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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Isabella Sannino Leonardo Iannucci Luca Lombardo Marco Parvis Allegra Comba Pasquale Arpaia Emma Angelini Sabrina Grassini Department of Applied Science and Technology Politecnico di Torino Turin Italy Department of Electronics and Telecommunication Politecnico di Torino Turin Italy Department of Surgical Science Università degli Studi di Torino Turin Italy Dep. of Electrical Engineering and Information Technology Università degli Studi di Napoli Federico II Naples Italy Dep. of Applied Science and Technology Politecnico di Torino Turin Italy

This work deals with the design and development of a non invasive though rather effective solution to detect carious lesions. The main aim of the study is to develop a simple automatic approach for the assessment of the tooth demineralization, which represents the earlier stage of the dynamic carious process, by means of impedance spectroscopy. In particular, impedance measurements were carried out on 50 extracted human teeth. Teeth were demineralized in-vitro according to a validated protocol; then, a morphological analysis of the tooth surface was performed by scanning electron microscopy to confirm enamel demineralization. The proposed approach tries to take advantage of the change of impedance phase due to the demineralization process. Data analysis confirmed that the best frequency for discriminating between demineralized and non-demineralized teeth is about 15 Hz, and this parameter can be used for building up an automatic classifier based on Multilayer Perceptron (MLP) topology. Impedance data were processed by using a modified single neuron, which allows classifying demineralized and sound teeth with an error rate of about 7%, estimating therefore the presence of carious lesions in progress. The presented work can be considered as a feasibility study with a final goal to conceive a simple and low-cost measurement system to identify caries at an early stage

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Characterizations of symplectic polar spaces

arXiv

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arXiv 2022年

作者： Cardinali, Ilaria Pasini, Antonio Cuypers, Hans Giuzzi, Luca Pasini, Tonio Dep. Information Engineering and Mathematics University of Siena Via Roma 56 SienaI-53100 Italy Dep. Mathematics and Computer Sciences Eindhoven University of Technology Eindhoven5600 MB Netherlands D.I.C.A.T.A.M. University of Brescia Via Branze 43 BresciaI-25123 Italy

A polar space S is called symplectic if it admits a projective embedding ϵ: S → PG(V ) such that the image ϵ(S) of S by ϵ is defined by an alternating form of V. In this paper we characterize symplectic polar spaces in terms of their incidence properties, with no mention of peculiar properties of their embeddings. This is relevant especially when S admits different (non isomorphic) embeddings, as it is the case when S is defined over a field of characteristic *** Codes 51A50, 51b25, 51e24 Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Improving Obstacle Avoidance Strategies in Autonomous Navigation of a Sailboat

Improving Obstacle Avoidance Strategies in Autonomous Naviga...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Pedro Paiva Luís Gomes Dep. Electrical and Computer Engineering NOVA School of Science and Technology — FCT-NOVA NOVA University Lisbon Caparica Portugal School of Science and Technology Center of Technology and Systems (UNINOVA-CTS) and Associated Lab of Intelligent Systems (LASI) NOVA University Lisbon Caparica Portugal

This work explores the different mechanisms involved in creating a behavioural system for route planning and collision avoidance, taking into account the constraints imposed by the navigational characteristics of the sailboat. The implementation of these mechanisms is accompanied by an introduction to fundamental concepts, including Artificial Potential Fields, Histograms of Vector Fields, Navigation in the No-Go Zone, Differentiation of the Nature of Obstacles resulting in Different Operations, and the Constitution of Different Areas of Actuation. Finally, multiple simulations are conducted in a virtual environment to verify the resilience of the proposed navigation strategy.

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A Safety and Security Requirements Management Methodology in Reconfigurable Collaborative Human-Robot Application

A Safety and Security Requirements Management Methodology in...

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IEEE International Workshop on Factory Communication Systems (WFCS)

作者： Ali M. Hosseini Clara Fischer Mukund Bhole Wolfgang Kastner Thilo Sauter Sebastian Schlund Institute of Computer Technology TU Wien Vienna Austria Institute of Management Science TU Wien Vienna Austria Institute of Computer Engineering TU Wien Vienna Austria Dep. of Integrated Sensor Systems Danube Univ. Krems Wiener Neustadt Austria

The current industry has to adapt to rapidly changing customers' needs. Reconfigurable manufacturing, therefore, provides capacity and functionality on demand which is essential for competitiveness in fast-changing markets. Furthermore, Industry 4.0 or even more so, Industry 5.0 emphasizes human-centred production with collaborative robots, Cobots, to create human-robot interactions. In such scenarios, safety and security are difficult to address due to the intrinsic features of reconfigurable manufacturing, like exposure to numerous requirements changes in a short period. As safety and security can conflict in different phases of the system life-cycle, one of the earliest activities to avoid conflicts is requirements engineering which can significantly diminish the cost and time of fixing issues compared to later phases like operation. This paper proposes a methodology for safety and security requirements interaction management, including conflict detection and resolution, and shows its applicability through a reconfigurable collaborative human-robot use case. Based on the proposed methodology, we detected and resolved two safety and security requirement conflicts.

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Sentiment Analysis of Persian Language: Review of Algorithms, Approaches and Datasets

arXiv

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arXiv 2022年

作者： Nazarizadeh, Ali Banirostam, Touraj Sayyadpour, Minoo Computer Eng. Dep. Faculty of Technology and Engineering Islamic Azad University Central Tehran Branch Tehran Iran Kharazmi University Tehran Iran

Sentiment analysis aims to extract people's emotions and opinion from their comments on the web. It widely used in businesses to detect sentiment in social data, gauge brand reputation, and understand customers. Most of articles in this area have concentrated on the English language whereas there are limited resources for Persian language. In this review paper, recent published articles between 2018 and 2022 in sentiment analysis in Persian Language have been collected and their methods, approach and dataset will be explained and analyzed. Almost all the methods used to solve sentiment analysis are machine learning and deep learning. The purpose of this paper is to examine 40 different approach sentiment analysis in the Persian Language, analysis datasets along with the accuracy of the algorithms applied to them and also review strengths and weaknesses of each. Among all the methods, transformers such as BERT and RNN Neural Networks such as LSTM and Bi-LSTM have achieved higher accuracy in the sentiment analysis. In addition to the methods and approaches, the datasets reviewed are listed between 2018 and 2022 and information about each dataset and its details are provided. Copyright © 2022, The Authors. All rights reserved.

关键词： Sentiment analysis

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Efficient COVID-19 Identification Using Deep Learning for IoT 5

Efficient COVID-19 Identification Using Deep Learning for Io...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Veeraiah, Vivek Pankajam, A. Vashishtha, Ela Dhabliya, Dharmesh Karthikeyan, P. Chandan, Radha Raman Adichunchanagiri University Department of R & D Computer Science Karnataka Mandya India Avinashilingam Institute for Home Science and Higher Education for Women Dep of Business Administration Tamil Nadu India University of North Texas Health Science Center Healthcare Consultant TX United States Vishwakarma Institute of Information Technology Dept. of Information Technology Maharashtra Pune India School of Management Studies KonguEngineering College Perundurai Tamil Nadu Erode India Shambhunath Institute of Engineering & Technology Jhalwa Dept. of Computer Science & Engineering Uttar Pradesh Prayagraj India

ISBN: (纸本)9798350398267

Internet of Things (IoT) has made it possible to diagnose and treat patients remotely, as well as to expedite the transportation of essential drugs and medical equipment to locations that are geographically separated. This has occurred at a time when society has become more socially distant. During the Ebola and COVID-19 outbreaks, the Internet of Things (IoT) technology was put to use in remote patient monitoring and the management of the vaccine cold chain. Concurrently, this study reflects on the variables that are required for IoT to scale. Since December 2019, the COVID-19 outbreak on a worldwide scale has developed into a significant problem. In order for medical treatment to be successful, it is essential to make a prompt and accurate diagnosis of persons who may be infected with the COVID-19 virus. In order to put a halt to the spread of COVID-19, it is important to construct an automated system that is based on deep transfer learning and is capable of detecting the virus based on chest X-rays. The authors of this study present an internet-of-things (IoT) system that makes use of ensemble deep transfer learning to diagnose COVID-19 patients at an earlier stage. It is feasible to keep an eye on potentially hazardous COVID-19 incidents as they occur so long as suitable procedures are adhered to. Inceptions A variety of different deep learning models are included into the framework that has been proposed for the Internet of Things. According to the findings of the study, the method that was suggested assisted radiologists in accurately and quickly identifying patients who could have COVID-19. The proposed effort focuses on developing an effective identification system based on the COVID-19 standard for use in an IoT setting. © 2022 IEEE.

关键词： COVID-19

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Deep Learning technology Used High Dynamic Range Image Reconstruction And Detection

Deep Learning Technology Used High Dynamic Range Image Recon...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： M. Bheemalingaiah R. Srinivasan V. Vijey Nathan Raju Kolluri Amit Gupta Department of CSE J.B Institute of Engineering and Technology Hyderabad Telangana Department of CSE School of Computer Science and Engineering Galgotia University U.P India Department of CSE SRM TRP Engineering College Tiruchirappalli Tamilnadu India Department of ECE Narasaraopet Engineering College Narasaraopet Andhra Pradesh India Dep. of AI & ML J.B Institute of Engineering and Technology Hyderabad Telangana

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

The suggested remedy for saturated pixels in digital camera photos introduces a brand-new learning-based method for reconstructing images with high dynamic range (HDR). While current techniques concentrate on increasing brightness range, they frequently fall short in producing realistic textures, leading to errors in places that are too saturated. Our method reconstructs an HDR image by using aesthetically beautiful saturated pixels from a low dynamic range (LDR) image. We provide a feature masking strategy to address problems discovered during training, when saturated and well-exposed pixels got comparable convolutional filters, resulting in uncertainty and checkerboard and halo aberrations. This method improves the overall quality of the reconstruction by reducing the contribution of features from saturated regions. Furthermore, in order to guarantee visually pleasing textures in the reconstructed HDR photos, we adjust the perceptual loss function based on *** use a two-step procedure in order to properly train our system. We first train the system for picture in painting on a sizable dataset, and then we modify it for HDR reconstruction. Our method performs exceptionally well even with a small number of HDR photos because it uses an advanced sampling strategy to choose hard training patches from a wide range of settings. Convolutional neural networks, feature masking, perceptual loss, and high dynamic range imagery are important components of our methodology.

关键词： Training Uncertainty Filters Image color analysis Supervised learning Digital cameras High dynamic range Convolutional neural networks Image reconstruction Painting

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Reinforcement Learning Based Joint Resource Allocation and User Fairness Optimization in mmWave-NOMA HetNets

Reinforcement Learning Based Joint Resource Allocation and U...

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Iranian Conference on Electrical engineering (ICEE)

作者： Sima Sobhi-Givi Mahdi Nouri Mahrokh G. Shayesteh Hashem Kalbkhani Zhiguo Ding Electrical and Computer Engineering Department Urmia University Urmia Iran Elect. Eng. Dep. Sharif university of technology Mobile Telecommunication Company of Iran (MCI) International Telecommunication Union (ITU) Tehran Iran ACRI Elec. Eng. Dep. Wireless Research Lab Sharif University of Technology Tehran Iran Faculty of Elec. Eng. Urmia University of technology Urmia Iran School of Electrical and Electronic Engineering The University of Manchester Manchester UK

In this paper, we propose heterogeneous network (HetNet) with mmWave and hybrid non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmission, where uplink (UL) of macrocell users (MCUs) and downlink (DL) of small cell users (SCUs) share the same resource block (RB) to increase the network capacity. Also, an imperfect successive interference cancelation (I-SIC) decoding is considered due to hardware impairment of the real-world NOMA systems. We formulate the problem of joint power and resource block (RB) allocation to maximize rate-fairness and sum-rate by considering minimum quality of service (QoS) requirements. Reinforcement learning (RL) named Q-learning (QL) and deep Q-learning network (DQN) algorithms are employed to solve these problems. The results indicate that the proposed QL and DQN algorithms increase the fairness and sum-rate while low number of iterations is required.

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LLM Acceleration on FPGAs: A Comparative Study of Layer and Spatial Accelerators

LLM Acceleration on FPGAs: A Comparative Study of Layer and ...

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IEEE Central America and Panama Convention (CONCAPAN)

作者： Luis D. Prieto-Sibaja Luis G. Leόn-Vega Indra Leόn-Vega Jorge Castro-Godínez Stefano Cozzini Computer Engineering School Costa Rica Institute of Technology Cartago Costa Rica Math. Inf. and Geoscience Dep. University of Trieste Trieste Italy Industrial Design Engineering School Instituto Tecnolόgico de Costa Rica Cartago Costa Rica Electronics Engineering School Costa Rica Institute of Technology Cartago Costa Rica Institute of Research and Technology AREA Science Park Trieste Italy

ISBN: (数字)9798350366723

ISBN: (纸本)9798350366730

Large Language Models (LLMs) have emerged as the leading technique in Natural Language Processing due to their remarkable capabilities. These models are exceptionally complex, often utilising billions of parameters and consuming tens of gigabytes of memory unless optimised. Typically, LLMs are trained and accelerated using general-purpose Graphics Processing Units (GPUs), with a single inference potentially occupying an entire GPU. Consequently, cloud services may require hundreds of GPUs to deliver high-quality AI assistant services. This demand for extensive hardware acceleration allows exploring alternative architectures that offer improved execution time and energy *** study evaluates Field-Programmable Gate Arrays (FP-GAs), renowned for their flexibility and efficiency, to accelerate LLMs. We specifically compare the resource consumption and latency of two distinct architectures: layer and spatial accelerators. Analysing the Llama 2-7B model, we identified potential optimisation opportunities within its composition and operational graphs. Our most successful implementations demonstrate a performance improvement ranging from 1.37× to 10.98× over two AMD EPYC processors with 64 cores each. Moreover, our results indicate that, with further refinement, FPGAs have the potential to surpass GPU performance for LLM inference, showcasing their feasibility as a viable alternative for this demanding application.

关键词： Large language models High performance computing Graphics processing units computer architecture Logic gates Distance measurement Natural language processing Field programmable gate arrays Hardware acceleration Optimization

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An Overview of information Hiding Techniques Challenges and Applications 23

An Overview of Information Hiding Techniques Challenges and ...

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Proceedings of the 7th International Conference on Future Networks and Distributed Systems

作者： Ali Noori Kareem Omar Adil Deheyab Aws Zuhair Sameen Mohammed Hasan Alwan Yousif I Hammadi Mayssam Saad Kadhum Ahmed Aziz Muthana Ali Salem Ali Department of Computer engineering University of Diyala Iraq Department of Communications Engineering Bilad Alrafidain University College Iraq Department of Medical Instrumentation Engineering Techniques Al-Farahidi University Iraq Department of Communications Engineering University of Diyala Iraq Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Department of Computer-aided Design and Engineering Design National University of Science and Technology Russia

ISBN: (纸本)9798400709036

The internet became a common communication platform for many people. However, information transmission is not secure, and there is a high probability of copying and altering the information easily. In the public network, transmitting sensitive or private information needs some kind of protection. Unless a secure channel is utilized for the transmission. Hiding the information is one of the efficient techniques that provides a solution for transmitting confidential data over communication channels. These techniques should meet various application requirements such as high robustness against attacks, real-time, and high hiding capacity. Generally, information-hiding techniques suffer from many challenges. This paper provides an insight into these techniques, their applications, and challenges to fill the gap of missing such an overview.

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