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In this paper, we propose a novel robust algorithm for image recovery via affine transformations, the weighted nuclear, L ∗ , w , and the L 2,1 norms. The new method considers the spatial weight matrix to account the correlated samples in the data, the L 2,1 norm to tackle the dilemma of extreme values in the high-dimensional images, and the L ∗ , w norm newly added to alleviate the potential effects of outliers and heavy sparse noises, enabling the new approach to be more resilient to outliers and large variations in the high-dimensional images in signal processing. The determination of the parameters is involved, and the affine transformations are cast as a convex optimization problem. To mitigate the computational complexity, alternating iteratively reweighted direction method of multipliers (ADMM) method is utilized to derive a new set of recursive equations to update the optimization variables and the affine transformations iteratively in a round-robin manner. The new algorithm is superior to the state-of-the-art works in terms of accuracy on various public databases. [ABSTRACT FROM AUTHOR]

In this paper, a three-point interpolation algorithm based on the discrete Fourier transform (DFT) is presented for a damped real-valued sinusoid, and the algorithm considers comprehensively the contribution of negative frequency to the parameter estimation (including that of the frequency and the damping factor). The proposed algorithm uses the DFT spectral bin with the maximum amplitude and two other arbitrary ones within the main lobe of the spectrum of the rectangular window to remove the negative-frequency interference completely. First, the effect of changed length of samples on the performance of the proposed algorithm is investigated, and the influence of the zero-padding technique and the selection of two arbitrary spectral bins on the ratio of the mean-square error (MSE) to the Crámer–Rao lower bound are also analyzed by simulations. Second, the MSEs of the estimated parameters are calculated to evaluate the performances of the proposed algorithm and state-of-the-art DFT-based algorithms in the literature. Third, the computational complexities of the proposed and comparative algorithms are analyzed. The results confirm the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Active sound profiling (ASP) is gaining ever-increasing attention owing to its powerful ability to balance the sound pressure levels of certain frequency points or frequency bands of target noise for sound quality enhancement. Most of the current ASP algorithms, however, only focus on narrowband or broadband noise alone and the computational efficiency is very low. To alleviate this problem, a feedforward time–frequency-domain hybrid active sound profiling (TFD-HASP) algorithm is proposed in this paper. The algorithm consists of three subsystems, in which the narrowband ASP (NASP) subsystem utilizes the time-domain internal model FXLMS algorithm along with the local secondary path estimation approach, the broadband ASP (BASP) subsystem is based on the delayless frequency-domain internal model FXLMS algorithm, and the sinusoidal noise canceller (SNC) subsystem is designed to eliminate narrowband interference to the BASP subsystem. In this way, the presented TFD-HASP algorithm not only has good spectral reshaping capability for both narrowband and broadband noise components, but also requires notably low computational effort. A series of simulations are performed to evaluate the performance of the TFD-HASP algorithm. In addition, the comfortable sound quality of a real vehicle interior noise at high speed is designed and realized. Experimental results demonstrate that the proposed algorithm achieves satisfactory active sound profiling for noise with both narrowband and broadband characteristics. [ABSTRACT FROM AUTHOR]

An important step in the identification of cellular automata (CA) is to detect the correct neighborhood before parameter estimation. Many authors have suggested procedures based on the removal of redundant neighbors from a very large initial neighborhood one by one to find the real model, but this often induces ill conditioning and overfitting. This is true particularly for a large initial neighborhood where there are few significant terms, and this will be demonstrated by an example in this paper. By introducing a new criteria and three new techniques, this paper proposes a new adaptive fast CA orthogonal-least-square (Adaptive-FCA-OLS) algorithm, which cannot only adaptively search for the correct neighborhood without any preset tolerance but can also considerably reduce the computational complexity and memory usage. Several numerical examples demonstrate that the Adaptive-FCA-OLS algorithm has better robustness to noise and to the size of the initial neighborhood than other recently developed neighborhood detection methods in the identification of binary CA. [ABSTRACT FROM AUTHOR]

In this paper, we combine two techniques together, i.e., the fast Fourier transform-twofold subspace-based optimization method (FFT-TSOM) and multiplicative regularization (MR) to solve inverse scattering problems. When applying MR to the objective function in the FFT-TSOM, the new method is referred to as MR-FFT-TSOM. In MR-FFT-TSOM, a new stable and effective strategy of regularization has been proposed. MR-FFT-TSOM inherits not only the advantages of the FFT-TSOM, i.e., lower computational complexity than the TSOM, better stability of the inversion procedure, and better robustness against noise compared with the SOM, but also the edge-preserving ability from the MR. In addition, a more relaxed condition of choosing the number of current bases being used in the optimization can be obtained compared with the FFT-TSOM. Particularly, MR-FFT-TSOM has even better robustness against noise compared with the FFT-TSOM and multiplicative regularized contrast source inversion (MR-CSI). Numerical simulations including both inversion of synthetic data and experimental data from the Fresnel data set validate the efficacy of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

• A cost-efficient LFE-ASQC algorithm with similar or even better control accuracy than the ANE and ASP algorithms is proposed. • The proposed algorithm reduces the computational complexity more than one order of magnitude compared to the ANE or ASP algorithms. • The proposed LSP modeling methods A and B have significantly higher modeling accuracy. • Simulated and experimental studies confirm the equalization capability of the proposed algorithm in both stationary and non-stationary cases. Current active noise equalization or active sound profiling algorithms with a parallel structure are typically used for sound quality improvement of multi-harmonic noise. The computational efficiency and system performance may, however, be severely degraded due to an increase in the length of estimated secondary path or the number of controlled frequencies. To alleviate this problem, a local secondary path estimation and filtered-error structure based active sound quality control (LFE-ASQC) algorithm is proposed in this paper. The algorithm utilizes a set of low-order local secondary path (LSP) models to perform filtering operations and uses the filtered pseudo-error signals instead of usual filtered reference signals to update control filter weights, thus it not only has good spectral reshaping capability but also requires notably low computational effort. Two new LSP modeling methods are also proposed. Moreover, a computational complexity analysis of the proposed LFE-ASQC algorithm is provided. Numerical simulations are conducted to evaluate the accuracy of the LSP modeling methods and the performance of the LFE-ASQC algorithm. In addition, the powerful sound quality of a real vehicle interior noise during acceleration is designed and realized. Experimental results demonstrate that the proposed algorithm achieves satisfactory active sound quality control for stationary and non-stationary multi-tonal noises. [ABSTRACT FROM AUTHOR]

In this paper, time–frequency (TF) decomposition (TFD) is studied in the framework of sparse regularization theory. The short-time Fourier transform is first formulated as a convex constrained optimization where a mixed \ell1{-}\ell2 norm of the coefficients is minimized subject to a data fidelity constraint. Such formulation leads to a novel invertible decomposition with adjustable TF resolution. Then, a fast and efficient algorithm based on the alternating split Bregman technique is proposed to carry out the optimization with computational complexity [N^2 \log(N)]. Window length is a key parameter in windowed Fourier transform which affects the TF resolution; a novel method is also presented to determine the optimum window length for a given signal resulting to maximum compactness of energy in the TF domain. Numerical experiments show that the proposed sparsity-based TFD generates high-resolution TF maps for a wide range of signals having simple to complicated patterns in the TF domain. The performance of the proposed algorithm is also shown on real oil industry examples, such as ground roll noise attenuation and direct hydrocarbon detection from seismic data. [ABSTRACT FROM AUTHOR]

Node localization is a main application of wireless sensor networks. However, the measurement error and noise in a real environment make the positioning of information nonlinear and have a great effect on the performance of localization methods. In this paper, we propose a novel isometric mapping (Isomap) node localization algorithm based on partial least squares (PLS-Isomap). For topological stability, the critical outlier points are eliminated by comparing the contribution rate of all data points. Then, we employ the PLS method to solve the Isomap. The adoption of PLS reduces the noise sensitivity of Isomap, which achieves solution by least squares. Moreover, the proposed approach applies a projection method to construct a new kernel matrix between new and original data points. Compared with Isomap and the multidimensional scale method, experimental and simulation results indicate that the PLS-Isomap algorithm has good topological stability, robustness, positioning accuracy, and lower computational complexity. [ABSTRACT FROM PUBLISHER]

This paper investigates the problem of nonfragile H\infty and H2 filter designs for continuous-time linear systems. Additive filter gain variations to reflect the imprecision in filter implementation are considered. The nonfragile filter design is first formulated as a robust convex optimization problem. Then, both deterministic and randomized algorithms are employed to solve the obtained robust convex optimization problem. Compared with the deterministic algorithm, the proposed randomized one has two advantages: On one hand, it has acceptable computational complexity for systems with high dimensions; on the other hand, it can alleviate the conservatism of deterministic algorithms. Several examples are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel hybrid algorithm for fault diagnosis of rotary kiln based on a binary ant colony (BACO) and support vector machine (SVM). The algorithm can find a subset selection which is attained through the elimination of the features that produce noise or are strictly correlated with other already selected features. The BACO algorithm can improve classification accuracy with an appropriate feature subset and optimal parameters of SVM. The proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through two real Rotary Cement kiln datasets. The results show that our algorithm outperforms existing algorithms. [ABSTRACT FROM AUTHOR]

Real-time harmonic estimation and frequency tracking are well known pivotal problems in many engineering domains. Incidentally, similar challenges arise in tokamak plasma diagnostics' data processing where the spectral complexity of characteristic signals poses additional obstacles. Building up on previous Kalman filter based developments, this paper describes an adaptive real-time algorithm for simultaneous multi-component frequency tracking and harmonic estimation in noisy signals. Furthermore, the proposed gain adaptation method is shown to improve estimation performance in cases where the amplitude ratio of signal components differs by roughly up to three orders of magnitude. A series of selectively devised tests were carried out for challenging the performance and determining the operational limits of the algorithm when aiming to provide accurate estimates, in real-time, of both instantaneous amplitude and phase plus the instantaneous frequency evolution of dominant tones in noisy signals. Finally, results and performance issues are discussed. [ABSTRACT FROM PUBLISHER]

The block orthogonal projection algorithm exhibits an excellent convergence property in a noise-free environment. However, a compromise must be sought between the speed and the precision of the convergence when there exists a noise. This paper considers the block orthogonal projection algorithm using the conjugate-gradient method, which is one of the realizations of the block orthogonal projection algorithm. The convergent value is evaluated, and it is shown that one of the major factors that determine the convergent value is the number of iterations in the algorithm. Based on the result of the error analysis, the optimal number of iterations for the block orthogonal projection algorithm is derived so that the effect of the noise can be reduced without sacrificing the high-speed property. For this purpose, the block adaptive algorithm is proposed, based on the block orthogonal projection algorithm using the conjugate-gradient method. computational complexity can be reduced drastically compared to the block orthogonal projection algorithm. [ABSTRACT FROM AUTHOR]