Bimodal conductance distribution of Kitaev edge modes in topological superconductors

A two-dimensional superconductor with spin-triplet p-wave pairing supports chiral or helical Majorana edge modes with a quantized (length $L$-independent) thermal conductance. Sufficiently strong anisotropy removes both chirality and helicity, doubling the conductance in the clean system and imposing a super-Ohmic $1/\sqrt{L}$ decay in the presence of disorder. We explain the absence of localization in the framework of the Kitaev Hamiltonian, contrasting the edge modes of the two-dimensional system with the one-dimensional Kitaev chain. While the disordered Kitaev chain has a log-normal conductance distribution peaked at an exponentially small value, the Kitaev edge has a bimodal distribution with a second peak near the conductance quantum. Shot noise provides an alternative, purely electrical method of detection of these charge-neutral edge modes.Comment: 11 pages, 13 figure

Accurate Iris Localization Using Edge Map Generation and Adaptive Circular Hough Transform for Less Constrained Iris Images

This paper proposes an accurate iris localization algorithm for the iris images acquired under near infrared (NIR) illuminations and having noise due to eyelids, eyelashes, lighting reflections, non-uniform illumination, eyeglasses and eyebrow hair etc. The two main contributions in the paper are an edge map generation technique for pupil boundary detection and an adaptive circular Hough transform (CHT) algorithm for limbic boundary detection, which not only make the iris localization more accurate but faster also. The edge map for pupil boundary detection is generated on intersection (logical AND) of two binary edge maps obtained using thresholding, morphological operations and Sobel edge detection, which results in minimal false edges caused by the noise. The adaptive CHT algorithm for limbic boundary detection searches for a set of two arcs in an image instead of a full circle that counters iris-occlusions by the eyelids and eyelashes. The proposed CHT and adaptive CHT implementations for pupil and limbic boundary detection respectively use a two-dimensional accumulator array that reduces memory requirements. The proposed algorithm gives the accuracies of 99.7% and 99.38% for the challenging CASIA-Iris-Thousand (version 4.0) and CASIA-Iris-Lamp (version 3.0) databases respectively. The average time cost per image is 905 msec. The proposed algorithm is compared with the previous work and shows better results

The Canny edge detector revisited

yesCanny (IEEE Trans. Pattern Anal. Image Proc. 8(6):679-698, 1986) suggested that an optimal edge detector should maximize both signal-to-noise ratio and localization, and he derived mathematical expressions for these criteria. Based on these criteria, he claimed that the optimal step edge detector was similar to a derivative of a gaussian. However, Canny's work suffers from two problems. First, his derivation of localization criterion is incorrect. Here we provide a more accurate localization criterion and derive the optimal detector from it. Second, and more seriously, the Canny criteria yield an infinitely wide optimal edge detector. The width of the optimal detector can however be limited by considering the effect of the neighbouring edges in the image. If we do so, we find that the optimal step edge detector, according to the Canny criteria, is the derivative of an ISEF filter, proposed by Shen and Castan (Graph. Models Image Proc. 54:112-133, 1992). In addition, if we also consider detecting blurred (or non-sharp) gaussian edges of different widths, we find that the optimal blurred-edge detector is the above optimal step edge detector convolved with a gaussian. This implies that edge detection must be performed at multiple scales to cover all the blur widths in the image. We derive a simple scale selection procedure for edge detection, and demonstrate it in one and two dimensions

Detection, identification and localization of R/C electronic devices through their unintended emissions

The accurate and reliable detection of unintended emissions from radio receivers has a broad range of commercial and security applications. This thesis presents detection, identification, and localization methods for multiple RC electronic devices in a realistic environment. First, a Hurst parameter based detection method for super-regenerative receivers (SRR) has been used for detection. Hurst parameter based detection method exploits a self-similarity property of the SRR receiver emissions to distinguish it from background noise. Second paper presents a novel detection and localization scheme of multiple RC electronic devices called Edge-Synthetic Aperture Radar (Edge-SAR). It employs cost-effective, mobile antenna-array detectors. Two types of RC devices are considered: SRR with H parameter method and super heterodyne receivers (SHR) with peak detection method. Third paper improves detection of multiple devices by proposing a dynamic antenna-array processing method called VIVEK-MVDR-GA. It combines multi-constrained genetic algorithm (GA) and minimum variance distortion-less response (MVDR) method to increase accuracy of detection and localization of multiple devices. Finally, a 4-element array mounted on an unmanned aerial vehicle (UAV) is proposed to overcome multipath and reflection due to environmental surroundings and improve the response time in compromised scenarios. Also, a time based correlation method is proposed for array detectors to identify the line of sight (LOS) and non-line of sight (N-LOS) signals. A normalized error correlation function has been implemented to improve the estimation of angle of arrival (AOA) in the presence of strong non-line of sight (N-LOS) signals --Abstract, page iv

Implementation of an algorithm for cylindrical object identification using range data

One of the problems in 3-D object identification and localization is addressed. In robotic and navigation applications the vision system must be able to distinguish cylindrical or spherical objects as well as those of other geometric shapes. An algorithm was developed to identify cylindrical objects in an image when range data is used. The algorithm incorporates the Hough transform for line detection using edge points which emerge from a Sobel mask. Slices of the data are examined to locate arcs of circles using the normal equations of an over-determined linear system. Current efforts are devoted to testing the computer implementation of the algorithm. Refinements are expected to continue in order to accommodate cylinders in various positions. A technique is sought which is robust in the presence of noise and partial occlusions

Detecting and localizing edges composed of steps, peaks and roofs

It is well known that the projection of depth or orientation discontinuities in a physical scene results in image intensity edges which are not ideal step edges but are more typically a combination of steps, peak and roof profiles. However most edge detection schemes ignore the composite nature of these edges, resulting in systematic errors in detection and localization. We address the problem of detecting and localizing these edges, while at the same time also solving the problem of false responses in smoothly shaded regions with constant gradient of the image brightness. We show that a class of nonlinear filters, known as quadratic filters, are appropriate for this task, while linear filters are not. A series of performance criteria are derived for characterizing the SNR, localization and multiple responses of these filters in a manner analogous to Canny's criteria for linear filters. A two-dimensional version of the approach is developed which has the property of being able to represent multiple edges at the same location and determine the orientation of each to any desired precision. This permits junctions to be localized without rounding. Experimental results are presented

Edge and Line Feature Extraction Based on Covariance Models

age segmentation based on contour extraction usually involves three stages of image operations: feature extraction, edge detection and edge linking. This paper is devoted to the first stage: a method to design feature extractors used to detect edges from noisy and/or blurred images. The method relies on a model that describes the existence of image discontinuities (e.g. edges) in terms of covariance functions. The feature extractor transforms the input image into a “log-likelihood ratio” image. Such an image is a good starting point of the edge detection stage since it represents a balanced trade-off between signal-to-noise ratio and the ability to resolve detailed structures. For 1-D signals, the performance of the edge detector based on this feature extractor is quantitatively assessed by the so called “average risk measure”. The results are compared with the performances of 1-D edge detectors known from literature. Generalizations to 2-D operators are given. Applications on real world images are presented showing the capability of the covariance model to build edge and line feature extractors. Finally it is shown that the covariance model can be coupled to a MRF-model of edge configurations so as to arrive at a maximum a posteriori estimate of the edges or lines in the image