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IEEE International Conference on Radar

作者： Marius Lippke Maurice Quach Sascha Braun Daniel Köhler Michael Ulrich Bastian Bischoff Wei Yap Tan Robert Bosch GmbH Corporate Research Germany Institute of Information Technology Mannheim University of Applied Sciences Germany Robert Bosch GmbH Cross-Domain Computing Solutions Germany

ISBN: (数字)9798350362381

ISBN: (纸本)9798350362398

Having precise perception of the environment is crucial for ensuring the secure and reliable functioning of autonomous driving systems. Radar object detection networks are one fundamental part of such systems. Although CNN-based object detectors have shown good performance in this context, they require significant computational resources. This paper investigates sparse convolutional object detection networks, which combine powerful grid-based detection with low compute resources. We investigate radar specific challenges and propose sparse kernel point pillars (SKPP) and dual voxel point convolution (DVPC) networks as remedies for the grid rendering and sparse backbone architectures. We evaluate our SKPP-DPVCN architecture on nuScenes, which outperforms the baseline by 5.89% and the previous state of the art by 4.19% in Car AP4.0. Moreover, SKPP-DPVCN reduces the average scale error (ASE) by 21.41% over the baseline.

关键词： Point cloud compression Radar detection Radar Object detection Computer architecture Detectors Feature extraction Rendering (computer graphics) Reliability Kernel

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FreqRISE: Explaining time series using frequency masking

arXiv

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arXiv 2024年

作者： Brüsch, Thea Wickstrøm, Kristoffer Knutsen Schmidt, Mikkel N. Alstrøm, Tommy Sonne Jenssen, Robert Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Physics and Technology UiT The Arctic University of Norway Norway Norwegian Computing Center Oslo Norway

Time-series data are fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision making. To develop explainable artificial intelligence in these domains therefore implies explaining salient information in the time series. Current methods for obtaining saliency maps assume localized information in the raw input space. In this paper, we argue that the salient information of a number of time series is more likely to be localized in the frequency domain. We propose FreqRISE, which uses masking-based methods to produce explanations in the frequency and time-frequency domain, and outperforms strong baselines across a number of tasks. The source code is available here: https://***/theabrusch/FreqRISE © 2024, CC BY.

关键词： Time series

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An Improved Approach for Semantic Segmentation of Fundus Lesions using R2U-Net 46

An Improved Approach for Semantic Segmentation of Fundus Les...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Pereira, Alejandro Santos, Carlos Aguiar, Marilton Welfer, Daniel Dias, Marcelo De Menezes, Rafaela Ferreira, Paulo Roberto Rossi, Fábio D'Ornellas, Marcos Haygert, Carlos Jesus Kazienko, Juliano Comim, Fabio Hoppe, Aurélio Weber, Juliano Tavares, Leandro Guths, Leonardo Guerra, Rodrigo Federal University of Pelotas - UFPel Postgraduate Program in Computing Pelotas Brazil Science and Technology Farroupilha - IFFar Federal Institute of Education Alegrete Brazil Federal University of Santa Maria - Ufsm Department of Applied Computing Santa Maria Brazil Federal University of Santa Maria - Ufsm Department of Clinical Medicine Santa Maria Brazil Federal University of Santa Maria - Ufsm Industrial Technical College Santa Maria Brazil Federal University of Minas Gerais - Ufmg Department of Clinical Medicine Belo Horizonte Brazil Regional University of Blumenau - Furb Department of Information Systems and Computing Blumenau Brazil Science and Technology Farroupilha - IFFar Federal Institute of Education Santo Ângelo Brazil Federal University of Rio Grande do Sul - Ufrgs Postgraduate Program in Computing Porto Alegre Brazil Federal University of Rio Grande - Furg Center of Computational Sciences Rio Grande Brazil

ISBN: (纸本)9798350371499

Diabetic Retinopathy (DR) is a microvascular complication related to diabetes that affects approximately 33% of individuals with this condition and, if not detected and treated early, can lead to irreversible vision loss. Fundus lesions such as Hard and Soft Exudates, Hemorrhages, and Microaneurysms typically identify DR. The development of computational methods to segment these lesions plays a fundamental role in the early diagnosis of the disease. This paper proposes a new approach that uses an R2U-Net combined with data augmentation techniques for segmenting fundus lesions. We trained, adjusted, and evaluated the proposed work in the DDR dataset, achieving an accuracy of 99.87% and a mean Intersection over Union (mIoU) equal 59.69%. Furthermore, we assessed it in the IDRiD dataset, achieving an mIoU of 49.92%. The results obtained in the experiments highlight the potential contribution of the model in the lesion annotations for creating new DR datasets, which is essential given the scarcity of annotations in publicly available datasets. © 2024 IEEE.

关键词： Semantic Segmentation

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Evaluation of resonances: adaptivity and AAA rational approximation of randomly scalarized boundary integral resolvents

arXiv

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arXiv 2024年

作者： Bruno, Oscar P. Santana, Manuel A. Trefethen, Lloyd N. Computing and Mathematical Sciences California Institute of Technology PasadenaCA91125 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States

This paper presents a novel algorithm, based on use of rational approximants of a randomly scalarized boundary integral resolvent in conjunction with an adaptive search strategy and an exponentially convergent secant-method termination stage, for the evaluation of acoustic and electromagnetic resonances in open and closed cavities;for simplicity we restrict treatment to cavities in two-dimensional space. The desired cavity resonances (also known as "eigenvalues" for interior problems, and "scattering poles" or "complex eigenvalues" for exterior and open-cavity problems) are obtained as the poles of associated rational approximants;both the approximants and their poles are obtained by means of the recently introduced AAA rational-approximation algorithm. In fact, the proposed resonance-search method applies to any nonlinear eigenvalue problem associated with a given function F : U → Cd×d, wherein, denoting F(k) = Fk, a complex value k is sought for which Fkw = 0 for some nonzero w ∈ Cd. For the scattering problems considered in this paper, which include interior, exterior and open cavity problems, Fk is taken to equal a spectrally discretized version of a Green function-based boundary integral operator at spatial frequency k. In all cases, the scalarized resolvent is given by an expression of the form u∗Fk−1v, where u, v ∈ Cd are fixed random vectors. The proposed adaptive search strategy relies on use of a rectangular subdivision of the resonance search domain which is locally refined to ensure that all resonances in the domain are captured. The approach works equally well in the case in which the search domain is a one-dimensional set, such as, e.g., an interval of the real line, in which case the rectangles used degenerate into subintervals of the search domain. A variety of numerical results are presented, including comparisons with well-known methods based on complex contour integration, and a discussion of the asymptotics that result as open cavities approach c

关键词： Poles

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A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Burak Dindar Can Berk Saner Dogukan Yigit Polat Hüseyin Kemâl Çakmak Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Department of Electrical and Computer Engineering National University of Singapore Singapore School of Computing National University of Singapore Singapore

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time.

关键词： Data privacy Computational modeling Europe Machine learning Computer architecture Generators Data models Standards Interoperability Load flow

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Nutritional Values and Effects of Selected Solvents on the Phytochemical Properties of Moringa Oleifera Leaves Obtained from Sokoto, Nigeria

Nutritional Values and Effects of Selected Solvents on the P...

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Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Aasia Muhammed Jamiu Ahmad Salma Gumi Dorcas Toyin Alabi Fatimah Ahmed-Umar Nabila Muhammed Habib Grace I Adebayo-Gege Adegunloye Deke Victoria Ojikah Obianuju Muftaudeen Taoheed Kolawole Ogundolie Frank Abimbola Faculty of Computing and Applied Sciences Baze University Abuja College of Medicine and Health Sciences Baze University Abuja Nigeria Department of Microbiology Federal University of Technology Akure Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Moringa Oliefera (M. oleifera) contains high amounts of antioxidants and phytochemicals which have been explored for their therapeutic effects. Solvents used in extraction have been noted to influence the availability of specific phytochemicals. In this study, the nutritional values of M. oleifera and effects of extracting solvents namely; ethanol, n-hexane, ethyl-acetate, and aqueous solution on the phytochemical constituents of *** leaves from Sokoto were investigated. M. oleifera leaves were obtained from Sokoto, Nigeria and dried. The Proximate and trace metal analysis were carried out by use of Atomic absorption spectrophotometer (AAS). *** sample was extracted by soxhlet extractor using the different solvents, and then concentrated. Concentrated compounds were further analyzed by GC-MS. Proximate analysis of the leaves yielded crucial nutritional values which includes, crude protein (13.31%),crude lipid (14.01%), and dietary fibre (6.37%). Furthermore, sodium and magnesium concentrations were notably elevated, with magnesium reaching 15.1 mg/l. Ethanol and aqueous extracts yielded positive results for flavonoids, carbohydrates, phenols, tannins, cardiac glucosides, and alkaloids. Conversely, n-hexane exhibited negative effects on these compounds, while ethyl-acetate demonstrated mixed outcomes.. The GC-MS analysis of the extracts showed the presence of 2-Heptanol, 6-meth 1,2-Benzenedicarboxylic acid, Phthalic acid, cyclobutyl-heptyl ester, Diethyl-Phthalate, ethylisopropyl ester, Trimethyl(4-tert.-butyl-phenoxy)silane, and 2,4,6-Cycloheptatrien-1-one. The findings from this work suggested that *** leaves possess important nutritional values ,and using solvents such as ethanol and aqueous solution to extract the bioactive compounds presents a rich reservoir of such compounds (especially cardiac glycosides) which could be used as therapeutics in management of cardiovascular disorders.

关键词： Proteins Solvents Ethanol Sodium Reservoirs Lipidomics Magnesium

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Two Physics-Based Schwarz Preconditioners for Three-Temperature Radiation Diffusion Equations in High Dimensions

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Communications in Computational Physics 2022年第8期32卷 829-849页

作者： Xiaoqiang Yue Jianmeng He Xiaowen Xu Shi Shu Libo Wang National Center for Applied Mathematics in Hunan Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationHunan Key Laboratory for Computation and Simulation in Science and EngineeringXiangtan UniversityXiangtan 411105P.R.China Laboratory of Computational Physics Institute of Applied Physics and ComputationalMathematicsBeijing 100094P.R.China College of Information Science and Technology JinanUniversityGuangzhou 510632P.R.China

We concentrate on the parallel,fully coupled and fully implicit solution of the sequence of 3-by-3 block-structured linear systems arising from the symmetrypreserving finite volume element discretization of the unsteady three-temperature radiation diffusion equations in high *** this article,motivated by[***,***,***,SIAM *** ***.33(2012)653–680]and[***,***,***,***.442(2021)110513],we aim to develop the additive and multiplicative Schwarz preconditioners subdividing the physical quantities rather than the underlying domain,and consider their sequential and parallel implementations using a simplified explicit decoupling factor approximation and algebraic multigrid subsolves to address such linear ***,computational efficiencies and parallel scalabilities of the proposed approaches are numerically tested in a number of representative real-world capsule implosion benchmarks.

关键词： Radiation diffusion equations Schwarz methods algebraic multigrid parallel and distributed computing

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Legacy Moderization: A Cloud Migration Strategy With Serverless Microservice Architecture

Legacy Moderization: A Cloud Migration Strategy With Serverl...

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Intelligent computing and Control (IC&C), International Conference on

作者： Qi Zhi Ang Peter ChunYu Yau Chin Sean Sum Qi Cao Dennis Wong School of Computing Science University of Glasgow United Kingdom Singapore Institute of Technology Singapore Faculty of Applied Sciences Macao Polytechnic University China

This paper provides an overview, presents the work-in-progress study of how software architecture can be modernized using the latest technologies. We will discuss the reasons why software modernization is required, how to choose an optimal solution from the architecture perspective, related migration strategy and the related points to note. The paper will also discuss some of the common issues faced while reviewing the architecture. These issues would provide some insights to some frequently faced problems in development and deployment. Finally, a discussion on how containerization technology and load balancer could help in resolving some of the common issues faced. In this paper, we have evaluated our proposed approach in a realistic case study involving an actual organization, which was anonymized for identity protection A certain organization would be used to provide the corresponding review mentioned.

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Machine Learning Neural Network Classifier Interfaced Skin Cancer Identification for Medical Diagnosis System

Machine Learning Neural Network Classifier Interfaced Skin C...

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2024 International Conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Chempavathy, B. Veerappan, Kavitha Jose, P. Theodore, Shaji. K. A. Rajasree, R. Prasanna, S. Gnana New Horizon College of Engineering Dept. of Cse Bangalore India Kongu Engineering College Dept. of Ece Erode India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Cse Chennai India College of Computing and Information Science University of Technology and Applied Sciences Oman Chennai Institution of Technology Dept. of Ece Chennai India St. Martins Engineering College Dept. of Cse Telangana Secunderabad India

ISBN: (纸本)9798350387230

Skin cancer, which lethal, is one of the top three tumours caused by DNA damage. This damaged DNA causes cells to grow uncontrolled, and they are currently growing swiftly. Numerous trainings performed on the automatic diagnosis of cancer in images of skin lesions. Analysis of these images is rather challenging, though, because of some disruptive factor like light observations from the skin's surface, differences in color enlightenment, and different forms and dimensions of the lesions. The accuracy and skill of analyzers in the early stages improved by machine learning (ML) based autonomous skin cancer diagnosis. A Deep Convolutional Neural Network (DCNN) model for identifying malignant and benign skin lesions is presented in this research. Applying a bilateral filter as the initial step in preprocessing eliminates noise and artefacts. The second phase involves utilizing U-Net to segment the input images and GLCM to extract features that assist with correct categorization. Data classification, the third phase, increases the quantity of images and improves classification precision. This work implements a skin cancer detection model using the ISIC dataset, which contains a large collection of medical images for training and evaluating ML algorithms. Proposed DCNN model is more dependable and resilient, according to the results. The training accuracy is 95% and the training loss is 0.01 after 35 epochs. © 2024 IEEE.

关键词： Lung cancer

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Blockchain-based Secure Health Records in the Healthcare Industry

Blockchain-based Secure Health Records in the Healthcare Ind...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Mohammad Tabrez Quasim Mohammad Mufareh Mobarak Khair Ul Nisa Mohammad Meraj Mohammad Zunnun Khan College of Computing and Information Technology University of Bisha Bisha Saudi Arabia College of Applied Computer Science King Saud University Saudi Arabia

Global healthcare systems are going through substantial changes in access, data processing, monitoring, and healthcare. By 2020, it's anticipated that connected and data-capturing equipment will have produced around 2314 exabytes of healthcare data. Cybercriminals work very hard to get access to medical information. Due to this challenge, the market for cybersecurity is predicted to reach $27.1 billion in 2026. To collect data for clinical research, a central repository will be formed with blockchain technology. This article proposes a secure mechanism utilizing Blockchain to guarantee the security of electronic health records (EHR). Databases, the Internet of Things, sensors, and other computing resources are part of the architecture. In comparison to the conventional healthcare system, the security and privacy of EHR will be improved by this framework for security

关键词： Industries Privacy Medical services Market research Blockchains Sensors Internet of Things

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