Journal of Telecommunication, Electronic and Computer Engineering (JTEC)

e-Waste Bin: An Interactive Waste Management and Monitoring System Via IoT Platform

2022-12-08T01:42:16+00:00

Nowadays, waste management has become a challenge faced by the developing and the developed countries. The main problem occurred when waste accumulates beyond capacity before schedule collection. This situation may led to huge environmental pollution and potential outbreaks of disease in the society. In this case, waste pickers are essential in waste management by ensuring timely waste collection and preventing bin overflow. However, monitoring bin levels in real-time poses a challenge for them. Hence, this research proposes a mobile application for solid waste management, designed to monitor the garbage fill level of the waste bin. The app alerts both users and waste pickers to potential overflowing bins, allowing users to communicate directly with waste management company. Employing an ESP8266 NodeMCU, HC-SR04 ultrasonic sensor, MQ-4 gas sensor, and solar power for energy efficiency, this system results in a user-friendly mobile application. This innovation is aimed at mitigating the issue of waste overloading and improves the environmental quality.

Design of Rectangular Patch Array 2x4 Microstrip Antenna on C-Band for Weather Radar Applications

2024-02-23T04:09:35+00:00

Weather radars are essential for detecting rainfall, calculating movement, and estimating the object type (rain, snow, or hail). For such applications, the C-Band frequency range of 3.7 GHz to 4.2 GHz is typically utilized. A key challenge lies in improving antenna gain for better system performance. This research aims to design a C-Band downlink microstrip antenna at a working frequency of 3.9 GHz. We used a 4 x 2 array to increase the gain. Before designing, we did calculations using the CST Studio Suite 2019 software. The results of our design are a return loss of -29,171 dB, a VSWR of 1.061, a bandwidth of 713 MHz, and a gain of 8.23 dB. This research contributes an improved gain and bandwidth contribution of this study is the better gain and bandwidth compared to similar studies.

New 7D and Memristor-Based 8D Chaotic Systems: Computer Modeling and Circuit Implementation

2024-02-09T08:56:46+00:00

The most pressing modern problem of using chaotic systems in practice is the development of multi-channel information encryption methods. To solve these problems, there is a need to develop multidimensional chaos generators. In this paper, novel multidimensional 7D and 8D hyperchaotic systems are presented. It details a technique for creating a 7D dynamic system derived from a pre-existing 6D dynamic system. Additionally, this paper outlines the development of an 8D nonlinear dynamic system utilizing a memristor. An examination of innovative 7D and 8D dynamic systems was conducted, focusing on the determination of Lyapunov exponents, the construction of bifurcation diagrams, and the identification of equilibrium points along with their corresponding stability conditions for each system. As a result of computer modeling of 7D and 8D hyperchaotic systems in MATLAB-Simulink and Labview, phase portraits of numerous strange attractors were obtained. Finally, using Multisim software, electronic circuits for new 7D and 8D chaos generators were built, which demonstrated similar behavior as in the MATLAB-Simulink and LabView models.

Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal

2024-02-15T06:52:36+00:00

A microsleep is an unintentional, transient loss of consciousness correlated with sleep that lasts up to fifteen seconds. Electroencephalogram (EEG), recordings have been extensively utilized to diagnose and study various neurological disorders. This study analyzes time series EEG signals to predict microsleep employing two deep learning models: Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN). The findings show that the ANN model achieves outstanding metrics in microsleep prediction, outperforming the LSTM in key performance metrics. The model demonstrated exceptional performance, as demonstrated by the outcomes of the Scatter Plot, R2 Score, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Between the two models, the ANN model achieved the most significant R2, MAE, MSE, and RMSE values (0.84, 1.10, 1.90, and 1.38) compared to the LSTM model. The critical contribution of this study lies in its development of comprehensive and effective methods for accurately predicting microsleep events from EEG signals.

Mobile Application for Data Acquisition in Livestock Farming Systems: Case Study of Goat Farming in East Java Indonesia

2024-01-22T02:12:59+00:00

The lack of real data availability in the agricultural sector, especially in the livestock sub-sector, has the potential and opportunities for optimal data processing. Stakeholders in agriculture require accurate field data to minimize costs and save time for profit maximization. This study aims to enrich and record livestock goat data with the help of an application that will assist in the managerial process so that the data collected can be analyzed and used by stakeholders for fast and precise decision-making. The application was created using the prototyping method for both mobile and web platforms. The application prototype was evaluated on functionality testing using a black box method, which resulted in a success in application tests. The application successfully performed real-data recording on the Android platform, offered a user-friendly display of goat information, and enabled documentation of goat livestock data on the website. This study contributes to current research by making data recordings of goats using an Android application for farmers and stakeholders. This tool provides significant benefits by delivering real-time data, enhancing decision-making processes, and offering insights that could lead to increased profitability and strategic planning for future business endeavors.