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APPLYING TRAJECTORY TRACKING AND POSITIONING techniques FOR REAL-TIME AUTONOMOUS FLIGHT PERFORMANCE ASSESSMENT OF UAV SYSTEMS

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Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University 2019年第3期54卷

作者： Mostafa, Salama A. Mustapha, Aida Ramli, Azizul Azhar Darman, Rozanawati Zeebaree, Subhi R.M. Mohammed, Mazin Abed Gunasekaran, Saraswathy Shamini Ibrahim, Dheyaa Ahmed Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Duhok Polytechnic University Duhok Iraq Tishk International University Erbil Iraq College of Computer Science and Information Technology University of Anbar Anbar31001 Iraq College of Computing and Informatics Universiti Tenaga Nasional Selangor43000 Malaysia Computer Engineering Techniques Department Imam Ja'afar Al-Sadiq University Baghdad Iraq

This research presents a Performance Visualized Assessment (PVA) archetype via which the performance of autonomous Unmanned Aerial Vehicles (UAV) or drone framework can be scrutinized and evaluated. The PVA is a performance-based assessment method that appropriately fits in assessing the flight point of the UAV in an indoor situation and outside the requirement of GPS aid. The PVA prototype came up with is comprised of Visualized Mission Grid (VMG) and Chi-square (CSI) modules. The VMG maps viewable field information of the surrounding including the situation of the route mission. In addition, the utilization of restriction and optical stream indoor direction monitoring and trailing is used in the CSI. The PVA follows and evaluates the execution of the UAV autonomous operations by checking and envisioning the direction and the UAV action amid route missions. The implementation outcomes demonstrate that the PVA is able to precisely assess and envision the execution quality of three aerial missions. © 2019 Science Press. All rights reserved.

关键词： Antennas

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Optimal loads scheduling using the intelligent optimization approach

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AIP Conference Proceedings 2024年第1期3051卷

作者： Bilal Naji Alhasnawi Nidhal Abd Mohammed Basil H. Jasim Sabah Mohammed Mlket Almutoki Hussein M. Hathal Thulfiqar H. Mandeel Ali M. Jasim Rabeh Abbassi Vladimír Bureš Bishoy E. Sedhom Department of Electricity Techniques Al-Furat Al-Awsat Technical University Technical Institute Samawa Iraq Electrical Engineering Department Basrah University Basrah 61001 Iraq Electrical Engineering Department College of Engineering Mustansiriyah University Baghdad 10047 Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna 66002 Iraq Department of Communications Engineering Iraq University College Basrah 61001 Iraq Department of Electrical Engineering College of Engineering University of Ha’il Ha’il City 81451 Saudi Arabia Faculty of Informatics and Management University of Hradec Králové 50003 Hradec Králové Czech Republic Electrical Engineering Department Faculty of Engineering Mansoura University Mansoura 35516 Egypt

Because of the need for energy growing exponentially, prices are constantly going up, which impacts both residential and business consumers. Researchers are looking for workable solutions to this problem, such as off-peak scheduling that reduces peak energy consumption while maintaining reasonable energy costs. The system design issues that regulate decision-making for the best operation management are optimized in this research using an Improved Crow Search Algorithm (ICSA). The simulation’s results show that the suggested algorithm-based ICSA reduces carbon emissions, peak-to-average ratio (PAR), and electricity costs. Using the ICSA method, the scheduling controller suggested in this study was successful in achieving an energy savings of 30.58%.

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Preface

Smart Innovation, Systems and Technologies

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Smart Innovation, Systems and Technologies 2020年 179卷 ix-x页

作者： Kountchev, Roumen Patnaik, Srikanta Shi, Junsheng Favorskaya, Margarita N. Technical University of Sofia Sofia Bulgaria Department of Electro-Optical Engineering Yunnan Normal University Yunnan China Department of Computer Science and Engineering SOA University BhubaneswarOdisha India Informatics and Computer Techniques Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia

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Preface

Smart Innovation, Systems and Technologies

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Smart Innovation, Systems and Technologies 2020年 180卷 ix-xi页

作者： Kountchev, Roumen Patnaik, Srikanta Shi, Junsheng Favorskaya, Margarita N. Department of Radio Communications and Video Technologies Technical University of Sofia Sofia Bulgaria Department of Computer Science and Engineering SOA University BhubaneswarOdisha India Department of Electro-Optical Engineering Yunnan Normal University Yunnan China Informatics and Computer Techniques Reshetnev Siberian State University of Science and Technology Krasnoyarsk Krai Russia

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Preface

Smart Innovation, Systems and Technologies

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Smart Innovation, Systems and Technologies 2020年 173卷 v页

作者： Jain, Lakhmi C. Favorskaya, Margarita N. Nikitin, Ilia S. Reviznikov, Dmitry L. University of Technology Sydney Sydney Australia Liverpool Hope University Liverpool United Kingdom KES International United Kingdom Department of Informatics and Computer Techniques Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Moscow Russia Moscow Russia Federal Research Centre "Information and Control" of the Russian Academy of Sciences Moscow Russia

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Enhancement of medical image using spatial optimized filters and OpenMP technology

Enhancement of medical image using spatial optimized filters...

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2018 International MultiConference of Engineers and computer Scientists, IMECS 2018

作者： Cadena, Luis Zotin, Alexander Cadena, Franklin Electric and Electronic Department Universidad de las Fuerzas Armadas ESPE Av. Gral Ruminahui s/n Sangolqui Ecuador Department of Informatics and Computer Techniques Reshetnev Siberian State University of Science and Technology 31 krasnoyarsky raboch av Krasnoyarsk660037 Russia College Juan Suarez Chacon Quito Ecuador

ISBN: (纸本)9789881404787

For remove noise digital filters are used which in the last decades have taken great impetus for the treatment of images in different fields of science and in the case of the medical image processing better results are obtained in order to make better interpretations. Medical images can contain some noise therefore it makes sense to suppress noise on preprocessing stage. For the research we used digital filters such as mean filter, Gaussian filter and median filter. The speed performance comparison of optimized and classical implementations of these filters was conducted. It shows great speed improvement of optimized implementation. The possibilities of modern hardware allow using parallel technology for image processing. The parallel implementations of noise reduction filters algorithms taking into account the data parallelism were made utilizing OpenMP technology. The comparison of parallel implementation of filters algorithm with sequential and classic implementation show great increase in performance. Thus the parallel implementation gives a performance boost in 2-3 times for optimized version of algorithms. © 2018 Newswood Limited. All rights reserved.

关键词： Median filters

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Improve method using spatial fast filters and GPU to detection of parcels of land in satellite images for cadaster purposes

Improve method using spatial fast filters and GPU to detecti...

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2018 World Congress on Engineering and computer Science, WCECS 2018

作者： Cadena, Luis Cadena, Franklin Legalov, Alexander Zotin, Alexander Electric and Electronic Department Universidad de las Fuerzas Armadas ESPE Av. Gral Ruminahui s/n Sangolqui Ecuador College Juan Suarez Chacon Quito Ecuador Siberian Federal University 79 Svobodny pr. Krasnoyarsk660041 Russia Department of Informatics and Computer Techniques Reshetnev Siberian State University of Science and Technology 31 krasnoyarsky raboch Krasnoyarsk660037 Russia

ISBN: (纸本)9789881404817

Nowadays, remotely sensed images are used for various purposes in different applications. One of them is the cadastral application using high resolution satellite imagery. The edge detection has an important role in image processing, especially in the detection and the extraction physical features, those which are useful to their enforcement in the analysis of cadasters. An improve methodology is shown for detection of parcels of land in satellite images using GPU and fast filter for improve time process to reduce the noise, dynamic histogram equalization instead classic histogram equalization and Canny edge detection which is the best of others as Sobel, Prewitt, etc, for accelerated time process we used parallel Matlab GPU. © 2018 Newswood Limited.

关键词： Graphics processing unit

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Corrigendum to "Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients" [J. Adv. Res. 37 (2022) 147-168]

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Journal of advanced research 2023年 45卷 193页

作者： O S Albahri A A Zaidan A S Albahri H A Alsattar Rawia Mohammed Uwe Aickelin Gang Kou F M Jumaah Mahmood M Salih A H Alamoodi B B Zaidan Mamoun Alazab Alhamzah Alnoor Jameel R Al-Obaidie Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq. Faculty of Engineering & IT British University in Dubai United Arab Emirates. Electronic address: aws.alaa@fskik.upsi.edu.my. Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq. Faculty of Engineering & IT British University in Dubai United Arab Emirates. School of Computing and Information Systems University of Melbourne 700 Swanston Street Victoria 3010 Australia. School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu 611130 China. Department of Advanced Applications and Embedded Systems Intel Corporation Plot 6 Bayan Lepas Technoplex 11900 Georgetown Pulau Pinang Malaysia. Department of Computer Science Computer Science and Mathematics College Tikrit University Tikrit Iraq. Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia. Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan ROC. College of Engineering IT and Environment Charles Darwin University NT Australia. School of Management Universiti Sains Malaysia Pulau Pinang Malaysia. Department of Biology Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Perak 35900 Malaysia.

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Processing MRI brain image using OpenMP and fast filters for noise reduction

Processing MRI brain image using OpenMP and fast filters for...

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2019 World Congress on Engineering and computer Science, WCECS 2019

作者： Cadena, Luis Castillo, Darwin Zotin, Aleksandr Diaz, Patricia Cadena, Franklin Cadena, Gustavo Jimenez, Yuliana Electric and Electronic Department Universidad de las Fuerzas Armadas ESPE Av. Gral Ruminahui s/n Sangolqui Ecuador Chemistry and Exact Sciences Department Universidad Técnica Particular de Loja UTPL Av. Marcelino Champagñat s/n Loja Ecuador I3M Universitat Politècnica de Valencia Camino de Vera Valencia46022 Spain Department of Informatics and Computer Techniques Reshetnev Siberian State University of Science and Technology 31 krasnoyarsky raboch av. Krasnoyarsk660037 Russia College Unidad Educativa Atahualpa Oyacoto-Quito Ecuador Medicine Faculty Universidad Central del Ecuador Iquique 132 Quito Ecuador Health and Medical Science Department Universidad Técnica Particular de Loja UTPL Av. Marcelino Champagñat s/n Loja Ecuador

ISBN: (纸本)9789881404879

The medical diagnosis of most pathologists requires the analysis of the image studies. Therefore, it is important to get the best quality of the images without noise and highlight the details of tissues. The principal aim of this work is to apply different algorithms and filters to reduce the noise of magnetic resonance brain images, due to the noise in these can cause to give a difficult diagnosis. The algorithms considered in this work are the fast mean filter, fast Gaussian filter, and fast median filter;also was used parallel programming in OpenMP. The results show that the parallel implementation of algorithms has more performance in the time processing, localization, and noise reduction than sequential and classic implementation. © Newswood *** right reserved.

关键词： Parallel programming

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The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions

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The Journal of Data Education 2025年第1期65卷

作者： Keng-Boon Ooi Garry Wei-Han Tan Mostafa Al-Emran Mohammed A. Al-Sharafi Alexandru Capatina Amrita Chakraborty Yogesh K. Dwivedi Tzu-Ling Huang Arpan Kumar Kar Voon-Hsien Lee Xiu-Ming Loh Adrian Micu Patrick Mikalef Emmanuel Mogaji Neeraj Pandey Ramakrishnan Raman Nripendra P. Rana Prianka Sarker Anshuman Sharma Ching-I Teng Samuel Fosso Wamba Lai-Wan Wong a UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiab FORE School of Management New Delhi Indiac Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Kuching Malaysia a UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiac Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Kuching Malaysiad College of Business Administration Adamson University Manila Philippines e Faculty of Engineering & IT The British University in Dubai Dubai UAEf Department of Computer Techniques Engineering Dijlah University College Baghdad Iraq g Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Selangor Malaysia h Department of Business Administration “Dunarea de Jos” University of Galati Galati Romania i Tech Talk New Delhi India j Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Bay Campus Swansea UKk Department of Management Symbiosis International (Deemed University) Pune India l Department of Information Management National Central University Taoyuan Taiwan m Department of Management Studies Indian Institute of Technology Delhi New Delhi India n Faculty of Business and Finance Universiti Tunku Abdul Rahman Kampar Malaysia o Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norwayp Department of Technology Management SINTEF Digital Trondheim Norway q Keele Business School Keele University Keele UK r Indian Institute of Management Mumbai Mumbai India k Department of Management Symbiosis International (Deemed University) Pune India s College of Business and Economics Qatar University Doha Qatar t Business School Manchester Metropolitan University Manchester UK u Department of Marketing College of Business Administration Ajman university Ajman UAE v Graduate Institute of Management Chang Gung University Taoyuan Taiw

ABSTRACTIn a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

关键词： Generative artificial intelligence machine learning large language model ChatGPT Bard

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