, and another is the datasets used for experiments. The third one is the detection measures used to evaluate the effectiveness of a hyperspectral anomaly detector. As for hyperspectral anomaly detectors, twelve commonly used anomaly detectors were evaluated and compared. To address the appropriate use of datasets for HAD, seven popular and widely used datasets were studied for HAD. As for the third issue, the traditional area under a receiver operating characteristic (ROC) curve of detection probability—PD versus false alarm probability, PF, (AUC(D,F))—was extended to 3D ROC analysis where a 3D ROC curve was developed to generate three 2D ROC curves from which eight detection measures could be derived to evaluate HAD in all round aspects, including anomaly detectability, BS and joint anomaly detectability and BS. Qualitative analysis showed that many works reported in the literature which claimed that their developed hyperspectral anomaly detectors performed better than other anomaly detectors are actually not true because they overlooked these two issues. Specifically, a comprehensive study via extensive experiments demonstrated that these 3D ROC curve-derived detection measures can be further used to address the various characterizations of different data scenes and also to provide explanations as to why certain data scenes are not suitable for HAD. © 2023 by the authors.

关键词： Curve fitting

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In situ construction of PtSe_(2)/Ge Schottky junction array with interface passivation for broadband infrared photodetection and imaging

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InfoMat 2024年第4期6卷 37-46页

作者： Xue Li Shuo-En Wu Di Wu Tianxiang Zhao Pei Lin Zhifeng Shi Yongtao Tian Xinjian Li Longhui Zeng Xuechao Yu School of Physics and Microelectronics Key Laboratory of Material Physics Ministry of EducationZhengzhou UniversityZhengzhouHenanthe People's Republic of China Department of Electrical and Computer Engineering University of CaliforniaLa JollaCaliforniaUSA National Research Center for Optical Sensors/Communications Integrated Networks School of Electronic Science and EngineeringSoutheast UniversityNanjingJiangsuthe People's Republic of China Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhouJiangsuthe People's Republic of China

Infrared(IR)detection is vital for various military and civilian *** research has highlighted the potential of two-dimensional(2D)topological semimetals in IR detection due to their distinctive advantages,including van der Waals(vdW)stacking,gapless electronic structure,and Van Hove singularities in the electronic density of ***,challenges such as large-scale patterning,poor photoresponsivity,and high dark current of photodetectors based on 2D topological semimetals significantly impede their wider applications in low-energy photon ***,we demonstrate the in situ fabrication of PtSe_(2)/Ge Schottky junction by directly depositing 2D PtSe_(2) films with a vertical layer structure on a Ge substrate with an ultrathin AlOx *** to high quality junction,the photodetector features a broadband response of up to 4.6μm,along with a high specific detectivity of�1012 Jones,and operates with remarkable stability in ambient conditions as ***,the highly integrated device arrays based on PtSe_(2)/AlOx/Ge Schottky junction showcases excellent Mid-IR(MIR)imaging capability at room *** findings highlight the promising prospects of 2D topological semimetals for uncooled IR photodetection and imaging applications.

关键词： broadband photodetection imaging platinum diselenide Schottky junction van der Waals integration

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Convex Equilibrium-Free Stability and Performance Analysis of Discrete-Time Nonlinear systems

arXiv

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arXiv 2024年

作者： Koelewijn, Patrick J.W. Weiland, Siep Tóth, Roland Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Hungary

This paper considers the equilibrium-free stability and performance analysis of discrete-time nonlinear systems. We consider two types of equilibrium-free notions. Namely, the universal shifted concept, which considers stability and performance w.r.t. all equilibrium points of the system, and the incremental concept, which considers stability and performance between trajectories of the system. In this paper, we show how universal shifted stability and performance of discrete-time systems can be analyzed by making use of the time-difference dynamics. Moreover, we extend the existing results for incremental dissipativity for discrete-time systems based on dissipativity analysis of the differential dynamics to more general state-dependent storage functions for less conservative results. Finally, we show how both these equilibrium-free notions can be cast as a convex analysis problem by making use of the linear parameter-varying framework, which is also demonstrated by means of an example. Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear systems

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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Breast Cancer Detection Using Aperture Coupled Patch Antenna 3

Breast Cancer Detection Using Aperture Coupled Patch Antenna

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3rd International Conference on Advanced electrical engineering, ICAEE 2024

作者： Boulerbah, Rachida Chaabane, Abdelhalim Attia, Hussein Aissaoui, Djelloul Ziane Achour University of Djelfa Telecommunications and Smart Systems Laboratory Faculty of Science and Technology Djelfa17000 Algeria Université 8 Mai 1945 Guelma Laboratoire des Telecommunications-LT BP 401 Guelma24000 Algeria King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems and Sensing Electrical Engineering Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350349351

The study introduces a simple technique for mammary cancer early detection. The technique is based on a high-performance aperture-coupled patch antenna designed for ISM-band applications, operating at 2.45 GHz with unidirectional radiation and a gain of approximately 6.27 dBi. Rigorous testing involving calculating the Specific Absorption Rate (SAR) of the antenna when exposed to the breast tissues, both with and without tumors, yielded highly encouraging results, affirming the antenna's potential for tumor detection. When a tumor is present, there is a substantial increase in SAR values. Particularly, for a tumor with a 20 mm radius, SAR10g increased from 0.96 W/kg to 3.44 W/kg, and SAR1g rose from 2.26 W/kg to 6.23 W/kg. These findings contribute to the development of non-invasive and potentially life-saving medical technologies, leveraging advanced antenna designs. © 2024 IEEE.

关键词： Microstrip antennas

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Kepler-aSI: Semantic Annotation for Tabular Data

Kepler-aSI: Semantic Annotation for Tabular Data

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2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, SemTab 2024

作者： Baazouzi, Wiem Kachroudi, Marouen Faiz, Sami RIADI Laboratory National School of Computer Science University of Manouba Manouba2010 Tunisia ESPRIT School of Engineering El Ghazala 2080 Tunisia University of Tunis El Manar Faculty of Sciences of Tunis LR11ES14 Tunis2092 Tunisia University of Tunis El Manar National School of Engineers of Tunis Laboratory of Remote Sensing and Spatial Reference Information Systems 99/UR/11-11 Tunis2092 Tunisia

The identification of semantic concepts in tabular data is crucial for numerous applications, including data integration, cleaning, retrieval, feature engineering, and model development in machine learning. Recently, various studies have introduced methods using supervised learning or heuristic models to annotate semantic types. However, these approaches have limitations, making it difficult for them to generalize to a wide range of concepts or examples. Additionally, many neural network-based methods struggle with scalability, and the majority of the existing techniques do not perform well with numerical data. We present Kepler-aSI, a column-to-concept mapping technique that employs a maximum likelihood estimation approach through sets. This method effectively leverages large amounts of publicly available table data, even in the presence of some noise. We showcase the effectiveness of Kepler-aSI in the Semtab2024 challenge. © 2024 Copyright for this paper by its authors.

关键词： Self-supervised learning

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-Visual Class Incremental Tasks 24

MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yue, Xianghu Zhang, Xueyi Chen, Yiming Zhang, Chengwei Lao, Mingrui Zhuang, Huiping Qian, Xinyuan Li, Haizhou Department of Electrical and Computer and Engineering National University of Singapore Singapore Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China School of Electronic Electrical and Communication Engineering University of the Chinese Academy of Sciences Beijing China National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangdong Guangzhou China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shenzhen China

ISBN: (纸本)9798400706868

Class-incremental learning poses a significant challenge under an exemplar-free constraint, leading to catastrophic forgetting and sub-par incremental accuracy. Previous attempts have focused primarily on single-modality tasks, such as image classification or audio event classification. However, in the context of Audio-Visual Class-Incremental Learning (AVCIL), the effective integration and utilization of heterogeneous modalities, with their complementary and enhancing characteristics, remains largely unexplored. To bridge this gap, we propose the Multi-Modal Analytic Learning (MMAL) framework, an exemplar-free solution for AVCIL that employs a closed-form, linear approach. To be specific, MMAL introduces a modality fusion module that re-formulates the AVCIL problem through a Recursive Least-Square (RLS) perspective. Complementing this, a Modality-Specific Knowledge Compensation (MSKC) module is designed to further alleviate the under-fitting limitation intrinsic to analytic learning by harnessing individual knowledge from audio and visual modality in tandem. Comprehensive experimental comparisons with existing methods show that our proposed MMAL demonstrates superior performance with the accuracy of 76.71%, 78.98%, and 76.19% on AVE, Kinetics-Sounds, and VGGSounds100 datasets, respectively, setting new state-of-the-art AVCIL performance. Notably, compared to those memory-based methods, our MMAL, being an exemplar-free approach, provides good data privacy and can better leverage multi-modal information for improved incremental accuracy. © 2024 ACM.

关键词： Contrastive Learning

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