Hue University Journal of Science: Techniques and Technology

Design of a high-speed data transmission link using free-space optics communication technology for metropolitan information networks

Wed, 31 Dec 2025 00:00:00 +0000

This paper presents a process of design and simulation of a high-speed data transmission link using free-space optical communication (FSO) technology which is suitable for metropolitan information network applications. Based on technical requirements of data speed and transmission distance, the key system components will be designed and configured with appropriate parameters. Important quality criteria such as quality factor (Q) and bit error rate (BER) will be evaluated and selected through the process of simulating and analyzing system performance. In particular, the investigation results show that a large antenna aperture diameter could significantly improve the signal reception and enhances the quality factor Q, minimizing BER. At the same time, a narrower beam divergence of the transmitting antenna helps to better concentrate the energy, limit the loss due to scattering and turbulence in the transmission environment. However, these two parameters need to be carefully considered and selected because they are also related to the physical size of the device and the cost of the system which are important factors affecting the feasibility of the system when deployed in practice. In addition, other factors such as cut-off frequency of the receiving power filter, visibility index of the environment and transmission power level affect the signal quality, significantly contributing to the design of the entire FSO system.

EVALUATION OF THE EFFECTIVENESS OF MODERN OBJECT DETECTION MODELS ON SPATIAL IMAGE DATA

Nguyễn Dũng, Van-Dung Hoang, Van-Tuong-Lan Le — Wed, 31 Dec 2025 00:00:00 +0000

Object detection in aerial imagery, especially from unmanned aerial vehicles (UAVs), presents numerous challenges due to varying altitudes, occlusions, and diverse object scales—particularly the detection of small objects. This paper provides a comparative evaluation of three advanced object detection models: YOLOv11, RT-DETR, and RF-DETR, using the VisDrone2019 dataset, which includes complex urban and suburban scenes captured from UAVs. We analyze the models based on key performance metrics such as mean average precision (mAP), inference speed, model size, and computational complexity. Experimental results show that YOLOv11 achieves the highest processing speed, making it especially suitable for real-time applications due to its fast inference and strong edge-device performance. RF-DETR, on the other hand, achieves the best accuracy, with the fastest mAP@0.5 and mAP@[0.5:0.95] scores of 46.9% and 26.6%, respectively, demonstrating effectiveness in complex scenarios with high object density and occlusions. RT-DETR offers a balanced trade-off between speed and accuracy, making it a practical choice for applications requiring both responsiveness and reliable detection quality. These findings clarify the strengths and limitations of each model and provide practical guidance for selecting suitable object detection models in UAV-based surveillance and tracking tasks.

AN INTEGRATED APPROACH COMBINING NEURAL NETWORKS AND GENETIC ALGORITHMS FOR MULTISOURCE TIME SERIES FORECASTING

Ngô Văn Sơn — Wed, 31 Dec 2025 00:00:00 +0000

This paper proposes a multisource data-driven approach to enhance time series forecasting in the tourism domain. Specifically, we integrate a multilayer perceptron (MLP) neural network with the NSGA-II genetic algorithm to optimize the model’s hyperparameters, replacing manual tuning or traditional GA-based methods. In addition to historical data on monthly international tourist arrivals to Vietnam, two exogenous data sources are incorporated: (i) sentiment indices extracted from tourist reviews on TripAdvisor, and (ii) search trend data from Google Trends. Key hyperparameters such as input window size, learning rate, number of epochs, early stopping, and normalization methods are optimized through the genetic algorithm. Experimental results indicate that the NSGA-II-MLP model using multisource data outperforms the standard MLP and maintains stable performance across both pre-COVID-19 and pandemic periods. The results underscore the effectiveness of combining multisource data with NSGA-II optimization for tourism demand forecasting.

Fine-tuning deep learning models on microscopic images of liver and intestine cells of shrimps using k-fold cross-validation

Thi Thu Thao Khong — Wed, 31 Dec 2025 00:00:00 +0000

We employ the fine-tuning technique to train compact and efficient deep convolutional neural networks—specifically MobileNet_V2, MobileNet_V3_Small, and MobileNet_V3_Large – to classify the nutritional status of farmed shrimp. The classification is based on microscopic images of liver and intestinal cells, enabling rapid and scalable assessment of shrimp health through image-based diagnostics. The experiment was conducted on a dataset comprising 854 cellular images, and used k-fold cross-validation to split the dataset into the training and test sets. The pre-trained MobileNet_V3_Large was fine-tuned on our cellular image dataset using 10-fold cross-validation, achieving the highest average classification accuracy of 90.89%. This study demonstrates the potential of applying deep learning techniques to the monitoring and nutritional management of farmed shrimp, aiming to enhance productivity in aquaculture operations.