SPTM-7.1

An Optimal Generalized Theory of Signal Representation
J. S. Goldstein (MIT Lincoln Laboratory, 244 Wood St., Lexington, MA 02420), Joseph R Guerci (SAIC, 4001 N. Fairfax Drive, Suite 400, Arlington, VA 22201), Irving S Reed (Dept. of EE, Univ. of Southern California, Los Angeles, CA 90089)

A new generalized statistical signal processing framework is introduced for optimal signal representation and compression. Previous work is extended by considering the multiple signal case, where a desired signal is observed only in the presence of other non-white signals. The solution to this multi-signal representation problem yields a generalization of the Karhunen-Loeve transform and generates a basis selection which is optimal for multiple signals and colored-noise random processes under the minimum mean-square error criterion. The important applications for which this model is valid include detection, prediction, estimation, compression, classification and recognition.

SPTM-7.2

Symbolic Signal Processing
Don H Johnson, Wei Wang (Rice University)

Symbolic signals are, in discrete-time, sequences of quantities that do not assume numeric values. In the most general case, these quantities have no mathematical structure other than that they are members of some set, but they can have a sequential structure. We show that processing such signals does not entail mapping them directly to the integers, which would impose more structure---ordering and arithmetic---than present in the data. We describe how linear estimation and prediction can be performed on symbolic sequences. We show how spectrograms can be computed from neural population responses and from DNA sequences.

SPTM-7.3

Using a new uncertainty measure to determine optimal bases for signal representations
Tomasz Przebinda (The University of Oklahoma, Department of Mathematics), Victor DeBrunner (The University of Oklahoma, School of Electrical and Computer Engineering), Murad Ozaydin (The University of Oklahoma, Department of Mathematics)

We use a new uncertainty measure,Hp, that predicts the compactness of digital signal representations to determine a good (non-orthogonal) set of basis vectors. The measure uses the entropy of the signal and its Fourier transform in a manner that is similar to the use of the signal and its Fourier transform in the Heisenberg uncertainty principle. The measure explains why the level of discretization of continuous basis signals can be very important to the compactness of representation. Our use of the measure indicates that a mixture of (non-orthogonal) sinusoidal and impulsive or �blocky� basis functions may be best for compactly representing signals.

SPTM-7.4

Detection of Extra Solar Planets Using Parametric Modeling
Andre Ferrari (UMR Astrophysique/I3S, Universite de Nice-Sophia-Antipolis), Jean-Yves Tourneret (ENSEEIHT/GAPSE), Francois-Xavier Schmider (UMR 6525 Astrophysique, Universite de Nice Sophia-Antipolis)

We present an algorithm for the detection of extra-solar planets by occultation on the satellite COROT. Under high flux assumption, the signal is modeled as an autoregressive process having equal mean and variance. A transit of a planet in front of a star will produce an abrupt jump in the mean/variance of the process. The Neyman-Pearson detector is derived when the abrupt change parameters are known. The theoretical distribution of the test statistic is obtained allowing the computation of the ROC curves. The generalized likelihood ratio detector is then studied for the practical case were the change parameters are unknown. This detector requires the maximum likelihood estimates of the parameters. ROC curves are then determined using computer simulations.

SPTM-7.5

A DSB-SC Signal Model for Nonlinear Regression-Based Quadrature Receiver Calibration
Roger A Green (EE Department, North Dakota State University)

Recent advances have been made regarding quadrature receiver I/Q mismatch calibration. In particular, Green/Anderson-Sprecher/Pierre present a nonlinear regression (NLR) -based algorithm that utilizes a pure sinusoidal test signal for sensor calibration [1]. This paper develops a double side-band suppressed carrier (DSB-SC) signal model for use with NLR-based calibration methods. The DSB-SC model not only provides a useful signal for calibration, it also demonstrates the model flexibility inherent to nonlinear regression techniques. Simulations illustrate the effectiveness of the DSB-SC signal model for the calibration of I/Q sensors.

SPTM-7.6

Model Selection: A Bootstrap Approach
Abdelhak M Zoubir (Cooperative Research Centre for Satellite Systems)

The problem of model selection is addressed. Bootstrap methods based on residuals are used to select the best model according to a prediction criterion. Both the linear and the nonlinear models are treated. It is shown that bootstrap methods are consistent and in simulations that in most cases they outperform classical techniques such as Akaike's information criterion and Rissanen's minimum description length. We also show how the methods apply to dependent data models such as autoregressive models.

SPTM-7.7

Color Texture Synthesis with 2-D Moving Average Model
Glen Andrews, Kie B Eom (The George Washington University)

An algorithm for synthesizing color textures from a small set of parameters is presented in this paper. The synthesis algorithm is based on the 2-D moving average model, and realistic textures resembling many real textures can be synthesized using this algorithm. A maximum likelihood estimation algorithm to estimate parameters from a sample texture is also presented. Using the estimated parameters, a texture larger than the original image can be synthesized from a small texture sample. In the experiment, various textures suitable for multimedia applications are synthesized from parameters estimated from real textures.

SPTM-7.8

Sampling Theorems for Linear Time-varying Systems with Bandlimited Inputs
Soonman Kwon, Daniel R Fuhrmann (Washington University)

We propose and prove an extended sampling theorem for linear time-varying systems. As a result, we establish a discrete-time equivalent model of the input-output relation of the system for the case of bandlimited inputs and bandlimited system variation. The sampling of the output signal and an equivalent discrete-time model of the system are discussed.

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Last Update: February 4, 1999 Ingo Höntsch