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Compressive sensing (CS) has recently drawn considerable attentions in signal and image processing communities as a joint sampling and compression approach. Generally, the image CS reconstruction can be formulated as an optimization problem with a properly chosen regularization function based on image priors. In this paper, we propose an efficient image block compressive sensing (BCS) reconstruction method, which combine the best of group-based sparse representation (GSR) model and nonlocal total variation (NLTV) model to regularize the solution space of the image CS recovery optimization problem. Specifically, the GSR model is utilized to simultaneously enforce the intrinsic local sparsity and the nonlocal self-similarity of natural images, while the NLTV model is explored to characterize the smoothness of natural images on a larger scale than the classical total variation (TV) model. To efficiently solve the proposed joint regularized optimization problem, an algorithm based on the split Bregman iteration is developed. The experimental results demonstrate that the proposed method outperforms current state-of-the-art image BCS reconstruction methods in both objective quality and visual perception. [ABSTRACT FROM AUTHOR]

This paper presents a new design method and a corresponding architecture for creating FIR filters that are significantly more hardware-efficient than presently known implementations. These optimally factored IFIR filters are also easily pipelined, thereby allowing operation at much higher data-rates. Using examples introduced by previous researchers, we show surprisingly better hardware efficiency. Two such examples show hardware reductions in the vicinity of 50%, relative to the conventional Remez structures, whereas previous research targeting this matter reports more modest results. We also show new features and further benefits that can be obtained by using optimally factored IFIR filters. [ABSTRACT FROM AUTHOR]

Controllability and observability of singular Boolean control networks are proposed in this paper from its algebraic form via semi-tensor product. First, the input-state incidence matrix of singular Boolean control networks is presented based on which the generalized input-state incidence matrix is also defined. Second, a necessary and sufficient condition for the controllability of singular Boolean control networks is given, as well as an algorithm to design controls which drive a state to a reachable state. At last, similar to Boolean control networks, a sufficient condition of observability of singular Boolean control networks is obtained. [ABSTRACT FROM AUTHOR]

This paper develops, simulates and experimentally evaluates a novel method based on non-contact low frequency (LF) ultrasound which can determine, from airborne reflection, whether the lips of a subject are open or closed. The method is capable of accurately distinguishing between open and closed lip states through the use of a low-complexity detection algorithm, and is highly robust to interfering audible noise. A novel voice activity detector is implemented and evaluated using the proposed method and shown to detect voice activity with high accuracy, even in the presence of high levels of background noise. The lip state detector is evaluated at a number of angles of incidence to the mouth and under various conditions of background noise. The underlying mouth state detection technique relies upon an inaudible LF ultrasonic excitation, generated in front of the face of a user, either reflecting back from their face as a simple echo in the closed mouth state or resonating inside the open mouth and vocal tract, affecting the spectral response of the reflected wave when the mouth is open. The difference between echo and resonance behaviours is used as the basis for automated lip opening detection, which implies determining whether the mouth is open or closed at the lips. Apart from this, potential applications include use in voice generation prosthesis for speech impaired patients, or as a hands-free control for electrolarynx and similar rehabilitation devices. It is also applicable to silent speech interfaces and may have use for speech authentication. [ABSTRACT FROM AUTHOR]

It is well known that the quality of ultrasound image is significantly degraded by the speckle noise, which has restricted the development of automatic diagnostic techniques for ultrasound images, especially for the breast ultrasound images. This necessitates the need to choose an optimal speckle filtering algorithm for the specific clinical application with different required criteria. In this paper, the study focuses on the comparison of despeckle filters for the breast ultrasound images. Firstly, the models of speckle noise for medical ultrasound images are discussed. After that, eleven despeckle filters which are classified into five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter, nonlocal means filter, and hybrid filter) are described. Then, the comparative experiments of eleven despeckle filters for the two types of simulated images and clinical ultrasound breast images are presented. Finally, to objectively and systematically compare the performance of eleven despeckle filters, several comparison methods are used, such as the full-reference image quality metrics, the nonreference/blind image quality metrics, observing the removed noise images, as well as the visual evaluation of experts. [ABSTRACT FROM AUTHOR]

Analysis and design of linear periodic control systems are closely related to the discrete-time periodic matrix equations. In this paper, we propose an iterative algorithm based on the conjugate gradient method on the normal equations (CGNE) for finding the solution group of the general coupled periodic matrix equations By proving some properties of the algorithm, we show that the solution group of the periodic matrix equations can be obtained within a finite number of iterations in the absence of roundoff errors. Numerical examples are given to illustrate the efficiency and accuracy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the problem of blind source separation (BSS). To solve the problem efficiently, a new algorithm is proposed. First, a parallel variable dual-matrix model (PVDMM) that considers all the numerical relations between a mixing matrix and a separating matrix is proposed. Different constrained terms are used to construct the cost function for every sub-algorithm. These constrained terms reflect a numerical relation. Therefore, a number of undesired solutions are excluded, the search region is reduced, and the convergence efficiency of the algorithm is ultimately improved. Parallel sub-algorithms are proven to converge to a separable matrix only if the cost function approaches zero. Second, a descent algorithm (DA) as a PVDMM optimizing approach is proposed in this paper as well. Apparently, the efficiency of the algorithm depends completely on the DA. Unlike the traditional descent method, the DA defines step length by solving inequality instead of merely utilizing the Wolfe- or Armijo-type search rule. Stimulation results indicate that the DA can improve computational efficiency. Under mild conditions, the DA has been proven to have strong convergence properties. Numerical results also show that the method is very efficient and robust. Finally, we applied the combinative algorithm to the BSS problem. Computer simulations illustrate its good performance. [ABSTRACT FROM AUTHOR]

In hearing aid (HA) systems, amplification of speech signals is done to compensate the hearing loss of patients. Background noise and feedback signals may also get amplified which degrade the intelligibility and quality of speech. To achieve high de-noise efficiency, signal processing unit in HA system has voice activity detector (VAD). The conventional VAD detects voice based on zero crossing rate or energy of input signals. However, these methods cannot perform well at low SNR or non-stationary noise environments. Since pitch is a special characteristic of speech and is basically independent of noise intensity, VAD based on pitch can have high accuracy even when the noise spectrum is changing drastically. In this paper, pitch-based VAD is presented and its accuracy is checked against zero crossing rate-based VAD (ZCR-VAD). For noise reduction, an improved multi-band spectral over-subtraction algorithm is employed along with the high accurate pitch-based VAD. For feedback cancellation, the performance of adaptive algorithms like NLMS, RLS and affine projection (AP) algorithms with pitch-based VAD is compared and it is observed that AP is suitable for feedback cancellation. The proposed noise reduction and feedback cancellation algorithm with pitch-based VAD method is tested with NOIZEUS speech database and with real-time noisy speech signals. The simulation results show that the accuracy of pitch-VAD is about 23% higher than that of ZCR-VAD. The SNR for the proposed noise reduction and feedback cancellation with pitch-VAD method is improved by 10 dB than conventional spectral subtraction and adaptive algorithms. Mean opinion score (MOS) obtained for the proposed method is 4.3 out of 5. [ABSTRACT FROM AUTHOR]

The paper presents an efficient VLSI architecture for fast Motion Estimation in video codec using modified Adaptive Rood Pattern Search Algorithm. The proposed architecture uses an interleaved memory arrangement and an early check technique to compute the Sum of Absolute Differences. The proposed design can process High Definition (1080p) video frames in real time while optimizing the hardware area. The architecture has been implemented in verilog HDL and mapped to 45 nm FPGA. It uses only 6.8K gates for the implementation of the datapath and the controller. It achieves a maximum frequency of 120 MHz. However, working at 100 MHz, it is able to process 60 HD (1920×1080) frames per second while consuming 39 mW of power. The proposed architecture achieves premium speed with an optimum power and area requirements and can be suitably incorporated in light-weight video-intensive devices like smart-phones, tablet computers. [ABSTRACT FROM AUTHOR]

In this paper, a novel robust design algorithm based on estimation of signal steering vector and covariance matrix is developed. The theoretical covariance matrix is first estimated via the shrinkage method. Subsequently, the desired signal steering vector is estimated based on maximizing array output power under the correlation coefficient constraint and norm constraint. The original nonconvex quadratic programming problem, whose relaxation is tight through analyzing the hidden convexity properties, can be solved by the relaxed semidefinite programming method. Moreover, the interference-plus-noise covariance matrix is estimated with the corrected steering vector and the subspace theorem, whose efficiency is analytically proven. Numerical experiments show that the proposed algorithm has the advantages of high efficiency and accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we consider the sparse filter design problem where some of the coefficients can be reduced to zeroes in order to lower implementation complexity. The objective is to choose the fewest number of nonzero filter coefficients to meet a given performance requirement. We formulate a discrete optimization problem to minimize the number of nonzero terms and develop a discrete search method to find the minimal nonzero terms. In each step, we need to consider a subproblem to design the filter coefficients with a given set of nonzero terms. We formulate this subproblem as a linear programming problem and apply an exchange algorithm to find the optimal coefficients. For illustration, we compare the proposed algorithm with existing methods and show that the proposed method gives better results in all our test cases. [ABSTRACT FROM AUTHOR]

Addition represents an important operation that significantly impacts the performance of almost every data processing system. Due to their importance and popularity, addition algorithms and their corresponding circuit implementations have consistently received attention in research circles, over the years. One of the most popular implementations for long adders is the carry skip adder. In this paper, we present the design space exploration for a variety of carry skip adder implementations. More specifically, the paper focuses on the implementation of these adders using traditional as well as novel dynamic circuit design styles. 8-16-32-64-bit adders were implemented using traditional domino, footless domino, and data driven dynamic logic (D3L) in ST Microelectronics 45 nm 1 V CMOS process. In order to further exploit the advantages of the domino and D3L approaches, a new hybrid methodology combining both strategies was implemented and presented in this work. The adders were analyzed for energy-delay trade-offs at different process corners. They were also examined for their sensitivity to process and supply voltage variations. Comparative simulation results reveal that the full D3L adder ensures a better energy-delay product over all process corners (down to 34 % and 25 % lower than the domino and hybrid implementations, respectively, at the typical corner), while showing at the same time similar performance in terms of process and supply voltage variability as compared to the other considered carry skip adder configurations. [ABSTRACT FROM AUTHOR]

Compressed sensing ensures the accurate reconstruction of sparse signals from far fewer samples than required in the classical Shannon-Nyquist theorem. In this paper, a generalized hard thresholding pursuit (GHTP) algorithm is presented that can recover unknown vectors without the sparsity level information. We also analyze the convergence of the proposed algorithm. Numerical experiments are given for synthetic and real-world data to illustrate the validity and the good performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of designing a robust fault-detection for uncertain T-S fuzzy models based on the delta operator approach. By means of the T-S fuzzy delta operator systems, a fuzzy fault detection filter system is constructed via the delta operator approach. The worst case fault sensitivity has been formulated in terms of linear matrix inequalities. The proposed fault-detection filter not only ensures the H-gain from a fault signal to a residual signal greater than a prescribed value, but also guarantees the H-gain from an exogenous input to a residual signal less than a prescribed value in terms of the solvability of linear matrix inequalities. The linear matrix inequalities can be solved by an effective algorithm. A numerical example is provided to illustrate the effectiveness of the proposed design techniques. [ABSTRACT FROM AUTHOR]

This paper concerns the application of Huber-based robust unscented Kalman filter (HRUKF) in nonlinear system with non-Gaussian measurement noise. The tuning factor γ is key factor in determining the form of Huber cost function. Traditionally, γ is mainly determined by experience and/or experiments. It is hard to acquire optimal parameter or achieve an optimal filtering. To solve this problem, the influence of tuning factor γ on the performance of HRUKF is analyzed, and then, an adaptive strategy based on projection statistics algorithm for this parameter is proposed to improve filtering performance under the conditions that the measurement noise is contaminated by heavier tails and/or outliers. Simulation results for the problem of Reentry Vehicle Tracking demonstrate the superiority of the proposed method over the traditional ones. [ABSTRACT FROM AUTHOR]

A conventional single-channel feedforward active noise control (ANC) system encounters noise from multiple sources. In many cases, there is a reference signal that is independently available which is correlated with the ambient noise. However, in some situations, a portion of the noise generated is uncorrelated with the reference signal. In this paper, a new hybrid ANC (HANC) system is proposed using a convex combination of time and frequency domain Filtered-X LMS (FXLMS) algorithms. The feedforward part uses a time domain FXLMS (TDFXLMS) algorithm to control disturbances that are correlated with the reference noise and the feedback part uses a multiresolution analysis-based frequency domain wavelet packet FXLMS (WPFXLMS) algorithm to control the uncorrelated disturbances encountered during the operation of an ANC system. An extra adaptive filter is used to smoothen the error signal in the HANC system. An effort is made to exploit the benefits of the structural design and time-frequency domain signal processing techniques for active control of disturbances that are both correlated and uncorrelated with the reference noise. As a result of the convex combination of the TDFXLMS and WPFXLMS algorithms in a HANC system, the proposed method is successful in canceling the disturbances encountered by the ANC system from multiple noise sources. [ABSTRACT FROM AUTHOR]

Acoustic feedback is one of the major issues associated with the hearing aid users which limits the maximum amount of gain available for amplification and degrades the sound quality. In this paper, partitioned time-domain block LMS (PTBLMS) algorithm is proposed for efficient hardware realization of acoustic feedback cancellers (AFCs) in hearing aids. A full-parallel and a folded structure is derived using the proposed PTBLMS algorithm. The folded structure utilizing the time multiplexing of convolution and correlation operation and performing them in one arithmetic unit enables better hardware utilization. A low-complexity design is employed for realization of power normalization unit (for calculating normalized convergence factor) which involves squaring and division operations. The theoretical analysis illustrates that the proposed AFC structures offer L times higher throughput rate and requires proportionately less hardware resource than the existing one where L is the block length. ASIC synthesis results reveal that the proposed folded structure involves nearly 79% less area-delay product and 86% less energy per sample compared to the existing structure. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new algorithm for recovery of block sparse signals in compressed sensing framework based on orthogonal matching pursuit. Furthermore, we point out that a major issue in conventional sparse signal recovery is the lack of prior knowledge about the order of sparsity. Consequently, block sparse signal recovery algorithms suffer even more from the same problem since two parameters are needed for exact recovery of the signal, order of active blocks and within-block sparsity order. Therefore, we propose a new approach to determining both of the active blocks and within-block sparsity order which is embedded in the proposed block sparse signal recovery algorithm. The simulation results illustrate the improved performance of the proposed method for recovery of block sparse signals compared to the conventional methods which are not aware of the prior information. We also apply our proposed algorithm to ECG signal compression, where the obtained results reveal its efficiency. [ABSTRACT FROM AUTHOR]

In this paper, robust complex-valued sparse signal recovery is considered in the presence of impulse noise. A generalized Lorentzian norm is defined for complex-valued signals. A complex Lorentzian iterative hard thresholding algorithm is proposed to realize the signal recovery. Simulations are given to demonstrate the validity of our results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel algorithm for the detection changes in the spatio-spectral dynamics of a multivariate time series based on a sliced cross-bispectrum. The singular value decomposition is performed on the matrix of the sliced cross-bispectrum at every frequency of the bandwidth, and a 2D spectrum of the eigenvalues is obtained. The changes in the dynamics are translated into differences between the reference and current values of the 2D eigenvalue spectrum. Hellinger divergence as the measure of the distance between eigenvalue spectrum matrices is used. The performance of the proposed approach is tested on model data and applied to multi-channel electroencephalogram recordings from epilepsy patients to detect ictal states. [ABSTRACT FROM AUTHOR]

In this paper, two arctangent-based normalized subband adaptive filter (Arc-NSAF) algorithms are proposed, which are derived from two different forms of arctangent cost function and their difference is the order of the arctangent computation and the summation operation of the normalized subband error signal. Benefiting from the excellent property of the arctangent function for suppressing impulsive interferences, the proposed Arc-NSAF algorithms not only possess good robustness against impulsive interferences, but also obtain faster convergence rate for the colored input signals. To further improve the convergence performance of the Arc-NSAFs when identifying sparse system, the proportionate versions of the Arc-NSAF algorithms are also proposed. Simulation results have demonstrated the superiority of the proposed algorithms in impulsive interferences environments as compared to their respective competitors. [ABSTRACT FROM AUTHOR]

Variable step size norm-constrained adaptive filtering algorithms are proposed in the paper. A variable step size is derived by minimizing the variance of the noise-free a posterior error. Thus, the update equation can obtain a reasonable step size at each iteration. Due to the introduction of variable step size, the proposed algorithms based on the constrained conditions of $$L_{1}$$ and $$L_{0}$$ norm have a significant advantage that the convergence rate is faster than some well-known algorithms in the sparse system. The simulation results illustrate the good performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a closed-form analytical expression for fractional order differentiation in the fractional Fourier transform (FrFT) domain is derived by utilizing the basic principles of fractional order calculus. The reported work is a generalization of the differentiation property to fractional (noninteger or real) orders in the FrFT domain. The proposed closed-form analytical expression is derived in terms of the well-known confluent hypergeometric function. An efficient computation method has also been derived for the proposed algorithm in the discrete-time domain, utilizing the principles of the discrete fractional Fourier transform algorithm. An application example of a low-pass finite impulse response (FIR) fractional order differentiator in the FrFT domain has also been investigated to show the practicality of the proposed method in signal processing applications. [ABSTRACT FROM AUTHOR]

This paper considers the stability problem for discrete-time systems with interval time-varying delays. By construction of a suitable Lyapunov-Krasovskii (L-K) functional and utilization of Finsler's lemma, novel delay-dependent criteria for asymptotic stability of the systems are established in terms of linear matrix inequalities (LMIs) which can easily be solved by various effective optimization algorithms. Three numerical examples are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is considered with the computation of upper bounds for the solution of continuous algebraic Riccati equations (CARE). A parameterized upper bound for the solution of CARE is proposed by utilizing some linear algebraic techniques. Based on this bound, more precise estimation can be achieved by means of carefully choosing the bound's parameters. Iterative algorithm is also developed to obtain more sharper solution bounds. Comparing with some existing results in the literature, the proposed bounds are less restrictive and more effective. The effectiveness and advantages of the proposed approach are illustrated via a numerical example. [ABSTRACT FROM AUTHOR]

Recently, data hiding by modifying network parameters like packet header, payload, and packet length has become popular among researchers. Different algorithms have been proposed during the last few years which have altered the network packets in different ways to embed the data bits. Some of these algorithms modify the network packet length for embedding. Although most of the packet length based embedding schemes try to imitate the normal network traffic distribution, they have altered the statistical distribution of network packet lengths during embedding. These statistical anomalies can be exploited to detect such schemes. In this paper, a second order detection scheme for packet length based steganography has been proposed. A comprehensive set of experiments have been carried out to show that the proposed detection scheme can detect network packet length based steganography with a considerably high accuracy. [ABSTRACT FROM AUTHOR]

This paper is concerned with the maximum-likelihood (ML) calibration methods tailored to the antenna arrays whose spatial responses are perturbed by mutual coupling effects and unknown sensor gain/phase responses. Unlike the existing work, the proposed methods are capable of jointly exploiting the spatial-domain information and time-domain waveform information of the calibration sources. Two kinds of numerical optimization algorithm are devised dependent on different array geometries. One is suitable for arbitrary irregular array manifold, while the other applies to some particular uniform arrays. Additionally, based on the maximum a posteriori probability (MAP) criterion, we extend the two algorithms to the scenario where the true values of the calibration source azimuths deviate slightly from the nominal ones with a priori known Gaussian distribution. The Cramér-Rao bound (CRB) expressions for the unknowns are derived in the absence and presence of the azimuth deviations, respectively. Simulation results support that the performances of the proposed algorithms are preferable to the ones which merely employs the spatial-domain information of the calibration sources, and are able to attain the corresponding CRB. [ABSTRACT FROM AUTHOR]

In this paper, the joint transceiver design issue is investigated for multi-cell systems with multiple mobile terminals (MTs). An optimization problem is formulated based on the criterion of mean squared error minimization for the joint design of the cooperative precoder of all multi-input multi-output (MIMO) cells and the cooperative decoder of all MIMO MTs. With a fixed decoder, the optimum precoder is first derived in a closed-form expression, where no exchange of data streams among all of the MIMO cells is required due to the limited capability of the backhaul. Subsequently, an optimum decoder is then obtained in an analytical expression with the fixed precoder. Based on these two expressions, an iterative algorithm with guaranteed convergence is finally developed to carry out the joint optimization of the cooperative precoders and decoders. Simulation results demonstrating the effectiveness of the proposed algorithm are also provided. [ABSTRACT FROM AUTHOR]

Due to the environmental noise, variance is uncertain and unknown. The energy-based detection (ED) technology has many shortcomings in cognitive radio. In the paper, an energy-autocorrelation detection (EAD) algorithm is proposed to overcome these challenges, taking advantage of the different characteristics of Gauss white noise and signal. Two statistics are structured based on energy and autocorrelation of samples. This spectrum sensing algorithm can lead to stable and accurate detection performance without any prior information on noise and signal. It is testified in the simulation that the energy-autocorrelation-based detection is much better than energy-based detection; moreover, the impact of some parameters of the algorithm is also simulated and discussed. [ABSTRACT FROM AUTHOR]

This technical paper studies robust H filtering design for 2-dimensional (2-D) systems and the focus is on finding other methods to further reduce the conservativeness when the 'polynomially dependent on the parameter' technique fails to do so. First, the possible sources of conservativeness of the existing results are explored, and then two algorithms are proposed. Since no special structures are required in the introduced variables, the algorithms can further reduce the conservativeness. In addition, the initial conditions of the algorithms are discussed and an example is provided to demonstrate the effectiveness of the conclusions. [ABSTRACT FROM AUTHOR]

For a multivariable controlled autoregressive system with autoregressive noises, its corresponding identification model contains a parameter matrix and a parameter vector. This paper presents the hierarchical gradient-based iterative (HGI) algorithm to interactively estimate the parameter matrix and the parameter vector by using the hierarchical identification principle and the gradient search. The simulation results show that the HGI algorithm is effective. [ABSTRACT FROM AUTHOR]

The paper is devoted to the analysis of diode-transistor circuits having multiple DC solutions. The transistors are characterized by the Ebers-Moll model and the circuits are described by the Sandberg-Willson equation, without any piecewise-linear approximations. A new method for finding bounds on the location of all the solutions is offered. The method contracts a hyperrectangular region that includes the solutions in a systematic manner, considering in succession all the individual equations. It does not require much computation power and is very fast. The method is very useful as a preliminary step of the algorithms for finding all the DC solutions, making them more efficient. A numerical example is given to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents new architectures for real-time implementation of the forward/inverse discrete wavelet transforms and their application to signal denoising. The proposed real-time wavelet transform algorithms present the advantage to ensure perfect reconstruction by equalizing the filter path delays. The real-time signal denoising algorithm is based on the equalized filter paths wavelet shrinkage, where the noise level is estimated using only few samples. Different architectures of these algorithms are implemented on FPGA using Xilinx System Generator for DSP and XUP Virtex-II Pro development board. These architectures are evaluated and compared in terms of reconstruction error, denoising performance and resource utilization. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive fuzzy controller for Nonlinear in Parameters (NLP) chaotic systems with parametric uncertainties. In the proposed controller, the unknown parameters are estimated by the novel Improved Speed Gradient (ISG) method, which is a modification of Speed Gradient (SG) algorithm. ISG employs the Lagrangian of two suitable objective functionals for on-line estimation of system parameters. The most significant advantage of ISG is that it is applicable to NLP systems and it results in a faster rate of convergence for the estimated parameters than the SG method. Estimated parameters are used to design the fuzzy controller and to calculate the Lyapunov exponents of the chaotic system adaptively. Furthermore, established on the well-known Takagi-Sugeno (T-S) fuzzy model, a LMI (Linear Matrix Inequality)-based fuzzy controller is designed and is tuned using estimated parameters and Lyapunov exponents. Throughout the controller design procedure, several important issues in fuzzy control theory including relaxed stability analysis, control input performance specifications, and optimality are taken into account. Combination of ISG parameter estimation method and T-S-based fuzzy controller yields an adaptive fuzzy controller capable to suppress uncertainties in parameters and initial states of NLP chaotic systems. Finally, simulation results are provided to show the effectiveness of the ISG and adaptive fuzzy controller on chaotic Lorenz system and Duffing oscillator. [ABSTRACT FROM AUTHOR]

This paper mainly deals with target detecting problem using polarimetric Multiple Input Multiple Output (MIMO) radar against Spherically Invariant Random Vector (SIRV) clutter. First, we develop the MIMO signal model to two polarimetric channels and SIRV clutter-dominated scenario, and then the Generalized Likelihood Ratio Test (GLRT) is derived with known covariance structure. Meanwhile, three estimation strategies of covariance, such that Sampled Covariance Matrix (SCM), Normalized Sampled Covariance Matrix (NSCM) and Fixed Point Estimation (FPE) matrix, are introduced to make derived receiver fully adaptive. A thorough performance assessment is given by several numerical examples, and the results show that the polarimetric diversity and the spatial diversity can be exploited to improve the detection performance, and it outperforms the conventional polarimetric phased-array counterpart. Meanwhile, the FPE strategy is more suitable to implement the adaptive detection algorithm, the adaptive loss of which is completely acceptable in practical applications. [ABSTRACT FROM AUTHOR]

In this paper, on the basis of the theories and methods of Watson's perceptual model and rational dither modulation (RDM), a hybrid quantization-based watermarking in the discrete wavelet transform (DWT) and discrete cosine transform (DCT) domains is studied. In the design of the quantization-based watermarking, quantization step-size plays an important role in many watermarking algorithms. RDM at both the embedder and decoder adopts a gain-invariant adaptive quantization step-size. Therefore, we investigated combining the modified Watson's perceptual model with RDM. Its improved robustness is due to the embedding in the high entropy region of low-frequency sub-band image and adaptive control of its quantization step-size. The Euclidean distance decoder is used to extract the watermark data. The performance of the proposed scheme is analytically calculated and verified by simulation. Experimental results confirm the imperceptibility of the proposed watermarking and its higher robustness against attacks compared to alternative watermarking methods in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes improved delay-range-dependent H performance conditions and a H filtering algorithm for linear systems with interval time-varying delays. The proposed filtering approach guarantees that the results are less conservative than those obtained by other existing approaches. Numerical examples well demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Vector quantization (VQ) is an efficient technique for data compression and has been successfully used in various applications. The methods most commonly used to generate a codebook are the Linde, Buzo, Gray (LBG) algorithm, fuzzy vector quantization (FVQ) algorithm, Kekre's Fast Codebook Generation (KFCG) algorithm, discrete cosine transform based (DCT-based) codebook generation method, and k-principle component analysis (K-PCA) algorithm. However, if the separation boundaries in codebook generation are nonlinear, their performance can degrade fast. In this paper, we present a kernel fuzzy learning (KFL) algorithm, which takes advantages of the distance kernel trick and the gradient-based fuzzy clustering method, to create a codebook automatically. Experiments with real data show that the proposed algorithm is more efficient in its performance compared to that of the LBG, FVQ, KFCG, and DCT-based method, and to the K-PCA algorithm. [ABSTRACT FROM AUTHOR]

The optimal microphone array, in the sense of minimum mean square errors (MMSE), includes two processing blocks: the minimum variance distortionless response (MVDR) beamformer and the single-channel Wiener filter, which acts as post-filter. In this paper, we propose a new post-filter algorithm based on assumptions that both the noise power attenuation factor (NPAF) and signal power attenuation factor (SPAF) are time invariant in the reverberant room. The algorithm recursively estimates both factors from available measurements and uses them in estimation of the post-filter parameters. Additionally, to overcome the problem of the poor performance of the MVDR beamformer in reverberant conditions, we propose the usage of the two-step (TS) MVDR algorithm. This algorithm improves the robustness of the beamformer and its ability to suppress the interferences using an estimate of the desired speaker transfer function. Although TS MVDR beamformer and proposed post-filter can work separately, or combined with other algorithms, the best performance is obtained when they work together. The performance of the proposed combination of new post-filter algorithm and TS MVDR beamformer is tested in a simulated reverberant room and compared with similar algorithms, which gave rather good results. [ABSTRACT FROM AUTHOR]

Multivariate ( n-D) polynomial matrix factorizations are basic research subjects in multidimensional ( n-D) systems and signal processing. In this paper, several results on general matrix factorizations are provided for extracting a matrix factor from a given n-D polynomial matrix whose lower order minors satisfy certain conditions. These results are further generalizations of previous results in (Lin et al. in Circuits Syst. Signal Process. 20(6):601-618, ). As a consequence, the application range of the constructive algorithm in (Lin et al. in Circuits Syst. Signal Process. 20(6):601-618, ) has been greatly extended. Three examples are worked out in detail to show the practical value of the proposed method for obtaining general factorizations for a class of n-D polynomial matrices. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive block-by-block least squares (LS) algorithm for the interpolation of infrared (IR) images. The suggested algorithm is based on the segmentation of the low resolution (LR) image into overlapping blocks and the interpolation of each block, separately. The purpose of the overlapping is to avoid edge effects between blocks. An iterative implementation of the proposed algorithm, which considers the image acquisition model, is used for the minimization of the estimation error in each block. A weight matrix of moderate dimensions is estimated in a small number of iterations to interpolate each block. This proposed algorithm avoids the large computational complexity resulting from the matrices of large dimensions required to interpolate the image as a whole. The performance of the proposed algorithm is compared with the standard as well as the warped distance optimal interpolation of maximal order with minimal support (O-MOMS) algorithm from the peak signal-to-noise ratio (PSNR) point of view. Numerical results reveal the superiority of the proposed LS algorithm to the cubic O-MOMS algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new MIMO instantaneous blind identification algorithm based on second-order temporal property and steepest-descent method. Second-order temporal structure is reformulated in a particular way, such that each column of the unknown mixing matrix satisfies a system of nonlinear multivariate homogeneous polynomial equations. The nonlinear system is solved by an improved steepest-descent method. We convert the nonlinear system into an optimal problem by constructing an optimal objective function which includes a 'valley-filled' item to obtain a valid solution. Our algorithm allows estimating the mixing matrix for scenarios with 4 sources and 3 sensors, etc. Finally, simulations show its effectiveness with more accurate solutions than the algorithm with homotopy method. [ABSTRACT FROM AUTHOR]

control-theoretic approach in designing Digital-to-Analogue Converters and Digital Amplifiers which leads to improved performance in Audio and Multimedia applications is presented in this paper. The design involves an over-sampling and a pulse modulation component which is driven by a pulse generation algorithm based on the characteristics of the output filter. The theoretical model results in a family of digital circuits whose operation is verified by computer simulations achieving a performance of Signal-to-Noise Ratio of 147 dB at a switching rate of 90 MHz. Implementation and hardware complexity issues are discussed based on a FPGA realization of the algorithm. [ABSTRACT FROM AUTHOR]

In order to generate volumetric underwater acoustic images, planar arrays are often used to collect the signals coming from a 3-D scene in underwater environment. These acquired signals are commonly processed using the 2-D conventional beamforming method. The 2-D conventional beamforming is robust and the most commonly used method. However, the 2-D conventional beamforming suffers from fat beams and high-level sidelobes. High-resolution beamforming, based on the inverse of the signal covariance matrix, such as minimum-variance distortionless response, produces narrow beamwidth and low sidelobe levels but is sensitive to signal mismatch and requires many snapshots of data. In order to overcome the above-mentioned limitations, this paper applies a 2-D deconvolution algorithm to the 2-D conventional beamforming result of planar array in order to improve the beamwidth and suppress the high-level sidelobes. The 2-D deconvolved beam power produces narrow beams, and low sidelobe levels without compromising the robustness of 2-D conventional beamforming. The 2-D deconvolution algorithm provides higher output signal-to-noise ratio than the 2-D conventional beamforming for isotropic noise. The vector extrapolation method is improved to make it applicable on two-dimensional data. The detailed implementations of the original 2-D deconvolved beamforming and the efficient 2-D deconvolved beamforming are presented. Simulations and experiments are conducted to validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation has paramount importance in array signal processing tasks. A practical issue regarding DOA estimation is the uncertainty in gain/phase of some of the sensor elements. In this paper, we focus on the 2-dimensional DOA (2D-DOA) estimation in the presence of gain/phase uncertainty. We propose a grid-based modeling for the 2D-DOA estimation of the uniform rectangular array considering the array gain/phase uncertainty. In order to solve the modeled problem, we suggest an algorithm to exploit the joint sparse properties of the problem. Furthermore, we have used a zooming-based technique to estimate the 2D-DOAs with much lower computational complexity. The efficiency of the proposed method has been validated through various simulation scenarios which confirm the superiority of the suggested algorithm over its counterparts. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce a novel similarity measure using fractional-order derivative for patch comparison in low-rank image denoising approach. Recently, several outstanding low-rank image denoising algorithms have been proposed. However, these methods have limitations in the sense that certain irrelevant patches can be selected during patch comparison. These undesired patches affect singular values shrinkage and aggregation phases of these approaches. Thus, the fine details and edges of denoised image may not be well preserved. To address this issue, a novel method is proposed in which gradient information is injected in patch comparison using discretized fractional-order derivatives. The advantages of proposed approach are twofold: firstly, the patch comparison becomes more reliable by combining intensity and gradient information; secondly, the fractional-order gradient provides an additional degree of freedom to quantify the gradient information for patch comparison in an efficient way. In addition, the proposed algorithm estimates noise level using geometric details encoded in the image patches. The noise estimation strategy may help in terminating the iterative low-rank approximation. Experimental results on test images reveal that the proposed method performs better than several outstanding algorithms, specifically, in the presence of severe noise levels. [ABSTRACT FROM AUTHOR]

Under the framework of compressed sensing theory, the greedy algorithm achieves good reconstruction performance with known signal sparsity. However, unknown sparsity of sparse signals in practical applications brings obstacles for signal reconstruction. Specifically, the conventional sparsity adaptive adjustment algorithm takes long time to finish the reconstruction, and the accuracy of reconstruction is not good enough. To solve this problem, this paper proposes a new matching pursuit reconstruction algorithm based on bidirectional sparsity adaptive adjustment and weak selection of atoms (BSA-WSAMP). In this algorithm, the optimization strategy for atom weak selection is employed to update the support set, and the idea of "zoom" bidirectional variable step-size is applied to achieve the sparsity adaptive adjustment. Based on this, the number of iterations can be reduced effectively, and the accurate reconstruction of the sparse signal is obtained. Simulation results indicate that the proposed BSA-WSAMP algorithm achieves better adaptive characteristic of the sparsity, higher reconstruction quality, lower reconstruction complexity, and less reconstruction time than some existing reconstruction algorithms. [ABSTRACT FROM AUTHOR]

This paper presents the design and realization of a tunable fractional-order series resonator. It is made of an acute-angled capacitive fractor and an obtuse-angled inductive fractor and has theoretically infinite quality factor. This work establishes a relation between resonating frequency and fractor's coefficients, and that makes the tuning of such resonator feasible in practice. Similar work, by same authors, already exists for fractional-order parallel resonator. The present work along with the said parallel resonator completes the study on tunable fractional-order resonator. The fractional-order series resonator, proposed here, is realized in hardware, tuned at various frequencies, and shows very high quality fractor (about 350). The paper also goes through detailed sensitivity analysis of the proposed resonator as well as discusses the practical aspects of proposed tuning in detail. [ABSTRACT FROM AUTHOR]

This paper proposes a robust set-membership affine projection algorithm with coefficient vector reuse (RSM-APA-CVR) for high background noise environment. In the proposed algorithm, the sum of the squared $$L_{2}$$ norms of the differences between the updated weight vector and past weight vectors is minimized and a new robust error bound is designed. Simulation results in acoustic echo cancellation context show that the proposed algorithm has faster convergence rate and smaller steady-state misalignment as compared to the conventional RSM-APA. [ABSTRACT FROM AUTHOR]