Journal of Telecommunication, Electronic and Computer Engineering (JTEC)

An Incident Management System for the Police Force

2024-05-30T08:20:19+00:00

In our modern era, where advancement in healthcare and technology have greatly contributed to the increase in the world population, the resilience of police Incident Management Systems, especially traditional ones, is being questioned. This is especially relevant as incident rates rise as a direct consequence of population growth. In this context, factors such as prioritization, response time and optimum use of already scarce resources at disposal of police forces become critical. While some of these aspects are already being addressed to some extent, this paper leverages technologies such as Robotic Process Automation (RPA), Geographic Information Systems (GIS) as well as Business Intelligence (BI) for visualization and Artificial Intelligence (AI) for prioritization to assist in Incident Management and Response. The aim of this paper is to critically analyze several existing incident management systems and propose a system prototype that uses a centralized database to cater everything from incident reporting from the citizen’s perspective to its resolution across several actors. The system was evaluated through a survey targeted at Mauritian citizens. The paper concludes by presenting feedback on the system prototype and pave the way for future works. The feedback gathered highlights the public’s enthusiasm for the implementation of such intelligent technologies, with many believing it would significantly enhance their incident reporting experience.

Smart Microcontroller-Controlled Hydroponic System for Optimized Environmental Monitoring in Small-Scale Farming

2024-09-19T05:40:30+00:00

Smart farming is increasingly popular and serves as a support tool for renewable agricultural cultivation systems. However, hydroponic control tools remain expensive and are primarily limited to large-scale agrarian industries. The novelty of this research lies in addressing the lack of hydroponic control tools specifically designed for small-scale and affordable cultivation systems. This study aims to design and develop a simple operational hydroponic control system suitable for small-scale cultivation. The method involves adopting a programming system using microcontroller devices and utilizing Nutrient Film Technique (NFT) hydroponic installation components as the working mechanism. The results of this study indicate that the simple hydroponic control tool effectively responds to several testing parameters, including air temperature, water temperature, nutrient concentration, and humidity, with optimal data readings over 24 hours. This work provides a recommendation for developing cheaper and more affordable tools with mechanism that can be further improved.

Utilizing GPS Data Tracking Design for Stopped Truck Analysis in an Industrial Loading Process

2024-10-25T13:41:55+00:00

Economic growth has become essential for improving people’s livelihoods, particularly as more enterprises are established. This expansion has increased the demand for transportation, where timely delivery of goods is crucial for the success of growing businesses. Consequently, vehicles have become a vital component of the logistics chain, transporting items from one location to another. However, traffic delays often hinder timely deliveries, and administrators struggle to locate trucks during unexpected stops. Additionally, some truck drivers waste time, causing unnecessary delivery delays and increasing operating costs. This study proposes a solution that integrates an RFID sensor module to address traffic congestion issues at loading bays and a GPS module with TTGO TCALL ESP 32 to track the whereabouts of trucks. This approach aims to improve operational efficiency, reduce costs and streamline the logistics process.

Classification of Malaysian and Indonesian Batik Designs Using Deep Learning Models

2024-10-11T04:26:15+00:00

Batik is a traditional textile art form native to Southeast Asia, especially prominent in Malaysia and Indonesia, where unique patterns reflect significant cultural value. The intricate designs of batik, often embodying floral, geometric, and symbolic elements, make automated classification challenging and time intensive. This study presents a method for classifying Malaysian and Indonesian batik patterns using deep learning models. A curated dataset of 1,825 batik images was compiled, consisting of 949 Indonesian batik images and 876 Malaysian batik images. Three popular Convolutional Neural Network (CNN) architectures: MobileNet v2, YOLO-v8, and LeNet-5 were evaluated based on classification accuracy, loss, and training efficiency. Results show that YOLO-v8 achieved the highest accuracy at 98.80%, followed by MobileNet v2 with 97.79%, and LeNet-5 with 92.94%. These findings indicate that CNN models can effectively distinguish between Malaysian and Indonesian batik designs, offering valuable applications in cultural preservation and industry documentation. Future work could focus on refining these models for real-time use and expanding the dataset to capture additional regional variations in batik design.

Machine Learning-Based Solar Irradiance Forecasting Model Using GPS

2024-09-25T05:52:06+00:00

Accurate solar irradiance forecasting is critical for optimizing photovoltaic (PV) systems, enhancing grid stability, and enabling effective energy management. This study explores the integration of machine learning (ML) techniques with Global Positioning System (GPS) data to improve the accuracy of solar irradiance prediction. By incorporating Total Electron Content (TEC) and Integrated Water Vapor (IWV) derived from GPS data, alongside meteorological variables such as pressure and temperature, a robust forecasting model was developed. Among the three backpropagation algorithms tested—Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient—the Bayesian Regularization algorithm with a 10-layer neural network achieved the best performance, with the lowest Mean Square Error (MSE) and highest Correlation Coefficient (R). The model's predictions closely aligned with measured solar irradiance, demonstrating its reliability. Despite challenges such as data availability and computational complexity, the study highlights the potential of integrating GPS-derived data into ML-based solar irradiance forecasting. This approach offers a promising solution for advancing renewable energy management and supporting the transition to sustainable energy systems.

AI-Driven Automation for Analog and Mixed-Signal Circuit Design: Schematic to GDSII Layout

2024-10-11T03:32:22+00:00

As technology propels forward and circuits evolve into intricate and complex designs, the traditional manual circuit design approach finds itself at a crossroads. With cutting-edge processes introducing many challenges, the journey from concept to creation has become increasingly arduous, demanding significant time investments. To overcome these challenges, automation emerges as a key innovation, accelerating product development while ensuring precision. This study explores analog circuit design by investigating the architecture of an analog and digital circuit generator and pioneering an automated synthesis method called “correct-by-construction”. This innovative approach optimizes the design process while prioritizing accuracy from the outset. Additionally, this study evaluates the performance of analog generators, focusing on accuracy and circuit metrics using AutoCkt. Tools such as ALIGN for automated layout generation and OpenFASoC for digital design automation further enhance efficiency and accessibility in analog circuit design. The integration of these tools, alongside their compatibility with open-source CAD platforms, demonstrates significant advancements in automation. Furthermore, the development of a graphical user interface (GUI) provides a user-friendly platform for interacting with various functionalities related to circuit design and simulation, enhancing the overall design workflow.

Development of a Web-Based Application for Monitoring and Measuring the Physical Condition of Badminton Athletes

2024-08-20T04:15:45+00:00

The research aimed to develop a digital solution for monitoring and controlling the physical condition of badminton athletes at Bina Darma University, enabling coaches to track whether athletes’ physical conditions have decreased, remained stable, or improved. Previously, physical condition measurements were recorded manually in notebooks, requiring coaches to manually search and compare past records to assess badminton athletes’ progress. This manual process was time-consuming and inefficient. To address these challenges, a responsive, web-based application was developed for real-time monitoring and management of athletes’ physical data. This application, accessible from both laptops and smartphones with internet connectivity, allows for convenient access from any location. By streamlining data collection and tracking, the application provides a more efficient tool for coaches to evaluate badminton athletes’ physical condition over time. The transition from a manual to a digital system improves data security and accessibility, supporting enhanced decision-making in athlete management and training.

An Integrated Architecture for Validation of Simulation Models and Validation Methods Classification

2024-08-20T04:12:16+00:00

Validation of simulation models is a crucial aspect of the software engineering simulation modelling process. It aims to reduce costs, save time, mitigate risks, and enhance understanding of the validation process’s functions. This paper reviewed existing literature on simulation model validation, focusing on definitions, architectures, and methods. However, the study identified that it remains unclear which attributes should be prioritized when validating simulation models. Furthermore, the literature lacks a comprehensive architecture and classification to effectively describe the validation process. To address these gaps, this paper proposes an integrated architecture for the Simulation Model Validation Process (SMVP). Furthermore, it introduces a novel classification that incorporates three essentials elements: simulation validation phases, simulation models, and validation methods. The findings indicate that the primary focus of verification method is to verify the accuracy of model behavior. In contrast, structural validation emphasizes historical data validation, historical methods, and internal validation. Moreover, combining the structural and behavioral methods ensure a more robust and effective validation process. In conclusion, the proposed architecture and classification are expected to significantly contribute to future research by ensuring that simulation models are thoroughly validated during the validation processes.