A digital background calibration technique using signal-dependent dithering is proposed, to correct the nonlinear errors which results from capacitor mismatches and finite opamp gain in pipelined analog-to-digital converter (ADC). Large magnitude dithers are used to measure and correct both errors simultaneously in background. In the proposed calibration system, the 2.5-bit capacitor-flip-over multiplying digital-to-analog converter (MDAC) stage is modified for the injection of large magnitude dithering by adding six additional comparators, and thus only three correction parameters in every stage subjected to correction were measured and extracted by a simple calibration algorithm with multibit first stage. Behavioral simulation results show that, using the proposed calibration technique, the signal-to-noise-and-distortion ratio improves from 63.3 to 79.3dB and the spurious-free dynamic range is increased from 63.9 to 96.4dB after calibrating the first two stages, in a 14-bit 100-MS/s pipelined ADC with σ=0.2% capacitor mismatches and 60dB nonideal opamp gain. The time of calibrating the first two stages is around 1.34 seconds for the modeled ADC.

Cluster computation has been used in the applications that demand performance, reliability, and availability, such as cluster server groups, large-scale scientific computations, distributed databases, distributed media-on-demand servers and search engines etc. In those applications, multicast can play the vital roles for the information dissemination among groups of servers and users. This paper proposes a set of novel efficient fault-tolerant multicast routing algorithms on hypercube interconnection of cluster computers using multicast shared tree approach. We present some new algorithms for selecting an optimal core (root) and constructing the shared tree so as to minimize the average delay for multicast messages. Simulation results indicate that our algorithms are efficient in the senses of short end-to-end average delay, load balance and less resource utilizations over hypercube cluster interconnection networks.

6DoF pose estimation from a monocular RGB image is a challenging but fundamental task. The methods based on unit direction vector-field representation and Hough voting strategy achieved state-of-the-art performance. Nevertheless, they apply the smooth l1 loss to learn the two elements of the unit vector separately, resulting in which is not taken into account that the prior distance between the pixel and the keypoint. While the positioning error is significantly affected by the prior distance. In this work, we propose a Prior Distance Augmented Loss (PDAL) to exploit the prior distance for more accurate vector-field representation. Furthermore, we propose a lightweight channel-level attention module for adaptive feature fusion. Embedding this Adaptive Fusion Attention Module (AFAM) into the U-Net, we build an Attention Voting Network to further improve the performance of our method. We conduct extensive experiments to demonstrate the effectiveness and performance improvement of our methods on the LINEMOD, OCCLUSION and YCB-Video datasets. Our experiments show that the proposed methods bring significant performance gains and outperform state-of-the-art RGB-based methods without any post-refinement.