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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Alozie, Emmanuel Faruk, Nasir Abdulkarim, Abubakar Usman, Aliyu D. Imam-Fulani, Yusuf Olayinka Garba, Salisu Adewole, Kayode S. Oloyede, Abdulkarim A. Sowande, Olugbenga A. Baba, Bashir Abdullahi Musa, Abdulwaheed Imoize, Agbotiname Lucky Adediran, Yinusa A. Olagunju, Hawau I. Taura, Lawan S. Sule Lamido University Department of Information Technology Kafin Hausa Nigeria Sule Lamido University Directorate of Information and Communication Technology Kafin Hausa Nigeria Ahmadu Bello University Department of Electrical Engineering Nigeria Kampala International University Department of Electrical and Telecommunications Engineering Kansanga Kampala Uganda Ahmadu Bello University Department of Electronics and Telecommunication Engineering Nigeria University of Ilorin Department of Telecommunication Science Ilorin Nigeria Sule Lamido University Department of Software Engineering Kafin Hausa Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria Sule Lamido University Department of Cyber Security Kafin Hausa Nigeria Kwara State University Department of Electrical and Computer Engineering Malete Nigeria University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Lagos Department of Electrical and Electronics Engineering Lagos Akoka Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum Germany University of Ilorin Department of Electrical and Electronics Engineering Ilorin Nigeria Sule Lamido University Department of Physics Kafin Hausa Nigeria

ISBN: (纸本)9798350358834

Signal propagation in a particular region differs from another due to differences in atmospheric, climatic and environmental properties, distinct terrain and clutter features. Adequate analysis is essential to understand the radio propagation behavior in a particular region. The ITU-R designated four rain regions, M, N, P, and Q, for Nigeria representing the rain rate distribution and also provided further classifications based on ground conductivity, among other salient parameters. Based on these classifications, this paper utilized the EDX Signal Pro software® to model and simulate a typical Point-to-Point (P2P), Non-Line of Sight (NLOS) link scenarios for 5G networks. The objective was to estimate and compare the total loss, excess loss, and flat fade margins for each rain region in Nigeria. Results obtained from the comparison showed that signals propagating in region N experience the highest level of losses and fading, while, region Q has the least losses and fading. © 2023 IEEE.

关键词： 5G mobile communication systems

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Field Measurements and Energy Performance Modelling of Water Side Free Cooling System for Datacenters

Field Measurements and Energy Performance Modelling of Water...

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East-West Design & Test Symposium (EWDTS)

作者： Hande İlkor Gül Nihal Güğül Mustafa Kemerci Tugğba Güneş Department of Computer Engineering Faculty of Technology Institute of Science Selcuk University Konya Turkey Department of Computer Engineering Faculty of Technology Selcuk University Konya Turkey Information Processing Department Republic of Turkey Ministry of Environment Urbanization and Climate Change General Directorate of Land Registry and Cadastre Ankara Türkiye Cadastre Department Republic of Turkey Ministry of Environment Urbanization and Climate Change General Directorate of Land Registry and Cadastre Ankara Türkiye

ISBN: (数字)9798331515768

ISBN: (纸本)9798331515775

In recent years, number of free cooling system (FCS) applied in datacenters (DC) is increasing due to the provided significant decrease in the electricity consumption and short payback period. However, since FCS is a partially new technology, energy savings potential of FCS has not been sufficiently investigated using real time data series in the literature. In this study, the field measurements and energy performance analysis were performed in an operational DC with water-side free cooling, located in Ankara, Türkiye. Mathematical model of FCS is developed using electricity consumption data of IT devices and FCS, and climate data of Ankara using MS Excel. Electricity consumption of FCS and the PUE of the DC in case of installation of the existing DC in six different climate regions of Turkey were calculated using the developed model. In case of using the FCS, PUE value is obtained less than 1.1 for each location. The result shows that, very low temperatures are not required for the FCS, and the important factor is the number of hours that is lower than the indoor temperature of DC. In case of installation of the DC in Antalya and Şanlıurfa, which are among hottest cities of Türkiye, the electricity consumption of the FCS increased by 8 % and 9 %. However, since the energy consumption of FCS is still much lower than the IT consumption compared to the old type cooling systems, the use of the FCS provided adequate saving.

关键词： Temperature measurement Performance evaluation Analytical models Temperature dependence Cooling Electricity Atmospheric modeling Energy measurement Mathematical models Meteorology

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Investigation of the Impact of Height and Bandwidth on Path Loss Exponent of 5G FR1 Wireless Networks at 800, 3500, and 5900 MHz 2

Investigation of the Impact of Height and Bandwidth on Path ...

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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Imam-Fulani, Yusuf Olayinka Faruk, Nasir Usman, Aliyu D. Abdulkarim, Abubakar Tekanyi, Abdoulie M.S Yaro, Abdulmalik S. Alozie, Emmanuel Garba, Salisu Adewole, Kayode S. Oloyede, Abdulkarim A. Sowande, Olugbenga A. Abdullahi Baba, Bashir Musa, Abdulwaheed Imoize, Agbotiname Lucky Adediran, Yinusa A. Olagunju, Hawau I. Taura, Lawan S. University of Ilorin Department of Telecommunication Science Ilorin Nigeria Ahmadu Bello University Department of Electronics and Telecommunication Engineering Zaria Nigeria Sule Lamido University Department of Information Technology Kafin Hausa Nigeria Sule Lamido University Directorate of Information and Communication Technology Kafin Hausa Nigeria Ahmadu Bello University Department of Electrical Engineering Zaria Nigeria Kampala International University Department of Electrical and Telecommunication Engineering Kampala Uganda University of Hradec Králové Faculty of Informatics and Management Department of Informatics and Qualitative Methods Hradec Králové Czech Republic Sule Lamido University Department of Software Engineering Kafin Hausa Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria Kwara State University Department of Electrical and Computer Engineering Malete Nigeria University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Lagos Department of Electrical and Electronics Engineering Lagos Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum Germany University of Ilorin Department of Electrical and Electronics Engineering Ilorin Nigeria Sule Lamido University Department of Physics Kafin Hausa Nigeria

ISBN: (纸本)9798350358834

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency Range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond. © 2023 IEEE.

关键词： 5G mobile communication systems

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AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning

AZTR: Aerial Video Action Recognition with Auto Zoom and Tem...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Xijun Wang Ruiqi Xian Tianrui Guan Celso M. de Melo Stephen M. Nogar Aniket Bera Dinesh Manocha Dept. of Computer Science University of Maryland College Park MD USA Dept. of Electrical and Computer Engineering University of Maryland College Park MD USA Computational and Information Sciences Directorate DEVCOM U.S. Army Research Laboratory Adelphi MD USA Dept. of Computer Science Purdue University West Lafayette IN USA

We propose a novel approach for aerial video action recognition. Our method is designed for videos captured using UAVs and can run on edge or mobile devices. We present a learning-based approach that uses customized auto zoom to automatically identify the human target and scale it appropriately. This makes it easier to extract the key features and reduces the computational overhead. We also present an efficient temporal reasoning algorithm to capture the action information along the spatial and temporal domains within a controllable computational cost. Our approach has been implemented and evaluated both on the desktop with high-end GPUs and on the low power Robotics RB5 Platform for robots and drones. In practice, we achieve $6.1-7.4 \%$ improvement over SOTA in Top-1 accuracy on the RoCoG-v2 dataset, 8.3-10.4% improvement on the UAV-Human dataset and 3.2% improvement on the Drone Action dataset.

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AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning

arXiv

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arXiv 2023年

作者： Wang, Xijun Xian, Ruiqi Guan, Tianrui de Melo, Celso M. Nogar, Stephen M. Bera, Aniket Manocha, Dinesh Dept. of Computer Science University of Maryland College ParkMD United States The Dept. of Electrical and Computer Engineering University of Maryland College ParkMD United States The Computational and Information Sciences Directorate DEVCOM U.S. Army Research Laboratory AdelphiMD United States The Dept. of Computer Science Purdue University West LafayetteIN United States

We propose a novel approach for aerial video action recognition. Our method is designed for videos captured using UAVs and can run on edge or mobile devices. We present a learning-based approach that uses customized auto zoom to automatically identify the human target and scale it appropriately. This makes it easier to extract the key features and reduces the computational overhead. We also present an efficient temporal reasoning algorithm to capture the action information along the spatial and temporal domains within a controllable computational cost. Our approach has been implemented and evaluated both on the desktop with high-end GPUs and on the low power Robotics RB5 Platform for robots and drones. In practice, we achieve 6.1-7.4% improvement over SOTA in Top-1 accuracy on the RoCoG-v2 dataset, 8.3-10.4% improvement on the UAV-Human dataset and 3.2% improvement on the Drone Action dataset. © 2023, CC BY.

关键词： Drones

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Retraction Note: Resnet-based deep learning multilayer fault detection model-based fault diagnosis

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Multimedia Tools and Applications 2024年第38期83卷 86323-86324页

作者： Jaber, Mustafa Musa Ali, Mohammed Hasan Abd, Sura Khalil Jassim, Mustafa Mohammed Alkhayyat, Ahmed Majid, Mohammed Sh. Alkhuwaylidee, Ahmed Rashid Alyousif, Shahad Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Department of Computer Science Al-Turath University College Baghdad Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Najaf Iraq Directorate of Research and development Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq College of Technical Engineering The Islamic University Najaf Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Computer Technical Engineering Mazaya University College Thi Qar Iraq Department of Electrical and Electronic Engineering College of Engineering Gulf University Sanad Kingdom of Bahrain Research Centre University of Mashreq Baghdad Iraq

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An Enhanced Deep Neural Network Enabled with Cuckoo Search Algorithm for Intrusion Detection in Wide Area Networks

An Enhanced Deep Neural Network Enabled with Cuckoo Search A...

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information Technology for Education and Development (ITED)

作者： Rasheed Gbenga Jimoh Agbotiname Lucky Imoize Joseph Bamidele Awotunde Stephen Ojo Mulikat Bola Akanbi Jesufemi Ayotomide Bamigbaye Nasir Faruk Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Department of Electrical and Electronics Engineering University of Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Electrical and Computer Engineering Anderson University Anderson SC USA Department of Computer Science Kwara State Polytechnic Ilorin Nigeria Department of Physics Sule Lamido University Nigeria Directorate of ICT SLU Nigeria

A diversity of harmful software has been created as a result of the dramatic increase in internet usage, posing major risks to computer security. There is a great probability that the numerous computing operations performed through the network will be interfered with or altered, and as a result, effective intrusion detection systems are imperative. In addition, the attacks on the network are unpredictable, something that emphasizes the value of creating effective classification and prediction models. Machine learning (ML) and Deep Learning techniques have been used to evaluate datasets for intrusion detection systems (IDS). The employment of the DL-based approach enabled by feature selection helps to address challenges with data quality, handling high-dimensional data, and other related issues. Therefore, due to the large nature and volume of the IDS datasets, and the ability of DL-based models to learn categories incrementally through their hidden layer architecture to produce more accurate results in big data, this study proposes a Long-Short-Term-Memory (LSTM) model, and to further enhance the classification capacity of the projected DL method, the cuckoo search algorithm was introduced to select optimal features from the wireframe. The accuracy and subsequent detection of the suggested model positive and negative rates were evaluated. The experimental results show that the LSTM outperformed some other existing models with the highest classification accuracy of 99.7% and an error rate of 0.006.

关键词： Deep learning Wide area networks Training Adaptation models Solid modeling Telecommunication traffic Feature extraction

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VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics

arXiv

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arXiv 2022年

作者： Karnan, Haresh Sikand, Kavan Singh Atreya, Pranav Rabiee, Sadegh Xiao, Xuesu Warnell, Garrett Stone, Peter Biswas, Joydeep The University of Texas at Austin Department of Mechanical Engineering United States The University of Texas at Austin Department of Computer Science United States Sony AI United States Computational and Information Sciences Directorate Army Research Laboratory United States

One of the key challenges in high-speed off-road navigation on ground vehicles is that the kinodynamics of the vehicle-terrain interaction can differ dramatically depending on the terrain. Previous approaches to addressing this challenge have considered learning an inverse kinodynamics (IKD) model, conditioned on inertial information of the vehicle to sense the kinodynamic interactions. In this paper, we hypothesize that to enable accurate high-speed off-road navigation using a learned IKD model, in addition to inertial information from the past, one must also anticipate the kinodynamic interactions of the vehicle with the terrain in the future. To this end, we introduce Visual-Inertial Inverse Kinodynamics (VI-IKD), a novel learning based IKD model that is conditioned on visual information from a terrain patch ahead of the robot in addition to past inertial information, enabling it to anticipate kinodynamic interactions in the future. We validate the effectiveness of VI-IKD in accurate high-speed off-road navigation experimentally on a scale 1/5 UT-AlphaTruck off-road autonomous vehicle in both indoor and outdoor environments and show that compared to other state-of-the-art approaches, VI-IKD enables more accurate and robust off-road navigation on a variety of different terrains at speeds of up to 3.5m/s. © 2022, CC BY.

关键词： Navigation

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On the Robustness of AlphaFold: A COVID-19 Case Study

arXiv

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arXiv 2023年

作者： Alkhouri, Ismail Jha, Sumit Beckus, Andre Atia, George Velasquez, Alvaro Ewetz, Rickard Ramanathan, Arvind Jha, Susmit Electrical & Computer Engineering Department University of Central Florida OrlandoFL32816 United States Computer Science Department University of Texas at San Antonio TX78249 United States Information Directorate Air Force Research Laboratory RomeNY13441 United States Defense Advanced Research Projects Agency ArlingtonVA22203 United States Data Science and Learning Argonne National Laboratory LemontIL60439 United States Computer Science Laboratory SRI International Menlo ParkCA94709 United States

Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks has heretofore not been explored. This is particularly relevant given the broad social implications of such technologies and the fact that biologically small perturbations in the protein sequence do not generally lead to drastic changes in the protein structure. In this paper, we demonstrate that AlphaFold does not exhibit such robustness despite its high accuracy. This raises the challenge of detecting and quantifying the extent to which these predicted protein structures can be trusted. To measure the robustness of the predicted structures, we utilize (i) the root-mean-square deviation (RMSD) and (ii) the Global Distance Test (GDT) similarity measure between the predicted structure of the original sequence and the structure of its adversarially perturbed version. We prove that the problem of minimally perturbing protein sequences to fool protein folding neural networks is NP-complete. Based on the well-established BLOSUM62 sequence alignment scoring matrix, we generate adversarial protein sequences and show that the RMSD between the predicted protein structure and the structure of the original sequence are very large when the adversarial changes are bounded by (i) 20 units in the BLOSUM62 distance, and (ii) five residues (out of hundreds or thousands of residues) in the given protein sequence. In our experimental evaluation, we consider 111 COVID-19 proteins in the Universal Protein resource (UniProt), a central resource for protein data managed by the European Bioinformatics Institute, Swiss Institute of Bioinformatics, and the US Protein information Resource. These result in an overall GDT similarity test score average of around 34%, demonstrating a substantial drop in the performance of AlphaFold. Copyright © 2023, The Authors. All rights reserved.

关键词： Protein folding

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Towards Automated Multiclass Severity Prediction Approach for COVID-19 Infections Based on Combinations of Clinical Data

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Mobile information Systems 2022年第1期2022卷

作者： Dinar, Ahmed M. Raheem, Enas A. Abdulkareem, Karrar Hameed Mohammed, Mazin Abed Oleiwie, Marwan Ghazi Zayr, Fawzi Hasan Al-Boridi, Omar Al-Mhiqani, Mohammed Nasser Al-Andoli, Mohammed Nasser Computer Engineering Department University of Technology Baghdad Iraq College of Agriculture Al-Muthanna University Samawah66001 Iraq College of Computer Science and Information Technology University of Anbar Anbar31001 Iraq Azizia Primary Health Care Sector Wasit Health Directorate Ministry of Health Al Aziziyah Saudi Arabia Department of Biochemistry College of Medicine University of Wasitc Wasit Iraq School of Engineering RMIT University Melbourne Australia Center for Advanced Computing Technology Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Malacca Malaysia Computer Science and Information Systems Department Faculty of Science Sa'Adah University Sa'adah Yemen

The recent dramatic expansion of the COVID-19 outbreak is placing enormous strain on human society as a whole. Numerous biomarkers are being investigated in an effort to track the condition of the patient. This could interfere with signs of many other illnesses, making it more difficult for a specialist to diagnose or predict the severity level of the case. As a result, the focus of this research was on the development of a multiclass prediction system capable of dealing with three severity cases (severe, moderate, and mild). The lymphocyte to CRP ratio (C-reactive protein blood test) and SpO2 (blood oxygen saturation level) indicators were ranked and used as prediction system attributes. A machine learning model based on SVMs is created. A total of 78 COVID-19 patients were recruited from the Azizia primary health care sector/Wasit Health directorate/Ministry of Health to form different combinations of COVID-19 clinical dataset. The outcomes demonstrate that the proposed approach had an average accuracy of 82%. The established prediction system allows for the early identification of three severity cases, which reduces deaths. © 2022 Ahmed M. Dinar et al.

关键词： COVID-19

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