A new class of orthonormal symmetric wavelet bases using a complexallpass filter

This paper considers the design of the whole sample symmetric (WSS) paraunitary filterbanks composed of a single complex allpass filter and gives a new class of real-valued orthonormal symmetric wavelet bases. First, the conditions that the complex allpass filter has to satisfy are derived from the symmetry and orthonormality conditions of wavelets, and its transfer function is given to satisfy these conditions. Second, the paraunitary filter banks are designed by using the derived transfer function from the viewpoints of the regularity and frequency selectivity. A new method for designing the proposed paraunitary filterbanks with a given degrees of flatness is presented. The proposed method is based on the formulation of a generalized eigenvalue problem by using the Remez exchange algorithm. Therefore, the filter coefficients can be easily obtained by solving the eigenvalue problem, and the optimal solution is attained through a few iterations. Furthermore, both the maximally flat and minimax solutions are also included in the proposed method as two specific cases. The maximally flat filters have a closed-form solution without any iteration. Finally, some design examples are presented to demonstrate the effectiveness of the proposed method     

On cosine-modulated wavelet orthonormal bases

Multiplicity M, K-regular, orthonormal wavelet bases (that have implications in transform coding applications) have previously been constructed by several authors. The paper describes and parameterizes the cosine-modulated class of multiplicity M wavelet tight frames (WTFs). In these WTFs, the scaling function uniquely determines the wavelets. This is in contrast to the general multiplicity M case, where one has to, for any given application, design the scaling function and the wavelets. Several design techniques for the design of K regular cosine-modulated WTFs are described and their relative merits discussed. Wavelets in K-regular WTFs may or may not be smooth, Since coding applications use WTFs with short length scaling and wavelet vectors (since long filters produce ringing artifacts, which is undesirable in, say, image coding), many smooth designs of K regular WTFs of short lengths are presented. In some cases, analytical formulas for the scaling and wavelet vectors are also given. In many applications, smoothness of the wavelets is more important than K regularity. The authors define smoothness of filter banks and WTFs using the concept of total variation and give several useful designs based on this smoothness criterion. Optimal design of cosine-modulated WTFs for signal representation is also described. All WTFs constructed in the paper are orthonormal bases.     

Shear madness: new orthonormal bases and frames using chirpfunctions

The proportional-bandwidth and constant-bandwidth time-frequency signal decompositions of the wavelet, Gabor, and Wilson orthonormal bases have attracted substantial interest for representing nonstationary signals. However, these representations are limited in that they are based on rectangular tessellations of the time-frequency plane. While much effort has gone into methods for designing nice wavelet and window functions for these frameworks, little consideration has been given to methods for constructing orthonormal bases employing nonrectangular time-frequency tilings. The authors take a first step in this direction by deriving two new families of orthonormal bases and frames employing elements that shear, or chirp, in the time-frequency plane, in addition to translate and scale. The new scale-shear fan bases and shift-shear chevron bases are obtained by operating on an existing: wavelet, Gabor (1946), or Wilson basis set with two special unitary warping transformations. In addition to the theoretical benefit of broadening the class of valid time-frequency plane tilings, these new bases could possibly also be useful for representing certain types of signals, such as chirping and dispersed signals     

Design of robust envelope-constrained filter with orthonormal bases

In the continuous-time envelope-constrained (EC) filtering problem using an orthonormal filter structure, the aim is to synthesize an orthonormal filter such that the noise enhancement is minimized while the noiseless output response of the filter with respect to a specified input signal stays within the upper and lower bounds of the envelope. The noiseless output response of the optimum filter to the prescribed input signal touches the output boundaries at some points. Consequently, any disturbance in the prescribed input signal or error in the implementation of the optimal filter will result in the output constraints being violated. In this paper, we review a semi-infinite envelope-constrained filtering problem in which the constraint robustness margin of the filter is maximized, subject to a specified allowable increase in the optimal noisy power gain. Using a smoothing technique, it is shown that the solution of the optimization problem can be obtained by solving a sequence of strictly convex optimization problems with integral cost. An efficient optimization algorithm is developed based on a combination of the golden section search method and the quasi-Newton method     

Detection in correlated impulsive noise using fourth-ordercumulants

We consider detection and estimation in correlated impulsive noise. The non-Gaussian impulsive noise is modeled as the sum of two linear processes: a nominal part and an impulsive part. This model admits correlated impulsive bursts lasting many data samples. Identifiability of the noise model is established using fourth- and second-order cumulants. Under this model, the correlated time series can be whitened and an appropriate memoryless nonlinearity applied to attenuate the impulsive events. A detection statistic is then formed from the output of the nonlinearity. In the threshold detection case, the use of cumulants allows identification of the noise in the presence of the signal to be detected, obviating the need for noise-only training records. Simulation results with a sample size of 512 show small loss in detector performance versus an ideal detector with no impulsive part present     

On variable overlapped windows and weighted orthonormal bases

A generalized lapped orthonormal transform (GLOT) formulation is presented for the analysis and synthesis of signals. This formulation is composed of a variable width window and a linear combination of weighted orthonormal functions. Tradeoffs in the specification of windows are examined. A sinusoidal example is considered, and a fast algorithm is provided for its evaluation     

Efficient design of orthonormal wavelet bases for signal representation

The efficient representation of a signal as a linear combination of elementary "atoms" or building blocks is central to much signal processing theory and many applications. Wavelets provide a powerful, flexible, and efficiently implementable class of such atoms. In this paper, we develop an efficient method for selecting an orthonormal wavelet that is matched to a given signal in the sense that the squared error between the signal and some finite resolution wavelet representation of it is minimized. Since the squared error is not an explicit function of the design parameters, some form of approximation of this objective is required if conventional optimization techniques are to be used. Previous approximations have resulted in nonconvex optimization problems, which require delicate management of local minima. In this paper, we employ an approximation that results in a design problem that can be transformed into a convex optimization problem and efficiently solved. Constraints on the smoothness of the wavelet can be efficiently incorporated into the design. We show that the error incurred in our approximation is bounded by a function that decays to zero as the number of vanishing moments of the wavelet grows. In our examples, we demonstrate that our method provides wavelet bases that yield substantially better performance than members of standard wavelet families and are competitive with those designed by more intricate nonconvex optimization methods.     

SVD-updating using orthonormal μ-rotations

In this paper the implementation of the SVD-updating algorithm using orthonormal μ-rotations is presented. An orthonormal μ-rotation is a rotation by an angle of a given set of μ-rotation angles (e.g., the angles Φ_i = arctan2^-i) which are choosen such that the rotation can be implemented by a small amount of shift-add operations. A version of the SVD-updating algorithm is used where all computations are entirely based on the evaluation and application of orthonormal rotations. Therefore, in this form the SVD-updating algorithm is amenable to an implementation using orthonormal μ-rotations, i.e., each rotation executed in the SVD-updating algorithm will be approximated by orthonormal μ-rotations. For all the approximations the same accuracy is used, i.e., onlyr?w (w: wordlength) orthonormal μ-rotations are used to approximate the exact rotation. The rotation evaluation can also be performed by the execution of μ-rotations such that the complete SVD-updating algorithm can be expressed in terms of orthonormal μ-rotations. Simulations show the efficiency of the SVD-updating algorithm based on orthonormal μ-rotations.     

Reduction of noise and distortion in amplifiers using adaptivecancellation

Application of adaptive cancellation techniques to feed-forward networks for amplifier linearization is discussed. Results of a test showing over 25 dB reduction in third order intermodulation products are presented     

On adaptive beamforming in correlated noise

This paper deals with the problem of adaptive beamforming in the presence of fully coherent (correlated) noise sources. Two different techniques are developed for minimizing the effects of coherent interference. The first method employs spatial interpolation of the array aperture, followed by spatial smoothing in order to decorrelate the desired signal and the interference. The second technique is based on a simple algebraic transformation for restoring the rank of the array signal correlation matrix, which is normally rank deficient in such situations. This technique is shown to work for nonuniform adaptive arrays as well. Extensive computer simulation results are presented to illustrate the effectiveness of the proposed techniques.This week was supported by the N.R.C., Resident Research Associateship Programme.     

Reduction of harmonic distortion and noise in a semiconductor optical amplifier using bias current feedback

The application of bias current feedback to a semiconductor optical amplifier is shown to improve considerably its noise and distortion performance in analogue applications. Improvements in harmonic distortion of up to 12.75dB, and in noise levels of up to 7.5dB, have been achieved.<>     

Smooth functional and structural maps on the neocortex via orthonormal bases of the Laplace-Beltrami operator

Functional and structural maps, such as a curvature, cortical thickness, and functional magnetic resonance imaging (MRI) maps, indexed over the local coordinates of the cortical manifold play an important role in neuropsychiatric studies. Due to the highly convoluted nature of the cerebral cortex and image quality, these functions are generally uninterpretable without proper methods of association and smoothness onto the local coordinate system. In this paper, we generalized the spline smoothing problem (Wahba, 1990) from a sphere to any arbitrary two-dimensional (2-D) manifold with boundaries. We first seek a numerical solution to orthonormal basis functions of the Laplace-Beltrami (LB) operator with Neumann boundary conditions for a 2-D manifold M then solve the spline smoothing problem in a reproducing kernel Hilbert space (r.k.h.s.) of real-valued functions on manifold M with kernel constructed from the basis functions. The explicit discrete LB representation is derived using the finite element method calculated directly on the manifold coordinates so that finding discrete LB orthonormal basis functions is equivalent to solving an algebraic eigenvalue problem. And then smoothed functions in r.k.h.s can be represented as a linear combination of the basis functions. We demonstrate numerical solutions of spherical harmonics on a unit sphere and brain orthonormal basis functions on a planum temporale manifold. Then synthetic data is used to quantify the goodness of the smoothness compared with the ground truth and discuss how many basis functions should be incorporated in the smoothing. We present applications of our approach to smoothing sulcal mean curvature, cortical thickness, and functional statistical maps on submanifolds of the neocortex.     

Reduction of phase noise in linear HBT amplifiers using low-frequency active feedback

In this paper, we investigate the effect of low-frequency active feedback in the 1/f phase modulation (PM) noise of a linear SiGe heterojunction bipolar transistor amplifier operating at 1 GHz. The voltage gain and the output resistance of the feedback amplifier were varied and their effect on the baseband collector voltage noise reduction and PM noise reduction were observed. Our results show that the measured reduction in the baseband noise when using active feedback was in close agreement with the expected reduction (within 2 dB) for all the configurations tested. While the PM noise was also reduced when active feedback was used, in some feedback configurations the PM noise reduction was not as large as the reduction observed in the baseband noise. The best amplifier PM noise obtained when using low-frequency feedback was L(f)/spl cong/-140 dBc/Hz at 100 Hz from the carrier, a reduction of 21 dB compared to the amplifier without feedback.     

Broadband macromodelling of passive components using orthonormal vector fitting

Vector fitting is widely accepted as a robust macromodelling tool for efficient frequency domain identification of passive components. The orthonormal vector fitting technique is introduced, which improves the numerical stability of the method, by using orthonormal rational functions. This leads to better conditioned equations, reduces the numerical sensitivity to the choice of starting poles significantly, limits the number of required iterations, and reduces the overall computation time.     

Threshold detection in correlated non-Gaussian noise fields

Classical threshold detection theory for arbitrary noise and signals, based on independent noise samples, i.e., using only the first-order probability density of the noise, is generalized to include the critical additional statistical information contained in the (first-order) covariances of the noise. This is accomplished by replacing the actual, generalized noise by a “quasi-equivalent” (QE-)model employing both the first-order PDF and covariance. The result is a “near-optimum” approach, which is the best available to date incorporating these fundamental statistical data. Space-time noise and signal fields are specifically considered throughout. Even with additive white Gaussian noise (AWGN) worthwhile processing gains per sample (Γ^(c)) are attainable, often O(10-20 dB), over the usual independent sampling procedures, with corresponding reductions in the minimum detectable signal. The earlier moving average (MA) noise model, while not realistic, is included because it reduces in the Gaussian noise cases to the threshold optimum results of previous analyses, while the QE-model remains suboptimum here because of the necessary constraints imposed in combining the PDF and covariance information into the detector structure. Full space-time formulation is provided in general, with the important special cases of adaptive and preformed beams in reception. The needed (first-order) PDF here is given by the canonical Class A and Class B noise models. The general analysis, including the canonical threshold algorithms, correlation gain factors Γ^(c), detection parameters for the QE-model, along with some representative numerical results for both coherent and incoherent detection, based on four representative Toeplitz covariance models is presented     

Reduction of musical residual noise using perceptual tools with classic speech denoising techniques

Single channel enhancement techniques based on short-time spectral amplitude (STSA) estimation have the major drawback of generating an artificial and annoying residual noise with musical character, due mainly to the unwanted peaks in the denoised signal spectrum. The detection and reduction of spectral peaks which have a musical characteristic are the main objectives of this paper. The proposed perceptual technique to reduce musical residual noise operates as a post-processing. Based on human auditory properties, the perceptual post-processing is established in a number of steps. First, we detect musical peaks by comparing tonality coefficients in each critical band of both denoised signal and reference signal. Detected musical peaks are audible only if they exceed the clean speech masking threshold (MT). However, the clean MT is not available. It is estimated by modifying the Johnston model. Secondly, we reduce the musical residual noise by removing only audible musical peaks which exceed the estimated MT. The proposed method is tested and compared with classic STSA technique and perceptual techniques at various levels of white and colored noise. Results show the validity of the proposed technique.     

Estimation of frequency-delay of arrival (FDOA) using fourth-orderstatistics in unknown correlated Gaussian noise sources

A new family of nonparametric and parametric methods based on fourth-order statistics for the estimation of the frequency-delay of arrival (FDOA) between two sensor signal measurements, corrupted by correlated Gaussian noise sources in an unknown way, is presented. The new family of FDOA estimation methods utilizes the fourth-order cumulants or 1-D Fourier transforms of sliced fourth-order cumulants of the two signal measurements. It is demonstrated that the new family of FDOA estimation methods suppresses the effect due to correlated Gaussian measurement noises and outperforms existing second-order statistics-based FDOA estimation methods using either cross-ambiguity function or MUSIC algorithm. Various simulation results are presented for different types of signals, different lengths of data, and different signal-to-noise ratios     

An orthonormal Laguerre expansion yielding Rice's envelope densityfunction for two sine waves in noise

Through an orthonormal Laguerre expansion, expressions are derived for a lesser known Rician probability distribution-the probability density function (PDF) of the envelope of two fixed-amplitude randomly phased sine waves in narrowband Gaussian noise-and for the integral of the density, the cumulative distribution function (CDF). The principal formula derived has been checked analytically, numerically, and (approximately) graphically. Analytically, the moment-generating function for the PDF of the square of the envelope has been found to be a three-term product of elementary functions times an I₀ Bessel function (and thus to be in closed form); in confirmation, the same result has been secured via another, more direct route     

A modified likelihood function approach to DOA estimation in thepresence of unknown spatially correlated Gaussian noise using a uniformlinear array

The problem of modified ML estimation of DOAs of multiple source signals incident on a uniform linear array (ULA) in the presence of unknown spatially correlated Gaussian noise is addressed here. Unlike previous work, the proposed method does not impose any structural constraints or parameterization of the signal and noise covariances. It is shown that the characterization suggested here provides a very convenient framework for obtaining an intuitively appealing estimate of the unknown noise covariance matrix via a suitable projection of the observed covariance matrix onto a subspace that is orthogonal complement of the so-called signal subspace. This leads to a formulation of an expression for a so-called modified likelihood function, which can be maximized to obtain the unknown DOAs. For the case of an arbitrary array geometry, this function has explicit dependence on the unknown noise covariance matrix. This explicit dependence can be avoided for the special case of a uniform linear array by using a simple polynomial characterization of the latter. A simple approximate version of this function is then developed that can be maximized via the-well-known IQML algorithm or its variants. An exact estimate based on the maximization of the modified likelihood function is obtained by using nonlinear optimization techniques where the approximate estimates are used for initialization. The proposed estimator is shown to outperform the MAP estimator of Reilly et al. (1992). Extensive simulations have been carried out to show the validity of the proposed algorithm and to compare it with some previous solutions