Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Exhibition and Seminar on Science and Creative Technology – Al Azhar Proceeding
Sofian Hamid
Institute of High Frequency Technology RWTH Aachen University
Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
FPGA Implementation of Kalman Filter for Visible Light Imam Wahyudi; Dwi Astharini; Danny M Gandana; Sofian Hamid; Denny Hermawan; Budi Aribowo
EXSACT-A Vol 1, No 1 (2023)
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/exc.v1i1.2280

Abstract

Visible Light (VL) has received significant attention due to its benefits in energy efficiency, wide bandwidth availability, and resistance to electromagnetic interference. This final project discusses VL utilizing the Kalman Filter (KF) to predict and estimate the position of related data. The development of the VL method is carried out using Xilinx FPGA Arty A7 hardware, and the KF implementation is carried out in a two-dimensional framework with the Linear KF approach. The main objective of this Final Project is to implement VL using Photodetectors (Photodiode and Photoresistor LM393) on FPGA. The use of Xilinx FPGA Arty A7 hardware and Xilinx SDK software provides the flexibility and reliability required for system implementation. The results indicate that the implementation of Xilinx FPGA Arty A7-35T with KF and the use of 16 LED and 8 LED configurations yield relatively accurate estimations. While the Photodiode LM393 (PD LM393) sensor does not exhibit superior results compared to the Photoresistor LM393 (PR LM393) sensor, this research effectively optimizes light measurements by utilizing the sensor and KF algorithm. The Root Mean Squared Error (RMSE) results show that for the system with 16 LEDs, KF with PR LM393 has an RMSE of approximately ). This RMSE value indicates that KF with PR LM393 can provide relatively more accurate estimations. Similarly, for the system with 8 LEDs, KF with PR LM393 has an RMSE of around ). In this case, KF with PR LM393 again provides relatively more accurate estimations. Meanwhile, the RMSE result for 2D KF in this system is approximately ), indicating that the KF estimation has a relatively small error value compared to the actual measurement value. This demonstrates that KF effectively reduces noise and measurement data fluctuations in the LM393 Photodetector system with 16 LEDs.
Light-Based Positioning System Using Arduino Nurul Imam Assidqi; Dwi Astharini; Sofian Hamid; Denny Hermawan; Budi Aribowo
EXSACT-A Vol 1, No 1 (2023)
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/exc.v1i1.2286

Abstract

The Light Positioning System (LPS) represents an innovative technology employed for precise object localization by utilizing light as a positional reference. This method encompasses the utilization of light sources, such as LED lights or other visible light emitters, which can be strategically positioned at various orientations and angles. This research centers on the practical implementation of the LPS paradigm through the application of Arduino. Additionally, the study involves the integration of the Kalman filter algorithm within the Arduino framework to enhance the accuracy of sensor data estimations. The LPS implementation employs distinct sensors, namely the Photoresistor LM393, Photodiode LM393, and TF-Luna Lidar. The programming is accomplished using the Arduino Integrated Development Environment (IDE), while the hardware framework is based on the Arduino Mega 2560 microcontroller. In this research, the ESP32 module plays a pivotal role as it establishes a seamless connection between the sensor data and the Blynk platform. This integration empowers effective and comprehensive data monitoring and analysis, facilitating real-time tracking and evaluation of the LPS system's performance. The photoresistor exhibits better reading accuracy compared to the photodiode, as evident from the obtained RMSE values. The KF PR with 16 LEDs has the smallest RMSE value, which is 0,03. The TF-Luna LiDAR sensor readings are more accurate and effective under well-lit conditions as opposed to low-light conditions. The RMSE value at lux 160 is 1,28 , while the RMSE value at lux 2 is 3,32
Co-Authors Budi Aribowo Danny M Gandana Denny Hermawan Dwi Astharini Imam Wahyudi Nurul Imam Assidqi