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BER Improvement of Cooperative Spectrum Sharing of NOMA in 5G Network

BER Improvement of Cooperative Spectrum Sharing of NOMA in 5...

sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mohamed Hassan Rashid A. Saeed Rania A. Mokhtar Khalid Hamid Elmustafa Sayed Ali Nidhal Odeh Manwinder Singh Othman O. Khalifa Shayla Islam Department of Wireless Communication Lovely Professional University Phagwara Punjab India Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Communication Systems Engineering University of Science & Technology Omdurman Sudan Department of Electronics Engineering Sudan University of Science and Technologu (SUST) Khartoum Sudan School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia Department of Electrical and Computer Engineering International Islamic University Malaysia Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Department of Computer Science Institute of Computer Science and Digital Innovation UCSI University Malaysia Kuala Lumpur Malaysia

Non-orthogonal multiple access is one of the best methods for addressing the needs of 5G wireless services (NOMA). The proliferation of mobile devices in recent years has increased the importance of cooperative spectrum sharing and utilization in wireless communication due to increases in the bit error rate (BER) brought on by collisions and interference. In this study, MIMO and Massive MIMO (M-MIMO) in the downlink (DL) NOMA power domain (PD) in conjunction with the Cooperative Cognitive Radio Network are proposed as two new ways for improving and evaluating BER in the 5G network (CCRN). Customers of NOMA compete for available channels on the CCRN in this first strategy. The second approach creates a dedicated channel for NOMA users. Three scenarios with varying distances, power location coefficients, and transmission power are used to evaluate the proposed methods in the MATLAB software program. It is assumed that four users will share a 90 MHz BW using QPSK modulation in all three scenarios. When evaluating the performance of the suggested system under the presumption of frequency-selective Rayleigh fading, concurrent interference cancellation (SIC) and unstable channel conditions are also considered.

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Trends, Opportunities, and Challenges in Detecting Depressiv...

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computer System, Information Technology, and Electrical engineering (COSITE), International Conference on

作者： Gregorius Natanael Elwirehardja Mahmud Isnan Anzaludin Samsinga Perbangsa Kahlil Muchtar Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches.

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Graph Auto-Encoder Via Neighborhood Wasserstein Reconstruction

arXiv

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arXiv 2022年

作者： Tang, Mingyue Yang, Carl Li, Pan Department of Engineering Systems and Environment University of Virginia United States Department of Computer Science Emory University United States Department of Computer Science Purdue University United States

Graph neural networks (GNNs) have drawn significant research attention recently, mostly under the setting of semi-supervised learning. When task-agnostic representations are preferred or supervision is simply unavailable, the auto-encoder framework comes in handy with a natural graph reconstruction objective for unsupervised GNN training. However, existing graph auto-encoders are designed to reconstruct the direct links, so GNNs trained in this way are only optimized towards proximity-oriented graph mining tasks, and will fall short when the topological structures matter. In this work, we revisit the graph encoding process of GNNs which essentially learns to encode the neighborhood information of each node into an embedding vector, and propose a novel graph decoder to reconstruct the entire neighborhood information regarding both proximity and structure via Neighborhood Wasserstein Reconstruction (NWR). Specifically, from the GNN embedding of each node, NWR jointly predicts its node degree and neighbor feature distribution, where the distribution prediction adopts an optimal-transport loss based on the Wasserstein distance. Extensive experiments on both synthetic and real-world network datasets show that the unsupervised node representations learned with NWR have much more advantageous in structure-oriented graph mining tasks, while also achieving competitive performance in proximity-oriented ones. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Risk-Averse Self-Scheduling of Storage in Decentralized Markets

Risk-Averse Self-Scheduling of Storage in Decentralized Mark...

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IEEE General Meeting Power& Energy Society

作者： Ogun Yurdakul Farhad Billimoria Energy Systems and Infrastructure Analysis Division Argonne National Laboratory Lemont IL USA Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Department of Engineering Science University of Oxford Oxford United Kingdom

Storage is expected to be a critical source of firming in low-carbon grids. A common concern raised from ex-post assessments is that storage resources can fail to respond to strong price signals during times of scarcity. While commonly attributed to forecast error or failures in operations, we posit that this behavior can be explained from the perspective of risk-averse scheduling. Using a stochastic self-scheduling framework and real-world data harvested from the Australian National Electricity Market, we demonstrate that risk-averse storage resources tend to have a myopic operational perspective, that is, they typically engage in near-term price arbitrage and chase only few extreme price spikes and troughs, thus remaining idle in several time periods with markedly high and low prices. This has important policy implications given the non-transparency of unit risk aversion and apparent risk in intervention decision-making.

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Three-Filters-to-Normal+: Revisiting Discontinuity Discrimination in Depth-to-Normal Translation

arXiv

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arXiv 2023年

作者： Yang, Jingwei Xue, Bohuan Feng, Yi Wang, Deming Fan, Rui Chen, Qijun The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong SAR Hong Kong

This article introduces three-filters-to-normal+ (3F2N+), an extension of our previous work three-filters-to-normal (3F2N), with a specific focus on incorporating discontinuity discrimination capability into surface normal estimators (SNEs). 3F2N+ achieves this capability by utilizing a novel discontinuity discrimination module (DDM), which combines depth curvature minimization and correlation coefficient maximization through conditional random fields (CRFs). To evaluate the robustness of SNEs on noisy data, we create a large-scale synthetic surface normal (SSN) dataset containing 20 scenarios (ten indoor scenarios and ten outdoor scenarios with and without random Gaussian noise added to depth images). Extensive experiments demonstrate that 3F2N+ achieves greater performance than all other geometry-based surface normal estimators, with average angular errors of 7.85◦, 8.95◦, 9.25◦, and 11.98◦ on the clean-indoor, clean-outdoor, noisy-indoor, and noisy-outdoor datasets, respectively. We conduct three additional experiments to demonstrate the effectiveness of incorporating our proposed 3F2N+ into downstream robot perception tasks, including freespace detection, 6D object pose estimation, and point cloud completion. Our source code and datasets are publicly available at https://***/3F2Nplus. Note to Practitioners—The primary motivation behind this work arises from the need to develop a high-performing surface normal estimator for practical robotics and computer vision applications. While geometry-based surface normal estimators have been widely used in these domains, the existing solutions focus merely on discontinuity discrimination. To tackle this problem, this article introduces a plug-and-play module that leverages both depth curvature and correlation coefficient to quantify discontinuity levels, thereby optimizing surface normal estimation, particularly near or on discontinuous regions. Moreover, this article also introduces a large-scale public dataset wit

关键词： Gaussian noise (electronic)

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Exploring Cognitive Fatigue: Machine Learning, Forecasting, Generalization, Personalization, Inference and Labels

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2023年第1期67卷 978 - 979页

作者： David Nartey Rohith Karthikeyan Theodora Chaspari Ranjana Mehta Department of Industrial and Systems Engineering Texas A&M University College Station TX USA Department of Mechanical Engineering Texas A&M University College Station TX USA Department of Computer Science and Engineering Texas A&M University College Station TX USA

Fatigue is a feeling of tiredness or weakness that can be physical, mental or both. Interpretable machine learning methods can be used for inference while having the dimension of time series prediction for developing intelligent health systems to manage fatigue. The study explored forecasting perception and performance scores associated with fatigue manifestation using cardiac activity with generalized and personalized models at a forecast interval of 10-minutes during a cognitively fatiguing task. Participants underwent a 2-hour cognitively fatiguing working memory task with subjective fatigue responses obtained every 10-minutes. Participants’ performance was calculated over the 10-minute interval. Performance labeled models had the lowest mean absolute error for forecasting in both generalized and personalized models using Gradient Boosting Regression. This provides the ability to forecast performance decrease due to fatigue and to generate fatigue mitigation interventions to reduce fatigue-related injuries.

关键词： Machine Learning Cognitive fatigue Fatigue perception Performance Time-series prediction heart rate cardiac activity

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Bio-Inspired Optimization Algorithm on Cloud based Image Retrieval System using Deep Features

Bio-Inspired Optimization Algorithm on Cloud based Image Ret...

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Augmented Intelligence and Sustainable systems (ICAISS), International Conference on

作者： T. Vijayakumar K Ramalakshmi C. Priyadharsini S. Vasanthakumar Shaina Abhishek Sharma Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Alliance College of Engineering and Design Benglure Karnataka Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering GNA University Phagwara Punjab Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore Madhya Pradesh

ISBN: (纸本)9781665489638

With the tremendous improvement of cloud computing (CC) technique, increasing count of users choosing to outsource image data to the clouds. Cloud resource provider decreases the storing problem on local hardware by outsourcing massive image database to cloud server and exploiting the cloud computation ability to image processing application. Learning effective feature representation and similarity measures are essential for the performance of content-based image retrieval (CBIR). Despite wide-ranging research efforts for decades, it remains a challenge that significantly hinders the success of real-time CBIR system. Since the trail and error hyperparameter selection is a tedious process, metaheuristic optimization can be used. This paper introduces an Evolutionary Optimization Algorithm for Cloud Based Image Retrieval System (EOA-CIRS) technique. The presented EOA-CIRS technique derives a new CBIR model for retrieving highly related images. To obtain this, the EOA-CIRS technique initially employs MobileNetv3 model as a feature extractor, which derives features from the query and database images. Next, the bamboo forest growth optimizer (BFGO) approach was applied as a hyperparameter optimizer to appropriately tune the hyperparameters of the MobileNetv3 approach. Finally, Euclidean distance based similarity measurement is utilized for retrieving the related images. The experimental validation of the EOA-CIRS model on Corel10K dataset demonstrates the effectual efficacy of the EOA-CIRS model over other recent approaches.

关键词： Cloud computing Biological system modeling Computational modeling Image retrieval Forestry Euclidean distance Feature extraction

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Dual Approach in Autonomous Directional Drilling: Innovations with Drillbotics 1.5 Inch Automated RSS and Virtual Rig Platforms

Dual Approach in Autonomous Directional Drilling: Innovation...

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2025 SPE/IADC International Drilling Conference and Exhibition, DC 2025

作者： Soilemezidis, C. Mensah, J.S. Hollstein, W. Al Maasarani, K. Alkhawaja, A. Jamali, F. Dörfler, G. Hölzner, N. Jarbouh, K.A. Ustaoglu, D. Alukkal, A.T. Gudhane, L.A.Y. Jaeger, P. Feldmann, E. Carvajal, C.A.P. Yin, A.O. Sinkuu, L. Quayson, J. Amoah, F.B. Yahaya, H.S. Owusu, K.A. Dongoyo, C.K.N. Owusu, A. Amorin, R. Florence, F. Institute of Subsurface Energy Systems Clausthal University of Technology Lower Saxony Clausthal-Zellerfeld Germany Department of Petroleum and Natural Gas Engineering University of Mines and Technology Tarkwa Ghana Institute of Mechanical Engineering Clausthal University of Technology Lower Saxony Clausthal-Zellerfeld Germany Institute of Economics Clausthal University of Technology Lower Saxony Clausthal-Zellerfeld Germany Institute of Informatics Clausthal University of Technology Lower Saxony Clausthal-Zellerfeld Germany Department of Electrical and Electronics Engineering University of Mines and Technology Tarkwa Ghana Department of Computer Science and Engineering University of Mines and Technology Tarkwa Ghana Department of Petroleum Geoscience University of Mines and Technology Tarkwa Ghana Rig Operations AustinTX United States

ISBN: (纸本)9781959025641

SPE’s Drilling systems Automation Technical Section (DSATS) sponsors the Drillbotics® autonomous drilling contest to promote advanced learning at participating universities. Two groups worked separately to drill a directional well hands-free and to connect to a well model to detect/close in a kick. This paper consolidates the efforts of both teams. Team A, the directional drilling team from the University of Mines and Technology (UMaT) in Ghana, developed a virtual model that replicated full-scale directional drilling operations, facilitating the development and testing of innovative control schemes and automation strategies. Leveraging state-of-the-art drilling software, real-time data analytics, and advanced control systems, the project demonstrated the potential of automated drilling technologies across both the planning and execution phases of drilling projects. Team B, from Technical University Clausthal (TUC) in Germany, designed and built a mini-rig that included a working scaled version of a rotary steerable system (RSS) tool to steer a wellbore through a block of sandstone to reach two non-planer targets. The work incorporates machine learning techniques to evaluate downhole sensor data and improve steering signal input. The modular design of the second-generation surface actuation system offers a more compact and efficient solution than its predecessor. The control architecture and network protocols are analyzed, demonstrating network delays of under 2 msec using a refined second-generation translator setup. The achievements of both teams are important to the future of drilling automation, as their approaches could be used to verify and validate third-party app’s communications prior to field release on full-scale rigs. Copyright 2025, SPE/IADC International Drilling Conference and Exhibition.

关键词： Directional drilling

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New Decision-Making Technique Based on Hurwicz Criteria for Fuzzy Ranking

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computers, Materials & Continua 2022年第12期73卷 4595-4609页

作者： Deepak Sukheja Javaid Ahmad Shah G.Madhu K.Sandeep Kautish Fahad A.Alghamdi Ibrahim.S.Yahia El-Sayed M.El-Kenawy Ali Wagdy Mohamed Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia Department of Mathematics Dr.APJ Abdul Kalam University Indore(M.P)India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia LBEF Campus Kathmandu44600Nepal Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Laboratory of Nano-Smart Materials for Science and Technology(LNSMST) Department of PhysicsFaculty of ScienceKing Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Research Center for Advanced Materials Science(RCAMS) King Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Nanoscience Laboratory for Environmental and Biomedical Applications(NLEBA) Semiconductor Lab.Department of PhysicsFaculty of EducationAin Shams UniversityRoxyCairo 11757Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Sciences Engineering The American University in CairoEgypt

Efficient decision-making remains an open challenge in the research community,and many researchers are working to improve accuracy through the use of various computational *** this case,the fuzzification and defuzzification processes can be very *** is an effective process to get a single number from the output of a fuzzy *** defuzzification as a center point of this research paper,to analyze and understand the effect of different types of vehicles according to their *** this paper,the multi-criteria decision-making(MCDM)process under uncertainty and defuzzification is discussed by using the center of the area(COA)or ***,to find the best solution,Hurwicz criteria are used on the defuzzified ***-making technique is proposed using Hurwicz criteria for triangular and trapezoidal fuzzy *** proposed technique considers all types of decision makers’perspectives such as optimistic,neutral,and pessimistic which is crucial in solving decisionmaking problems.A simple case study is used to demonstrate and discuss the Centroid Method and Hurwicz Criteria for measuring risk attitudes among *** significance of the proposed defuzzification method is demonstrated by comparing it to previous defuzzification procedures with its application.

关键词： Defuzzification decision-making fuzzy numbers Hurwicz multicriteria decision-making ranking order

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Problem-Solving Using Logic and Reasoning, Mathematics, Algorithms, Python and Generative AI

Problem-Solving Using Logic and Reasoning, Mathematics, Algo...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Weizheng Gao Julian D. Allagan Shanzhen Gao Jianning Su Olumide Malomo Ephrem Eyob Adeyemi Adekoya Department of Mathematics Computer Science and Engineering Technology Elizabeth City State University Elizabeth City NC USA Department of Mathematics Computer Science and Engineering Perimeter College Georgia State University Atlanta GA USA Department of Computer Information Systems Reginald F Lewis College of Business Virginia State University Petersburg VA USA

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Problem-solving is essential in various fields, including business, technology, and everyday life. It often involves a combination of experience, knowledge, intuition, and rational analysis. Furthermore, it requires integrating disciplines such as logic and reasoning, mathematics, algorithms, Python, and generative AI in today's complex world. We will provide detailed descriptions of how to solve problems based on integrating previously mentioned disciplines. Later, we will discuss how to guide students through making intelligent investment decisions.

关键词： Generative AI Scientific computing Programming Mathematics Cognition Problem-solving Logic

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