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This paper proposes a 16-bit 6-channel high-voltage successive approximation register (SAR) ADC with an optimized 5 + 5 + 6 segmented capacitor array. The lower 10 bits of the capacitor array are all composed of unit capacitors without any calibration unit. Without calibration, the lower 10 bits of the capacitor array can ensure 10-bit conversion accuracy. Every of the upper 6 bits of the capacitor array contains a linearity calibration unit. The linearity error of the upper 6 bits is calibrated by the linearity calibration unit. The 16-bit is manufactured by a $0.6~\mu m$ standard COMS process, and the total chip area of 6-channel ADC including pads is 6.6mm $\times6.6$ mm. As for single channel SAR ADC, the area is 0.9mm $\times2.0$ mm. The measurement results show that the effective conversion accuracy of the SAR ADC reaches 13 bits by using novel differential nonlinearity (DNL) and integral nonlinearity (INL) calibration methods. The power is 80mW, corresponding to a Figure of Merit (FOM) of 48 pJ/conv.-step.

Multi-spectral imaging (MSI) is a novel non-invasive tool for visualizing the entire span of the eye, from the internal limiting membrane to the choroid. However, spatial misalignments can be frequently observed in sequential MSI images because the eye saccade movement is usually faster than the MSI image acquisition speed. Therefore, registering MSI images is necessary for computer-based analysis of retinal degeneration via MSI. In this paper, we propose an early deep learning framework for achieving an accurate registration of MSI images in a group-wise fashion. The framework contains three parts: a template construction based on principal component analysis, a deformation field calculation, and a spatial transformation. The framework is uniquely capable of resolving two key challenges, i.e., the “multi-modal” characteristics in MSI images for the acquisition with different spectra and the requirement of joint registration of the sequential images. Our experimental results demonstrate the superior performance of our framework compared to several representative state-of-the-art techniques in both speed and accuracy.

With the rise of crowdsourcing economies, the home delivery business is now undergoing rapid development. The authentication schemes that are currently used in the home delivery business remain unanalyzed from the provable authenticity perspective. In this paper, we generalize the “cross-realm authentication path”of the Kerberos protocol and apply it to the online ordering and offline delivery business. We design a Kerberos-based scheme for the crowdsourcing delivery model to establish authenticity for principals, including customers, suppliers, and deliverymen. Then we extend it to some more complex models, such as the receiving agent model, the unified service of franchised chain model, and the relay delivery model. The authentication schemes can also be incorporated into the door access and guard system to enhance the physical security in smart cities.

Electrification of transportation has drawn increasing attention for the sake of low-pollution emission and high-fuel economy. Scientific scale forecasting of electric vehicles (EVs) is fundamental to promote EVs' integration for transportation and electric power industries. To capture the evolution pattern, a system dynamics (SD) approach is proposed to simulate and forecast the scale of the EVs. The proposed SD model can integrate various factors and quantify their relationship by comprehensive reasoning. Causal loop diagrams are designed to describe the relationship between factors and variables, and their quantifications are formulated by different business models and surveys. The main procedure for simulation includes survey, problem analysis, variable definition, feedback analysis, and model building. The effectiveness of the SD approach is verified by case studies. Furthermore, sensitivity analysis of the key factors, such as fuel price, subsidy policy, and so on, is also conducted.