Recently, weakly supervised methods for scene text spotter are increasingly popular with researchers due to their potential to significantly reduce dataset annotation efforts. The latest progress in this field is text spotter based on single or multi-point annotations. However, this method struggles with the sensitivity of text recognition to the precise annotation location and fails to capture the relative positions and shapes of characters, leading to impaired recognition of texts with extensive rotations and flips. To address these challenges, this paper develops a novel method named Coarse-point-supervised Scene Text Spotter (Cps-STS). Cps-STS first utilizes a few approximate points as text location labels and introduces a learnable position modulation mechanism, easing the accuracy requirements for annotations and enhancing model robustness. Additionally, we incorporate a Spatial Compatibility Attention (SCA) module for text decoding to effectively utilize spatial data such as position and shape. This module fuses compound queries and global feature maps, serving as a bias in the SCA module to express text spatial morphology. In order to accurately locate and decode text content, we introduce features containing spatial morphology information and text content into the input features of the text decoder. By introducing features with spatial morphology information as bias terms into the text decoder, ablation experiments demonstrate that this operation enables the model to effectively identify and utilize the relationship between text content and position to enhance the recognition performance of our model. One significant advantage of Cps-STS is its ability to achieve full supervision-level performance with just a few imprecise coarse points at a low cost. Extensive experiments validate the effectiveness and superiority of Cps-STS over existing approaches.

In this paper, we propose a novel method termed as the histogram and ultrafast segmented model identification of linearity errors (H-USMILE), in order to implement an efficient linearity test of ADCs. We use the standard histogram method with only a small number of test samples to obtain coarse INL results, along with the segmented non-parametric model of the ultrafast segmented model identification of linearity errors (USMILE) algorithm employed to convert the coarse INL results into precise ones. Experimental results show that, by using the proposed method, we can significantly reduce the required test data and test time while maintaining or even improving the precision of the standard histogram method.

In the workflow of SKA-SDP (Square Kilometer Array Radio Telescope-Scientific Data Processing), FFT (Fast Fourier Transform) calculation takes a significant proportion of computation overhead. Moreover, FFT computation has to be done within the tight power budget, which existing generic high performance computing architectures cannot meet. To explore power efficiency of FFT computation, this study is designed with initial evaluation of FFT implementation in variable precision on Xilinx ML605 FPGA (field programmable gate array). The FPGA-based implementation of FFT includes a Xilinx IP Core version 7.1, which supports fixed-point and floating-point computation in single precision. The input data width and phase factor width of fixed-point computation can be varied from 8 bits to 34 bits, allowing that calculation accuracy can be adjusted by setting the width. Since single precision is redundant to accuracy requirement of SKA-SDP, fixed-point calculation is designed to emulate single-precision floating point computation. Calculational power dissipation and throughput with different phase factors on FPGA was measured respectively. The final result demonstrates that the implementation on FPGA ensures sufficient precision at a much less power cost compared with floating-point FFT. In other words, this study indicates variable precision computation would be an efficient way to improve power efficiency.