Presentations -
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Optimizing Network Message Regulations Using AI-Enhanced Dynamic Programming Methods International conference
Thi Thi Zin, Tunn Cho Lwin, H. Hama, Pyke Tin
IEEE International Conference on Consumer Electronics – Taiwan (ICCE-TW), 2025 (Kaohsiung, Taiwan) 2025.7.17 IEEE Consumer Technology Society
Event date： 2025.7.16 - 2025.7.18
Language：English Presentation type：Oral presentation (general)
Venue：Kaohsiung, Taiwan Country：Taiwan, Province of China
Network message transmission efficiency faces increasing challenges in multi-server systems due to complex traffic patterns and resource allocation demands. This paper presents an AI-enhanced dynamic programming approach for optimizing message flow regulations. By formulating the problem as a Markov Decision Process (MDP) and integrating reinforcement learning techniques, we develop an adaptive framework for network message regulation. Experimental results show our approach achieves 25% reduction in queue length and 30% improvement in resource utilization compared to conventional methods.
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Behavior Estimation of Calf Groups Using RGB Cameras and Deep Learning International conference
D. Nishimoto, Thi Thi Zin, M. Aikawa
IEEE International Conference on Consumer Electronics – Taiwan (ICCE-TW), 2025 (Kaohsiung, Taiwan) 2025.7.17 IEEE Consumer Technology Society
Event date： 2025.7.16 - 2025.7.18
Language：English Presentation type：Oral presentation (general)
Venue：Kaohsiung, Taiwan Country：Taiwan, Province of China
This paper presents a non-contact, real-time behavior estimation system for calf groups on large-scale farms. Leveraging an RGB camera and deep learning techniques, the proposed method detects calves via YOLO and tracks them using a Hungarian + Weighted IoU + Re-identification framework to maintain consistent IDs. The Segment Anything Model2 is employed to extract calf regions from each frame, and EfficientNetv2-L is used to identify individuals from these regions. By classifying postures (sitting, standing) and detecting specific intake behaviors (drinking milk, drinking water, eating), the system enables comprehensive health monitoring of each calf. Experiments conducted on 16 calves (Holstein and Jersey) achieved 91.33% MOTA in multi-object tracking, approximately 80% accuracy for posture classification, and 50–70% for intake behaviors. Furthermore, the integrated system processes five frames in about 0.70 seconds, meeting real-time requirements. These results suggest that the proposed approach can effectively reduce labor burdens, support early disease detection, and facilitate scalable livestock management.
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A study on action recognition for the elderly using depth camera International conference
Remon Nakashima, Thi Thi Zin, Hiroki Tamura, Shinji Watanabe, Etsuo Chosa
IEEE International Conference on Consumer Electronics – Taiwan (ICCE-TW), 2025 (Kaohsiung, Taiwan) 2025.7.17 IEEE Consumer Technology Society
Event date： 2025.7.16 - 2025.7.18
Language：English Presentation type：Oral presentation (general)
Venue：Kaohsiung, Taiwan Country：Taiwan, Province of China
In Japan, the rapid aging of the population has exacerbated the shortage of caregiving staff, making the optimization of care environments imperative. Conventional video surveillance methods extract human regions to perform action recognition; however, these approaches often fail to capture detailed motion analysis. In this study, a depth camera-based system is proposed to achieve non-contact, privacy-preserving action recognition using human skeleton recognition. Specifically, human regions are first extracted using bounding box detection (BB), followed by action recognition based on Keypoint-based pose estimation. The estimated Keypoints capture detailed joint positions, and their structural relationships are modeled using a Graph Convolutional Network (GCN). Furthermore, a Transformer is employed to capture the temporal features of the skeletal data. This Keypoint-centric method distinguishes this approach from conventional methods and significantly enhances the granularity of action recognition.
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Research on Feature Extraction for Prediction of Dystocia in Cows Using Image Processing Technology International conference
T. Murayama, Thi Thi Zin, I. Kobayashi, M. Aikawa
IEEE International Conference on Consumer Electronics – Taiwan (ICCE-TW), 2025 (Kaohsiung, Taiwan) 2025.7.17 IEEE Consumer Technology Society
Event date： 2025.7.16 - 2025.7.18
Language：English Presentation type：Oral presentation (general)
Venue：Kaohsiung, Taiwan Country：Taiwan, Province of China
In dairy farming, an aging operator population and a shortage of successors have led to a decline in the number of farms rearing milking cows, while the number of milking cows per farm is increasing. Under these circumstances, effective calving management has become critical. Calving fatalities cause significant economic losses to dairy operations, and dystocia accounts for approximately 20% of these cases. Without proper intervention during dystocia, the risk of fatal incidents rises and the labor burden on farmers increases. Therefore, there is a strong demand for technology that can detect early signs of calving and reduce accidents. In this study, a ceiling-mounted 360° camera was used to record cow behavior before calving. Quantitative features—including posture changes, tail-up behavior, and walking distance—were extracted and computed to develop indicators effective for predicting the onset of calving and detecting dystocia.
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Machine Learning-Based Classification of Umbilical Cord Blood Gas Using Fetal Heart Rate Variability International conference
Tunn Cho Lwin, Thi Thi Zin, Pyke Tin, E. Kino, T. Ikenoue
IEEE International Conference on Consumer Electronics – Taiwan (ICCE-TW), 2025 (Kaohsiung, Taiwan) 2025.7.17 IEEE Consumer Technology Society
Event date： 2025.7.16 - 2025.7.18
Language：English Presentation type：Oral presentation (general)
Venue：Kaohsiung, Taiwan Country：Taiwan, Province of China
Fetal heart rate variability (FHRV) is a key indicator of fetal well-being and has potential in predicting umbilical cord blood gas, an essential biomarker for fetal health assessment. Machine learning techniques can enhance fetal pH classification using FHRV features. This study aims to develop a machine learning-based classification model for fetal pH levels, leveraging FHRV data to support early risk detection during childbirth. To achieve this, we classify fetal pH into two categories using Mahalanobis Distance, Support Vector Machine (SVM), and k-Nearest Neighbors (kNN) based on statistical FHRV features. Model performance was evaluated using confusion matrices for both training and testing datasets. Among the classifiers, SVM demonstrated the best generalizability, suggesting its potential for FHRV-based fetal pH prediction. Future work will focus on refining feature selection and improving classification accuracy to enhance fetal monitoring.
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Minimizing Resource Usage for Real-Time Network Camera Tracking of Black Cows International conference
Aung Si Thu Moe, Thi Thi Zin, Pyke Tin, M. Aikawa, I. Kobayashi
2025 10th International Conference on Multimedia and Image Processing (ICMIP 2025) (Okinawa, Japan) 2025.4.27 University of the Ryukyus, Japan and Ritsumeikan University, Japan
Event date： 2025.4.26 - 2025.4.28
Language：English Presentation type：Oral presentation (general)
Venue：Okinawa, Japan Country：Japan
Livestock plays a crucial role in the farming industry to meet consumer demand. A livestock monitoring system helps track animal health while reducing labor requirements. Most livestock farms are small, family-owned operations. This study proposes a real-time black cow detection and tracking system using network cameras in memory and disk constrained environments. We employ the Detectron2 Mask R-CNN ResNeXt-101 model for black cow region detection and the ByteTrack algorithm for tracking. ByteTrack tracks multiple objects by associating each detection box. Unlike other deep learning tracking algorithms that use multiple features such as texture, color, shape, and size. ByteTrack effectively reduces tracking ID errors and ID switches. Detecting and tracking black cows in real-time is challenging due to their uniform color and similar sizes. To optimize performance on low-specification machines, we apply ONNX (Open Neural Network Exchange) to the Detectron2 detection model for optimization and quantization. The system processes input images from network cameras, enhances color during preprocessing, and detects and tracks black cows efficiently. Our system achieves 95.97% mAP@0.75 detection accuracy and 97.16 % in daytime video and 94.83 % in nighttime accuracy of tracking are effectively tracks individual black cows, minimizing duplicate IDs and improving tracking after missed detections or occlusions. The system is designed to operate on machines with minimal hardware requirements.
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A Depth Camera-Based Dangerous Action Detection System in Elderly Care Center International conference
Remon Nakashima, Thi Thi Zin, Hiroki Tamura, Hideoki Terada
2025 10th International Conference on Multimedia and Image Processing (ICMIP 2025) (Okinawa, Japan) 2025.4.27 University of the Ryukyus, Japan and Ritsumeikan University, Japan
Event date： 2025.4.26 - 2025.4.28
Language：English Presentation type：Oral presentation (general)
Venue：Okinawa, Japan Country：Japan
In Japan’s aging society, monitoring and early detection of dangerous actions in elderly care facilities is crucial. This paper presents a novel non-contact system that focuses on analyzing residents’ body poses relative to predetermined semantic regions within the care environment. The proposed framework first employs a state-of-the-art pose estimation network to extract skeletal Keypoints, particularly focusing on critical joint positions such as the shoulders and hips. Next, a semantic segmentation algorithm is applied to delineate key furniture regions (e.g., beds and chairs) within the facility. A spatial analysis is then performed to determine the degree of overlap between the resident’s bounding box and these segmented regions, thus identifying deviations from expected safe postures. A temporal smoothing mechanism, based on a sliding window with majority voting, is incorporated to correct transient misclassifications and stabilize the detection results. The system was validated through experiments conducted in an actual elderly care facility, where various dangerous actions such as abrupt postural changes and unsupervised bed-leaving were simulated. Quantitative analysis of the experimental data demonstrated that the method reliably classifies actions into three distinct risk levels: Safe, Attention, and Danger. This detailed analytical approach provides a solid basis for early detection of hazardous actions by continuously monitoring posture dynamics and spatial relationships, thereby facilitating timely intervention. The results of this study underscore the potential of the proposed method to contribute significantly to the proactive management of safety risks in elderly care settings.
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Machine learning-based prediction of cattle body condition score using 3D point cloud surface features International conference
Pyae Phyo Kyaw, Thi Thi Zin, Pyke Tin, M. Aikawa, I. Kobayashi
2025 10th International Conference on Multimedia and Image Processing (ICMIP 2025) (Okinawa, Japan) 2025.4.27 University of the Ryukyus, Japan and Ritsumeikan University, Japan
Event date： 2025.4.26 - 2025.4.28
Language：English Presentation type：Oral presentation (general)
Venue：Okinawa, Japan Country：Japan
Body Condition Score (BCS) of dairy cattle is a crucial indicator of their health, productivity, and reproductive performance throughout the production cycle. Recent advancements in computer vision techniques has led to the development of automated BCS prediction systems. This paper proposes a BCS prediction system that leverages 3D point cloud surface features to enhance accuracy and reliability. Depth images are captured from a top-view perspective and processed using a hybrid depth image detection model to extract the cattle’s back surface region. The extracted depth data is converted into point cloud data, from which various surface features are analyzed, including normal vectors, curvature, point density, and surface shape characteristics (planarity, linearity, and sphericity). Additionally, Fast Point Feature Histograms (FPFH), triangle mesh area, and convex hull area are extracted and evaluated using three optimized machine learning models: Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting (GB). Model performance is assessed using different tolerance levels and error metrics, including Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Among the models, Random Forest demonstrates the highest performance, achieving accuracy rates of 51.36%, 86.21%, and 97.83% at 0, 0.25, and 0.5 tolerance levels, respectively, with an MAE of 0.161 and MAPE of 5.08%. This approach enhances the precision of BCS estimation, offering a more reliable and automated solution for dairy cattle monitoring and health management.
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Advanced Multimodal Analysis of Black Cattle Mounting Behavior Using YOLO and Open-World Object Detection Techniques International conference
Su Myat Noe, Thi Thi Zin, Pyke Tin, and I. Kobayashi
2025 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'24) 2025.3.2
Event date： 2025.2.27 - 2025.3.2
Language：English Presentation type：Oral presentation (general)
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A Markovian Queueing Model for Internet of Things International conference
Pyke Tin, Thi Thi Zin, H. Hama
2025 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'24) 2025.3.2
Event date： 2025.2.27 - 2025.3.2
Language：English Presentation type：Oral presentation (general)
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Vision-Based Person Re-Identification Through Gait Recognition Using Long Short-Term Memory International conference
Cho Nilar Phyo, R. Tanno, Thi Thi Zin, Pyke Tin
2025 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'24) 2025.3.2
Event date： 2025.2.27 - 2025.3.2
Language：English Presentation type：Oral presentation (general)
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Quantifying Elderly Walking States Using Keypoint Data from OpenPose and Image Processing International conference
R. Tanno, Cho Nilar Phyo and Thi Thi Zin
2025 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'24) 2025.3.2
Event date： 2025.2.27 - 2025.3.2
Language：English Presentation type：Oral presentation (general)
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Quantitative assessment of fetal heart rate variability using Mahalanobis distance for pH classification
2024.12.21
Event date： 2024.12.21 - 2024.12.22
Language：English Presentation type：Oral presentation (general)
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軽量なPointNet++モデルを用いたカラー点群に基づく牛識別システム
Pyae Phyo Kyaw, Thi Thi Zin, Pyke Tin, 相川 勝, 小林 郁雄
第26回日本知能情報ファジィ学会九州支部学術講演会 2024.12.21
Event date： 2024.12.21 - 2024.12.22
Language：English Presentation type：Oral presentation (general)
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深度カメラを用いた高齢者の行動推定に関する研究
中嶋麗文，Thi Thi Zin，近藤 千尋，渡邉 信二
第37回バイオメディカル・ファジィ・システム学会年次大会 (BMFSA2024) 2024.12.14
Event date： 2024.12.14 - 2024.12.15
Language：Japanese Presentation type：Oral presentation (general)
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Analyzing Parameter Patterns in YOLOv5-based Elderly Person Detection Across Variations of Data International conference
Ye Htet, Thi Thi Zin, Pyke Tin, H. Tamura, K. Kondo, S. Watanabe, E. Chosa
2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS) 2024.9.25
Event date： 2024.9.23 - 2024.9.25
Language：English Presentation type：Oral presentation (general)
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Fusion of Strategic Queueing Theory and AI for Smart City Telecommunication System International conference
Thi Thi Zin, Aung Si Thu Moe, Cho Nilar Phyo, Pyke Tin
2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS) 2024.9.25
Event date： 2024.9.23 - 2024.9.25
Language：English Presentation type：Oral presentation (general)
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Comparative Study of Predicting Fetal pH Level based on Heart Rate Variability International conference
Cho Nilar Phyo, Tunn Cho Lwin, Pyae Phyo Kyaw, E. Kino, T. Ikenoue, Pyke Tin and Thi Thi Zin
The 18th International Conference on Innovative Computing, Information and Control (ICICIC2024) 2024.9.12
Event date： 2024.9.10 - 2024.9.13
Language：English Presentation type：Oral presentation (general)
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Integrating Entropy Measures of Fetal Heart Rate Variability with Digital Twin Technology to Enhance Fetal Monitoring International conference
Tunn Cho Lwin, Thi Thi Zin, Pyae Phyo Kyaw, Pyke Tin, E. Kino and T. Ikenoue
The 18th International Conference on Innovative Computing, Information and Control (ICICIC2024) 2024.9.12
Event date： 2024.9.10 - 2024.9.13
Language：English Presentation type：Oral presentation (general)
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A Study on the Analysis and Classification of Gait Status Using Gait Information International conference
R. Tanno, Thi Thi Zin and Cho Nilar Phyo
The 18th International Conference on Innovative Computing, Information and Control (ICICIC2024) 2024.9.12
Event date： 2024.9.10 - 2024.9.13
Language：English Presentation type：Oral presentation (general)