Quantum automated object detection algorithm

Automated object detection algorithm is an important research challenge in many applications such as computer vision and urban surveillance systems. Recently, quantum computing and information processing become emerging technologies which attract researchers' increasing attention and some work has been attempted in image detection using quantum computing approach. In this paper, we propose a novel quantum automated object detection algorithm for urban surveillance systems. This novel quantum based method is shown to be capable of detecting an object in images accurately and is able to deal with the measurement errors caused by the quantum measurements in the image retrieving process

Automated characterization of single-photon avalanche photodiode

We report an automated characterization of a single-photon detector based on commercial silicon avalanche photodiode (PerkinElmer C30902SH). The photodiode is characterized by I-V curves at different illumination levels (darkness, 10 pW and 10 uW), dark count rate and photon detection efficiency at different bias voltages. The automated characterization routine is implemented in C++ running on a Linux computer.Comment: Have permission to post journal-formatted version on arXi

The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch

Recent and forthcoming advances in instrumentation, and giant new surveys, are creating astronomical data sets that are not amenable to the methods of analysis familiar to astronomers. Traditional methods are often inadequate not merely because of the size in bytes of the data sets, but also because of the complexity of modern data sets. Mathematical limitations of familiar algorithms and techniques in dealing with such data sets create a critical need for new paradigms for the representation, analysis and scientific visualization (as opposed to illustrative visualization) of heterogeneous, multiresolution data across application domains. Some of the problems presented by the new data sets have been addressed by other disciplines such as applied mathematics, statistics and machine learning and have been utilized by other sciences such as space-based geosciences. Unfortunately, valuable results pertaining to these problems are mostly to be found only in publications outside of astronomy. Here we offer brief overviews of a number of concepts, techniques and developments, some "old" and some new. These are generally unknown to most of the astronomical community, but are vital to the analysis and visualization of complex datasets and images. In order for astronomers to take advantage of the richness and complexity of the new era of data, and to be able to identify, adopt, and apply new solutions, the astronomical community needs a certain degree of awareness and understanding of the new concepts. One of the goals of this paper is to help bridge the gap between applied mathematics, artificial intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in Astronomy, special issue "Robotic Astronomy

A Wide-Field CCD Survey for Centaurs and Kuiper Belt Objects

A modified Baker-Nunn camera was used to conduct a wide-field survey of 1428 square degrees of sky near the ecliptic in search of bright Kuiper Belt objects and Centaurs. This area is an order of magnitude larger than any previously published CCD survey for Centaurs and Kuiper Belt Objects. No new objects brighter than red magnitude m=18.8 and moving at a rate 1"/hr to 20"/hr were discovered, although one previously discovered Centaur 1997 CU26 Chariklo was serendipitously detected. The parameters of the survey were characterized using both visual and automated techniques. From this survey the empirical projected surface density of Centaurs was found to be SigmaCentaur(m<18.8)=7.8(+16.0 -6.6)x10^-4 per square degree and we found a projected surface density 3sigma upper confidence limit for Kuiper Belt objects of SigmaKBO(m< 18.8)<4.1x10^-3 per square degree. We discuss the current state of the cumulative luminosity functions of both Centaurs and Kuiper Belt objects. Through a Monte Carlo simulation we show that the size distribution of Centaurs is consistent with a q=4 differential power law, similar to the size distribution of the parent Kuiper Belt Objects. The Centaur population is of order 10^7 (radius > 1 km) assuming a geometric albedo of 0.04. About 100 Centaurs are larger than 50 km in radius, of which only 4 are presently known. The current total mass of the Centaurs is 10^-4 Earth Masses. No dust clouds were detected resulting from Kuiper Belt object collisions, placing a 3sigma upper limit <600 collisionally produced clouds of m<18.8 per year.Comment: 13 pages, 5 figures, Accepted for Publication in A

Exploring the Variable Sky with LINEAR. I. Photometric Recalibration with the Sloan Digital Sky Survey

We describe photometric recalibration of data obtained by the asteroid survey LINEAR. Although LINEAR was designed for astrometric discovery of moving objects, the data set described here contains over 5 billion photometric measurements for about 25 million objects, mostly stars. We use Sloan Digital Sky Survey (SDSS) data from the overlapping ~10,000 deg^2 of sky to recalibrate LINEAR photometry and achieve errors of 0.03 mag for sources not limited by photon statistics with errors of 0.2 mag at r ~ 18. With its 200 observations per object on average, LINEAR data provide time domain information for the brightest four magnitudes of the SDSS survey. At the same time, LINEAR extends the deepest similar wide-area variability survey, the Northern Sky Variability Survey, by 3 mag. We briefly discuss the properties of about 7000 visually confirmed periodic variables, dominated by roughly equal fractions of RR Lyrae stars and eclipsing binary stars, and analyze their distribution in optical and infrared color-color diagrams. The LINEAR data set is publicly available from the SkyDOT Web site