Detection of small-sized maritime targets is an important task for a marine surveillance radar. Recently, with the emergence of a marine surveillance radar system that has a narrow azimuth beamwidth and rapidly rotating antennas, the available dwell time for detecting a maritime target is usually very short. This short dwell time considerably degrades the performance of conventional detectors, especially those focusing on small-sized targets. In this paper, we propose an efficient detector for small-sized maritime targets to provide a reliable detection performance, even in short dwell times. The proposed scheme is based on a new joint metric, which results from the product of the magnitude and difference features in the Doppler spectra. We discriminate the target bins from sea clutter bins using a statistical discriminator based on the joint metric, whose probability density function follows the product distribution of standard gamma distributions. Compared to conventional detectors, the proposed scheme can provide a robust performance in terms of the average signal-to-clutter ratio as well as the detection rate, especially in shorter dwell times.

한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)

3D pose estimation of multi-person in real-time is a challenging problem which is essential for a various virtual reality (VR) applications. In this paper, we propose a real-time multi-person 3D pose estimation system for live streaming of 3D animation with multiple RGB-D cameras. The proposed system comprises the several edge devices connected to a central server via networks. 2D pose detection and depth sensing is locally conducted on each edge device. The edge device transmits the results to the central server. 3D pose reconstruction is performed on the central server. The central server aligns the multiple camera coordinates with a specific world plane. Then, the central server matches the 2D pose result across the multiple cameras to a person based on the distance. Finally, the 3D poses are reconstructed based on multi-view triangulation. The proposed system is capable of processing in real-time. To demonstrate the proposed system estimates multi-person 3D poses in real-time, we implement the prototypes and show the proposed system can be applied to the live streaming of 3D animation such as PC and Web.

Reliable environmental monitoring in remote or sparsely instrumented regions is hindered by the cost, maintenance demands, and inaccessibility of dense physical sensor deployments. To address these challenges, this study introduces VSG-SGL, a unified virtual sensor generation framework that integrates Sparse Gaussian Process Regression (SGPR) and Bayesian Ridge Regression (BRR) with deep generative learning via Variational Autoencoders (VAE) and Conditional Tabular GANs (CTGAN). Real meteorological datasets from multiple South Korean cities were preprocessed using thresholding and Isolation Forest anomaly detection and evaluated using distributional alignment (KDE) and sequence-learning validation with BiLSTM and BiGRU models. Experimental findings demonstrate that VAE-augmented virtual sensors provide the most stable and reliable performance. For temperature, VAE maintains predictive errors close to those of BRR and SGPR, reflecting the already well-modeled dynamics of this variable. In contrast, humidity and wind-related variables exhibit measurable gains with VAE; for example, SGPR-based wind speed MAE improves from 0.1848 to 0.1604, while BRR-based wind direction RMSE decreases from 0.1842 to 0.1726. CTGAN augmentation, however, frequently increases error, particularly for humidity and wind speed. Overall, the results establish VAE-enhanced VSG-SGL virtual sensors as a cost-effective and accurate alternative in scenarios where physical sensing is limited or impractical.

KSII Transactions on Internet and Information Systems TIIS Paper Details

Among the weather networks in South Korea, although basic variables are recorded at 600 to 744 stations nationwide, only approximately 67 and 31 are equipped with sensors for detecting solar radiation (GSR) and particulate matter (PM10), respectively. Exploiting this asymmetry, we propose a machine-learning-based virtual densification to facilitate estimations of sparse variables from co-located basic-weather variables, evaluated based on leave-station-out (LSO) cross-validation, excluding entire stations to emulate sensor-free locations. Solar radiation was found to be effectively densified (LSO R2 = 0.76 ± 0.03), far exceeding the Hargreaves-Samani baseline (R2 = 0.54) and was stable without coordinates, indicating a physical relationship. Comparatively, PM10 reaches only R2 = 0.28 from weather alone, a control for emission-driven variables. Although machine learning (RandomForest, XGBoost) achieves significantly higher predictive accuracy than the empirical and linear baselines (p < 0.001), the two models are mutually indistinguishable. Given that solar radiation is a required wet-bulb globe temperature input, the virtual solar field can directly support heat-disaster risk assessment in locations in which sensors are absent.