A Robust Information Source Estimator with Sparse Observations

In this paper, we consider the problem of locating the information source with sparse observations. We assume that a piece of information spreads in a network following a heterogeneous susceptible-infected-recovered (SIR) model and that a small subset of infected nodes are reported, from which we need to find the source of the information. We adopt the sample path based estimator developed in [1], and prove that on infinite trees, the sample path based estimator is a Jordan infection center with respect to the set of observed infected nodes. In other words, the sample path based estimator minimizes the maximum distance to observed infected nodes. We further prove that the distance between the estimator and the actual source is upper bounded by a constant independent of the number of infected nodes with a high probability on infinite trees. Our simulations on tree networks and real world networks show that the sample path based estimator is closer to the actual source than several other algorithms

Transport equation for 2D electron liquid under microwave radiation plus magnetic field and the Zero Resistance State

A general transport equation for the center of mass motion is constructed to study transports of electronic system under uniform magnetic field and microwave radiation. The equation is applied to study 2D electron system in the limit of weak disorder where negative resistance instability is observed when the radiation field is strong enough. A solution of the transport equation with spontaneous AC current is proposed to explain the experimentally observed Radiation-Induced Zero Resistance State.Comment: 9 pages, 1 figur

Evaluation and Assessment of ZigBee wireless Sensor

The research evaluated a type of widely used smart wireless sensor - ZigBee sensor. This article explained the significance of monitoring key indexes of merchandise (temperature, humidity, shock and vibration - g value) during its transportation, introduced methodologies and involved equipment of evaluating an existing ZigBee sensor, and shown the analysis methods as well as the conclusions and suggestions for future researches. All data presented in this article was originally captured in Dynamics lab and APC innovation Center of the Packaging Science Department at RIT and a local company warehouse. As result of my research, the testing ZigBee sensor was considered accurate only in temperature test. The effective broadcasting range has been proved larger than seventy meters with or without blocking materials. Maximum signal strength was captured at mid top of truck trailer, minimum signal strength was captured from mid bottom of truck trailer

Learning Deep CNN Denoiser Prior for Image Restoration

Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance; in the meanwhile, discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task. Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring). Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning. However, the study of integration with fast discriminative denoiser prior is still lacking. To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems. Experimental results demonstrate that the learned set of denoisers not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.Comment: Accepted to CVPR 2017. Code: https://github.com/cszn/ircn