Patiyuth Pramkeaw

Wildfires need a fast response, and a small unmanned aerial vehicle (UAV) carrying its own detector is an appealing way to find them early. Almost every published UAV fire detector is validated on a single corpus, which leaves open the question that matters to an operator: how much of the reported accuracy survives a change in scene. We trained unmodified YOLOv8n, YOLOv8s, and YOLOv8m on two public benchmarks that ship official test splits, D-Fire and the UAV subset of the Flame and Smoke Detection Dataset, under one fixed recipe with three seeds on D-Fire, then evaluated every checkpoint on both test splits with frozen weights. In domain, YOLOv8m reached 79.04 ± 0.21% mAP@0.5 on D-Fire, within 0.04 points of the published value for the same architecture on the same split, and 92.71% on FASDD_UAV. Moved across corpora, the same weights fell 38 and 65 points below a locally trained model, with fire degrading about twice as far as smoke. A perceptual-hash audit then found near-duplicates of training images in 46.0% and 90.7% of the two test sets; re-evaluating on the uncontaminated remainder costs 2.8 and 12.4 points, closes two-thirds of the apparent difficulty gap between the corpora, and leaves corrected transfer shortfalls near 26 and 62 points. Onboard, an F450 carrying a Pixhawk 2.4.8 and a Jetson Nano A02 flew and detected a controlled fire, but full-resolution inference ran 4.0 to 41.4 times slower than the video it consumed. Both gaps must close before a platform of this class can support operational wildfire monitoring.

Regular screening and timely treatment play a crucial role in addressing the progression and visual impairment caused by cataracts, the leading cause of blindness in Thailand and many other countries. Despite the potential for prevention and successful treatment, patients often delay seeking medical attention due to the gradual and relatively asymptomatic nature of cataracts. To address this challenge, this research focuses on the identification of cataract abnormalities using image processing techniques and machine learning for preliminary assessment. The LeNet-convolutional neural network (LeNet-CNN) model is employed to train a dataset of digital camera images, and its performance is compared to the support vector machine (SVM) model in categorizing cataract abnormalities. The evaluation demonstrates that the LeNet-CNN model achieves impressive results in the testing phase. It attains an accuracy rate of 96%, exhibiting a sensitivity of 95% for detecting positive cases and a specificity of 96% for accurately identifying negative cases. These outcomes surpass the performance of previous studies in this field. This highlights the accuracy and effectiveness of the proposed approach, particularly the superior performance of LeNet-CNN. By utilizing image processing technology and convolutional neural networks, this research provides an effective tool for initial cataract screening. Patients can independently assess their eye health by capturing self-images, facilitating early intervention and medical consultations. The proposed method holds promise in enhancing the preliminary assessment of cataracts, enabling early detection and timely access to appropriate care.

This research focuses on the development of a foot-worn device to assist individuals with limited mobility in walking. The device aims to reduce the risk of accidents by closely moni-toring the wearer's movements and detecting potential hazards. It incorporates sensors, in-cluding the MPU6050 gyro sensor and KY-031 vibration sensor, to track the wearer's walking behavior. In case of abnormalities, such as falls, the device triggers an auditory alert and noti-fies the caregiver via LINE Notify. The system is controlled by the Node MCU ESP8266, enabling seamless integration with the Internet of Things (IoT). The objective of this study is to design an IoT system for medical assistance. The researchers created a prototype of a smart ankle device capable of detecting falls and sending notifications to the user's smartphone. The effectiveness of the notification system was evaluated using the Line Notify application. Re-sults demonstrated a successful detection of falls in 48 out of 50 trials, with two instances of false alarms during normal walking. The overall efficiency of the smart ankle device was 98%. In summary, the proposed smart ankle device demonstrates promising potential for var-ious applications in fall detection and safety alert systems. Further improvements and collab-orations with healthcare experts are recommended to enhance its performance and make it commercially viable. The device's connectivity and responsiveness to IoT systems are vital aspects to be considered. Future research should aim to develop additional features in collab-oration with physiotherapists and healthcare professionals to meet user requirements. The de-vice holds significant benefits in terms of fall detection and safety alerts, and efforts should be made to enhance its durability, robustness, and commercialization prospects.

The work is presented for arm paresis patients in rehabilitation phase, consisting of an arm movement but slower than normal ones. Since most patients with lacking motivation affect the physical therapy, they are not willing to follow program of rehabilitation. This work aims to design a game for arm paresis patients in rehabilitation phase with virtual reality. The control system is designed using unity, oculus rift and oculus touch. For the system, the game contains three different levels. A goal of level 1 is to drag and drop an object in X radius. Level 2 is to pick up books to bookshelf in Y radius, and level 3 is to pick ingredients to cauldron in X and Y radius. As a results, the satisfaction survey on usage is satisfied. Besides, the game's development should be more varied to make patients have more fun and motivated in physical therapy.