Anuwongpinit, Thanavit

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Anuwongpinit, Thanavit
Alternative Name
Anuwongpinit, T.
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Email
thanavit.an@kmitl.ac.th

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    Design and Implementation of a Hybrid Real-Time Salinity Intrusion Monitoring and Early Warning System for Bang Kachao, Thailand
    (2025-07-01)
    Seeboonruang, Uma
    ;
    Tanachaichoksirikun, Pinit
    ;
    Anuwongpinit, Thanavit
    ;
    Sirikaew, Uba
    Salinity intrusion is a growing threat to freshwater resources, particularly in low-lying coastal and estuarine regions, necessitating the development of effective early warning systems (EWS) to support timely mitigation. Although various water quality monitoring technologies exist, many face challenges related to long-term sustainability, ongoing maintenance, and accessibility for local users. This study introduces a novel hybrid real-time salinity intrusion early warning system that uniquely integrates fixed and portable monitoring technologies with strong community participation—an approach not yet widely applied in comparable urban-adjacent delta regions. Unlike traditional systems, this model emphasizes local ownership, flexible data collection, and system scalability in resource-constrained environments. This study presents a real-time salinity intrusion early warning system for Bang Kachao, Thailand, combining eight fixed monitoring stations and 20 portable salinity measurement devices. The system was developed in response to community needs, with local input guiding both station placement and the design of mobile measurement tools. By integrating fixed stations for continuous, high-resolution data collection with portable devices for flexible, on-demand monitoring, the system achieves comprehensive spatial coverage and adaptability. A core innovation lies in its emphasis on community participation, enabling villagers to actively engage in monitoring and decision-making. The use of IoT-based sensors, Remote Telemetry Units (RTUs), and cloud-based data platforms further enhances system reliability, efficiency, and accessibility. Automated alerts are issued when salinity thresholds are exceeded, supporting timely interventions. Field deployment and testing over a seven-month period confirmed the system’s effectiveness, with fixed stations achieving 90.5% accuracy and portable devices 88.7% accuracy in detecting salinity intrusions. These results underscore the feasibility and value of a hybrid, community-driven monitoring approach for protecting freshwater resources and building local resilience in vulnerable regions.
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    INTERNET OF THINGS BASED PRACTICAL SMART ENVIRONMENTAL MONITORING SYSTEM FOR POULTRY FARM
    (2026-01-01)
    Anuwongpinit, Thanavit
    ;
    Manthawornsiri, Chananont
    ;
    Archevapanich, Tuanjai
    ;
    Purahong, Boonchana
    In this article, we propose developing digital innovation of smart monitoring systems in the poultry farm using the Internet of Things (IoT) technology. This work aimed to design and develop a monitoring system based on an IoT system that transforms a traditional farm that uses a manual management system to apply an IoT system for environmental monitoring in a poultry farm. The main components include a hardware component that was designed and implemented to gather data of the poultry houses. Temperature and humidity sensor nodes are applied to monitoring the environment of poultry houses. The LoRa communication module in the sensor node will forward data to the gateway. The second principal component is the cloud server for data acquisition from the gateway. The cloud will be responsible for back-end processing and a web-based dashboard displaying mechanism. This system can work as an alarm notification system using LINE notify API for the LINE application that is the most popular communication application in Thailand. The system was implemented practically in one of the poultry farms in Prachinburi province, Thailand. The results indicate that the proposed system provides significant advantages, including enhanced monitoring accuracy, reduced energy consumption, and improved real-time environmental tracking for poultry farms.
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    Optimized Gaussian Pulse Design for UWB System Using Particle Swarm Optimization Based-on Generalized Bessel Polynomials
    (2022-01-01)
    Chutchavong, Vanvisa
    ;
    Anuwongpinit, Thanavit
    ;
    Purahong, Boonchana
    ;
    Archevapanich, Tuanjai
    ;
    Janchitrapongvej, Kanok
    The ultrawideband system operates a very short pulse with enormous bandwidth to provide high data rates for data transmission. To design the UWB pulse, considering the pulse shape is very necessary, and a spectral emission mask of the designed pulse should meet the FCC spectral mask requirement between frequency range 3.1 GHz to 10.6 GHz. The traditional UWB pulse design is based on the Gaussian derivative. However, the frequency spectrum is not satisfied the FCC spectral mask requirement. In this study, the Gaussian pulse can be designed from the mathematical characteristic of the generalized Bessel polynomial. The spectral efficiency of the proposed pulse can be improved by the combination of the derivative of Gaussian pulse with a weight coefficient optimization with particle swarm optimization (PSO). PSO is a population-based optimization algorithm inspired by animal behavior. PSO is applied with generalized Bessel polynomial transfer function to gain the best weight coefficient, we proposed to optimize its weight vector to design a pulse that exceeds to FCC spectral mask. The results were found in MATLAB software show that generalized Bessel polynomials can approximate the proposed pulse with combination method and PSO. The spectral efficiency is improved to 89.30% and the spectrum is greater close to the FCC spectral mask requirement. To confirm an improved spectral efficiency compared to the previous works.
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    Medical Drone Managing System for Automated External Defibrillator Delivery Service
    (2022-04-01)
    Purahong, Boonchana
    ;
    Anuwongpinit, Thanavit
    ;
    Juhong, Aniwat
    ;
    Kanjanasurat, Isoon
    ;
    Pintaviooj, Chuchart
    One of the common causes of a heart attack is fibrillation, a condition that causes an irregular and often abnormally fast heart rate. There is scientific evidence that the survival rate of sudden cardiac arrest patients who are rescued with cardiopulmonary resuscitation (CPR) and with the use of an automated external defibrillator (AED) is significantly increased. Despite the recommendation that automated external defibrillators should be installed in the workplace, along with a proper management system and training for employees on how to use the device, less than 70% of non-residential areas have an AED installed. The situation is even worse in residential areas, with less than 30% having an AED installed. This research concerns the development of a medical drone managing system that can deliver an AED in case of emergency. An application was developed that can be installed on the mobile phone and/or tablet of the patient or the accompanying person. In the event of a heart attack, the patient or the accompanying person can call a medical drone by sending coordinates to the drone station and a notification to medical staff. The drone station administrator can respond by sending the drone, which automatically lands at the patient’s location. After being tested in a simulation situation, the operational field test yielded satisfactory results. The medical drone can land within 1.5 meters of the destination. The designed AED drone can be used not only to deliver AEDs, but also first aid kits and prescribed drugs suitable for medical care. Such a system is especially useful in the current context of the COVID‐19 pandemic.
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    Image Enhancement and 27 Pretrained Convolutional Neural Network Models for Diabetic Retinopathy Grading
    (2023-01-01)
    Kanjanasurat, Isoon
    ;
    Anuwongpinit, Thanavit
    ;
    Purahong, Boonchana
    Diabetic retinopathy (DR) affects the retina's blood vessels and causes vision loss. Fundus images are used to diagnose DR, which is a lengthy process because experienced clinicians must accurately diagnose the disease and identify microlesions early to prevent blindness. Computer vision can be used for retinal image classification. The APTOS dataset contains 5990 normal, moderate, mild, proliferate, and severe retinal images. In this study, we proposed a convolutional neural network (CNN) ensemble for DR fundus grading. Each image channel was enhanced by contrast-limited adaptive histogram equalization (CLAHE) and gamma correction and then fed to 27 pretrained CNN models for one-time training to examine the DR grading. The results showed that MobileNet's green channel with the CLAHE technique is sufficiently fast and accurate for disease classification. The grading retinal images had an accuracy of 96.95%, a precision of 96.17%, a sensitivity of 97.80%, an F1 score of 96.98%, and a specificity of 97.75%. In addition, the proposed method improves the speed and robustness of retinal DR grading.