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.

In integrated optics, it is very important to provide efficient optical coupling between fibers and waveguide devices. In this letter, we propose a fiber-waveguide coupler, whose guide width varies in propagation direction, and show its better coupling behavior than an uniform coupler.

A simple and practically accurate method for studying rectangular dielectric waveguides is proposed. This method can provide good evaluations of propagation constant and field distribution of the waveguides by analyzing improved equivalent guide models. In this paper, it is applied in studying a novel taper-formed waveguides system. The waveguides system was experimentally demonstrated of being advantageous in its lightwave transmission characteristics. Experimentally obtained results exhibited good agreement with the performances predicted by the system numerical analyses using the proposed waveguide study technique.

A real-time road-direction point detection model is developed based on convolutional neural network architecture which can adapt to complex environment. Firstly, the concept of road-direction point is defined for either single road or crossroad. For single road, the predicted road-direction point can serve as a guiding point for a self-driving vehicle to go ahead. In the situation of crossroad, multiple road-direction points can also be detected which will help this vehicle to make a choice from possible directions. Meanwhile, different types of road surface can be classified by this model for both paved roads and unpaved roads. This information will be beneficial for a self-driving vehicle to speed up or slow down according to various road conditions. Finally, the performance of this model is evaluated on different platforms including Jetson TX1. The processing speed can reach 12 FPS on this portable embedded system so that it provides an effective and economic solution of road-direction estimation in the applications of autonomous navigation.