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Laser Pigeon Deterrent
(2026-01-01)
Viriyakul, Vachara
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Chumsri, Paramee
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Chayawattana, Faikaew
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Sattapornnara, Peerapat
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Vongchumyen, Charoen
Urban environments face increasing challenges due to pigeon infestations, leading to property damage, environmental degradation, and health concerns. This research presents the development of a laser-based pigeon deterrent system that leverages image processing, embedded systems, and web applications to provide an automated and efficient bird deterrence solution. The system detects pigeons and humans in real time, ensuring that laser deterrence is applied safely and ethically. Through controlled field tests, the system achieved a pigeon detection accuracy of 78.18% and a successful deterrence rate of 49.09%. The results indicate the potential of laser-based deterrents as a viable alternative to conventional bird control methods.
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A Low-cost Autonomous Lawn Mower with AI-Based Obstacle Avoidance and GPS Guidance System
(2025-07-01)
Kosri, Thanapon
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Seekhamharn, Tossawat
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Phoonsrichaiyasit, Phasawut
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Khungpo, Poowadon
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Sirisuk, Phaophak
This paper presents a cost-effective robotic system capable of manual control via RF remote and autonomous navigation using GPS-based information. The system employs artificial intelligence to dynamically classify and avoid non-grass obstacles, ensuring safe operation in real environments. The prototype integrates affordable hardware including Arduino board, sensors, actuators and Raspberry Pi with lightweight algorithms to balance performance and cost. Experimental validation confirms its ability to follow predefined paths with ±1.5 meters deviation in open area and 90% obstacle avoidance success rate. With a total hardware cost under $200, this prototype highlights feasibility for larger-scale implementation.
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A novel approach to enhanced fall detection using STFT and magnitude features with CNN autoencoder
(2025-02-01)
Soontornnapar, Tomorn
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Ploysuwan, Tuchsanai
The ability to accurately detect and classify falls is critical for ensuring timely medical intervention, especially for the elderly, who face a significantly higher risk of severe injuries, loss of independence, or fatal outcomes from falls. This paper introduces a novel fall detection approach that addresses these urgent needs by using short-time Fourier transform (STFT) images and the magnitude of quaternion (MQ) signals, fused into STFT-MQ images. The proposed method leverages STFT’s time–frequency representation to capture rapid changes and dynamic characteristics in human motion data from wearable sensors, enhancing its ability to distinguish between fall and non-fall incidents. Utilizing a convolutional neural network autoencoder (CNN-AE), an unsupervised learning model, this approach analyzes transformed data without extensive labeled datasets, offering a scalable solution in diverse settings. Tested on the HIFD dataset with heart rate and IMU sensor data, the STFT-MQ-AE method achieves remarkable sensitivity of 98.08%, specificity of 98.78%, and an overall accuracy of 98.57%, setting a new benchmark in fall detection accuracy. Furthermore, the model’s reliance on an N-way K-shot learning approach enables it to manage unforeseen fall cases effectively without retraining, enhancing adaptability and real-world utility. The model achieves the highest Youden’s index (YI) of 96.85%, underlining balanced performance between fall and non-fall classification. Consistent performance across varied training scenarios yields an average accuracy of 96.10%, making this approach highly reliable. This advancement in fall detection technology offers a practical, effective solution to reduce fall-related injuries and enable timely assistance, thereby promoting safer, more independent living for at-risk populations.
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Digital Image Analysis for Gender Identification on Panoramic Dental X-Rays
(2024-01-01)
Mungpayabarn, Harikarn
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Tuntiwong, Kuson
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Taertulakarn, Somchat
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Sritart, Hiranya
Computerized panoramic dental X-ray imaging technology uses information recorded from dental treatment history. Teeth are considered a biometric tool to identify people in cases where their bodies have been severely affected, such as to distinguish victims of disasters and fire accidents. In cases where evidence such as DNA, fingerprints, and iris cannot be examined, gender identification is an important factor in establishing personal identity. Tooth enamel is the strongest material in the human body. Resistant to burns from temperature up to 500 degrees Celsius. And the jawbone is the part that is the strongest and most durable. Including showing high levels of gender differences gender. Additionally, the jawbone is the strongest and most durable part of the human body, exhibiting high levels of gender differences. These characteristics of the teeth form the basis for this research. Using medical technology, panoramic dental X-rays taken from living individuals' dental records can determine gender when compared with post-mortem panoramic dental X-rays used as a database to compare traces of events involving lawsuits. Consequently, this guideline is for verifying personal identity by identifying gender from computerized panoramic dental X-rays. For this reason, this study aims to measuring inter-canine distance and finding the ratio of crown width to root length on computerized panoramic dental X-rays in males and females using the Image J program and analyzing with the GraphPad Prism 8 program. The results showed that the average ratio of crown width to root length was higher in males than in females, and inter-canine distance was greater in males than in females. These results emphasize the critical role of panoramic dental X-rays and image processing in biomedical engineering for accurate gender identification.
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Manufacturing Process Improvement by Barcode Reader using Image Processing
(2024-01-01)
Sukasem, Sutikamon
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Maneerat, Noppadol
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Thudthong, Jakkrit
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Wongsomboon, Chanathip
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Yajima, Kuniaki
Manufacturing industries get perspective on the efficiency of their production process. This study focuses on improving the cycle time of the work process at the station related to barcode reading. This operation should be of minimal duration to result in shorter cycle times and resulting in increased production volumes while still maintaining quality and reducing production costs. Image processing methodology is applied to reading barcodes instead of using a barcode reader which creates clear image quality along with developing an application using Android Bridge (ABD) and ZXing libraries that can read 1D barcodes in many formats. More essentially, application development by multithreading programming can process barcode reading simultaneously at a time that meets the target cycle time. The result of this study proves that the process of reading barcodes by human work has taken 60 times per job cycle and the reading time was about 29 seconds, becoming reduced to 19.85 seconds, manpower can be reduced by 1 person and production cost as well. Moreover, this implementation eliminates human errors that are likely to occur from scanning barcodes in the wrong position. Therefore, this study has benefited the manufacturing industry achieve significantly increased productivity and efficiency.
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Writing Test with Image Processing Technique
(2023-01-01)
Charoy-Boon, Kamolchanok
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Kruesang, Nattakan
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Anuntachai, Anuntapat
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Raksadawan, Natte
Nowadays, Dysgraphia frequently discovers as same as other intellectual disabilities. The dysgraphia diagnosis method is to do monitoring by human then this paper represents the applied technology for improving the diagnosis performance. The writing test is to estimate the writing ability and alphabet perception. The image processing is applied to evaluate writing test for students (grade 1-3 or 6-8 years). The student wrote sentences accordingly to an example article in a time frame. The example article composed of 4 lines, 40 words and 161 alphabets of Thai language.
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Machine learning-based prediction of nutritional status in oil palm leaves using proximal multispectral images
(2022-07-01)
Chungcharoen, Thatchapol
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Donis-Gonzalez, Irwin
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Phetpan, Kittisak
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Udompetaikul, Vasu
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Sirisomboon, Panmanas
This study evaluated the application of proximal multispectral images accompanied by 4 machine learning approaches for estimating the nutritional status of oil palm leaves. The image responded for five bands: blue, green, red, red edge, and near-infrared regions with a center wavelength of 475, 560, 668, 717, and 840 nm. Average and standard deviation (SD) values from the leaf pixels of each band were extracted, obtaining 5 average and 5 SD values from 5 bands. Thirty-four vegetation variables were generated based on those average and SD values. In total, forty-four variables consisted of 10 average-and SD-based features, and 34 vegetation variables were used as the input candidates for analyses against 10 target variables: nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), zinc (Zn), boron (B), and chlorophyll (SPAD). No significant input came out for modeling with P and Zn based on the stepwise selection. Therefore, 8 nutritional models were proposed in this study. A training set with 50 samples was used to be modeled for each target, and a test set with 15 samples was employed to evaluate the models' performances. Based on random forest (RF), support vector regression (SVR), partial least square regression (PLSR), and artificial neuron network (ANN) applied to be modeled, the models for chlorophyll, N, and Ca predictions were acceptable for screening, and those for K and Mg predictions were acceptable for rough screening. The chlorophyll model developed based on the RF had the predictive statistics in terms of coefficient of determination for prediction (r2), root mean square error of prediction (RMSEP), and standard error of prediction (SEP) of 0.752, 5.46 SPAD, and 5.65 SPAD, respectively. The other 2 screening models developed based on SVR and RF for N and Ca, respectively, gave the performances with the r2, RMSEP, and SEP ranging from 0.655 to 0.718, 0.12 to 0.17%, and 0.12 to 0.18%, respectively. In the case of the 2 rough screening models established using the RF algorithm, the predictive statistics ranged from 0.496 to 0.530 for the r2 and 0.07–0.16% for both RMSEP and SEP. In this study, the Fe, Mn, and B models had poor results presenting the range of r2, RMSEP, and SEP of 0.308–0.491, 2.39–72.9 ppm, and 2.45–62.8 ppm, respectively. Based on the results, this study confirmed that the proximal multispectral information of oil palm leaves had enough significance to account for the status of chlorophyll and macro-nutrients: N, K, Ca, and Mg in the leaves.
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Design and Development of an Assistive System Based on Eye Tracking
(2022-02-01)
Paing, May Phu
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Juhong, Aniwat
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Pintavirooj, Chuchart
This research concerns the design and development of an assistive system based on eye tracking, which can be used to improve the quality of life of disabled patients. With the use of their eye movement, whose function is not affected by their illness, patients are capable of communicating with and sending notifications to caretakers, controlling various appliances, including wheelchairs. The designed system is divided into two subsystems: Stationary and mobile assistive systems. Both systems provide a graphic user interface (GUI) that is used to link the eye tracker with the appliance control. There are six GUI pages for the stationary assistive system and seven for the mobile assistive system. GUI pages for the stationary assistive system include the home page, smart appliance page, eye-controlled television page, eye-controlled air conditional page, i-speak page and entertainment page. GUI pages for the mobile assistive system are similar to the GUI pages for the stationary assistive system, with the additional eye-controlled wheelchair page. To provide hand-free secure access, an authentication based on facial landmarks is developed. The operational test of the proposed assistive system provides successful and promising results.
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Turbidity of Coconut Oil Determination Using the MAMoH Method in Image Processing
(2021-01-01)
Palananda, Attapon
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Kimpan, Warangkhana
In general, considering standard production, as well as coconut oil production, in oil consumption industries is an important factor. Oil color is an important element, as it is an important factor for consumers or buyers in selecting coconut oil. In the process of producing coconut oil, the cold-pressed method has been chosen to maintain the essential quality of coconut oil. The quality of the coconut oil is inspected from the production process by means of light passing through the coconut oil. Then, the production staff compares the turbidity of coconut oil with the master sample. The turbidity of coconut oil in every production must be compared with a master sample to maintain standards control. According to previous studies, there are many methods for determining coconut oil turbidity. One method that has been utilized is determining turbidity from light passing through the medium in which the transmitted light can be absorbed through the turbidity of the variable medium. This process is applied together with image processing to determine the coconut oil turbidity. In this research, we propose a method for measuring coconut oil turbidity by the Moving Average Median of Hue (MAMoH), which is better in detecting the coconut oil turbidity than the Median of Gray Scale (MoGS) method, Median of Hue (MoH) method, and Random Position Median of Hue (RPMoH) method. In terms of the percentage accuracy of the efficiency test; the MAMoH method has 99 percent accuracy, while the MoGS method is not applicable, the MoH method has 88.04 percent accuracy, and the RPMoH method has 85.91 percent accuracy. Thus, the MAMoH method is considered an appropriate method for measuring coconut oil turbidity.
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Auto focusing ophthalmoscope for smartphone
(2020-10-29)
Navaitthiporn, Nitipon
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Rithcharung, Preeyarat
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Wongpa, Chuttima
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Treebupachatsakul, Treesukon
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Pechprasarn, Suejit
Several retinal pathologies can cause severe damages and may lead to permanent vision loss. Early diagnosis is highly recommended to take a precautionary measure to reduce the risk of ocular diseases. Therefore, an eye test by an ophthalmologist plays a crucial role. This does, however, require paying visit to a hospital, which becomes a burden for elders and patients with physical disabilities. Consequently, these patients usually get a late diagnosis and treatment, which the symptoms may have progressed and developed. Here, we develop an optical toolkit with engineered software for smartphones enabling the smartphone to take images of human retina as a direct ophthalmoscope. Thanks to the modern smartphone specifications, which usually come with high resolution cameras and sufficient computing power for the software. The optical toolkit illuminates an eye with an appropriate wavelength and light intensity and it has been provisionally tested for health and safety based on the ISO-10942 and IEC 62471 standards. An image processing algorithm is embedded in the software providing an auto focusing capability.

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