This paper presents a cloud-native Lakehouse architecture designed for real-time semiconductor analytics, with a focus on optimizing storage tiering, data lineage, and cost-energy co-optimization. As semiconductor data analytics require processing massive amounts of real-time data, traditional data warehouses are often insufficient in addressing the need for low-latency, high-concurrency queries. The proposed framework leverages cloud-native technologies, such as AWS, Azure, and distributed databases like Apache Doris, to design a dynamic multi-tier storage system that segregates data based on access frequency and volatility, incorporating columnar compression techniques for efficient storag... More >

Human Action Recognition (HAR) in unconstrained video remains difficult due to cluttered backgrounds, camera motion, and long-range temporal dependencies. The recognition of human action is the most complex study in the area of Artificial Intelligence (AI) and Computer Vision (CV). Machine vision for online and offline video processing is typically employed in the development of human behavior recognition systems. In video broadcasting and analysis, identifying the type and content of human actions present in the footage is a fundamental requirement. In this article, we propose a compact and deployable Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) framework that combines a 2... More >

The proliferation of unmanned aerial vehicles (UAVs) has driven the emergence of Flying Ad-hoc Networks (FANETs) as distributed cyber-physical computing systems for surveillance, disaster management, precision agriculture, and logistics. Optimizing computational and communication resource utilization within these constrained aerial systems is essential to extend network lifespan and maintain satisfactory quality of service (QoS). This study proposes integrating the Fisheye State Routing (FSR) protocol with a 3$\times$3 Manhattan Grid mobility model as a computing-systems solution for reliable data exchange in dynamic UAV swarm networks. FSR maintains distributed topology tables and applies... More >

Vietnam's Electric Vehicle (EV) market is expanding rapidly, yet public charging infrastructure development lags significantly, exhibiting pronounced spatial imbalance in dense urban cores. This study addresses this gap through an integrated demand forecasting and location optimization framework for District 1, Ho Chi Minh City. We develop a log-linear regression model using Vietnam's macroeconomic data (2003–2023), identifying GDP and CPI as dominant determinants of vehicle ownership (R$^2$ = 0.962). Forecasted vehicle stocks for 2026–2030 are translated into public charging demand through vehicle-type disaggregation and service-capacity modeling. Spatially, we propose a four-stage opti... More >

AI-driven metaheuristic optimization algorithms have demonstrated strong potential in solving complex, high-dimensional engineering design problems. This paper applies and systematically compares three representative swarm-intelligence and evolutionary computation methods—particle swarm optimization (PSO), whale optimization algorithm (WOA), and grey wolf optimizer (GWO)—to the parameter design of LCL filters in single-phase grid-tied inverter systems, a class of problems characterized by nonlinear constraints and competing performance objectives. A structured optimization framework is developed, incorporating an objective function targeting total harmonic distortion (THD) minimization a... More >