Sample-time error between channels degrades resolution of time-interleaved analog-to-digital converters (TIADCs). A calibration method implemented in mixed circuits with low-complexity and fast-convergence is proposed in this paper. The algorithm for detecting sample-time error, which is widely applied to wide-sense stationary input signals, is based on correlation. The detected sample-time error is corrected by a voltage-controlled sampling switch. Experimental result of a 2-channel 200-MS/s 14-bit TIADC shows that the signal-to-noise-and-distortion ratio improves 19.1 dB, and the spurious-free dynamic range improves 34.6 dB for a 70.12-MHz input after calibration. The convergence time of the calibration is about 20000 sampling intervals.

In recent years, assessing action quality from videos has attracted growing attention in computer vision community and humancomputer interaction. Most existing approaches usually tackle this problem by directly migrating the model from action recognition tasks, which ignores the intrinsic differences within the feature map such as foreground and background information. To address this issue, we propose a Tube Self-Attention Network (TSA-Net) for action quality assessment (AQA). Specifically, we introduce a single object tracker into AQA and propose the Tube Self-Attention Module (TSA), which can efficiently generate rich spatio-temporal contextual information by adopting sparse feature interactions. The TSA module is embedded in existing video networks to form TSA-Net. Overall, our TSA-Net is with the following merits: 1) High computational efficiency, 2) High flexibility, and 3) The state-of-theart performance. Extensive experiments are conducted on popular action quality assessment datasets including AQA-7 and MTL-AQA. Besides, a dataset named Fall Recognition in Figure Skating (FR-FS) is proposed to explore the basic action assessment in the figure skating scene. Our TSA-Net achieves the Spearman's Rank Correlation of 0.8476 and 0.9393 on AQA-7 and MTL-AQA, respectively, which are the new state-of-the-art results. The results on FR-FS also verify the effectiveness of the TSA-Net. The code and FR-FS dataset are publicly available at https:// github.com/ Shunli-Wang/ TSA-Net.

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