Kawuu Weicheng Lin

Precise timing of ball-racket contact is a critical determinant of stroke performance in tennis, yet few studies have objectively quantified this variable across different players’ groups. This study developed and validated a hybrid vision-based framework combining K-nearest neighbors (KNN) background subtraction with Hue-Saturation-Value (HSV) color segmentation to analyze the spatial relationship between the contact point and the apex of the ball’s trajectory. Twelve participants were recruited and stratified by skill level and gender, each performing 30 forehand and 30 backhand strokes, yielding 720 stroke events. Contact point overlap ratios and vertical height distributions were extracted, and two-way ANOVA was used to examine main and interaction effects. Results indicated a significant effect of the skill level, with top-level players demonstrating higher overlap ratios and reduced vertical deviations compared to average-level players (p < 0.001, partial η² = 0.386). Gender differences were negligible among top-level players but pronounced in the average-level group, particularly for backhand strokes (p < 0.001). These findings highlight the skill level as the dominant factor influencing contact point precision, while gender disparities emerge primarily among less experienced athletes. The proposed computer vision approach offers a low-cost and scalable tool for objective stroke assessment and has potential applications in individualized training feedback and performance monitoring.

This study presents CoD-CAST+, a distributed clustering algorithm tailored for large-scale and high-dimensional data analysis. Built upon the CoD-CAST-GPU architecture, CoD-CAST+ preserves the core idea of on-demand affinity computation while introducing a hierarchical acceleration strategy that integrates multi-threading, GPU parallelism, and inter-node coordination. A visualization module is embedded to enhance interpretability, and the system design is guided by a systematic review of clustering algorithms developed over the past decade. Experimental results on a dataset containing 2,048,000 gene records demonstrate that CoD-CAST-GPU achieves a 12.44× speedup over the original CAST algorithm. Building on this, CoD-CAST+(Local) and the fully distributed CoD-CAST+ further extend the performance, with the final system achieving a 110.33× total speedup. Additionally, CoD-CAST+ is the first in the CAST family to support result visualization, making it an innovative and scalable solution for clustering analysis in ultra-large-scale data environments.

The amount of information nowadays is rapidly growing. Aside from valuable information, information that is unrelated to a target or is meaningless is also growing. Big data and broader digital technologies are considered the primary components of smart city governance and planning. Big data analysis is considered to define a new era in urban planning, research, and policy. Effective data mining and pattern detection techniques are becoming very important these days. Processing such a large amount of data entails the use of data mining, a technique that clarifies the association between valid information and excludes irrelevant data to implement a practical decision tree. A large amount of data affects processing time and I/O costs during data mining. This study proposes to distribute data among multiple clients and distribute a large amount of data computation equally to improve the resource cost problem of exploration. Following that, the main server consolidates the computation results and generates the survey results. Experiment results show that the proposed algorithm is superior, thus allowing a larger amount of data to be processed while producing high-quality results.

In recent years, knowledge discovery in databases provides a powerful capability to discover meaningful and useful information. For numerous real-life applications, frequent pattern mining and association rule mining have been extensively studied. In traditional mining algorithms, data are centralized and memory-resident. As a result of the large amount of data, bandwidth limitation, and energy limitations when applying these methods to distributed databases, especially in this era of big data, the performance is not effective enough. Hence, data mining on distributed environments has emerged as an important research area. To improve the performance, we propose a set of algorithms based on FP growth that discover FPs that are capable of providing fast and scalable service in distributed computing environments and a brief data structure to store items and counts to minimize the data for transmission on the network. To ensure completeness and execution capability, DistEclat and BigFIM were considered for the experiment comparison. Experiments show that the proposed method has superior cost-effectiveness for processing massive datasets and good capabilities under various experiment conditions. The proposed method on average required only 33% of the execution time and 45% of the transmission cost of DistEclat. Compared to BigFIM, The proposed method on average required 23.3% of the execution time and 14.2% of the transmission cost of BigFIM.

Data mining is a set of methods used to mine hidden information from data. It mainly includes frequent pattern mining, sequential pattern mining, classification, and clustering. Frequent pattern mining is used to discover the correlation among various sets of items within large databases. The rapid upward trend in data size slows the mining of frequent patterns. Numerous studies have attempted to develop algorithms that operate in distributed computing environments to accelerate the mining process. FLR-mining (Fast, Load balancing and Resource efficient mining algorithm) is one of the fastest methods of mining with efficient consideration of load balancing and resources. FLR-mining can automatically determine the appropriate number of computing nodes. However, FLR-mining and existing methods assume that the network bandwidth is constant. In practical distributed and many-task computing systems, this assumption fails because there are packet collisions caused by many mining tasks that run in a simultaneous manner. Therefore, a method that can consider the varying network bandwidth is necessary. In this study, we propose a method that can rapidly mine frequent patterns under the varying network bandwidth. The proposed method can also determine the appropriate number of computing nodes to efficiently utilize computing resources and achieve load balancing. Through empirical evaluation, the proposed method is shown to deliver excellent performance in terms of execution efficiency and load balancing.

Along with the development of communication technology, the degree of involvement of location-based service in daily life increases accordingly. Through further combination between mobile devices and micro-location technology, mobile devices can now offer more precise and high-quality services which allow users to obtain required information while moving. The proposed intelligent device-oriented control system is based on the devices to be controlled and the location they use devices, providing a control service system with dynamic operation interface: when detecting the approaching users, the system would automatically notify users the available devices and provide users with the control options for the devices. The system function would come along with the device to be controlled, and the user interface would change with user's location and devices in the location, reducing the complexity of operation and enabling users to focus on the present objects to be operated. This system can be combined with I/O or smart devices, providing users with location-based device-oriented control service, making the update and maintenance of IoT application control system easier.