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International Conference on Modern Problems of Radio engineering, Telecommunications and computer science

作者： Illia Zhydenko Halyna Klym Ivan Karbovnyk Yuriy Kostiv Ivanna Vasylchyshyn Dmytro Chalyy Department of fire tactics and emergency rescue work Lviv State University of Life Safety Lviv Ukraine Specialized Computer Systems Dpt. Lviv Polytechnic National University Lviv Ukraine Radiophysics and Computer Technology Dpt. Ivan Franko National University of Lviv Lviv Ukraine Information Technology Security Dpt. Lviv Polytechnic National University Lviv Ukraine Department of Theoretical and General Electrical Engineering Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

Thermal properties of refractory nanocomposites with embedded single-walled carbon nanotubes were investigated using proposed automated approach. In this approach, differences in nanocomposites with varying parameters, observed under temperature differentials and thermal loads, are highlighted. These findings are crucial for their integration into sensor systems and for applications requiring enhanced fire resistance. The results indicate that nanocomposites with a nanotube dispersion time of 2 hours exhibit a peak temperature difference of 55 “C, while those with a 1-hour dispersion time show a maximum difference of 50 °C. A comparative analysis of heat transfer dynamics suggests that extending the dispersion time by 1 hour increases fire resistance by 15%.

关键词： Temperature sensors Temperature distribution Nanocomposites Thermal resistance Sensor systems Telecommunications Thermal loading Dispersion Heat transfer Resistance heating

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A Comprehensive Methodology for Assessing the Economic Viability of EV Charging Infrastructure in Brazil

A Comprehensive Methodology for Assessing the Economic Viabi...

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Smart Power & Internet Energy Systems (SPIES), International Conference on

作者： Daywes Pinheiro Neto António Paulo Coimbra Aníbal T. De Almeida Tony R. Almeida Pedro Moura Letícia C. Ucker Raphael A. Gomes Alana S. Magalhães Center for Experimental and Technological Research and Studies Federal Institute of Goiás Brazil Dpt. of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal

ISBN: (数字)9798350368864

ISBN: (纸本)9798350368871

This study proposes a comprehensive methodology to assess the economic viability of electric vehicle (EV) charging infrastructure projects in Brazil, specifically targeting connections with a capacity of up to 112.5 kW, which are subject to flat electricity tariffs. The methodology employs a novel EV energy consumption model, integrates the regulatory framework of net metering and relevant tax considerations, explores the potential cost-saving benefits of photovoltaic (PV) system integration, and utilizes key economic indicators like Net Present Value and Discounted Payback Time to quantify project profitability. By accounting for a set of EV demand scenarios (low, moderate, and high), the methodology provides a multifaceted assessment of project viability. A case study demonstrates the methodology's application, highlighting the potential economic benefits of such investments. The results suggest that the minimum charging service fee required for economic viability ranges from 0.17 to 0.29 USD/kWh, with a clear cost advantage associated with PV integration observed across all scenarios.

关键词： Economics Photovoltaic systems Costs Finance Transportation Tariffs System integration Charging stations Electric vehicle charging Sustainable development

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Electromigration Effects in Processes of Nano- Structured Thin Films Growth 12

Electromigration Effects in Processes of Nano- Structured Th...

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12th IEEE International Conference "Nanomaterials: Applications and Properties", NAP 2022

作者： Dvornichenko, Alina Kharchenko, Vasyl Kharchenko, Dmitrii Petrov, Serhii Sumy State University Dpt. Applied Mathematics And Modeling Of Complex Systems Sumy Ukraine Sumy State University Institute Of Applied Physics National Academy Of Sciences Of Ukraine Sumy Ukraine Institute Of Applied Physics National Academy Of Sciences Of Ukraine Sumy Ukraine Sumy State University Dpt. Computer Science Sumy Ukraine

ISBN: (数字)9781665489829

ISBN: (纸本)9781665489829

We discuss effects of electromigration in processes of nano-structured thin films growth at condensation from gaseous phase end epitaxial growth in the framework of numerical simulations. It will be shown that an increase in the strength of the electric field applied to the substrate promotes formation of percolate structures of adsorbate. We will analyze dynamics of the surface morphology change in details. This study provides insight into details of electromigration effects at self-organization of adatoms into percolating adsorbate islands during nano-structured thin films growth at both condensation from the gaseous phase and epitaxial growth of multi-layer pyramidal-like adsorbate structures. © 2022 IEEE.

关键词： Surface morphology

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Decision Support Framework for Automating the Optimization of Edge Computing Federations

Decision Support Framework for Automating the Optimization o...

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Annual Modeling and Simulation Conference (ANNSIM)

作者： Antonio F. Rodríguez-Liria Román Cárdenas Patricia Arroba José M. Moya José L. Risco-Martín Gabriel Wainer Laboratorio de Sistemas Integrados (LSI) CCS–Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain Dpt. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain Dpt. of Systems and Computer Engineering Carleton University Ottawa ON Canada

Modern IoT applications present demanding Quality of Service (QoS) requirements that can no longer rely on a centralized solution to offload compute-intensive tasks. Edge Computing tackles these limitations by extending the Cloud to the edge of the network. However, the distributed and complex nature of Edge infrastructures introduces new technical challenges that must be addressed to develop a successful solution. Modeling, Simulation, and Optimization (M&S&O) tools can aid in the automatic design of modern Edge deployments. Here we present a decision support module that allows practitioners to automatically improve aspects of their Edge infrastructure by applying different optimization algorithms, such as simulated annealing, tabu search, and stochastic hill climbing. Furthermore, the structure of this module allows us to extend the available optimization methods if needed. We illustrate how this tool can help us to improve energy consumption policies of Edge Computing facilities and reduce their operational costs.

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A Survey of Recent Developments in Testability, Safety and Security of RISC-V Processors

A Survey of Recent Developments in Testability, Safety and S...

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IEEE European Test Symposium (ETS)

作者： Jens Anders Pablo Andreu Bernd Becker Steffen Becker Riccardo Cantoro Nikolaos I. Deligiannis Nourhan Elhamawy Tobias Faller Carles Hernandez Nele Mentens Mahnaz Namazi Rizi Ilia Polian Abolfazl Sajadi Mathias Sauer Denis Schwachhofer Matteo Sonza Reorda Todor Stefanov Ilya Tuzov Stefan Wagner Nuša Zidarič Institute of Smart Sensors University of Stuttgart Stuttgart Germany Universitat Politècnica de València Spain Dpt. of Computer Science University of Freiburg Freiburg Germany Institute of Software Engineering University of Stuttgart Stuttgart Germany Dpt. of Control and Computer Engineering Politecnico di Torino Turin Italy Institute of Computer Architecture and Computer Engineering University of Stuttgart Stuttgart Germany LIACS Leiden University Leiden Netherlands Advantest Europe Boeblingen Germany

With the continued success of the open RISC-V architecture, practical deployment of RISC-V processors necessitates an in-depth consideration of their testability, safety and security aspects. This survey provides an overview of recent developments in this quickly-evolving field. We start with discussing the application of state-of-the-art functional and system-level test solutions to RISC-V processors. Then, we discuss the use of RISC-V processors for safety-related applications; to this end, we outline the essential techniques necessary to obtain safety both in the functional and in the timing domain and review recent processor designs with safety features. Finally, we survey the different aspects of security with respect to RISC-V implementations and discuss the relationship between cryptographic protocols and primitives on the one hand and the RISC-V processor architecture and hardware implementation on the other. We also comment on the role of a RISC-V processor for system security and its resilience against side-channel attacks.

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Understanding the Conceptual Structure of Large Language Models through Bibliographical Network

Understanding the Conceptual Structure of Large Language Mod...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： V. Duarte-Martínez I.J. Perez P. Ducange M.J Cobo Escuela Superior Politécnica del Litoral ESPOL Facultad de Ingeniería en Electricidad y Computación ESPOL Polytechnic University Guayaquil Ecuador Dpt. of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain Dpt. of Information Engineering University of Pisa Italy

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

Large Language Models represent a transformative technology at the forefront of artificial intelligence and natural language processing, with applications spanning diverse domains. This study conducts a comprehensive science mapping analysis of the LLMs research field, leveraging bibliometric techniques to uncover its thematic structure, trends, and global actors involved. Utilizing data from the Web of science, a corpus of 1303 research documents from 2010 to 2023 is analyzed, revealing a notable surge in research activity, particularly in recent years. Key thematic areas driving research within the field are identified, including chatbot, code generation, augmented reality, transformers, and machine learning paradigms. Foundational technologies such as transformers are pivotal in shaping the research landscape, while emerging themes like prompt learning hint at future directions. This study offers valuable insights for researchers, practitioners, and policymakers seeking to navigate the dynamic landscape of LLMs research and harness its full potential for societal benefit.

关键词： Technological innovation Codes Navigation Collaboration Market research Transformers Chatbots

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A Multispectral Dataset for the Detection of Tutaabsoluta and Leveillula Taurica in Tomato Plants

SSRN

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SSRN 2022年

作者： Georgantopoulos, Panagiotis S. Papadimitriou, Dimitris Constantinopoulos, Constantinos Manios, Thrasyvoulos Daliakopoulos, Ioannis N. Kosmopoulos, Dimitrios Computer Engineering & Informatics Dpt. University of Patras Rio26504 Greece Dpt. of Agriculture Hellenic Mediterranean University Heraklion71410 Greece

Tomato (Solanum lycopersicum) is one of the most importantvegetables for human nutrition and its cultivation employs amounts of resourcesworldwide. However, tomato cultivation is plagued by several diseases and peststhat increase the production cost and introduce additional environmental andhealth risks due to pesticide use. Timely disease and pest detection is of highimportance for tomato crop production and the environment, as plant protec-tion input can be optimized. Here, we present a dataset of multispectral images(RGB and NIR) of tomato plants, at various stages of infection with Tuta ab-soluta and Leveillula taurica, which to our knowledge is unique. The datasetcomprised of 263 images collected from a real greenhouse. Additionally, weapplied a baseline Faster-RCNN object detector for the localization and classi-fication of lesions. Our experiments include (i) a version for the RGB channelsand (ii) a custom backbone architecture version for feature fusion using thesame Faster-RCNN head. Results of mAP 18.5% and mAP 20.2% respectively,highlight the added value of NIR spectral bands for detecting these diseases. © 2022, The Authors. All rights reserved.

关键词： Cultivation

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computer-Aided Lung Auscultation Screening and Radiographic Evaluation of Pediatric Pneumonia

Computer-Aided Lung Auscultation Screening and Radiographic ...

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

作者： Annapurna Kala Daniel Chong Abdullah H. Baqui Salahuddin Ahmed ASMD Ashraful Islam Nabid Chowdhury Arunangshu Roy Eric D. McCollum Mounya Elhilali Dpt. Electrical & Computer Engineering Lab. for Computational Audio Perception Johns Hopkins University Dpt. International Health Bloomberg School of Public Health Johns Hopkins University Projahnmo Research Foundation Dhaka Bangladesh Dpt. Pediatrics Global Program in Pediatric Respiratory Sciences Eudowood Division of Pediatric Respiratory Sciences Johns Hopkins School of Medicine Baltimore USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

While chest auscultations provide an accessible and low-cost tool for pediatric pneumonia diagnosis, its subjectivity and low reliability continues to hinder its inclusion in global pneumonia guidelines; eventhough more robust tools like chest radiography also suffer from cost and accessibility issues. Advances in computer-aided analytics is offering more robust tools for interpreting digital auscultation signals though little has been done to explore variations of lung sounds across different chest positions and the correspondence between auscultations and specific radiographic findings. The present study explores interpretation of lung auscultations across chest positions in a pediatric pneumonia population, using a deep neural network classification of normal and abnormal breathing patterns. The results reveal a strong alignment between computer-aided auscultation findings and radiographic interpretation not only in terms of presence of adventitious lung patterns, but also in terms of localizing the abnormality along the left or right lung. Though evaluated in a small clinical population, this research underscores the potential of computer-aided auscultation analysis as a cost-effective substitute for radiography in resource-limited settings.

关键词： Pneumonia Costs Lungs Artificial neural networks Reliability engineering in medicine and biology Diagnostic radiography Guidelines

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A Multispectral Dataset for the Detection of Tuta Αbsoluta and Leveillula Τaurica in Tomato Plants

SSRN

引用

SSRN 2022年

作者： Georgantopoulos, Panagiotis S. Papadimitriou, Dimitris Constantinopoulos, Constantinos Manios, Thrasyvoulos Daliakopoulos, Ioannis Kosmopoulos, Dimitrios Computer Engineering & Informatics Dpt. University of Patras Rio26504 Greece Dpt. of Agriculture Hellenic Mediterranean University Heraklion71410 Greece

Tomato ( Solanum lycopersicum ) is one of the most important vegetables for human nutrition and its cultivation employs amounts of resources worldwide. However, tomato cultivation is plagued by several diseases and pests that increase production cost and introduce additional environmental and health risks due to pesticide use. Timely disease and pest detection is of high importance for tomato crop output and the environment, since plant protection input can be optimized. Here, we present a dataset of multispectral images (RGB and NIR) of tomato plants, at various stages of infection with Tuta absoluta and Leveillula taurica , which to our knowledge is unique. The dataset comprised of 263 images collected from a real greenhouse. Additionally, we applied a baseline Faster-RCNN object detector for the localization and classification lesions. Our experiments include (i) a version for the RGB channels and (ii) a custom backbone architecture version for feature fusion using the same Faster-RCNN head. Results of mAP 18.5% and mAP 20.2% respectively, highlight the added value of NIR spectral bands for detecting these diseases. © 2022, The Authors. All rights reserved.

关键词： Fruits

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Analysis of classifier training on synthetic data for cross-domain datasets

arXiv

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arXiv 2024年

作者： Cortés, Andoni Rodríguez, Clemente Vélez, Gorka Barandiarán, Javier Nieto, Marcos Deparment of Intelligent Transportation Systems and Engineering Deparment Vicomtech Paseo Mikeletegi 57 San Sebastian20009 Spain Dpt. of Computer Architecture San Sebastian Spain

A major challenges of deep learning (DL) is the necessity to collect huge amounts of training data. Often, the lack of a sufficiently large dataset discourages the use of DL in certain applications. Typically, acquiring the required amounts of data costs considerable time, material and effort. To mitigate this problem, the use of synthetic images combined with real data is a popular approach, widely adopted in the scientific community to effectively train various detectors. In this study, we examined the potential of synthetic data-based training in the field of intelligent transportation systems. Our focus is on camera-based traffic sign recognition applications for advanced driver assistance systems and autonomous driving. The proposed augmentation pipeline of synthetic datasets includes novel augmentation processes such as structured shadows and gaussian specular highlights. A well-known DL model was trained with different datasets to compare the performance of synthetic and real image-based trained models. Additionally, a new, detailed method to objectively compare these models is proposed. Synthetic images are generated using a semi-supervised errors-guide method which is also described. Our experiments showed that a synthetic image-based approach outperforms in most cases real image-based training when applied to cross-domain test datasets (+10% precision for GTSRB dataset) and consequently, the generalization of the model is improved decreasing the cost of acquiring images. © 2024, CC BY-NC-ND.

关键词： Advanced driver assistance systems

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