Revisiting and testing stationarity

6 pages, 4 figures, 10 references. To be presented at the 2008 Euro American Workshop on Information Optics will be held during June 1 - 5, 2008 at Les Tresoms in Annecy, France.The concept of stationarity is revisited from an operational perspective that explicitly takes into account the observation scale. A general framework is described for testing such a relative stationarity via the introduction of stationarized surrogate data

Automatic recognition of radar signals based on time-frequency image shape character

Radar signal recognition is one of the key technologies of modern electronic surveillance systems. Time-frequency image provides a new way for recognizing the radar signal. In this paper, a series of image processing methods containing image enhancement, image threshold binarization and mathematical morphology is utilized to extract the shape character of smoothed pseudo wigner-ville time-frequency distribution of radar signal. And then the identification of radar signal is realized by the character. Simulation results of eight kinds of typical radar signal demonstrate that when signal noise ratio (SNR) is greater than -3 dB, the Legendre moments shape character of the time-frequency image is very stable. Moreover, the recognition rate by the character is more than 90 per cent except for the FRANK code signal when SNR > -3 dB. Test also show that the proposed method can effectively recognize radar signal with less character dimension through compared with exitsing algorithms.Defence Science Journal, 2013, 63(3), pp.308-314, DOI:http://dx.doi.org/10.14429/dsj.63.240

Stochastic Discrete Scale Invariance

International audienceA definition of stochastic discrete scale invariance (DSI) is proposed and its properties studied. It is shown how the Lamperti transformation, which transforms stationarity in self-similarity, is also a means to connect processes deviating from stationarity and processes which are not exactly scale invariant: in particular we interpret DSI as the image of cyclostationarity. This theoretical result is employed to introduce a multiplicative spectral representation of DSI processes based on the Mellin transform, and preliminar remarks are given about estimation issues

Multiscale analysis of geometric planar deformations: application to wild animals electronic tracking and satellite ocean observation data

International audienceThe development of animal tracking technologies (including for instance GPS and ARGOS satellite systems) and the increasing resolution of remote sensing observations call for tools extracting and describing the geometric patterns along a track or within an image over a wide range of spatial scales. Whereas shape analysis has largely been addressed over the last decades, the multiscale analysis of the geometry of opened planar curves has received little attention. We here show that classical multiscale techniques cannot properly address this issue and propose an original wavelet-based scheme. To highlight the generic nature of our multiscale wavelet technique, we report applications to two different observation datasets, namely wild animal movement paths recorded by electronic tags and satellite observations of sea surface geophysical fields

Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

The localized nature of curvelet functions, together with their frequency and dip characteristics, makes the curvelet transform an excellent choice for processing seismic data. In this work, a denoising method is proposed based on a combination of the curvelet transform and a whitening filter along with procedure for noise variance estimation. The whitening filter is added to get the best performance of the curvelet transform under coherent and incoherent correlated noise cases, and furthermore, it simplifies the noise estimation method and makes it easy to use the standard threshold methodology without digging into the curvelet domain. The proposed method is tested on pseudo-synthetic data by adding noise to real noise-less data set of the Netherlands offshore F3 block and on the field data set from east Texas, USA, containing ground roll noise. Our experimental results show that the proposed algorithm can achieve the best results under all types of noises (incoherent or uncorrelated or random, and coherent noise)

Optophone design: optical-to-auditory vision substitution for the blind

An optophone is a device that turns light into sound for the benefit of blind people. The present project is intended to produce a general-purpose optophone to be worn on the head about the house and in the street, to give the wearer a detailed description in sound of the'scene he is facing. The device will therefore consist'of an'electronic camera, some signal-processing electronics, earphones`, and a battery. The two major problems are the derivation of (a) the most suitable mapping from images to sounds, and (b) an algorithm to perform the mapping in real'time on existing electronic components. This thesis concerns problem (a). Chapter 2 goes into the general scene-to-sound mapping problem in some detail'and presents the work of earlier investigators. Chapter 3 1- discusses the design of tests to evaluate the performance of candidate mappings. A theoretical performance test (TPT) is derived. Chapter 4 applies the TPT to the most obvious mapping, the cartesian piano transform. Chapter 5 applies the TPT to a mapping based on the cosine transform. Chapter 6 attempts to derive a mapping by principal component analysis, using the inaccuracies of human sight and hearing and the statistical properties of real scenes and sounds. Chapter 7 presents a complete scheme, implemented in software, for representing digitised colour scenes by audible digitised stereo sound. Chapter 8 tries to decide how'many numbers are required to specify a steady spectrum with no noticeable degradation. Chapter 9 looks'at a scheme designed to produce more natural-sounding sounds related to more meaningful portions of the scene. This scheme maps windows in the scene to steady spectral patterns of short duration, the location of the window being conveyed by simulated free-field listening. Chapter 10 gives detailed recommendations as to further work

BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154104/1/kampfner1996.pd

Signal-adapted tomography as a tool for dust devil detection

Dust devils are important phenomena to take into account to understand the global dust circulation of a planet. On Earth, their contribution to the injection of dust into the atmosphere seems to be secondary. Elsewhere, there are many indications that the dust devil’s role on other planets, in particular on Mars, could be fundamental, impacting the global climate. The ability to identify and study these vortices from the acquired meteorological measurements assumes a great importance for planetary science. Here we present a new methodology to identify dust devils from the pressure time series testing the method on the data acquired during a 2013 field campaign performed in the Tafilalt region (Morocco) of the North- Western Sahara Desert. Although the analysis of pressure is usually studied in the time domain, we prefer here to follow a different approach and perform the analysis in a time signal-adapted domain, the relation between the two being a bilinear transformation, i.e. a tomogram. The tomographic technique has already been successfully applied in other research fields like those of plasma reflectometry or the neuronal signatures. Here we show its effectiveness also in the dust devils detection. To test our results, we compare the tomography with a phase picker time domain analysis. We show the level of agreement between the two methodologies and the advantages and disadvantages of the tomographic approach