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2020 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2020

作者： Ilagan, Lorena Concepcion Ii, Ronnie Cabatuan, Melvin Roque, Christian University of Perpetual Help System DALTA Electronics Engineering Department Las Pinãs City Philippines De la Salle University Electronics and Communications Engineering Department Manila Philippines Center for Applied Microelectronics and Programming Inc. Muntinlupa City Philippines

ISBN: (纸本)9781728172019

In response to the emerging challenges of providing intelligent dynamic integrated circuit (IC) layout checking, computer vision in IC design and constraint engineering highlights the opportunities of computational intelligence solutions. In this study, vision-based system architecture is integrated with deep transfer learning models to classify metal oxide semiconductor (MOS) transistor cell-level layout error using one-vs-rest (OvR) multilabel classification. Two layout errors, namely missing contact layer and excess structure around the gate, are generated using the developed tool command language (TCL) script that is executed on Synopsys platform. Missing contact layer error is furtherly subcategorized to metal 1 in place and not fully placed, off positioned contact and its combination. Excess structure around the transistor gate is characterized by excess p-type implant (PIMP) and n-type implant (NIMP) with misaligned diffusion (DIFF) and polysilicon (PO) layers. Feature extraction for MOS-level error classification explored on using MobileNetV2 and EfficientNet variants. It was found that EfficientNetB7 best MobileNetV2 and other variants of EfficientNet in predicting IC layout errors based on nine error subcategories. Hamming loss was found to decrease and inference time to increase as the input image size is increased. The deep transfer network EfficientNetB7 has accuracy of 96.889 %, precision of 88.778 %, recall of 97.444 % and F1score of 91.667 in predicting transistor-level layout errors. Overall, the developed approach in predicting MOS transistor cell-level layout error using integrated computer vision and deep learning proved to be accurate and easy to be replicated for further enhancement to provide advanced layout evaluation. © 2020 IEEE.

关键词： Metals

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Using the inverse Cauchy problem of the Laplace equation for wave propagation to implement a numerical regularization homotopy method

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AIP Conference Proceedings 2023年第1期2977卷

作者： H. K. Al-Mahdawi Zainalabideen Albadran Hussein Alkattan Mostafa Abotaleb Khder Alakkari Ali J. Ramadhan Electronic Computer Centre University of Diyala Diyala Iraq Department of Computer Techniques Engineering College of Technical Engineering University of Alkafeel Najaf Iraq Department of System Programming South Ural State University South Ural State Russia Department of Statistics and Programming Faculty of Economics University of Tishreen Latakia Syria

In this paper, we introduce an approach via regularization and Homotopy way for resolving the inverse Cauchy problem of the Laplace of system partial differential equation which appears in the wave propagation for communication networks. We considered the method of Homotopy Perturbation Metheod (HPM) for solving the integral equations of the first kind named Fredholm. In order to formulate the Laplace equation into the first-kind integral equation (Fredholm) the Fourier series used. Then the discretization method used to reduce the integral equation into a linear operator equation for the first kind. It is clear that this kind of problem is callsified as an ill-posed and the direct way to solve it unacceptably. Tikhonov’s regularization method with Homotopy Perturbation algorithm used for obtaing the approximation solution for the Laplace differential equation. Finally, the numerical example is proposed.

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Design and Implementation of a Smart Human Posture Monitoring Device Using a Wearable Vest

Design and Implementation of a Smart Human Posture Monitorin...

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International Conference on Advances in Biomedical Engineering (ICABME)

作者： Roy Abi Zeid Daou Elio Geha Youssef Aoun Ali Hayek Josef Boercsoek Jose Javier Serrano Olmedo Biomedical Engineering department Furn El Chebbak Université La Sagesse Beirut Lebanon Education and Research Center MART Learning Chananir Lebanon Biomedical Technologies Department Faculty of Public Health Lebanese German University Jounieh Lebanon Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 University of Applied Science Hamm-Lippstadt Hamm Department of Computer Architecture and System Programming Faculty of Electrical Engineering /Computer Science University of Kassel Germany Lab of Bioinstrumentation and Nanomedicine Centro de Tecnologia Biomedica Universidad Politecnica de Madrid Spain

Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant.

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Modeling of rainfall time series using NAR and ARIMA model over western Himalaya, India

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Arabian Journal of Geosciences 2022年第23期15卷 1-26页

作者： Rawat, Deepa Mishra, Pradeep Ray, Soumik Warnakulasooriya, Himasha Harshani Fernando Sati, Sarswati Prakash Mishra, Gaurav Alkattan, Hussein Abotaleb, Mostafa College of Forestry VCSG Uttarakhand University of Horticulture and Forestry Tehri Garhwal India College of Agriculture Rewa India Centurion University of Technology and Management Paralakhemundi India Department of Statistics & Computer Science Faculty of Science University of Kelaniya Kelaniya Sri Lanka Centre of Excellence on Sustainable Land Management Indian Council of Forestry Research and Education (MoEF & CC Govt. of India) Dehradun India Department of System Programming South Ural State University Chelyabinsk Russia

The high Himalayas in northern India are an essential source of climate generation and maintenance over the entire northern belt of the Indian subcontinent. It also affects extreme weather phenomena such as western disturbances in the region during winter. The work presented here describes the trends in 117-year precipitation changes and their impact on the western Himalayas and suggests some possible explanations in the context of changing rainfall patterns. Under the investigation, the forecasting efficiency and the prediction pattern of artificial neural network (ANN) and seasonal autoregressive integrated moving average (SARIMA) models for rainfall series in the western Himalayan states of India have been assessed. The results revealed significant changes in the monthly, seasonal, and annual rainfall series data for the three states of the Western Himalayan regions from the years 1900 to 2017. The study also concludes that the nonlinear autoregressive neural network (NARNN) models can be used to forecast the western Himalayan region data series well. According to the result interpretation, the highest rainfall may be estimated in August, 1632.63 mm (2023), whereas the lowest rainfall can be obtained in October (0.43 mm) during 2023. The model predicted a gradual decrease in annual rainfall trends in Uttarakhand and Himachal Pradesh from 2018 to 2023 despite heavy rainfall prediction in the monsoon season, whereas Jammu and Kashmir increase in annual rainfall has been predicted from 2018 to 2023. Possible explanations for the change in precipitation over the western Himalayas have also been proposed and explained.

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Knowledge-Based Solvers for RGT Combinatorial Problems 12

Knowledge-Based Solvers for RGT Combinatorial Problems

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12th International Conference on Computer Science and Information Technologies, CSIT 2019

作者： Grigoryan, Sedrak Hakobyan, Nairi Baghdasaryan, Tadevos IPIA NAS RA Division of Computational and Cognitive Networks Yerevan Armenia RAU Department of System Programming RAU Yerevan Armenia

ISBN: (纸本)9781728128580

In this work, we present means for usage of RGT (Reproducible Game Trees) Solvers, that include knowledge acquisition, matching and decision making algorithms for RGT problems. The RGT Solvers are being regularly improved and the current implementation tries to cover the drawbacks of previous versions, improves presentation of classifiers and mental doings, as well as provides enhancements in interface. We also discuss ways of knowledge acquisition for marketing and battlefield problems by RGT Solvers. © 2019 IEEE.

关键词： Knowledge acquisition

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Formalization of data derivative structures

Formalization of data derivative structures

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2020 International Conference on Information Science and Communications Technologies, ICISCT 2020

作者： Vosilovich Kabulov, Rustam Bakhtiyorovich Ruzibaev, Ortik Temur Ataxanovich, Kuchkorov Saidqulovich Muminov, Sanjar Tashkent University of Information Technologies Named after Muhammad Al-Khwarizimi Department of Software Engineering Amir Temur str. 108 Tashkent100200 Uzbekistan Department of System and Application Programming of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Amir Temur str. 108 Tashkent100200 Uzbekistan

ISBN: (纸本)9781728199696

The paper considers the topical issue of formal description of data structures. Data structures are widely used in modern object-oriented programming to solve various practical problems. The paper introduces functions for performing operations with such data structures as stack, queue, and priority queue. The conditions that these functions must satisfy are introduced as axioms. To describe the axioms, an approach based on an abstract data type, algebraic and programmatic approaches were used. © 2020 IEEE.

关键词： Object oriented programming

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Image noise reduction by denoising autoencoder

Image noise reduction by denoising autoencoder

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IEEE International Conference on Dependable systems, Services and Technologies (DESSERT)

作者： Lev Yasenko Yaroslav Klyatchenko Oksana Tarasenko-Klyatchenko Department of System Programming and Specialized Computer Systems National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine

ISBN: (数字)9781728199573

ISBN: (纸本)9781728199580

Neural networks are used in many tasks today. One of them is the images processing. Autoencoder is very popular neural networks for such problems. Denoising autoencoder is an important autoencoder because some tasks we need a preprocessed image to get less noisy result. This research describes ways to analyze noisy images produced by a physically-based render engine and how to reduce that noise. The results showed that the algorithms are logarithmic.

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Forecasting nonlinear nonstationary processes in machine learning task 3

Forecasting nonlinear nonstationary processes in machine lea...

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3rd IEEE International Conference on Data Stream Mining and Processing, DSMP 2020

作者： Bidyuk, Peter Gozhyj, Aleksandr Kalinina, Irina Vysotska, Victoria Vasilev, Mikhail Malets, Romanna Department of Mathematical Methods of System Analysis National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Petro Mohyla Black Sea National University Department of Intelligent Information Systems Nikolaev Ukraine National University Lvivska Politechnika Department of Information Systems and Networks Lviv Ukraine Ivan Franko Lviv National University Programming Department Lviv Ukraine

ISBN: (纸本)9781728132143

The article discusses the features of the solving the forecasting problems using machine learning techniques. The issues of accounting and correctly processing non-linear non-stationary processes in the problems of modeling and forecasting time series in various areas are considered. The analysis of stages and methods for solving machine learning problems. As an example, consider the problem of predicting currency pairs based on historical data. A comparative analysis of the normalization methods in data clustering is given. For the six currency pairs a short-term forecast is proposed. © 2020 IEEE.

关键词： Machine learning

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Quality of life during the Pandemic: A cross sectional study about Attitude, Individual perspective and behavior change affecting general population in daily life 6

Quality of life during the Pandemic: A cross sectional study...

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6th Smart Cities Symposium, SCS 2022

作者： Khatoon, Fahmida Kumar, Manish Khalid, Ayesha Akbar Alshammari, Amal Daher Khan, Farida Alshammari, Rashid D. Balouch, Zahid Verma, Deepmala Mishra, Pradeep Abotaleb, Mostafa Makarovskikh, Tatiana El-Kenawy, El-Sayed M. Dutta, P.K. Marques, Joao Alexandre Biochemistry Department College of Medicine University of Hail Saudi Arabia Department of Zoology Narain College Shikohabad India Queen Elizabeth the Queen Mother Hospital East Kent NHS Foundation Trust Margate United Kingdom Dept. of Public Health Community Medicine in College of Medicine Princess Nourah Bint Abdul Rahman University Saudi Arabia Sulaiman Al-Rajhi University Saudi Arabia Department of Community Medicine Community Medicine Hail University Saudi Arabia United Medical College Jinnah University Pakistan Department of Zoology SS Jain Subodh Autonomous College Raj Jaipur India J.N.K.V.V. College of Agriculture M.P. Powarkheda India Department of System Programming South Ural State University Chelyabinsk454080 Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt School of Engineering and Technology Amity University West Bengal Kolkata India Laboratory of Applied Neurosciences - LAN/USJ University of Saint Joseph China

ISBN: (纸本)9781839538544

Quality of life in general population before and during pandemic is topic need to be address by researcher in terms of mobility, self-care, usual activities, pain/discomfort and anxiety/depression. The study was carried out among Saudi population. Data were collected from general population using questionnaire during the period from 22 August 2021 to 10th January 2022. As a result, total 214 participants have included in this study. Among them prevalent age group include 40 years (n= 63, 29.4%) shadowed by the age group 25-35 (n= 61, 28.5%) while above 60 years group were least frequent (n= 1, 0.5%). On questioning the applicants whether they were satisfied with their health and how would they rate their quality of life, their answers were as follows: yes, or satisfied (n= 86, 40.2%), very Satisfied (n= 102, 47.7%) Dissatisfied (n= 11, 5.1%) and neither satisfied nor dissatisfied (n= 15, 7%). Due to pandemic, they were rate quality of life very good (n= 94, 43.9%), good (n= 63, 29.4 %) poor (n= 5, 2.3 %) and neither good and nor poor (n= 52, 24.3 %). During pandemic 96 participants feel no change in their weight but 110 participants respond that there is increase in coffee intake during the pandemic. Similarly increased in smoking habits and decrease rate in social activities (n=119,41.4%). The psychosomatic well-being of people has been interrupted by disturbing their social activities during pandemic. © 2022 IET Conference Proceedings. All rights reserved.

关键词： SARS

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Principles of organizing a hardware of information collection system perceptron for a quantum hashing problem

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AIP Conference Proceedings 2024年第1期2969卷

作者： V. D. Munister A. L. Zolkin A. I. Dzhangarov Department of mathematical methods and system analysis Mariupol State University Prospect Builders 129 a Mariupol 87500 Ukraine Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics Samara 443010 Russia Department of Programming and Information and Communication Kadyrov Chechen State University 32 Sheripova Street Groznyy 364093 Russia

The article raises the issue of organizing the signal processing process in existing classes of digital systems and analogue-to-digital converters. The authors indicate a significant problem of the synthesis of controlled action (and its adequate perception by the control system through the "sensory level of perception", in particular, the aspectology of redundancy in computing power and the problem of choice and delegation in stream processing of signals).A solution for an information system for collecting and managing data based on peripheral interrogation of sensors is generalized and formalized. For the first time authors are developing a new class of hardware devices that combine the principles of organization of neural pathogens (perceptrons) that work within the activation function and trend line (neural memory), and quantum logic in a Boolean basis. The functional testing of the developed device is presented, the simplicity and reliability of the hardware implementation is indicated. While formulating research methods, the goal is to develop a reliable integrative system for information support of the hardware circuit system. A structural block of controlled addiction has been implemented, taking into account quantum caching that is taken as a mean of ensuring the unity and consistency of data transmission from the measuring environment in case of increasing its structural component. Options for the practical implementation of the information and analytical structure of the hardware perceptron in the industrial Internet of Things are considered in the conclusion.

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