With the recent progress in sports analytics, deep learning approaches have demonstrated the effectiveness of mining insights into players' tactics for improving performance quality and fan engagement. This is attributed to the availability of public ground-truth datasets. While there are a few available datasets for turn-based sports for action detection, these datasets severely lack structured source data and stroke-level records since these require high-cost labeling efforts from domain experts and are hard to detect using automatic techniques. Consequently, the development of artificial intelligence approaches is significantly hindered when existing models are applied to more challenging structured turn-based sequences. In this paper, we present ShuttleSet, the largest publicly-available badminton singles dataset with annotated stroke-level records. It contains 104 sets, 3,685 rallies, and 36,492 strokes in 44 matches between 2018 and 2021 with 27 top-ranking men's singles and women's singles players. ShuttleSet is manually annotated with a computer-aided labeling tool to increase the labeling efficiency and effectiveness of selecting the shot type with a choice of 18 distinct classes, the corresponding hitting locations, and the locations of both players at each stroke. In the experiments, we provide multiple benchmarks (i.e., stroke influence, stroke forecasting, and movement forecasting) with baselines to illustrate the practicability of using ShuttleSet for turn-based analytics, which is expected to stimulate both academic and sports communities. Over the past two years, a visualization platform has been deployed to illustrate the variability of analysis cases from ShuttleSet for coaches to delve into players' tactical preferences with human-interactive interfaces, which was also used by national badminton teams during multiple international high-ranking matches.

In this study, an affordable data collection solution is proposed for the tactical and strategic analysis of badminton. To make mass technical data collection possible, broadcast video is considered as the data source, and an intuitive and efficient video labeling tool is designed and implemented. The contribution of this work includes three folds. First, in response to the needs of the next generation of badminton technical and tactical analysis, the concept of Shot-By-Shot Labeling is proposed and a prototype data representation is formulated. Second, UI designs and computer vision techniques are utilized to develop an efficient computer-aided labeling tool for shot-by-shot technical data collection from match videos. Third, a Shot-By-Shot Labeling dataset composed of six badminton matches is shared with the community to accelerate related research. The dataset is available at https://drive.google.com/drive/folders/1eweBFa_M_CnYcqkQ5IHJ-Y8ag3TqRwUh.

In a later stage of a VLSI design, it is quite often to modify a design implementation to accommodate the new specification, design errors, or to meet design constraints. In addition to meet the design schedule for the new implementation, the reduction of the mask set have become very critical. In this paper, we propose a new method to add a programmable rectification module to reduce the mask cost and to improve the turn around time. When a modification is needed, one can program the rectification module to achieve the new implementation. The rectification module can be designed by one mask programmable gate array, or an embedded FPGA. To reduce the size needed for the rectification module, we also propose algorithms, which can intelligently select some internal signals of the old implementation to become pseudo primary inputs and primary outputs. Our experimental results are very encouraging.