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The method principal component (PCA) allows to allocate from a matrix of these several objects with a large amount of signs only 1–3 vectors containing 90–95% of information. Usually measuring problem of assessment of these main components is solved by the iterative NIPALS procedure or the algebraic SVD procedure, however both of these methods often give ambiguous estimates. For the purpose of elimination...
This paper addresses the problem of speech quality enhancement and acoustic noise reduction by adaptive filtering algorithms. In this paper, we propose a new version of the set-membership partial-update normalized least mean square (SM-PU-NLMS) algorithm. The proposed algorithm is based on the use of the cross-correlation of the output error signal and the noisy to control the variable-step-size in...
This paper considers the identification of impulse responses of systems with multiple inputs. An existing technique utilizing signal sets which are correlated with one another is modified to improve its convergence properties. A detailed example is presented to compare the performance between identification using a set of correlated signals and identification using a set of uncorrelated signals. In...
This paper presents the application of runs test for indirect consideration of observation's autocorrelation in estimation of a standard uncertainty of arithmetic mean value. At first stage researches were performed by Monte Carlo (MC) simulation for two kind's random signals: first order autoregression (AR) and moving averaging (MA). Comparison of theoretical values of effective number of observations...
Multiview canonical correlation analysis (MCCA) is an effective tool for analyzing the relationships among group- aligned multidimensional samples, which has been applied to the fields of pattern recognition and computer vision. In MCCA, its first-stage canonical variables are solved by a multivariate eigenvalue problem that can be computed by Horst method. However, how to use the algorithm for effectively...
Disturbances originating in one control loop of a large industrial plant can propagate far from the source, giving rise to plant-wide oscillations. The underlying interactions among the different control loops make it hard to identify the origin of such large scale disturbances. This paper studies the application of the convergent cross mapping (CCM) based technique to isolate the source of a plant-wide...
The convergence analysis of the affine projection algorithm (APA) was intensively studied, however, it is far to be a trivial task. In this context, a set of assumptions have to be considered, in order to allow a tractable analytical approach. As a consequence, notable differences occur between the simulation results and the analytical model. In this paper, the convergence of APA is studied and a...
In this paper, we focus on promoting multi-label learning task with ensemble learning. Compared to traditional single algorithm methods, it has been recognized that ensemble methods could achieve much better performance than each constituent learned model, especially under the conditional independence of different classifiers. Existing multi-label ensemble algorithms mainly focus on creating diverse...
In this paper we revisit the well known and popular Normalized Subband Adaptive Filter (NSAF). Based on an analysis of the algorithm in the mean and using an analysis strategy presented in [1], we find that the NSAF can be seen as a Richardson iteration applied to a preconditioned augmented Wiener-Hopf equation. This equation is formulated in such a way that its convergence speed can be predicted...
Remote Photoplethysmography (rPPG) is a technique that consists in estimating physiological parameters such as heart rate from live or recorded video sequences taken by conventional camera or even webcams. This technique is increasingly used in many application fields thanks to its simplicity and affordability. The basic idea is that the arterial blood flow shows regularity due to the heartbeat. This...
Convergence of Gibbs fields modeling procedures is evaluated. Evaluation of convergence time is based on analysis of a dynamics of random field realizations space-correlation characteristics during the process of Gibbs fields modeling. Gibbs fields modeling procedure stop time is estimated.
The paper analyses historical cross border commercial flow (CBCF) of electricity for Germany for the years 2012 to 2015. Based on graphical solution for CBCF it is hypothesized that with increasing shares of fluctuating renewable energy sources in Germany, it would be exporting more electricity at lower prices while importing at relatively higher prices. For this the relation between CBCF, spot prices...
This paper presents a technique to estimate the time skew in time-interleaved ADCs. The proposed method estimates all of the time skew parameters jointly based on observations from a bank of correlators. The proposed method works for an arbitrary number of sub-ADCs. For implementation of the correlator bank, we propose the use of Mitchell's logarithmic multiplier and a hardware reuse mechanism, thereby...
Speech enhancement using adaptive filtering methods are known to give good signal recovery from the noisy speech signal. Among these, Least Mean Square (LMS) and Recursive Least Squares (RLS) algorithms are more popular. These algorithms have a constraint that correlating noise should be given as the reference signal for denoising. Therefore in all the adaptive algorithms, two microphones are used,...
A new method for designing single/multiple unimodular waveforms with good weighted correlation properties, which is based on minimizing the weighted integrated sidelobe levels of waveforms, is developed. The main contributions of the paper lie in formulating the objective as a quartic form where Hadamard product of matrices is involved, converting the non-convex quartic optimization problem into a...
While most existing video summarization approaches aim to extract an informative summary of a single video, we propose an unsupervised framework for summarizing topic-related videos by exploring complementarity within videos. We develop a novel sparse optimization method to extract a diverse summary that is both interesting and representative in describing the video collection. To efficiently solve...
Unlike dimensionality reduction (DR) tools for single-view data, e.g., principal component analysis (PCA), canonical correlation analysis (CCA) and generalized CCA (GCCA) are able to integrate information from multiple feature spaces of data. This is critical in multi-modal data fusion and analytics, where samples from a single view may not be enough for meaningful DR. In this work, we focus on a...
With the advent of hands free devices, speech recognition is of utmost importance but miserably fails to be perfect in a cock-tail party environment without speech separation or speech denoising. There are various techniques available for speech separation but the one technique used nowadays is non-negative matrix factorization (NMF). Non-negative matrix factorization decomposes the mixed signal into...
Multi-label data with high dimensionality arise frequently in data mining and machine learning. It is not only time consuming but also computationally unreliable when we use high-dimensional data directly. Supervised dimensionality reduction approaches are based on the assumption that there are large amounts of labeled data. It is infeasible to label a large number of training samples in practice...
In the ensemble learning methods for training individual learners in a committee machine, two learning items should be optimized, including minimization of both the squared difference between the target and the learner's output and the estimated correlation between the learner and the rest of learners in the ensemble. The first term is to force each learner to learn the given data. The second term...
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