Synchronization of spatiotemporal semiconductor lasers and its application in color image encryption

Optical chaos is a topic of current research characterized by high-dimensional nonlinearity which is attributed to the delay-induced dynamics, high bandwidth and easy modular implementation of optical feedback. In light of these facts, which adds enough confusion and diffusion properties for secure communications, we explore the synchronization phenomena in spatiotemporal semiconductor laser systems. The novel system is used in a two-phase colored image encryption process. The high-dimensional chaotic attractor generated by the system produces a completely randomized chaotic time series, which is ideal in the secure encoding of messages. The scheme thus illustrated is a two-phase encryption method, which provides sufficiently high confusion and diffusion properties of chaotic cryptosystem employed with unique data sets of processed chaotic sequences. In this novel method of cryptography, the chaotic phase masks are represented as images using the chaotic sequences as the elements of the image. The scheme drastically permutes the positions of the picture elements. The next additional layer of security further alters the statistical information of the original image to a great extent along the three-color planes. The intermediate results during encryption demonstrate the infeasibility for an unauthorized user to decipher the cipher image. Exhaustive statistical tests conducted validate that the scheme is robust against noise and resistant to common attacks due to the double shield of encryption and the infinite dimensionality of the relevant system of partial differential equations.Comment: 20 pages, 11 figures; Article in press, Optics Communications (2011

A Novel Optical/digital Processing System for Pattern Recognition

This paper describes two processing algorithms that can be implemented optically: the Radon transform and angular correlation. These two algorithms can be combined in one optical processor to extract all the basic geometric and amplitude features from objects embedded in video imagery. We show that the internal amplitude structure of objects is recovered by the Radon transform, which is a well-known result, but, in addition, we show simulation results that calculate angular correlation, a simple but unique algorithm that extracts object boundaries from suitably threshold images from which length, width, area, aspect ratio, and orientation can be derived. In addition to circumventing scale and rotation distortions, these simulations indicate that the features derived from the angular correlation algorithm are relatively insensitive to tracking shifts and image noise. Some optical architecture concepts, including one based on micro-optical lenslet arrays, have been developed to implement these algorithms. Simulation test and evaluation using simple synthetic object data will be described, including results of a study that uses object boundaries (derivable from angular correlation) to classify simple objects using a neural network

Molecular Gas in the Low Metallicity, Star Forming Dwarf IC 10

We present a complete survey of CO 1->0 emission in the Local Group dwarf irregular IC 10. The survey, conducted with the BIMA interferometer, covers the stellar disk and a large fraction of the extended HI envelope with the sensitivity and resolution necessary to detect individual giant molecular clouds (GMCs) at the distance of IC 10 (950 kpc). We find 16 clouds with a total CO luminosity of 1 x 10^6 K km s^-1 pc^2, equivalent to 4 x 10^6 Msun of molecular gas using the Galactic CO-to-H2 conversion factor. Observations with the ARO 12m find that BIMA may resolve out as much as 50% of the CO emission, and we estimate the total CO luminosity as 2.2 x 10^6 K km s^-1 pc^2. We measure the properties of 14 GMCs from high resolution OVRO data. These clouds are very similar to Galactic GMCs in their sizes, line widths, luminosities, and CO-to-H2 conversion factors despite the low metallicity of IC 10 (Z ~ 1/5 Zsun). Comparing the BIMA survey to the atomic gas and stellar content of IC 10 we find that most of the CO emission is coincident with high surface density HI. IC 10 displays a much higher star formation rate per unit molecular (H2) or total (HI+H2) gas than most galaxies. This could be a real difference or may be an evolutionary effect - the star formation rate may have been higher in the recent past.Comment: 21 pages, 14 figures, Accepted to Ap

DNN-Based Source Enhancement to Increase Objective Sound Quality Assessment Score

We propose a training method for deep neural network (DNN)-based source enhancement to increase objective sound quality assessment (OSQA) scores such as the perceptual evaluation of speech quality (PESQ). In many conventional studies, DNNs have been used as a mapping function to estimate time-frequency masks and trained to minimize an analytically tractable objective function such as the mean squared error (MSE). Since OSQA scores have been used widely for soundquality evaluation, constructing DNNs to increase OSQA scores would be better than using the minimum-MSE to create highquality output signals. However, since most OSQA scores are not analytically tractable, i.e., they are black boxes, the gradient of the objective function cannot be calculated by simply applying back-propagation. To calculate the gradient of the OSQA-based objective function, we formulated a DNN optimization scheme on the basis of black-box optimization, which is used for training a computer that plays a game. For a black-box-optimization scheme, we adopt the policy gradient method for calculating the gradient on the basis of a sampling algorithm. To simulate output signals using the sampling algorithm, DNNs are used to estimate the probability-density function of the output signals that maximize OSQA scores. The OSQA scores are calculated from the simulated output signals, and the DNNs are trained to increase the probability of generating the simulated output signals that achieve high OSQA scores. Through several experiments, we found that OSQA scores significantly increased by applying the proposed method, even though the MSE was not minimized

Source finding, parametrization and classification for the extragalactic Effelsberg-Bonn HI Survey

Context. Source extraction for large-scale HI surveys currently involves large amounts of manual labor. For data volumes expected from future HI surveys with upcoming facilities, this approach is not feasible any longer. Aims. We describe the implementation of a fully automated source finding, parametrization, and classification pipeline for the Effelsberg-Bonn HI Survey (EBHIS). With future radio astronomical facilities in mind, we want to explore the feasibility of a completely automated approach to source extraction for large-scale HI surveys. Methods. Source finding is implemented using wavelet denoising methods, which previous studies show to be a powerful tool, especially in the presence of data defects. For parametrization, we automate baseline fitting, mask optimization, and other tasks based on well-established algorithms, currently used interactively. For the classification of candidates, we implement an artificial neural network which is trained on a candidate set comprised of false positives from real data and simulated sources. Using simulated data, we perform a thorough analysis of the algorithms implemented. Results. We compare the results from our simulations to the parametrization accuracy of the HI Parkes All-Sky Survey (HIPASS) survey. Even though HIPASS is more sensitive than EBHIS in its current state, the parametrization accuracy and classification reliability match or surpass the manual approach used for HIPASS data.Comment: 13 Pages, 13 Figures, 1 Table, accepted for publication in A&