Topological Crystalline Insulator Nanomembrane with Strain-Tunable Band Gap

The ability to fine-tune band gap and band inversion in topological materials is highly desirable for the development of novel functional devices. Here we propose that the electronic properties of a free-standing nanomembrane of topological crystalline insulator (TCI) SnTe and Pb$_{1-x}$Sn$_x$(Se,Te) are highly tunable by engineering elastic strain and controlling membrane thickness, resulting in tunable band gap and giant piezoconductivity. Membrane thickness governs the hybridization of topological electronic states on opposite surfaces, while elastic strain can further modulate the hybridization strength by controlling the penetration length of surface states. We propose a frequency-resolved infrared photodetector using force-concentration induced inhomogeneous elastic strain in TCI nanomembrane with spatially varying width. The predicted tunable band gap accompanied by strong spin-textured electronic states will open up new avenues for fabricating piezoresistive devices, thermoelectrics, infrared detectors and energy-efficient electronic and optoelectronic devices based on TCI nanomembrane.Comment: 10 pages, 9 figure

Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave Systems

Enabling highly-mobile millimeter wave (mmWave) systems is challenging because of the huge training overhead associated with acquiring the channel knowledge or designing the narrow beams. Current mmWave beam training and channel estimation techniques do not normally make use of the prior beam training or channel estimation observations. Intuitively, though, the channel matrices are functions of the various elements of the environment. Learning these functions can dramatically reduce the training overhead needed to obtain the channel knowledge. In this paper, a novel solution that exploits machine learning tools, namely conditional generative adversarial networks (GAN), is developed to learn these functions between the environment and the channel covariance matrices. More specifically, the proposed machine learning model treats the covariance matrices as 2D images and learns the mapping function relating the uplink received pilots, which act as RF signatures of the environment, and these images. Simulation results show that the developed strategy efficiently predicts the covariance matrices of the large-dimensional mmWave channels with negligible training overhead.Comment: to appear in Asilomar Conference on Signals, Systems, and Computers, Oct. 201

The Improved Riccati Equation Method and Exact Solutions to mZK Equation

We utilize the improved Riccati equation method to construct more general exact solutions to nonlinear equations. And we obtain the travelling wave solutions involving parameters, which are expressed by the hyperbolic functions, the trigonometric functions, and the rational functions. When the parameters are taken as special values, the method provides not only solitary wave solutions but also periodic waves solutions. The method appears to be easier and more convenient by means of a symbolic computation system. Of course, it is also effective to solve other nonlinear evolution equations in mathematical physics

Remote measurement calibration in power system

Power system reliability and economy of operation require accurate measurements of current, voltage, real and reactive powers. These measurements are transmitted to a control center of a power system for monitoring, display, and use in power system real-time analysis. The number of measurements is in thousands. Routinely field technicians must calibrate transducers and/or determine other sources of metering errors. Due to the large number of measurements and the time required to check each individual measurement, field calibration procedures are impractical, expensive, and not timely. There has been a need for a more efficient approach to measurement calibration and identification of defective instruments. This paper describes an approach which meets the need. The collection of measurements over time are used to correct for systematic errors, (caused by instrument transformers, transducers, secondary leads between these devices, analog-digital converters, and the scaling procedure). The volts, watts, and vars scales are then adjusted to compensate for these errors, thus providing more accurate measurements