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arXiv 2021年

作者： Untracht, Gavrielle R. Matos, Rolando Dikaios, Nikolaos Bapir, Mariam Durrani, Abdullah K. Butsabong, Teemapron Campagnolo, Paola Sampson, David D. Heiss, Christian Sampson, Danuta M. Optical+Biomedical Engineering Laboratory School of Electrical Electronic and Computer Engineering The University of Western Australia 35 Stirling Highway PerthWA6009 Australia Surrey Biophotonics Advanced Technology Institute School of Physics School of Biosciences and Medicine University of Surrey Surrey GuildfordGU2 7XH United Kingdom Department of Biochemical Sciences Department of Clinical and Experimental Medicine School of Biosciences and Medicine University of Surrey Surrey GuildfordGU2 7XH United Kingdom Mathematics Research Center Academy of Athens Athens106 79 Greece Surrey and Sussex Healthcare NHS Trust East Surrey Hospital Surrey RedhillRH1 5RH United Kingdom Surrey Biophotonics Centre for Vision Speech and Signal Processing School of Biosciences and Medicine The University of Surrey GuildfordGU2 7XH United Kingdom

Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease. © 2021, CC BY-NC-SA.

关键词： Optical tomography

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Ranking-based deep cross-modal hashing

arXiv

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arXiv 2019年

作者： Liu, Xuanwu Yu, Guoxian Domeniconi, Carlotta Wang, Jun Ren, Yazhou Guo, Maozu College of Computer and Information Sciences Southwest University Chongqing China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China Department of Computer Science George Mason University Fairfax United States SMILE Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China

Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level features of objects, which may not be optimally compatible with the coding process. Besides, these hashing methods are mainly designed to handle simple pairwise similarity. The complex multilevel ranking semantic structure of instances associated with multiple labels has not been well explored yet. In this paper, we propose a ranking-based deep cross-modal hashing approach (RDCMH). RDCMH firstly uses the feature and label information of data to derive a semi-supervised semantic ranking list. Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions. Experiments on real multi-modal datasets show that RDCMH outperforms other competitive baselines and achieves the state-of-the-art performance in cross-modal retrieval applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Secure Edge Caching Placement and Delivery for Ultra-Reliable and Low-Latency Vehicular Networks

Secure Edge Caching Placement and Delivery for Ultra-Reliabl...

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IEEE Globecom Workshops

作者： Haojun Yang Kuan Zhang Kan Zheng Yi Qian Intelligent Computing and Communications (IC) Lab Wireless Signal Processing and Networks (WSPN) Lab Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering University of Nebraska-Lincoln Omaha NE USA

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

To meet the urgent requirements of ultra-reliable and low-latency communications (URLLCs) in vehicular networks, vehicular edge caching (VEC) becomes a promising solution to reduce backhaul costs and mitigate network congestion. However, the data requested by vehicular users may be maliciously tampered by attackers during the caching delivery phase. In this paper, we investigate the problem of secure caching placement and delivery for a VEC URLLC system. Specifically, we first analyze the lower bound of spectral efficiency for URLLCs and security metric, i.e., vulnerability. Then, an optimization problem of minimizing the maximum vulnerability among all vehicular users is formulated based on the analysis. Subsequently, we propose a secure caching placement and delivery algorithm to obtain the solutions of our formulated problem by reasonable complexity. Simulation results show that the proposed algorithm can significantly improve the performance of security for the VEC URLLC system.

关键词： Security Optimization Roads Reliability Fading channels Signal to noise ratio Antennas

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Mitigate the obstructing effect of vehicles on the propagation of VANETs safety-related information

Mitigate the obstructing effect of vehicles on the propagati...

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作者： Huang, Rui Wu, Jing Long, Chengnian Zhu, Yanmin Li, Bo Lin, Yi-Bing Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Computer Science School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Computer Science and Information Engineering National Chiao Tung University Taiwan

As physical obstacles, vehicles have significant impacts on the efficient propagations of safety-related information in vehicular ad hoc networks (VANETs) by frequently obstructing the LOS links between transmitters and receivers. Obstructing effect will diminish the effective coverage of safety-related information and incur severe impact on road safety. However, this impact has not been addressed effectively. In this paper, we first present the definition of broadcast efficiency as the metric for quantizing the obstructing effect. We investigate the optimization issue for mitigating the impact of obstructing effect on the propagation of safety-related information and propose a graph theory-based optimization algorithm. For a distributed implementation of this optimization in VANETs, we further propose the maximum broadcast efficiency relaying (MBER) algorithm. MBER maximizes the effective coverage of information, meanwhile, meets delay and reliability constraints by incorporating broadcast efficiency and propagation distance in relay contention. Simulation results demonstrate that MBER promotes the effective coverage of safety-related information in VANETs with varying vehicular distribution. © 1967-2012 IEEE.

关键词： Vehicular ad hoc networks

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Scaling Limitations of Line TFETs at Sub-8-nm Technology Node

Scaling Limitations of Line TFETs at Sub-8-nm Technology Nod...

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International Symposium on VLSI Technology, Systems and Applications

作者： Narasimhulu Thoti Yiming Li Sekhar Reddy Kola Parallel and Scientific Computing Laboratory National Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu Taiwan EECS International Graduate Program National Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu Taiwan Institute of Communications Engineering National Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu Taiwan Department of Electrical and Computer Engineering National Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu Taiwan Center for mmWave Smart Radar System and Technologies National Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu Taiwan

ISBN: (数字)9781728142326

ISBN: (纸本)9781728142333

The scope of the work is to investigate limitations in device scaling by identifying various parameters of short channel effects (SCEs) in current challenging geometries of the line tunnel field effect transistors (TFETs). Key factors of the considered device, such as the doping and the thickness (t n ) of n-epitaxial region, and source-to-drain length (L SDeff ) scaling cannot be tuned anymore to boost the device characteristics for the sub-8-nm technology node. The main results of this study indicates that the engineering acceptable performance are achieved at a low doping of 5 × 10 18 cm -3 , an optimal t n as low (about 0.5 nm), and a L SDeff greater than 12.5 nm. Hence, the line TFETs below sub-8-nm faces serious bottleneck of scaling and cannot be further scaled with the conventional scaling rule at all.

关键词： Electron devices Solids

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Tunable superconducting josephson dielectric metamaterial

arXiv

引用

arXiv 2019年

作者： Trepanier, M. Zhang, Daimeng Filippenko, L.V. Koshelets, V.P. Anlage, Steven M. Center for Nanophysics and Advanced Materials Department of Physics University of Maryland College ParkMD20742-4111 United States Department of Electrical and Computer Engineering University of Maryland College ParkMD20742-3285 United States Laboratory of Superconducting Devices for Signal Detection and Processing Kotelnikov Institute of Radio Engineering and Electronics Moscow125009 Russia

We demonstrate a low-dissipation dielectric metamaterial with tunable properties based on the Josephson effect. Superconducting wires loaded with regularly spaced Josephson junctions (critical current Ic ≈ 0.25 µA) spanning a K-band waveguide and aligned with the microwave electric fields create a superconducting dielectric metamaterial. Applied dc current tunes the cutoff frequency and effective permittivity of this unique electric metamaterial. The results are in agreement with an analytical model for microwave transmission through the artificial dielectric medium. Copyright © 2019, The Authors. All rights reserved.

关键词： Metamaterials

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Weighted linear discriminant analysis based on class saliency information

arXiv

引用

arXiv 2018年

作者： Xu, Lei Iosifidis, Alexandros Gabbouj, Moncef Laboratory of Signal Processing Tampere University of Technology Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University Aarhus Denmark

In this paper, we propose a new variant of Linear Discriminant Analysis to overcome underlying drawbacks of traditional LDA and other LDA variants targeting problems involving imbalanced classes. Traditional LDA sets assumptions related to Gaussian class distribution and neglects influence of outlier classes, that might hurt in performance. We exploit intuitions coming from a probabilistic interpretation of visual saliency estimation in order to define saliency of a class in multi-class setting. Such information is then used to redefine the between-class and within-class scatters in a more robust manner. Compared to traditional LDA and other weight-based LDA variants, the proposed method has shown certain improvements on facial image classification problems in publicly available datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Visualization

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WEIGHTED LINEAR DISCRIMINANT ANALYSIS BASED ON CLASS SALIENCY INFORMATION

WEIGHTED LINEAR DISCRIMINANT ANALYSIS BASED ON CLASS SALIENC...

引用

IEEE International Conference on Image processing

作者： Lei Xu Alexandros Iosifidis Moncef Gabbouj Laboratory of Signal Processing Tampere University of Technology Tampere Finland Department of Engineering Electrical & Computer Engineering Aarhus University Aarhus Denmark

关键词： Estimation Probabilistic logic Visualization Optimization Linear discriminant analysis Task analysis Robustness

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Radiomics analysis of baseline F-FDG PET/CT images for improved prognosis in nasopharyngeal carcinoma 15

Radiomics analysis of baseline F-FDG PET/CT images for impro...

引用

15th IEEE International Symposium on Biomedical Imaging, ISBI 2018

作者： Lv, Wenbing Yuan, Qingyu Wang, Quanshi Ma, Jianhua Feng, Qianjin Chen, Wufan Rahmim, Arman Lu, Lijun School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University China Nanfang PET Center Nanfang Hospital Southern Medical University China Department of Radiology Johns Hopkins University United States Department of Electrical and Computer Engineering Johns Hopkins University United States

ISBN: (纸本)9781538636367

The purpose of this study is to investigate the prognostic performance of radiomics features on nasopharyngeal carcinoma (NPC) patients imaged with baseline 18F-FDG PET/CT. 128 NPC patients were retrospectively enrolled with 3348 radiomics features and 13 clinical features. Kaplan-Meier analysis was used to estimate progression-free survival (PFS), and log-rank test was used to screen the significant features. Cox proportional hazards regression modal with forward stepwise feature selection was adopted to identify independent predictors of PFS. 24 radiomics features and 8 clinical features were found to be significantly associated with PFS in univariate analysis. Radiomics features HGZE-GLSZM-HLL-32 (p=0.0061, HR: 0.66, 95%CI: 0.49-0.89), as well as clinical features N-stage, M-stage, antibody VCA-IgA and platelet count (PLT) retained the independent prognostic significance for PFS in multivariate analysis. Overall, radiomics features can provide complementary prognostic information for NPC patients imaged with baseline 18F-FDG PET/CT compared to clinical features. © 2018 IEEE.

关键词： Multivariant analysis

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