Imaging and burst location with the EXIST high-energy telescope

The primary instrument of the proposed EXIST mission is a coded mask high energy telescope (the HET), that must have a wide field of view and extremely good sensitivity. It will be crucial to minimize systematic errors so that even for very long total integration times the imaging performance is close to the statistical photon limit. There is also a requirement to be able to reconstruct images on-board in near real time in order to detect and localize gamma-ray bursts. This must be done while the spacecraft is scanning the sky. The scanning provides all-sky coverage and is key to reducing systematic errors. The on-board computational problem is made even more challenging for EXIST by the very large number of detector pixels. Numerous alternative designs for the HET have been evaluated. The baseline concept adopted depends on a unique coded mask with two spatial scales. Monte Carlo simulations and analytic analysis techniques have been used to demonstrate the capabilities of the design and of the proposed two-step burst localization procedure

Current-Mode Techniques for the Implementation of Continuous- and Discrete-Time Cellular Neural Networks

This paper presents a unified, comprehensive approach to the design of continuous-time (CT) and discrete-time (DT) cellular neural networks (CNN) using CMOS current-mode analog techniques. The net input signals are currents instead of voltages as presented in previous approaches, thus avoiding the need for current-to-voltage dedicated interfaces in image processing tasks with photosensor devices. Outputs may be either currents or voltages. Cell design relies on exploitation of current mirror properties for the efficient implementation of both linear and nonlinear analog operators. These cells are simpler and easier to design than those found in previously reported CT and DT-CNN devices. Basic design issues are covered, together with discussions on the influence of nonidealities and advanced circuit design issues as well as design for manufacturability considerations associated with statistical analysis. Three prototypes have been designed for l.6-pm n-well CMOS technologies. One is discrete-time and can be reconfigured via local logic for noise removal, feature extraction (borders and edges), shadow detection, hole filling, and connected component detection (CCD) on a rectangular grid with unity neighborhood radius. The other two prototypes are continuous-time and fixed template: one for CCD and other for noise removal. Experimental results are given illustrating performance of these prototypes

Doping dependence of the (π,π)(\pi,\pi) shadow band in La-based cuprates studied by angle-resolved photoemission spectroscopy

The $(\pi,\pi)$ shadow band (SB) in La-based cuprate family (La214) was studied by angle-resolved photoemission spectroscopy (ARPES) over a wide doping range from $x=0.01$ to $x=0.25$. Unlike the well-studied case of the Bi-based cuprate family, an overall strong, monotonic doping dependence of the SB intensity at the Fermi level ($E_F$) was observed. In contrast to a previous report for the presence of the SB only close to $x=1/8$, we found it exists in a wide doping range, associated with a doping-independent $(\pi,\pi)$ wave vector but strongly doping-dependent intensity: It is the strongest at $x\sim 0.03$ and systematically diminishes as the doping increases until it becomes negligible in the overdoped regime. This SB with the observed doping dependence of intensity can in principle be caused by the antiferromagnetic fluctuations or a particular form of low-temperature orthorhombic lattice distortion known to persist up to $x\sim 0.21$ in the system, with both being weakened with increasing doping. However, a detailed binding energy dependent analysis of the SB at $x=0.07$ does not appear to support the former interpretation, leaving the latter as a more plausible candidate, despite a challenge in quantitatively linking the doping dependences of the SB intensity and the magnitude of the lattice distortion. Our finding highlights the necessity of a careful and global consideration of the inherent structural complications for correctly understanding the cuprate Fermiology and its microscopic implication.Comment: Note the revised conclusion and author list; To appear in New J. Phy

A fully-convolutional neural network for background subtraction of unseen videos

Background subtraction is a basic task in computer vision and video processing often applied as a pre-processing step for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have been proposed, however nearly all of the top-performing ones are supervised. Crucially, their success relies upon the availability of some annotated frames of the test video during training. Consequently, their performance on completely “unseen” videos is undocumented in the literature. In this work, we propose a new, supervised, backgroundsubtraction algorithm for unseen videos (BSUV-Net) based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we also introduce a new data-augmentation technique which mitigates the impact of illumination difference between the background frames and the current frame. On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of several metrics including F-measure, recall and precision.Accepted manuscrip