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Improved Dynamic Collision Recovery in Wireless Ad-Hoc Networks: System and RTL Modeling 14

Improved Dynamic Collision Recovery in Wireless Ad-Hoc Netwo...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Murad, Hussam Newagy, Fatma El-Kharashi, M. Watheq Ain Shams University Computer and Systems Eng. Dept. Cairo Egypt Ain Shams University Electronics and Communication Eng. Dept. Cairo Egypt

ISBN: (纸本)9798350335095

Wireless ad-hoc networks are local area networks that are built without a centralized administration point. They are widely utilised in many applications, particularly those involving the Internet of Things (IoT), such as environmental monitoring, smart homes, smart agriculture, and many other IoT applications. However, when the network's size grows, network throughput rapidly declines, mainly because of more collisions. According to the IEEE 802.11 standard, the carrier sense multiple access with collision avoidance protocol is the basic medium access control technique used by the 802.11 standard, also known as the distributed coordination function. It uses the Binary Exponential Backoff (BEB) algorithm for collision avoidance. Avoiding collisions will optimize the network with regard to throughput. This paper proposes an improved dynamic backoff algorithm called Dynamic Enhanced Collision Resolution Algorithm (D-ECRA), which improves the recently introduced Enhanced Collision Resolution Algorithm (ECRA) by replacing the contention window exponential increment with a less aggressive and dynamic contention window increment based on the network's size. The Register Transfer Level (RTL) design of D-ECRA is proposed using Verilog HDL. Results demonstrate that D-ECRA surpasses BEB and ECRA with regard to throughput with a maximum improvement percentage of 4% and 1.2% respectively. Also, results showed that D-ECRA performed better than ECRA with regard to delay with a maximum improvement percentage of 43%. This thesis shows the assessment process for D-ECRA including simulation parameters and test scenarios for both system and RTL modelling. © 2023 IEEE.

关键词： Ad hoc networks

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Traffic Jam Detection Using Real-Time Bus Operation Data Considering Timetable Information in Various Conditions 18

Traffic Jam Detection Using Real-Time Bus Operation Data Con...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Hatanka, Nozomi Aoyagi, Hiroki Fujita, Tomoya Yamana, Hayato Oguchi, Masato Ochanomizu University Dept. of Information Sciences Tokyo Japan Waseda University Dept. of Computer Sci. and Eng. Tokyo Japan Waseda University Fuculty of Sci. and Eng. Tokyo Japan

ISBN: (纸本)9798350331011

Because of the significant losses caused by traffic congestion, the detection of traffic congestion is an urgent issue for reducing such losses. In this study, we propose a model that performs a binary classification of two consecutive bus stops as one section, using the bus speed calculated from the bus departure time, the time of day, and the difference between the time the bus actually leaves the bus stop and the time it arrives. Two types of learning were performed, one with a single learner in the system and one with a single learner each time period, with better results when one learner was placed in the system. © 2024 IEEE.

关键词： Buses

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Towards Web-Based Automation: A Comparative Analysis of Feature Extraction Approaches and Applications for Quality Control 9

Towards Web-Based Automation: A Comparative Analysis of Feat...

引用

9th International Conference on Web Research, ICWR 2023

作者： Cheraghi, Shiva Chalechale, Abdolah Safari, Faezeh Razi University Dept. of Computer Eng. and Info. Tech Kermanshah Iran

ISBN: (纸本)9798350399691

In recent years, with the development of science and technology, the emerging competitive markets in various industries, and the need for quality control, quantitative and qualitative measurements of products' properties have become significantly important. Quality production is the most vital part of a production line in a way that a few factories exist, with some of their sectors not being controlled by intelligent computer Vision (CV) and image processing systems. Accordingly, Real-time quality management can increase production efficiency. Moreover, quality management focuses on customer experience with agents and products. In this regard, Sentiment Analysis (SA) can also determine the success rate of services and products, identify customers' thoughts, and mirror their true voice. This paper aims to conduct a comparative analysis of feature extraction approaches used by CV and Natural Language Processing (NLP) for quality control of products and SA in the industry. These approaches and trends can further be integrated into Web-based quality control systems in industry. Therefore, this paper also investigates how such technologies can enhance quality control through large-scale textual and image data analytics. © 2023 IEEE.

关键词： Quality control

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Large Language Models versus Natural Language Understanding and Generation 27

Large Language Models versus Natural Language Understanding ...

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27th Pan-Hellenic Conference on Progress in Computing and Informatics with International Participation, PCI 2023

作者： Karanikolas, Nikitas Manga, Eirini Samaridi, Nikoletta Tousidou, Eleni Vassilakopoulos, Michael Dept. of Informatics and Computer Engineering University of West Attica Athens Greece Dept. of Electrical and Computer Eng. University of Thessaly Volos Greece

ISBN: (纸本)9798400716263

In recent years, the process humans adopt to learn a foreign language has moved from the strict "Grammar -Translation"method, which is based mainly on grammar and syntax rules, to more innovative processes, resulting to the more modern "Communicative approach". As its name states, this approach focuses on the coherent communication with native speakers and the cultivation of oral skills, without taking into consideration, at least at the first stages, the rules that govern the language. The same trend seems to have been applied to the way machinery can be "educated"to comprehend and reproduce the unfamiliar, human language. The "rule based"Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms, on one hand, and the "text based"Large Language Models (LLMs), on the other, are two, analogous to the two human foreign language learning processes, subareas of Natural Language Processing (NLP). This paper presents these two alternative approaches, LLMs (a technology having surfaced as an influential catalyst of NLP, during last years) on the one hand and NLG/NLU on the other, highlighting their applications, their technologies, their capabilities, their differences, their streng.hs and weaknesses and the challeng.s they present, contributing to a deeper comprehension of the evolving landscape of Artificial Intelligence and human-computer communication. © 2023 Owner/Author.

关键词： Natural language processing systems

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Three Coupling Models Compared in a Distributed Port

Three Coupling Models Compared in a Distributed Port

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2024 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2024

作者： Dhombridge, E.A.D. Hussey, T.W. Peng. Z.V. Bremner, P. Schamiloglu, E. Sandia National Laboratories ABQNM United States Unm Dept. of Electrical and Computer Eng. ABQNM United States Uiuc Dept. of Electrical and Computer Eng. Urbana-ChampaignIL United States RobustPhysics San DiegoCA United States

ISBN: (纸本)9798350360394

It is well known that electromagnetic fields in overmoded high Q cavities are difficult to characterize deterministically because of extreme sensitivity to small variations in boundary conditions. The acoustics community first explored a variant of this problem where, in the highly overmoded limit, the distribution of pressure within a room could be characterized statistically. © 2024 IEEE.

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Environmental Monitoring with Machine Learning 1

Environmental Monitoring with Machine Learning

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sikarwar, Shailendra Singh Kumar, Sunil Meghwal, Lakshita Varshney, Manoj Roy, Swapnila Kumar, Ravi PGV College Dept. of Computer Science Gwalior India Madhav University Dept. of Pharmacy Pindwara India Mangalayatan University Dept. of Computer Eng. and Applications Aligarh India Lingayas Vidyapeeth Dept. of Chemistry Faridabad India J.S. University Dept. of Agriculture Shikohabad India

ISBN: (纸本)9798350356816

Data science is poised to transform environmental monitoring by boosting the precision, scope, and effectiveness of environmental assessments. Emerging technologies, including powerful machine learning algorithms, big data analytics, and remote sensing, are offering unprecedented capabilities in acquiring and evaluating environmental data. These developments permit more precise projections of ecological changes, real-time monitoring through vast sensor networks, and thorough climate modeling. Looking ahead, data science will drive creative approaches to environmental management, enabling tools for proactive decision-making and more effective resource management. The democratization of data science tools will also broaden stakeholder eng.gement, enabling communities and policymakers alike. By harnessing these future breakthroughs, data science has the prospect addressing important environmental concerns and creating sustainable solutions for a healthy planet. © 2024 IEEE.

关键词： Environmental monitoring

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Advanced Particle Swarm Optimization: An Innovative Approach Towards Addressing Constrained Optimization Challeng.s

Advanced Particle Swarm Optimization: An Innovative Approach...

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2024 IEEE International Women in eng.neering (WIE) Conference on Electrical and computer eng.neering, WIECON-ECE 2024

作者： Hasan, Ashik Sarker, Sowmik Bhowmik, Angkita Ahmed, Pathik Chowdhury, Abdullahi Islam, Mohammad Manzurul Khulna University of Eng. and Tech. Dept. of Electrical and Electronic Eng. Khulna9203 Bangladesh Khulna University of Eng. and Tech. Dept. of Urban and Regional Planning Khulna9203 Bangladesh University of South Australia UniSA Online Adelaide Australia East West University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331535476

A refined version of the traditional particle swarm optimization (PSO) method for handling constraint-based optimization is described in this paper. Numerous academic fields, including economics, finance, a nd eng.neering, frequently encounter bounded optimization challeng.s. The presence of constraints that restrict the possible search area makes solving these problems particularly difficult. This study reviews various approaches to handling constraints in metaheuristic optimization algorithms, with a focus on particle swarm optimization (PSO). The proposed PSO approach incorporates a mutation operator to enhance the diversity of the search process and a penalty function to manage constraints effectively. Several benchmark problems from the literature are used to assess the performance of the proposed technique. The outcomes are compared with those of various PSO variations and conventional optimization algorithms, demonstrating competitive performance by improving both convergence speed and solution quality. The analysis reveals that, in terms of convergence time and solution quality, the proposed method outperforms previous PSO variations and compares favorably with state-of-the-art optimization techniques. With potential applications across numerous fields, the proposed PSO method represents a promising approach to solving constraint-based optimization problems. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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An IoT-Based Hardware System for Road Accident Detection and Notification Using CNN-LSTM Algorithm 3

An IoT-Based Hardware System for Road Accident Detection and...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Chatterjee, Tanusree Dalui, Shibasish Roy, Sanjib Debnath, Rituraj Mondal, Avijit Future Institute of Technology Dept. of Computer Sc. & Eng. Kolkata India Kolkata India Kolkata India

ISBN: (纸本)9798350374643

According to the World Health Organization (WHO), approximately 1.3 million people die each year globally, due to road accidents and the deaths have increased by 17% in 2021 compared to 2020. Fatalities and injuries caused by the accidents are now a matter of great concern. There are certain causes behind this but the most crucial issue is not receiving medical emergency services at time which eventually leads to a dead-end situation. In this scenario, a complete solution for automatic accident detection and notification is tile most needed. This does not only help to save millions of lives rather can create a positive impact on society. In this paper, we aim to develop an Internet of Things (IoT) based hardware system that can be installed in a car as an auxiliary device to collect data, detect an accident, and deliver notifications of the same with the exact location, to the relatives and the nearest emergency service providers. The device is composed of Raspberry Pi along with other sensors like an Accelerometer, Gyroscope, Global Positioning System (GPS), and also a Global System for Mobile Communications (GSM) module. To analyze and classify the collected data, the Convolution Neural Network and Long Short-Term Memory (CNN-LSTM) hybrid machine learning model is proposed. The model is trained with a time-series dataset and it predicts the occurrence with 92 % accuracy. The comparative performance of the proposed framework claims its supremacy over other models in terms of Accuracy, Precision, Recall, and F1-Scorp. © 2024 IEEE.

关键词： Global positioning system

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Modeling Lunar Surface Charging Using Physics-Informed Neural Networks

Modeling Lunar Surface Charging Using Physics-Informed Neura...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Zendehdel, Niloofar Mosharrof, Adib Delgado, Katherine Han, Daoru Liang, Xin Shu, Tong Missouri University of Science & Technology Mechanical & Aerospace Eng. Dept. Rolla United States University of Kentucky Dept. of Computer Science Lexington United States Missouri University of Science & Technology Mechanical & Aerospace Engr. Dept. Rolla United States University of North Texas Dept. of Computer Science & Engr. Denton United States

ISBN: (纸本)9798350362480

Modeling the electric potential profile above the lunar surface is critical for understanding surface charging and interactions with the space environment. Traditional methods like Particle-in-Cell (PIC) simulations are highly accurate but computationally expensive. To address this, we propose a hybrid approach using a Multi-Layer Perceptron (MLP) architecture in both data-driven neural networks and Physics-Informed Neural Networks (PINNs). The PINN component incorporates physical laws directly into the training process, ensuring physical consistency, while the data-driven component captures complex patterns. This combination offers a significant reduction in computational cost compared to PIC methods while maintaining high modeling accuracy. Our results show that the proposed method effectively represents the electric potential profile above the lunar surface, even with limited data. © 2024 IEEE.

关键词： Multilayer neural networks

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Solving Audio Inverse Problems with a Diffusion Model 48

Solving Audio Inverse Problems with a Diffusion Model

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Moliner, Eloi Lehtinen, Jaakko Välimäki, Vesa Aalto University Acoustics Lab Dept. of Information & Communications Eng. Espoo Finland Aalto University Dept. of Computer Science Espoo Finland

ISBN: (纸本)9781728163277

This paper presents CQT-Diff, a data-driven generative audio model that can, once trained, be used for solving various different audio inverse problems in a problem-agnostic setting. CQT-Diff is a neural diffusion model with an architecture that is carefully constructed to exploit pitch-equivariant symmetries in music. This is achieved by preconditioning the model with an invertible Constant-Q Transform (CQT), whose logarithmically-spaced frequency axis represents pitch equivariance as translation equivariance. The proposed method is evaluated with solo piano music, using objective and subjective metrics in three different and varied tasks: audio bandwidth extension, inpainting, and declipping. The results show that CQT-Diff outperforms the compared baselines and ablations in audio bandwidth extension and, without retraining, delivers competitive performance against modern baselines in audio inpainting and declipping. This work represents the first diffusion-based general framework for solving inverse problems in audio processing. © 2023 IEEE.

关键词： Audio systems deep learning inverse problems signal restoration

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