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Meta learning uses information from base learners (e.g. classifiers or estimators) as well as information about the learning problem to improve upon the performance of a single base learner. For example, the Bayes error rate of a given feature space, if known, can be used to aid in choosing a classifier, as well as in feature selection and model selection for the base classifiers and the meta classifier...
In service engineering it is important to estimate when and what a worker did, because they include crucial evidences to improve service quality and working environments. For Service Operation Estimation (SOE), acoustic information is one of useful and key modalities; particularly environmental or background sounds include effective cues. This paper focuses on two aspects: (1) extracting powerful...
The paper deals with the task of robust nonlinear regression in the presence of outliers. The problem is dealt in the context of reproducing kernel Hilbert spaces (RKHS). In contrast to more classical approaches, a recent trend is to model the outliers as a sparse vector noise component and mobilize tools from the sparsity-aware/compressed sensing theory to impose sparsity on it. In this paper, three...
With the progress of adaptive optics systems, ground-based telescopes acquire images with improved resolutions. However, compensation for atmospheric turbulence is still partial, which leaves good scope for digital restoration techniques to recover fine details in the images. A blind image deblurring algorithm for a single long-exposure image is proposed, which is an instance of maximum-a-posteriori...
Semi-local Hurst estimation is considered by incorporating a Markov random field model to constrain a wavelet-based pointwise Hurst estimator. This results in an estimator which is able to exploit the spatial regularities of a piecewise parametric varying Hurst parameter. The pointwise estimates are jointly inferred along with the parametric form of the underlying Hurst function which characterises...
We propose a new hybrid (or morphological) generative model that decomposes a signal into two (and possibly more) layers. Each layer is a linear combination of localised atoms from a time-frequency dictionary. One layer has a low-rank time-frequency structure while the other as a sparse structure. The time-frequency resolutions of the dictionaries describing each layer may be different. Our contribution...
We propose the use of multivariate version of Whittle's methodology to estimate periodic autoregressive moving average models. In the literature, this estimator has been widely used to deal with large data sets, since, in this context, its performance is similar to the Gaussian maximum likelihood estimator and the estimates are obtained much faster. Here, the usefulness of Whittle estimator is illustrated...
In chronobiology a periodic components variation analysis for the signals expressing the biological rhythms is needed. Therefore precise estimation of the periodic components is required. The classical approaches, based on FFT methods, are inefficient considering the particularities of the data (non-stationary, short length and noisy). In this paper we propose a new method using inverse problem and...
In this paper, a novel array-based method to estimate the path loss exponent (PLE) is developed. The method is designed as a part of an automatic calibration step, prior to localization of a source transmitting in the near-far field of the array. The method only requires the knowledge of the ranges between the array elements. By making the antenna elements transmit in turn, the array response model...
In the analysis of Electroencephalograms (EEG), notably in their graphical modeling, the estimation of the spectral matrix and associated variables is of central importance. Often, when adjusting for the bandwidth of the spectral matrix estimate, singularity issues arise and information derived from the inverse spectral matrix is intractable. This requires the use of regularization methods, which...
We consider the problem of joint estimation of structured inverse covariance matrices. We assume the structure is unknown and perform the estimation using groups of measurements coming from populations with different covariances. Given that the inverse covariances span a low dimensional affine subspace in the space of symmetric matrices, our aim is to determine this structure. It is then utilized...
Many array-processing algorithms or applications require the estimation of a target signal subspace, e.g., for source localization or for signal enhancement. In wireless sensor networks, the straightforward estimation of a network-wide signal subspace would require a centralization of all the sensor signals to compute network-wide covariance matrices. In this paper, we present a distributed algorithm...
This paper presents a method to estimate a linearly time-varying delay between two continuous signals. The joint estimation of the time delay and Doppler shift by analyzing the cross-ambiguity function is state of the art, however, this method has high computational demands as it relies on a bi-variate search. It is shown that, by using previous estimation results to initialize the analysis, a similar...
Despite the popularity of linear process models in signal and image processing, various real life phenomena exhibit nonlinear characteristics. Compromising between the realistic and computationally heavy nonlinear models and the simplicity of linear estimation methods, linear in the parameters nonlinear models such as polynomial autoregressive (PAR) models have been accessible analytical tools for...
Doppler-shift target localization has recently attracted renewed interest due to its wide range of applications. In this paper we analyze the optimal sensor-target geometries for the Doppler-shift target localization problem where the position and velocity of a moving target are estimated from Doppler-shift measurements taken at stationary sensors. The analysis is based on minimizing the estimation...
Existing methods for smart data reduction are typically sensitive to outlier data that do not follow postulated data models. We propose robust censoring as a joint approach unifying the concepts of robust learning and data censoring. We focus on linear inverse problems and formulate robust censoring through a sparse sensing operator, which is a non-convex bilinear problem. We propose two solvers,...
We present method for in-flight signal processing and calibration of a strap-down inertial navigation system (SINS) for the information mini-satellites at low Earth orbit. We have applied the SINS correction based on the signals of a sun-magnetic system and consider the problem at a long-term forecasting of the mini-satellite's orbital motion.
High sampling rate Analog-to-Digital Converters (ADCs) can be obtained by time-interleaving low rate (and thus low cost) ADCs into so-called Time-Interleaved ADCs (TI-ADCs). Nevertheless increasing the sampling frequency involves an increasing sensibility of the system to desynchronization between the different ADCs that leads to time-skew errors, impacting the system with non linear distortions....
In this paper we propose a distributed and adaptive algorithm for collaborative processing of the complex signals. The proposed algorithm, which will be referred to as the incremental augmented affine projection algorithm (IncAAPA), not only utilizes the full second order statistical information in complex domain but also exploits the spatial diversity which is provided by the distribution of the...
The coin classification, recognition and validation is an important issue for vending machines and other coin handling equipment. One approach to investigate and classify the coins (often in combination with other methods-like optical and electromagnetic sensor signal processing) is the analyzing of the acoustical signature of the coin, falling against the special metal part (e.g. in the form of small...
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