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Fairness with a α parameter is an important consideration to provide uniform and high quality service for each user when designing a wireless communication system. In general, α -fairness problem includes the proportional fairness and the max-min fairness. In this paper, we consider the problem of α -fairness-based joint power allocation and power splitting for massive multiple-input and multiple-output (MIMO) systems with simultaneous wireless information and power transfer (SWIPT). Different from the existing works, we utilize a low-complexity parametric optimization method to solve the non-convex proportional fairness problem. Furthermore, we develop a joint iterative algorithm to solve the general α -fairness problem and obtain the suboptimal solutions on power allocation and power splitting. Simulation results illustrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Ensuring conflicting requirements such as imperceptibility, payload capacity, and robustness, becomes a great challenge for a robust audio/speech watermarking algorithm. To achieve this, this paper proposes a blind and robust audio/speech watermarking algorithm that combines the discrete Tchebichef moment transform (DTMT), the chaotic system of the mixed linear–nonlinear coupled map lattices (MLNCML), and discrete wavelet transform (DWT). The watermark is encrypted by the MLNCML system, and then it is embedded in the norm of the Tchebichef moments of the low frequency components. DTMT and MLNCML are used to achieve high robustness, high payload capacity, and high security, while DWT is used to achieve a satisfactory level of imperceptibility. In addition, the adopted strategy has a blind nature, where no original audio/speech is needed in watermark extraction. Compared to other existing audio/speech watermarking algorithms, the proposed algorithm gives better results in terms of robustness and payload capacity while keeping the embedding effect non-perceptible and undetectable. [ABSTRACT FROM AUTHOR]

The sparsity characteristic, which is the foundation of the compressive sensing (CS) theory, has been widely used in the inverse synthetic aperture radar (ISAR) imaging field. However, the performance of the traditional CS-based sparse aperture ISAR imaging method is limited in low signal to noise ratio (SNR) and/or sampling rate (SPR). Inspired by the low-rank property of the final ISAR image, a new sparse aperture ISAR imaging model by jointly using the sparse characteristic and low-rank property is proposed in this paper. Considering the effective solution of the proposed reconstruction model, the optimization problem is decomposed into several sub-problems under the framework of linearized alternating direction method with adaptive penalty (LADMAP) approach. Furthermore, in order to improve the computing efficiency of the proposed algorithm, a fast singular value decomposition (SVD) free algorithm is finally proposed. Both simulated and measured data experiments verify the effectiveness of the proposed algorithms, especially under the condition of low SNR and/or SPR. [ABSTRACT FROM AUTHOR]

Time-frequency distributions (TFD) provide a set of powerful tools for the non-stationary signal analysis. Although TFD overcomes signal representation limitations, the most commonly used TFDs generate unwanted artefacts, also called the cross-terms, which make the TFD application less feasible for noise-corrupted real-life signals. In this paper, we investigate the advantages of the TFD sparsity by using the compressive sensing based methods. We propose a sparse reconstruction algorithm which reconstructs a TFD from a small sub-set of samples taken from the signal ambiguity function. The proposed algorithm is based on the iterative shrinkage algorithm which performance and robustness have been improved by utilizing the short-term and narrow-band Rényi entropies. Furthermore, we have circumvented the limitations of global concentration measure by coupling it with the measure based on the local Rényi entropy. The introduced concentration measures have been used as objective functions in a multi-objective meta-heuristic optimization of the proposed algorithm parameters, resulting in high-resolution TFDs while avoiding disjunctions within signal components. The obtained results have been compared to the state-of-the-art sparse reconstruction algorithms, for both noisy synthetic and real-life signals. • TFD sparsity by using compressive sensing based methods has been investigated. • Sparse reconstruction has been augmented with the local Rényi entropy. • The algorithm parameters have been optimized using meta-heuristic optimization. • A TFD with a highly concentrated auto-terms has been achieved. [ABSTRACT FROM AUTHOR]

Adaptive filter has been successfully considered for the system identifications. Under some circumstances, due to the unknown system's intrinsic characteristics, nonnegativity is an desired constraint imposed on the parameters to be estimated, which will affect the performance of devised adaptive filtering. This paper studies a nonnegative least mean mixed-norm algorithm (NNLMMN) with fourth-order moment error and square-order error, which are jointly considered and devised to design a new cost function for dealing with the system identification problems with a nonnegativity constraint in mixed Gaussian noise environment. We also provide the theoretical behavior prediction of the mean weight, the transient analysis and steady-state excess mean square error (EMSE) of the NNLMMN algorithm. Experiment results show that the superior performance of the NNLMMN algorithm and verify the effectiveness of the presented theory analysis. [ABSTRACT FROM AUTHOR]

The aim of this paper is to deal with the problem of conditional heteroscedastic noise in multivariate systems. In this regard, a new multivariate equation-error system with colored noise is introduced, in which the noise conditional variance varies with time and the noise exhibits a GARCH process. Thus, in this our new approach, both the time dependency and probability distribution of noise samples shall be identified. Based on the maximum likelihood principle and orthogonality of parameters, the problem of multivariate system identification is reduced into two separate maximum likelihood problems. An iterative algorithm is proposed, which can efficiently estimate the problem parameters based on gradient reduction and nonlinear optimization. Besides, a bootstrap algorithm is applied for assessing the accuracy of estimators. Also, some asymptotic result of proposed method in large sample size is provided. According to bootstrap simulation results, the proposed algorithm can effectively estimate the parameters of the system with conditional heteroscedastic noise and provides more accurate parameter estimates with a much lower confidence interval, compared to the least square method. [ABSTRACT FROM AUTHOR]

A key challenge in the field of graph signal processing is to design proper transform methods to extract valuable information from signals on weighted graphs. This paper first defines convolution, translation and modulation operators in graph fractional domain, and proposes windowed graph fractional Fourier transform (WGFRFT). Related properties are proved. Then, a fast algorithm of WGFRFT is designed to increase flexibility. The robustness and superiority of the fast algorithm are verified via simulations with synthetic signals on graphs. Finally, an application to graph clustering is presented to show the practicability of WGFRFT. [ABSTRACT FROM AUTHOR]

In real life, many engineering problems are modeled in the complex domain (CD). This paper proposes two complex-valued proportionate adaptive filtering (CPAF) algorithms in the CD for identifying the complex sparse systems in impulsive noises. The proposed CPAF algorithms are derived by maximizing the reuse of Versoria cost subjected to the weighted squared Euclidean norm of the filter tap-weight vector difference with the proportionate matrices as the weight of the weighted Euclidean norm. To address the tradeoff problem between the fast convergence rate and low steady-state misadjustment of the CPAF algorithms, two combined-step-size (CSS) CPAF algorithms are also proposed in the CD, which are obtained by introducing a time-varying step-size bound (TVSSB) into the constraint condition of the cost function of the CPAF algorithms. The TVSSB is achieved adaptively by using a modified sigmoidal function (MSF) to combine a large step-size and a small one. In order to be robust against impulsive noises, the MSF is adapted with the aid of a stochastic gradient ascent algorithm which is obtained by maximizing the Versoria cost function with respect to the real part of the a priori prediction error. Simulations on complex sparse system identification under impulsive noises in the CD demonstrate that the performance of the proposed CPAF algorithms is superior to that of the complex-valued variants of the affine projection Versoria, affine projection sign (APS), proportionate APS (PAPS), and improved PAPS (IPAPS) algorithms, also validate the effectiveness of the proposed CSS-CPAF algorithms. [ABSTRACT FROM AUTHOR]

Frequency diverse array multiple-input multiple-output (FDA-MIMO) produces an angle-range-dependent and time-varying transmit beampattern due to the small frequency increment across its array elements, which provides potential applications in new radar techniques. Due to this particularity, multiple signal classification (MUSIC) algorithm can be used to simultaneously estimate the angle and range of the target through a two-dimensional search. The MUSIC need to estimate the covariance matrix. In this paper, we introduce a shrinkage processing for estimating large covariance matrices when the number of samples is substantially fewer than the number of variables which is more common in FDA-MIMO than phase array (PA). Firstly, we propose a receiving model of multi-channel mixing multi-matched filter to eliminate time-varying effect. Secondly, the taper processing, Toeplitz rectification processing and shrinkage-to-identity processing are analyzed. Then, we propose two algorithms, namely, shrinkage-to-taper and shrinkage-to-Toeplitz-rectification on the basis of these algorithms. The proposed processing method improves upon both shrinkage and Toeplitz rectification or tapering processing by shrinking the sample covariance matrix (SCM) to its Toeplitz rectification or tapered version. Thirdly, a closed form expression of shrinkage coefficient for the shrinkage-to-Toeplitz-rectification, and shrinkage-to-taper are used. Finally, simulations results for FDA-MIMO radar show that the proposed algorithm is superior to the sample covariance matrix, shrinkage LW algorithm proposed by Ledoit-Wolf and shrinkage RBLW algorithm which is improved shrinkage LW according to the Rao-Blackwell theorem, in the sense of minimum mean-squared error (MMSE). [ABSTRACT FROM AUTHOR]

Aiming at the problem that existing fuzzy clustering with quadratic polynomial prototype cannot effectively deal with the uneven illumination images, a robust kernel-based fuzzy local neighborhood clustering-related algorithm based on quadratic polynomial is proposed. In this paper, the samples and cluster centers of fuzzy clustering with quadratic polynomial prototype are firstly mapped into the high-dimensional feature space. Furthermore, an optimization model of kernel-based fuzzy local neighborhood information clustering with quadratic polynomial prototype is constructed by the induced kernel distance metric. Then the iterative algorithm of membership and coefficient matrix of the quadratic polynomial prototype is proposed by Lagrange multiplier method and numerical algebra. In the end, the convergence of the proposed is strictly proved by using the combination of Zangwill theorem and bordered Hessian matrix. Experimental results show that the proposed algorithm outperforms existing fuzzy clustering with quadratic polynomial prototype, and it can effectively resolve the segmentation problem of inhomogeneous image and has very high accuracy in segmenting MRI brain images with a certain intensity of noise or bias field. • A robust kernel-based fuzzy clustering quadratic polynomial-centers is proposed. • The proposed algorithm is strictly proved to be convergent by Zangwill theorem. • A inter-similarity measurement is constructed to verify the algorithm's validity. • Test results show the proposed algorithm has ability for uneven grayscale image. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new technique for designing IIR filters that have minimum deviation from equiripple response. The algorithm is also able to find transfer functions with unequal numerator and denominator orders, which are suitable for both digital and analog IIR sampled-data realizations. Elliptic filters are produced as a particular case, when equal numerator and denominator orders are specified. Pole-zero mapping is used for scalar update of optimization parameters, thereby reducing the algorithm complexity. Zeros are structurally allocated on the unit circumference for efficient stopband shaping. Moreover, filter stability is easily enforced by restricting the radii of the poles to be lower than 1. A Taylor series expansion is employed to determine the step size of the parameter updates. The proposed approach is based on evaluations of partial cost functions to avoid local minima, and hence increase the robustness and the convergence rate of the optimization process. Design examples are shown to illustrate the efficacy of the proposed design technique, compared to alternative design techniques. [Copyright &y& Elsevier]

Abstract: Multi-element synthetic aperture focusing (n-SAF) methods have been proposed as a suitable way to reduce cost and size for complex ultrasonic imaging systems. In this method, the larger the sub-aperture, the better the image contrast. However, when the number n of elements in the sub-aperture increases, some problems arise due to the great number of signals involved in the beamforming process, demanding a more complex electronic architecture, a higher bandwidth to manage the signals, and a greater computational power to compose the images in real time. This paper presents the n-SAF method in combination with a data reduction algorithm that reduces the number of signals intervening in the image data processing. The proposed method, called nR-SAF, simplifies the beamforming process; the hardware adds all signals in phase from identical coarray elements (similar spatial frequencies) thus a reduction of approximately 2/n of the data intervening in the dynamical focusing process is attained. The phase errors due to the simplified algorithms are also analyzed. We concluded that there are some limitations for sub-aperture size when image points are very near the transducer. Finally, an electronic architecture is presented, which is able to implement high velocity images from nR-SAF methods. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a novel speech enhancement technique based on an improved minimum statistics (MS) approach incorporating acoustic environmental noise awareness. A relevant noise estimation approach, known as MS, tracks the minimal values if a smoothed power estimate of the noisy signal is within a finite search window. From an investigation of previous MS-based methods, it is discovered that a fixed size of the minimum search window is assumed regardless of the environmental conditions. To overcome this limitation, we initially determine the optimal window sizes in terms of the perceived speech quality according to a variety of noise types. We then assign a different search window size according to the determined noise type, for which we use a real-time noise classification algorithm based on the Gaussian mixture model (GMM). The performance of the proposed approach is evaluated by a quantitative comparison method and by objective tests under various noise environments. It was found to yield better results compared to the previous MS method. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a source localization algorithm based on a sparse Fast Fourier Transform (FFT)-based feature extraction method and spatial sparsity. We represent the sound source positions as a sparse vector by discretely segmenting the space with a circular grid. The location vector is related to microphone measurements through a linear equation, which can be estimated at each microphone. For this linear dimensionality reduction, we have utilized a Compressive Sensing (CS) and two-level FFT-based feature extraction method which combines two sets of audio signal features and covers both short-time and long-time properties of the signal. The proposed feature extraction method leads to a sparse representation of audio signals. As a result, a significant reduction in the dimensionality of the signals is achieved. In comparison to the state-of-the-art methods, the proposed method improves the accuracy while the complexity is reduced in some cases. [Copyright &y& Elsevier]

Abstract: A new perceptual audio hashing algorithm based on maximum-likelihood watermarking detection is proposed in this paper. The idea is justified by the fact that the maximum-likelihood watermark detector responds similarly to perceptually close audio using a non-embedded watermark (i.e. virtual watermark). The feature vector, which is composed of the total amplitude of low-order Zernike moments of each audio frame, is modeled by the Gaussian or Rayleigh distribution. Then, the maximum-likelihood watermark detection is performed on the feature vector with the virtual watermarks generated by pseudo-random number generator to construct the hash vector. Extensive experiments over three large audio databases of different type (speech, instrumental music, and sung voice) demonstrate the efficiency of the proposed scheme in terms of discrimination, perceptual robustness and identification rate. It is also verified that the proposed scheme outperforms state-of-the-art techniques in perceptual robustness and can be applied in content-based search, successfully. [Copyright &y& Elsevier]

Abstract: MP3Stego is a typical steganographic tool for MP3 audio, which embeds secret message into MP3 audio according to the parity of the block length. In this paper, a detection method for MP3Stego based on the differential statistics of quantization step is present. By analyzing the algorithm of MP3Stego, we find that the quantization step which is an important parameter of MP3 encoding is affected during embedding. The standard deviation of the second-order differential sequence from quantization step is adopted as the classification feature. Experimental results indicate that the proposed algorithm is effective in detecting MP3Stego and can achieve better detection performance than the existing algorithms. [Copyright &y& Elsevier]

Abstract: In this paper, an edge preserving fast requantization algorithm is proposed for color image coding. In this work, a block classifier in the orthogonal polynomials based transform domain is proposed to classify the color image region as containing either smooth or edge. The smooth regions are clustered by finding the central tendency adjacent smooth regions separated by edge block. The reduced training vectors are obtained from both edge blocks and clustered smooth blocks for quantization process. The quantization codebook is then constructed from the reduced training vectors and so reduces the codebook training process. In this proposed work, the edge block training vectors and the quantization codebook are utilized to construct the requantization codebook so as to eliminate the edge degradation problem present in existing requantization techniques. The self-organizing feature map algorithm is employed in both quantization and requantization stage for codebook generation process. The computation time of color image encoding is further reduced in this proposed work by constructing a single codebook for all the R, G and B color components utilizing the inter-correlation property of the proposed transform. The experimental results demonstrate that the proposed work gives superior results compared with existing techniques. [Copyright &y& Elsevier]

Abstract: A hybrid method of block truncation coding (BTC) and differential pulse code modulation (DPCM) offers better visual quality than the standard BTC for small block sizes due to its inherent multitone representation. Recently, a two-level quantizer design method has been proposed to increase the coding performance of the DPCM-BTC framework. However, the design method is near optimal in the sense that its coding performance depends on the initial bit plane patterns. In this paper, we propose a bit plane modification (BPM) algorithm to achieve further performance improvement. The BPM algorithm, inspired by error diffusion, effectively distributes large quantization error at a certain pixel to its neighboring pixels having small quantization errors by changing partial bit patterns. Experimental results show that the proposed algorithm successfully achieves much higher coding performance than various conventional BTC methods. The average PSNR performance of the proposed method is 2.31 dB, 5.15 dB, and 5.15 dB higher than that of BTC, DPCM-BTC, and a recently developed BTC scheme using error diffusion and bilateral filtering, respectively. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a robust color image retrieval method using visual interest point feature of significant bit-planes. We firstly extract the visually significant bit-plane image from the original color image according to the bit-plane theory and noise attack characteristic. And then, we extract the visual interest points from the original color image by using the significant bit-plane image and multi-scale Harris–Laplace detector, and construct the fuzzy color histogram of visual interest points. We finally compute the similarity between color images by using the fuzzy color histogram of visual interest points. Experiments on large databases show that the proposed algorithm is significantly more effective than the state-of-the-art approaches. Especially, it can retrieve the noise-attacked (including blurring, sharpening, and illumination, etc.) image effectively. [Copyright &y& Elsevier]

Abstract: When modeling a linear system in a parametric way, one needs to deal with (i) model structure selection, (ii) model order selection as well as (iii) an accurate fit of the model. The most popular model structure for linear systems has a rational form which reveals crucial physical information and insight due to the accessibility of poles and zeros. In the model order selection step, one needs to specify the number of poles and zeros in the model. Automated model order selectors like Akaikeʼs Information Criterion (AIC) and the Minimum Description Length (MDL) are popular choices. A large model order in combination with poles and zeros lying closer to each other in frequency than the frequency resolution indicates that the modeled system exhibits some fractional behavior. Classical integer order techniques cannot handle this fractional behavior due to the fact that the poles and zeros are lying to close to each other to be resolvable and not enough data is available for the classical integer order identification procedure. In this paper, we study the use of fractional order poles and zeros and introduce a fully automated algorithm which (i) estimates a large integer order model, (ii) detects the fractional behavior, and (iii) identifies a fractional order system. [Copyright &y& Elsevier]

Abstract: A new procedure for design of linear phase IIR notch filters is presented in this paper. Resulted filter is realized using parallel connection of two IIR all-pass filters with approximately linear phase. Adequate amplitude characteristic is obtained by approximation of all-pass filter phase. Amplitude characteristic satisfies all prescribed demands but it is not equiripple no matter both all-pass filters have equiripple phase. Algorithm exhibits fast convergence and easy initial values determination. [Copyright &y& Elsevier]

Abstract: This paper presents an ECG compression algorithm based on the optimal selection of wavelet filters and threshold levels in different subbands that achieve maximum data volume reduction while guaranteeing reconstruction quality. The proposed algorithm starts by segmenting the ECG signal into frames; where each frame is decomposed into m subbands through optimized wavelet filters. The resulting wavelet coefficients are thresholded and those having absolute values below specified threshold levels in all subbands are deleted and the remaining coefficients are appropriately encoded with a modified version of the run-length coding scheme. The threshold levels to use, before encoding, are adjusted in an optimum manner, until predefined compression ratio and signal quality are achieved. Extensive experimental tests were made by applying the algorithm to ECG records from the MIT-BIH Arrhythmia Database. The compression ratio (CR), the root-mean-square difference (PRD) and the zero-mean percent root-mean-square difference () measures are used for measuring the algorithm performance (high CR with excellent reconstruction quality). From the obtained results, it can be deduced that the performance of the optimized signal dependent wavelet outperforms that of Daubechies and Coiflet standard wavelets. However, the computational complexity of the proposed technique is the price paid for the improvement in the compression performance measures. [Copyright &y& Elsevier]

Abstract: A robust template matching using occlusion-free correlation (OFC) coefficient is presented in this paper for the purpose of locating objects partially occluded under low PSNR (Peak Signal to Noise Ratio) environment. The OFC coefficient can effectively eliminate the negative effect of occlusion on matching score, resulting in providing better accuracy in recognizing and locating objects under bad environment. This algorithm provides a closed-loop scheme through a reliability assessment of occlusion detection which is able to check if the occlusion detection is well made. Extensive experimental results through occluded images with various noise levels demonstrate that the proposed algorithm is robust against objects partially occluded under low PSNR and superior in terms of the false alarm and miss rates in the identification problem. [Copyright &y& Elsevier]

Abstract: The copyright protection of two-dimensional (2D) vector map has attracted a lot of research focus due to the increasing security issues raised in recent years. One promising direction seeking the optimal tradeoff between adding watermarks and maintaining minimal distortion is the so-called lossless watermarking, i.e., after watermark extraction the 2D vector maps are fully lossless. This paper presents a novel lossless watermarking scheme for 2D vector maps based on a novel recursive embedding algorithm. In our algorithm, feature points of individual polylines are first grouped into united, upon which highly correlated unites are selected as cover data to carry out a recursive modification of its mean vertex coordinates. Such operation not only ensures lossless compression, but also enables higher payload capacity and, to a certain degree, the perception invisibility before and after the watermark extraction. We have conduced experiments on several real-world 2D vector map applications to show the effectiveness, efficiency of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, an encryption algorithm for gray images using a secret key of 128-bits size is proposed. Initially, visual quality of image is degraded by the mixing process. Resultant image is partitioned into key dependent dynamic blocks and, further, these blocks are passed through key dependent diffusion and substitution processes. Total sixteen rounds are used in the encryption algorithm. Proposed technique is simple to implement and has high encryption rate. Simulation experiment results have been given to validate the high security features and effectiveness of proposed system. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a reversible data hiding algorithm for grayscale images. Specifically, our algorithm is based on the histogram modification technique. The premise of this algorithm is that a histogram is constructed from the differences between each pixel and its neighbors. In the data embedding process, a modified histogram shifting scheme is used to embed a secret message into the pixels whose pixel difference is located at the peak value within the histogram. Experimental results show that our algorithm can achieve higher embedding capacity and imperceptible distortion. Performance comparisons with other existing algorithms are also provided to demonstrate the feasibility of our proposed algorithm in reversible data hiding. [Copyright &y& Elsevier]

Abstract: Accurate estimation of the attitude of unmanned aerial vehicles (UAVs) is crucial for their control and displacement. Errors in the attitude estimate may misuse the limited battery energy of UAVs or even cause an accident. For attitude estimation, proprioceptive sensors such as inertial measurement units (IMUs) are widely applied, but they are susceptible to inertial guidance error. With antenna arrays currently being installed in UAVs for communication with ground base stations, we can take advantage of the array structure in order to improve the estimates of IMUs via data fusion. In this paper, we therefore propose an attitude estimation system based on a hexagon-shaped 7-element electronically steerable parasitic antenna radiator (ESPAR) array. The ESPAR array is well-suited for installment in the UAVs with broad wings and short bodies. Our proposed solution returns an estimation for the pitch and roll based on the inter-element phase delay estimates of the line-of-sight path of the impinging signal over the antenna array. By exploiting the parallel and centrosymmetric structure in the hexagon-shaped ESPAR array, the 3-dimensional Unitary ESPRIT algorithm is applied for phase delay estimation to achieve high accuracy as well as computational efficiency. We devise an attitude estimation algorithm by exploiting the geometrical relationship between the UAV attitude and the estimated phase delays. An analytical closed-form expression of the attitude estimates is obtained by solving the established simultaneous nonlinear equations. Simulations results show the feasibility of our proposed solution for different signal-to-noise ratio levels as well as multipath scenarios. [Copyright &y& Elsevier]

Abstract: Locating an unknown-position source using received signal strength (RSS) measurements in an accurate and low-complexity manner is addressed in this paper. Given that the source transmit power is unknown, we employ the differential RSS information to devise two computationally attractive localization methods based on the weighted least squares (WLS) approach. The main ingredients in the first algorithm development are to obtain the unbiased estimates of the squared ranges and introduce an extra variable. The second method improves the first version by implicitly exploiting the relationship between the extra variable and source location through a second WLS step. The performance of the two estimators is analyzed in the presence of zero-mean white Gaussian disturbances. Numerical examples are also included to evaluate their localization accuracy by comparing with the maximum likelihood approach and Cramér–Rao lower bound. [Copyright &y& Elsevier]

Abstract: Adaptive arrays suffer from performance degradation in the presence of steering vector errors. The doubly constrained robust Capon beamformer (DCRCB) can deal with the problem, utilizing all the eigenvalues and eigenvectors of the covariance matrix, which leads to high computational complexity. This paper presents a robust beamforming method which is computationally efficient, exploiting principal eigenpairs only. The eigenpairs can be estimated based on the projection approximation subspace tracking with deflation (PASTd). The original PASTd algorithm, which does not provide orthonormal eigenvectors in general, is modified so that the orthonormalization of eigenvectors can be efficiently made using the structure of the modified algorithm. The proposed beamforming method significantly reduces the computational load, particularly when the number of the directional signals is much less than that of sensor elements, and substantially has the same performance as the conventional one utilizing all the eigenpairs. [Copyright &y& Elsevier]

Abstract: Face recognition is an increasingly important problem in biometric applications; consequently many recognition algorithms have been proposed during the last three decades. It is accepted that the use of a pre-processing step can extract more discriminating features and increase the classification rates. Although, Gabor filters have been widely employed they do not provide satisfying classification results. This paper proposes the use of directional filters as a pre-processing step to demonstrate that a Directional Filter Bank is capable of enhancing existing face recognition classifiers such as PCA, ICA, LDA and SDA. The proposed method is tested using two different databases: the Yale face database and the FERET database. Experimental results demonstrate that the pre-processing phase enhances the classification rates. A comparative study has also been carried out to demonstrate that a DFB based classification outperforms a Gabor type one. [Copyright &y& Elsevier]

Abstract: Blind source separation (BSS) consists of recovering the statistically independent source signals from their linear mixtures without knowing the mixing coefficients. Pre-whitening is a useful pre-processing technique in BSS. However, BSS algorithms based on the pre-whitened data lack the equivariance property, one of the significant properties in BSS. By transforming the pre-whitening into a weighted orthogonal constraint condition, this paper proposes a new definition of the contrast function. In light of the constrained optimization method, various weighted orthogonal constrained BSS algorithms with equivariance property are developed. Simulations on man-made signals and practical speech signals show the proposed weighted orthogonal constrained BSS algorithms have better separation ability, convergent speed and steady state performance. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate level-crossing (LC) analog-to-digital converters (ADC)s in a competitive algorithm framework. In particular, we study how the level sets of an LC ADC should be selected in order to track the dynamical changes in the analog signal for effective sampling. We introduce a sequential LC sampling algorithm asymptotically achieving the performance of the best LC sampling method which can choose both its LC sampling levels (from a large class of possible level sets) and the intervals (from the continuum of all possible intervals) that these levels are used based on observing the whole analog signal in hindsight. The results we introduce are guaranteed to hold in an individual signal manner without any stochastic assumptions on the underlying signal. [Copyright &y& Elsevier]

Abstract: The Vertical Bell Laboratories Layer Space–Time (V-BLAST) system using ordered successive interference cancellation (OSIC) detection is a popular system architecture featuring high data rate and moderate computational complexity. The performance of the V-BLAST system can be further improved by exploiting the diversity and closed-loop gain provided by the technique of combined antenna selection (CAS). In this paper, we investigate the design of CAS algorithms with a goal to minimize the systemʼs error rate. As the error rate of a system varies dramatically with the transceiver architecture, the proposed CAS algorithms explicitly take the VBLAST detection structure into account. Despite the optimal CAS can be achieved by exhaustively searching all the transmit and receive antenna subsets, the applicability of this brute-force approach is excessively limited due to its huge computational complexity. To this end, we propose two computationally efficient CAS algorithms by implementing a greedy strategy incrementally or decrementally to fully exploit the recursive structure of mean-square-error matrices between consecutive V-BLAST detection stages. Near optimal error rate performance can therefore be obtained with significantly lower complexity as verified by the extensive computer simulations. [Copyright &y& Elsevier]

Abstract: In this paper, a multi-resolution feature extraction algorithm for palm-print recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a palm-print image. The entire image is segmented into several small spatial modules and the effect of modularization in terms of the entropy content of the palm-print images has been investigated. A palm-print recognition scheme is developed based on extracting dominant wavelet features from each of these local modules. In the selection of the dominant features, a threshold criterion is proposed, which not only drastically reduces the feature dimension but also captures precisely the detail variations within the palm-print image. It is shown that, because of modularization of the palm-print image, the discriminating capabilities of the proposed features are enhanced, which results in a very high within-class compactness and between-class separability of the extracted features. The effect of using different mother wavelets for the purpose of feature extraction has been also investigated. A principal component analysis is performed to further reduce the feature dimension. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods. [Copyright &y& Elsevier]

Abstract: This paper presents a new method for differential diagnosis of erythemato-squamous diseases based on Genetic Algorithm (GA) wrapped Bayesian Network (BN) Feature Selection (FS). With this aim, a GA based FS algorithm combined in parallel with a BN classifier is proposed. Basically, erythemato-squamous dataset contains six dermatological diseases defined with 34 features. In GA–BN algorithm, GA makes a heuristic search to find most relevant feature model that increase accuracy of BN algorithm with the use of a 10-fold cross-validation strategy. The subsets of features are sequentially used to identify six dermatological diseases via a BN fitting the corresponding data. The algorithm, in this case, produces 99.20% classification accuracy in the diagnosis of erythemato-squamous diseases. The strength of feature model generated for BN is furthermore tested with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Simple Logistics (SL) and Functional Decision Tree (FT). The resultant classification accuracies of algorithms are 98.36%, 97.00%, 98.36% and 97.81% respectively. On the other hand, BN algorithm with classification accuracy of 99.20% is quite a high diagnosis performance for erythemato-squamous diseases. The proposed algorithm makes no more than 3 misclassifications out of 366 instances. Furthermore, FS power of GA is also compared with two alternative search algorithms, i.e. Best First (BF) and Sequential Floating (SF). The obtained results have all together shown that the proposed GA–BN based FS and prediction strategy is very promising in diagnosis of erythemato-squamous diseases. [Copyright &y& Elsevier]

Abstract: Mixing matrix estimation in instantaneous blind source separation (BSS) can be performed by exploiting the sparsity and disjoint orthogonality of source signals. As a result, approaches for estimating the unknown mixing process typically employ clustering algorithms on the mixtures in a parametric domain, where the signals can be sparsely represented. In this paper, we propose two algorithms to perform discriminative clustering of the mixture signals for estimating the mixing matrix. For the case of overdetermined BSS, we develop an algorithm to perform linear discriminant analysis based on similarity measures and combine it with K-hyperline clustering. Furthermore, we propose to perform discriminative clustering in a high-dimensional feature space obtained by an implicit mapping, using the kernel trick, for the case of underdetermined source separation. Using simulations on synthetic data, we demonstrate the improvements in mixing matrix estimation performance obtained using the proposed algorithms in comparison to other clustering methods. Finally we perform mixing matrix estimation from speech mixtures, by clustering single source points in the time-frequency domain, and show that the proposed algorithms achieve higher signal to interference ratio when compared to other baseline algorithms. [Copyright &y& Elsevier]

Abstract: Selective partial update of the adaptive filter coefficients has been a popular method for reducing the computational complexity of least mean-square (LMS)-type adaptive algorithms. These algorithms use a fixed step-size that forces a performance compromise between fast convergence speed and small steady state misadjustment. This paper proposes a variable step-size (VSS) selective partial update LMS algorithm, where the VSS is an approximation of an optimal derived one. The VSS equations are controlled by only one parameter, and do not require any a priori information about the statistics of the system environment. Mean-square performance analysis will be provided for independent and identically distributed (i.i.d.) input signals, and an expression for the algorithm steady state excess mean-square error (MSE) will be presented. Simulation experiments are conducted to compare the proposed algorithm with existing full-update VSS LMS algorithms, which indicate that the proposed algorithm performs as well as these algorithms while requiring less computational complexity. [Copyright &y& Elsevier]

Abstract: Multiframe image super-resolution is a technique to reconstruct a high-resolution image by fusing a sequence of low-resolution images of the same scene. In this paper, we propose a new multiframe image super-resolution algorithm built on the regularization framework. The objective functional to be minimized for the regularization framework consists of a fidelity term and a regularization term. A new adaptive norm combining the advantages of traditional and norms is used in both terms. The fidelity term is then formed by an adaptive strategy depending on the accuracies of the estimated low-resolution image observation models. This strategy serves to adaptively weight low-resolution images according to their reliability and can add robustness in practical implementation of super-resolution. The proposed regularization term can preserve sharp edges well without producing visual artifacts. Our experimental results using both synthetic and real data show the performance improvement of the proposed algorithm over other methods. [Copyright &y& Elsevier]

Abstract: A reliable speech presence probability (SPP) estimator is important to many frequency domain speech enhancement algorithms. It is known that a good estimate of SPP can be obtained by having a smooth a-posteriori signal to noise ratio (SNR) function, which can be achieved by reducing the noise variance when estimating the speech power spectrum. Recently, the wavelet denoising with multitaper spectrum (MTS) estimation technique was suggested for such purpose. However, traditional approaches directly make use of the wavelet shrinkage denoiser which has not been fully optimized for denoising the MTS of noisy speech signals. In this paper, we firstly propose a two-stage wavelet denoising algorithm for estimating the speech power spectrum. First, we apply the wavelet transform to the periodogram of a noisy speech signal. Using the resulting wavelet coefficients, an oracle is developed to indicate the approximate locations of the noise floor in the periodogram. Second, we make use of the oracle developed in stage 1 to selectively remove the wavelet coefficients of the noise floor in the log MTS of the noisy speech. The wavelet coefficients that remained are then used to reconstruct a denoised MTS and in turn generate a smooth a-posteriori SNR function. To adapt to the enhanced a-posteriori SNR function, we further propose a new method to estimate the generalized likelihood ratio (GLR), which is an essential parameter for SPP estimation. Simulation results show that the new SPP estimator outperforms the traditional approaches and enables an improvement in both the quality and intelligibility of the enhanced speeches. [Copyright &y& Elsevier]

Abstract: The environment of the communication system will be more complicated in the future. Therefore, the adaptive array alone cannot cancel all the interferences in future communication system. Many space–temporal joint equalizers (STE) have been proposed to further enhance the capability of canceling the co-channel interferences (CCI) and the multi-path inter-symbol interferences (ISI). The recursive least square (RLS) algorithm of those joint methods will suffer the problems of system structure is complex, the convergent rate of performance is slow or number of calculations is large. In this paper, we propose a simple RLS algorithm (SRLS) of the STE to reduce the computation load, simplify the system structure and improve the system performance. The SRLS algorithm uses the output signal of STE to adjust the weights of adaptive array and equalizer simultaneously without using a transversal filter. Since there is no transversal filter required in the system, the system is simpler in comparison to the conventional STE. Besides, the RLS algorithm of conventional STE adjusts the weights of equalizer after the weights of adaptive array. The convergent rate of weights is slowly in those STE systems. The SRLS algorithm adjusts the weights of adaptive array and equalizer simultaneously. Therefore, the convergent rate of system will improve by the SRLS. Furthermore, the SRLS algorithm is found with input vectors of adaptive array and equalizer separately. Another STE method, which combines the input vectors of adaptive array and equalizer into a large dimension input vector, needs more number of calculations than the SRLS method. Thus, the SRLS of STE can simplify the complexity of system structure, reduce the computation load and improve the convergent performance of system effectively. [Copyright &y& Elsevier]

Abstract: This paper focuses on the identification problem of nonlinear discrete-time systems using Volterra filter series model. Generally, to update the kernels of Volterra model, the most commonly used method is the gradient adaptive algorithm. However, this method probably traps at the local minimum for searching parameter solutions. In this study, a new intelligence swarm computation of the global search is considered. We utilize an improved particle swarm optimization (IPSO) algorithm to design the Volterra kernel parameters. It is somewhat different from the original algorithm due to modifying its velocity updating formula and this can promote the algorithmʼs searching ability for solving the optimization problem. Using the IPSO algorithm to minimize the mean square error (MSE) between the actual output and model output, the identification problem for nonlinear discrete-time systems can be fulfilled. Finally, two different kinds of examples are provided to demonstrate the efficiency of the proposed method. Moreover, some examinations including the Volterra model memory size and algorithm initial condition are further considered. [Copyright &y& Elsevier]

Abstract: We study the reconstruction of a Gaussian random signal, subject to extreme clipping. The reconstruction is achieved by adding a high frequency sinusoidal reference signal prior to the hard-limiter, and by low pass filtering the output. Such a scheme belongs to the area of signal reconstruction from Sine Wave Crossings (SWC). In the present paper we study in detail the effect of sampling in time domain on the reconstruction algorithm, and we carry out an analysis, valid for high sampling rates, leading to approximate analytical expressions of the cross-correlation coefficient between the signal and its reconstructed version. As a result of our analysis, the best achievable cross-correlation coefficient, together with the corresponding setting of the configuration parameters, i.e., the frequency and power of the reference signal, is obtained as a function of the sampling rate. Asymptotic closed form formulas are derived in the limit of very large sampling rates. [Copyright &y& Elsevier]

Abstract: This paper aims at analyzing the performance of various fast methods for the computation of pseudo Zernike moments (PZMs) with respect to their time complexity and numerical stability. Based on these different types of existing methods, four new methods are also proposed. A computational framework is provided which integrates the fast calculation of pseudo Zernike polynomials (PZPs), the angular functions and symmetry/anti-symmetry property of complex kernel functions. It is shown that the recursive methods that compute PZMs of all orders for an image of resolution pixels reduce the time complexity from to . Experimental analysis for numerical stability of various methods is provided which could be useful for the evaluation of these methods. It is observed that recursive methods are numerically stable for high orders of moments and their reconstruction error declines with increase in image size and moment order. [Copyright &y& Elsevier]

Abstract: Commercial iris recognition systems do not perform well for non-ideal data, because their iris localization algorithms are specifically developed for controlled data. This paper presents a robust iris localization algorithm for less constrained data. It includes: (i) suppressing specular reflections; (ii) localizing the iris inner (pupil circle) and outer (iris circle) boundaries in a two-phase strategy. In the first phase, we use Hough transform, gray level statistics, adaptive thresholding, and a geometrical transform to extract the pupil circle in a sub-image containing a coarse pupil region. After that, we localize iris circle in a sub-image centered at the pupil circle. However, if the first phase fails, the second phase starts, where first we localize a coarse iris region in the eye image. Next, we extract pupil circle within the coarse iris region by reusing procedure of first phase. Following that, we localize iris circle. In either of the two phases, we validate the pupil location by using an effective occlusion transform; and (iii) regularizing the iris circular boundaries by using radial gradients and the active contours. Experimental results show that the proposed technique is tolerant to off-axis eye images, specular reflections, non-uniform illumination; glasses, contact lens, hair, eyelashes, and eyelids occlusions. [Copyright &y& Elsevier]

Abstract: In this paper, we have proposed two extensions to pixon-based image modeling. The first one is using bicubic interpolation instead of bilinear interpolation and the second one is using fuzzy filtering method, aiming to improve the quality of the pixonal image. Finally, partial differential equations (PDEs) are applied on the pixonal image for noise removing. The proposed algorithm has been examined on variety of standard images and their performance compared with the existing algorithms. Experimental results show that in comparison with the other existing methods, the proposed algorithm has a better performance in denoising and preserving image edges. [Copyright &y& Elsevier]

Abstract: Spectral estimation of data sequences with randomly missing samples is considered in this paper. A nonparametric missing-data method is proposed based on interpolation followed by a deconvolution procedure. Sample-and-hold interpolation is considered here. The method is based on the analytic expression of the autocorrelation function of the interpolated data as a linear function of the autocorrelation function of the data to be estimated. Bias and standard deviation of both autocorrelation function and power spectral density are detailed for simulated data based on Monte Carlo analysis. The method is also compared with a fuzzy slotting technique with local normalization and weighting algorithm. Based on the results of these simulations, it is concluded that the performance of the proposed method is better than those of the slotting technique. [Copyright &y& Elsevier]

Abstract: Bayes factors have been advocated as superior to -values for assessing statistical evidence in data. Despite the advantages of Bayes factors and the drawbacks of -values, inference by -values is still nearly ubiquitous. One impediment to the adoption of Bayes factors is a lack of practical development, particularly a lack of ready-to-use formulas and algorithms. In this paper, we discuss and expand a set of default Bayes factor tests for ANOVA designs. These tests are based on multivariate generalizations of Cauchy priors on standardized effects, and have the desirable properties of being invariant with respect to linear transformations of measurement units. Moreover, these Bayes factors are computationally convenient, and straightforward sampling algorithms are provided. We cover models with fixed, random, and mixed effects, including random interactions, and do so for within-subject, between-subject, and mixed designs. We extend the discussion to regression models with continuous covariates. We also discuss how these Bayes factors may be applied in nonlinear settings, and show how they are useful in differentiating between the power law and the exponential law of skill acquisition. In sum, the current development makes the computation of Bayes factors straightforward for the vast majority of designs in experimental psychology. [Copyright &y& Elsevier]

Abstract: This paper addresses the problem of joint block diagonalization (JBD) of a set of given matrices. As is known that the nonunitary JBD algorithm has some advantages over the existing orthogonal one for convolutive blind source separation (CBSS). However, the nonunitary JBD algorithm is prone to converge to some unexpected degenerate solutions (singular or ill-conditioned solutions). Especially for the matrices of large dimension or the case that the number of the diagonal blocks is relatively large, the performances of the nonunitary JBD algorithm degrade more severely. To eliminate the degenerate solutions, we optimize a penalty term based weighted least-squares criterion and thus develop a fast efficient algorithm. The performance of the proposed algorithm is evaluated by computer simulations and compared with the existing state-of-the-art nonunitary JBD algorithm. The simulation results demonstrate the robustness and performance improvement of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: This paper presents a new time domain noise reduction approach based on Singular Value Decomposition (SVD) technique. In the proposed approach, the noisy signal is initially represented in a Hankel Matrix. Then SVD is applied on the Hankel Matrix to divide the data into signal subspace and noise subspace. Since singular vectors are the span bases of the matrix, reducing the effect of noise from the singular vectors and using them in reproducing the matrix leads to considerable enhancement of information embedded in the matrix. The noise-reduced singular vectors from the signal subspace are utilized to reconstruct the data matrix. This matrix is finally used to obtain the time-series signal. The results of applying the proposed method to different synthetic noisy signals indicate a better efficiency in noise reduction compared to the other time series methods. [Copyright &y& Elsevier]

Abstract: This paper proposes an optimization-based method to design orthonormal wavelet filters with improved frequency separation. The proposed approach adopts a parameterization of orthogonal filter banks which ensures that the resulting wavelets have at least two vanishing moments. The filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to cut-off sharpness. In comparison with standard orthonormal filters, the proposed method is shown to provide better trade-off between frequency selectivity and time resolution. For illustration, the optimized filters are employed in an application example involving the use of a wavelet-packet system identification scheme. As a result, the identification errors are smaller than those obtained by using a non-optimized filter with the same length. [Copyright &y& Elsevier]