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YOLO Based IoT Tracking for Academic Labs on Raspberry Pi
(2026-06-16)
Jiamrachada, Tamakorn
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Nonsiri, Sarayut
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Kamin, Pichitchai
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Nanthajirapong, Nathaphon
Academic IoT laboratories often rely on shared equipment, making efficient borrow-return management essential. Conventional management methods depend on manual recording, which can cause verification delays, data entry errors, and increased staff workload. This study proposes a YOLO based IoT equipment tracking system that uses a camera to detect and count devices inside student equipment boxes for borrow-return recording and inventory monitoring. The system runs on a Raspberry Pi 5 for real-time edge-based processing, while detection results are stored in a database and displayed through a web-based dashboard. Experimental results show that the YOLO12n model achieved an F1-score of 0.996 with a real-time inference speed of 12.01 FPS, demonstrating the system's effectiveness in reducing human error and improving laboratory inventory management efficiency.
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Machine Learning-Optimized Dual-Band LoRa Elliptical Patch Antenna in LoRa Communication System for Waterborne Microplastic Detection
(2026-02-01)
Romputtal, Adisak
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Phongcharoenpanich, Chuwong
This research proposes a dual-band LoRa elliptical patch antenna for the LoRa communication system to detect waterborne microplastics. The proposed LoRa communication system comprises a LoRa sensor node board and an IoT-LoRa gateway board. The LoRa sensor node board is used to capture microplastic images using a digital camera and collect analog signal data from an 8 × 8 photodiode array which detects the reflected light from microplastic fragments. The data are transmitted using a LoRa elliptical patch antenna in the sensor node board, operating at 0.915 GHz for long-range data transfer. The IoT-LoRa gateway board is used to forward data received from the LoRa sensor node board to a cloud server via the internet, and the stored data are accessible and viewable via a smartphone. In this research, the antenna design is optimized by using machine learning (ML) algorithms, unlike conventional antenna design methods which rely on the manual and iterative process. The ML-optimized dual-band LoRa elliptical patch antenna covers the LoRa, UHF RFID, and ZigBee frequency bands, with an omnidirectional radiation pattern. The measured impedance bandwidths (IBWs) are 8.93% (0.868–0.949 GHz) and 12.69% (2.36–2.68 GHz) for the lower and upper frequency bands, respectively, with the corresponding impedance matching (|S<inf>11</inf>|) of –23.02 dB at 0.907 GHz and −27.27 dB at 2.52 GHz. Two ML-optimized LoRa elliptical patch antennas are subsequently integrated into the LoRa communication system, that is, one on the LoRa sensor node board and other on the IoT-LoRa gateway board. Furthermore, prior to indoor and outdoor experiments, the ML-driven waterborne microplastic detection scheme with the LoRa communication system is trained and tested using camera-captured images and analog signal-converted images from the photodiode array. The ML-driven microplastic detection scheme can classify different types of microplastics in water, achieving an accuracy of 100% for all types of microplastics. The detection scheme is also capable of identifying the presence of microplastics in water, achieving an overall accuracy of 98.5%. The originality of this work lies in the use of ML algorithm to optimize the antenna design and to streamline identification and detection of microplastics in water.
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Wireless Carbon Monoxide Level Control and Monitoring System
(2025-01-01)
Rerkratn, Apinai
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Riewruja, Vanchai
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Petchmaneelumka, Wandee
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Tammaruckwattana, Sirichai
This paper proposes an online carbon monoxide control and monitoring system. The proposed system comprises an MQ-7 CO gas sensor module, a DHT11 digital temperature and humidity sensor module, an ESP8266 module, and a Relay Module. The ThingSpeak platform is used to create HMI for online monitoring of CO level, temperature, humidity, and status of the ventilation fan. This proposed system can measure CO and monitor all parameters of measurement data, alert users when CO values exceed the set value. In addition, the measured CO values are used to control the ventilation system operation (ON or OFF) to maintain the CO at a non-harmful level. The experiment testing and results with three conditions show that the proposed system can measure and monitor CO level, temperature, and humidity online with satisfying values. The ventilation fan can operate with function as a design procedure and maintain CO at a non-harmful level.
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Impact of the Internet of Medical Things on Artificial Intelligence-enhanced medical imaging systems from 2019 to 2023
(2025-01-01)
Locharoenrat, Kitsakorn
This review addresses the disease diagnosis from brain, eye, and lung scan images based on non-invasive imaging technologies using the Internet of Medical Things (IoMT) and Artificial Intelligence (AI) systems, a topic that has been neglected in the recent literature. Combining imaging modalities with IoMT and AI is expected to enhance both medical diagnoses and personalized treatment plans. We searched various scientific databases for details on IoMT and AI in medical imaging technologies from 2019 to 2023, focusing on different imaging modalities. We investigated the performance of AI-based algorithms in imaging modalities such as X-ray, Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography, and Optical Coherence Tomography using the following metrics: accuracy, precision, recall, sensitivity, specificity, and F-1 score, and then analyzed their balanced performance in six issues: enhancement of medical image quality, improvement of clinical diagnoses, support for clinical decision-making, consideration of input data, time efficiency, and data management. Advanced understanding of the IoMT and AI applications in medical imaging technologies would help identify unexplored opportunities and provide directions for future research to enhance the clinical applicability.
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Implementation of Cloud Computing and Internet of Things (IoT) by Performance Evaluation
(2024-01-01)
Sithiyopasakul, Jiran
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Archevapanich, Tuanjai
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Sithiyopasakul, Saran
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Lasakul, Attasit
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Purahong, Boonchana
The integration of cloud computing and the Internet of Things (IoT) holds transformative potential across diverse industries. Performance assessment is essential to gauge the quality and efficiency of cloud computing and IoT systems. This paper presents a comprehensive performance evaluation of cloud computing and IoT systems, focusing on three major platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Experimental results encompass various scenarios, including normal operation, heavy load conditions, IoT applications, and scalability testing. The outcomes reveal distinct performance metrics such as response time, throughput, latency, and reliability for each cloud platform.
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T-Slot Antennas-Embedded ZigBee Wireless Sensor Network System for IoT-Enabled Monitoring and Control Systems
(2023-12-01)
Romputtal, Adisak
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Phongcharoenpanich, Chuwong
This research proposes a 2.4 GHz T-slot antennas-embedded ZigBee wireless sensor network system, consisting of an Internet of Things (IoT) gateway board and a sensor node board, for IoT applications. Simulations were first carried out to optimize the parameters for the T-shaped slot patch antenna. The prototypes of the IoT gateway and sensor node boards were subsequently fabricated and measurements undertaken. The measured impedance matching (|S11|), bandwidth, and gain of the proposed ZigBee sensor network system were -18 dB, 15.38%, and 1.722 dBi, respectively. Furthermore, the ZigBee IoT-based monitoring and control schemes based on the ZigBee wireless sensor network system were set up and experiments carried out in an enclosed area for the monitoring scheme and in an open area for the control scheme. The experimental results revealed that the proposed IoT-enabled 2.4 GHz ZigBee sensor network system with embedded T-slot patch antennas could efficiently be utilized in IoT-based monitoring and control systems. In essence, the novelty of this research lies in the integration of the IoT and ZigBee sensor network technologies to store data in a cloud server in a real-time fashion, as opposed to in the microcontroller memory which is common in conventional ZigBee systems. In addition, the data stored in the cloud server are retrievable and viewable via the Blynk application on smartphone, rendering the proposed 2.4 GHz T-slot antennas-embedded ZigBee wireless sensor network system operationally suitable for IoT-based monitoring and control systems.
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LPG Leakage Risk Predictions from an IoT-Based Detection System Using Machine Learning
(2023-01-01)
Lorthong, Saksiwa
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Janjarassuk, Udom
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Jayranaiwachira, Nuthvipa
LPG has the potential to ignite and explode if it spills since it is a flammable gas. Explosions and flames caused by LPG leaks can damage or kill people who are working in the hazardous area. The industrial sector currently lacks efficient warning systems to identify and anticipate leaks, necessitating efficient equipment to identify and monitor gas leaks. This work aims to investigate and identify a device designed to detect and keep track of LPG leaks by utilizing straightforward yet efficient IoT technologies. The experimental and carefully vetted data are applied to build an Artificial Neural Network (ANN) model for predicting the risk of gas leaks. Gas leaks in the workplace are monitored, alerted to, and controlled by using gas detection systems based on IoT technology. The dataset is then submitted to factor analysis for feature selection, which made use of the model's expertise from its examination of the information gathered from the detecting device in the Cloud system. Several measures were utilized to evaluate the model, including Accuracy, Precision, Recall, F1-Score and Area Under the Curve (AUC). The investigation led to clustering the Risk Ranking Number into three levels, which were then utilized in conjunction with the Risk Matrix to evaluate risk. The net processing time was 1.34 minutes, and the forecast accuracy was 96.05%. This study will assist in improving the model that establishes the alarm system's alert level.

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