An overview of the holographic display related tasks within the European 3DTV project

A European consortium has been working since September 2004 on all video-based technical aspects of three-dimensional television. The group has structured its technical activities under five technical committees focusing on capturing 3D live scenes, converting the captured scenes to an abstract 3D representations, transmitting the 3D visual information, displaying the 3D video, and processing of signals for the conversion of the abstract 3D video to signals needed to drive the display. The display of 3D video signals by holographic means is highly desirable. Synthesis of high-resolution computer generated holograms with high spatial frequency content, using fast algorithms, is crucial. Fresnel approximation with its fast implementations, fast superposition of zonelens terms, look-up tables using pre-computed holoprimitives are reported in the literature. Phase-retrieval methods are also under investigation. Successful solutions to this problem will benefit from proper utilization and adaptation of signal processing tools like waveletes, fresnelets, chirplets. and atomic decompositions and various optimization algorithms like matching pursuit or simulated annealing

Nonparametric Detection and Estimation of Highly Oscillatory Signals

This thesis considers the problem of detecting and estimating highly oscillatory signals from noisy measurements. These signals are often referred to as chirps in the literature; they are found everywhere in nature, and frequently arise in scientific and engineering problems. Mathematically, they can be written in the general form A(t) exp(ilambda varphi(t)), where lambda is a large constant base frequency, the phase varphi(t) is time-varying, and the envelope A(t) is slowly varying. Given a sequence of noisy measurements, we study two problems seperately: 1) the problem of testing whether or not there is a chirp hidden in the noisy data, and 2) the problem of estimating this chirp from the data. This thesis introduces novel, flexible and practical strategies for addressing these important nonparametric statistical problems. The main idea is to calculate correlations of the data with a rich family of local templates in a first step, the multiscale chirplets, and in a second step, search for meaningful aggregations or chains of chirplets which provide a good global fit to the data. From a physical viewpoint, these chains correspond to realistic signals since they model arbitrary chirps. From an algorithmic viewpoint, these chains are identified as paths in a convenient graph. The key point is that this important underlying graph structure allows to unleash very effective algorithms such as network flow algorithms for finding those chains which optimize a near optimal trade-off between goodness of fit and complexity. Our estimation procedures provide provably near optimal performance over a wide range of chirps and numerical experiments show that both our detection and estimation procedures perform exceptionally well over a broad class of chirps. This thesis also introduces general strategies for extracting signals of unknown duration in long streams of data when we have no idea where these signals may be. The approach is leveraging testing methods designed to detect the presence of signals with known time support. Underlying our methods is a general abstraction which postulates an abstract statistical problem of detecting paths in graphs which have random variables attached to their vertices. The formulation of this problem was inspired by our chirp detection methods and is of great independent interest.</p

Gravitational wave peak luminosity model for precessing binary black holes

When two black holes merge, a tremendous amount of energy is released in the form of gravitational radiation in a short span of time, making such events among the most luminous phenomenon in the Universe. Models that predict the peak luminosity of black hole mergers are of interest to the gravitational wave community, with potential applications in tests of general relativity. We present a surrogate model for the peak luminosity that is directly trained on numerical relativity simulations of precessing binary black holes. Using Gaussian process regression, we interpolate the peak luminosity in the seven-dimensional parameter space of precessing binaries with mass ratios q ≤ 4 and spin magnitudes χ₁, χ₂ ≤ 0.8. We demonstrate that our errors in estimating the peak luminosity are lower than those of existing fitting formulas by about an order of magnitude. In addition, we construct a model for the peak luminosity of aligned-spin binaries with mass ratios q ≤ 8 and spin magnitudes |χ₁_z|,|χ₂_z| ≤ 0.8. We apply our precessing model to infer the peak luminosity of the GW event GW190521 and find the results to be consistent with previous predictions

Physical Layer Identification and authentication of electronic devices

In this thesis, I have investigated the problem of identification and authentication of electronic devices through their physical layer intrinsic features or fingerprints. The concept is that small differences in the electronic components of electronic devices leave small but significant traces in the digital output generated by the electronic device. Then, an analysis of the digital output provides the capability to identify and/or authenticate an electronic device from its digital output with a degree of accuracy, which is based on various factors including environmental effects. This research area has become more prominent in recent times due to the increasing computing power available for signal processing and analysis, which allows a more efficient and accurate extraction of the fingerprints. Even if there is considerable research in this area, which has proven the concept both with theoretical analysis and experimental results, there are still many aspects to be investigated both for the different types of electronic devices and for the analysis of the digital output through signal processing and machine learning techniques. The PhD activities have investigated various novel aspects in comparison to the existing literature. This thesis describes most of the results and describes the novelty in comparison to previous research literature. Three specific use cases were considered: identification of wireless devices, microphones and magnetometers

Development of Eccentric Black Hole Binary Searches in the LIGO and PTA Regimes

In the past several years, a plethora of gravitational wave events have been detected leading to better understanding of binary black holes, binary neutron stars, and neutron star black hole binaries. All of these transient detections have helped us better understand the dynamics of these systems as well as the populations of these objects, but each of these sources was detected with models that neglected eccentricity. Eccentricity is one of several potential markers for determining the formation of binary systems. Detecting gravitational waves from eccentric sources can better our understanding of such systems and help constrain theories about their formation. In the ground-based gravitational-wave regime, most eccentric binary black hole sources will be detected with little to no eccentricity (e0.1), but the lack of eccentricity-based models implemented into current search methods will make detecting such systems difficult. In the pulsar timing array regime, previous implementations of eccentricity-based models proved to be too computationally expensive. Recent developments in eccentric modeling of supermassive black hole binary systems have made it possible to incorporate eccentricity in a search for continuous gravitational waves from eccentric supermassive black hole binary sources. This work details the methods developed to aid in searching for eccentric stellar-mass black hole binary sources in the ground-based gravitational-wave regime and eccentric supermassive black hole binaries in the pulsar timing array regime