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5th International Conference on Advancements in Computing, ICAC 2023

作者： Saranga, R.G.V. Jayasinghe, A.H.N. Divyantha, U.A.S. De Silva, S.W.L.P. Swarnakantha, N.H.P. Ravi Supunya Kumari, P. K. Suriya Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children. © 2023 IEEE.

关键词： Image enhancement

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Synthetic Data Generator for an E-Learning Platform in a Big Data Environment

Synthetic Data Generator for an E-Learning Platform in a...

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International Conference in Information Technology and Education, ICITED 23

作者： Moreno, Yaneth Montero, Anthony Hidrobo, Francisco Infante, Saba CEMISID Faculty of Engineering University of Los Andes Mérida Venezuela Department of Computer Science Faculty of Engineering University of Los Andes Mérida Venezuela School of Mathematical and Computational Sciences Yachay Tech University Imbabura San Miguel de Urcuquí Ecuador

ISBN: (纸本)9789819954131

In this paper, we present the implementation of a big data environment to extract, filter, and classify data to use tools that anticipate its growth and that allow scaling resources in order for results to be used as a basis for future analysis or, as in this case study, for the generation of synthetic data. Specifically, we focus on the information available in a learning management system, concretely Moodle, where numerous interactions occur between users and the learning platform. All actions of users, mainly students, are recorded and can be used to reproduce behaviors. Then, we create a request simulator to emulate the behavioral pattern of students, using data from a database taken from an operative learning management system for training. Results were quite similar concerning student behavior, as reflected in the statistics on the operational learning management system database and the data synthetically generated. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Big data

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Enhancing the Efficiency of the Grouping-Scoring-Modeling Framework with Statistical Pre-Scoring Component for Transcriptomic Data Analysis

Enhancing the Efficiency of the Grouping-Scoring-Modeling Fr...

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18th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

作者： Khokhar, Maham Bakir-Gungor, Burcu Yousef, Malik Department of Data Science Social Sciences Institute Abdullah Gul University Kayseri38080 Turkey Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Information Systems Galilee Digital Health Research Center Zefat Academic College Zefat13206 Israel

The advent of high-throughput transcriptomic technologies has generated vast transcriptomic datasets, challenging current analytical methodologies with their sheer volume and complexity. The Grouping-Scoring Modeling (G-S-M) approach is one of the recent approaches that treat groups of genes (or clusters of genes) by embedding prior biological knowledge with machine learning in order to detect the most significant groups for classification tasks. The G-S-M might need to treat thousand ten thousand of groups (scoring those groups) which might affect the speed and performance of the algorithm. In response, this study introduces the Pre Scoring G-S-M model, an enhancement of the established Grouping-Scoring-Modeling (G-S-M) framework. This approach incorporates a Pre-Scoring component that leverages the Limma package for its empirical Bayes methods to optimize initial transcriptomic data evaluation through a percentage-based selection of statistically significant gene groups. Aimed at reducing computational demand and streamlining feature selection, the model also addresses data redundancy by eliminating duplicate gene-disease associations. Application to nine human gene expression datasets from the GEO database showed promising results. It demonstrated improvements in computational efficiency and analytical precision while reducing the number of features selected per dataset compared to the traditional G-S-M approach, without compromising accuracy. These initial findings highlight the Pre-Scoring G-S-M model's potential to enhance transcriptomic data analysis, indicating a promising direction for future bioinformatics research. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Data accuracy

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The Influence of Input Variable Selection on Deep Learning-Based Sea Ice Parameter Inversion from Multi-Sensor Satellite Data

The Influence of Input Variable Selection on Deep Learning-B...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Chen, Xinwei Patel, Muhammed Cantu, Fernando J. Pena Park, Jinman Turnes, Javier Noa Jiang, Mingzhe Xu, Linlin Scott, K. Andrea Clausi, David A. Huang, Weimin University of Waterloo Vision and Image Processing Lab Department of System Design Engineering Canada Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1B 3X5 Canada

ISBN: (纸本)9798218142186

The automated mapping of Arctic sea ice using satellite data from various sources plays a critical role in maritime shipping and climate science research. Deep learning models, particularly convolutional neural networks (CNNs), have shown significant potential in generating more accurate and reliable automated sea ice maps from synthetic aperture radar (SAR) images. This study focuses on investigating the impact of integrating multi-source data on enhancing the performance of CNN-based sea ice mapping models. By employing a multitask U-Net architecture, the paper aims to estimate three essential sea ice parameters, namely sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE), through different combinations of input data. The AI4Arctic Sea Ice Challenge dataset with multi-sensor satellite data is utilized for model training and evaluation. The experimental findings confirm the effectiveness of incorporating passive microwave data and spatial-temporal information in conjunction with SAR imagery, as well as feature redundancy removal, resulting in improved accuracy for sea ice parameter estimation. © 2023 The Marine Technology Society (MTS).

关键词： Sea ice

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Intelligent Headlights for Adapting Beam Patterns with Raspberry Pi and Convolutional Neural Networks 2

Intelligent Headlights for Adapting Beam Patterns with Raspb...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Maheswari, P. Gowriswari, S. Balasubramani, S. Ramesh Babu, A. Nk, Jijith Murugan, S. Srm Institute of Science and Technology Faculty of Management Studies Tamil Nadu Chennai India Sri Sairam Engineering College Department of Instrumentation and Communication Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Computer Applications Vidya Vikas Institute of Engineering and Technology Karnataka Mysore India Dxn Holdings Bhd Maharashtra Pune India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350372847

This study uses intelligent headlights to improve car safety and economy. The suggested system uses Raspberry Pi and Convolutional Neural Networks (CNNs) to dynamically modify beam patterns to real-time ambient circumstances and context. The Raspberry Pi is a flexible computer platform, and CNNs automatically analyze sensor and video data to modify headlight beam patterns. A trained CNN model recognizes impending vehicles, pedestrians, and road conditions. By analyzing this information, the headlights dynamically optimize the beam pattern to maximize visibility and minimize road user glare. This flexibility improves road safety by optimizing lighting when required, minimizing driver fatigue, and enhancing response times. Raspberry Pi allows for a cost-effective and easy retrofitting of current vehicles with intelligent headlights. The system's scalability and adaptability make it suitable for many automotive applications, helping to construct smart and linked transportation systems. This discovery is a major step towards intelligent and adaptable lighting systems that improve vehicle safety and economy. © 2024 IEEE.

关键词： Vehicle safety

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Evaluating Datagram Congestion Control Protocol (DCCP) for Real-Time Applications: A Comparative Study with TCP in Multi-Node Networks 10

Evaluating Datagram Congestion Control Protocol (DCCP) for R...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Hassan, Aqib Memon, Vengas Shaikh, Farhan Bashir Alahmari, Saad Iqbal, Mansoor Azam, Farooq School of Computing and Mathematical Sciences University of Greenwich London United Kingdom Universiti Tunku Faculty of Information and Communication Tech. Abdul Rahman Perak Malaysia Northern Border University Department of Computer Science Arar Saudi Arabia University of Wah Department of Computer Science Wah Cantt Pakistan Nust Dept. of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798331532895

The rise in demand for real-time applications, such as live streaming, online gaming, and Internet telephony, has highlighted the necessity for transport protocols that offer low latency and network stability. Traditional protocols, namely Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), have inherent limitations. TCP ensures reliable data delivery but suffers from latency, making it unsuitable for real-time needs, while UDP provides fast data transmission but lacks congestion control, which can lead to network instability. This gap underscores the need for a protocol that combines high-speed data transfer with effective congestion management. To address this issue, the Internet engineering Task Force (IETF) introduced the Datagram Congestion Control Protocol (DCCP), designed to optimize data delivery speed while managing congestion. This study evaluates DCCP's performance against TCP in network scenarios of 8 and 16 nodes using the NS2 simulation environment. By comparing throughput and delay across various bandwidth and latency settings, the study finds that DCCP offers up to 35% higher throughput in 8-node setups and reduces packet loss by 20% in high-bandwidth scenarios. These results suggest that DCCP is well-suited for real-time applications, achieving a crucial balance between fast data transmission and network stability. © 2024 IEEE.

关键词： Congestion control (communication)

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An Optimal Scheme for WSN Based on Compressed Sensing

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computers, Materials & Continua 2022年第7期72卷 1053-1069页

作者： Firas Ibrahim AlZobi Ahmad Ali AlZubi Kulakov Yurii Abdullah Alharbi Jazem Mutared Alanazi Sami Smadi Department of Information Systems and Networks Faculty of Information TechnologyThe World Islamic Sciences&Education UniversityJordan Computer Science Department Community CollegeKing Saud UniversitySaudi Arabia Department of Computer Engineering National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic InstituteUkraine

Wireless sensor networks(WSNs)is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks,bio-medical engineering,agriculture,industry and many *** has been used in the internet-of-things(IoTs)applications.A method for data collecting utilizing hybrid compressive sensing(CS)is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network *** cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes,and then the cluster heads are selected in order based on the distance between the determined cluster head node and the undetermined candidate cluster head ***,each ordinary node joins the cluster that is nearest to *** greedy CS is used to compress data transmission for nodes whose data transmission volume is greater than the threshold in a data transmission tree with the Sink node as the root node and linking all cluster head *** simulation results demonstrate that when the compression ratio is set to ten,the data transfer volume is reduced by a factor of *** compared to clustering and SPT without CS,it is reduced by 75%and 65%,*** compared to SPT with Hybrid CS and Clustering with hybrid CS,it is reduced by 35%and 20%,*** and SPT without CS are compared in terms of node data transfer volume standard *** with Hybrid CS and clustering with Hybrid CS were both reduced by 62%and 80%,*** compared to SPT with hybrid CS and clustering with hybrid CS,the latter two were reduced by 41%and 19%,respectively.

关键词： Compressed sensing computer networks sensor networks ad hoc networks

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Automatic Repeat Request Design in AoI-Aware Broadcast With Heterogeneous Direct and Relay-Assisted Users

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IEEE Internet of Things Journal 2025年

作者： Wang, Xueer Pan, Haoyuan Chan, Tse-Tin Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology The College of Computer Science and Software Engineering Shenzhen China

This paper investigates the design of automatic repeat request (ARQ) protocols in age of information (AoI)-aware broadcast systems with heterogeneous users, including both direct and relay-assisted users. In this setup, a direct user receives status updates directly via a single-hop link, while a relay-assisted user can receive status updates through either a direct single-hop link or a two-hop relay-assisted link. While ARQ is commonly used to ensure reliable transmission in error-prone wireless networks, previous studies suggest that ARQ does not improve the average AoI in single-hop networks. However, its impact on AoI in systems with relays, particularly those involving heterogeneous users, remains unclear. We address this gap by analyzing the average AoI under different ARQ strategies, introducing a transmission limit k ≥ 0 at the relay. Here, k = 0, k = 1, and k > 1 correspond to non-relay, non-ARQ-at-relay, and truncated-ARQ-at-relay strategies, respectively. Utilizing a unified Markov chain framework that models the transmission processes for each user type, we derive the theoretical average AoI. Our results show that the direct user benefits most from non-relay and non-ARQ strategies, similar to single-hop systems. In contrast, the relay-assisted user achieves optimal performance with truncated-ARQ-at-relay, leveraging both direct and relay-assisted links. For the overall system, applying a non-ARQ-at-relay approach strikes a balance in AoI between the direct and relay-assisted users, leading to a more stable and lower system-wide average AoI. © 2014 IEEE.

关键词： Mobile telecommunication systems

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Design of Slotted Substrate Integrated Waveguide Antenna for Ka-band Automotive Wireless Communication

Design of Slotted Substrate Integrated Waveguide Antenna for...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Ashish Singh, Gurprince Sahoo, Bikash Chandra Kumar, Ashwini Singh, Gurmeet Chitkara University Chitkara University Institute of Engineering and Technology Punjab India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Indira Gandhi Institute of Technology Department of ETC Engg. Odisha Sarang India Poornima University Faculty of Engineering and Technology Rajasthan Jaipur India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350378092

In this paper, a slotted multi-input-multi-output (MIMO) substrate integrated waveguide (SIW) antenna is presented for mm wave frequency bands and 5G wireless applications. The proposed design resonates in n257 frequency band dedicated for 5G communication according to 3rd generation partnership project (3GPP). The presented antenna design exhibits excellent impedance matching of -29 dB with the gain of about 8.5 dB. Also, the antenna covers required impedance bandwidth of 500 MHz with mutual coupling of about -35 dB. Further, in the context of beamforming feature, the proposed design achieves the ±15° with the progressive phase shift of feeds of the antenna structure and also the design demonstrates the excellent performance in terms of diversity parameters. The proposed design is suitable for various 5G automotive use cases with the calculated data rate according to link budget calculations. © 2024 IEEE.

关键词： Budget control

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Convergent Wheeled Robot Navigation Based on an Interpolated Potential Function and Gradient

SSRN

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SSRN 2023年

作者： Seder, Marija Klančar, Gregor Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Croatia Department of System Control and Cybernetics Faculty of Electrical Engineering University of Ljubljana Slovenia

The article presents a novel idea to construct a smooth navigation function based on the grid-based search that enables replanning in dynamic environments. Since the dynamic constraints of the robot are also considered, the navigation function is combined with the model predicted control (MPC) to guide the robot safely to the defined goal location. The main novelty of this work is the definition of this navigation function and its MPC application with guaranteed closed-loop convergence in finite time for a non-holonomic robot with speed and acceleration constraints. The navigation function consists of an interpolated potential function derived from the grid-based search and a term that guides the orientation of the robot on continuous gradients. The navigation function guarantees convergent trajectories to the desired goal, results in smooth motion between obstacles, has no local minima, and is computationally efficient. The proposed navigation is also suitable in dynamic environments, as confirmed by experiments with a Husky mobile robot. © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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