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This paper outlines the use of an Evolutionary Algorithm (EA) to perform the Equalisation of a non minimum phase channel. Conventional techniques utilising first and second order approximations of the error surface, have been demonstrated to be ineffective in achieving an optimal solution in continuous simulations, and have proved incapable of dealing with the more difficult non minumum phase problems...
A method of incorporating implementation aspects in the algorithm-level design of nonlinear filters is proposed. As a case study, the trade-off between the visual properties and the complexity of soft morphological filters is studied using training-based optimization methods. Specifically, it is shown that the use of the complexity constraints can provide the filter designer valuable information on...
Estimating the frequencies, amplitudes and phases of sinusoids in noise is a problem which arises in many-applications. The aim of the methods in this paper is to achieve computational efficiency and near-ML performance (i.e. low bias, variance and threshold SNR), in problems such as vibration or audio analysis where the number of tones may be large (e.g. > 20). An approach has recently been published...
Multi-delay predictive FIR filters utilizing a small number of multipliers are proposed. These filters are shown to have substantially lower noise gain than the standard minimum-length predictors using the same amount of multipliers. These filters are formulated for both arbitrary-order polynomial and sinusoidal signal prediction. The use of dynamic programming for the efficient optimization of these...
The dot diffusion method for digital halftoning has the advantage of parallelism unlike the error diffusion method. However, image quality offered by error diffusion is still regarded as superior to other known methods. In this paper we show how the dot diffusion method can be improved by optimization of the so-called class matrix. By taking the human visual characteristics into account we show that...
This paper analyzes adaptive linear prediction and the effects of the underlying optiniality criterion on the prediction error. It is well known that the signal-dependent optimization process converts the linear filter into a nonlinear signal processing device and that this will influence the statistics of the filter output in a way not expected from linear filter theory. For minimum-phase Lp-optimal...
The desire to view smaller and smaller attributes within biological specimens means that confocal microscopes are often used at the limit of their resolution. For quantitative analysis of smaller sized attributes, and as a necessary preprocessing stage for automatic recognition and classification of objects it is essential that confocal images are restored. A fast new hybrid statistical restoration...
The need for decomposing a signal into its optimal representation arises in many applications. In such applications, one can usually represent the signal as a combination of an over-complete dictionary elements. The non-uniqueness of signal representation, in such dictionaries, provides us with the opportunity to adapt the signal representation to the signal. The adaptation is based on sparsity, resolution...
A new recursive algorithm is proposed for finding the minimum of an objective function whose gradient is not obtainable directly but is approximated from the noisy observations of the function. The algorithm is based on the simultaneous perturbation stochastic approximation method (SPSA) combined with randomly varying truncations, and provides the estimate, which is convergent under weaker conditions...
It is shown how noisy closed-loop frequency response measurements may be used to obtain pointwise in frequency bounds on the possible difference between an unknown closed-loop system and a nominal model of the closed-loop. To this end, the ν-gap metric framework for robustness analysis plays a central role.
In this paper the normal and the failed behaviors of a sampleddata system are modeled using two distinct uncertain models. A proper auxiliary signal is an input signal for which the behaviors of the two systems do not intersect making guaranteed failure detection possible. Algorithms for the design of optimal proper auxiliary signals are developed.
This paper presents a methodology for enhancing the robustness of a GPC controlled system by convex optimisation of the Youla parameter. This methodology requires, as a first step, the design of an initial GPC controller; this controller is then robustified considering frequency and temporal constraints. By means of the Youla parametrisation, frequency and temporal constraints are formulated within...
In many signal processing applications, one has to solve an overdetermined system of linear equations Ax ≈ b, while minimizing the errors on A and b. The Total Least Squares (TLS) method calculates corrections ΔA and Δb such that (A + ΔA)x = b + Δb and ||[ΔA Δb]||F is minimal. The resulting parameter vector x is ä Maximum Likelihood (ML) estimate when the noise on the different entries of [A b] is...
In many acoustic conditions the recorded speech signals may be severely affected by reverberation, leading to a reduced speech quality and intelligibility. In this paper we focus on a blind speech dereverberation method based on multi-channel linear prediction (MCLP) in the short-time Fourier transform domain, which is typically performed in each frequency bin independently without taking into account...
The task of determining informative sensors and clustering the sensor measurements according to their information content is considered. To this end, the standard canonical correlation analysis (CCA) framework is equipped with norm-one and norm-two regularization terms to estimate the unknown number of field sources and identify informative groups of sensors. Coordinate descent techniques are combined...
In this paper, we present a novel framework for parcellation of a brain region into functional sub-regions based on connectivity patterns between brain regions. The proposed method takes the prior neurological information into consideration and aims at finding spatially continuous and functionally consistent sub-regions in a given brain area. The proposed framework relies on 1) a sparse spatially...
In this paper, we focus on solving the decentralized consensus optimization problem defined over a networked multi-agent system. All the agents shall cooperatively find a common minimizer of the overall objective while each agent holds its own local objective and can only communicate with its neighbors. Motivated by many applications in which the local objective is the sum of a differentiable part...
A classical problem that arises in numerous signal processing applications asks for the reconstruction of an unknown, k-sparse signal x0 ∈ ℝn from underdetermined, noisy, linear measurements y = Ax0 + z ∈ ℝm. One standard approach is to solve the following convex program x̂ = arg minx ∥y − Ax∥2+λ∥x∥1, which is known as the ℓ2-LASSO. We assume that the entries of the sensing matrix A and of the noise...
Images and videos are often captured in poor light conditions, resulting in low-contrast images that are corrupted by acquisition noise. To recreate a high-quality image for visual observation, the captured image must be denoised and contrastenhanced. Conventional methods perform these two tasks in two separate stages: an image is first denoised, followed by an enhancement procedure. In this paper,...
In hypothesis testing, the phenomenon of label noise, in which hypothesis labels are switched at random, contaminates the likelihood functions. In this paper, we develop a new method to determine the decision rule when we do not have knowledge of the uncontaminated likelihoods and contamination probabilities, but only have knowledge of the contaminated likelihoods. In particular we pose a minimax...
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