Showing papers on "White noise published in 2001"

A simple white noise analysis of neuronal light responses.

Abstract: A white noise technique is presented for estimating the response properties of spiking visual system neurons. The technique is simple, robust, efficient and well suited to simultaneous recordings from multiple neurons. It provides a complete and easily interpretable model of light responses even for neurons that display a common form of response nonlinearity that precludes classical linear systems analysis. A theoretical justification of the technique is presented that relies only on elementary linear algebra and statistics. Implementation is described with examples. The technique and the underlying model of neural responses are validated using recordings from retinal ganglion cells, and in principle are applicable to other neurons. Advantages and disadvantages of the technique relative to classical approaches are discussed.

An introduction to long-memory time series models and fractional differencing

Abstract: The idea of fractional differencing is introduced in terms of the infinite filter that corresponds to the expansion of (1 - B)d. When the filter is applied to white noise, a class of time series is generated with distinctive properties, particularly in the very low frequencies and provides potentially useful long-memory forecasting properties. Such models are shown possibly to arise from aggregation of independent components. Generation and estimation of these models are considered and applications on generated and real data presented.

Model-based estimation of ultrasonic echoes. Part I: Analysis and algorithms

Abstract: The patterns of ultrasonic backscattered echoes represent valuable information pertaining to the geometric shape, size, and orientation of the reflectors as well as the microstructure of the propagation path. Accurate estimation of the ultrasonic echo pattern is essential in determining the object/propagation path properties. In this study, we model ultrasonic backscattered echoes in terms of superimposed Gaussian echoes corrupted by noise. Each Gaussian echo in the model is a nonlinear function of a set of parameters: echo bandwidth, arrival time, center frequency, amplitude, and phase. These parameters are sensitive to the echo shape and can be linked to the physical properties of reflectors and frequency characteristics of the propagation path. We address the estimation of these parameters using the maximum likelihood estimation (MLE) principle, assuming that all of the parameters describing the shape of the echo are unknown but deterministic. In cases for which noise is characterized as white Gaussian, the MLE problem simplifies to a least squares (LS) estimation problem. The iterative LS optimization algorithms when applied to superimposed echoes suffer from the problem of convergence and exponential growth in computation as the number of echoes increases. In this investigation, we have developed expectation maximization (EM)-based algorithms to estimate ultrasonic signals in terms of Gaussian echoes. The EM algorithms translate the complicated superimposed echoes estimation into isolated echo estimations, providing computational versatility. The algorithm outperforms the LS methods in terms of independence to the initial guess and convergence to the optimal solution, and it resolves closely spaced overlapping echoes.

White-noise magnetization fluctuations in magnetoresistive heads

Abstract: Thermal magnetization fluctuations in magnetoresistive (MR) heads for magnetic hard-disk storage are a fundamental limit on their signal-to-noise ratio. The resultant noise is essentially frequency flat (white), scales with head sensitivity as signal does, but increases inversely with sensor volume. It will impact the present course of industrial R&D efforts toward geometric scaling and increasing raw head sensitivity to achieve increased areal storage densities and data rates. Magnetization noise is shown to exceed Johnson noise in 0.4 μm sensor size, giant-MR spin-valve heads designed for ∼20 Gbit/in.2 areal storage density. The basic physics underlying the experimental results is shown to be consistent with predictions from the fluctuation–dissipation theorem.

Maximum-likelihood direction-of-arrival estimation in the presence of unknown nonuniform noise

Abstract: We consider the problem of estimating directions of arrival (DOAs) of multiple sources observed on the background of nonuniform white noise with an arbitrary diagonal covariance matrix. A new deterministic maximum likelihood (ML) DOA estimator is derived. Its implementation is based on an iterative procedure which includes a stepwise concentration of the log-likelihood (LL) function with respect to the signal and noise nuisance parameters and requires only a few iterations to converge. New closed-form expressions for the deterministic and stochastic direction estimation Cramer-Rao bounds (CRBs) are derived for the considered nonuniform model. Our expressions can be viewed as an extension of the well-known results by Stoica and Nehorai (1989, 1990) and Weiss and Friedlander (1993) to a more general noise model than the commonly used uniform one. In addition, these expressions extend the results obtained by Matveyev et al. (see Circuits, Syst., Signal Process., vol.18, p.479-87, 1999) to the multiple source case. Comparisons with the above-mentioned earlier results help to discover several interesting properties of DOA estimation in the nonuniform noise case. To compare the estimation performance of the proposed ML technique with the results of our CRB analysis and with the performance of conventional "uniform" ML, simulation results are presented. Additionally, we test our technique using experimental seismic array data. Our simulations and experimental results both validate essential performance improvements achieved by means of the approach proposed.

An adaptive KLT approach for speech enhancement

Abstract: An adaptive Karhunen-Loeve transform (KLT) tracking-based algorithm is proposed for enhancement of speech degraded by colored additive interference. This algorithm decomposes noisy speech into its components along the axes of a KLT-based vector space of clean speech. It is observed that the noise energy is disparately distributed along each eigenvector. These energies are obtained from noise samples gathered from silence intervals between speech samples. To obtain these silence intervals, we proposed an efficient voice activity detector based on outputs of the principle component eigenfilter; the greatest eigenvalue of speech KLT. Enhancement is performed by modifying each KLT component due to its noise and clean speech energies. The objective is to minimize the produced distortion when residual noise power is limited to a specific level. At the end, the inverse KLT is performed and an estimation of the clean signal is synthesized. Our listening tests indicated that 71% of our subjects preferred the enhanced speech by the above method over former methods of enhancement of speech degraded by computer generated white Gaussian noise. Our method was preferred by 80% of our subjects when we processed real samples of noisy speech gathered from various environments.

Cramer-Rao bounds for estimating range, velocity, and direction with an active array

Abstract: We derive Cramer-Rao bound (CRB) expressions for the range (time delay), velocity (Doppler shift), and direction of a point target using an active radar or sonar array. First, general CRB expressions are derived for a narrowband signal and array model and a space-time separable noise model that allows both spatial and temporal correlation. We discuss the relationship between the CRB and ambiguity function for this model. Then, we specialize our CRB results to the case of temporally white noise and the practically important signal shape of a linear frequency modulated (chirp) pulse sequence. We compute the CRB for a three-dimensional (3-D) array with isotropic sensors in spatially white noise and show that it is a function of the array geometry only through the "moments of inertia" of the array. The volume of the confidence region for the target's location is proposed as a measure of accuracy. For this measure, we show that the highest (and lowest) target location accuracy is achieved if the target lies along one of the principal axes of inertia of the array. Finally, we compare the location accuracies of several array geometries.

Chaotic resonance — methods and applications for robust classification of noisy and variable patterns

Abstract: A fundamental tenet of the theory of deterministic chaos holds that infinitesimal variation in the initial conditions of a network that is operating in the basin of a low-dimensional chaotic attractor causes the various trajectories to diverge from each other quickly. This "sensitivity to initial conditions" might seem to hold promise for signal detection, owing to an implied capacity for distinguishing small differences in patterns. However, this sensitivity is incompatible with pattern classification, because it amplifies irrelevant differences in incomplete patterns belonging to the same class, and it renders the network easily corrupted by noise. Here a theory of stochastic chaos is developed, in which aperiodic outputs with 1/f2 spectra are formed by the interaction of globally connected nodes that are individually governed by point attractors under perturbation by continuous white noise. The interaction leads to a high-dimensional global chaotic attractor that governs the entire array of nodes. An example is our spatially distributed KIII network that is derived from studies of the olfactory system, and that is stabilized by additive noise modeled on biological noise sources. Systematic parameterization of the interaction strengths corresponding to synaptic gains among nodes representing excitatory and inhibitory neuron populations enables the formation of a robust high-dimensional global chaotic attractor. Reinforcement learning from examples of patterns to be classified using habituation and association creates lower dimensional local basins, which form a global attractor landscape with one basin for each class. Thereafter, presentation of incomplete examples of a test pattern leads to confinement of the KIII network in the basin corresponding to that pattern, which constitutes many-to-one generalization. The capture after learning is expressed by a stereotypical spatial pattern of amplitude modulation of a chaotic carrier wave. Sensitivity to initial conditions is no longer an issue. Scaling of the additive noise as a parameter optimizes the classification of data sets in a manner that is comparable to stochastic resonance. The local basins constitute dynamical memories that solve difficult problems in classifying data sets that are not linearly separable. New local basins can be added quickly from very few examples without loss of existing basins. The attractor landscape enables the KIII set to provide an interface between noisy, unconstrained environments and conventional pattern classifiers. Examples given here of its robust performance include fault detection in small machine parts and the classification of spatiotemporal EEG patterns from rabbits trained to discriminate visual stimuli.

On the behavior of information theoretic criteria for model order selection

Abstract: The Akaike (1974) information criterion (AIC) and the minimum description length (MDL) are two well-known criteria for model order selection in the additive white noise case. Our aim is to study the influence on their behavior of a large gap between the signal and the noise eigenvalues and of the noise eigenvalue dispersion. Our results are mostly qualitative and serve to explain the behavior of the AIC and the MDL in some cases of great practical importance. We show that when the noise eigenvalues are not clustered sufficiently closely, then the AIC and the MDL may lead to overmodeling by ignoring an arbitrarily large gap between the signal and the noise eigenvalues. For fixed number of data samples, overmodeling becomes more likely for increasing the dispersion of the noise eigenvalues. For fixed dispersion, overmodeling becomes more likely for increasing the number of data samples. Undermodeling may happen in the cases where the signal and the noise eigenvalues are not well separated and the noise eigenvalues are clustered sufficiently closely. We illustrate our results by using simulations from the effective channel order determination area.

Convergence of numerical schemes for the solution of parabolic stochastic partial differential equations

Abstract: We consider the numerical solution of the stochastic partial differential equation ∂u/∂t = ∂ 2 u/∂x 2 + σ(u)W(x,t), where W is space-time white noise, using finite differences. For this equation Gyongy has obtained an estimate of the rate of convergence for a simple scheme, based on integrals of W over a rectangular grid. We investigate the extent to which this order of convergence can be improved, and find that better approximations are possible for the case of additive noise (σ(u) = 1) if we wish to estimate space averages of the solution rather than pointwise estimates, or if we are permitted to generate other functionals of the noise. But for multiplicative noise (σ(u) = u) we show that no such improvements are possible.

On velocity estimation and correlation properties of narrow-band mobile communication channels

Abstract: The estimation of the maximum Doppler spread or, equivalently, the vehicle velocity, is useful in improving handoff algorithms and necessary for the optimal tuning of parameters for systems that adapt to changing channel conditions. We provide a novel velocity estimator based on the spectral moments of the in-phase and the quadrature phase components or the envelope of the received signal. We characterize the joint effects of the Ricean K factor, the directivity and the angle of nonisotropic scattering, and the effects of additive white noise on our estimator and other covariance-based velocity estimators analytically. We also prove the mean-square consistency of the covariance-based velocity estimators under some assumptions on the angle of arrival distribution. Simulations illustrate our approach and compare with existing techniques.

Adaptive Wiener denoising using a Gaussian scale mixture model in the wavelet domain

Abstract: We describe a statistical model for images decomposed in an overcomplete wavelet pyramid. Each coefficient of the pyramid is modeled as the product of two independent random variables: an element of a Gaussian random field, and a hidden multiplier with a marginal log-normal prior. The latter modulates the local variance of the coefficients. We assume subband coefficients are contaminated with additive Gaussian noise of known covariance, and compute a MAP estimate of each multiplier variable based on observation of a local neighborhood of coefficients. Conditioned on this multiplier, we then estimate the subband coefficients with a local Wiener estimator. Unlike previous approaches, we (a) empirically motivate our choice for the prior on the multiplier; (b) use the full covariance of signal and noise in the estimation; (c) include adjacent scales in the conditioning neighborhood. To our knowledge, the results are the best in the literature, both visually and in terms of squared error.

Robust H/sub /spl infin// filtering of stationary continuous-time linear systems with stochastic uncertainties

Abstract: The problem of applying H/sub /spl infin// filters on stationary, continuous-time, linear systems with stochastic uncertainties in the state-space signal model is addressed. These uncertainties are modeled via white noise processes. The relevant cost function is the expected value of the standard H/sub /spl infin// performance index with respect to the uncertain parameters. The solution is obtained via a stochastic bounded real lemma that results in a modified Riccati inequality. This inequality is expressed in the form of a linear matrix inequality whose solution provides the filter parameters. The method proposed is also applied to the case where, in addition to the stochastic uncertainty, other deterministic parameters of the system are not perfectly known and are assumed to lie in a given polytope. The problem of mixed H/sub 2//H/sub /spl infin// filtering for the above system is also treated. The theory developed is demonstrated by a practical example.

Efficient detection in hyperspectral imagery

Abstract: Hyperspectral sensors collect hundreds of narrow and contiguously spaced spectral bands of data. Such sensors provide fully registered high resolution spatial and spectral images that are invaluable in discriminating between man-made objects and natural clutter backgrounds. The price paid for this high resolution data is extremely large data sets, several hundred of Mbytes for a single scene, that make storage and transmission difficult, thus requiring fast onboard processing techniques to reduce the data being transmitted. Attempts to apply traditional maximum likelihood detection techniques for in-flight processing of these massive amounts of hyperspectral data suffer from two limitations: first, they neglect the spatial correlation of the clutter by treating it as spatially white noise; second, their computational cost renders them prohibitive without significant data reduction like by grouping the spectral bands into clusters, with a consequent loss of spectral resolution. This paper presents a maximum likelihood detector that successfully confronts both problems: rather than ignoring the spatial and spectral correlations, our detector exploits them to its advantage; and it is computationally expedient, its complexity increasing only linearly with the number of spectral bands available. Our approach is based on a Gauss-Markov random field (GMRF) modeling of the clutter, which has the advantage of providing a direct parameterization of the inverse of the clutter covariance, the quantity of interest in the test statistic. We discuss in detail two alternative GMRF detectors: one based on a binary hypothesis approach, and the other on a "single" hypothesis formulation. We analyze extensively with real hyperspectral imagery data (HYDICE and SEBASS) the performance of the detectors, comparing them to a benchmark detector, the RX-algorithm. Our results show that the GMRF "single" hypothesis detector outperforms significantly in computational cost the RX-algorithm, while delivering noticeable detection performance improvement.

Application of ARMAV models to the identification and damage detection of mechanical and civil engineering structures

Abstract: In this paper, the application of auto-regressive moving average vector models to system identification and damage detection is investigated. These parametric models have already been applied for the analysis of multiple input-output systems under ambient excitation. Their main advantage consists in the capability of extracting modal parameters from the recorded time signals, without the requirement of excitation measurement. The excitation is supposed to be a stationary Gaussian white noise. The method also allows the estimation of modal parameter uncertainties. On the basis of these uncertainties, a statistically based damage detection scheme is performed and it becomes possible to assess whether changes of modal parameters are caused by, e.g. some damage or simply by estimation inaccuracies. The paper reports first an example of identification and damage detection applied to a simulated system under random excitation. The `Steel-Quake' benchmark proposed in the framework of COST Action F3 `Structural Dynamics' is also analysed. This structure was defined by the Joint Research Centre in Ispra (Italy) to test steel building performance during earthquakes. The proposed method gives an excellent identification of frequencies and mode shapes, while damping ratios are estimated with less accuracy.

LV volume quantification via spatiotemporal analysis of real-time 3-D echocardiography

Abstract: This paper presents a method of four-dimensional (4-D) (3-D+Time) space-frequency analysis for directional denoising and enhancement of real-time three-dimensional (RT3D) ultrasound and quantitative measures in diagnostic cardiac ultrasound. Expansion of echocardiographic volumes is performed with complex exponential wavelet-like basis functions called brushlets. These functions offer good localization in time and frequency and decompose a signal into distinct patterns of oriented harmonics, which are invariant to intensity and contrast range. Deformable-model segmentation is carried out on denoised data after thresholding of transform coefficients. This process attenuates speckle noise while preserving cardiac structure location. The superiority of 4-D over 3-D analysis for decorrelating additive white noise and multiplicative speckle noise on a 4-D phantom volume expanding in time is demonstrated. Quantitative validation, computed for contours and volumes, is performed on in vitro balloon phantoms. Clinical applications of this spatiotemporal analysis tool are reported for six patient cases providing measures of left ventricular volumes and ejection fraction.

Quantification of molecular cloud structure using the Delta -variance

Abstract: We present a detailed study of the Δ -variance as a method to quantify molecular cloud structure. The Δ -variance was introduced by Stutzki et al. (1998) to analyze the drift behaviour of scalar functions and is used to characterize the spatial structure of observed molecular cloud images. For fractional Brownian motion structures ( fBm-fractals ), characterized by a power law power spectrum and random phases, the Δ -variance allows to determine the power spectral index β . We present algorithms to determine the Δ -variance for discretely sampled maps and study the influence of white noise, beam smoothing and the finite spatial extent of the maps. We find that for images with , edge effects can bias the structure parameters when determined by means of a Fourier transform analysis. In contrast, the Δ -variance provides a reliable estimate for the spectral index β , if determined in the spatial domain. The effects of noise and beam smoothing are analytically represented in a leading order approximation. This allows to use the Δ -variance of observed maps even at scales where the influence of both effects becomes significant, allowing to derive the spectral index β over a wider range and thus more reliably than possible otherwise. The Δ -variance is applied to velocity integrated spectral line maps of several clouds observed in rotational transitions of 12 CO and 13 CO. We find that the spatial structure of the emission is well characterized by a power law power spectrum in all cases. For linear scales larger than ∼ 0.5 pc the spectral index is remarkably uniform for the different clouds and transitions observed (). Significantly larger values () are found for observations made with higher linear resolution toward the molecular cloud MCLD 123.5+24.9 in the Polaris Flare, indicating a smoother spatial structure of the emission at small scales (<0.5 pc).

Effects of colored noise on stochastic resonance in a bistable system subject to multiplicative and additive noise

Abstract: The effects of colored noise on stochastic resonance (SR) in a bistable system driven by multiplicative colored noise and additive white noise and a periodic signal are studied by using the unified colored noise approximation and the theory of signal-to-noise ratio (SNR) in the adiabatic limit. In the case of no correlations between noises, there is an optimal noise intensities ratio R at which SNR is a maximum that identifies the characteristics of the SR when the correlation time tau of the multiplicative colored noise is small. However, when tau is increased, a second optimal value of R appears, and two peaks appear in the SNR simultaneously. In the case of correlations between noises, the SNR is not only dependent on the correlation time tau, but also on the intensity of correlations between noises. Moreover, the double peak phenomenon can also appear as tau is increased in certain situations.

Method and apparatus for implementing a class D driver and speaker system

Abstract: An audio path is constructed to include a multi-bit sigma-delta converter for converting an N-bit digital input to an n-bit output representing an over-sampled, lower resolution n-bit version of the N-bit digital input; a formatter for converting the n-bit output to an m signal output (e.g., as a thermometer code, a SDM format or a PWM format); an m-by-m switching matric for receiving the m output signals and for reordering the m output signals, m class-D drivers individual ones of which are driven by one of the reordered m output signals for driving one of m speakers; and a dynamic element matching (DEM) block coupled to the switching matric for controlling the reordering of the m output signals driving the m class-D drivers for spreading the distortion due at least to driver-speaker pair mismatch to wide band noise. The DEM may operate to generate white noise, or it may generate shaped (colored) noise.

Blind detection of independent dynamic components

Abstract: In certain applications of independent component analysis (ICA) it is of interest to test hypotheses concerning the number of components or simply to test whether a given number of components is significant relative to a "white noise" null hypothesis We estimate probabilities of such competing hypotheses for ICA based on dynamic decorrelation The probabilities are evaluated in the so-called Bayesian information criterion approximation, however, they are able to detect the content of dynamic components as efficiently as an unbiased test set estimator

Blind turbo equalization in Gaussian and impulsive noise

Abstract: We consider the problem of simultaneous parameter estimation and restoration of finite-alphabet symbols that are blurred by an unknown linear intersymbol interference (ISI) channel and contaminated by additive Gaussian or non-Gaussian white noise with unknown parameters. Non-Gaussian noise is found in many wireless channels due to the impulsive phenomena of radio-frequency interference. Bayesian inference of all unknown quantities is made from the blurred and noisy observations. The Gibbs sampler, a Markov chain Monte Carlo procedure, is employed to calculate the Bayesian estimates. The basic idea is to generate ergodic random samples from the joint posterior distribution of all unknowns and then to average the appropriate samples to obtain the estimates of the unknown quantities. Blind Bayesian equalizers based on the Gibbs sampler are derived for both Gaussian ISI channel and impulsive ISI channel. A salient feature of the proposed blind Bayesian equalizers is that they can incorporate the a priori symbol probabilities, and they produce as output the a posteriori symbol probabilities. (That is, they are "soft-input soft-output" algorithms.) Hence, these methods are well suited for iterative processing in a coded system, which allows the blind Bayesian equalizer to refine its processing based on the information from the decoding stage and vice versa-a receiver structure termed as blind turbo equalizer.

Filterbank optimization with convex objectives and the optimality of principal component forms

Abstract: This paper proposes a general framework for the optimization of orthonormal filterbanks (FBs) for given input statistics. This includes as special cases, many previous results on FB optimization for compression. It also solves problems that have not been considered thus far. FB optimization for coding gain maximization (for compression applications) has been well studied before. The optimum FB has been known to satisfy the principal component property, i.e., it minimizes the mean-square error caused by reconstruction after dropping the P weakest (lowest variance) subbands for any P. We point out a much stronger connection between this property and the optimality of the FB. The main result is that a principal component FB (PCFB) is optimum whenever the minimization objective is a concave function of the subband variances produced by the FB. This result has its grounding in majorization and convex function theory and, in particular, explains the optimality of PCFBs for compression. We use the result to show various other optimality properties of PCFBs, especially for noise-suppression applications. Suppose the FB input is a signal corrupted by additive white noise, the desired output is the pure signal, and the subbands of the FB are processed to minimize the output noise. If each subband processor is a zeroth-order Wiener filter for its input, we can show that the expected mean square value of the output noise is a concave function of the subband signal variances. Hence, a PCFB is optimum in the sense of minimizing this mean square error. The above-mentioned concavity of the error and, hence, PCFB optimality, continues to hold even with certain other subband processors such as subband hard thresholds and constant multipliers, although these are not of serious practical interest. We prove that certain extensions of this PCFB optimality result to cases where the input noise is colored, and the FB optimization is over a larger class that includes biorthogonal FBs. We also show that PCFBs do not exist for the classes of DFT and cosine-modulated FBs.

Blind estimation and equalization of MIMO channels via multidelay whitening

Abstract: Blind channel estimation and blind minimum mean square error (MMSE) equalization of multiple-input multiple-output (MIMO) communications channels arising in multiuser systems is considered, using primarily the second-order statistics of the data. The basis of the approach is the design of multiple zero-forcing equalizers that whiten the noise-free data at multiple delays. In the past such an approach has been considered using just one zero-forcing equalizer at zero-delay. Infinite impulse response (IIR) channels are allowed. Moreover, the multichannel transfer function need not be column-reduced. The proposed approach also works when the "subchannel" transfer functions have common zeros so long as the common zeros are minimum-phase zeros. The channel length or model orders need not be known. Using second-order statistics, the sources are recovered up to a unitary mixing matrix, and are further "unmixed" using higher order statistics of the data. Two illustrative simulation examples are provided where the proposed method is compared with its predecessors and an existing method to show its efficacy.

Detection Theory: Applications and Digital Signal Processing

Abstract: INTRODUCTION General Philosophy Detection and Estimation Philosophy Description of Spaces involved in the Decision Summary REVIEW OF DETERMINISTIC AND RANDOM SYSTEM AND SIGNAL CONCEPTS Some Mathematical and Statistical Background Systems and Signals (Deterministic and Random) Transformation of Random Variables Summary INTRODUCTION TO SIGNAL PROCESSING Introduction Data Structure and Sampling Discrete-Time Transformations Filtering Finite Impulse Response Filter The Fast Fourier Transform Fast Correlation Periodogram (Power Spectral Density Estimate) Wavelets Summary HYPOTHESIS TESTING Introduction Bayes Detection Maximum A Posteriori (MAP) Detection Maximum Likelihood (ML) Criterion Minimum Probability of Error Criterion Min-Max Criterion Neyman-Pearson Criterion Multiple Hypothesis Testing Composite Hypothesis Testing Receiver Operator Characteristic Curves and Performance Summary NON-PARAMETRIC AND SEQUENTIAL LIKELIHOOD RATIO DETECTORS Introduction Non-Parametric Detection Wilcoxon Detector Sequential Detection Summary DETECTION OF SIGNALS IN GAUSSIAN WHITE NOISE Introduction The Binary Detection Problem Matched Filters Matched Filter Approach M-ary Communication Systems Detection of Signals with Random Parameters Multiple Pulse Detection Summary DETECTION OF SIGNALS IN COLORED GAUSSIAN NOISE Introduction Series Representation Derivation of the Correlator Structure Using an Arbitrary Complete Ortho-Normal (CON) Set Gram-Schmidt Procedure Detection of a Known Signal in Additive White Gaussian Noise Using the Gram-Schmidt Procedure Series Expansion for Continuous Time Detection for Colored Gaussian Noise Detection of Known Signals in Additive Colored Gaussian Noise Discrete Time Detection - Known Signals Embedded in Colored Gaussian Noise Summary ESTIMATION Introduction Basic Estimation Schemes Properties of Estimators Cramer-Rao Bound Waveform Estimation Summary APPLICATIONS TO DETECTION, PARAMETER ESTIMATION, AND CLASSIFICATION Introduction The Periodogram and the Spectrogram Correlation Instantaneous Correlation Function, Wignerville Distribution, Spectral Correlation, and the Ambiguity Function Cyclo-Stationary Processing Higher Order Moments and Poly-Spectra Coherence Processing Wavelet Processing Adaptive Techniques Summary APPENDICES Probability, Random Processes and Systems Signals and Transforms Mathematical Structures Some Mathematical Expressions and Moments of Probability Density Function Wavelet Transforms INDEX

Noise characterization of a CMOS technology for the LHC experiments

Abstract: After having reviewed the main noise sources in an MOS transistor the paper presents results about the noise performance of a 0.25 μm CMOS technology which is being extensively used to design radiation tolerant ASICs for the LHC experiments (the Large Hadron Collider at present under construction at CERN). The 1/f and white noise are studied for n- and p-channel devices with five different gate lengths, in weak, moderate and strong inversion and for different drain to source and bulk to source biases. The noise degradation is measured after irradiation with 10 keV X-rays and after annealing. The results are commented in view of the use of these transistors in low-noise front-end circuits.

A stereo echo canceler with correct echo-path identification based on an input-sliding technique

Abstract: A new stereo echo canceler with correct echo-path identification based on an input-sliding technique is proposed. A time-varying filter located in one of the two channels periodically delays the input signal. By this input sliding, the correct echo-path identification is achieved. Aliasing components and audible clicks by input-sliding are made inaudible by selecting appropriate parameter values for the time-varying filter. Simulations with the NLMS algorithm and a white Gaussian signal confirm the correct echo-path identification. The subjective quality of the input signal with slides is 4.38 based on the ITU-R five-grade impairment scale. Experimental results based on an implementation by 32-bit floating-point digital signal processors show that ERLE is not degraded by talker changes in the remote room. The mean opinion score is as much as 0.55-point higher than the conventional stereo echo canceler for different round-trip delays.

Frequency estimation in the presence of Doppler spread: performance analysis

Abstract: We are concerned with the estimation of the frequency of a complex sinusoid that has been corrupted by complex-valued multiplicative and additive noise. This problem is important in many applications including array processing in the case of spatially distributed sources and synchronization in the context of time-selective channels. The multiplicative noise smears the spectral line due to the sinusoid. This smearing, which is often called Doppler spreading, may significantly degrade the estimation accuracy. The goal of this paper is to analytically assess this degradation. The finite-sample Cramer-Rao bounds (CRBs) are derived, and closed-form expressions are given for the large-sample CRB. The latter gives insights into the effective coherent and noncoherent SNRs for frequency estimation. We then analyze the accuracy of frequency estimators that are based on the angles of the sample covariances. Simulations results are presented to illustrate the theoretical results.