On adaptive filter structure and performance

SIGLEAvailable from British Library Document Supply Centre- DSC:D75686/87 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Digital Signal Processing

Contains reports on twelve research projects.U. S. Navy - Office of Naval Research (Contract N00014-75-C-0951)National Science Foundation (Grant ENG76-24117)National Aeronautics and Space Administration (Grant NSG-5157)Joint Services Electronics Program (Contract DAABO7-76-C-1400)U.S. Navy-Office of Naval Research (Contract N00014-77-C-0196)Woods Hole Oceanographic InstitutionU. S. Navy - Office of Naval Research (Contract N00014-75-C-0852)Department of Ocean Engineering, M.I.T.National Science Foundation subcontract to Grant GX 41962 to Woods Hole Oceanographic Institutio

A New Adaptive Filter Approach for Acoustic Echo Canceller in Teleconference Systems

A flexible Frequency domain Block Recursive Least Squares (FBRLS) algorithm using the Multi-Delay Filter (MDF) is presented throughout this paper. In term of performances, the MDF-FBRLS adaptive filter introduces smaller block delay and is usually faster and suitable for ideal time-varying system such as an acoustic echo in a teleconference room. The implementation of the FBRLS algorithm using MDF adaptive filter allows reducing the FFT size and consequently optimizing the hardware implementation that could be performed using standard DSP chips. These good performances are achieved by using smaller block size and updating frequently the weight vectors which will reduce the total execution time of the adaptive process. Simulation results show that the MDF-FBRLS algorithm is better than the FBRLS algorithm in terms of the total execution time and the efficiency of the computational complexity

Dynamical Systems in Spiking Neuromorphic Hardware

Dynamical systems are universal computers. They can perceive stimuli, remember, learn from feedback, plan sequences of actions, and coordinate complex behavioural responses. The Neural Engineering Framework (NEF) provides a general recipe to formulate models of such systems as coupled sets of nonlinear differential equations and compile them onto recurrently connected spiking neural networks – akin to a programming language for spiking models of computation. The Nengo software ecosystem supports the NEF and compiles such models onto neuromorphic hardware. In this thesis, we analyze the theory driving the success of the NEF, and expose several core principles underpinning its correctness, scalability, completeness, robustness, and extensibility. We also derive novel theoretical extensions to the framework that enable it to far more effectively leverage a wide variety of dynamics in digital hardware, and to exploit the device-level physics in analog hardware. At the same time, we propose a novel set of spiking algorithms that recruit an optimal nonlinear encoding of time, which we call the Delay Network (DN). Backpropagation across stacked layers of DNs dramatically outperforms stacked Long Short-Term Memory (LSTM) networks—a state-of-the-art deep recurrent architecture—in accuracy and training time, on a continuous-time memory task, and a chaotic time-series prediction benchmark. The basic component of this network is shown to function on state-of-the-art spiking neuromorphic hardware including Braindrop and Loihi. This implementation approaches the energy-efficiency of the human brain in the former case, and the precision of conventional computation in the latter case

Phase extraction of non-stationary signals produced in dynamic interferometry involving speckle waves

It is now widely acknowledged, among communities of researchers and engineers of very different horizons, that speckle interferometry (SI) offers powerful techniques to characterize mechanical rough surfaces with a submicronic accuracy in static or quasi-static regime, when small displacements are involved (typically several microns or tens of microns). The issue of dynamic regimes with possibly large deformations (typically several hundreds of microns) is still topical and prevents an even more widespread use of speckle techniques. This is essentially due to the lack of efficient processing schemes able to cope with non-stationary AM-FM interferometric signals. In addition, decorrelation-induced phase errors represent an hindrance to accurate measurement when such large displacements and classical fringe analysis techniques are considered. This work is an attempt to address those issues and to endeavor to make the most of speckle interferometry signals. Our answers to those problems are located on two different levels. First of all, we adopt the temporal analysis approach, i.e. the analysis of the temporal signal of each pixel of the sensor area used to record the interferograms. A return to basics of phase extraction is operated to properly identify the conditions under which the computed phase is meaningful and thus give some insight on the physical phenomenon under analysis. Due to their intrinsic non-stationary nature, a preprocessing tool is missing to put the SI temporal signals in a shape which ensures an accurate phase computation, whichever technique is chosen. This is where the Empirical Mode Decomposition (EMD) intervenes. This technique, somehow equivalent to an adaptive filtering technique, has been studied and tailored to fit with our expectations. The EMD has shown a great ability to remove efficiently the random fluctuating background intensity and to evaluate the modulation intensity. The Hilbert tranform (HT) is the natural quadrature operator. Its use to build an analytical signal from the so-detrended SI signal, for subsequent phase computation, has been studied and assessed. Other phase extraction techniques have been considered as well for comparison purposes. Finally, our answer to the decorrelation-induced phase error relies on the well-known result that the higher the pixel modulation intensity, the lower the random phase error. We took benefit from this result – not only linked to basic SNR considerations, but more specifically to the intrinsic phase structure of speckle fields – with a novel approach. The regions within the pixel signal history classified as unreliable because under-modulated, are purely and simply discarded. An interpolation step with the Delaunay triangulation is carried out with the so-obtained non-uniformly sampled phase maps to recover a smooth phase which relies on the most reliable available data. Our schemes have been tested and discussed with simulated and experimental SI signals. We eventually have developed a versatile, accurate and efficient phase extraction procedure, perfectly able to tackle the challenge of dynamic behaviors characterization, even for displacements and/or deformations beyond the classical limit of the correlation dimensions

Ultrasound Imaging

In this book, we present a dozen state of the art developments for ultrasound imaging, for example, hardware implementation, transducer, beamforming, signal processing, measurement of elasticity and diagnosis. The editors would like to thank all the chapter authors, who focused on the publication of this book

Function-valued Mappings and SSIM-based Optimization in Imaging

In a few words, this thesis is concerned with two alternative approaches to imag- ing, namely, Function-valued Mappings (FVMs) and Structural Similarity Index Measure (SSIM)-based Optimization. Briefly, a FVM is a mathematical object that assigns to each element in its domain a function that belongs to a given function space. The advantage of this representation is that the infinite dimensionality of the range of FVMs allows us to give a more accurate description of complex datasets such as hyperspectral images and diffusion magnetic resonance images, something that can not be done with the classical representation of such data sets as vector-valued functions. For instance, a hyperspectral image can be described as a FVM that assigns to each point in a spatial domain a spectral function that belongs to the function space L2(R); that is, the space of functions whose energy is finite. Moreoever, we present a Fourier transform and a new class of fractal transforms for FVMs to analyze and process hyperspectral images. Regarding SSIM-based optimization, we introduce a general framework for solving op- timization problems that involve the SSIM as a fidelity measure. This framework offers the option of carrying out SSIM-based imaging tasks which are usually addressed using the classical Euclidean-based methods. In the literature, SSIM-based approaches have been proposed to address the limitations of Euclidean-based metrics as measures of vi- sual quality. These methods show better performance when compared to their Euclidean counterparts since the SSIM is a better model of the human visual system; however, these approaches tend to be developed for particular applications. With the general framework that it is presented in this thesis, rather than focusing on particular imaging tasks, we introduce a set of novel algorithms capable of carrying out a wide range of SSIM-based imaging applications. Moreover, such a framework allows us to include the SSIM as a fidelity term in optimization problems in which it had not been included before

Methods for transform, analysis and rendering of complete light representations

Recent advances in digital holography, optical engineering and computer graphics have opened up the possibility of full parallax, three dimensional displays. The premises of these rendering systems are however somewhat different from traditional imaging and video systems. Instead of rendering an image of the scene, the complete light distribution must be computed. In this thesis we discuss some different methods regarding processing and rendering of two well known full light representations: the light field and the hologram. A light field transform approach, based on matrix optics operators, is introduced. Thereafter we discuss the relationship between the light field and the hologram representations. The final part of the thesis is concerned with hologram and wave field synthesis. We present two different methods. First, a GPU accelerated approach to rendering point-based models is introduced. Thereafter, we develop a Fourier rendering approach capable of generating angular spectra of triangular mesh models.Aktuelle Fortschritte in den Bereichen der digitalen Holographie, optischen Technik und Computergrafik ermöglichen die Entwicklung von vollwertigen 3D-Displays. Diese Systeme sind allerdings auf Eingangsdaten angewiesen, die sich von denen traditioneller Videosysteme unterscheiden. Anstatt für die Visualisierung ein zweidimensionales Abbild einer Szene zu erstellen, muss die vollständige Verteilung des Lichts berechnet werden. In dieser Dissertation betrachten wir verschiedene Methoden, um dies für zwei verschiedene gebräuchliche Darstellungen der Lichtverteilung zu erreichen: Lichtfeld und Hologramm. Wir stellen dafür zunächst eine Methode vor, die Operatoren der Strahlenoptik auf Lichtfelder anzuwenden, und diskutieren daraufhin, wie die Darstellung als Lichtfeld mit der Darstellung als Hologramm zusammenhängt. Abschliessend wird die praktische Berechnung von Hologrammen und Wellenfeldern behandelt, wobei wir zwei verschiedene Ansätze untersuchen. Im ersten Ansatz werden Wellenfelder aus punktbasierten Modellen von Objekten erzeugt, unter Einsatz moderner Grafikhardware zur Optimierung der Rechenzeit. Der zweite Ansatz, Fourier-Rendering, ermöglicht die Generierung von Hologrammen aus Oberflächen, die durch Dreiecksnetze beschrieben sind